Electromechanical system function module construction method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of electromechanical system design, and particularly relates to an electromechanical system function module construction method and device, equipment and a storage medium. The method specifically comprises the following steps: step 1, acquiring a demand-function-structure mapping relation of the electromechanical system; step 2, based on the mapping relation, performing electromechanical system function module division; the method specifically comprises the following steps: S201, constructing a demand-function matrix and a function-structure mapping matrix based on a demand-function-structure mapping relationship, determining the association degree of structural components on function attributes and the association relationship on structural attributes, and establishing the comprehensive association degree of the structural components; s202, determining module division indexes including cohesion of the functional modules and coupling degree between the functional modules, and establishing an objective function based on a criterion of high clustering degree and low coupling degree; and S203, based on the objective function, identifying a module grouping mode, and forming functional module division.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromechanical system design, and in particular relates to a method, device, equipment and storage medium for constructing an electromechanical system functional module. Background Art

[0002] Currently, the functional reconstruction of multidisciplinary coupled systems mainly relies on traditional modular design and optimization methods.

[0003] Modular design: The system is decomposed into several independent modules, each of which is responsible for a specific function, and the interoperability between modules is achieved through predefined interfaces. This method can improve the flexibility and reusability of the design to a certain extent.

[0004] Optimization method: Use linear programming, dynamic programming and other methods to optimize the combination of modules, usually with certain specific performance indicators (such as cost, weight, time) as the optimization target.

[0005] These methods usually simplify the design process by decomposing the system into several independent modules, but they are insufficient when dealing with multidisciplinary coupling relationships. For example, some existing technologies use linear programming or dynamic programming methods for module combination and optimization, but these methods can usually only handle simple coupling relationships of a single discipline. There is a lack of effective solutions for coupling relationships in complex systems involving multiple disciplines, especially optimization under resource constraints.

[0006] 1) Limited processing of complex coupling relationships: When dealing with multidisciplinary coupling systems, existing technologies can usually only handle relatively simple module combinations. There is a lack of effective solutions for complex coupling relationships between disciplines, especially optimization under resource constraints. This limitation can easily lead to conflicts or incoordination between functional modules in system design, thus affecting the overall optimization effect of the system.

[0007] 2) Lack of multi-objective optimization capabilities: Existing technologies usually use single-objective optimization methods, which can only achieve the best performance on a certain performance indicator, but cannot take into account the optimization of multiple objectives (such as cost, resource utilization and system performance) at the same time. This limitation makes it difficult for the system to achieve overall optimization when facing multi-objective constraints, affecting the overall performance of the system and the effectiveness of resource allocation.

[0008] 3) Inefficient resource utilization: As existing methods do not adequately consider resource constraints, the design scheme is inefficient in resource utilization, which may lead to waste or insufficiency of resources. This not only affects the economy of the system, but may also have a negative impact on the overall performance of the system.

[0009] 4) Poor flexibility and adaptability of module combination: The modular design of existing technologies usually lacks sufficient flexibility and adaptability when dealing with dynamically changing design requirements or external environments, and it is difficult to respond and adjust quickly. This limitation makes it difficult for the system to maintain efficient and stable performance in a changing operating environment. Summary of the invention

[0010] The present invention aims to solve the deficiencies of existing technologies in the design of multidisciplinary coupling systems in terms of dealing with complex coupling relationships and resource optimization, which often lead to low overall system performance and resource utilization efficiency. The present invention proposes a multidisciplinary functional reconstruction modeling method based on resource optimization, which ensures that the system achieves global optimization of resources and performance while meeting multiple constraints by optimizing the combination of various functional modules in the system. This method can effectively cope with the challenges brought by resource constraints and multidisciplinary coupling in complex system design and improve the overall efficiency of the system.

[0011] The technical solution for implementing the present invention is as follows:

[0012] In a first aspect, an embodiment of the present application provides a method for constructing a mechatronic system functional module, and the specific process is as follows:

[0013] Step 1: Obtain the demand-function-structure mapping relationship of the electromechanical system;

[0014] Step 2: Based on the mapping relationship, the electromechanical system functional modules are divided; the specific process is as follows:

[0015] S201, based on the demand-function-structure mapping relationship, construct a demand-function matrix and a function-structure mapping matrix, determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and establish the comprehensive correlation of the structural components;

[0016] S202, determining the module division index includes: the cohesion of the functional modules and the coupling between the functional modules, and establishing the objective function based on the criteria of high cohesion and low coupling;

[0017] S203: Based on the objective function, identify the module grouping pattern and form a functional module division.

[0018] Furthermore, the functional attributes described in the present invention are: the similarity and correlation between two structural components in terms of functional composition and functional relationship; the structural attributes are: the correlation between two structural components in multiple disciplines.

[0019] Furthermore, the comprehensive correlation degree of the present invention is:

[0020]

[0021] Among them, α and β are the set weight parameters, is the functional relevance, is the correlation degree of structural attributes.

[0022] Further, the cohesion of the present invention is calculated as:

[0023]

[0024] Where |S k | is the functional module S k The size of the set, c ij For functional module S k The comprehensive correlation between structural component i and structural component j.

[0025] Furthermore, the two functional modules S of the present invention k , S k′ The coupling calculation formula between them is:

[0026]

[0027] Furthermore, step S203 of the present invention is implemented by using a fuzzy C-means clustering method, and the specific process is as follows:

[0028] First, clustering methods are used to group functional components, and components with similar functions or structural interdependence are classified into the same module to form a preliminary set of module division schemes;

[0029] Secondly, the evaluation and selection of groups are carried out based on the objective function;

[0030] Finally, the final module division plan is determined by evaluating the functional integrity, independence and cost factors of each module.

[0031] Furthermore, the step S203 of the present invention uses a genetic algorithm to implement functional module division, specifically:

[0032] The constraints set include: (1) complete coverage constraint: ensuring that each component is assigned to exactly one module, (2) module non-emptiness constraint: ensuring that each module contains at least one component;

[0033] Set the objective function:

[0034]

[0035] Among them, λ is the equilibrium parameter.

[0036] Based on the constraints and the set objective function, the genetic algorithm is used to realize the functional module division.

[0037] Furthermore, the present invention also includes a step of realizing rapid functional reconstruction based on the collaborative optimization method CO. Before realizing functional reconstruction using CO, basic assumptions are put forward for four aspects: modular design, resource constraints, rapid reconstruction, and collaborative optimization; wherein,

[0038] Modular design assumption: Based on the modular concept, the overall system is divided into multiple levels, including the top system level, subsystem level and module level;

[0039] Source constraint assumption: It is assumed that during the entire reconstruction process, the budget C, time T, material M, and manufacturing process are finite and known;

[0040] Rapid Reconfiguration Assumption: Rapid reconfiguration not only involves performance optimization within the module, but also requires ensuring coordinated operation between various functional subsystems at the system level;

[0041] Collaborative optimization: Objective function Z for system-level optimization sys (X) and subsystem-level optimization goals Discipline-level optimization objective function The coupling is done through the following relationships:

[0042]

[0043] Among them, w k and w i is the weight coefficient, which represents the trade-off between the system-level goal and the goals of each discipline. kl ) represents the constraint function of the coupling relationship between subsystem k and subsystem l, g(z ij ) represents the constraint function of the coupling relationship between module i and module j, λ kl and λ ij It is the penalty system of coupling relationship;

[0044] Constraints for module-level optimization:

[0045]

[0046] subject tog(z ij )=0

[0047] In a second aspect, an embodiment of the present invention further provides a device for constructing a mechatronic system functional module, comprising:

[0048] A first processing module is used to obtain a demand-function-structure mapping relationship of the electromechanical system;

[0049] The second processing module is used to divide the electromechanical system functional modules based on the mapping relationship, specifically:

[0050] Based on the demand-function-structure mapping relationship, the demand-function matrix and the function-structure mapping matrix are constructed to determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and to establish the comprehensive correlation degree of the structural components;

[0051] The indicators for determining module division include: the cohesion of functional modules and the coupling between functional modules. The objective function is established based on the criteria of high cohesion and low coupling.

[0052] Based on the objective function, a module grouping pattern is identified and a functional module division is formed.

[0053] In a third aspect, an embodiment of the present invention further provides a device for constructing an electromechanical system functional module, comprising: a transceiver, a processor, a memory, and a program or instruction stored in the memory and executable on the processor; when the processor executes the program or instruction, the steps in the electromechanical system functional module construction method as described above are implemented.

[0054] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium having a program or instruction stored thereon, which, when executed by a processor, implements the steps in the method for constructing a mechatronic system functional module as described in any one of the above.

[0055] Beneficial effects:

[0056] The electromechanical system functional module construction method provided by the scheme of the present invention determines the functional modules in the electromechanical system and the structural components in the functional modules according to the system functional requirements of the electromechanical system. Taking into account the task requirements of low-cost design, the optimal module division principle is high cohesion and low coupling. This design principle not only helps to improve the reliability and performance of the system, but also simplifies the manufacturing process and improves the efficiency of tooling switching. Based on this design principle, design resources are allocated to each of the functional modules to obtain a constructed electromechanical system. The constructed electromechanical system reduces the situation of improper resource allocation or functional module conflicts that may occur in the later development, reduces the overall design and development costs, improves the efficiency of system development, can quickly respond to dynamic changes in design requirements, ensures efficient operation in various environments, and improves the adaptability and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The basic process of dividing the functional modules of the embodiment of the present invention;

[0058] Figure 2 A flow chart of the demand-function-structure mapping relationship of the electromechanical system is obtained for the embodiment of the present invention;

[0059] Figure 3 A fuzzy C-means clustering flow chart provided for an embodiment of the present invention;

[0060] Figure 4 A basic flow chart of cross processing provided by an embodiment of the present invention;

[0061] Figure 5 A flow chart of a genetic algorithm provided by an embodiment of the present invention;

[0062] Figure 6 A technical flow chart of a multidisciplinary functional reconstruction mathematical model considering resource optimization provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0064] Basic concept definition:

[0065] System functions: In complex electromechanical systems, functions refer to the specific functions of each subsystem at different operating stages, such as target detection, tracking, navigation, motion control, task execution, etc. Through the parallel or serial operation of each function within the system, the system can accurately and reliably complete the predetermined mission objectives. The realization of these functions depends on the coordinated coupling between the subsystems to ensure the optimization of the overall performance.

[0066] Requirements: In the design and application of complex electromechanical systems, requirements determine how the system should operate in a specific working environment to meet the expected goals. Requirements may change throughout the life cycle of the system from design to use. For example, the system may need to be able to accurately identify and track targets in a high noise or interference environment while performing complex operating tasks.

[0067] Modules or functional modules: The modular design of complex electromechanical systems can greatly simplify the design process and enable the system to better adapt to different operating environments and task requirements. A complex electromechanical system is usually composed of multiple independent modules, such as perception modules, drive modules, control modules, and execution modules. Each module consists of several parts and sub-assemblies, is responsible for a set of related functions in the subsystem, and is connected and adapted to other modules through predefined interfaces. For complex electromechanical systems, each module can be divided into general modules, special function modules, and series modules according to its functional structure characteristics. General modules are usually standard components in the system, using a common standardized architecture and have high compatibility, such as navigation modules. Special function modules are highly customized modules designed for specific tasks or operating environments, such as recognition modules for specific targets and high-precision control modules. Series modules are between general modules and special modules, usually a series of functional modules of different models using a common interface, such as drive modules of different powers or perception modules of different precisions.

[0068] Basic components: Components are the basic components and subunits of each module in a complex electromechanical system. Taking a complex electromechanical system as an example, a navigation module may contain multiple sensors (such as infrared sensors, radar sensors), signal processors, and communication equipment. Components are the basic units that realize the functions of each functional module, and are integrated with other components through the interface inside the module to realize the overall function of the system.

[0069] The design concept of the present invention is:

[0070] In the design of complex multidisciplinary coupled systems, reasonable module division is the basis for achieving system optimization and efficient operation. In order to ensure that the system can achieve optimal performance under resource constraints, it is necessary to reasonably divide the system into functional modules. The principle of modular design is high cohesion and low coupling, that is, each module should have highly related functions, and the dependencies between modules should be minimized.

[0071] When designing complex electromechanical systems, considering the task requirements of low-cost design, the optimal module division principle is high cohesion and low coupling (also known as separation). This design principle not only helps to improve the reliability and performance of the system, but also simplifies the manufacturing process and improves the efficiency of tooling switching. In addition, when dividing modules, it is also necessary to comprehensively consider additional indicators such as the feasibility of module manufacturing, cost-effectiveness, and interface standardization between modules to ensure the overall efficiency and flexibility of the system in design, manufacturing, and maintenance.

[0072] like Figure 1 As shown, the present application embodiment provides a method for constructing a mechatronic system functional module, such as Figure 1 As shown, the specific process is:

[0073] Step 1: Based on the module function-structure mapping technology of Function-Behavior-Structure (FBS), the demand-function-structure mapping relationship of the electromechanical system is obtained.

[0074] In the modular design process of complex electromechanical systems, it is often necessary to consider complex operational task requirements, multiple functional constraints, and the coupling relationship between various structural components. Therefore, the premise and basis for module division is to clarify the task requirements and functional details, and to clarify the functional structure mapping relationship between the various module components of the system.

[0075] The module function structure mapping technology based on Function-Behavior-Structure (FBS) is a commonly used method for system function structure analysis and modeling. This method realizes the modeling and decomposition between function and structure through the hierarchical mapping mechanism of function mapping behavior and behavior mapping structure. Specifically, the FBS method regards the function of the system as a high-level design goal, the behavior is the specific manifestation of the function, and the structure is the specific physical implementation required to realize these behaviors.

[0076] In complex electromechanical systems, modular design uses the FBS method to provide important input for subsequent module function division by conducting a detailed analysis of the system's functions, behaviors, and structures. The specific process is:

[0077] Requirements analysis: clarify the overall function of the system and the specific functional requirements of each subsystem. These functional requirements may include operation accuracy, response speed, environmental adaptability, etc.

[0078] Behavior mapping: functions are mapped to specific behaviors. In this step, each function is decomposed into multiple executable behaviors. For example, the navigation function may be mapped to a series of specific behaviors such as path planning, speed adjustment, and attitude control.

[0079] Structural mapping: Behaviors are further mapped to the physical structure of the system. This includes determining the physical implementation of each behavior, such as the arrangement of sensors, the design of actuators, the architecture of the control system, etc.

[0080] Through the above FBS method, the relationship between the system's requirements, behaviors, and structures is clarified, providing clear guidance for module division. The goal of module division is to ensure that each module has the ability to independently implement specific functions, while effectively collaborating with other modules through standardized interfaces. The application of the FBS method in modular design helps to optimize the overall performance of the system and improve the flexibility and scalability of the system. In order to be more suitable for the modular design of modern complex electromechanical systems, the module function structure mapping technology based on FBS is introduced. The specific process is as follows Figure 2 shown.

[0081] In the full life cycle demand analysis phase: the overall requirements of the complex electromechanical system need to be collected and analyzed as comprehensively as possible, including operating scenarios, operating requirements, technical specifications and expected performance. The intended use, performance indicators, operating environment and budget constraints of the system need to be defined in detail. This includes specific requirements for the key technical specifications of the system. At the same time, the demand analysis should also consider manufacturing, maintainability and upgrade requirements. The results of the analysis will directly guide the subsequent system layer division, function identification and module structure design to ensure that the design scheme can meet all operational requirements while having future flexibility and scalability. In the design phase, considering the design goals of modular design and scalable adjustment, the addition, deletion or modification of demand analysis and functional decomposition will also be carried out during the system design iteration process, and the future scalability requirements of the module should be considered. In the maintenance phase, each module should be easy to access and replace to facilitate daily maintenance and upgrades, thereby reducing life cycle maintenance costs. Core requirements include reusability, compatibility, and convenient transportation. In the tooling phase, the core requirements that should be considered include modular assembly, easy module disassembly, standardized interfaces, etc., to improve tooling efficiency. In the use phase, that is, the operation and execution phase of the system, the system should be guaranteed to have a certain degree of effectiveness to achieve specific mission objectives. Common task requirements include flexible operation, guaranteed control accuracy, and high performance.

[0082] In the system level division and functional decomposition stage, the complex electromechanical system is divided into several subsystems according to its functions. Taking a complex electromechanical system as an example, it is divided into several main subsystems according to its functions:

[0083] Structural subsystem: The structural subsystem is the supporting framework of the entire complex electromechanical system. It is responsible for maintaining the physical integrity and structural stability of the system. The main functions include providing a load-bearing and supporting platform, ensuring the stability of the connection between modules, achieving electrical and signal connections, and providing the necessary physical protection for the system. The structural subsystem is the foundation of all other subsystems, and its design needs to consider multiple aspects such as material strength, durability, and thermal protection.

[0084] Control subsystem: The control subsystem is responsible for the operation control and monitoring of the entire system, ensuring that the system can perform tasks according to the predetermined instructions during operation. The main functions of this subsystem include executing operation control, real-time monitoring of system status, and managing the communication between the system and external devices. The control subsystem usually integrates advanced electronic control units and software to achieve precise control and efficient management in complex operating environments.

[0085] Power subsystem: The power subsystem provides the necessary power support for complex electromechanical systems to ensure that the system can operate stably under various operating conditions. This subsystem is mainly responsible for energy storage and management, power output control, and adjusting power distribution during operation. The design of the power subsystem needs to consider energy efficiency, power management, and power output stability to ensure that the system has sufficient energy and power support during operation.

[0086] Navigation subsystem: The navigation subsystem is responsible for providing accurate navigation and positioning information for the system, supporting the system to perform effective path planning and target tracking in a dynamic environment. The navigation subsystem integrates a variety of sensing technologies, such as GPS, inertial navigation systems, radar, and infrared sensors, to ensure high-precision navigation in complex environments.

[0087] Through this system-level functional decomposition, not only can the functions and corresponding tasks of each subsystem be clearly identified, but also a solid foundation can be laid for subsequent modular design and optimization. The functional design of each subsystem needs to be closely centered around the overall goal of the system to ensure the overall coordination and consistency of the system. In addition, functional decomposition also needs to consider the coupling relationship between subsystems, and achieve efficient communication and collaboration between subsystems through standardized interfaces and protocols to further improve the overall performance and reliability of the system.

[0088] Function-structure mapping analysis phase: For each subsystem, further, function-behavior-structure (FBS) mapping and solution are performed at the function-structure level. Take the power subsystem as an example:

[0089] The main functions of the power subsystem are to manage energy supply and provide the system's operating power. These two functions are implemented by different components. For the energy management part, the power subsystem is mainly responsible for storing, supplying and regulating the flow of energy to ensure that the drive unit or other power device can obtain the right amount of energy under various operating conditions. Key components include energy storage units, energy pumps, energy transmission pipelines, electronic control units, etc.

[0090] By refining the functions of the power subsystem, we can clarify the role of each component in the overall function of the system and the relationship between the components. This mapping analysis of the functional structure provides an important basis for the modular design of the system, which helps to optimize system performance and improve system reliability and maintainability.

[0091] Step 2: Basic process of dividing the functional modules of complex electromechanical systems

[0092] Based on the aforementioned demand-behavior-structure analysis and functional structure mapping analysis, the functional module division of complex electromechanical systems can be further carried out. Module division is a key step in system design to ensure the reasonable distribution of system functions and the efficient operation of modules. The following is the basic process of functional module division:

[0093] S201, based on the demand-function-structure mapping relationship, construct a demand-function matrix and a function-structure mapping matrix, determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and establish the comprehensive correlation of the structural components; the specific process of this step is:

[0094] Requirements-function-structure mapping decomposition: Analyze the input requirements list and function decomposition in detail and map them to the structural components that can realize these functions. Through this process, the requirements-function matrix and function-structure matrix are constructed. On this basis, combined with the physical relationship between each component, a comprehensive design structure matrix between components is established.

[0095] In order to systematically evaluate the correlation between structural components, a comprehensive evaluation method can be used to combine functional attributes and structural attributes for quantitative calculation. Functional attributes: the similarity and correlation between two structural components in terms of functional composition and functional relationship; structural attributes: the correlation between two structural components in mechanical, electrical and other disciplines.

[0096] The correlation of functional attributes can be quantified based on the aforementioned "function-structure" correlation matrix. The functional correlation can be calculated by calculating the sum of the correlation strengths of each pair of components corresponding to the functions under certain weights, and defining the "function-structure" correlation matrix as follows:

[0097]

[0098] Among them, w k is the weight of the kth subject, A ik is the component weight vector of function i, A ij is the component weight vector of function j.

[0099] For the correlation between structural attributes The expert scoring method can be used to score the structural correlation from the mechanical, electrical and other disciplines, and then weighted according to a certain weight.

[0100] By combining the evaluation results of functional attributes and structural attributes, the final evaluation of the correlation between components can be obtained. This application adopts a simple additive linear combination to achieve this, that is, the comprehensive correlation calculation formula is:

[0101]

[0102] Among them, α and β are the set weight parameters.

[0103] After quantitative analysis of the above process, the association matrix of the structural components can be obtained.

[0104] S202, determining the module division index includes: the cohesion of the functional modules and the coupling between the functional modules, and establishing the objective function based on the criteria of high cohesion and low coupling;

[0105] Determine the basic criteria for module division: Based on the actual goals of the project, such as low-cost design, performance reliability, technical feasibility, etc., formulate the most suitable basic criteria for functional module division. These criteria will provide guidance for module division and ensure that modular design can take into account both system performance and cost-effectiveness.

[0106] The basic principle is high cohesion and low coupling: Here we first give two indicators for quantifying the results of module division: cohesion and coupling.

[0107] Cohesion is an indicator used to measure the strength of the correlation between the components within a module. The higher the cohesion of a functional module, the tighter the components within it are, and the components within it achieve the module function through synergy. k The formula for calculating the cohesion is:

[0108]

[0109] Where |S k | is the functional module S k The size of the set, c ij For functional module S k The comprehensive correlation between structural component i and structural component j.

[0110] Coupling is an indicator used to measure the degree of dependency or coupling between two functional modules. The lower the coupling between two functional modules, the more independent they are. If the coupling between a functional module and other functional modules is very low, it means that its independence is high, and changes in other modules have little impact on this module, which also means that it has high compatibility. Define two functional modules S k , S k′ The coupling calculation formula between them is:

[0111]

[0112] On the one hand, the cohesion is maximized to ensure that the functions and parts or components within each module are closely related, and the functional modules can realize their functions as independently as possible. At the same time, the coupling is minimized to reduce the direct dependencies between modules as much as possible, so that a single module can be flexibly replaced or modified without affecting the functions of other modules.

[0113] S203: Based on the objective function, identify the module grouping pattern and form a functional module division scheme.

[0114] In this embodiment, the clustering division component method can be used to determine the division scheme of the functional modules, specifically: use the clustering analysis method to group the functional components, and classify the components with similar functions or structural interdependence into the same module. Through cluster analysis, the module grouping pattern is identified, and a preliminary set of module division schemes is formed for further evaluation and selection. Specifically, the fuzzy C-means clustering (FCM) algorithm is selected here to implement the functional module clustering of components. The fuzzy C-means clustering (FCM) algorithm is a soft clustering clustering method that uses membership to represent the relationship between each data, thereby determining the clustering cluster (i.e., functional module) to which each functional component belongs. Therefore, the fuzzy C-means clustering algorithm can be used to automatically identify the potential relationships and dependencies between functions, thereby determining which functions should be classified into one module. The specific process is as follows: Figure 3 shown.

[0115] Analysis and adjustment of module division results: Finally, the final module division plan is determined by evaluating the functional integrity, independence and cost of each module. If necessary, the module division results are adjusted to ensure the rationality and feasibility of the system design.

[0116] Furthermore, in the above embodiment, the functional module division can also be implemented based on genetic algorithm.

[0117] Fuzzy clustering algorithm provides an effective method for preliminary division of functional modules, but this method still has certain limitations in many more complex practical constraints and objectives. Genetic algorithm is a commonly used meta-heuristic optimization technology that can generate more sophisticated module division solutions. Compared with directly calling clustering algorithm, genetic algorithm processing can consider more complex optimization objectives, and can also consider relevant constraints predefined by users, and output possible functional module division schemes based on given constraints.

[0118] Module partitioning of structural components is equivalent to dividing a certain number of sets (all components) into several subsets (functional modules). Therefore, the module partitioning problem of components can be transformed into a set partitioning mathematical model with specific objectives and constraints. By solving this mathematical model, the final functional module partitioning result can be obtained.

[0119] Problem definition: Given a set of n components S = {c1, c2, ..., c n}, it is required to be divided into k mutually disjoint subsets S1, S2, …, S k , each subset represents a functional module, and each component belongs to and can only belong to one module. It is known that the correlation between structural component i and structural component j is c ij .

[0120] Decision variable: Define a two-dimensional binary decision variable x ik , where x ik =1 indicates that the structural component i is divided into the module set k.

[0121] Constraints:

[0122] (1) Full coverage constraint: ensures that each component is assigned to exactly one module.

[0123]

[0124] (2) Module non-empty constraint: ensure that each module contains at least one component

[0125]

[0126] (3) The above decision variable-based constraints can also be expressed in a set-based form:

[0127]

[0128] Objective function: The basic goal is to maximize the cohesion within the module while minimizing the coupling between modules. Define f cohesion (S k )Module S k The cohesion, f coupling (S i ,S j ) indicates module S i and S j The degree of coupling between them. For the optimization goal of maximizing cohesion, it can be expressed as:

[0129]

[0130] For the optimization goal of minimizing coupling, it can be expressed as:

[0131]

[0132] This is a multi-objective optimization problem. For convenience, a balance parameter λ∈[0,1] is introduced to adjust the importance between cohesion and coupling. Then the comprehensive optimization goal can be obtained:

[0133]

[0134] Based on the above definition, the basic process of functional module division based on genetic algorithm is as follows: Figure 3 As shown,

[0135] First, the initial functional module division scheme is generated, and then the fitness function is constructed according to the objective function, and the fitness function is used to evaluate the fitness of the initial population composed of multiple allocation schemes; secondly, the chromosomes in the population are processed through operations such as selection, crossover and mutation to generate new individuals with higher fitness, and after generations of inheritance and evolution, the allocation scheme with the highest fitness is obtained. The specific process is as follows Figure 4 shown.

[0136] (1) Coding design

[0137] In this embodiment, a complete functional module division scheme is used to represent a chromosome (individual). In order to intuitively represent the meaning of chromosome coding, integer coding is used here. Each gene represents a component, and the value of the gene represents the functional module grouping assigned to the component. i The value of the i-th gene position in chromosome X[k], that is, the module grouping of the i-th component, the mathematical representation of this chromosome is:

[0138] X k =(x1,…,x i ,…,x n ),x i ∈1,2,…k

[0139] Similarly, N functional module partitioning schemes can be generated as the initial population for subsequent genetic algorithm iterations. Generally, the quality and diversity of the initial population solution have a significant impact on the convergence speed and final solution of the algorithm. Therefore, in the module partitioning problem, a heuristic strategy based on the characteristics of the problem can be used to generate the initial population, such as pre-grouping according to the degree of correlation between components.

[0140] (2) Fitness function

[0141] The fitness function is a function used in the optimization algorithm to measure the quality of each individual in the problem optimization. In the genetic algorithm for functional module division in this project, the fitness function is used to evaluate the quality of a given module division solution. The independence between components can be evaluated by calculating the correlation between component pairs in different modules.

[0142] Based on the mathematical model in the previous section, for a given module partitioning scheme X, its fitness function is:

[0143]

[0144] (3) Select strategy

[0145] The purpose of the selection strategy is to select some individuals with higher fitness from the current population according to certain rules to generate the next generation of population. Common selection strategies include Roulette Wheel Selection, Elitism, Tournament Selection, etc. In Roulette Wheel Selection, the probability of each individual being selected is proportional to its fitness. Elitism will directly copy some of the individuals with the highest fitness in the current population to the next generation. Tournament Selection randomly selects a group of individuals and then selects the individual with the highest fitness from them.

[0146] (4) Crossover operator

[0147] The crossover operator in the genetic algorithm is an operation used to generate new individuals. Crossover is used to simulate the gene crossover process in biological evolution. New offspring are generated by exchanging and combining chromosome fragments of two parent individuals. Common crossover operators include single-point crossover, double-point crossover, and multi-point crossover. After completing the crossover, it is necessary to check whether the offspring after the crossover meets the constraints. If not, it is necessary to cross over again. Taking two-point crossover as an example, the basic process of crossover is as follows: Figure 5 As shown:

[0148] (5) Mutation operator

[0149] The mutation operation is a random perturbation in the genetic algorithm that imitates biological gene mutation. It is mainly used to introduce new gene combinations to help the algorithm avoid falling into the local optimal solution, while increasing the possibility of exploring new solutions.

[0150] For an existing chromosome population, the steps of the mutation operation are as follows:

[0151] 1. Select mutant individuals: according to the preset mutation probability P mut , determine which individuals need to be mutated.

[0152] 2. Select the mutation site: For the selected chromosome / individual, for each gene site, it has a certain probability P′ mut A mutation has occurred.

[0153] 3. Perform mutation: For the selected ones, randomly select one or more gene positions for mutation. There are many ways to mutate, such as simple bit flipping (for binary coding), randomly assigning new values ​​(for integer or real number coding), etc.

[0154] 4. Constraint check: Check whether the new genome meets the constraints. If yes, go to step 5; otherwise, return to step 2.

[0155] 5. Apply the mutation results: Update the individual’s genome and re-evaluate its fitness.

[0156] When the number of iterations is large, the mutation probability can usually be dynamically adjusted according to the evolutionary algebra, so that at the beginning of the iteration, a wide search is performed in the solution space to ensure the diversity of the population and prevent falling into the local optimal solution; and at the iteration convergence stage, a detailed search is ensured in the solution space to prevent the optimal solution from being destroyed and improve the convergence speed. The calculation formula for the adaptive mutation probability is as follows:

[0157]

[0158] Among them, p mmax 、p mmin They represent the maximum mutation probability and the minimum mutation probability respectively; A is the convergence constant, generally A=9.903438. max is the fitness of the best individual in the population; f′ is the fitness of the individual to be crossed; f is the fitness of the individual to be mutated; is the average fitness of the population.

[0159] The genetic algorithm is used to iterate and solve the problem, aiming to optimize the cohesion within the module and the coupling between modules. Through iterative selection, crossover and mutation operations, the genetic algorithm continuously improves the quality of the module partitioning scheme and finally obtains the optimal module partitioning scheme.

[0160] The above method is used to divide the complex electromechanical system into modules in a scientific and reasonable manner, which not only helps to improve the overall design and development efficiency of the system, but also ensures the independence and maintainability of each module. Each module focuses on a specific functional task, reducing the dependency between modules and thus reducing the complexity of the system. This design method can quickly modify and upgrade a single module during the system's design iteration or functional expansion process, and minimizes the impact on the entire system, ensuring the flexibility and scalability of the system.

[0161] Furthermore, another embodiment of the present application also considers the multidisciplinary functional reconstruction of resource optimization.

[0162] Functional module division and optimization are intended to improve the structural rationality and design efficiency of the system, but in practical applications, the limited resources (such as time, cost, energy, etc.) are often the key factors affecting the overall performance of the system. Resource optimization methods can further improve the overall efficiency and economy of the system by optimizing resource allocation within the established functional module framework. At the same time, during the design iteration and functional expansion process, the system often needs to improve performance without increasing resource consumption. Through the resource optimization model, a flexible framework can be provided for the system, making it more flexible when refactoring functions and minimizing the impact on existing resources.

[0163] The technical flow chart of the mathematical model of multidisciplinary functional reconstruction considering resource optimization is as follows Figure 6 shown.

[0164] In the framework of module division and optimization, complex electromechanical systems are divided into multiple functional modules, and the design of each module involves the integration and optimization of multiple disciplines. Although this modular design significantly improves the flexibility and adaptability of the system, enabling it to better cope with different task requirements and environmental changes, it also increases the coupling complexity between disciplines. The multidisciplinary design optimization (MDO) method is introduced to optimize multiple disciplines simultaneously during the design of complex systems using distributed computing technology and parallel engineering ideas to improve the overall efficiency of the design. The design benefit of the MDO method can be expressed as:

[0165] Δ Design =∑ i Δ Disciplinary +Δ MDO ,

[0166] Among them, Δ Design represents the total benefit of the MDO method design, ∑ i Δ Disciplinary represents the sum of the design benefits of a single discipline, Δ MDO It represents the increase in benefits after the introduction of the MDO method. This formula shows that the MDO method can indeed increase the benefits of the design.

[0167] Collaborative Optimization (CO) is a multi-level multidisciplinary design optimization (MDO) method designed to handle the coupling relationship between multiple disciplines in complex systems. However, in the CO method, the optimization task of the entire system is divided into two levels: system-level optimization and discipline-level optimization. However, in the design of complex electromechanical systems, there is often a subsystem level below the system level, so the improved CO method is introduced.

[0168] In order to facilitate the establishment of a multidisciplinary functional reconstruction mathematical model considering resource optimization, basic assumptions are first proposed in four aspects: modular design, resource constraints, rapid reconstruction, and collaborative optimization.

[0169] Modular design assumption: Based on the modular concept, the overall system is divided into multiple levels, including the top-level system level, subsystem level and module level. Each level handles different optimization tasks and coupling relationships. The top-level system level mainly deals with the coupling between subsystems and global resource allocation; the subsystem level is responsible for optimizing the performance and coordination of internal functional modules; the module level specifically optimizes the design variables of functional modules. Modular design improves the operability of the design and also allows the system to respond quickly and adaptably through independent reconstruction of modules when resources are prioritized. Therefore, this assumption is reasonable and necessary. The system is divided into m subsystems, and the set of variables involved in each subsystem k (k=1,2,…,m) is X={X 1 ,X 2 ,…,X m In each subsystem k, there are n k Functional modules, the design variable set of each module i is x i , then the design variable set of the subsystem is The coupling relationship between subsystems is achieved through the interface variable Z kl It indicates the dependency relationship between subsystem k and subsystem l.

[0170] Resource constraint assumption: It is assumed that throughout the reconstruction process, resources (such as budget C, time T, materials M, and manufacturing processes) are limited and known, and these resource constraints remain unchanged throughout the design process. The design and optimization of each functional module must be carried out under these strict resource constraints to ensure the feasibility and economy of the final solution. In actual engineering projects, resource limitations are an inevitable reality. By clarifying resource constraints, we can effectively control costs and shorten development cycles during the design process, and ensure the practical applicability of the system in complex environments. Therefore, the resource constraint assumption is also necessary. Let the total budget of the system be C total , the time limit is T total , the material limit is M total , the constraints can be expressed as:

[0171]

[0172] Among them, C i (X k ), T i (X k )、M i (X k ) represent the budget, time and material consumption of the ith subsystem respectively.

[0173] Rapid Reconfiguration Assumption: It is assumed that the functional subsystems of the system can be quickly reconfigured when the requirements change. Rapid reconstruction not only involves the performance optimization within the module, but also requires ensuring the coordinated operation between the functional subsystems at the system level to avoid system performance degradation or functional failure due to reconstruction. Let the new task requirement be d, then the system needs to find a new set of design variables X′ within a limited time, so that:

[0174]

[0175] Among them, Z(X′,d) represents the comprehensive performance objective function of the system under the new task requirements, which needs to be minimized or maximized through the reconstructed design variable X′.

[0176] Collaborative optimization system: It is assumed that system-level optimization and discipline-level optimization can be separated and reversed, but through iteration and coordination mechanisms, it is ensured that the functional reconstruction of each module ultimately meets the overall system-level goal. System-level optimization is responsible for dealing with the coupling relationship and resource allocation between subsystems, while discipline-level optimization optimizes the functional modules of each subsystem under the given resource constraints. The two coordinate through information transmission and feedback mechanisms to ultimately achieve the optimization of the overall system performance. In the case of complex systems, it is difficult to effectively deal with the complex coupling relationship between multiple disciplines by relying solely on centralized optimization. The CO method achieves efficient management of complex systems through hierarchical optimization, especially when resources are limited, to ensure the consistency of the independent optimization of each discipline with the overall goal. System and optimization objective function Z sys (X) and subsystem-level optimization goals Discipline-level optimization objective function The coupling is done through the following relationships:

[0177]

[0178] Among them, w k and w i is the weight coefficient, which represents the trade-off between the system-level goal and the goals of each discipline. kl ) represents the constraint function of the coupling relationship between subsystem k and subsystem l, g(z ij) represents the constraint function of the coupling relationship between module i and module j, λ kl and λ ij is the penalty coefficient of the coupling relationship. The following are the constraints for module-level optimization:

[0179]

[0180] subject to g(z ij )=0

[0181] Specific application process of the functional module rapid reconstruction model

[0182] In the rapid reconstruction of complex electromechanical systems, CO is used as the core optimization framework to ensure that each functional module can be reconstructed quickly and efficiently under the condition of resource priority. The CO method reduces the difficulty of handling complex coupling relationships and improves the overall optimization efficiency through hierarchical optimization at the system level, subsystem level, and module level. The following is the specific application process of the CO method in this model.

[0183] (1) Input:

[0184] Requirement d: The requirement will directly affect the design goals and optimization direction of the system.

[0185] Resource Limitation: C total ,T total ,M total Resource constraints include specific limitations of budget, time, materials, and manufacturing processes.

[0186] Initial design state x0: the current design state of the system and the initial configuration of each subsystem.

[0187] (2) Internal logic:

[0188] The CO method consists of two parts: system level and discipline level, and finally the two are iteratively optimized.

[0189] Top-level system optimization

[0190] Task decomposition: Based on the new requirements d, the system-level optimization module decomposes the task into specific goals x for each subsystem module i This includes the performance requirements, resource requirements of each module, and interface requirements between modules.

[0191] Resource allocation: System-level optimization allocates resource quotas to each functional module based on current resource constraints. i ,T i ,M i , these resource quotas need to be determined through optimization calculations.

[0192] Coupling coordination: System-level optimization is responsible for handling the coupling relationship between subsystems to ensure that the subsystems can work in coordination after functional reconstruction. This process is achieved by minimizing the coupling constraint function g(z ij ) to achieve it.

[0193] Subsystem Level Optimization

[0194] Independent optimization: Under the framework of system-level optimization settings, discipline-level optimization is performed within the constraints of their respective resources. i ,T i ,M i Under this condition, the design variables x of each module are optimized independently. i , and calculate the subject-level objective function The optimal value of .

[0195] Feedback mechanism: The results of discipline-level optimization are fed back to system-level optimization as feedback information to adjust the global design variables x and coupling relationships. The feedback process is achieved by updating the coupling variables z. kl accomplish.

[0196] Module-level optimization: Within the framework of subsystem-level optimization settings, module-level optimization specifically deals with the design variables of each functional module, such as material selection, structural parameters, etc., to ensure that the module has optimal performance under the subsystem-level goals.

[0197] Iterative optimization: The system level and the discipline level are coordinated through multiple rounds of iterations until the system level objective function Z sys (X) Converge to the optimal solution. In each iteration, the system-level optimization updates the global design variables, and the discipline-level optimization updates the module design variables.

[0198] (3) Output:

[0199] Reconstruction scheme X′: The final output includes the optimized configuration of each subsystem module.

[0200] System performance evaluation E(X′): The performance evaluation result E(X′) of the reconstructed system is analyzed by calculating the comprehensive performance index Z(X′,d) to ensure that the system achieves optimal performance under the new task requirements.

[0201] Resource usage report: The detailed resource usage report R(X′) includes the resource consumption of each subsystem and module, the remaining resource allocation, etc. This report helps the design team to further optimize resource management and provide a reference for future refactoring tasks.

[0202] Aiming at the multi-level optimization design problem of complex electromechanical systems, this paper proposes a solution based on genetic algorithm and elaborates its solution steps in detail. Through hierarchical optimization strategy and collaborative optimization method, we can effectively deal with the multidisciplinary coupling and resource constraint problems in complex electromechanical systems.

[0203] The present application can also use linear programming or dynamic programming instead of multi-objective genetic algorithm to optimize module combination. These methods may be more efficient when dealing with simple systems, but in complex multidisciplinary systems, they are prone to fall into local optimal solutions and are difficult to achieve global optimal solutions.

[0204] The present application can also adopt a hierarchical optimization strategy, splitting the multidisciplinary system into several subsystems for optimization respectively, and then integrating the whole. Although this method is feasible in some scenarios, its optimization effect is often not as good as the overall optimization method.

[0205] The embodiments of the present application have the following effects:

[0206] Improve resource utilization efficiency: By introducing a modeling method based on resource optimization, the present invention can effectively analyze and allocate limited resources during the system design phase to maximize resource utilization. Compared with the prior art, this method reduces resource waste and reduces the overall cost of the system, thereby improving the economic benefits of the system.

[0207] Enhance the overall performance of the system: By using optimization tools such as multi-objective genetic algorithms, the present invention can achieve global optimization of system performance in complex multidisciplinary coupling relationships. Compared with the prior art, the present invention not only performs well in a single performance indicator, but also achieves the best balance between multiple performance indicators, meets diverse design requirements, and thus comprehensively improves the overall performance of the system.

[0208] Improve the flexibility and adaptability of the system: The present invention provides a flexible module combination strategy, which enables the system to better cope with the changing requirements under different tasks and environmental conditions. Compared with the existing technology, the system can quickly respond to the dynamic changes of design requirements, ensure efficient operation in various environments, and improve the adaptability and scalability of the system.

[0209] Reduce design and development costs: By implementing global optimization in the system design phase, the present invention effectively reduces the situation of improper resource allocation or functional module conflicts that may occur in the later development, reduces the overall design and development costs, and improves the efficiency of system development. Compared with existing methods, the present invention not only reduces rework and adjustments in the development process, but also shortens the time to market of the product.

[0210] The embodiment of the present invention further provides a device for constructing a mechatronic system functional module, comprising:

[0211] A first processing module is used to obtain a demand-function-structure mapping relationship of the electromechanical system;

[0212] The second processing module is used to divide the electromechanical system functional modules based on the mapping relationship, specifically:

[0213] Based on the demand-function-structure mapping relationship, the demand-function matrix and the function-structure mapping matrix are constructed to determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and to establish the comprehensive correlation degree of the structural components;

[0214] The indicators for determining module division include: the cohesion of functional modules and the coupling between functional modules. The objective function is established based on the criteria of high cohesion and low coupling.

[0215] Based on the objective function, a module grouping pattern is identified and a functional module division is formed.

[0216] An embodiment of the present invention also provides a device for constructing an electromechanical system functional module, comprising: a transceiver, a processor, a memory, and a program or instruction stored in the memory and executable on the processor; when the processor executes the program or instruction, the steps in the electromechanical system functional module construction method described above are implemented.

[0217] An embodiment of the present invention further provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps in the method for constructing a mechatronic system functional module as described above are implemented.

[0218] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for constructing a mechatronic system functional module, characterized in that: The specific process is: Step 1: Obtain the demand-function-structure mapping relationship of the electromechanical system; Step 2: Based on the mapping relationship, the electromechanical system functional modules are divided; the specific process is as follows: S201, based on the demand-function-structure mapping relationship, construct a demand-function matrix and a function-structure mapping matrix, determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and establish the comprehensive correlation of the structural components; S202, determining the module division index includes: the cohesion of the functional modules and the coupling between the functional modules, and establishing the objective function based on the criteria of high cohesion and low coupling; S203: Based on the objective function, identify the module grouping pattern and form a functional module division.

2. The method for constructing a mechatronic system functional module according to claim 1, characterized in that: The functional attributes are: the similarity and correlation between the two structural components in terms of functional composition and functional relationship; the structural attributes are: the correlation between the two structural components in multiple disciplines.

3. The method for constructing a mechatronic system functional module according to claim 2, characterized in that: The comprehensive correlation is: Among them, α and β are the set weight parameters, is the functional relevance, The correlation cohesion on the structural attributes is calculated as: Where |S k | is the functional module S k The size of the set, c ij For functional module S k The comprehensive correlation between structural component i and structural component j.

4. The method for constructing a mechatronic system functional module according to claim 3, characterized in that: Two functional modules S k , S k′ The coupling calculation formula between them is:

5. The method for constructing a mechatronic system functional module according to claim 1, characterized in that: The step S203 is implemented by using the fuzzy C-means clustering method, and the specific process is as follows: First, clustering methods are used to group functional components, and components with similar functions or structural interdependence are classified into the same module to form a preliminary set of module division schemes; Secondly, the evaluation and selection of groups are carried out based on the objective function; Finally, the final module division plan is determined by evaluating the functional integrity, independence and cost factors of each module.

6. The method for constructing a mechatronic system functional module according to claim 1, characterized in that: The step S203 uses a genetic algorithm to implement functional module division, specifically: The constraints set include: (1) complete coverage constraint: ensuring that each component is assigned to exactly one module, (2) module non-emptiness constraint: ensuring that each module contains at least one component; Set the objective function: Among them, λ is the equilibrium parameter. Based on the constraints and the set objective function, the genetic algorithm is used to realize the functional module division.

7. The method for constructing a mechatronic system functional module according to claim 1, characterized in that: It also includes the steps of realizing rapid functional reconstruction based on the collaborative optimization method CO. Before using CO to realize functional reconstruction, basic assumptions are put forward in four aspects: modular design, resource constraints, rapid reconstruction, and collaborative optimization. Modular design assumption: Based on the modular concept, the overall system is divided into multiple levels, including the top system level, subsystem level and module level; Source constraint assumption: It is assumed that during the entire reconstruction process, the budget C, time T, material M, and manufacturing process are finite and known; Rapid Reconfiguration Assumption: Rapid reconfiguration not only involves performance optimization within the module, but also requires ensuring coordinated operation between various functional subsystems at the system level; Collaborative optimization: Objective function Z for system-level optimization sys (X) and subsystem-level optimization goals Discipline-level optimization objective function The coupling is done through the following relationships: Among them, w k and w i is the weight coefficient, which represents the trade-off between the system-level goal and the goals of each discipline. kl ) represents the constraint function of the coupling relationship between subsystem k and subsystem l, g(z ij ) represents the constraint function of the coupling relationship between module i and module j, λ kl and λ ij It is the penalty system of coupling relationship; Constraints for module-level optimization: subject to g(z ij )=0 8. A device for constructing a mechatronic system functional module, characterized in that: include: A first processing module is used to obtain a demand-function-structure mapping relationship of the electromechanical system; The second processing module is used to divide the electromechanical system functional modules based on the mapping relationship, specifically: Based on the demand-function-structure mapping relationship, the demand-function matrix and the function-structure mapping matrix are constructed to determine the correlation degree of the structural components in terms of functional attributes and the correlation relationship in terms of structural attributes, and to establish the comprehensive correlation degree of the structural components; The indicators for determining module division include: the cohesion of functional modules and the coupling between functional modules. The objective function is established based on the criteria of high cohesion and low coupling. Based on the objective function, a module grouping pattern is identified and a functional module division is formed.

9. A device for constructing a mechatronic system functional module, characterized in that: include: A transceiver, a processor, a memory, and a program or instruction stored on the memory and executable on the processor; When the processor executes the program or instruction, the steps in the method for constructing a mechatronic system functional module as described above are implemented.

10. A readable storage medium, characterized in that: Programs or instructions are stored thereon, and when the programs or instructions are executed by the processor, the steps in the method for constructing a mechatronic system functional module as described in any one of the above are implemented.

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