Modular parametric design method for electrical system of construction robot

Abstract

The present invention relates to the field of parametric design technology, and in particular to a module parametric design method for a construction robot electrical system. The method comprises the following steps: acquiring construction robot design data, and extracting component function design features and robot structure features from the construction robot design data to obtain component function design data and robot structure data; performing robot topology structure analysis on the robot structure data to obtain a robot topology structure model; clustering functional components according to the component function design data to obtain functional component clustering data; performing module marking on the robot topology structure model according to the functional component clustering data to obtain a robot module structure model; performing module assembly strategy analysis on the robot module structure model to obtain a module assembly strategy; describing the robot topology structure model to obtain a construction robot module parametric model. The present invention can improve the flexibility and adaptability of electrical system design.

Description

Technical Field

[0001] The present invention relates to the technical field of parametric design, and in particular to a module parametric design method for an electrical system of a construction robot. Background Art

[0002] With the modernization and automation trend of the construction industry, the application of construction robots has gradually become an important means to improve production efficiency and work safety. Construction robots usually contain complex electrical systems to control and perform various construction tasks, such as bricklaying, plastering and installing structures. However, traditional construction robots are usually designed with static and fixed electrical systems, which are difficult to quickly adapt to different construction tasks and environmental changes. For example, the same robot may need to operate in different types of building structures, but existing designs often cannot be effectively adjusted to meet these different needs. Due to the relatively fixed electrical system design, traditional construction robot designs often cannot meet the personalized requirements of specific projects. For example, some projects require higher precision or more complex control logic, and traditional designs cannot flexibly respond to these changes in requirements. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a modular parametric design method for the electrical system of a construction robot to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above object, a module parameterized design method for a construction robot electrical system comprises the following steps:

[0005] Step S1: Acquire construction robot design data, and perform component function design feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function design data and robot structure data; perform robot topology structure analysis on the robot structure data, thereby obtaining a robot topology structure model;

[0006] Step S2: performing component function similarity evaluation according to the component function design data, thereby obtaining component function similarity data, and performing functional component clustering according to the component function similarity data, thereby obtaining functional component clustering data;

[0007] Step S3: dividing the robot topology structure model into structural modules according to the functional component clustering data, thereby obtaining a robot structure module set and an electrical wiring harness module set, and marking the robot topology structure model according to the robot structure module set and the electrical wiring harness module set, thereby obtaining a robot module structure model;

[0008] Step S4: performing module assembly priority evaluation on the robot module structure model to obtain module assembly priority data, and performing module assembly strategy analysis on the robot structure module set and the electrical harness module set according to the module assembly priority data to obtain a module assembly strategy;

[0009] Step S5: Performing a module assembly parameterized description on the robot module structure model according to the module assembly strategy, thereby obtaining a construction robot module parameterized model.

[0010] The present invention extracts the functional characteristics of each component, such as purpose, operation method, etc., by analyzing the design data of the construction robot. It is helpful to understand the role and requirements of each component in the overall system. The physical structural characteristics of the robot, such as size, connection method, etc., are analyzed. These characteristics are crucial for subsequent modular design and optimization. By evaluating the functional similarity between components, it can be determined which components can share similar electrical system design or control logic. This helps to reduce system complexity and design costs. Components with similar functions are clustered to form component groups. This can simplify the design and maintenance of the electrical system and improve the flexibility and maintainability of the system. According to the clustering results of the functional components, the robot topology is decomposed into modular parts. Each module can be designed, tested and maintained independently, improving the reliability and maintainability of the overall system. An identifier or a specific electrical harness is assigned to each module so that each component can be clearly identified and managed during the subsequent assembly and integration process. The assembly priority of each module is evaluated to ensure that the gradual integration process of the system meets the design and safety requirements while maximizing the assembly efficiency. An assembly strategy is formulated based on the priority data, including the sequence, test and verification process, to ensure that the function and performance of each module and the overall system meet the expected requirements. According to the obtained assembly strategy, the robot topology model is parametrically described. This makes the system assembly process more standardized and automated, while reducing the possibility of human error. Overall, this technical process can not only improve the flexibility and adaptability of construction robot design, but also optimize the overall efficiency, maintainability and safety of the system. Through more accurate data analysis and modular design, the specific needs of different projects can be effectively responded to.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire construction robot design data, and perform component definition function feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function data and robot structure data;

[0013] Step S12: Structural classification of the robot structural data, thereby obtaining robot electrical structural data and robot mechanical structural data;

[0014] Step S13: performing an electrical topological structure analysis of the robot according to the electrical structure data of the robot, thereby obtaining an electrical topological structure model of the robot;

[0015] Step S14: performing a robot mechanical topology structure analysis according to the robot mechanical structure data, thereby obtaining a robot mechanical topology structure model;

[0016] Step S15: merging the robot electrical topology structure model and the robot mechanical topology structure model into a robot topology structure, thereby obtaining a robot topology structure model.

[0017] The present invention obtains the data required for the design of a construction robot. By extracting the component definition function features of these data, the functions and features of each robot component can be identified. This is critical for subsequent design and optimization because it ensures that each component can accurately perform its intended function in the design. The structural data of the robot is classified and analyzed. By dividing the robot structure into electrical structure and mechanical structure, the different parts of the robot can be understood and processed more clearly. This classification helps the subsequent electrical and mechanical topology analysis, making the design process more systematic and organized. Electrical topology analysis is a process of systematically evaluating and optimizing the wiring, connection and layout of electronic components of the electrical part of the robot. It helps the design team to ensure the rationality of the layout of the electrical part, avoid electrical interference and functional conflicts, and improve the reliability and efficiency of the robot. Mechanical topology analysis involves the assembly, connection and kinematic characteristics evaluation of each mechanical component of the robot. It helps the design team to ensure the stability of the mechanical structure, the smoothness of the movement and the durability of the structure. Reasonable mechanical structure design is crucial to the performance and operational safety of the robot. The electrical and mechanical topology models are merged into a comprehensive robot topology model. This ensures the coordination and integration of the electrical and mechanical parts so that the entire robotic system is designed to be integrated and work together. This comprehensive model can help the design team work more efficiently in subsequent development and testing stages.

[0018] Optionally, step S13 is specifically:

[0019] Step S131: extracting circuit structure features and electrical component structure features from the robot electrical structure data, thereby obtaining circuit structure data and electrical component structure data;

[0020] Step S132: classifying the circuit structure data into circuit connection modes, thereby obtaining series circuit structure data and parallel circuit structure data;

[0021] Step S133: performing series component association on the series circuit structure data and the electrical component structure data, thereby obtaining electrical component series circuit connection data; performing parallel component association on the parallel circuit structure data and the electrical component structure data, thereby obtaining electrical component parallel circuit connection data;

[0022] Step S134: constructing an electrical block diagram according to the electrical component series circuit connection data and the electrical component parallel circuit connection data, thereby obtaining an electrical block diagram of the robot;

[0023] Step S135: Perform electrical connection topology analysis according to the robot electrical block diagram to obtain the robot electrical topology model.

[0024] The present invention extracts the structural features of the circuit and the structural features of each electrical component from the electrical structure data of the robot, wherein the circuit structure features include the topological relationship of the circuit, node information, etc., and the electrical component structure features involve the type, parameters, connection mode, etc. of each component. Through these extractions, a detailed foundation of circuit structure data can be established. According to the circuit structure data, the connection mode of the circuit is classified, mainly including a series circuit structure and a parallel circuit structure. A series circuit means that the electrical components are connected in sequence and the current flows along a path; while a parallel circuit means that the electrical components are connected in parallel and the current can choose different paths. Classifying these connection modes is helpful for subsequent analysis and design. According to the classified series and parallel circuit structure data, the electrical components are associated. The series component association establishes the connection relationship of the electrical components in the series circuit, while the parallel component association determines the connection mode of the electrical components in the parallel circuit. These association data provide specific configuration information of the circuit. According to the obtained series and parallel circuit connection data of the electrical components, the electrical block diagram of the robot can be constructed. The electrical block diagram shows the connection relationship and layout of each component in the electrical system of the robot, and is an important tool for understanding and designing the electrical system. The electrical connection topology structure analysis is performed according to the electrical block diagram. This analysis helps understand the flow paths of electrical signals and power in the robot's electrical system, and can check the effectiveness and performance of the circuit. The electrical topology model provides a high-level perspective to help engineers optimize and improve the design of the electrical system.

[0025] Optionally, step S14 is specifically:

[0026] Step S141: classifying the robot mechanical structure data into functional structures, thereby obtaining mechanical component structure data and connection structure data;

[0027] Step S142: extracting component connection structure features from the mechanical component structure data, thereby obtaining mechanical component connection structure data;

[0028] Step S143: performing similarity calculation on the mechanical component connection structure data and the connection structure data, thereby obtaining connection structure similarity data;

[0029] Step S144: performing component connection association on the mechanical component structure data according to the connection structure similarity data, thereby obtaining mechanical component connection data;

[0030] Step S145: Perform mechanical connection topology analysis on the mechanical component connection data to obtain a robot mechanical topology model.

[0031] In the present invention, the mechanical structure data of the robot is first classified by functional structure. This means that according to the functions of each component of the robot, it is classified and sorted for further processing and analysis. For example, for a robot, it involves the classification of functional modules such as motion systems, actuators, and sensors. For the structural data of each mechanical component, the connection structure features are extracted. These features include information such as the shape, size, connection method (such as bolts, welding, etc.), and material properties of the component. By extracting these features, detailed connection structure data of each mechanical component can be established. Using the extracted connection structure feature data, the connection structure between the mechanical components is similarly calculated. These calculations can help determine the structural similarity between components, thereby finding potential opportunities for possible reuse of components or design optimization. Based on the obtained connection structure similarity data, this will associate the connection of the mechanical components. This means that according to similar connection structures, similar mechanical components are classified or possible connection methods between them are suggested. Such association helps to optimize the design, reduce the number of parts, improve manufacturing efficiency, etc. According to the obtained mechanical component connection data, the mechanical connection topology structure is analyzed. This analysis can show the connection method and relationship between the various components in the overall mechanical structure of the robot, thereby forming a mechanical topology structure model. This model helps engineers understand the complexity of mechanical systems, optimize layouts, assess structural strength and stability, and more.

[0032] Optionally, step S2 specifically includes:

[0033] Step S21: extracting component performance parameter features and component function definition features from the component function design data, thereby obtaining a component performance parameter set and component function definition data;

[0034] Step S22: Calculating component performance parameter similarity on the component performance parameter set, thereby obtaining component performance parameter similarity data;

[0035] Step S23: Calculating the component definition cosine similarity according to the component function definition data, thereby obtaining component function definition similarity data;

[0036] Step S24: performing similarity comprehensive integration on the component performance parameter similarity data and the component function definition similarity data, thereby obtaining component function similarity data;

[0037] Step S25: clustering the component function design data into functional components according to the component function similarity data, thereby obtaining functional component clustering data.

[0038] The present invention analyzes the functional design data of the component and extracts the performance parameters and functional definition features of the component. The performance parameters may include physical characteristics (such as size, weight, power demand, etc.) and operating characteristics (such as speed, accuracy, efficiency, etc.). The functional definition features cover the functional role and operating mode of the component in the system. These extracted feature data will serve as the basic data for subsequent steps. The similarity of the performance parameters between different components is calculated using the extracted component performance parameter set. This similarity calculation helps to identify and compare components that are similar in function and operating characteristics. In this way, alternatives or optimization solutions can be found to improve the flexibility and efficiency of the design. Based on the extracted component function definition data, the cosine similarity of the function definitions between the components is calculated. This similarity metric can evaluate the similarity of the components in terms of functional roles and operating modes. By comparing the similarity of the function definitions, components with similar functions in the system can be found, thereby performing more effective component selection, custom component addition and design optimization. The obtained component performance parameter similarity data and component function definition similarity data are integrated. The similarity of the two aspects of performance parameters and function definition is comprehensively considered to obtain more comprehensive and accurate component function similarity data. The integrated data can help engineers more accurately evaluate and compare the similarities between different components, providing a basis for subsequent design decisions. Using the obtained component function similarity data, cluster analysis is performed on the component function design data. Components with similar functions are classified into the same category or group. Functional component clustering helps to systematically manage and analyze large amounts of component data, discover functional modules in the system, optimize design, reduce duplication and cost, and improve the maintainability and performance of the overall system.

[0039] Optionally, step S23 is specifically:

[0040] Step S231: extracting function description text information from component function definition data, thereby obtaining function definition text data;

[0041] Step S232: performing feature vector conversion on the function definition text data, thereby obtaining a function definition feature vector set;

[0042] Step S233: performing cosine similarity measurement according to the function definition feature vector set, thereby obtaining component function definition similarity data.

[0043] The present invention extracts text information of function description from the function definition data of the component. The function definition may include a detailed description of the role of the component in the system, the mode of operation, the functions implemented, etc. The purpose of extracting these text information is to provide basic data for the subsequent steps of text feature processing and similarity analysis. The extracted function definition text data is converted into a feature vector set. This usually involves natural language processing (NLP) technology, such as processing the text with word frequency statistics, TF-IDF (word frequency-inverse document frequency) weighting, etc., to convert the text information into a numerical feature vector that can be processed by a machine. These feature vectors capture the key features and important information of each component function definition, and provide a mathematical expression for subsequent similarity measurement. The obtained function definition feature vector set is used to perform cosine similarity measurement. Cosine similarity is a commonly used similarity measurement method for comparing the differences between two vectors in direction. Here, it is used to measure the similarity between different component function definitions. Specifically, the cosine similarity measurement can help determine which components are closer in function description, even if their specific text descriptions are not exactly the same, but components with similar functional roles and operation modes will have higher similarity scores. The similarity between different components in terms of functional definition can be accurately quantified. This is very important for finding alternatives or optimizing design solutions during the design process, which can reduce duplication of design work and improve design efficiency and flexibility. Based on the similarity data of functional definitions, engineers can make component selection and design decisions more based on evidence. This data-based approach can reduce the impact of subjective judgment and improve the accuracy and reliability of design decisions. Converting functional definition data into feature vector sets and performing similarity measurements can help establish a systematic component management framework. This framework can help manage large-scale component data, conduct systematic analysis and comparison, and further optimize the performance and cost-effectiveness of the overall system.

[0044] Optionally, step S3 specifically includes:

[0045] Step S31: extracting structural component features and wiring harness component features from the functional component clustering data, thereby obtaining structural component clustering data and wiring harness component clustering data;

[0046] Step S32: extracting mechanical structure features and electrical structure features from the robot topology structure model, thereby obtaining a robot mechanical topology structure model and a robot electrical topology structure model;

[0047] Step S33: dividing the robot mechanical topology structure model into mechanical structure modules according to the structure clustering component data, thereby obtaining a robot structure module set;

[0048] Step S34: dividing the robot electrical topology structure model into electrical harness modules according to the robot structure module set and the harness component clustering data, thereby obtaining an electrical harness module set;

[0049] Step S35: marking the robot topology structure model according to the robot structure module set and the electrical harness module set, thereby obtaining the robot module structure model.

[0050] The present invention can better understand the commonalities and differences of different functional components through clustering and feature extraction, which is helpful for subsequent design and optimization. The extracted features can be used as input data for subsequent analysis and model building, providing a basis for the overall design of the robot. Through the structural feature extraction of the components, possible overlaps and conflicts can be identified in the design stage, which helps to simplify the design and assembly process. Through the feature extraction of mechanical and electrical structures, the physical and electrical characteristics of the robot can be deeply understood, providing detailed information for system-level design. Combining the features of mechanical structure and electrical structure helps to design an integrated robot system and reduce problems in the subsequent integration and debugging stages. The extracted features can be used to optimize the structural design of the robot and enhance its performance and reliability. By dividing into modules, it is easier to manage and maintain the various parts of the robot. Modular design helps different teams to develop various mechanical structure modules in parallel and speed up the progress of the overall design. Modular design makes the structure of the robot more flexible and can be customized and expanded according to different tasks and requirements. According to the characteristics of the wiring harness assembly and the set of structural modules, the electrical wiring of the robot can be optimized to reduce electrical interference and power consumption. Modular electrical design makes the integration and maintenance of the electrical part more simplified and controllable. Modular electrical design helps to perform system-level testing and improve the reliability and safety of the robot. Through module labeling, the various functional modules of the robot can be clearly identified and managed, which is convenient for subsequent maintenance and upgrades. Module labeling helps to document the design process and design decisions, and improve communication efficiency among teams. Labeled modules can be easily revised and iterated to adapt to new design requirements or technical changes.

[0051] Optionally, step S34 is specifically:

[0052] Step S341: extracting wiring harness connection features from the robot topology model to obtain wiring harness connection data;

[0053] Step S342: performing structural module-wiring harness connection association on the robot structural module set and the robot electrical topological structure model according to the wiring harness connection data, thereby obtaining structural module-wiring harness connection data;

[0054] Step S343: dividing the robot electrical topology model into a connection harness model according to the structure module-harness connection data, thereby obtaining a harness connection structure model;

[0055] Step S344: dividing the wiring harness connection structure model into electrical wiring harness modules according to the wiring harness component clustering data, thereby obtaining an electrical wiring harness module set.

[0056] The present invention can obtain key electrical connection information, such as connection points, communication protocols, voltage requirements, etc., by extracting the features of the harness connection of the robot topology model. These data are the basis of electrical design and help to understand the communication and energy transmission requirements between the various parts of the robot. Feature extraction can help detect possible harness conflicts or crossover problems and prevent problems from occurring during the actual integration process. Associating the harness connection data with the robot's structural module set can ensure that each structural module has corresponding electrical support and accurate harness layout. By associating the structural module with the harness connection, the system integration process can be optimized to ensure that the electrical connection requirements are considered when designing each part of the robot. This association can ensure the consistency and maintainability of the design and simplify future maintenance and upgrade work. Based on the structural module-harness connection data, the electrical topology of the robot can be divided into specific harness connection models. This division helps to visualize and document the robot's electrical layout more clearly, which is convenient for the team to understand and communicate. The divided model can be used to predict electrical problems that may be encountered during the system integration process, and make adjustments and preventive measures in advance. According to the clustering data of the harness assembly, the electrical harness can be further modularized to improve the flexibility and maintainability of the design. Modular electrical harness design helps optimize material costs and manufacturing efficiency, especially in mass production and customization requirements. Modular design makes troubleshooting and maintenance easier, reducing repair time and costs.

[0057] Optionally, step S4 is specifically:

[0058] Step S41: extracting module space features from the robot module structure model to obtain module space data;

[0059] Step S42: performing module vertical spatial sorting according to the module spatial data, thereby obtaining module vertical spatial data;

[0060] Step S43: extracting connection structure features of the robot module structure model, thereby obtaining robot module connection structure data;

[0061] Step S44: evaluating the module assembly priority according to the robot module connection structure data and the module vertical space data, thereby obtaining the module assembly priority data;

[0062] Step S45: performing module assembly sorting on the robot structure module set and the electrical harness module set according to the module assembly priority data, thereby obtaining module assembly sequence data;

[0063] Step S46: performing assembly strategy analysis on the module assembly sequence data to obtain the module assembly strategy.

[0064] The present invention extracts the spatial features of the module, such as size, volume, position and other data, by analyzing the robot module structure model. The obtained data is crucial for the subsequent assembly process because they determine the layout and position arrangement of each module within the robot volume. Through the spatial data, collision detection and space planning can be performed to avoid interference or conflict between modules and ensure the smooth progress of the assembly process. Through the analysis of the module spatial data, the layout order of the modules in the vertical direction is determined, that is, which modules should be placed above or below which modules. Through reasonable vertical sorting, the utilization efficiency of the robot's internal space can be maximized, and the compact arrangement of the modules can be ensured, thereby improving the efficiency and performance of the overall design. Feature extraction is performed for the connection mode between the modules, including connection points, interface types, fixing methods, etc. Obtaining these data can help determine the physical connection mode between the modules and ensure the stability and reliability during assembly. Based on the connection structure data and vertical space data, the assembly priority of each module is evaluated to determine which modules should be installed first. Through reasonable priority evaluation, the efficiency of assembly can be improved, the number of repeated adjustments and disassembly can be reduced, thereby saving time and cost. According to the assembly priority data, the structural modules and electrical harness modules of the robot are sorted to determine the best assembly order. An orderly assembly process can reduce assembly conflicts and errors, ensuring that each module is installed in the right place at the right time. Analyze assembly sequence data and formulate reasonable assembly strategies, taking into account the dependencies between different modules and the optimization of assembly sequence. Through strategy analysis, the overall assembly process can be optimized, and assembly efficiency and assembly quality can be improved.

[0065] Optionally, step S44 is specifically:

[0066] Step S441: classifying the structural connection modes according to the robot module connection structure data, thereby obtaining horizontal connection structure data and vertical connection structure data;

[0067] Step S442: sorting the vertical connection order of the modules on the vertical space data according to the vertical connection structure data, thereby obtaining the vertical connection order data of the modules;

[0068] Step S443: extracting the same-horizontal space module data of the robot module structure model according to the module vertical space data, thereby obtaining the same-horizontal space module data;

[0069] Step S444: sorting the module horizontal connection order of the same horizontal space module data according to the horizontal connection structure data, thereby obtaining the module horizontal connection sequence data;

[0070] Step S445: performing module assembly priority division on the module vertical connection sequence data and the module horizontal connection sequence data, thereby obtaining module assembly priority data.

[0071] The connection structure of the robot module in the present invention is classified into a horizontal connection structure and a vertical connection structure. This classification helps to understand how the modules are arranged and connected in space, and the classification can help determine the best module arrangement to improve the stability and efficiency of the robot as a whole. Understanding the connection structure can reduce the use of connecting parts, thereby reducing manufacturing costs. Sorting the connection order of the modules in the vertical direction can reduce assembly time and process complexity, making module installation more efficient. The optimized vertical connection order can ensure that the connection between the modules is more firm and stable. Correctly sorting the horizontal connection can maximize space utilization. A reasonable horizontal connection order can reduce conflicts and overlaps in operation, thereby improving the operating efficiency and response speed of the robot. Determining the assembly priority can make the order of module assembly more reasonable and efficient, and reduce waiting and adjustment time during assembly. The correct assembly order can reduce assembly errors and duplication of work, and improve the accuracy and success rate of assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0073] Figure 1 A schematic diagram of the steps of the module parameterized design method for the electrical system of a construction robot according to the present invention;

[0074] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0075] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0076] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0080] To achieve this, please refer to Figures 1 to 3 The present invention provides a module parameterized design method for an electrical system of a construction robot, the method comprising the following steps:

[0081] Step S1: Acquire construction robot design data, and perform component function design feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function design data and robot structure data; perform robot topology structure analysis on the robot structure data, thereby obtaining a robot topology structure model;

[0082] In this embodiment, it is first necessary to obtain the design data of the construction robot, which includes the various components that constitute the robot and the structural features of the robot as a whole. For example, the size, shape, material and other information of the robot, as well as the functional description and function of each component are extracted from the CAD design file. These data are not limited to the appearance, but also include key features such as the configuration of sensors and actuators. Through functional design feature extraction, key functional features can be extracted from each component, such as the measurement range of the sensor, the movement speed and force output of the actuator. At the same time, the robot structural feature extraction focuses on the structural characteristics of the robot as a whole, such as the center of gravity position, the structural parameters required for stability analysis, etc. Then, the robot topology is constructed according to the connection relationship and distribution of the various components of the robot contained in the robot structural data.

[0083] Step S2: performing component function similarity evaluation according to the component function design data, thereby obtaining component function similarity data, and performing functional component clustering according to the component function similarity data, thereby obtaining functional component clustering data;

[0084] In this embodiment, in the component function similarity evaluation process, a feature vector-based similarity calculation method, such as cosine similarity or Euclidean distance, can be used. For example, for sensor components, their sensing range, accuracy, response time and other characteristics can be compared. By clustering functional components, similar components are classified together, such as temperature sensors of different brands are classified into one category, so as to facilitate subsequent modular design and standardized production.

[0085] Step S3: dividing the robot topology structure model into structural modules according to the functional component clustering data, thereby obtaining a robot structure module set and an electrical wiring harness module set, and marking the robot topology structure model according to the robot structure module set and the electrical wiring harness module set, thereby obtaining a robot module structure model;

[0086] In this embodiment, when the robot topology is divided into modules according to the functional component clustering data, the graph theory method can be used to perform topological structure analysis to find out the connection relationship and dependency relationship between the modules. For example, the robot is divided into a power module, a perception module and a control module, and the functional boundary and interface specification of each module are determined. The division of the electrical wiring harness module set can determine the layout and installation position of each electrical wiring harness according to the electrical connection requirements and power transmission requirements. Then, the robot topology model is marked according to the spatial position information of the divided module set.

[0087] Step S4: performing module assembly priority evaluation on the robot module structure model to obtain module assembly priority data, and performing module assembly strategy analysis on the robot structure module set and the electrical harness module set according to the module assembly priority data to obtain a module assembly strategy;

[0088] In this embodiment, when evaluating the module assembly priority, the analytic hierarchy process (AHP) or other multi-factor evaluation methods can be used to consider the module function urgency, assembly complexity and factors affecting assembly efficiency. For example, the layout of the electrical harness will affect the electrical performance and safety of the robot, so it is necessary to give priority to its sequence and method in the overall assembly process. According to the module assembly priority data, the module assembly strategy analysis is performed. This includes determining the best assembly sequence and method to maximize the efficiency and assembly quality of the assembly process. The actual assembly scenario can be combined to analyze the impact of different strategies on the total assembly time and cost, and the best strategy can be selected.

[0089] Step S5: Performing a module assembly parameterized description on the robot module structure model according to the module assembly strategy, thereby obtaining a construction robot module parameterized model.

[0090] In the present embodiment, according to the module assembly strategy, the module assembly parameter description is performed on the robot module structure model. This means that the specific assembly process and parameters of each module are described in detail, including assembly sequence, fastener specifications, connection interfaces, etc. Through the module assembly parameter description, a parametric model of the construction robot can be generated, and this model can accurately reflect the assembly details and associated information of each module. This model can be used for simulation, maintenance and future design improvements. Parametric modeling software, such as SolidWorks or CATIA, can be used to define the assembly position, connection method and position of the fixing parts of each module in the robot module structure model. Constraints and assembly sequence can be set by CAD software to ensure that the relative position and functional interface between each module meet the design requirements, thereby realizing the modular design and customized production of the construction robot.

[0091] The present invention extracts the functional characteristics of each component, such as purpose, operation method, etc., by analyzing the design data of the construction robot. It is helpful to understand the role and requirements of each component in the overall system. The physical structural characteristics of the robot, such as size, connection method, etc., are analyzed. These characteristics are crucial for subsequent modular design and optimization. By evaluating the functional similarity between components, it can be determined which components can share similar electrical system design or control logic. This helps to reduce system complexity and design costs. Components with similar functions are clustered to form component groups. This can simplify the design and maintenance of the electrical system and improve the flexibility and maintainability of the system. According to the clustering results of the functional components, the robot topology is decomposed into modular parts. Each module can be designed, tested and maintained independently, improving the reliability and maintainability of the overall system. An identifier or a specific electrical harness is assigned to each module so that each component can be clearly identified and managed during the subsequent assembly and integration process. The assembly priority of each module is evaluated to ensure that the gradual integration process of the system meets the design and safety requirements while maximizing the assembly efficiency. An assembly strategy is formulated based on the priority data, including the sequence, test and verification process, to ensure that the function and performance of each module and the overall system meet the expected requirements. According to the obtained assembly strategy, the robot module structural model is parametrically described. This makes the system assembly process more standardized and automated, while reducing the possibility of human error. Overall, this technical process can not only improve the flexibility and adaptability of construction robot design, but also optimize the overall efficiency, maintainability and safety of the system. Through more accurate data analysis and modular design, the specific needs of different projects can be effectively responded to.

[0092] Optionally, step S1 specifically includes:

[0093] Step S11: Acquire construction robot design data, and perform component definition function feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function data and robot structure data;

[0094] In this embodiment, the construction robot design data is obtained from the construction robot manufacturer, and the component definition function feature extraction and robot structure feature extraction are performed on the construction robot design data, thereby obtaining component function data and robot structure data. For example, when obtaining the design data, CAD software or building information model (BIM) can be used to extract the geometric shape, material properties and functional characteristics of each component of the construction robot, such as the location and type of the sensor, the range of motion of the actuator, etc. These data form the basis of the component function data and robot structure data.

[0095] Step S12: Structural classification of the robot structural data, thereby obtaining robot electrical structural data and robot mechanical structural data;

[0096] In this embodiment, the robot structure data is structurally classified to obtain the robot electrical structure data and the robot mechanical structure data. For example, by classifying various structural components of the robot, such as the electrical system and the mechanical system, the electrical structure data includes the motor, sensor layout and connection method, and the mechanical structure data includes the joint configuration, the length of the connecting rod, etc.

[0097] Step S13: performing an electrical topological structure analysis of the robot according to the electrical structure data of the robot, thereby obtaining an electrical topological structure model of the robot;

[0098] In this embodiment, the electrical topology structure of the robot is analyzed based on the electrical structure data of the robot, so as to obtain the electrical topology structure model of the robot. For example, by analyzing the connection mode and communication protocol between various components in the electrical structure data, the electrical topology structure model is established to ensure that each electronic device is connected to the control system according to the design requirements and can correctly exchange data and commands.

[0099] Step S14: performing a robot mechanical topology structure analysis according to the robot mechanical structure data, thereby obtaining a robot mechanical topology structure model;

[0100] In this embodiment, the robot mechanical topology structure analysis is performed based on the robot mechanical structure data to obtain the robot mechanical topology structure model. For example, by analyzing the relative position and motion range of each joint and connecting rod in the mechanical structure data, a mechanical topology structure model is established to ensure that the robot can achieve the required motion and operation under various working conditions.

[0101] Step S15: merging the robot electrical topology structure model and the robot mechanical topology structure model into a robot topology structure, thereby obtaining a robot topology structure model.

[0102] In this embodiment, the robot electrical topology model and the robot mechanical topology model are combined to obtain the robot topology model. For example, the electrical topology model and the mechanical topology model are integrated to ensure the coordination between the electrical and mechanical systems to optimize the overall performance. The electronic control unit in the robot electrical topology model can be integrated with the mechanical components of each sensor and actuator in the robot mechanical topology model to ensure stability and reliability in different operation scenarios.

[0103] The present invention obtains the data required for the design of a construction robot. By extracting the component definition function features of these data, the functions and features of each robot component can be identified. This is critical for subsequent design and optimization because it ensures that each component can accurately perform its intended function in the design. The structural data of the robot is classified and analyzed. By dividing the robot structure into electrical structure and mechanical structure, the different parts of the robot can be understood and processed more clearly. This classification helps the subsequent electrical and mechanical topology analysis, making the design process more systematic and organized. Electrical topology analysis is a process of systematically evaluating and optimizing the wiring, connection and layout of electronic components of the electrical part of the robot. It helps the design team to ensure the rationality of the layout of the electrical part, avoid electrical interference and functional conflicts, and improve the reliability and efficiency of the robot. Mechanical topology analysis involves the assembly, connection and kinematic characteristics evaluation of each mechanical component of the robot. It helps the design team to ensure the stability of the mechanical structure, the smoothness of the movement and the durability of the structure. Reasonable mechanical structure design is crucial to the performance and operational safety of the robot. The electrical and mechanical topology models are merged into a comprehensive robot topology model. This ensures the coordination and integration of the electrical and mechanical parts so that the entire robotic system is designed to be integrated and work together. This comprehensive model can help the design team work more efficiently in subsequent development and testing stages.

[0104] Optionally, step S13 is specifically:

[0105] Step S131: extracting circuit structure features and electrical component structure features from the robot electrical structure data, thereby obtaining circuit structure data and electrical component structure data;

[0106] In this embodiment, first, the circuit structure feature extraction is performed on the robot electrical structure data. This includes analyzing the basic structural features of each circuit from the design data, such as the parameters of components such as resistors, capacitors, inductors, and their connection methods. For example, the circuit connection requirements from the main controller to each sensor and actuator are extracted from the robot design data, including electrical characteristics such as voltage level and signal transmission rate. For example, for an intelligent monitoring robot, its design data is analyzed to determine the component types and parameters contained in each circuit, such as the pixels and lens type of the camera module, and the bandwidth and rate requirements of the data transmission circuit. These feature extractions will help to accurately describe the functions and performance requirements of the circuit.

[0107] Step S132: classifying the circuit structure data into circuit connection modes, thereby obtaining series circuit structure data and parallel circuit structure data;

[0108] In this embodiment, the circuit structure data is classified into circuit connection modes. For example, the circuits in the electrical system of the robot are divided into two connection modes: series and parallel. For series circuits, the serial connection between the controller and each sensor can be analyzed to ensure the orderliness and stability of data transmission and control signals. For parallel circuits, the power lines or data buses shared by multiple sensors can be reviewed to ensure the effective distribution and transmission of energy and signals.

[0109] Step S133: performing series component association on the series circuit structure data and the electrical component structure data, thereby obtaining electrical component series circuit connection data; performing parallel component association on the parallel circuit structure data and the electrical component structure data, thereby obtaining electrical component parallel circuit connection data;

[0110] In this embodiment, after the circuit structure data is classified, the series component association and parallel component association are performed. This involves combining the electrical components according to their connection methods to form complete electrical connection data. For example, the circuit design of connecting multiple sensors in series to the same controller ensures that the signal of each sensor can be smoothly transmitted and accurately received by the controller. At the same time, for parallel connections with a shared power supply, it is ensured that each sensor or actuator can obtain the required power support.

[0111] Step S134: constructing an electrical block diagram according to the electrical component series circuit connection data and the electrical component parallel circuit connection data, thereby obtaining an electrical block diagram of the robot;

[0112] In this embodiment, the electrical block diagram of the robot is constructed by using the series circuit connection data and the parallel circuit connection data of the electrical components. For example, based on the previously determined circuit connection mode and component association, the electrical block diagram of the robot is drawn to clearly show the connection mode and physical layout of each component in the circuit. This includes using CAD software to create a detailed circuit diagram so that engineers and technicians can understand and modify the circuit design.

[0113] Step S135: Perform electrical connection topology analysis according to the robot electrical block diagram to obtain the robot electrical topology model.

[0114] In this embodiment, in the electrical connection topology analysis stage, the topology analysis is performed according to the electrical block diagram of the robot. For example, by analyzing the physical connection relationship and electrical characteristics between the components in the block diagram, the stability of the circuit, the signal transmission efficiency and the potential electromagnetic interference problem are evaluated. Circuit simulation software, such as SPICE (Simulation Program and Integrated Circuit Simulation Program), MATLAB or PSPICE, can be used to mathematically model and simulate the circuit, analyze the current path, voltage distribution and signal transmission mode between the components in the circuit, evaluate the stability and efficiency of the circuit, and thus obtain the electrical topology model of the robot.

[0115] The present invention extracts the structural features of the circuit and the structural features of each electrical component from the electrical structure data of the robot, wherein the circuit structure features include the topological relationship of the circuit, node information, etc., and the electrical component structure features involve the type, parameters, connection mode, etc. of each component. Through these extractions, a detailed foundation of circuit structure data can be established. According to the circuit structure data, the connection mode of the circuit is classified, mainly including a series circuit structure and a parallel circuit structure. A series circuit means that the electrical components are connected in sequence and the current flows along a path; while a parallel circuit means that the electrical components are connected in parallel and the current can choose different paths. Classifying these connection modes is helpful for subsequent analysis and design. According to the classified series and parallel circuit structure data, the electrical components are associated. The series component association establishes the connection relationship of the electrical components in the series circuit, while the parallel component association determines the connection mode of the electrical components in the parallel circuit. These association data provide specific configuration information of the circuit. According to the obtained series and parallel circuit connection data of the electrical components, the electrical block diagram of the robot can be constructed. The electrical block diagram shows the connection relationship and layout of each component in the electrical system of the robot, and is an important tool for understanding and designing the electrical system. The electrical connection topology structure analysis is performed according to the electrical block diagram. This analysis helps understand the flow paths of electrical signals and power in the robot's electrical system, and can check the effectiveness and performance of the circuit. The electrical topology model provides a high-level perspective to help engineers optimize and improve the design of the electrical system.

[0116] Optionally, step S14 is specifically:

[0117] Step S141: classifying the robot mechanical structure data into functional structures, thereby obtaining mechanical component structure data and connection structure data;

[0118] In this embodiment, the mechanical structure data of the robot is classified into functional structures. This means identifying and classifying the various functional components of the robot, such as joints, transmission systems, actuators, etc. By classifying these functional structures, the structural data of each mechanical component can be obtained, such as the geometric shape, size, material and other features of each component. At the same time, it is also necessary to obtain the structural data connecting these components, including information such as connection methods and interface types. For a multi-joint robot, first identify and classify each joint, driver and the transmission system between them. For example, identify the rotation axis of the joint structure, the working range and torque requirements of the joint, and the gear transmission or belt transmission connection structure of the transmission system. These data will help in the structural feature extraction and similarity calculation in the subsequent steps.

[0119] Step S142: extracting component connection structure features from the mechanical component structure data, thereby obtaining mechanical component connection structure data;

[0120] In this embodiment, it is necessary to extract the component connection structure features for the obtained mechanical component structure data. This includes extracting key connection features from the data of each mechanical component, such as the location of the connection point, the connection method (such as bolt connection, welding, plug-in, etc.), the connection strength requirements, etc. These feature data will be used to describe the connection structure between mechanical components. For the arm structure of a robot, various connection methods are involved, such as spherical hinge connection at the joint, keyway connection from the motor to the transmission gear, etc. By extracting and describing the geometric features and material properties (such as strength and wear resistance) of each connection structure, a detailed data set of the connection structure can be established.

[0121] Step S143: performing similarity calculation on the mechanical component connection structure data and the connection structure data, thereby obtaining connection structure similarity data;

[0122] In this embodiment, the extracted connection structure data is used to calculate the similarity of the connection structures between mechanical components. This means comparing the similarity of the connection methods between different mechanical components and the functional equivalence of various connection structures. Through similarity calculation, it can be determined which connection structures are similar in function and design, thereby providing a basis for subsequent connection associations. For two joint connection structures of a robot, the similarity between them can be calculated, including the position accuracy of the connection point, the stability of the connection, and the carrying capacity of the mechanical load. For example, by calculating the distance or similarity index (such as cosine similarity) of these features, the degree of similarity in function and performance can be evaluated.

[0123] Step S144: performing component connection association on the mechanical component structure data according to the connection structure similarity data, thereby obtaining mechanical component connection data;

[0124] In this embodiment, based on the connection structure similarity data obtained in the previous step, the structural data of the mechanical components are connected and associated. This means determining which mechanical components can share similar connection structures or can be assembled using similar connection methods. This association can optimize the design and manufacturing process of the robot and reduce the number of parts and inventory. If the connection structures of two mechanical components are found to be very similar in the similarity calculation, they can be associated and sharing the same type of connectors or components can be considered in the design. For example, two similar joint structures can be regarded as being assembled with the same type of bearings or connecting bolts, thereby simplifying the manufacturing process and parts management.

[0125] Step S145: Perform mechanical connection topology analysis on the mechanical component connection data to obtain a robot mechanical topology model.

[0126] In this embodiment, the obtained mechanical component connection data is subjected to a topological structure analysis to establish a mechanical topological structure model of the robot. This includes determining the hierarchical relationship between mechanical components, the connection path, and the stability and reliability of the overall structure. The mechanical topological structure model can provide an important reference for the design optimization, motion planning, and maintenance of the robot. Based on the connection data obtained in the previous steps, the mechanical structure of the robot can be modeled using a graph theory method or a hierarchical analysis method. For example, each mechanical component is regarded as a node, the connection relationship is regarded as an edge, and a directed or undirected graph of mechanical connections is constructed. By analyzing this graph, the dependencies between the various components of the robot and the stability of the overall structure can be determined.

[0127] In the present invention, the mechanical structure data of the robot is first classified by functional structure. This means that according to the functions of each component of the robot, it is classified and sorted for further processing and analysis. For example, for a robot, it involves the classification of functional modules such as motion systems, actuators, and sensors. For the structural data of each mechanical component, the connection structure features are extracted. These features include information such as the shape, size, connection method (such as bolts, welding, etc.), and material properties of the component. By extracting these features, detailed connection structure data of each mechanical component can be established. Using the extracted connection structure feature data, the connection structure between the mechanical components is similarly calculated. These calculations can help determine the structural similarity between components, thereby finding potential opportunities for possible reuse of components or design optimization. Based on the obtained connection structure similarity data, this will associate the connection of the mechanical components. This means that according to similar connection structures, similar mechanical components are classified or possible connection methods between them are suggested. Such association helps to optimize the design, reduce the number of parts, improve manufacturing efficiency, etc. According to the obtained mechanical component connection data, the mechanical connection topology structure is analyzed. This analysis can show the connection method and relationship between the various components in the overall mechanical structure of the robot, thereby forming a mechanical topology structure model. This model helps engineers understand the complexity of mechanical systems, optimize layouts, assess structural strength and stability, and more.

[0128] Optionally, step S2 specifically includes:

[0129] Step S21: extracting component performance parameter features and component function definition features from the component function design data, thereby obtaining a component performance parameter set and component function definition data;

[0130] In this embodiment, in the feature extraction stage of component function design data, data mining and feature engineering techniques are usually used to extract key information from a large number of design documents or specifications. For example, for the design of electronic products, important parameters such as component dimensions, electrical characteristics, material requirements, etc. can be extracted from technical specifications or CAD files. This involves extracting keywords or phrases from documents or design instructions, such as descriptions of specific functions, input and output requirements, performance parameters, etc. Text analysis, pattern recognition, and information extraction techniques can be used to ensure that the required information is accurately captured from a variety of design data to provide data support for subsequent performance evaluation and function definition.

[0131] Step S22: Calculating component performance parameter similarity on the component performance parameter set, thereby obtaining component performance parameter similarity data;

[0132] In this embodiment, the similarity calculation of the component performance parameter set is to evaluate the performance similarity between different components. This can be achieved through mathematical methods such as Euclidean distance or similarity measurement based on feature vectors. For example, for multiple engine designs, the differences between their displacement, fuel efficiency and other parameters can be calculated, and the similarity between them can be determined through numerical similarity measurement.

[0133] Step S23: Calculating the component definition cosine similarity according to the component function definition data, thereby obtaining component function definition similarity data;

[0134] In this embodiment, the cosine similarity calculation based on the component function definition data is to compare the similarity between the components in the function definition. This method usually converts the text description into a vector representation, and then uses the cosine similarity formula for calculation. For example, the function descriptions of different car engines are converted into text vectors, and then the cosine similarity between them is calculated to determine the similarity between them in the function definition.

[0135] Step S24: performing similarity comprehensive integration on the component performance parameter similarity data and the component function definition similarity data, thereby obtaining component function similarity data;

[0136] In this embodiment, the component performance parameter similarity data and component function definition similarity data are integrated to comprehensively evaluate the functional similarity between components. This can be achieved by weighted average or other aggregation methods to ensure that the similarities in both aspects are properly considered. For example, if a certain car engine is very similar in performance parameters and its function definition is also very close, then their functional similarity data should be high.

[0137] Step S25: clustering the component function design data into functional components according to the component function similarity data, thereby obtaining functional component clustering data.

[0138] In this embodiment, clustering functional components according to functional similarity data means grouping components with similar functions into one category. This can be done using clustering algorithms such as k-means or hierarchical clustering. For example, based on the functional similarity data calculated previously, automobile engine designs that are similar in performance parameters and functional definitions can be grouped into the same cluster group for further analysis or decision making.

[0139] The present invention analyzes the functional design data of the component and extracts the performance parameters and functional definition features of the component. The performance parameters may include physical characteristics (such as size, weight, power demand, etc.) and operating characteristics (such as speed, accuracy, efficiency, etc.). The functional definition features cover the functional role and operating mode of the component in the system. These extracted feature data will serve as the basic data for subsequent steps. The similarity of the performance parameters between different components is calculated using the extracted component performance parameter set. This similarity calculation helps to identify and compare components that are similar in function and operating characteristics. In this way, alternatives or optimization solutions can be found to improve the flexibility and efficiency of the design. Based on the extracted component function definition data, the cosine similarity of the function definitions between the components is calculated. This similarity metric can evaluate the similarity of the components in terms of functional roles and operating modes. By comparing the similarity of the function definitions, components with similar functions in the system can be found, thereby performing more effective component selection, custom component addition and design optimization. The obtained component performance parameter similarity data and component function definition similarity data are integrated. The similarity of the two aspects of performance parameters and function definition is comprehensively considered to obtain more comprehensive and accurate component function similarity data. The integrated data can help engineers more accurately evaluate and compare the similarities between different components, providing a basis for subsequent design decisions. Using the obtained component function similarity data, cluster analysis is performed on the component function design data. Components with similar functions are classified into the same category or group. Functional component clustering helps to systematically manage and analyze large amounts of component data, discover functional modules in the system, optimize design, reduce duplication and cost, and improve the maintainability and performance of the overall system.

[0140] Optionally, step S23 is specifically:

[0141] Step S231: extracting function description text information from component function definition data, thereby obtaining function definition text data;

[0142] In this embodiment, when extracting function description text information from component function definition data, it is first necessary to extract key text information describing the component function from the document or design specification. This may include a detailed description of the component's specific functions, operating requirements, performance expectations, etc. For example, for a control system component of a mechanical device, the function definition text data may include a specific description of its control algorithm, input and output interface requirements, and expected response time.

[0143] Step S232: performing feature vector conversion on the function definition text data, thereby obtaining a function definition feature vector set;

[0144] In this embodiment, the feature vector conversion of the function definition text data is performed to convert the text data into a numerical form that can be processed by a computer. This usually involves using natural language processing technology, such as a bag-of-words model or word embedding technology, to convert the text into a vector representation. For example, the text describing the function of a control system component is converted into a numerical representation containing a word frequency or a word embedding vector for subsequent similarity calculation or cluster analysis.

[0145] Step S233: performing cosine similarity measurement according to the function definition feature vector set, thereby obtaining component function definition similarity data.

[0146] In this embodiment, the cosine similarity measurement is performed based on the function definition feature vector set to evaluate the similarity between different function definitions. Cosine similarity is a commonly used measurement method for comparing the difference between the direction and size of two vectors. Here, cosine similarity will be used to measure the similarity between function definition text data, so as to determine the closeness of components in function description. For example, by calculating the cosine similarity between the feature vectors of different control system component function definitions, their similarity in function description can be evaluated, thereby providing a basis for subsequent functional component clustering.

[0147] The present invention extracts text information of function description from the function definition data of the component. The function definition may include a detailed description of the role of the component in the system, the mode of operation, the functions implemented, etc. The purpose of extracting these text information is to provide basic data for the subsequent steps of text feature processing and similarity analysis. The extracted function definition text data is converted into a feature vector set. This usually involves natural language processing (NLP) technology, such as processing the text with word frequency statistics, TF-IDF (word frequency-inverse document frequency) weighting, etc., to convert the text information into a numerical feature vector that can be processed by a machine. These feature vectors capture the key features and important information of each component function definition, and provide a mathematical expression for subsequent similarity measurement. The obtained function definition feature vector set is used to perform cosine similarity measurement. Cosine similarity is a commonly used similarity measurement method for comparing the differences between two vectors in direction. Here, it is used to measure the similarity between different component function definitions. Specifically, the cosine similarity measurement can help determine which components are closer in function description, even if their specific text descriptions are not exactly the same, but components with similar functional roles and operation modes will have higher similarity scores. The similarity between different components in terms of functional definition can be accurately quantified. This is very important for finding alternatives or optimizing design solutions during the design process, which can reduce duplication of design work and improve design efficiency and flexibility. Based on the similarity data of functional definitions, engineers can make component selection and design decisions more based on evidence. This data-based approach can reduce the impact of subjective judgment and improve the accuracy and reliability of design decisions. Converting functional definition data into feature vector sets and performing similarity measurements can help establish a systematic component management framework. This framework can help manage large-scale component data, conduct systematic analysis and comparison, and further optimize the performance and cost-effectiveness of the overall system.

[0148] Optionally, step S3 specifically includes:

[0149] Step S31: extracting structural component features and wiring harness component features from the functional component clustering data, thereby obtaining structural component clustering data and wiring harness component clustering data;

[0150] In this embodiment, when extracting structural component features and wiring harness component features from functional component clustering data, it is first necessary to extract key structural features and wiring harness features from the functional component clustering data of the robot design. For example, for a robot's structural component, its size, shape, material, and connection method can be extracted; for a wiring harness component, its electrical connection interface type, voltage requirements, communication protocol, and other features can be extracted. These feature extraction processes involve technologies such as image processing, sensor data parsing, and text information analysis.

[0151] Step S32: extracting mechanical structure features and electrical structure features from the robot topology structure model, thereby obtaining a robot mechanical topology structure model and a robot electrical topology structure model;

[0152] In this embodiment, when extracting mechanical and electrical features from the robot topology model, it is necessary to extract important mechanical and electrical features from the overall robot design. For example, for the mechanical structure, features such as joint type, joint degree of freedom, and transmission mode can be extracted; for the electrical structure, features such as motor type, sensor position, and control circuit can be extracted. These feature extractions can be based on CAD design data, sensor feedback data, or engineer's design specifications.

[0153] Step S33: dividing the robot mechanical topology structure model into mechanical structure modules according to the structure clustering component data, thereby obtaining a robot structure module set;

[0154] In this embodiment, the mechanical structure module division of the robot mechanical topology structure model according to the structural clustering component data is to decompose the robot's mechanical structure into manageable modular units. For example, according to the previously extracted mechanical structure features, the robot can be decomposed into different structural modules, such as arms, chassis, joints, etc. This module division can be optimized based on the functions and assembly requirements of the mechanical design to improve the manufacturing efficiency and maintenance convenience of the robot.

[0155] Step S34: dividing the robot electrical topology structure model into electrical harness modules according to the robot structure module set and the harness component clustering data, thereby obtaining an electrical harness module set;

[0156] In this embodiment, the electrical wiring harness module of the robot electrical topology structure model is divided according to the robot structure module set and the wiring harness component clustering data, in order to effectively manage and design the electrical system of the robot. For example, according to the characteristics of the wiring harness component and the electrical topology structure model, the electrical system can be decomposed into different modules, such as power module, communication module, sensor module, etc. Such division can help engineers better design and debug the electrical system of the robot to ensure its stability and performance optimization.

[0157] Step S35: marking the robot topology structure model according to the robot structure module set and the electrical harness module set, thereby obtaining the robot module structure model.

[0158] In this embodiment, the robot topology model is marked with modules according to the robot structure module set and the electrical harness module set in order to identify and manage the relationship and function between the various modules of the robot. For example, by marking the previously defined mechanical and electrical modules, the interface relationship and data flow between the modules can be established to ensure the coordinated operation of the entire robot system. This module marking can be implemented based on software tools or physical markings to facilitate engineers and technicians to design, test and maintain the system.

[0159] The present invention can better understand the commonalities and differences of different functional components through clustering and feature extraction, which is helpful for subsequent design and optimization. The extracted features can be used as input data for subsequent analysis and model building, providing a basis for the overall design of the robot. Through the structural feature extraction of the components, possible overlaps and conflicts can be identified in the design stage, which helps to simplify the design and assembly process. Through the feature extraction of mechanical and electrical structures, the physical and electrical characteristics of the robot can be deeply understood, providing detailed information for system-level design. Combining the features of mechanical structure and electrical structure helps to design an integrated robot system and reduce problems in the subsequent integration and debugging stages. The extracted features can be used to optimize the structural design of the robot and enhance its performance and reliability. By dividing into modules, it is easier to manage and maintain the various parts of the robot. Modular design helps different teams to develop various mechanical structure modules in parallel and speed up the progress of the overall design. Modular design makes the structure of the robot more flexible and can be customized and expanded according to different tasks and requirements. According to the characteristics of the wiring harness assembly and the set of structural modules, the electrical wiring of the robot can be optimized to reduce electrical interference and power consumption. Modular electrical design makes the integration and maintenance of the electrical part more simplified and controllable. Modular electrical design helps to perform system-level testing and improve the reliability and safety of the robot. Through module labeling, the various functional modules of the robot can be clearly identified and managed, which is convenient for subsequent maintenance and upgrades. Module labeling helps to document the design process and design decisions, and improve communication efficiency among teams. Labeled modules can be easily revised and iterated to adapt to new design requirements or technical changes.

[0160] Optionally, step S34 is specifically:

[0161] Step S341: extracting wiring harness connection features from the robot topology model to obtain wiring harness connection data;

[0162] In this embodiment, the features of the wiring harness connection are extracted from the topological structure model of the robot. This usually involves using computer vision technology or CAD software to directly obtain the physical structure information of the robot by scanning or model files. For example, image processing algorithms can be used to identify the connection points and paths between different components, as well as the types of connection points (such as plugs, interfaces, etc.). In CAD software, the relevant data of the wiring harness connection can be extracted through the connection relationship between lines, patches or parts in the 3D model. The robot is scanned using a machine vision system to generate its three-dimensional model, and the connection point coordinates and connection method of each component are extracted from it. These data are exported as structured files, containing the starting point, end point coordinates and their types (such as power lines, sensor signal lines, etc.) of each connection.

[0163] Step S342: performing structural module-wiring harness connection association on the robot structural module set and the robot electrical topological structure model according to the wiring harness connection data, thereby obtaining structural module-wiring harness connection data;

[0164] In this embodiment, the extracted harness connection data is associated with the robot's set of structural modules. This includes mapping each harness connection to a corresponding robot component or system module to establish a relationship between the structural module and the harness connection. This association helps with subsequent electrical wiring design and fault diagnosis. Using automated scripts or data processing tools, the various components or system modules of the robot are matched with the previously extracted harness connection data. For example, the identified power harness is connected to the robot's power unit, and the sensor signal harness is connected to the corresponding sensor module. These associated data are recorded in a structured database or spreadsheet for further analysis and use.

[0165] Step S343: dividing the robot electrical topology model into a connection harness model according to the structure module-harness connection data, thereby obtaining a harness connection structure model;

[0166] In this embodiment, the electrical topology model of the robot is further segmented and divided using the structural module-wiring harness connection data. This involves dividing the overall electrical connection of the robot into multiple smaller wiring harness connection structure models, each model representing an independent functional unit or system. Based on the established association data, the electrical wiring of the robot is logically grouped. For example, the wiring harness connections of all control circuits are divided into one model, and the electrical connections of sensors and actuators are divided into another model. Each model contains the necessary connection information, such as the number, length, color coding, etc. of the wiring harness, for subsequent maintenance and debugging.

[0167] Step S344: dividing the wiring harness connection structure model into electrical wiring harness modules according to the wiring harness component clustering data, thereby obtaining an electrical wiring harness module set.

[0168] In this embodiment, the previously divided harness connection structure model is further refined based on the harness component clustering data to form a complete set of electrical harness modules. This includes further organizing and classifying the harnesses according to their functions and locations to facilitate subsequent maintenance, updating, and troubleshooting. For example, a clustering algorithm is used to automatically classify the harnesses in each harness connection structure model. For example, all electrical harnesses related to the power system are classified into the power harness module, and electrical harnesses related to the sensor are classified into the sensor harness module. Each module has detailed documentation, including the function of the harness, connection diagrams, and troubleshooting guides, so that technicians can quickly locate and solve problems when needed.

[0169] The present invention can obtain key electrical connection information, such as connection points, communication protocols, voltage requirements, etc., by extracting the features of the harness connection of the robot topology model. These data are the basis of electrical design and help to understand the communication and energy transmission requirements between the various parts of the robot. Feature extraction can help detect possible harness conflicts or crossover problems and prevent problems from occurring during the actual integration process. Associating the harness connection data with the robot's structural module set can ensure that each structural module has corresponding electrical support and accurate harness layout. By associating the structural module with the harness connection, the system integration process can be optimized to ensure that the electrical connection requirements are considered when designing each part of the robot. This association can ensure the consistency and maintainability of the design and simplify future maintenance and upgrade work. Based on the structural module-harness connection data, the electrical topology of the robot can be divided into specific harness connection models. This division helps to visualize and document the robot's electrical layout more clearly, which is convenient for the team to understand and communicate. The divided model can be used to predict electrical problems that may be encountered during the system integration process, and make adjustments and preventive measures in advance. According to the clustering data of the harness assembly, the electrical harness can be further modularized to improve the flexibility and maintainability of the design. Modular electrical harness design helps optimize material costs and manufacturing efficiency, especially in mass production and customization requirements. Modular design makes troubleshooting and maintenance easier, reducing repair time and costs.

[0170] Optionally, step S4 is specifically:

[0171] Step S41: extracting module space features from the robot module structure model to obtain module space data;

[0172] In this embodiment, module space feature extraction is performed in the robot module structure model, which usually involves the use of 3D modeling software or professional CAD tools to accurately capture the size, shape and position relationship of each module. For example, detailed geometric modeling of each module is performed through CAD software, and its geometric center and bounding box information are marked to facilitate accurate calculation of subsequent vertical space sorting.

[0173] Step S42: performing module vertical spatial sorting according to the module spatial data, thereby obtaining module vertical spatial data;

[0174] In this embodiment, the vertical spatial sorting of modules according to the module spatial data can be realized by algorithms and spatial analysis tools. For example, the position and size of each module in the three-dimensional space are calculated using a spatial envelope curve (bounding box), and then the vertical stacking order of each module is determined using a sorting algorithm such as quick sort or heap sort.

[0175] Step S43: extracting connection structure features of the robot module structure model, thereby obtaining robot module connection structure data;

[0176] In this embodiment, the connection structure feature extraction of the robot module structure model usually requires extracting key connection information such as connection interfaces, bolt holes, sensor installation points, etc. from the CAD model. For example, the element analysis tool of the CAD software is used to extract the coordinates and type of the connection points of each module to ensure accurate docking and connection in the subsequent assembly process.

[0177] Step S44: evaluating the module assembly priority according to the robot module connection structure data and the module vertical space data, thereby obtaining the module assembly priority data;

[0178] In this embodiment, module assembly priority evaluation is performed based on the robot module connection structure data and module vertical space data. This includes weighing and evaluating multiple factors such as physical contact requirements and electrical signal transmission requirements. For example, by analyzing the physical strength of the connection structure, data transmission rate requirements, etc., the priority of each module in the assembly sequence is determined to ensure the stability of the overall structure of the robot and the functional integrity.

[0179] Step S45: performing module assembly sorting on the robot structure module set and the electrical harness module set according to the module assembly priority data, thereby obtaining module assembly sequence data;

[0180] In this embodiment, the robot structure module set and the electrical harness module set are sorted according to the module assembly priority data to determine the best assembly sequence. For example, the assembly priority information is used in combination with the physical size and weight of the module to determine the best installation position and sequence of each module through software simulation or scheduling algorithm, so as to optimize the assembly efficiency and quality of the overall robot.

[0181] Step S46: performing assembly strategy analysis on the module assembly sequence data to obtain the module assembly strategy.

[0182] In this embodiment, the module assembly sequence data is analyzed for assembly strategy to ensure efficiency and safety during the assembly process. For example, a specific assembly strategy is formulated in combination with the constraints of the actual production environment, such as workspace size, human resources, and time constraints. This includes determining the sequence of assembly stages, the tools and equipment used, and pre-planned risk management measures to ensure that each assembly step proceeds smoothly and achieves the desired results.

[0183] The present invention extracts the spatial features of the module, such as size, volume, position and other data, by analyzing the robot module structure model. The obtained data is crucial for the subsequent assembly process because they determine the layout and position arrangement of each module within the robot volume. Through the spatial data, collision detection and space planning can be performed to avoid interference or conflict between modules and ensure the smooth progress of the assembly process. Through the analysis of the module spatial data, the layout order of the modules in the vertical direction is determined, that is, which modules should be placed above or below which modules. Through reasonable vertical sorting, the utilization efficiency of the robot's internal space can be maximized, and the compact arrangement of the modules can be ensured, thereby improving the efficiency and performance of the overall design. Feature extraction is performed for the connection mode between the modules, including connection points, interface types, fixing methods, etc. Obtaining these data can help determine the physical connection mode between the modules and ensure the stability and reliability during assembly. Based on the connection structure data and vertical space data, the assembly priority of each module is evaluated to determine which modules should be installed first. Through reasonable priority evaluation, the efficiency of assembly can be improved, the number of repeated adjustments and disassembly can be reduced, thereby saving time and cost. According to the assembly priority data, the structural modules and electrical harness modules of the robot are sorted to determine the best assembly order. An orderly assembly process can reduce assembly conflicts and errors, ensuring that each module is installed in the right place at the right time. Analyze assembly sequence data and formulate reasonable assembly strategies, taking into account the dependencies between different modules and the optimization of assembly sequence. Through strategy analysis, the overall assembly process can be optimized, and assembly efficiency and assembly quality can be improved.

[0184] Optionally, step S44 is specifically:

[0185] Step S441: classifying the structural connection modes according to the robot module connection structure data, thereby obtaining horizontal connection structure data and vertical connection structure data;

[0186] In this embodiment, when classifying the structural connection mode according to the robot module connection structure data, graph theory and network analysis methods can be used. For example, a network analysis tool is used to graphically represent the connection structure, and the connection mode of each module is converted into edges and nodes in the graph, and then different connection modes are classified. In this way, horizontal connection structure data (such as horizontally transmitted electrical signals) and vertical connection structure data (such as upstream and downstream connection relationships between sensors and actuators) can be accurately captured.

[0187] Step S442: sorting the vertical connection order of the modules on the vertical space data according to the vertical connection structure data, thereby obtaining the vertical connection order data of the modules;

[0188] In this embodiment, the vertical space of the modules is sorted using the vertical connection structure data, which usually involves the application of space analysis and sorting algorithms. For example, according to the vertical stacking requirements of the modules, the spatial analysis tool is used to calculate the upper and lower relative positions of each module, and an appropriate sorting algorithm (such as topological sorting or greedy algorithm) is applied to determine the connection order in the vertical direction. This ensures the safe stacking and effective connection of the modules during the actual assembly process.

[0189] Step S443: extracting the same-horizontal space module data of the robot module structure model according to the module vertical space data, thereby obtaining the same-horizontal space module data;

[0190] In this embodiment, the module data of the same horizontal space of the robot module structure model is extracted according to the vertical space data of the module, which can be achieved by CAD software or automated extraction tools. For example, the automated batch processing function in the CAD software is used to extract the geometric information and connection features of the modules on the same horizontal line, so as to facilitate the subsequent horizontal connection sorting and assembly priority division.

[0191] Step S444: sorting the module horizontal connection order of the same horizontal space module data according to the horizontal connection structure data, thereby obtaining the module horizontal connection sequence data;

[0192] In this embodiment, the data of the modules in the same horizontal space are sorted based on the horizontal connection structure data, and the sorting algorithm and the support of the CAD tool can also be used. For example, the modules on the same horizontal line are sorted according to the horizontal connection requirements by using the constraints and relationship definitions in the CAD software, ensuring the reasonable layout and connection mode of each module in space, laying the foundation for the subsequent assembly steps.

[0193] Step S445: performing module assembly priority division on the module vertical connection sequence data and the module horizontal connection sequence data, thereby obtaining module assembly priority data.

[0194] In this embodiment, when dividing the module assembly priority for the module vertical connection sequence data and the module horizontal connection sequence data, the physical structure, electrical connection and functional requirements need to be comprehensively considered. For example, by combining the vertical and horizontal connection information, a decision analysis method (such as AHP) is used to determine the appropriate assembly priority for each module to ensure the safety and efficiency of the overall robot structure during the assembly process.

[0195] The connection structure of the robot module in the present invention is classified into a horizontal connection structure and a vertical connection structure. This classification helps to understand how the modules are arranged and connected in space, and the classification can help determine the best module arrangement to improve the stability and efficiency of the robot as a whole. Understanding the connection structure can reduce the use of connecting parts, thereby reducing manufacturing costs. Sorting the connection order of the modules in the vertical direction can reduce assembly time and process complexity, making module installation more efficient. The optimized vertical connection order can ensure that the connection between the modules is more firm and stable. Correctly sorting the horizontal connection can maximize space utilization. A reasonable horizontal connection order can reduce conflicts and overlaps in operation, thereby improving the operating efficiency and response speed of the robot. Determining the assembly priority can make the order of module assembly more reasonable and efficient, and reduce waiting and adjustment time during assembly. The correct assembly order can reduce assembly errors and duplication of work, and improve the accuracy and success rate of assembly.

[0196] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0197] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A modular parametric design method for the electrical system of a construction robot, characterized in that: The following steps are involved: Step S1: Acquire construction robot design data, and perform component function design feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function design data and robot structure data; Performing robot topology analysis on robot structure data to obtain a robot topology model; Step S2: performing component function similarity evaluation according to the component function design data, thereby obtaining component function similarity data, and performing functional component clustering according to the component function similarity data, thereby obtaining functional component clustering data; Step S3: dividing the robot topology structure model into structural modules according to the functional component clustering data, thereby obtaining a robot structure module set and an electrical wiring harness module set, and marking the robot topology structure model according to the robot structure module set and the electrical wiring harness module set, thereby obtaining a robot module structure model; Step S4: performing module assembly priority evaluation on the robot module structure model to obtain module assembly priority data, and performing module assembly strategy analysis on the robot structure module set and the electrical harness module set according to the module assembly priority data to obtain a module assembly strategy; Step S5: Performing a module assembly parameterized description on the robot module structure model according to the module assembly strategy, thereby obtaining a construction robot module parameterized model.

2. The modular parameterized design method for the electrical system of a construction robot according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire construction robot design data, and perform component definition function feature extraction and robot structure feature extraction on the construction robot design data, thereby obtaining component function data and robot structure data; Step S12: Structural classification of the robot structural data, thereby obtaining robot electrical structural data and robot mechanical structural data; Step S13: performing an electrical topological structure analysis of the robot according to the electrical structure data of the robot, thereby obtaining an electrical topological structure model of the robot; Step S14: performing a robot mechanical topology structure analysis according to the robot mechanical structure data, thereby obtaining a robot mechanical topology structure model; Step S15: merging the robot electrical topology structure model and the robot mechanical topology structure model into a robot topology structure, thereby obtaining a robot topology structure model.

3. The modular parameterized design method for the electrical system of a construction robot according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: extracting circuit structure features and electrical component structure features from the robot electrical structure data, thereby obtaining circuit structure data and electrical component structure data; Step S132: classifying the circuit structure data into circuit connection modes, thereby obtaining series circuit structure data and parallel circuit structure data; Step S133: performing series component association on the series circuit structure data and the electrical component structure data, thereby obtaining electrical component series circuit connection data; performing parallel component association on the parallel circuit structure data and the electrical component structure data, thereby obtaining electrical component parallel circuit connection data; Step S134: constructing an electrical block diagram according to the electrical component series circuit connection data and the electrical component parallel circuit connection data, thereby obtaining an electrical block diagram of the robot; Step S135: Perform electrical connection topology analysis according to the robot electrical block diagram to obtain the robot electrical topology model.

4. The modular parameterized design method for the electrical system of a construction robot according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: classifying the robot mechanical structure data into functional structures, thereby obtaining mechanical component structure data and connection structure data; Step S142: extracting component connection structure features from the mechanical component structure data, thereby obtaining mechanical component connection structure data; Step S143: performing similarity calculation on the mechanical component connection structure data and the connection structure data, thereby obtaining connection structure similarity data; Step S144: performing component connection association on the mechanical component structure data according to the connection structure similarity data, thereby obtaining mechanical component connection data; Step S145: Perform mechanical connection topology analysis on the mechanical component connection data to obtain a robot mechanical topology model.

5. The modular parameterized design method for the electrical system of a construction robot according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting component performance parameter features and component function definition features from the component function design data, thereby obtaining a component performance parameter set and component function definition data; Step S22: Calculating component performance parameter similarity on the component performance parameter set, thereby obtaining component performance parameter similarity data; Step S23: Calculating the component definition cosine similarity according to the component function definition data, thereby obtaining component function definition similarity data; Step S24: performing similarity comprehensive integration on the component performance parameter similarity data and the component function definition similarity data, thereby obtaining component function similarity data; Step S25: clustering the component function design data into functional components according to the component function similarity data, thereby obtaining functional component clustering data.

6. The modular parameterized design method for the electrical system of a construction robot according to claim 5, characterized in that: Step S23 is specifically as follows: Step S231: extracting function description text information from component function definition data, thereby obtaining function definition text data; Step S232: performing feature vector conversion on the function definition text data, thereby obtaining a function definition feature vector set; Step S233: performing cosine similarity measurement according to the function definition feature vector set, thereby obtaining component function definition similarity data.

7. The modular parameterized design method for the electrical system of a construction robot according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting structural component features and wiring harness component features from the functional component clustering data, thereby obtaining structural component clustering data and wiring harness component clustering data; Step S32: extracting mechanical structure features and electrical structure features from the robot topology structure model, thereby obtaining a robot mechanical topology structure model and a robot electrical topology structure model; Step S33: dividing the robot mechanical topology structure model into mechanical structure modules according to the structural component clustering data, thereby obtaining a robot structure module set; Step S34: dividing the robot electrical topology structure model into electrical harness modules according to the robot structure module set and the harness component clustering data, thereby obtaining an electrical harness module set; Step S35: marking the robot topology structure model according to the robot structure module set and the electrical harness module set, thereby obtaining the robot module structure model.

8. The modular parameterized design method for the electrical system of a construction robot according to claim 1, characterized in that: Step S34 is specifically as follows: Step S341: extracting wiring harness connection features from the robot topology model to obtain wiring harness connection data; Step S342: performing structural module-wiring harness connection association on the robot structural module set and the robot electrical topological structure model according to the wiring harness connection data, thereby obtaining structural module-wiring harness connection data; Step S343: dividing the robot electrical topology model into a connection harness model according to the structure module-harness connection data, thereby obtaining a harness connection structure model; Step S344: dividing the wiring harness connection structure model into electrical wiring harness modules according to the wiring harness component clustering data, thereby obtaining an electrical wiring harness module set.

9. The modular parameterized design method for the electrical system of a construction robot according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: extracting module space features from the robot module structure model to obtain module space data; Step S42: performing module vertical spatial sorting according to the module spatial data, thereby obtaining module vertical spatial data; Step S43: extracting connection structure features of the robot module structure model, thereby obtaining robot module connection structure data; Step S44: evaluating the module assembly priority according to the robot module connection structure data and the module vertical space data, thereby obtaining the module assembly priority data; Step S45: performing module assembly sorting on the robot structure module set and the electrical harness module set according to the module assembly priority data, thereby obtaining module assembly sequence data; Step S46: performing assembly strategy analysis on the module assembly sequence data to obtain the module assembly strategy.

10. The modular parameterized design method for the electrical system of a construction robot according to claim 9, characterized in that: Step S44 is specifically as follows: Step S441: classifying the structural connection modes according to the robot module connection structure data, thereby obtaining horizontal connection structure data and vertical connection structure data; Step S442: sorting the vertical connection order of the modules on the vertical space data according to the vertical connection structure data, thereby obtaining the vertical connection order data of the modules; Step S443: extracting the same-horizontal space module data of the robot module structure model according to the module vertical space data, thereby obtaining the same-horizontal space module data; Step S444: sorting the module horizontal connection order of the same horizontal space module data according to the horizontal connection structure data, thereby obtaining the module horizontal connection sequence data; Step S445: performing module assembly priority division on the module vertical connection sequence data and the module horizontal connection sequence data, thereby obtaining module assembly priority data.

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