Assembly planning solving method and device for complex product assembly

Abstract

The invention relates to the technical field of product assembly, in particular to an assembly planning solving method and device for complex product assembly, and the method comprises the steps: constructing a target system model corresponding to target complex product assembly through employing a model-based system engineering MBSE strategy, so as to obtain a dependency relationship and a function sequence between function modules; generating a function tree according to the decomposition result of the function modules and the dependency relationship between the function modules, and generating a structure tree based on the dependency relationship of the physical components and the physical structure of the target system model; and calling the target constraint conditions from the target constraint knowledge base to generate at least one constraint condition set, and generating a planning result of target complex product assembly in combination with the function sequence, the at least one constraint condition set, the mapping information between the function tree and the structure tree and the resource information. According to the method, the assembly sequence can be solved and generated through the planning algorithm through mapping of the function tree and the structure tree, related constraints of the knowledge base are called, and automatic optimization in the assembly process is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of product assembly, and in particular to an assembly planning solution method and device for complex product assembly. Background Art

[0002] In related technologies, traditional assembly processes often rely on manual experience. For example, according to the sequential planning method, parts are assembled one by one in sequence according to product structure and process; the module planning method divides the product into multiple modules, assembles each module independently first, and then performs module assembly.

[0003] However, in the related art, the assembly process of complex products usually involves the design of functional test nodes, and the test nodes need to be arranged in time after the functions are fully assembled. It is difficult to determine the test nodes by relying on manual experience. If the test nodes are arranged too early and some parts are not assembled, the test cannot be carried out. Sometimes the test nodes are arranged too late. Once a problem occurs during the test, the parts may be blocked and inconvenient to disassemble and assemble. The uncertainty of the test nodes further increases the difficulty of assembling complex products. When it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional system and high-precision assembly requirements corresponding to the assembly of complex products, and it is impossible to achieve the global optimal assembly of complex products, which needs to be solved urgently. Summary of the invention

[0004] The present application provides an assembly planning solution method and device for complex product assembly to solve the problem of functional test node design usually involved in the complex product assembly process in related technologies. However, it is difficult to determine the test nodes by relying on manual experience, which increases the difficulty of assembling complex products. Moreover, when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional systems and high-precision assembly requirements corresponding to the complex product assembly, and it is impossible to achieve the global optimal assembly of complex products.

[0005] The first aspect of the present application provides an assembly planning and solving method for complex product assembly, comprising the following steps: constructing a target system model corresponding to the target complex product assembly using a model-based systems engineering (MBSE) strategy to obtain dependencies and function sequences between functional modules in the target system model; generating a function tree of the target system model based on the decomposition results of the functional modules and the dependencies between the functional modules, and generating a structure tree of the target system model based on the dependencies of physical components in the target complex product assembly and the physical structure of the target system model; calling target constraints from a target constraint knowledge base to generate at least one constraint set in the target complex product assembly process, and generating a planning result of the target complex product assembly that meets preset optimization conditions in combination with the function sequence, the at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly.

[0006] Optionally, in one embodiment of the present application, obtaining the dependency relationship and functional order between functional modules in the target system model includes: modeling the dependency relationship between the functional modules and their corresponding dependency matrix based on the assembly requirements of the target complex product assembly; collecting the execution conditions of the functional modules to determine the functional order through the execution conditions and the dependency matrix.

[0007] Optionally, in one embodiment of the present application, it also includes: obtaining a node set of the function tree and a node set of the structure tree; completing the mapping of the function tree and the structure tree based on the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and obtaining the mapping information.

[0008] Optionally, in one embodiment of the present application, calling the target constraint conditions from the target constraint knowledge base to generate at least one constraint condition set in the assembly process of the target complex product includes: automatically retrieving corresponding functional constraints, logical constraints and physical constraints from the target constraint knowledge base based on the assembly requirements and the functional modules; determining the at least one constraint condition set based on the functional constraints, logical constraints and physical constraints.

[0009] Optionally, in one embodiment of the present application, the combination of the function sequence, the at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly generates a planning result of the target complex product assembly that meets preset optimization conditions, including: using a target optimization algorithm to solve the optimal solution that meets the at least one constraint set; based on the optimal solution, generating a planning result of the target complex product assembly that meets the preset optimization conditions.

[0010] Optionally, in one embodiment of the present application, after generating a planning result of the target complex product assembly that meets preset optimization conditions by combining the function sequence, the at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, it also includes: obtaining real-time feedback information of the target complex product assembly, wherein the real-time feedback information includes the real-time feedback information fed back by the on-site workers at the current moment and / or the corresponding real-time feedback information fed back by the suppliers; based on the real-time feedback information fed back by the on-site workers at the current moment and / or the corresponding real-time feedback information fed back by the suppliers, adjusting the at least one constraint set to generate at least one new constraint set that meets the preset conditions; adjusting the optimization planning result of the target complex product assembly through the at least one new constraint set until a new planning result of the target complex product assembly that meets the at least one new constraint set is obtained.

[0011] The second aspect of the present application provides an assembly planning and solving device for complex product assembly, including: a first acquisition module, used to use a model-based system engineering (MBSE) strategy to construct a target system model corresponding to the target complex product assembly, so as to obtain the dependency relationship and function order between functional modules in the target system model; a generation module, used to generate a function tree of the target system model according to the decomposition results of the functional modules and the dependency relationship between the functional modules, and to generate a structure tree of the target system model based on the dependency relationship of physical components in the target complex product assembly and the physical structure of the target system model; a solving module, used to call the target constraint conditions from a target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process, so as to combine the function order, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets the preset optimization conditions.

[0012] Optionally, in one embodiment of the present application, the acquisition module includes: a modeling unit, used to model the dependency relationship between the functional modules and their corresponding dependency matrix based on the assembly requirements of the target complex product assembly; and a collection unit, used to collect the execution conditions of the functional modules to determine the functional order through the execution conditions and the dependency matrix.

[0013] Optionally, in one embodiment of the present application, it also includes: a second acquisition module, used to acquire the node set of the function tree and the node set of the structure tree; a third acquisition module, used to complete the mapping of the function tree and the structure tree based on the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and acquire the mapping information.

[0014] Optionally, in one embodiment of the present application, the solution module includes: a retrieval unit, used to automatically retrieve corresponding functional constraints, logical constraints and physical constraints from the target constraint knowledge base based on the assembly requirements and the functional modules; and a determination unit, used to determine the at least one constraint condition set based on the functional constraints, logical constraints and physical constraints.

[0015] Optionally, in one embodiment of the present application, the solution module includes: a solution unit, used to use a target optimization algorithm to solve the optimal solution that satisfies the at least one set of constraint conditions; a generation unit, used to generate a planning result of the target complex product assembly that satisfies preset optimization conditions based on the optimal solution.

[0016] Optionally, in one embodiment of the present application, it also includes: a fourth acquisition module, which is used to obtain real-time feedback information of the target complex product assembly after generating a planning result of the target complex product assembly that meets the preset optimization conditions in combination with the function sequence, the at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, wherein the real-time feedback information includes the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers; a first adjustment module, which is used to adjust the at least one constraint set based on the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers to generate at least one new constraint set that meets the preset conditions; a second adjustment module, which is used to adjust the optimization planning result of the target complex product assembly through the at least one new constraint set until a new planning result of the target complex product assembly that meets the at least one new constraint set is obtained.

[0017] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the assembly planning solution method for complex product assembly as described in the above embodiment.

[0018] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned assembly planning solution method for complex product assembly.

[0019] A fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned assembly planning solution method for complex product assembly.

[0020] The embodiment of the present application can generate a target system model for the assembly of a target complex product based on the MBSE method, so as to generate the planning result of the assembly of the target complex product according to the mapping information between the function tree and the structure tree of the target system model, at least one set of constraint conditions in the assembly process of the target complex product, the function sequence and the resource information. Thus, it is realized that the function decomposition relationship and dependency relationship of the complex product are automatically extracted and generated using MBSE, and the function tree is generated, so as to realize the efficient assembly process and the orderly execution of the functions according to the relationship between the functions; through the mapping between the function tree and the structure tree, the optimal matching between the function module and the physical components can be realized, so as to ensure that multiple functions can be realized through the physical components; by constructing a target constraint knowledge base including function test constraints, logical constraints, and physical component assembly constraints, it is possible to automatically generate constraint conditions suitable for the current task, and solve the optimal assembly sequence through the optimization algorithm; by continuously obtaining feedback information from the assembly site and the supply chain, new dynamic constraints are generated in real time in combination with the existing constraints, and then the assembly sequence is adjusted in time to ensure that the assembly process will not be interrupted while improving the assembly efficiency and ensuring feasibility and accuracy. As a result, the problem of functional test node design usually involved in the assembly process of complex products in related technologies is solved. It is difficult to determine the test nodes by relying on manual experience, which increases the difficulty of assembling complex products. Moreover, when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional systems and high-precision assembly requirements corresponding to the assembly of complex products, and it is impossible to achieve the global optimal assembly of complex products.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A schematic diagram of the framework of an assembly planning solution system for complex product assembly according to an embodiment of the present application;

[0024] Figure 2 A flowchart of an assembly planning solution method for complex product assembly provided according to an embodiment of the present application;

[0025] Figure 3 A flow chart of generation and mapping of a function tree and a structure tree according to an embodiment of the present application;

[0026] Figure 4 A flow chart of constraint generation and solution based on a knowledge base according to an embodiment of the present application;

[0027] Figure 5A schematic diagram of the structure of an assembly planning solution device for complex product assembly provided according to an embodiment of the present application;

[0028] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0029] Reference numerals:

[0030] 10-Assembly planning solving device for complex product assembly: 100-first acquisition module, 200-generation module and 300-solving module; 601-memory, 602-processor and 603-communication interface. DETAILED DESCRIPTION

[0031] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] The following describes the assembly planning solution method and device for complex product assembly of the embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology, the complex product assembly process usually involves the design of functional test nodes, and it is difficult to determine the test nodes by relying on manual experience, which increases the difficulty of assembling complex products; and when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multifunctional system and high-precision assembly requirements corresponding to the complex product assembly, and it is impossible to achieve the global optimal problem of complex product assembly. The present application provides an assembly planning solution method for complex product assembly, in which a target system model of the target complex product assembly can be generated based on the MBSE method, so as to generate the planning result of the target complex product assembly according to the mapping information between the function tree and the structure tree of the target system model, at least one constraint condition set in the target complex product assembly process, the function sequence and the resource information. Thus, MBSE is used to automatically extract and generate the functional decomposition relationship and dependency relationship of complex products and generate a functional tree, so as to realize the efficient assembly process and orderly execution of functions according to the relationship between functions; through the mapping between the function tree and the structure tree, the optimal matching between the functional modules and the physical components can be achieved, ensuring that the various functions in the assembly process of the target complex product can be realized through the physical components; by building a target constraint knowledge base containing functional test constraints, logical constraints, and physical component assembly constraints, it is possible to automatically generate constraints suitable for the current task, and solve the optimal assembly sequence through the optimization algorithm; by continuously obtaining feedback information from the assembly site and the supply chain, new dynamic constraints are generated in real time in combination with existing constraints, and then the assembly sequence is adjusted in time to ensure that the assembly process will not be interrupted while improving the assembly efficiency and ensuring feasibility and accuracy. Thus, the problem that the test node design of the function is usually involved in the assembly process of complex products in the related technology is solved, and it is difficult to determine the test node by relying on manual experience, which increases the difficulty of assembling complex products; and when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional system and high-precision assembly requirements corresponding to the assembly of complex products, and it is impossible to achieve the global optimal assembly of complex products.

[0033] Figure 1 This is a schematic diagram of the assembly planning solution system framework for complex product assembly according to an embodiment of the present application. Figure 1 As shown, the assembly planning solution system for complex product assembly in the embodiment of the present application can be divided into, but not limited to, four parts: an input module, a core processing module, an auxiliary module, and an output module.

[0034] Among them, the input module can receive MBSE strategies, component BOM and geometric distribution information involved in the complex product assembly process, and resource information.

[0035] The core processing module can be further divided into a function tree extraction and generation module, a function tree and product structure tree mapping module, a knowledge base-based assembly sequence generation module, and a real-time feedback adjustment module. Among them, the function tree extraction and generation module can use the MBSE model to automatically extract and generate the functional decomposition relationship and dependency relationship of complex products, set test nodes according to product characteristics, and generate the function module execution order through a topological sorting algorithm. The function tree and product structure tree mapping module can generate a function tree through a certain algorithm, and use a certain algorithm to map the function tree to the product structure tree. The knowledge base-based assembly sequence generation module can automatically solve the assembly sequence of complex product assembly by integrating assembly rules and historical experience in the constraint knowledge base. The real-time feedback adjustment module can capture the on-site assembly execution status and supplier feedback information in real time and generate real-time new dynamic constraints to adjust the assembly sequence in time according to the new dynamic constraints, improve assembly efficiency, and ensure feasibility and accuracy.

[0036] The auxiliary module mainly includes a constraint knowledge base and an optimization algorithm module. The constraint knowledge base contains various historical experiences, design specifications and assembly rules about complex product assembly; the optimization algorithm module includes some related algorithms for solving constraint sets.

[0037] The output module is to output the optimized functional flow path and assembly plan.

[0038] Specifically, Figure 2 A flowchart of an assembly planning solution method for complex product assembly provided in an embodiment of the present application.

[0039] like Figure 2 As shown, the assembly planning solution method for the complex product assembly includes the following steps:

[0040] In step S201, a target system model corresponding to a target complex product assembly is constructed using a model-based systems engineering (MBSE) strategy to obtain dependencies and functional sequences between functional modules in the target system model.

[0041] It can be understood that complex product assembly refers to the process of assembling multiple parts or components according to design requirements and technical specifications to form a complete product with specific functions and performance. The target complex product assembly can be understood here as the assembly (process) of related complex products that require assembly sequence planning, that is, there is no specific restriction on the complex products in actual application, and they can be complex products of various types. The model-based systems engineering (MBSE) strategy can be understood here as taking the entire complex product assembly process (system) as the research object, and better managing the entire life cycle of the system through modeling, including but not limited to demand analysis, system design, implementation, testing, maintenance, etc. It can transform the traditional document-based system engineering method into a model-based method, use digital modeling instead of writing documents for system solution design, and convert all nouns, verbs, adjectives, parameters, etc. in the design documents that describe the system structure, function, performance, and specification requirements into digital model expressions.

[0042] In some embodiments, the present application can use the MBSE strategy to model the assembly process of a complex product, thereby obtaining a target system model corresponding to the assembly process of the complex product, and then obtain the dependency relationship and functional order between each functional module based on the target system model.

[0043] The target system model here can be understood as the system model obtained by modeling complex product assembly using the MBSE strategy, and is uniformly referred to as the system in the subsequent description. Therefore, the relationship between product functions or component functions involved in the complex product assembly process can be converted into the dependency relationship between functional modules. For example, the physical dependency relationship between parts, the logical dependency relationship between functional components, and the hierarchical dependency relationship between systems and subsystems.

[0044] Furthermore, in order to better plan the assembly of complex products, the embodiments of the present application can also obtain the sequential relationship between functions, that is, the functional order, such as the need to perform product testing before product packaging during the assembly process of complex products.

[0045] Optionally, in one embodiment of the present application, the dependency relationship and functional order between functional modules in the target system model are obtained, including: based on the assembly requirements of the target complex product assembly, modeling the dependency relationship between functional modules and their corresponding dependency matrix; collecting the execution conditions of the functional modules to determine the functional order through the execution conditions and the dependency matrix.

[0046] During the actual execution process, when obtaining the dependencies and functional order between functional modules in the target system model, the present application can model the dependencies between functional modules and their corresponding dependency matrices based on the assembly requirements of the target complex product assembly; then collect the execution conditions of the functional modules, and determine the order between the functional modules in combination with the dependency matrix and the execution conditions.

[0047] Among them, the dependency relationship between functional modules is a crucial link in the system during assembly planning and execution. In order to construct an accurate functional flow path, that is, the dependency relationship and functional sequence of functional modules, it is first necessary to obtain the dependency relationship between functional modules. In the embodiment of the present application, the dependency relationship of functional modules can be achieved through, but not limited to, the following process:

[0048] (1) Requirements analysis and decomposition

[0049] The functional modules in the system are usually derived from user requirements or system design requirements. In the MBSE model in the embodiment of the present application, the requirements in the target complex product assembly process can be modeled using, but not limited to, the SysML (System Modeling Language) requirement diagram. The requirement diagram can map the user's high-level requirements to specific system functional requirements, and further decompose them into functional modules through system modeling.

[0050] For example, the present application may assume that the system requirement is D = {D 1 ,D 2 ,…,D n}, where D i By analyzing the requirements, the embodiment of the present application can decompose them into multiple functional modules to generate a functional module set F = {F 1 ,F 2 ,…,F n}. Among them, each functional module F i It can be but is not limited to one or more requirements D i The decision can be expressed in the form of a formula, but is not limited to:

[0051]

[0052] (2) Functional module dependency modeling

[0053] The dependencies between functional modules are usually derived from aspects such as the task flow or data flow in the target complex product assembly process, and can be, but not limited to, extracting the relevant information of the task flow or data flow in the target complex product assembly process from block definition diagrams, internal block diagrams, sequence diagrams, activity diagrams, and state machine diagrams. In the system engineering in the embodiments of the present application, the dependencies between these functional modules can be modeled by, but not limited to, hierarchical functional decomposition (HFD). Among them, the HFD method can form a hierarchical structure between modules according to the order and data exchange path between the functional modules.

[0054] For example, the present application can firstly obtain the dependency relationship between functional modules through methods such as data analysis and task sequence analysis, but is not limited to the above. Among them, data analysis can determine the dependency relationship between functional modules by analyzing the data input and output relationship between functional modules; task sequence analysis can find out the execution order between functional modules through the analysis of product tasks, thus forming the dependency relationship between functional modules.

[0055] Furthermore, in the embodiment of the present application, the dependency relationship between the functional modules can be represented by, but not limited to, a dependency matrix M, where M ij =1 indicates function module F i Depends on function module F j , that is, function F j Must precede function F i Execute. The form of the matrix M can be, but is not limited to, expressed as:

[0056]

[0057] The dependency matrix M is a complete description of the dependency relationship between functional modules, which indicates that each functional module needs to rely on the completion of the predecessor function before it can be started. This dependency relationship may be in data transfer (for example, the output of one function is the input of another function) or it may be specified by the task design.

[0058] Furthermore, the embodiment of the present application can also clarify the hierarchical relationship of the dependency relationship between functional modules through hierarchical modeling. Among them, each high-level functional module can be further refined into multiple sub-functions by recursive decomposition. The construction of a hierarchical model can not only help understand the overall functional structure of the system, but also provide an intuitive mapping for the dependency relationship between functional modules.

[0059] Assume that the high-level function of the system is F root , whose subfunction is F root ={F 1 ,F 2 ,…,Fm Each sub-function can be further decomposed into smaller functional modules until it cannot be decomposed any further. The dependency relationship between the functional modules of the entire system can be described by, but is not limited to, the following recursive formula:

[0060]

[0061] Through recursive decomposition, the embodiment of the present application can obtain the dependency hierarchy of each functional module in the system, forming a tree representation of the functional modules.

[0062] (3) The assembly process of some complex products involves the design of functional test nodes, but not every functional module corresponds to a test node one by one, but the test nodes are set according to the necessity of the product. The test depends not only on the dependency relationship, but also on the resource availability, time window and other factors in the assembly process of the target complex product. Based on this, the embodiment of the present application can also design a test node for each functional module F i Define the corresponding execution condition C i , which indicates the necessary conditions for starting this functional module.

[0063] For example, the present application may, but is not limited to, use resource availability and time window as execution conditions. i It can be understood that the functional module needs to rely on certain specific resources, such as equipment, materials, and personnel. Time window T i It can be understood that the execution of the functional module must be completed within the specified time, which may be constrained by the overall progress of the system.

[0064] Therefore, the functional module F i The execution condition can be expressed as:

[0065] C i =R i ∧T i ,

[0066] Among them, R i is the condition of resource availability and can be expressed as:

[0067]

[0068] T i It is a time-limited condition, which means that the execution time of the function must be within the time window allowed by the system:

[0069] T i =(t i ≤T max ).

[0070] (4) Sorting of functional modules

[0071] In the embodiment of the present application, the function sequence, i.e., the ordering problem between the function modules, can be solved by, but not limited to, the dependency matrix M and the execution condition C. i Combined with topological sorting algorithm to solve.

[0072] In the matrix M, if M ij =1, then F i Depends on F j , so F j Must be in F i Based on this, the embodiment of the present application can determine the execution order of each functional module by topologically sorting the dependency matrix, and the process can be but not limited to the following:

[0073] First, for each functional module, its in-degree (i.e., the number of other modules it depends on) is calculated;

[0074] Next, all function modules with in-degree 0 are added to the execution queue, indicating that they can be executed first;

[0075] Finally, the functional modules are taken out from the queue one by one, removed after execution, and the in-degree of their dependent modules is updated. If the in-degree of a module becomes 0, it is added to the queue.

[0076] Through this process, the embodiment of the present application can obtain the execution order P={F1, F2, ..., Fm} of all functional modules, and ensure that all dependencies are satisfied. Among them, the formula of topological sorting can be expressed as but not limited to:

[0077] P = TopologicalSort (M),

[0078] The execution order generated by topological sorting can ensure that each functional module F i All dependent functions F j All have been executed, and during the execution of all modules, the system's resource, time and space constraints have been met.

[0079] The embodiment of the present application can obtain the dependency relationship between functional modules through demand decomposition and MBSE model, and can generate the function sequence based on the dependency matrix M and execution condition C. In addition, the embodiment of the present application can introduce a hierarchical model, a dependency matrix, an execution condition formula, and a topological sorting algorithm to ensure that the system can effectively and automatically generate the function sequence in the complex product assembly process, ensuring the orderly execution of functions and the efficient assembly process.

[0080] Step S202, generating a function tree of the target system model according to the decomposition results of the function modules and the dependencies between the function modules, and generating a structure tree of the target system model based on the dependencies of the physical components in the target complex product assembly and the physical structure of the target system model.

[0081] In certain embodiments, the present application can also generate a functional tree (Functional Tree) of the target system model based on the decomposition results of the functional modules and the dependencies between the functional modules, and generate a structural tree (Structural Tree) of the target system model based on the dependencies of the physical components in the target complex product assembly and the physical structure of the target system model.

[0082] Specifically, the function tree is a hierarchical representation of the system function modules, the root node of which can be used to represent the high-level functions of the system, and the leaf nodes represent the specific function implementation. In the embodiment of the present application, the generation of the function tree can be achieved by, but not limited to, recursively decomposing the system functions, wherein the goal of the recursive decomposition can be understood as decomposing the complex system functions layer by layer into fine-grained sub-functions until they can be directly implemented or mapped to specific physical components. Figure 3 This is a flow chart of the generation and mapping of a function tree and a structure tree according to an embodiment of the present application, as shown in FIG. Figure 3 As shown:

[0083] (1) Functional decomposition process

[0084] First, the embodiment of the present application may set the overall function of the system as F root , through recursive decomposition, the embodiment of the present application can obtain the structure of the function tree. Among them, the function decomposition formula of each layer can be but is not limited to the following:

[0085] F root ={F 1 ,F 2 ,…,F n},

[0086] Among them, F 1 ,F 2 ,…,F n is a sub-function of the system. For each sub-function F i , continue the recursive decomposition:

[0087]

[0088] This recursive decomposition process will continue until a certain functional module cannot be further decomposed. These functional modules that cannot be decomposed any further can become leaf nodes of the functional tree. The entire functional tree can be represented as, but not limited to:

[0089] T f ={Froot ,F 1 ,F 2 ,…,F n ,F 11 ,F 12 ,…,F nm},

[0090] Through this recursive decomposition, the embodiment of the present application can establish a logical relationship between each layer of system functions and their sub-functions to form a tree-like functional structure, wherein each node of the function tree can represent a specific function, and the hierarchical structure of the tree can represent the hierarchical relationship and dependency relationship between functions.

[0091] (2) Functional Dependency Modeling

[0092] It is understandable that each layer of the function tree follows certain dependencies. Based on this, the embodiment of the present application can add certain dependency conditions to each node of the function tree, such as the completion of the upstream function, the availability of resources, etc.

[0093] The product structure tree (structure tree) is a hierarchical representation of components in a complex product, wherein the root node may represent the overall structure of the product, and the leaf nodes may represent specific components or certain subsystems. In the embodiment of the present application, the structure tree may be generated by, but is not limited to, converting the product BOM (Bill of Material).

[0094] (1) Product physical structure tree generation

[0095] First, the overall structure of the system in the embodiment of the present application can be set as S root , through recursive splitting, the obtained physical structure tree can be expressed as but not limited to:

[0096] S root ={S 1 ,S 2 ,…,S n},

[0097] Each structural node S i The recursive decomposition into substructures can be continued:

[0098]

[0099] This recursive process is similar to the generation of a function tree, that is, the physical structure is decomposed until it reaches a physical component that cannot be decomposed further. These indecomposable physical components become leaf nodes of the structure tree. The entire structure tree can then be represented as:

[0100] T s ={S root ,S 1,S 2 ,…,S n ,S 11 ,S 12 ,…,S nm},

[0101] Through this recursive decomposition, hierarchical relationships and dependency relationships can be established between each layer of the system structure and its substructure.

[0102] (2) Physical Dependency Modeling

[0103] In the embodiment of the present application, each node of the structure tree can represent the dependency relationship between physical components, which can be represented by the dependency matrix M s The method is the same as the function dependency matrix, except that the present embodiment of the present application needs to consider the dependency relationship of components in space in the structure tree. Therefore, the present embodiment of the present application can be combined with the interference matrix IM s Based on the 3D CAD system, the geometric relationship between parts is calculated to determine whether interference occurs. At this time, the product dependency can be expressed as, but not limited to:

[0104] I s =M s ×IM s .

[0105] The embodiment of the present application can generate a function tree by recursive decomposition, and generate a structure tree by converting the product BOM. The system can hierarchically model the functional modules and physical components of the system. In the assembly of complex products, the function tree helps to clarify and organize complex functional relationships, clearly display the dependencies between various functions, so as to better plan and allocate resources and improve work efficiency; and the structure tree can clearly describe the hierarchy and composition relationship between various components and parts of the product, providing an important basis for material procurement, production planning, product configuration and warehouse management, and ensuring the smooth progress of the assembly process.

[0106] Optionally, in one embodiment of the present application, it also includes: obtaining a node set of the function tree and a node set of the structure tree; completing the mapping of the function tree and the structure tree based on the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and obtaining mapping information.

[0107] It can be understood by professionals in this technical field that the mapping of the function tree and the product structure tree is a core task in assembly, which can achieve the best match between the functional modules of the system and the physical components, ensuring that the functions can be realized through the physical components.

[0108] Based on this, the embodiment of the present application can complete the mapping between the function tree and the structure tree through the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and obtain the mapping information between the function tree and the structure tree. Among them, the target mapping constraint condition can be understood here as a series of constraints that need to be satisfied in the mapping process between the function tree and the structure tree.

[0109] For example, the mapping process between the function tree and the structure tree in the present application can be implemented, but not limited to, through a certain optimization algorithm. Figure 3 :

[0110] (1) Definition of mapping relationship

[0111] In the embodiment of the present application, the node set of the function tree may be set to N f , the node set of the structure tree is N s , then the mapping relationship φ can be defined as, but not limited to, the following mapping function:

[0112] φ:N f →N s .

[0113] Through this mapping relationship, the embodiment of the present application can map each functional module in the functional tree to one or more physical components in the structure tree.

[0114] And, the goal of the mapping in the embodiment of the present application is to find a mapping that maximizes the matching degree between the function and the structure. In the process of implementing the mapping, the embodiment of the present application also takes into account the compatibility and operability of the function and the physical implementation. Therefore, the matching algorithm used in the mapping process and certain mapping constraints must also be considered.

[0115] (2) Selection of matching algorithm

[0116] Bipartite matching algorithm: In the embodiment of the present application, the mapping problem between the function tree and the product structure tree can be formalized as a bipartite matching problem. In this problem, there are two disjoint node sets: the function tree node set N f and product structure tree node set N s The mapping of the function tree and the product structure tree can be converted into a match between the two sets, with the goal of maximizing the matching weight, wherein the matching weight can come from a product design history knowledge base or other data sources, etc., which is only used as an example in the embodiments of the present application without specific limitation.

[0117] At this time, the mapping problem can be defined as a weight optimization problem. Therefore, the embodiment of the present application can define a weight matrix W, where each element W in the matrix ij Represents the function node F i and structure node S jThe goal is to find an optimal matching solution that maximizes the total matching weight. The formula can be, but is not limited to, expressed as:

[0118]

[0119] In this formula, φ ij =1 indicates function node F i Mapped to structure node S j , otherwise φ ij =0.

[0120] The implementation process of the bipartite matching algorithm can be expressed as follows: Initialize the mapping matrix φ, set all elements to 0; for each functional node F i , traverse all structure nodes S j , according to the weight W ij Determine whether it matches; if the matching condition is met, set φ ij =1, and record the matching information.

[0121] Furthermore, considering that the bipartite matching algorithm works better when the number of functional modules and physical components is small, but the computational complexity will increase as the scale increases, the embodiment of the present application may consider a more optimized algorithm to achieve mapping when the number of functional modules and physical components is large.

[0122] For example, when the function tree and the product structure tree are large, the present application may, but is not limited to, use linear programming (LP) to solve the matching problem, wherein linear programming can be optimized through constraints to ensure that the best match is found under multiple objective conditions.

[0123] Among them, the goal of the linear programming problem in the embodiment of the present application can be expressed as:

[0124]

[0125] The constraints can be expressed as:

[0126]

[0127] That is, each function node can only be mapped to one structure node, and vice versa.

[0128] Linear programming can be used to quickly find the optimal mapping between large-scale functions and structures. Commonly used linear programming algorithms include the simplex algorithm and the interior point method, both of which can find the optimal solution within an effective time.

[0129] (3) Constraints in the Mapping Process

[0130] In the process of mapping functions and structures in the embodiments of the present application, some constraints are also considered, including but not limited to the following constraints:

[0131] Functional and structural compatibility: ensuring that the functional requirements can be achieved through the physical structure, therefore taking into account the capabilities and limitations of the physical components.

[0132] Spatial constraints: The execution of functions may be limited by space, and the mapping process should ensure that there is no spatial conflict between components.

[0133] These constraints can be handled by introducing a penalty function P(φ). If a mapping does not satisfy the constraints, a penalty term is added to the objective function to avoid invalid mappings:

[0134]

[0135] The embodiment of the present application can implement the mapping process of the function tree and the structure tree through optimization methods such as bisection matching algorithm and linear programming, thereby ensuring the best match between functional requirements and physical implementation.

[0136] Step S203, calling the target constraint conditions from the target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process, so as to combine the function sequence, at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets the preset optimization conditions.

[0137] It is understandable to those skilled in the art that various constraints may be imposed on the assembly of complex products, and the constraints may vary depending on the current assembly process. Moreover, in the assembly of complex products, the constraints on product assembly may also include resource-related and worker-related assembly experience-related constraints. Therefore, the embodiment of the present application may construct a target constraint knowledge base to quickly and easily call related constraints. The target constraint knowledge base may be understood here as a database established based on the constraints that may be imposed on the assembly of complex products obtained from a variety of data sources.

[0138] Among them, the target constraint knowledge base in the embodiment of the present application includes but is not limited to storing the rules, standards and historical experience of system design. When various rules and standards are updated, the target constraint knowledge base will also be updated accordingly, and it can generate suitable constraints in different scenarios through the reasoning engine and automatically solve these constraints. The constraint set here refers to a set of constraints that integrates multiple generated constraints.

[0139] For example, the core part of the target constraint knowledge base in this application includes but is not limited to the following types of information:

[0140] Resource dependency constraints: The assembly and testing of components require corresponding tools, equipment and other resources. The resource requirements of each physical component are stored in the knowledge base. The resource constraints can be expressed as follows:

[0141] C f =R i ∧T i ∧Q i ,

[0142] Among them, R i It is resource dependency, indicating that component S i Resources required; i is the execution time window; Q i It is the space limitation of tools and equipment. Components can be assembled only when resources are available, time is suitable and space is not conflicting.

[0143] On-site workers’ experience summary: On-site workers may encounter many unforeseen problems during the assembly process, which may be caused by operational difficulties, parts mismatch, tool failure, etc. When workers encounter these problems, they can report them through the feedback system. The system generates new constraints based on the feedback and summarizes them into experience to guide subsequent assembly.

[0144] On-site workers manually input feedback to report actual problems encountered during the assembly process: for example, workers may report that a component cannot be installed correctly, or that it is difficult to continue the task due to limited operating space. After receiving the feedback, the system first classifies and analyzes the feedback content to determine the type and severity of the feedback. Among them, the types of feedback from on-site workers can be divided into two types, including but not limited to operating difficulties and assembly conflicts:

[0145] Operational difficulties: Workers are unable to continue to perform tasks under current operating conditions (e.g., insufficient installation space, unavailable tools, etc.).

[0146] Assembly conflict: Interference or assembly conflict occurs between components and they cannot be installed as originally planned.

[0147] Each feedback type may affect different constraints. For example, operational difficulties may generate new spatial constraints, while assembly conflicts require a re-evaluation of the fit between function and physical structure.

[0148] The system can dynamically generate new constraints based on worker feedback: for example, if a worker feedback component S i Failure to install may be due to space limitations. In this case, the system needs to regenerate space constraints to avoid component interference:

[0149] C p ′=if Space i <minSpace,S i = False,

[0150] And, the target constraint knowledge base in the embodiment of the present application also includes a constraint rule base. The constraint rule base is a key part of the target constraint knowledge base, including but not limited to assembly rules with resource constraints and worker experience feedback. The constraint rule base can automatically generate constraint conditions applicable to the current assembly task based on current functional requirements. The constraints in the rule base can be derived based on international standards, design specifications, manufacturing experience, etc., and can also be updated as various information is updated.

[0151] For example, the constraint rule base may store the following rule: “If component S i The geometric dimensions exceed d min , then it cannot be combined with component S j Assemble in the same space area. ", "Component S i In the resource R i When it is not available, it cannot be executed." etc.

[0152] Therefore, the embodiment of the present application can use the target constraint knowledge base to automatically generate at least one constraint condition set in the target complex product assembly process, so as to generate an optimization planning result of the target complex product assembly that meets the preset optimization conditions by combining the sequential relationship between the functional modules, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly.

[0153] The preset optimization conditions here can be understood as certain optimization conditions that the optimization planning results need to meet, for example, the optimization planning results meet all constraints in the constraint condition set and can run normally under these constraints. Resource information here can be understood as the current resource information of the target complex product assembly, such as which resources can be called and which resources cannot be called. The optimization planning results include but are not limited to resource allocation and task sequence in the target complex product assembly process.

[0154] Optionally, in one embodiment of the present application, target constraints are called from a target constraint knowledge base to generate at least one constraint set in the target complex product assembly process, including: based on assembly requirements and functional modules, automatically retrieving corresponding functional constraints, logical constraints and physical constraints from the target constraint knowledge base; determining at least one constraint set based on the functional constraints, logical constraints and physical constraints.

[0155] Based on the relevant descriptions of other embodiments, it can be understood that the present application can utilize the target constraint knowledge base to automatically generate at least one new constraint condition set in the target complex product assembly process.

[0156] During the actual execution process, the system will automatically retrieve the relevant constraints involved in the current target complex product assembly from the knowledge base to generate new constraints based on testing and product component assembly, and then generate a complete set of constraints based on these new constraints. Figure 4 This is a flow chart of constraint generation and solution based on a knowledge base according to an embodiment of the present application. Figure 4 As shown, the constraint types generated in the embodiment of the present application include but are not limited to the following:

[0157] (1) Functional test constraint generation

[0158] In the embodiment of the present application, the process of generating functional test constraints is automatically generated according to resource requirements, and the resources required by each functional test node can be directly obtained from the knowledge base. The system generates corresponding constraints based on this information to ensure that the test execution order and resource allocation are reasonable.

[0159] Among them, the logic of generating functional test constraints in the embodiment of the present application can be, but is not limited to, expressed as follows:

[0160] First, the function F that sets the test node is retrieved from the knowledge base. i Resource requirements R i and dependencies;

[0161] Then, if a resource is unavailable, a corresponding functional constraint is generated:

[0162] C f =if R i is not available,F i = False,

[0163] If F i Depends on another function F j The execution order constraints are generated if

[0164]

[0165] (2) Logical constraint generation

[0166] The generation of logical constraints in the embodiments of the present application is to ensure that the functions of the system are logically consistent. For example, when certain functional modules need to be executed in parallel or must be executed in sequence, the system will generate corresponding rules based on the logical constraint knowledge. Logical constraints are usually used to ensure that the operation of the system will not conflict, especially when multiple tasks are running in parallel.

[0167] The generation logic of the logical constraints in the embodiment of the present application may be, but is not limited to, expressed as follows:

[0168] First, retrieve the functional modules F that need to be executed in parallel i and F j ;

[0169] Then, generate the synchronization constraints:

[0170]

[0171] If two functional modules cannot be executed at the same time, a mutual exclusion constraint is generated:

[0172] C l =F i ∧F j =False.

[0173] (3) Generation of physical component assembly constraints

[0174] In the embodiment of the present application, the process of generating physical component assembly constraints is automatically generated according to resource requirements, and the resources required for each component assembly can be directly obtained from the knowledge base. The system can generate corresponding constraints based on this information to ensure reasonable assembly execution and resource allocation.

[0175] The logic of generating physical component assembly constraints in the embodiment of the present application can be, but is not limited to, expressed as follows:

[0176] First, retrieve the component S from the knowledge base i Resource requirements R i ;

[0177] Then determine if a resource is unavailable and generate the corresponding resource constraints:

[0178] C f =if R i is not available,S i =False.

[0179] Optionally, in one embodiment of the present application, a planning result of the target complex product assembly that meets preset optimization conditions is generated by combining the function order, at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, including: using a target optimization algorithm to solve the optimal solution that meets at least one constraint set; based on the optimal solution, generating a planning result of the target complex product assembly that meets the preset optimization conditions.

[0180] As a possible implementation method, after generating at least one set of constraints, the embodiment of the present application can also use a certain target optimization algorithm to solve the at least one set of constraints, find the optimal solution that satisfies all constraints, and obtain the planning result of the target complex product assembly. Thus, the embodiment of the present application can ensure that the system can satisfy all constraints and optimize the target complex product assembly process to the maximum extent.

[0181] Here, the target optimization algorithm can be understood as a related algorithm that can solve the optimal solution that satisfies all constraints in the constraint set. Figure 4 The embodiments of the present application may, but are not limited to, use a linear programming (LP) method to express the constraint conditions as linear inequalities to find the optimal solution. For example, space constraints and resource allocation constraints can be solved by linear programming.

[0182] Among them, the objective function of solving the problem can be expressed as:

[0183] max∑ i ∑ j W ij ·x ij ,

[0184] Among them, x ij Indicates functional module F i and physical component S j The match between ij It is the function module F i and physical component S j The matching weight between .

[0185] Furthermore, during the solution process, the embodiment of the present application can also detect conflicts between constraints so as to make timely adjustments. For example, if a physical component is requested by multiple functional modules at the same time, the embodiment of the present application will generate a conflict prompt and resolve it through an adjustment strategy.

[0186] At this point, the system can gradually resolve conflicts and generate solutions that meet all constraints through iterative optimization. If a constraint cannot be met, the embodiment of the present application can solve it by adjusting the mapping of functions and structures or changing the order of task execution.

[0187] The embodiment of the present application can automatically generate constraint conditions suitable for the current task by constructing a target constraint knowledge base including functional test constraints, logical constraints, and physical component assembly constraints, and obtain the optimal assembly sequence of the target complex product through an optimization algorithm.

[0188] Optionally, in one embodiment of the present application, after generating a planning result of the target complex product assembly that meets preset optimization conditions by combining the function sequence, at least one constraint set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, it also includes: obtaining real-time feedback information of the target complex product assembly, wherein the real-time feedback information includes the real-time feedback information fed back by the on-site workers at the current moment and / or the corresponding real-time feedback information fed back by the suppliers; based on the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers, adjusting at least one constraint set to generate at least one new constraint set that meets the preset conditions; adjusting the optimization planning result of the target complex product assembly through at least one new constraint set until a new planning result of the target complex product assembly that meets at least one new constraint set is obtained.

[0189] In other embodiments, in the complex assembly process of complex complex products, in addition to relying on the constraints generated by the knowledge base, real-time feedback and adjustment mechanisms are also crucial, especially when the on-site status and supplier supply status fluctuate, these situations may have a significant impact on the assembly process. In order to cope with these dynamic changes, the embodiment of the present application can obtain (receive) in real time the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers, so as to adjust the constraints in at least one constraint set based on the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers, generate at least one new constraint set, and then adjust the optimization planning result of the target complex product assembly through at least one new constraint set, until a new planning result of the target complex product assembly that satisfies at least one new constraint set is obtained.

[0190] For example, the embodiment of the present application can generate new constraints based on the real-time assembly status reported by workers during the assembly of the target complex product or the supply chain situation reported by suppliers, and dynamically adjust and replan the assembly task. Among them, the core of the real-time feedback and adjustment mechanism in the embodiment of the present application is to dynamically adjust the task execution order and resource scheduling by continuously obtaining feedback information from the assembly site and the supply chain, combined with existing constraints, to ensure that the assembly process will not be interrupted.

[0191] For example, on-site workers can report that a task cannot be continued due to installation difficulties, tool failures, or unexpected assembly conflicts; or that the quality of assembled components is found to be substandard and they need to wait for redelivery of components, etc.

[0192] Suppliers may report that they are unable to deliver on time, i.e., materials, components or tools required for assembly are missing, etc., which may result in a functional module not being able to be executed as planned.

[0193] After obtaining these real-time feedback information, the embodiment of the present application will make certain reactions and processes, and the process can be but is not limited to the following:

[0194] 1. Feedback from on-site workers (feedback on on-site assembly execution status)

[0195] On-site workers may encounter many unforeseen problems during the assembly process, which may be caused by operational difficulties, parts mismatch, tool and equipment failure, etc. When workers encounter these problems, they can report the problems to the central control unit of the system through the feedback system, generate new constraints based on the feedback, and adjust the assembly plan.

[0196] (1) Feedback on-site status generation constraints

[0197] On-site workers can manually input feedback to the system to report actual problems encountered during the assembly process, such as operational difficulties or assembly conflicts and tool failures, which make the task unable to continue. At this time, new constraints need to be generated.

[0198] If workers feedback the operation tool T i If a failure occurs and the operation cannot be continued, the embodiment of the present application can generate a new functional constraint:

[0199] C f ′ =if T i is not available,F i = False,

[0200] These newly generated constraints are immediately fed back to the system, which adjusts the execution order of functional modules or reallocates resources based on these constraints to ensure the continuity of the assembly process.

[0201] (2) Adjust the execution order of functional modules

[0202] Problems reported by workers may cause some tasks to fail to proceed as planned, so the execution order of the functional modules will be re-evaluated in the present embodiment. i Unable to execute, but this module is a subsequent function module F j The embodiment of the present application can re-plan and adjust the priority of the functional modules.

[0203] A new function order can be generated by, but is not limited to, re-evaluating the dependency matrix M:

[0204] if F i = False, Recalculate M,

[0205] The system can automatically adjust the execution order of subsequent functional modules according to the new dependency matrix to avoid the situation where the entire assembly task is interrupted due to the failure of a certain function.

[0206] 2. Supplier out-of-stock feedback processing

[0207] In the actual assembly process, supply chain issues are one of the important factors that lead to assembly progress delays. If the supplier fails to deliver certain key components, materials or tools on time, the embodiment of the present application can respond in a timely manner and adjust the execution order of tasks and resource allocation.

[0208] (1) Capturing and analyzing supplier feedback

[0209] When a supplier is unable to deliver key supplies (such as parts, materials or tools) on time, the embodiment of the present application can promptly obtain information fed back by the supplier through the supply chain management system, so as to determine the impact of the current shortage of materials on the assembly process based on the information fed back by the supplier.

[0210] The information provided by the supplier in the present application embodiment includes but is not limited to the following types:

[0211] Type of out-of-stock material: Which specific component, material, or tool failed to be delivered.

[0212] Estimated delivery times: Estimated delivery times provided by suppliers help assess the specific impact of stockouts on assembly schedules.

[0213] Alternative solutions: If a supplier can provide alternative materials or components, the embodiment of the present application can generate a new material usage plan based on the alternative solutions.

[0214] (2) Generate new resource constraints

[0215] This embodiment of the application can generate new constraints based on supplier feedback. For example, if a key component S i If the delivery fails to be made on time, the embodiment of the present application can immediately generate a shortage constraint to prevent the execution of the functional modules related to the component:

[0216] C p ′=if S i is not available,F i = False,

[0217] If the supplier provides an alternative component S j , then the embodiment of the present application will generate new constraints to verify whether the replacement component meets the design requirements. For example, whether the size, material strength, etc. of the replacement component are compatible with the original component:

[0218] C p′=if S j matches specifications,Fi=True,

[0219] (3) Re-plan the order of task execution

[0220] Due to the shortage of key materials, the embodiment of the present application will timely adjust the execution order of the functional modules. i If it is unable to continue, the embodiment of the present application will re-evaluate the dependency matrix M and the resource allocation matrix R, adjust the execution order of subsequent functional modules or reallocate resources to ensure that other tasks can continue.

[0221] The new task execution order can be generated by dynamic programming or heuristic algorithms. The embodiment of the present application reorders the functional modules according to the availability of materials and gives priority to executing tasks that do not depend on out-of-stock components.

[0222] 3. Dynamic constraint solving and optimal adjustment

[0223] After generating new constraints, the embodiment of the present application can re-perform assembly sequence planning and solving to find the optimal solution that satisfies all constraints. The goal of the solution process is to minimize assembly delays due to feedback (such as worker feedback or supplier shortages) while ensuring that the system can continue to operate effectively under all constraints. Through real-time adjustment, the embodiment of the present application can avoid interruptions in the assembly process to the greatest extent possible.

[0224] (1) Assembly sequence planning solution and optimization

[0225] For example, in the embodiments of the present application, linear programming or heuristic algorithms may be used, but are not limited to, for constraint solving. The goal is to optimize the overall assembly efficiency of the system by adjusting factors such as resource allocation and task sequence, and to ensure the continuity of assembly tasks and the optimal allocation of resources.

[0226] The linear programming problem can be formalized as the following objective function:

[0227] max∑ i ∑ j W ij ·x ij ,

[0228] Among them, W ij Indicates functional module F i With physical components S j The matching weight between ij is a binary variable, indicating the functional module F i Is it assigned to component S? j :

[0229]

[0230] The embodiment of the present application dynamically adjusts the weight W in the objective function according to new material availability, worker feedback or component status. ij , to prioritize the most critical functions. For example, when some components provided by a supplier are out of stock, the system may reduce the weights associated with the out-of-stock components to avoid assigning critical tasks to these unavailable components.

[0231] (2) Application of heuristic algorithms

[0232] For complex assembly systems, linear programming may have difficulty handling large-scale constraint sets. In this case, the embodiment of the present application can use a heuristic algorithm (such as a genetic algorithm or a simulated annealing algorithm) to quickly generate an approximate optimal solution. The advantage of a heuristic algorithm is that it can quickly adapt to a changing environment, especially when resource supply and task execution are constantly changing.

[0233] The basic steps of genetic algorithm can be expressed as follows but are not limited to:

[0234] Initialization: Generate a population of initial solutions, each individual represents an allocation scheme of functional modules and physical components;

[0235] Fitness function: According to the objective function max∑ i ∑ j W ij ·x ij , evaluate the fitness of each individual;

[0236] Selection and crossover: select individuals with high fitness and crossover to generate new solutions;

[0237] Mutation: Randomly mutate some solutions to increase the exploration of the solution space;

[0238] Termination condition: When the specified number of iterations or convergence criteria is reached, the algorithm terminates and outputs the optimal solution.

[0239] Through the genetic algorithm, the embodiment of the present application can generate a nearly optimal assembly plan in a short time and continuously adjust it according to actual feedback.

[0240] (3) Iterative Optimization and Feedback Loop

[0241] During the solution process, the embodiment of the present application may repeatedly adjust and optimize the constraints according to the on-site status or further feedback from the supplier. This forms a feedback closed loop, that is, the embodiment of the present application can continuously obtain feedback from the site, adjust the constraints according to the feedback, and then re-plan the assembly task. Each optimized solution will be applied to the current assembly process and adjusted again when new feedback arrives.

[0242] The iterative optimization process can be, but is not limited to, expressed as follows:

[0243] Receive feedback: Worker feedback or supplier feedback affects the assembly process;

[0244] Generate new constraints: Generate new physical, functional or logical constraints based on feedback;

[0245] Optimize mapping: adjust the matching relationship between functions and physical components;

[0246] Constraint solving: Solve new constraints and generate the optimal assembly solution;

[0247] Execute New Solution: Apply the new assembly solution to the system and continue monitoring.

[0248] Through the feedback closed loop, the embodiment of the present application can maintain a high degree of flexibility of the system, ensuring that the system can still maintain efficient operation in a dynamically changing assembly environment, thereby ensuring the smooth operation of the assembly.

[0249] The embodiments of the present application can dynamically generate new constraints by capturing feedback information in the case of on-site assembly status feedback and supplier out-of-stock situations, and perform constraint solving and optimization through linear programming or heuristic algorithms, thereby ensuring that the assembly process can proceed smoothly in the face of uncertainty and emergencies, and minimizing delays and waste of resources.

[0250] According to the assembly planning and solving method for complex product assembly proposed in the embodiment of the present application, a target system model for target complex product assembly can be generated based on the MBSE method, so as to generate the planning result of the target complex product assembly according to the mapping information between the function tree and the structure tree of the target system model, at least one constraint condition set in the assembly process of the target complex product, the function sequence and the resource information. Thus, it is realized that the function decomposition relationship and dependency relationship of the complex product are automatically extracted and generated using MBSE, and the function tree is generated, so as to realize the efficient progress of the assembly process and the orderly execution of the functions according to the relationship between the functions; through the mapping between the function tree and the structure tree, the optimal matching between the function module and the physical components can be realized, so as to ensure that the functions in the assembly process of the target complex product can be realized by the physical components; by constructing a target constraint knowledge base including function test constraints, logical constraints, and physical component assembly constraints, it is possible to automatically generate constraints suitable for the current task, and solve the optimal assembly sequence through the optimization algorithm; by continuously obtaining feedback information from the assembly site and the supply chain, new dynamic constraints are generated in real time in combination with the existing constraints, and then the assembly sequence is adjusted in time, so as to ensure that the assembly process will not be interrupted while improving the assembly efficiency and ensuring feasibility and accuracy. As a result, the problem of functional test node design usually involved in the assembly process of complex products in related technologies is solved. It is difficult to determine the test nodes by relying on manual experience, which increases the difficulty of assembling complex products. Moreover, when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional systems and high-precision assembly requirements corresponding to the assembly of complex products, and it is impossible to achieve the global optimal assembly of complex products.

[0251] Next, the assembly planning solving device for complex product assembly proposed in accordance with the embodiment of the present application will be described with reference to the accompanying drawings.

[0252] Figure 5 It is a structural schematic diagram of an assembly planning solving device for complex product assembly according to an embodiment of the present application.

[0253] like Figure 5 As shown, the assembly planning solution device 10 for complex product assembly includes: a first acquisition module 100 , a generation module 200 and a solution module 300 .

[0254] The first acquisition module 100 is used to construct a target system model corresponding to a target complex product assembly by using a model-based systems engineering (MBSE) strategy, so as to obtain dependencies and functional sequences between functional modules in the target system model.

[0255] The generation module 200 is used to generate a function tree of the target system model according to the decomposition results of the function modules and the dependencies between the function modules, and to generate a structure tree of the target system model based on the dependencies of the physical components in the target complex product assembly and the physical structure of the target system model.

[0256] The solution module 300 is used to call the target constraint conditions from the target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process, so as to combine the function sequence, at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets the preset optimization conditions.

[0257] Optionally, in one embodiment of the present application, the first acquisition module 100 includes: a modeling unit and a collection unit.

[0258] The modeling unit is used to model the dependency relationship between functional modules and their corresponding dependency matrix based on the assembly requirements of the target complex product assembly.

[0259] The collection unit is used to collect the execution conditions of the functional modules to determine the function sequence through the execution conditions and the dependency matrix.

[0260] Optionally, in one embodiment of the present application, it further includes: a second acquisition module and a third acquisition module.

[0261] The second acquisition module is used to acquire the node set of the function tree and the node set of the structure tree.

[0262] The third acquisition module is used to complete the mapping of the function tree and the structure tree based on the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and acquire the mapping information.

[0263] Optionally, in one embodiment of the present application, the solution module 300 includes: a retrieval unit and a determination unit.

[0264] The retrieval unit is used to automatically retrieve corresponding functional constraints, logical constraints and physical constraints from the target constraint knowledge base based on the assembly requirements and the functional modules.

[0265] The determining unit is used to determine at least one constraint condition set according to the functional constraints, the logical constraints and the physical constraints.

[0266] Optionally, in one embodiment of the present application, the solution module 300 includes: a solution unit and a generation unit.

[0267] The solution unit is used to use a target optimization algorithm to solve the optimal solution that satisfies at least one set of constraints.

[0268] The generation unit is used to generate a planning result of the target complex product assembly that meets the preset optimization conditions based on the optimal solution.

[0269] Optionally, in one embodiment of the present application, it further includes: a fourth acquisition module, a first adjustment module and a second adjustment module.

[0270] Among them, the fourth acquisition module is used to obtain real-time feedback information of the target complex product assembly after generating a planning result of the target complex product assembly that meets the preset optimization conditions in combination with the function sequence, at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, wherein the real-time feedback information includes the real-time feedback information received from on-site workers at the current moment and / or the real-time feedback information feedback from suppliers.

[0271] The first adjustment module is used to adjust at least one constraint condition set based on the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the supplier in response to the feedback, so as to generate at least one new constraint condition set that meets the preset conditions.

[0272] The second adjustment module is used to adjust the optimization planning result of the target complex product assembly through at least one new constraint condition set until a new planning result of the target complex product assembly that satisfies at least one new constraint condition set is obtained.

[0273] It should be noted that the aforementioned explanation of the embodiment of the assembly planning solution method for complex product assembly is also applicable to the assembly planning solution device for complex product assembly of this embodiment, and will not be repeated here.

[0274] According to the assembly planning and solving device for complex product assembly proposed in the embodiment of the present application, a target system model for target complex product assembly can be generated based on the MBSE method, so as to generate the planning result of the target complex product assembly according to the mapping information between the function tree and the structure tree of the target system model, at least one set of constraint conditions in the assembly process of the target complex product, the function sequence and the resource information. Thus, it is realized that the function decomposition relationship and dependency relationship of the complex product are automatically extracted and generated using MBSE, and the function tree is generated, so as to realize the efficient progress of the assembly process and the orderly execution of the functions according to the relationship between the functions; through the mapping between the function tree and the structure tree, the optimal matching between the function module and the physical components can be realized, so as to ensure that the functions in the assembly process of the target complex product can be realized by the physical components; by constructing a target constraint knowledge base including function test constraints, logical constraints, and physical component assembly constraints, it is possible to automatically generate constraint conditions suitable for the current task, and solve the optimal assembly sequence through the optimization algorithm; by continuously obtaining feedback information from the assembly site and the supply chain, new dynamic constraints are generated in real time in combination with the existing constraints, and then the assembly sequence is adjusted in time, so as to ensure that the assembly process will not be interrupted while improving the assembly efficiency and ensuring feasibility and accuracy. As a result, the problem of functional test node design usually involved in the assembly process of complex products in related technologies is solved. It is difficult to determine the test nodes by relying on manual experience, which increases the difficulty of assembling complex products. Moreover, when it comes to problems such as spatial interference and stress distribution, manual experience is also difficult to handle the multi-functional systems and high-precision assembly requirements corresponding to the assembly of complex products, and it is impossible to achieve the global optimal assembly of complex products.

[0275] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0276] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0277] When the processor 602 executes the program, the assembly planning solution method for complex product assembly provided in the above embodiment is implemented.

[0278] Furthermore, the electronic device further comprises:

[0279] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0280] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0281] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0282] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0283] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0284] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0285] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the assembly planning solution method for complex product assembly as described above is implemented.

[0286] An embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the assembly planning solution method for complex product assembly provided by the embodiment of the present application is implemented.

[0287] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0288] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0289] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0290] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0291] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0292] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0293] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0294] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for solving assembly planning for complex product assembly, characterized in that: The following steps are involved: Using the MBSE strategy of model-based systems engineering to build a target system model corresponding to the target complex product assembly, so as to obtain the dependency relationship and function sequence between the functional modules in the target system model; Generate a function tree of the target system model according to the decomposition results of the function modules and the dependency relationships between the function modules, and generate a structure tree of the target system model based on the dependency relationships of the physical components in the target complex product assembly and the physical structure of the target system model; The target constraint conditions are called from the target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process, so as to combine the function sequence, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets the preset optimization conditions.

2. The method according to claim 1, characterized in that The obtaining of the dependency relationship and function sequence between the function modules in the target system model includes: Modeling the dependency relationship between the functional modules and their corresponding dependency matrix based on the assembly requirements of the target complex product assembly; The execution conditions of the functional modules are collected to determine the function sequence through the execution conditions and the dependency matrix.

3. The method according to claim 1, characterized in that Also includes: Obtaining a node set of the function tree and a node set of the structure tree; The mapping between the function tree and the structure tree is completed based on the node set of the function tree, the node set of the structure tree and the target mapping constraint condition, and the mapping information is obtained.

4. The method according to claim 1, characterized in that The step of calling the target constraint condition from the target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process includes: Based on the assembly requirements and the functional modules, automatically retrieving corresponding functional constraints, logical constraints and physical constraints from the target constraint knowledge base; The at least one constraint condition set is determined according to the functional constraints, the logical constraints and the physical constraints.

5. The method according to claim 4, characterized in that The step of combining the function sequence, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets a preset optimization condition includes: Using a target optimization algorithm to find an optimal solution that satisfies the at least one set of constraints; Based on the optimal solution, a planning result of the target complex product assembly that meets preset optimization conditions is generated.

6. The method according to claim 1, characterized in that After generating a planning result of the target complex product assembly that meets a preset optimization condition by combining the function sequence, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly, the method further includes: Acquire real-time feedback information of the assembly of the target complex product, wherein the real-time feedback information includes the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the supplier; Based on the real-time feedback information fed back by the on-site workers at the current moment and / or the real-time feedback information fed back by the suppliers, adjusting the at least one constraint condition set to generate at least one new constraint condition set that meets the preset conditions; The optimization planning result of the target complex product assembly is adjusted by the at least one new set of constraints until a new planning result of the target complex product assembly that satisfies the at least one new set of constraints is obtained.

7. An assembly planning solution device for complex product assembly, characterized in that: include: An acquisition module is used to construct a target system model corresponding to a target complex product assembly by using a model-based systems engineering (MBSE) strategy to obtain dependencies and function sequences between functional modules in the target system model; A generating module, used to generate a function tree of the target system model according to the decomposition results of the function modules and the dependency relationships between the function modules, and to generate a structure tree of the target system model based on the dependency relationships of the physical components in the target complex product assembly and the physical structure of the target system model; A solution module is used to call target constraint conditions from a target constraint knowledge base to generate at least one constraint condition set in the target complex product assembly process, so as to combine the function sequence, the at least one constraint condition set, the mapping information between the function tree and the structure tree, and the resource information of the target complex product assembly to generate a planning result of the target complex product assembly that meets the preset optimization conditions.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the assembly planning solution method for complex product assembly as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the assembly planning solution method for complex product assembly as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the assembly planning solution method for complex product assembly as described in any one of claims 1-6.

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