IMA system interface data conversion device layout and interface resource allocation method

Abstract

The invention discloses an IMA system interface data conversion equipment layout and interface resource allocation method, which comprises the following steps: S1) determining an onboard installation position according to the function of airborne equipment, and determining the interface type, the interface number, the interface constraint and the access cost of the IMA system data conversion equipment used by the airborne equipment interface; s2) determining the number of IMA system interface data conversion equipment, determining the total number of available interface types, and determining a layout limitation area; s3) amp is carried out; and S4) formulating a comprehensive optimization strategy of hierarchical optimization layout and interface allocation according to constraint conditions of interface resource requirements, interface data conversion equipment resource capability and the like, and executing a two-stage collaborative optimization method based on a genetic algorithm and mixed integer programming. According to the method, the problems of IMA system interface data conversion equipment layout and interface resource allocation under the multi-constraint condition can be solved, and global optimization is achieved.

Description

Technical Field

[0001] This invention relates to a method for system equipment layout and interface resource allocation, and more particularly to a method for IMA system interface data conversion equipment layout and interface resource allocation. 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, easy scalability, and high flexibility. The IMA system relies on interface data conversion equipment to provide interface data conversion services for various airborne systems. Current large aircraft have tens of thousands of interfaces from different vendors, which are rapidly converted using these interface data conversion devices. How to rationally deploy these interface data conversion devices on board, while meeting constraints and providing data conversion resources for a large number of different types of airborne equipment interfaces, 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 reasonable layout and resource allocation of interface data conversion devices can reduce aircraft weight. However, the layout and allocation of interface data conversion devices is an NP-hard problem. Existing methods primarily rely on manual 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 centroid methods can be used for single interface data conversion device location, and mixed-integer programming can be used for interface allocation, but these employ static strategies and require determining the interface data conversion device layout. Current methods, which optimize interface data conversion device layout and resource allocation separately, result in suboptimal overall costs, struggle to quickly generate feasible solutions under multiple constraints, and exhibit poor dynamic adaptability, limiting their application.

[0004] Therefore, how to reasonably deploy multiple interface data conversion devices on the machine, meet the machine layout constraints, and reasonably allocate a large number of interfaces from different vendors to each interface data conversion device to ensure that the interface relationship constraints and the total amount of interface conversion resources of different types of interfaces of each interface data conversion device are met, and the overall access cost is low, has always been a research hotspot in this field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for the layout and resource allocation of interface data conversion devices in IMA system, which can solve the problem of layout and resource allocation of interface data conversion devices in IMA system under multiple constraints and achieve global optimization.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is to provide a method for the layout and resource allocation of interface data conversion equipment in an IMA system, comprising 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 data conversion equipment used by the IMA system; S2) Determine the number of interface data conversion equipment in the IMA system, network the IMA system, determine the total number of each available interface type, and layout the restricted area; S3) Formulate a comprehensive optimization strategy for hierarchical optimization layout and interface allocation based on interface resource requirements, interface data conversion equipment resource capabilities, and constraints, and execute the first stage optimization of a two-stage collaborative optimization method based on genetic algorithm and mixed integer programming; S4) Execute the second stage optimization of the two-stage collaborative optimization method based on genetic algorithm and mixed integer programming based on interface resource requirements, constraints, and interface data conversion equipment resource capabilities.

[0007] Further, step S1 quantifies the access interfaces according to the following formula: Access interface set: IO m ={io1, io2, ..., io m There are a total of m interfaces for connecting to the IMA system interface data conversion device; io i =(posi i type i ,num i cost i bind i ); posi 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 access interfaces include A429 transmitting interface, A429 receiving interface, A825 interface, analog interface, discrete interface, and sensor interface; for interface devices without special installation location requirements, they can be installed at the shortest suitable distance and then connected to the interface data conversion device.

[0008] Further, step S2 quantifies the devices according to the following formula: Resource device set: IOM = {iom1, iom2, ..., iom} n There are a total of n interface data conversion modules; 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 data conversion device in the IMA system. j Indicates the location of the data conversion device for this interface, 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 data conversion 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 location of the layout restriction area, specifically through zone_p_x. s zone_p_y s zone_p_z s Confirmed, zone_par1 s zone_par2 s zone_par3 s This represents the parameters that define the restricted area of ​​this layout.

[0009] Furthermore, step S3 employs an improved genetic algorithm to optimize the spatial layout of the interface data conversion devices, sets no-distribution area constraints and device connection constraints, ensures that each interface data conversion device is located outside the no-distribution area, and imposes device connection constraints by controlling the sum of the interconnection distances of each interface data conversion device.

[0010] Further, step S3 includes: Step S301, initializing the population: representing feasible solutions to the problem as chromosomes in the solution space through encoding, with each gene bit corresponding to an interface data conversion device and its location coordinates, and generating an initial solution based on forbidden region constraints; Step S302, fitness evaluation: calculating individual fitness based on joint cost; Step S303, selecting parent individuals: selecting high-quality individuals as parents for reproduction based on fitness; Step S304, genetic crossover and mutation operations: using simulated binary crossover, randomly crossing all gene bits of two individuals as needed. The algorithm generates a new gene locus by combining two gene positions and generating a random variable that follows an exponential distribution. The offspring gene locus is then randomly perturbed by Gaussian or random mutation, and boundary constraints are applied to the mutated gene locus. Step S305: Population Update: The new population consists of offspring and some parents, or offspring individuals replace individuals with lower fitness in the old population. Step S306: Termination Condition Judgment: The algorithm terminates when the preset number of iterations and / or the change in the optimal fitness value is less than a preset threshold, and the current best individual is output as the final solution.

[0011] Furthermore, step S302 evaluates the merits of each individual through a fitness function. The fitness function is designed as a joint cost evaluation function, which integrates the interface access cost by converting the device location layout optimization cost through interface data conversion. The layout optimization cost includes the device connection cost and the no-displacement area penalty cost. In the evolution process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest fitness is selected.

[0012] Furthermore, the fitness function is: Fitness = α·cost_dist + β·cost_ penalty + γ·cost_ Allocate ； cost_dist : Indicates the connection cost of the interface data conversion device; cost_ penalty : Represents the number of interface data conversion devices deployed to the no-access zone × penalty coefficient, ensuring that the layout constraints are met; cost_ Allocate : Represents the cost of all interfaces being allocated to all interface data conversion devices; α, β, γ are optimized through experiments.

[0013] Furthermore, step S303 can be one of the following selection methods: roulette wheel selection: allocating selection probabilities according to fitness ratio; tournament selection: randomly selecting individuals for comparison and retaining the best; ranking selection: allocating probabilities according to fitness ranking.

[0014] Further, step S4 includes: step S401, defining decision variable x i,j and y k,j Each interface is assigned to a dedicated interface data conversion device; x i,j =

[0015] y k,j =

[0016] ,Right now It belongs to one of the g types of interfaces provided by the interface data conversion device; Step S402: Set allocation constraints to assign interfaces with interface association requirements to the same interface data conversion device; define allocation weight factors based on a greedy sorting allocation strategy, sort all interfaces according to the weight factors, and prioritize the allocation of interfaces with larger weight factors; set a total constraint on the number of interface types and allocate them to the interface data conversion device IOM. j any type of interface i The sum of the interface resources does not exceed the total interface resources of the data conversion device for that interface, max_type_g jAnd any type of interface i The sum of interface resources does not exceed the interface resource threshold of the interface data conversion device; Step S403: Set the objective function according to the minimum allocation cost and complete the allocation of all interfaces.

[0017] Further, step S403 includes: defining the cost of allocating a single interface as... cost_ io i This indicates the cost of connecting the interface to a certain interface data conversion device; cost_ io i =io_dist×num i ×cost i ; io_dist represents the distance from the access interface to the data conversion device of 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 data conversion devices; cost_ Allocate = .

[0018] Compared with the prior art, the present invention has the following beneficial effects: The IMA system interface data conversion device layout and interface resource allocation method provided by the present invention adopts a two-stage collaborative optimization method based on genetic algorithm and mixed integer programming. It uses an improved genetic algorithm to optimize the spatial layout of the interface data conversion device, uses mixed integer programming for dynamic allocation of access interfaces, uses population initialization that satisfies the preprocessing mechanism to improve the algorithm convergence efficiency, and finally achieves global optimum through joint cost evaluation of layout cost and allocation cost, thus solving the problem of IMA system interface data conversion device layout and interface resource allocation under multiple constraints. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the layout and interface resource allocation of the IMA system interface data conversion device of the present invention; Figure 2 This is a flowchart illustrating the layout and resource allocation of the interface data conversion equipment in the IMA system of this invention. Figure 3 This is a flowchart of the fitness algorithm based on joint cost in this invention; Figure 4 This is a schematic diagram of the interface allocation steps based on mixed integer programming in this invention. Detailed Implementation

[0020] The present invention will now be further described with reference to the accompanying drawings and embodiments.

[0021] This invention provides a method for the layout and resource allocation of interface data conversion devices in an IMA system. It requires determining the location of these devices and allocating a large number of interfaces to them while maintaining a relatively low overall cost. The layout and resource allocation method for IMA system interface data conversion devices studied in this invention are as follows: Figure 1 As shown, it is necessary to determine the location of the interface data conversion device, assign interfaces to the interface data conversion device, and satisfy constraints, thereby enabling simultaneous device layout and interface allocation. The specific implementation steps of this invention are as follows: Figure 2 As shown.

[0022] Step S1: Quantize the access interface, determine the location of each airborne device using the interface resources of the IMA system interface data conversion device, the interface type, quantity, interface constraints, and access cost of the airborne device, and the corresponding quantization formula is: Access interface set: IO = {io1, io2, ..., io2} m There are a total of m interfaces for connecting to the IMA system interface data conversion device; io i =(posi i type i ,num i cost i bind i ); posi 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 This indicates the type of the access interface, such as A429 transmit interface, A429 receive interface, A825 interface, analog interface, discrete interface, sensor interface, etc.; num 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 iThis indicates the associated requirements of the access interface. Some interfaces come from the same device and are expected to be connected to the same interface data conversion device, or interfaces that are closely related to each other need to be connected to the same interface data conversion device.

[0023] For interface devices that do not require a specific installation location, after the interface allocation is completed using the algorithm described in the invention, the device can be installed at the shortest possible distance and then connected to the interface data conversion device.

[0024] Step S2: Quantify equipment capabilities, determine the number of IMA system interface data conversion devices, layout restriction areas, and the types and quantities of interface resources available to the interface data conversion devices. The quantification formula is as follows: Resource device set: IOM = {iom1, iom2, ..., iom} n There are a total of n interface data conversion modules; 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 data conversion device in the IMA system. j This indicates the location of the data conversion device for this interface, iom_p_x j ,iom_p_yj ,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 the first, second, ... g-th type interfaces of the data conversion device, i.e., the maximum number of interface resources of each 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 data conversion device.

[0025] 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 s zone_par2 s zone_par3 s This represents the parameters that define the restricted area of ​​this layout.

[0026] Step S3: Based on the interface resource requirements and the resource capabilities of the interface data conversion equipment, and their constraints, formulate a comprehensive optimization strategy for hierarchical layout and interface allocation. Execute the first stage of optimization using a two-stage collaborative optimization method based on genetic algorithms and mixed integer programming, i.e., use an improved genetic algorithm to optimize the spatial layout of the interface data conversion equipment, such as... Figure 3 As shown. Considering the large number of onboard devices, the present invention establishes the following mathematical model for layout constraints: 1) Restrictions on prohibited areas: iom_posi j No-distribution zone: No interface data conversion device should be placed within the no-distribution zone.

[0027] 2) Device Connection Constraints: Since the interface data conversion devices need to be connected to network devices after deployment, a certain degree of clustering of these devices is required. This is represented by distance cost, and the model is established as follows: Minimize cost_dist= , where iom_posi n+1 =iom_posi1, This represents the distance from the j-th interface data conversion device to the (j+1)-th interface data conversion device; cost_dist represents the connection cost of the interface data conversion devices. Additionally, the device connection constraints can also be represented by the sum of the interconnection distances of the data conversion devices at each interface.

[0028] Step S301: Initialize the population 1) Encoding Rules: Feasible solutions to the problem are represented as chromosomes (individuals) in the solution space through encoding. A chromosome is represented as an array of length equal to the total number of interface data conversion devices. Each gene position corresponds to an interface data conversion device and its location coordinates, and the gene value... n represents the interface data conversion device number.

[0029] 2) Constraint preprocessing: To accelerate convergence and improve efficiency, this invention generates initial solutions based on forbidden region constraints, thereby reducing invalid searches.

[0030] Forbidden Zone Constraint: For each individual in the randomly initialized population, check the location of each interface data conversion device to ensure it meets the forbidden zone constraint, i.e., it is outside the forbidden zone.

[0031] 3) Parameter settings Define control parameters such as population size, crossover probability (Pc), mutation probability (Pm), and maximum number of iterations.

[0032] Step S302: Fitness Assessment Individual fitness is calculated based on joint cost: the fitness function evaluates the quality of each individual, and the fitness value reflects the optimization objective of the solution. The fitness function of this invention is designed as a joint cost evaluation function, which integrates interface access cost with interface data conversion device location layout optimization cost. The layout optimization cost includes device connection cost and no-displacement area penalty cost. During the evolution process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest fitness is selected.

[0033] Fitness function: Fitness = α·cost_dist + β·cost_ penalty + γ·cost_ Allocate ； in: cost_dist : Indicates the connection cost of the interface data conversion device; cost_ penalty : Represents the number of interface data conversion devices deployed to the no-access zone × penalty coefficient, ensuring that the layout constraints are met; cost_ Allocate: This represents the cost of allocating all interfaces to all interface data conversion devices, calculated in step 4, interface allocation; α, β, γ can Optimize through experiments.

[0034] Step S303: Select operation Selecting parent individuals: Choosing high-quality individuals based on fitness as parents for breeding. Common methods include: i Roulette wheel selection: Allocate selection probability according to fitness percentage; ii. Tournament selection: Randomly select individuals for comparison and retain the best performer; iii Ranking selection: Assign probabilities based on fitness ranking.

[0035] Step S304: Genetic manipulation 1) Crossover Typically, two individuals are randomly selected from the parent generation, and offspring are generated through single-point / multi-point crossover recombination. In this invention, a simulated binary crossover optimization algorithm is used. All gene loci of the two individuals are randomly crossovered as needed, i.e., a random variable alpha is generated. If this alpha is less than the crossover probability (Pc), a random variable beta following an exponential distribution is generated. This variable, combined with the two gene loci, recombines to generate new gene loci, thus producing offspring. An example of gene locus changes is as follows: Random variable: beta; The positions of gene j and gene j+1: iom_posi1, iom_posi j+1 ; new_p1=0.5×((1+beta)×iom_posi j +(1-beta)×iom_posi j+1 ); new_p2=0.5×((1-beta)×iom_posi j +(1+beta)×iom_posi j+1 ). New gene loci: new_p1, new_p2.

[0036] 2) Mutation Randomly perturb (adjust values) the offspring genes to increase population diversity. The perturbation can be Gaussian mutation or random mutation, and boundary constraints can be applied to the mutated gene loci.

[0037] Step S305: Population Update The population renewal strategies are as follows, and one of the population renewal strategies can be selected.

[0038] New generation population: replacing old population individuals with offspring individuals.

[0039] Hybrid strategy: The new population consists of offspring and some parents, or offspring individuals replace individuals with lower fitness in the old population, in order to balance exploration and development.

[0040] Step S306: Termination condition determination 1) Stopping Criteria The algorithm terminates if one or a combination of the following conditions are met: Reach the preset number of iterations; The optimal fitness value no longer increases significantly (convergence); 2) Output Results The current optimal individual is output as the final solution, which is the optimal interface data conversion device layout and interface resource allocation scheme. Before the stopping condition is met, each iteration must proceed to step S4 to allocate interfaces.

[0041] Step S4: Based on the interface resource requirements and constraints, and the resource capabilities of the interface data conversion equipment, execute the second stage of the two-stage collaborative optimization method based on genetic algorithms and mixed-integer programming. This involves using mixed-integer programming for dynamic allocation of access interfaces. Figure 4 As shown; finally, this invention achieves global optimality by jointly evaluating layout cost and allocation cost (fitness calculation in step 3), considering dynamic constraint processing and cost weight balancing, thus realizing the layout of interface data conversion devices and the allocation of interface resources. Specifically, as follows: Step S401: Define decision variables To describe the interface access to the interface data conversion device, define the following decision variables: x i,j =

[0042] A decision variable of 1 indicates that the j-th interface data conversion device provides interface resources for that interface, i.e., the I / O... i Assigned to the j-th interface data conversion device; the decision variable is 0, indicating that the I / O is... i Data conversion device IOM not assigned to this interface j .

[0043] Uniqueness of interface allocation: Each interface must be assigned to a single interface data conversion device; , j = 1, 2,..., n。

[0044] To describe the connection of a certain type of interface to an interface data conversion device, define the following decision variables: yk,j =

[0045] ,Right now It belongs to one of the g types of interfaces provided by the interface data conversion device.

[0046] The decision variable is 1, and the interface type is io i Assigned to the j-th interface data conversion device; the decision variable is 0, indicating that the I / O is... i Data conversion device IOM not assigned to this interface j .

[0047] Step S402: Define assignment constraints 1) Define interface association constraints Describe the interface association requirements, interface I / O i io l Data must be assigned to the same interface data conversion device: x i,j =x l,j , l m, j n; When io i When allocated to a certain IOM, I / O l It must be allocated to this IOM.

[0048] A greedy sorting and allocation strategy is used, defining allocation weight factors, and an interface with no relational relationships, where the weight factor = num. i ×cost i For interfaces with related relationships, the weight factor is defined as the number of the current interface. i ×cost i and the associated interface's num i ×cost i The sum of these factors. All interfaces are sorted according to weighting factors, with those having larger weighting factors allocated first, ensuring a more reasonable allocation in this invention.

[0049] 2) Define the total number of interface types constraints The resource constraints for the interface data conversion device's interface type are described, and the resources allocated to the interface data conversion device's IOM are determined. j any type of interface i The sum of the interface resources does not exceed the total interface resources of the data conversion device for that interface, max_type_g j .

[0050] , j = 1, 2,..., n , k = 1, 2,..., g; ... , j = 1, 2,..., n, k = 1, 2,..., g。

[0051] To balance the availability and allocation of interface data conversion equipment, any type of interface... i The sum of interface resources can be limited to not exceeding the interface resource threshold of the data conversion device for that interface. The interface resource threshold can be determined by referring to the average proportion of the total number of interfaces of that type to be allocated; this fully considers resource margin and facilitates compatibility with subsequent interface expansion. For example, the threshold for type i interfaces can be defined as (typei_total_num / n) × Gamma, where typei_total_num is the total number of type i interfaces, represents the number of interface data conversion devices, and Gamma represents the balance coefficient, which can be greater than 1, but must be less than max_type_gj / (typei_total_num / n).

[0052] Step S403: Define the objective function 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 data conversion device.

[0053] cost_ io i =io_dist×num i ×cost i ; `io_dist` represents the distance from the access interface to the data conversion device of that interface. `num` i Indicates the number of access interfaces, ost i This represents the access cost coefficient of the access interface, which is closely related to the access interface type.

[0054] In two-dimensional space, io_dist can define the distance between two points from the access interface to the data conversion device of that interface.

[0055] In three-dimensional space, io_dist defines the third point, point_right, with coordinates (posi_x). i posi_y i ,iom_p_z j ), posi_x i posi_y iLet x, y coordinates of the access point be iom_p_z. j The z-coordinate of the interface data conversion device is io_dist, which is defined as the sum of the distance from the access interface coordinates to the third point coordinates and the distance from the third point coordinates to the interface data conversion device position coordinates.

[0056] cost_ Allocate This represents the cost of allocating all interfaces to all interface data conversion devices; cost_ Allocate = 。

[0057] Step S404: Assignment Solution Based on the quantified interfaces and allocation constraints, and combined with the interface data conversion device input in step 3, all interface allocations are completed. It should be noted that the joint cost evaluation of fusion layout cost and interface resource allocation cost is implemented through fitness function design (step S302).

[0058] In summary, this invention provides a method for the layout and interface resource allocation of network transmission and interface conversion equipment in an IMA system. Based on the installation locations, interface types, and quantities of equipment from different vendors, and considering the total resource volume of each interface type and on-board layout constraints, a two-stage collaborative optimization method based on genetic algorithms and mixed-integer programming is employed. This includes spatial layout optimization and dynamic allocation optimization. An improved genetic algorithm is used to optimize the spatial layout of the interface data conversion equipment, and mixed-integer programming is used for the dynamic allocation of access interfaces. Finally, through joint cost evaluation of layout and allocation costs, and considering dynamic constraint processing and cost weight balancing, global optimality is achieved, thus solving the problem of interface data conversion equipment layout and interface resource allocation in an IMA system under multiple constraints.

[0059] 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 the layout of interface data conversion devices and the allocation of interface resources in an IMA system, characterized in that, Includes 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 IMA system data conversion equipment used by the airborne equipment interface; S2) Determine the number of IMA system interface data conversion devices, determine the total number of each available interface type, and determine the layout restriction area; S3) Based on the interface resource requirements, the resource capabilities of the interface data conversion equipment, and their constraints, formulate a comprehensive optimization strategy for hierarchical optimization layout and interface allocation, and execute the first stage optimization based on a two-stage collaborative optimization method using genetic algorithms and mixed integer programming; S4) Based on the interface resource requirements and constraints, and the resource capabilities of the interface data conversion equipment, perform the second stage optimization in the two-stage collaborative optimization method based on genetic algorithm and mixed integer programming.

2. The IMA system interface data conversion device layout and interface resource allocation method as described in claim 1, characterized in that, Step S1 quantifies the access interface according to the following formula: Access interface set: IO m ={io1, io2, ..., io m There are a total of m interfaces for connecting to the IMA system interface data conversion device; 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 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; The access interfaces include A429 transmitting interface, A429 receiving interface, A825 interface, analog interface, discrete interface, and sensor interface; for interface devices without special installation location requirements, they can be installed at the shortest suitable distance and then connected to the interface data conversion device.

3. The method for layout and allocation of interface resources for IMA system interface data conversion equipment as described in claim 1, characterized in that, The device is quantified in step S2 according to the following formula: Resource device set: IOM = {iom1, iom2, ..., iom} n There are a total of n interface data conversion modules; 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 data conversion device in the IMA system. j Indicates the location of the data conversion device for this interface, 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 data conversion 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 location of the layout restriction area, specifically through zone_p_x. s zone_p_y s zone_p_z s Confirmed, zone_par1 s zone_par2 s zone_par3 s This represents the parameters that define the restricted area of ​​this layout.

4. The method for layout and allocation of interface resources for IMA system interface data conversion equipment as described in claim 1, characterized in that, Step S3 uses an improved genetic algorithm to optimize the spatial layout of the interface data conversion devices, sets no-distribution area constraints and device connection constraints, and ensures that each interface data conversion device is located outside the no-distribution area. Device connection constraints are also set by controlling the sum of the interconnection distances of each interface data conversion device.

5. The method for layout and allocation of interface resources for IMA system interface data conversion equipment as described in claim 1, characterized in that, Step S3 includes: Step S301: Initialize the population: Represent the feasible solutions to the problem as chromosomes in the solution space through encoding. Each gene position corresponds to an interface data conversion device and its location coordinates. Generate an initial solution based on the forbidden region constraint. Step S302, Fitness Assessment: Calculate individual fitness based on joint cost; Step S303: Select parent individuals: Select high-quality individuals as parents for breeding based on fitness; Step S304, Genetic Crossover and Mutation Operations: Simulated binary crossover is used to randomly cross over all gene loci of two individuals as needed. By generating random variables that follow an exponential distribution, the variables are combined with the two gene positions to recombine and generate new gene loci, thereby generating offspring. The offspring genes are randomly perturbed by Gaussian mutation or random mutation, and the mutated gene loci are subject to boundary constraints. Step S305, Population Update: The new population consists of offspring and some parents, or offspring individuals replace individuals with low fitness in the old population; Step S306: Termination condition judgment: If the preset number of iterations and / or the change in the optimal fitness value is less than a preset threshold, the algorithm terminates and the current best individual is output as the final solution.

6. The IMA system interface data conversion device layout and interface resource allocation method as described in claim 5, characterized in that, Step S302 evaluates the merits of each individual using a fitness function designed as a joint cost evaluation function. This function integrates interface access costs with device location layout optimization costs through interface data conversion. The layout optimization costs include device connection costs and no-displacement zone penalty costs. During the evolutionary process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest fitness is selected.

7. The IMA system interface data conversion device layout and interface resource allocation method as described in claim 6, characterized in that, The fitness function is: Fitness=α·cost_dist+β·cost_ penalty +γ·cost_ Allocate ； cost_dist : Indicates the connection cost of the interface data conversion device; cost_ penalty : Represents the number of interface data conversion devices deployed to the no-access zone × penalty coefficient, ensuring that the layout constraints are met; cost_ Allocate : Represents the cost of all interfaces being allocated to all interface data conversion devices; α, β, γ are optimized through experiments.

8. The IMA system interface data conversion device layout and interface resource allocation method as described in claim 5, characterized in that, Step S303 can be selected in one of the following ways: Roulette wheel selection: The probability of selection is allocated according to the fitness ratio; Tournament selection: Randomly select individuals for comparison and retain the best performer; Ranking selection: Probability is assigned based on fitness ranking.

9. The method for layout and allocation of interface resources for IMA system interface data conversion equipment as described in claim 1, characterized in that, Step S4 includes: Step S401: Define decision variable x i,j and y k,j Each interface is assigned to a dedicated interface data conversion device; x i,j = y k,j = ,Right now It belongs to one of the g types of interfaces provided by the interface data conversion device; Step S402: Set allocation constraints and assign interfaces with interface association requirements to the same interface data conversion 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 data conversion device IOM. j any type of interface i The sum of the interface resources does not exceed the total interface resources of the data conversion device for that interface, 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 data conversion device. Step S403: Set the objective function according to minimize the allocation cost and complete the allocation of all interfaces.

10. The method for layout and allocation of interface resources for IMA system interface data conversion equipment as described in claim 5, characterized in that, Step S403 includes: The cost of allocating a single interface is defined as follows: cost_ io i This indicates the cost of connecting the interface to a certain interface data conversion device; cost_ I i =io_dist×num i ×cost i ; io_dist represents the distance from the access interface to the data conversion device of 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 data conversion devices; cost_ Allocate = 。

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