A performance platform building method, device and related system

Abstract

This application provides a method, apparatus, and related system for constructing a performance platform, belonging to the field of robotics technology. The control platform acquires equipment parameters from multiple robots and the construction requirements of the target performance platform, and controls a drone to acquire site image information of the target performance platform. Then, based on the equipment parameters, construction requirements, and site image information, a collaborative operation strategy involving multiple robots is determined. Finally, the multiple robots are controlled to execute the construction task of the target performance platform according to the collaborative operation strategy. This improves the consistency of multi-robot collaborative operation and the accuracy of constructing the performance platform.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to a method, apparatus and related system for building a performance platform. Background Technology

[0002] In the construction of outdoor performance platforms, multiple types of robots are involved in collaborative tasks such as handling and assembly. Existing methods typically employ decentralized control to schedule each robot, lacking a unified control center. This leads to problems such as unreasonable task allocation and low coordination consistency among different robots, thus affecting the overall construction efficiency. Furthermore, existing methods often rely on individual robots or local visual perception to obtain environmental information, making it difficult to acquire global site information of the performance platform construction area. This results in a lack of globality and accuracy in task planning, making it difficult to achieve unified collaborative operation of multiple robots.

[0003] Therefore, in the scenario of building a performance platform, how to improve the consistency of multi-robot collaborative operation and the accuracy of building the performance platform is an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method, apparatus, and related system for building a performance platform, which addresses the problems of low coordination consistency and inaccurate platform building among multiple robots in performance platform building scenarios. By implementing the method provided in this application, the consistency of multi-robot collaborative operations and the accuracy of performance platform building can be improved.

[0005] In a first aspect, this application provides a method for building a performance platform, applied to a control platform device in a performance platform building system. The performance platform building system further includes multiple robots and drones communicating with the control platform device. The method includes: Obtain the device parameters of the multiple robots and the construction requirements of the target performance platform; Control the drone to acquire site image information of the target performance platform; A collaborative operation strategy involving the multiple robots is determined based on the equipment parameters, the setup requirements, and the site image information. The multiple robots are controlled to perform the construction task of the target performance platform according to the collaborative operation strategy.

[0006] Secondly, this application provides a performance platform construction device, applied to a control center device in a performance platform construction system. The performance platform construction system further includes multiple robots and drones communicating with the control center device. The device includes: The acquisition unit is used to acquire the equipment parameters of the multiple robots and the construction requirements of the target performance platform; The control unit is used to control the drone to acquire site image information of the target performance platform; The computing unit is used to determine a collaborative operation strategy including the multiple robots based on the equipment parameters, the setup requirements information, and the site image information. The control unit is also used to control the multiple robots to perform the construction task of the target performance platform according to the collaborative operation strategy.

[0007] Thirdly, this application provides an electronic device including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any of the methods of the first aspect of this application.

[0008] Fourthly, this application provides a performance platform construction system, wherein the performance platform construction system can perform some or all of the steps described in any method of the first aspect of this application.

[0009] Fifthly, this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0010] Sixthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0011] By implementing the performance platform construction method, apparatus, and related system provided in this application, the system acquires equipment parameters of multiple robots and construction requirements information of the target performance platform. It then controls a drone to acquire site image information of the target performance platform. Based on the equipment parameters, construction requirements information, and site image information, a collaborative operation strategy involving multiple robots is determined. The system then controls the multiple robots to execute the construction task of the target performance platform according to the collaborative operation strategy. On one hand, by uniformly scheduling the collaborative operations of humanoid robots, robot dogs, and drones through a master control node, the consistency of the performance platform construction system is improved. On the other hand, by decoupling complex tasks such as performance platform construction and dismantling into standardized operation steps of transportation, assembly, disassembly, and recycling, and having the master control node uniformly control the operations of drones, robot dogs, and humanoid robots, the accuracy of performance platform construction is improved. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is an architecture diagram of a performance platform building system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a method for building a performance platform according to an embodiment of this application; Figure 4 This is a flowchart illustrating a method for detecting the results of a performance platform setup, as provided in an embodiment of this application. Figure 5 This is a schematic diagram of a performance platform setup provided in an embodiment of this application; Figure 6 This is a flowchart illustrating another method for building a performance platform provided in an embodiment of this application; Figure 7 This is a block diagram of the functional modules of a performance platform construction device provided in an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0015] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0016] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0017] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0018] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] The following is an explanation of the relevant terms used in this application: Model Context Protocol (MCP) is a standardized communication protocol between robots and large language models or language action models. It is used for command issuance, status feedback, knowledge base mounting, and coordination scheduling to ensure consistent and reliable interaction among multiple devices.

[0021] For scenarios involving multiple devices collaboratively building a performance platform, which involves various types of robots coordinating handling, assembly, and disassembly tasks, current methods typically employ decentralized control to schedule individual robots. This lack of a unified control center can lead to issues such as unreasonable allocation of tasks among different robots and low consistency in collaboration. Furthermore, most methods rely on the local visual perception of the environment by humanoid robots and robotic dogs, making it difficult to obtain the performance platform building area and global information during the building process. This results in insufficient accuracy in the building of the performance platform and makes it difficult to achieve unified collaborative operations.

[0022] To address the aforementioned issues, this application provides a method, apparatus, and related system for constructing a performance platform. A control platform device is applied within the performance platform construction system. This system also includes multiple robots and drones communicating with the control platform device. The method includes: acquiring equipment parameters of the multiple robots and construction requirements information for the target performance platform; controlling the drones to acquire site image information of the target performance platform; determining a collaborative operation strategy involving the multiple robots based on the equipment parameters, construction requirements information, and site image information; and controlling the multiple robots to execute the construction task of the target performance platform according to the collaborative operation strategy. This achieves consistency in multi-robot collaborative operation and improves the accuracy of performance platform construction.

[0023] Please see Figure 1 , Figure 1 This is an architecture diagram of a performance platform building system provided in an embodiment of this application. The performance platform building system 100 includes: a control center device 110, a humanoid robot 120, a robot dog 130, and a drone 140.

[0024] The control platform device 110 includes an equipment parameter library 111, a performance platform template library 112, and a task execution library 113. The control platform device 110 is used for unified modeling, task decomposition, strategy generation, and multi-robot collaborative scheduling control of the target performance platform construction task. It interacts with lower-level execution devices and issues commands through a model context protocol, thereby achieving centralized management and dynamic control of the humanoid robot 120, the robot dog 130, and the drone 140. The equipment parameter library 111 stores equipment parameter information related to the humanoid robot 120, the robot dog 130, and the drone 140. Equipment parameters include load capacity parameters, motion capability parameters, operational accuracy parameters, operational range parameters, and energy consumption parameters. The equipment parameter library 111 can use a structured data model to uniformly describe the capabilities of different types of equipment. For example, it stores the first load capacity parameter and operational flexibility parameter for the humanoid robot 120, the second load capacity parameter and support stability parameter for the robot dog 130, and the flight altitude range, endurance, and image acquisition accuracy parameter for the drone 140. The construction of this parameter library enables the control platform device 110 to perform precise capability matching and constraint judgment during task allocation. The performance platform template library 112 stores various types of performance platform structure templates and corresponding task models. The performance platform template library 112 can pre-configure platform construction templates of different sizes and structural forms, such as standard rectangular platform templates, multi-layer structure platform templates, and irregular structure platform templates. Each type of template corresponds to a set of standardized task phase division rules and task parameters, including the task division methods for the handling, assembly, and disassembly phases, as well as the component installation sequence and electrical system layout logic in each phase. By calling the template data in the performance platform template library 112, the control platform device 110 can quickly complete the task modeling and phase division of the target performance platform, reducing online computational complexity and improving system response efficiency. The task execution library 113 stores standardized task execution strategies and action instruction sets. The task execution library 113 includes various predefined operation strategy templates, such as handling path planning strategy templates, assembly action sequence templates, disassembly reverse execution templates, and multi-robot collaborative operation templates. When generating specific work strategies, the control platform device 110 can call and parameterize the strategy templates in the task execution library 113 based on the task parameters, thereby generating a specific execution instruction sequence suitable for the current task scenario and sending it to the corresponding execution device.

[0025] The humanoid robot 120 is used to perform high-precision operation tasks and complex structural assembly tasks, such as steel frame connection, curtain installation, and fine equipment placement. By calling the first operation strategy issued by the control center device 110, the humanoid robot 120 can complete various tasks such as handling, assembly, and disassembly at different task stages. The robot dog 130 is used to perform support and auxiliary operation tasks, such as providing temporary support during steel frame construction and performing cable laying during electrical system installation. The robot dog 130 receives the second operation strategy generated by the control center device 110 to achieve collaborative cooperation with the humanoid robot 120, thereby improving the stability and efficiency of the overall construction process. The drone 140 is used to perform site perception and detection tasks. By performing aerial scanning of the target performance platform area, the drone 140 can acquire high-precision site image information and perform tasks such as inspection, structural inspection, and compliance inspection based on the third operation strategy generated by the control center device 110, thereby providing real-time environmental perception data support for the system.

[0026] In one possible embodiment, the control platform device 110 is used for multi-source data fusion and decision generation. It acquires external input construction requirement information and site image information from the drone 140, comprehensively analyzes the structural features, spatial constraints, and operational requirements of the target performance platform, and combines this with various robot capability parameters stored in the device parameter library 111 to achieve matching calculations between tasks and device capabilities, thereby generating operational strategies for multi-robot collaborative execution. Furthermore, the control platform device 110 establishes a data interaction mechanism between the humanoid robot 120, the robot dog 130, and the drone 140 through a model context protocol. Specifically, this protocol is used to standardize the data format, communication process, and status feedback mechanism between the control platform device 110 and each execution device, enabling each device to interact with information within a unified context semantics. For example, during the handling or assembly process, the humanoid robot 120 can provide real-time feedback on its operational status parameters (such as current position, load status, and execution progress). Based on this feedback, the control platform 110 dynamically adjusts the strategies in the task execution library 113. When the drone 140 performs a site scanning task, it can upload the real-time collected site image information to the control platform 110 to support subsequent path planning and work area division. It is evident that by organically combining the equipment parameter library 111, the performance platform template library 112, and the task execution library 113, and by achieving efficient communication between the control platform 110 and various types of robotic devices through the model context protocol, this system can achieve closed-loop management of the entire process from task modeling and strategy generation to execution control.

[0027] Furthermore, by introducing multiple collaborative operation modes involving humanoid robots 120, robot dogs 130, and drones 140, different types of equipment can perform optimal tasks within their respective areas of strength, thus forming a collaborative operation system with complementary advantages. For example, humanoid robots 120 are responsible for high-precision assembly, robot dogs 130 are responsible for structural support and wiring, and drones 140 are responsible for environmental perception and quality inspection. Under the unified scheduling of the control platform equipment 110, the three form a collaborative closed loop.

[0028] It can be seen that through the architecture of the above-mentioned performance platform construction system 100, the complex performance platform construction task can be efficiently decomposed and intelligently scheduled, significantly improving the execution efficiency and operation accuracy of multi-robot collaborative operation, while enhancing the consistency of multi-robot collaborative operation.

[0029] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 2 As shown, the electronic device 200 includes a processor 210, a memory 220, a communication interface 230, and one or more programs 221. The processor 210 is communicatively connected to the memory 220 and the communication interface 230 via an internal communication bus.

[0030] The one or more programs 221 are stored in the memory 220 and configured to be executed by the processor 210. The one or more programs 221 include instructions for performing any step in the above method embodiments.

[0031] The processor 210 can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0032] The memory 220 can be volatile memory or non-volatile memory, or it can include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0033] It is understood that the electronic device 200 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device 200 may be equipped with... Figure 1 The architecture of a performance platform building system is described above.

[0034] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 3 This application describes a method for building a performance platform. Figure 3 This is a flowchart illustrating a method for building a performance platform according to an embodiment of this application, specifically including the following steps: Step S310: Obtain the equipment parameters of the multiple robots and the construction requirements of the target performance platform.

[0035] The "multiple robots" category includes several different types of humanoid robots, robot dogs, and drones. The "multiple humanoid robots" category can include combinations of different types, such as the HR-X1 and HR-Y2 humanoid robots. The equipment parameters for these robots represent their capabilities and operational status during the performance platform construction task. These parameters include load capacity, joint range of motion, force feedback accuracy, and visual perception accuracy for humanoid robots; support force, gait stability, and protection level for robot dogs; and flight stability, visual inspection accuracy, and electrical inspection range for drones. The construction requirements for the target performance platform describe the execution objectives and constraints of the target task. These include platform size parameters (e.g., length, width, and height), platform structural components (including steel frame system, curtain system, lighting system, and sound system), electrical system layout requirements (e.g., lighting and sound wiring layout rules), and construction sequence constraints. Specifically, the control platform establishes data interaction connections with the multiple robots through pre-established communication interfaces. Based on a unified communication protocol (e.g., the MCP interface mechanism), it sends parameter acquisition commands to each robot to trigger a self-check process, and the corresponding device parameters are uploaded to the control platform. Specifically, the humanoid robot collects operational status data from its joint drive module and force sensor feedback data through its internal control system to generate first device parameters characterizing its load capacity and operational accuracy. The robot dog obtains second device parameters related to its support capacity and stability through its motion control module and support platform sensing module. The drone obtains its flight performance parameters and detection capability parameters through its flight control system and detection module, forming third device parameters. The control platform performs unified analysis and standardization of these multi-source device parameters to construct a set of device capability parameters. Simultaneously, the control platform also obtains the construction requirements information of the target performance platform through a task input interface. Specifically, the setup requirements can be input by the user terminal or generated from a pre-set platform template library. For example, a standard platform structure model of the corresponding specifications (e.g., 15m × 10m) can be called from the platform template library, and information such as structural component parameters, connection method parameters (e.g., bolt connection torque standards) and circuit layout rules can be extracted. In addition, the standard template can be dynamically adjusted according to the actual task requirements to form target setup requirements information adapted to the current scenario.

[0036] It should be noted that during the acquisition of equipment parameters and setup requirements information for the multiple robots, the control platform not only performs data acquisition operations but also verifies the validity and ensures consistency of the acquired equipment parameters. For example, it uses threshold judgments to remove abnormal equipment parameters (such as abnormal load capacity or sensor failure) and synchronizes and aligns multi-source data based on timestamps to ensure the accuracy and timeliness of the data used in subsequent task planning. Furthermore, by storing the equipment parameters and setup requirements information in a structured manner, it provides basic data support for the generation of subsequent collaborative operation strategies, thereby enabling efficient collaboration and precise control of multiple robot systems in complex performance platform setup scenarios.

[0037] Step S320: Control the drone to acquire site image information of the target performance platform.

[0038] Among them, the site image information refers to the spatial environmental characteristics and environmental constraints of the target performance platform construction area. The site image information includes two-dimensional image data, three-dimensional point cloud data, spatial structure outline information, and obstacle distribution information. Furthermore, the site image information also includes ground flatness information, site area boundary information, and location information of transport vehicles and temporary storage materials.

[0039] Specifically, the control center first determines the expected construction area of ​​the target performance platform and sends flight control and data acquisition commands to the drone via a communication interface to control the drone to perform site scanning tasks according to a preset flight path. The flight path can be adaptively planned based on the area and shape of the target site, for example, using a grid scanning path or a surround scanning path to ensure full coverage of the target area. During flight, the drone uses its onboard visual sensors (such as high-definition cameras and depth cameras) and laser rangefinders to acquire data from multiple angles and altitudes, obtaining continuous image frames and depth information. Furthermore, during image acquisition, the drone can dynamically adjust its flight attitude based on its flight control system to improve the stability and accuracy of image acquisition. For example, attitude control algorithms can maintain the flight altitude within a preset range (e.g., 1m to 5m), and wind resistance strategies can be combined to reduce the impact of environmental interference on image quality. Simultaneously, the drone transmits the acquired raw image data to the control center in real time. The control center performs preprocessing operations on the image data, including image denoising, distortion correction, and multi-frame image stitching, thereby generating a complete two-dimensional image of the site. Furthermore, based on multi-view images and depth information, the control platform further executes 3D reconstruction algorithms (such as those based on structured light or multi-view geometry) to generate corresponding 3D point cloud models, thereby obtaining more accurate site spatial structure information. After obtaining 2D images and 3D point cloud data, the control platform can also identify and label key targets in the images using target detection algorithms, such as identifying the positions of transport vehicles, obstacles, and workable areas, and assigning corresponding semantic labels to different types of targets. Simultaneously, by analyzing the 3D point cloud data, information on the flatness and slope changes of the site surface can be extracted, providing a reference for the precision control of subsequent platform steel frame installation.

[0040] It is evident that by controlling drones to collect and process multi-dimensional site images of the target performance platform construction area, not only can a comprehensive perception of the complex site environment be achieved, but also a high-precision spatial environment model can be constructed. This provides accurate and real-time environmental data support for the formulation of multi-robot collaborative operation strategies. Furthermore, it avoids the problems of incomplete or large-error information caused by relying on manual measurement or single sensor collection, effectively improving the efficiency and accuracy of site information acquisition. This provides a reliable data foundation for subsequent path planning, task scheduling, and operation execution, thereby improving the automation level and execution reliability of the overall performance platform construction process.

[0041] Step S330: Determine a collaborative operation strategy including the multiple robots based on the equipment parameters, the setup requirements information, and the site image information.

[0042] The collaborative operation strategy refers to the division of labor, execution order, and collaborative relationships among various types of robots at different task stages during the construction of the target performance platform. The collaborative operation strategy includes task stage division information, task allocation information, path planning information, task order queues, and collaborative constraint parameters. Equipment parameters are the capabilities of each robot in terms of load capacity, operational accuracy, and environmental adaptability. Construction requirement information refers to the execution objectives and structural constraints of the target task. Site image information is used to provide the spatial distribution characteristics and obstacle constraints of the operational environment.

[0043] Specifically, the control platform first breaks down the target performance platform construction task into stages based on the construction requirements information, resulting in multiple task stages, such as the handling stage, assembly stage, and disassembly stage. Further, for each task stage, corresponding task parameters are extracted, such as the weight, size, and target location parameters of items in the handling stage; the steel frame connection sequence, installation accuracy requirements, and equipment installation location parameters in the assembly stage; and the disassembly sequence and classification storage requirements in the disassembly stage. Then, the control platform models the capabilities of each robot based on the equipment parameters. For example, based on the load capacity and operational accuracy parameters of a humanoid robot, it is determined that it is suitable for performing high-load handling and precision assembly tasks; based on the support capacity and motion stability parameters of a robot dog, it is determined that it is suitable for performing support assistance and cable laying tasks; and based on the visual inspection accuracy and flight capability parameters of a drone, it is determined that it is suitable for performing site scanning and quality inspection tasks. After completing the task phase division and equipment capability modeling, the control platform further combines the site image information to perform constraint modeling of the working environment. For example, it determines the passable area and obstacle distribution based on 3D point cloud data, and determines the spatial location of different functional areas (such as the steel frame assembly area, curtain installation area, and electrical testing area) based on 2D image information. Based on the environmental constraint information, a path planning algorithm (such as a grid map-based shortest path algorithm or a sampling-based path planning algorithm) is invoked to generate initial working paths for each robot. At the same time, according to the dependencies between task phases (such as "handle first, then assemble" and "power off first, then disassemble"), a task execution order constraint model is constructed.

[0044] Furthermore, the control platform allocates and schedules multi-robot tasks based on task parameters, equipment capability parameters, and environmental constraints. Specifically, by matching task requirements with equipment capabilities, high-load handling tasks are prioritized for humanoid robots with higher load capacity, tasks requiring temporary support or wiring are assigned to robot dogs, and environmental perception and quality inspection tasks are assigned to drones. Simultaneously, in scenarios involving multi-robot collaborative operations (such as collaborative handling of long steel frames), collaborative operation parameters (such as load allocation ratios and synchronization control time windows) are introduced to generate multi-robot collaborative execution strategies. In addition, the control platform can optimize and adjust the above allocation results based on a preset task execution library or model (such as a task planning module based on a large language model) to improve overall execution efficiency. After obtaining the corresponding operation strategies for each robot, the control platform further integrates these strategies to construct a unified collaborative operation strategy. For example, by constructing a global operation queue, the operation tasks of different robots at different stages are sorted by time, and the operation area is allocated based on the site space division results, thereby avoiding operation conflicts. Meanwhile, a dynamic adjustment mechanism is introduced into the strategy to update the work sequence and task allocation in real time based on the work status parameters (such as task completion progress, equipment status changes, etc.) fed back during subsequent execution, thereby forming a closed-loop control.

[0045] It is evident that by integrating and analyzing equipment parameters, setup requirements, and site image information, and through processes such as task decomposition, capability matching, and environmental constraint modeling, a collaborative operation strategy for multi-robot systems can be generated. This enables automated choreography and efficient execution of complex performance platform setup tasks, thereby improving setup accuracy and operational safety.

[0046] In one possible embodiment, the plurality of robots includes robot dogs and humanoid robots. The step of determining a collaborative operation strategy involving the plurality of robots based on the equipment parameters, the setup requirements information, and the site image information specifically includes the following steps: 331. Based on the construction requirement information, determine the task stages for the multiple robots to build the target performance platform, resulting in multiple task stages; the multiple task stages include a transportation stage, an assembly stage, and a disassembly stage; 332. Obtain the task parameters corresponding to each of the multiple task stages to obtain multiple task parameters; 333. Determine the first load capacity parameter of the humanoid robot and the second load capacity parameter of the robot dog in the equipment parameters; 334. Based on the first load capacity parameter and the multiple task parameters, determine the operation strategy for the humanoid robot to perform the performance platform construction task, and obtain the first operation strategy; 335. Based on the second load capacity parameter and the multiple task parameters, determine the operation strategy for the robot dog to perform the performance platform construction task, and obtain the second operation strategy; 336. Based on the site image information and the multiple task parameters, determine the operation strategy for the UAV to perform the performance platform construction task, and obtain the third operation strategy; 337. Based on the first operation strategy, the second operation strategy, and the third operation strategy, determine the strategy scheduling for the humanoid robot, the robot dog, and the drone to perform the performance platform construction task, and obtain the collaborative operation strategy.

[0047] The first load capacity parameter characterizes the humanoid robot's maximum load-bearing capacity and dynamic load stability under different postures and motion states. The second load capacity parameter characterizes the robot dog's support strength and stability maintenance ability under static and mobile support states. The collaborative operation strategy is used to analyze the task decomposition results, capability matching results, spatial constraint adaptation results, and cross-device collaborative execution relationships of multiple robots during the construction of the target performance platform. Essentially, it is a unified scheduling and control strategy generated based on the mapping relationship between task stages, equipment capabilities, and environmental constraints, used to guide the collaborative execution and dynamic adjustment of humanoid robots, robot dogs, and drones in different operational stages.

[0048] Specifically, the control platform first structurally breaks down and divides the overall construction task of the target performance platform into stages based on the construction requirements information, resulting in multiple task stages, including the transportation stage, assembly stage, and disassembly stage. The transportation stage corresponds to the transfer of materials from the transport carrier to the construction area; the assembly stage corresponds to the construction of the platform's physical structure and electrical system; and the disassembly stage corresponds to the platform's structural deconstruction and recycling. This stage division transforms the complex overall task into a set of sub-tasks with clear input-output relationships, providing a foundation for subsequent task allocation. Further, for each of the multiple task stages, the control platform extracts and structurally expresses the various task parameters, resulting in multiple sets of task parameters. These task parameters include task object attribute parameters (such as steel frame length, curtain size, and electronic device type), task execution constraint parameters (such as installation sequence constraints and power-off priority constraints), and spatial location parameters (such as target installation area coordinates and path endpoint location). After completing the task stage and parameter extraction, the control platform extracts the first load capacity parameter of the humanoid robot and the second load capacity parameter of the robot dog from the equipment parameters. This process involves differentiating and modeling the load capacities of different robot types to provide a foundation for subsequent task matching. Based on the initial load capacity parameter and multiple task parameters, the control platform performs capacity matching and task allocation for the humanoid robot, thereby generating the initial operational strategy. Specifically, in the handling phase, based on the weight and size parameters of the object, high-load or long-sized object handling tasks are assigned to humanoid robots with higher load capacities, and a handling path is generated using a path planning algorithm. In the assembly phase, based on the assembly sequence and precision requirements, high-precision steel frame installation tasks are assigned to humanoid robots, and a corresponding assembly action sequence is generated. In the disassembly phase, a reverse disassembly path is generated based on the structural disassembly sequence, and the execution actions of the humanoid robot at different disassembly nodes are determined, thus forming a complete humanoid robot operational strategy.

[0049] Furthermore, based on the second load capacity parameter and multiple task parameters, the control platform device performs functional adaptation analysis on the robot dog's tasks, thereby generating a second operational strategy. During the assembly phase, according to circuit layout and structural support requirements, the robot dog is configured as a temporary support node or wiring carrier. By analyzing the spatial relationships in the task parameters, the support position and movement path of the robot dog during steel frame installation are determined. Simultaneously, during the electrical system layout process, the robot dog's movement trajectory is generated based on the wiring path, enabling it to complete cable laying and fixing operations along a preset path, thus forming a collaborative operation strategy for the robot dog's support and wiring. In addition, based on site image information and the aforementioned multiple task parameters, the control platform device performs environmental perception and detection task planning for the UAV's tasks, thereby generating a third operational strategy. By analyzing the spatial structure and obstacle distribution in the site image information, the flight altitude, scanning range, and detection path of the UAV are determined. Combined with the detection requirement parameters for different task stages (such as structural flatness detection, electrical connection detection, and waterproof compliance detection), corresponding inspection paths and detection action sequences are generated, enabling the UAV to continuously provide environmental perception and quality detection support during the handling, assembly, and disassembly stages. After generating the first, second, and third operation strategies respectively, the control platform integrates and schedules these three strategies to generate the final collaborative operation strategy. Specifically, a global operation queue is constructed, sorting the tasks executed by different robots in different task stages according to time sequence and spatial region, and introducing collaborative constraints such as task dependency constraints (handling before assembly), spatial conflict constraints (avoiding multiple robots operating simultaneously in the same narrow area), and resource conflict constraints (avoiding load or path conflicts). Based on this, combined with real-time feedback of operation status parameters, the operation queue is dynamically adjusted to achieve closed-loop scheduling control of the multi-robot system.

[0050] It is evident that by introducing task phase division, equipment capability modeling, task parameter analysis, and multi-source environmental information fusion into the collaborative operation strategy generation process, differentiated task allocation and collaborative scheduling control for humanoid robots, robot dogs, and drones are achieved, improving the system's adaptability and execution stability in complex scenarios. This significantly enhances the overall efficiency, accuracy, and robustness of multi-robot collaborative performance platform construction tasks.

[0051] In one possible embodiment, determining the operational strategy for the humanoid robot to perform the performance platform construction task based on the first load capacity parameter and the plurality of task parameters, thereby obtaining the first operational strategy, specifically includes the following steps: 3341. Extract the task parameters of the humanoid robot corresponding to the handling stage, the assembly stage, and the disassembly stage from the multiple task parameters to obtain the handling task parameters, the assembly task parameters, and the disassembly task parameters; 3342. Based on the transport task parameters, determine multiple transport positions corresponding to the humanoid robot performing the transport task; and calculate the transport operation path of the humanoid robot based on a preset transport path planning algorithm and the multiple transport positions to obtain the transport path; 3343. Generate a handling operation strategy for the humanoid robot based on the handling path; 3344. Determine the platform assembly sequence of the performance platform construction task based on the assembly task parameters to obtain the platform assembly sequence; 3345. Based on the first load capacity parameter, determine the task parameters that require collaborative operation for the platform assembly sequence to obtain the first collaborative operation parameters for platform assembly; and generate the assembly operation strategy for the humanoid robot according to the first collaborative operation parameters. 3346. Determine the reverse sequence of the platform assembly sequence to obtain the platform disassembly sequence; 3347. Based on the first load capacity parameter, determine the task parameters that require collaborative operation for the platform disassembly sequence, and obtain the second collaborative operation parameters for platform disassembly; and generate the disassembly operation strategy for the humanoid robot according to the second collaborative operation parameters; 3348. The first operation strategy is obtained by integrating the handling operation strategy, the assembly operation strategy and the disassembly operation strategy.

[0052] The parameters include: the first load capacity parameter (e.g., 80kg for HR-X1, 50kg for HR-Y2), joint motion accuracy, force feedback sensor accuracy, and 3D visual modeling error); the task parameters (platform dimensions, component specifications, installation torque standards, transport channel parameters, and disassembly specifications); the transport task parameters (type and weight of the items to be transported, transport starting position, and assembly target position); the assembly task parameters (steel frame connection method, positioning accuracy requirements, installation sequence, and electrical system wiring specifications); the disassembly task parameters (power-off procedure, component removal sequence, storage classification standards, and loading specifications); the platform assembly sequence (standardized installation sequence of the physical platform and electrical system of the performance platform); the first collaborative operation parameter (load distribution ratio and force feedback synchronization threshold for multi-machine collaborative operation during assembly); the platform disassembly sequence (reverse execution sequence of the platform assembly sequence); the second collaborative operation parameter (parameters for multi-machine collaborative disassembly and load balance control during disassembly); and the first operation strategy (operation execution plan for humanoid robots to complete the entire process of transport, assembly, and disassembly).

[0053] Specifically, firstly, from the global task parameters of the performance platform setup, the humanoid robot task parameters corresponding to the three stages of handling, assembly, and disassembly are extracted and broken down, defining the working objects, execution standards, and constraints of the humanoid robots in each stage. Then, based on the weight, size, and transportation requirements of the items specified in the handling task parameters, the handling points such as the loading and unloading positions of transport vehicles and the material storage locations in the platform setup area are determined. At the same time, a preset obstacle avoidance shortest path planning algorithm is invoked, and combined with environmental information such as the width of the site passage and the distribution of obstacles, a conflict-free and highly efficient optimal handling path is calculated. Next, combined with the humanoid robot's first load capacity parameter, a single-robot handling or dual-robot collaborative handling mode is matched according to the weight level of different materials, incorporating rules such as real-time force feedback adjustment and load balancing control to generate a complete handling operation strategy. Based on the assembly task parameters, a standardized platform assembly sequence was determined for the performance platform: "first, build the front and back stage steel frame systems; then, install the curtain and lighting and sound equipment; and finally, connect the electrical system." Combining the first load capacity parameter with the collaborative operation requirements of the assembly process, first collaborative operation parameters were determined, including the dual-machine lifting load distribution ratio, synchronous control threshold, and bolt tightening torque standard. This generated an assembly operation strategy encompassing positioning calibration, component assembly, and precision testing. The platform assembly sequence was then reverse-engineered to form a platform disassembly sequence: "first, disconnect the electrical system; then, remove the lighting, sound, and curtain equipment; and finally, disassemble the steel frame structure." Based on the first load capacity parameter, second collaborative operation parameters were determined for the disassembly process, including collaborative load distribution, component storage and classification, and protective handling. This generated a disassembly operation strategy encompassing power-off operation, orderly disassembly, and categorized loading. Finally, the handling, assembly, and disassembly operation strategies were organically integrated according to the entire execution sequence of the performance platform construction process. The connection points and resource allocation of each stage were coordinated to ultimately form a first operation strategy for humanoid robots that is adapted to the performance platform construction and can be directly executed.

[0054] It is evident that by extracting task parameters in stages, accurately planning work paths, standardizing assembly and disassembly sequences, and combining load capacity with matching collaborative work parameters, a fully integrated work strategy can be built for humanoid robots. This enables robots to achieve precise execution and efficient collaboration of handling, assembly, and disassembly operations in complex scenarios set up on performance platforms, effectively improving the consistency, accuracy, and execution efficiency of humanoid robot operations.

[0055] In one possible embodiment, the process of fusing the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy to obtain the first operation strategy specifically includes the following steps: A1. Obtain the operational constraint parameters of the humanoid robot; A2. Based on the platform construction task execution order, the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy are integrated to obtain the first operation sequence of the performance platform construction task; A3. Perform platform construction constraint processing on the first job sequence according to the job constraint parameters to obtain the second job sequence; A4. If the second job sequence has a conflict with the platform building job, the second job sequence is conflict-handled according to the preset conflict handling rules to obtain the target job sequence; the first job strategy is generated by the task execution library according to the target job sequence. A5. If there is no conflict between the platform building operation and the second job sequence, the first job strategy is generated by the task execution library based on the second job sequence.

[0056] Among them, the operational constraint parameters are the hardware and scene limitations for the humanoid robot to perform the performance platform construction task, including indicators such as maximum load threshold, joint motion accuracy, force feedback control range, work space boundary, battery endurance, and equipment health status; the platform construction task execution sequence follows the standardized operation sequence of the performance platform, namely, first completing material handling, then assembling the platform, and finally disassembling and returning it to the warehouse, clearly defining the connection nodes and prerequisite dependencies of each stage; the first operation sequence is the original operation instruction queue formed by initially integrating handling, assembly, and disassembly strategies according to the construction sequence; the second operation sequence is the one verified by the constraint parameters. The adapted and compliant work sequence; platform-built work conflicts include execution anomalies such as overload, overlapping work paths, equipment resource contention, disordered timing, and interference in the work space; conflict handling rules include preset mechanisms such as load redistribution, path detour optimization, backup equipment scheduling, work sequence extension, and abnormal interruption; the task execution library is a standardized task execution basis that integrates handling path algorithms, assembly sequence logic, disassembly process specifications, and collaborative control rules; the first work strategy is a fully integrated work execution solution that adapts to the hardware capabilities of humanoid robots and the building scenario after constraint verification and conflict resolution.

[0057] Specifically, firstly, the operational constraints of the humanoid robot are acquired, including information such as the device's load limit, motion accuracy, space limitations, battery life, and health monitoring results. Then, following the standard execution sequence of "transportation first, assembly in the middle, disassembly finalization" for the performance platform, the transportation, assembly, and disassembly strategies are sequentially integrated to form a first operational sequence encompassing all operational instructions, execution nodes, and coordination requirements. Next, the first operational sequence is matched and verified against the operational constraints item by item. Instructions exceeding load capacity, failing to meet motion accuracy requirements, or exceeding spatial boundaries are adapted and adjusted, and non-compliant operational logic is eliminated to obtain the desired sequence that meets the basic requirements. The second job sequence is then constrained. Subsequently, conflict detection is performed on the second job sequence to determine whether there are job conflicts such as overload, path overlap, or resource preemption. If a conflict exists, it is resolved by using methods such as load redistribution, path detour optimization, backup equipment scheduling, and time sequence postponement according to preset rules, generating a conflict-free executable target job sequence. Then, the standardized rules and algorithms in the task execution library are called to generate the final first job strategy based on the target job sequence. If the second job sequence has no job conflicts, it is directly standardized and encapsulated and converted into instructions based on the task execution library to form a first job strategy adapted to the entire process of building the performance platform.

[0058] It is evident that by verifying operational constraint parameters, integrating multi-stage strategies according to the construction sequence, conducting conflict detection and resolution, and relying on the task execution library to complete strategy generation, the organic integration and compliant adaptation of humanoid robot handling, assembly, and disassembly operation strategies can be achieved. This effectively avoids operational conflicts and execution risks, ensures the rationality and feasibility of the operation sequence, and significantly improves the consistency and stability of humanoid robot task execution in complex construction scenarios.

[0059] In one possible embodiment, determining the operational strategy for the robot dog to perform the performance platform setup task based on the second load capacity parameter and the plurality of task parameters, thereby obtaining the second operational strategy, specifically includes the following steps: 3351. Obtain the attribute parameters of the building components for building the target performance platform; 3352. Filter out the supporting component attribute parameters corresponding to the required supporting components from the attribute parameters; 3353. Based on the second load capacity parameter and the support component attribute parameter, determine the task of the robot dog support operation to obtain multiple support operation tasks; 3354. Determine the support parameters and wiring parameters for the robot dog to perform the assembly of the performance platform based on the multiple support tasks; 3355. Determine the support pose parameters of the robot dog based on the support parameters; and generate the support operation strategy of the robot dog based on the support pose parameters; 3356. Determine the wiring operation path of the robot dog according to the wiring parameters, and generate the wiring operation strategy of the robot dog according to the wiring operation path; 3357. The support operation strategy and the wiring operation strategy are integrated based on the operation sequence of the performance platform to obtain the second operation strategy.

[0060] The second load capacity parameter refers to the robot dog's operational performance indicators, including maximum support force, support platform levelness error, waterproof rating, gait stability, and motion module accuracy. The assembly component attribute parameters are the properties of various assembly components of the performance platform, including the dimensions, weight, structural form, and installation requirements of steel frame components, curtain components, electrical cables, and connecting fasteners. The support component attribute parameters are the parameters of components requiring temporary support during the assembly of the performance platform, including the support points of the steel frame beams, stress requirements, installation height, and levelness requirements. The support posture parameter is used to limit the space for the robot dog's support operations. The posture and working position include the horizontal angle of the support platform, the leg gait lock state, and the coordinates of the working point; the wiring parameters are the standard indicators for the wiring of the electrical system of the performance platform, including cable type differentiation, laying path, fixed spacing and insulation protection requirements; the wiring operation path is the crawling route planned by the robot dog along the preset circuit layout; the support operation strategy is the execution plan for the robot dog to complete temporary support, posture maintenance and accuracy calibration; the wiring operation strategy is the specific execution plan for the robot dog to complete cable laying, classification and fixing and path compliance; the second operation strategy is the collaborative execution strategy that integrates the robot dog's support operation and wiring operation.

[0061] Specifically, the process begins by collecting attribute parameters of various components used in the performance platform, including the dimensions, stress, installation, and wiring characteristics of steel frames, cables, and support components. Then, from all component attribute parameters, the attribute parameters corresponding to components requiring temporary support during steel frame assembly and platform erection are selected. Next, combining the robot dog's own support parameters (i.e., the second load capacity parameter) with the support component attribute parameters, the timing, support strength, and posture maintenance requirements for each support point are determined, breaking down the work into multiple independent support tasks. Finally, based on the execution sequence and content of each support task, the support control parameters and electrical system wiring parameters required by the robot dog during the assembly stage are simultaneously determined, clarifying the precision standards for the support operations. The system first establishes a classification standard for cabling operations. Then, based on the support parameters, it calculates and sets the robot dog's support position and posture parameters, determines the horizontal angle of the support platform, the work points, and the gait locking mode, and generates a support operation strategy including point movement, posture calibration, and support force output. Simultaneously, based on the cabling parameters, it plans the robot dog's crawling path along the circuit layout, and combines the classification and laying requirements of lighting and audio cables to generate a cabling operation strategy including path crawling, cable fixing, and classified laying. Finally, following the operational sequence of first physically supporting and assembling the performance platform, and then wiring the electrical system, the support operation strategy and the cabling operation strategy are organically integrated in a sequential and task-coordinated manner to form a second robot dog operation strategy adapted to the performance platform setup scenario.

[0062] It is evident that by combining the support and motion performance parameters of the robot dog with the characteristics of the building components to plan specific support and wiring tasks, and generating a collaborative operation strategy for the robot dog's support and wiring, the robot dog can efficiently complete auxiliary support and circuit wiring operations during the construction of the performance platform. This effectively improves the consistency of cooperation among multiple robots and ensures the stability and standardization of the platform construction.

[0063] In one possible embodiment, determining the operational strategy for the UAV to perform the performance platform construction task based on the site image information and the multiple task parameters, to obtain a third operational strategy, specifically includes the following steps: 3361. Determine the detection requirement parameters for the UAV during the handling phase, the assembly phase, and the disassembly phase based on the multiple task parameters; the detection requirement parameters include the detection task type and the detection accuracy. 3362. Determine the detection range parameters of the target performance platform by the UAV based on the site image information, and obtain the detection range parameters; 3363. Determine the flight detection parameters of the UAV based on the detection range parameters; the flight detection parameters include flight altitude, flight path, and scan type; 3364. Generate the third operational strategy for the UAV based on the flight detection parameters and the detection requirement parameters.

[0064] The testing requirements parameters are the requirements for UAVs to perform testing operations in stages, consisting of testing task types and testing accuracy. Testing task types include global material scanning, steel frame verticality inspection, platform flatness inspection, electrical system leakage and waterproofing inspection, and disassembly panoramic inspection. Testing accuracy refers to the error thresholds for UAV visual and electrical inspections, including visual inspection ±2mm, flatness error ≤3mm, and leakage current ≤0.5mA. The testing range parameters are the UAV's operational coverage area, including transport channels, assembly areas, disassembly and storage areas, and the overall site space. Flight testing parameters are the flight control indicators for UAVs to perform testing, including low-altitude testing altitude, global scanning altitude, gridded flight path, along-structure inspection path, and scanning types such as 3D scanning and electrical inspection. The third operational strategy is an integrated testing flight execution solution for UAVs adapted to the entire assembly process.

[0065] Specifically, firstly, based on the task parameters of the three stages of performance platform handling, assembly, and disassembly, the corresponding inspection requirements parameters for each stage are determined. In the handling stage, global material inventory and path environment inspection tasks are set; in the assembly stage, steel frame verticality, platform flatness, and electrical system insulation and leakage inspection tasks are set; and in the disassembly stage, panoramic scanning and component storage verification tasks are set. Simultaneously, the accuracy thresholds for each inspection task are defined. Then, based on the site image information collected by the drone, the site boundaries, work zones, and obstacle locations are analyzed to delineate the inspection areas that the drone needs to cover, including the setup area, handling channels, and disassembly areas. The detection range parameters for the unloading area are determined. Next, the flight detection parameters for the drone are planned based on these parameters. A low-altitude flight altitude of 1m is set for local precision detection, and a high-altitude cruising altitude is set for global scanning. Flight paths are planned using a grid-like approach, following steel frame lines, and along electrical circuit layouts. Scanning types, such as 3D visual scanning and electrical probe detection, are selected according to the detection requirements. Finally, the flight detection parameters adapted to each stage are integrated with the detection requirement parameters to clarify the entire process detection sequence, flight control rules, data acquisition, and early warning feedback mechanisms, generating a third-stage drone operation strategy covering the entire lifecycle of the performance platform setup.

[0066] It is evident that by matching testing needs in stages, defining the testing range based on site information, and setting flight parameters in a refined manner, a testing operation strategy adapted to the entire process of building a performance platform can be generated for drones. This enables the integrated execution of site perception, quality inspection, and anomaly warning, effectively improving the real-time performance and accuracy of testing during the building process, and ensuring the accuracy and stability of the performance platform construction.

[0067] In one possible embodiment, the strategy scheduling of the performance platform construction task for the humanoid robot, the robot dog, and the drone based on the first operation strategy, the second operation strategy, and the third operation strategy to obtain the collaborative operation strategy specifically includes the following steps: 3371. Determine the collaborative operation parameters corresponding to the humanoid robot, the robot dog, and the drone based on the first operation strategy, the second operation strategy, and the third operation strategy to obtain multiple third collaborative operation parameters; 3372. Construct a task order queue for the target performance platform based on the multiple third collaborative task parameters to obtain a first task queue; 3373. Based on the site image information, determine the areas where the humanoid robot, the robot dog, and the drone will operate on the target performance platform, thus obtaining multiple operating areas; 3374. Based on the first task queue and the multiple task areas, the humanoid robot, the robot dog, and the drone are scheduled to perform tasks to obtain a collaborative scheduling strategy; 3375. Control the humanoid robot, the robot dog, and the drone to execute the cooperative scheduling strategy and obtain multiple operational status parameters of the multiple robots; 3376. Adjust the first job queue according to the multiple job status parameters to obtain the target job queue; 3377. Based on the target job queue, update the collaborative scheduling strategy to obtain the collaborative job strategy.

[0068] The third collaborative operation parameters include the humanoid robot load distribution ratio, robot dog support timing, drone detection frequency, inter-device communication latency threshold, and operation connection window period. The first operation queue is a sequence of multi-robot collaborative operation instructions initially arranged according to the performance platform construction process, including the start time, operation content, and end node of each robot. The operation area is a functional operation space divided based on the site image, including the transport channel area, steel frame assembly area, electrical wiring area, inspection and patrol area, and disassembly and storage area. The collaborative scheduling strategy is a unified scheduling instruction issued by the control center to multiple robots (multiple humanoid robots, multiple robot dogs, and drones), including scheduling rules such as task allocation, path planning, timing synchronization, and resource locking. The operation status parameters are the real-time execution status of each robot, including equipment health code, operation progress, load value, detection results, fault codes, and execution completion status. The target operation queue is a conflict-free and high-efficiency operation instruction queue optimized by real-time status feedback. The collaborative operation strategy is an integrated collaborative operation solution of multiple robots and drones that adapts to dynamic changes throughout the entire process and can be directly implemented.

[0069] Specifically, firstly, based on the first operational strategy of the humanoid robot, the second operational strategy of the robot dog, and the third operational strategy of the drone, the operational capabilities, execution timing, and interaction requirements of each device are extracted. The third collaborative operational parameters required for inter-device coordination are then matched and determined, clarifying the synchronization rules, connection conditions, and collaborative constraints for multi-robot operations. Next, based on the collaborative operational parameters, and following the "transportation-assembly-disassembly" operational logic of the performance platform, the operational instructions of each robot are arranged according to timing and logical dependencies, constructing an initial first operational queue. Then, by analyzing the site's spatial layout, obstacle distribution, and functional zoning through site image information, the respective operational areas of the humanoid robot, robot dog, and drone are delineated to avoid spatial interference and resource contention between devices. Subsequently, The first work queue is integrated with each work area to assign work tasks to each device within the corresponding area, coordinating task start timing, path planning, and resource allocation to form a standardized collaborative scheduling strategy. Then, individual robots are controlled to execute tasks according to the collaborative scheduling strategy, collecting real-time work status parameters such as work progress, load status, detection data, and fault information for each device. Following this, the first work queue is dynamically adjusted based on the work status parameters. For situations such as equipment failure, unqualified detection, or sudden environmental changes, optimization operations such as backup equipment replacement, work sequence extension, and task reassignment are performed to obtain a stable and reliable target work queue. Finally, the original collaborative scheduling strategy is updated and corrected in real-time using the target work queue, forming a collaborative work strategy that adapts to the on-site execution status and covers the entire closed-loop management process.

[0070] It is evident that by matching parameters for multi-device collaboration, constructing work queues, dividing spatial areas, and implementing dynamic scheduling and real-time optimization, the independent operational strategies of humanoid robots, robot dogs, and drones can be integrated into a unified collaborative operational strategy. This enables the synchronization of task execution across multiple devices, spatial collaboration, and status linkage, effectively avoiding operational conflicts and execution risks. It significantly improves the automation level, operational efficiency, and execution accuracy of the entire performance platform construction process, ensuring the stability and consistency of the multi-robot collaborative system in complex construction scenarios.

[0071] Step S340: Control the multiple robots to execute the construction task of the target performance platform according to the collaborative operation strategy.

[0072] Among them, the collaborative operation strategy is a full-process integrated execution rule formed by the control platform after integrating the operation schemes of humanoid robots, robot dogs, and drones. It includes task allocation, time synchronization, spatial partitioning, collaborative control, and abnormal response.

[0073] Specifically, firstly, the control platform synchronously distributes collaborative operation strategies to all humanoid robots and robot dogs via the MCP interface, while simultaneously coordinating with drones to perform detection tasks, completing instruction empowerment and status initialization for multiple devices, ensuring that each robot executes unified standards and collaborative constraints. Subsequently, the assembly task is advanced step by step according to the predetermined sequence of handling, assembly, and disassembly. During the handling phase, humanoid robots transport steel frames, curtains, and electronic equipment in stages according to their load capacity, automatically avoiding obstacles along the planned path. When two robots are working together to transport extra-long components, the load ratio is adjusted in real time to maintain posture balance, while drones simultaneously complete global material scanning and progress verification. During the assembly phase, humanoid robots accurately complete steel frame positioning, component fastening, and equipment installation, while robot dogs provide temporary support according to instructions and complete the classified laying of electrical cables. In the stationary phase, the drone periodically conducts platform flatness, steel frame verticality, and electrical safety checks. Any abnormalities detected are immediately marked and an automatic repair process is triggered. During disassembly, the system is first powered off, then the electrical system and physical platform components are dismantled according to specifications. Robots categorize and systematically transport the components back to the warehouse, while the drone completes a panoramic scan and final inspection. Throughout the process, each device uploads operational status parameters in real time. The control platform dynamically calibrates actions based on feedback data. In case of equipment failure, unqualified tests, or sudden environmental changes, an automatic anomaly handling mechanism is activated, dispatching backup equipment, sending a repair team, or initiating emergency protection to ensure continuous and stable mission progress. After the entire process is completed, each device returns to its standby position and performs a self-check, summarizing the execution data to complete the mission loop.

[0074] With the above Figure 3 For embodiments shown, please refer to [link to example]. Figure 4 , Figure 4 This is a flowchart illustrating a method for detecting the results of a performance platform setup, provided in an embodiment of this application. Applied to a control platform device in a performance platform setup system, the method includes the following steps before controlling the multiple robots to execute the setup task of the target performance platform according to the collaborative operation strategy: S401. Obtain the preset complete installation parameters and equipment installation parameters of the target performance platform; S402. Determine the platform structure feature information and platform equipment installation feature information in the site image information; S403. Determine the structural integrity of the performance platform based on the platform structural feature information, and obtain the platform construction integrity parameters; S404. Determine the installation integrity of the platform equipment based on the platform equipment installation feature information, and obtain the platform equipment installation parameters; S405. Based on the complete installation parameters, the equipment installation parameters, the complete platform setup parameters, and the platform equipment installation parameters, determine the complete platform setup deviation parameters and the platform equipment installation deviation parameters; S406. Based on preset compliance testing conditions, the platform construction completeness deviation parameters, and the platform equipment installation deviation parameters, the target performance platform is subjected to installation compliance testing to obtain the test results; S407. If the detection result meets the compliance detection conditions, then the step of controlling the multiple robots to execute the construction task of the target performance platform according to the collaborative operation strategy is executed; if the detection result does not meet the compliance detection conditions, then an error message is sent to the control center device to prompt manual processing.

[0075] The installation parameters are the pre-set standard parameters for the overall compliant installation of the performance platform structure, including platform dimensions, steel frame verticality threshold, platform flatness error, and structural connection fastening standards. The equipment installation parameters are the pre-set installation specifications for platform lighting, sound, and electrical systems, including equipment positioning accuracy, wiring standards, insulation resistance requirements, and waterproof sealing levels. The platform structural feature information is the platform's basic structural features extracted from the site image, including pre-buried positioning point locations, ground flatness, and spatial installation conditions. The platform equipment installation feature information is the environmental features extracted from the site image, such as equipment installation points, wiring channels, and installation space. The platform construction parameters are the measured data of the platform structure's construction integrity obtained based on the analysis of actual structural characteristics. The platform equipment installation parameters are based on the analysis of actual installation features. The obtained equipment installation conditions are measured data; the platform construction complete deviation parameter is the difference between the measured construction data and the preset installation standard; the platform equipment installation deviation parameter is the difference between the measured equipment installation conditions and the preset installation standard; the compliance test conditions are the pre-set allowable range of structural installation and equipment installation deviations, which are the basis for determining whether the working conditions meet the standards, including structural installation compliance conditions and equipment installation compliance conditions. For example, the structural installation compliance conditions limit the verticality error of the steel frame to ≤5mm, the platform flatness error to ≤3mm, and the positioning reference error to ≤2mm; the equipment installation compliance conditions limit the circuit insulation resistance to ≥10MΩ, the leakage current to ≤0.5mA, and the waterproof joint sealing air pressure to ≥0.2MPa; the control platform equipment is the control center of the multi-robot collaborative system, used for command issuance, data processing, anomaly prompts, and task management functions.

[0076] Specifically, firstly, the pre-set installation parameters of the target performance platform and equipment are retrieved from the knowledge base to clarify the legal and design standards for platform structure installation and equipment layout. Then, using site image information collected by drones, the platform structure feature information and platform equipment installation feature information are extracted. Next, based on the platform structure feature information, the structural integrity of the platform is calculated, forming the measured parameters for platform construction. Simultaneously, based on the platform equipment installation feature information, the integrity of the platform structure equipment installation space and passageways is calculated, forming the measured parameters for platform equipment installation. Finally, the measured construction integrity parameters and equipment installation parameters are compared with the pre-set standard parameters to obtain... The platform establishes complete deviation parameters and platform equipment installation deviation parameters to quantify the degree of difference between the actual site and standard requirements. Subsequently, the two deviation parameters are compared with preset compliance testing conditions to complete the compliance test before installation and generate test results. Finally, when the test results are within the allowable deviation range and meet the compliance conditions, the site and installation conditions are confirmed to be compliant, and the subsequent multi-robot collaborative construction task continues. When the test results exceed the compliance range and do not meet the installation conditions, an error message containing the deviation location, the type of exceedance, and rectification suggestions is immediately sent to the control center equipment to trigger the manual intervention process and avoid automated construction operations under unqualified conditions.

[0077] It is evident that conducting pre-construction compliance testing before performing multi-robot collaborative construction tasks, and quantifying installation condition deviations by comparing standard parameters with measured data, can identify non-compliance issues in the site structure and equipment installation environment in advance. This effectively avoids construction errors, equipment failures, and operational safety risks caused by substandard basic conditions, ensuring that the automated construction tasks of the performance platform are carried out in a compliant and controllable environment, and improving the accuracy of multi-robot collaborative construction tasks.

[0078] For easier understanding, please refer to Figure 5 , Figure 5This is a schematic diagram of a performance platform construction scenario provided in this application embodiment. Specifically, the control center first decomposes the overall structure of the platform's steel frame according to the preset performance platform construction requirements and generates corresponding operation strategies. The drone receives control commands from the control center and performs aerial inspection and image acquisition of the target construction area, obtaining site image information including the spatial distribution of the site, obstacle locations, and the boundaries of the steel frame construction area. This site image information is used to assist the control center in path planning and work area division, and also to monitor the real-time construction status of the "platform steel frame," thereby achieving dynamic feedback on the construction process. The humanoid robot is used to undertake the key structural assembly and fine-tuning tasks of the "platform steel frame." Located on one side of the steel frame, the humanoid robot performs operations such as handling, docking, and fixing, gradually assembling the steel frame components into a complete structure. During execution, the humanoid robot completes the connection between components according to the predetermined assembly sequence based on the operation strategy issued by the control center and collaborates with other equipment when necessary, such as providing support with a robot dog when transporting long steel frames. The robot dog is primarily used for auxiliary support and wiring-related tasks. Positioned on the other side of the "platform frame," it provides temporary support to the frame by adjusting its posture, ensuring stability during installation. In scenarios involving electrical system deployment, the robot dog can also execute cable laying tasks along preset paths according to the control center's instructions, thus assisting in the construction of the platform's functional systems. The robot dog and humanoid robot indirectly collaborate through the control center, switching roles and connecting tasks according to requirements at different operational stages.

[0079] Furthermore, drones are used not only for initial site scanning during the construction process but also for inspecting the structural integrity and installation accuracy of the platform's steel frame in the later stages. For example, by performing image recognition and analysis on the steel frame's levelness, connection point stability, and equipment installation status, the drones provide real-time feedback of the inspection results to the control platform, thus providing a basis for dynamic adjustments to subsequent operational strategies. Simultaneously, the two-way communication mechanism between the control platform and the drones, humanoid robots, and robotic dogs allows the control platform to monitor the operational status of each device in real time and dynamically optimize the task execution process based on feedback information. For instance, if congestion or conflict risks are detected in a certain work area, the operation path of the humanoid robot or robotic dog can be adjusted promptly, thereby ensuring the continuity and safety of the overall construction process.

[0080] It can be seen that by constructing a multi-robot collaborative operation system uniformly scheduled by the control center equipment, and combining the environmental perception capabilities of UAVs, the fine operation capabilities of humanoid robots, and the support and assistance capabilities of robot dogs, the intelligent construction of the performance platform has been realized. This system can not only complete the closed-loop process from site perception and task decomposition to execution control, but also make real-time adjustments in complex dynamic environments, thereby significantly improving the automation level, operational efficiency, and safety and reliability of the performance platform construction process.

[0081] Please see Figure 6 , Figure 6 This is a flowchart illustrating another performance platform construction method provided in this application embodiment. As can be seen, the performance platform construction method in this embodiment uses a control center for scheduling, combines various types of operating equipment such as humanoid robots, robot dogs, and drones, and constructs a closed-loop operation process covering the entire lifecycle, including task initiation, material handling, platform assembly, disassembly and return to the warehouse, to task completion, based on intelligent decision-making driven by a large vertical model.

[0082] Specifically, the execution phase T1, "Pre-task preparation phase: Activate the vertical VLA / LLM model for the performance platform's assembly and disassembly, mount the knowledge base through the MCP interface; and initiate equipment self-checks and status confirmations, starting the environmental perception and task planning process." Here, VLA / LLM is a vertical visual-language-action / large language model trained for the performance platform's assembly and disassembly scenarios, used for intelligent operation decision-making. MCP is the Model Context Protocol, a standardized communication interface between the large model, the knowledge base, and hardware devices. Through this interface, professional knowledge bases such as industry-standard libraries, equipment parameter libraries, and anomaly handling rule libraries are mounted, providing rule-based support for operations. This phase is led by the control platform. After activating the large model and mounting the knowledge base, self-checks and status confirmations of all operating equipment are initiated, verifying hardware health, load capacity, and communication links. Simultaneously, drones are dispatched to collect 3D images of the site, conducting environmental perception and task planning, laying the data foundation for subsequent operations. Next, the execution phase T2, "Transportation phase: Item identification and classification; begin orderly transportation to the assembly platform area, during which transportation path optimization, load balancing control, and transportation progress monitoring are performed." This stage is the material handling phase, primarily executed by humanoid robots assisted by robotic dogs. First, multimodal visual recognition identifies and classifies the materials to be moved (steel frame components, curtains, lighting and sound systems, cables, etc.) to match the robot's load capacity. Then, following the optimal path planned by the collaborative operation strategy, the materials are systematically moved to the assembly area. Throughout the process, obstacle avoidance algorithms dynamically optimize the path, and for extra-long and extra-heavy components, dual-robot collaboration and load balancing control ensure safe handling. Simultaneously, the control center monitors the moving progress in real time, while drones simultaneously perform material inventory and environmental inspections to avoid operational conflicts. Next, the execution phase T3, "Assembly Phase: Physical Platform Construction, including steel frame system installation, curtain and equipment installation, lighting and sound system installation; electrical system connection; and quality inspection and debugging," is completed through multi-device collaboration. First, a humanoid robot leads the construction of the physical platform, sequentially installing the steel frame system, curtains and equipment, lighting and speakers according to a pre-set assembly sequence. A robotic dog simultaneously provides temporary support and cabling. After the physical platform is built, the electrical system connections and functional debugging of the entire platform are performed. Finally, a drone conducts quality inspection according to a pre-set testing strategy, covering indicators such as steel frame verticality, platform flatness, and electrical insulation. Automatic repairs are triggered for any non-conforming parts, completing closed-loop quality control. Then, the T4 "Disassembly Phase: System Power Off and Preparation; Orderly Disassembly and Classification, including Electrical System Removal and Physical Platform Removal; and Return to Storage" phase is a reverse-standardized execution step, following the safety procedure of "Power Off First, Disassembly Then, Return to Storage."First, a full platform power outage and safety protection were implemented, followed by leakage detection. Then, disassembly was carried out systematically in reverse order of the assembly sequence, sequentially removing electrical systems and dismantling the physical platform to avoid structural damage. Finally, a humanoid robot categorized and transported materials back to the warehouse, a robot dog assisted in cable management and site cleanup, and a drone conducted a panoramic inspection of the disassembly process, ensuring orderly and complete disassembly. The final execution phase, T5, "Task Closure and Summary Phase: Equipment Return and Maintenance, and Data Summary and Report Generation," is led by the control platform. First, all equipment is returned to its designated position and autonomous maintenance is performed, including battery recharging, component calibration, and status self-checks. Then, all process data is summarized, including progress at each stage, equipment status, test results, and anomaly records, generating a standardized task report to provide data support for subsequent algorithm optimization and process iteration, achieving closed-loop management of the entire process.

[0083] It can be seen that by unifying the scheduling through the control platform, the deep integration of vertical-category large models and multi-robot systems is achieved. Each stage forms a complete logic of "perception-decision-execution-feedback-optimization" through state feedback and dynamic scheduling. At the same time, multiple devices work together in a division of labor. Humanoid robots undertake high-precision and high-load tasks, robot dogs undertake auxiliary support and wiring, and drones undertake full-process perception and detection, thereby improving operational efficiency and consistency of multi-robot collaboration.

[0084] As can be seen, the proposed method for building a performance platform involves controlling a central control unit to acquire equipment parameters of multiple robots and the platform's construction requirements, while controlling a drone to acquire site images of the platform's construction area and information on the multi-robot construction operations. This allows for a unified analysis of the multi-robot tasks based on task requirements and overall site information. Furthermore, a collaborative operation strategy involving multiple robots is generated, and each robot is controlled to execute the platform construction task according to this strategy. This achieves unified scheduling of multiple devices in task allocation, operation execution, and collaborative control, thereby improving the consistency of multi-robot collaborative operations, the accuracy of task execution, and the overall efficiency of the performance platform construction task.

[0085] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0087] When dividing each function into modules according to its corresponding function. Figure 7 This is a functional module block diagram of a performance platform construction device provided in an embodiment of this application. It is applied to a control center device in a performance platform construction system. The performance platform construction system also includes multiple robots and drones communicating with the control center device. The performance platform construction device 700 includes: The acquisition unit 701 is used to acquire the equipment parameters of the multiple robots and the construction requirements of the target performance platform; Control unit 702 is used to control the drone to acquire site image information of the target performance platform; The calculation unit 703 is used to determine a collaborative operation strategy including the multiple robots based on the equipment parameters, the construction requirement information and the site image information; The control unit 702 is also used to control the multiple robots to perform the construction task of the target performance platform according to the collaborative operation strategy.

[0088] In one possible embodiment, the plurality of robots includes robot dogs and humanoid robots, and the computing unit 703 is specifically used for determining the collaborative operation strategy including the plurality of robots based on the equipment parameters, the setup requirement information, and the site image information: Based on the construction requirements information, the task stages for the multiple robots to build the target performance platform are determined, resulting in multiple task stages; the multiple task stages include a transportation stage, an assembly stage, and a disassembly stage. Obtain the task parameters corresponding to each of the multiple task stages to obtain multiple task parameters; Determine the first load capacity parameter of the humanoid robot and the second load capacity parameter of the robot dog in the equipment parameters; Based on the first load capacity parameter and the multiple task parameters, the operation strategy for the humanoid robot to perform the performance platform construction task is determined, and the first operation strategy is obtained. Based on the second load capacity parameter and the multiple task parameters, the operation strategy for the robot dog to perform the performance platform construction task is determined, and the second operation strategy is obtained. Based on the site image information and the multiple task parameters, the operation strategy for the UAV to perform the performance platform construction task is determined, resulting in a third operation strategy; Based on the first operation strategy, the second operation strategy, and the third operation strategy, the strategy scheduling for the humanoid robot, the robot dog, and the drone to perform the performance platform construction task is determined, thus obtaining the collaborative operation strategy.

[0089] In one possible embodiment, the computing unit 703, in determining the operation strategy for the humanoid robot to perform the performance platform construction task based on the first load capacity parameter and the plurality of task parameters, and obtaining the first operation strategy, is specifically used for: The task parameters of the humanoid robot corresponding to the handling stage, the assembly stage and the disassembly stage are extracted from the multiple task parameters to obtain the handling task parameters, the assembly task parameters and the disassembly task parameters. Based on the transport task parameters, multiple transport positions corresponding to the transport task performed by the humanoid robot are determined; and based on the preset transport path planning algorithm and the multiple transport positions, the transport path of the humanoid robot is calculated to obtain the transport path; The humanoid robot's handling operation strategy is generated based on the handling path; The platform assembly sequence is obtained by determining the platform assembly order of the performance platform construction task based on the assembly task parameters. Based on the first load capacity parameter, the task parameters that require collaborative operation for the platform assembly sequence are determined to obtain the first collaborative operation parameters for platform assembly; and the assembly operation strategy of the humanoid robot is generated according to the first collaborative operation parameters. The platform assembly sequence is reversed to obtain the platform disassembly sequence; Based on the first load capacity parameter, the task parameters that require collaborative operation for the platform disassembly sequence are determined, and the second collaborative operation parameters for platform disassembly are obtained; and the disassembly operation strategy for the humanoid robot is generated according to the second collaborative operation parameters. The first operation strategy is obtained by integrating the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy.

[0090] In one possible embodiment, the computing unit 703, in terms of fusing the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy to obtain the first operation strategy, is specifically used for: Obtain the operational constraint parameters of the humanoid robot; The transport operation strategy, the assembly operation strategy, and the disassembly operation strategy are integrated based on the platform construction task execution order to obtain the first operation sequence of the performance platform construction task; The first job sequence is subjected to platform building constraint processing based on the job constraint parameters to obtain the second job sequence; If the second job sequence has a conflict with the platform building job, the second job sequence is conflict-handled according to the preset conflict handling rules to obtain the target job sequence; the first job strategy is generated by the task execution library based on the target job sequence. If there is no conflict with the platform building task in the second task sequence, the first task strategy is generated by the task execution library based on the second task sequence.

[0091] In one possible embodiment, the computing unit 703, in determining the operation strategy for the robot dog to perform the performance platform construction task based on the second load capacity parameter and the plurality of task parameters, and obtaining the second operation strategy, is specifically used for: Obtain the attribute parameters of the building components used to build the target performance platform; Filter out the supporting component attribute parameters corresponding to the components that need to be supported from the attribute parameters; Based on the second load capacity parameter and the support component attribute parameter, the task of the robot dog's support operation is determined, resulting in multiple support operation tasks; The support parameters and wiring parameters for the robot dog to perform the assembly of the performance platform are determined based on the multiple support tasks. The support pose parameters of the robot dog are determined based on the support parameters; and the support operation strategy of the robot dog is generated based on the support pose parameters. The wiring operation path of the robot dog is determined based on the wiring parameters, and the wiring operation strategy of the robot dog is generated based on the wiring operation path. The second operation strategy is obtained by merging the support operation strategy and the wiring operation strategy based on the operation sequence of the performance platform setup.

[0092] In one possible embodiment, the computing unit 703, in determining the operation strategy for the UAV to perform the performance platform construction task based on the site image information and the plurality of task parameters, and obtaining a third operation strategy, is specifically used for: The detection requirement parameters for the UAV during the handling, assembly, and disassembly stages are determined based on the multiple task parameters; the detection requirement parameters include the detection task type and detection accuracy. Based on the site image information, the range parameters for the drone to detect the target performance platform are determined, and the detection range parameters are obtained. The flight detection parameters of the UAV are determined based on the detection range parameters; the flight detection parameters include flight altitude, flight path, and scan type. The third operational strategy of the UAV is generated based on the flight detection parameters and the detection requirement parameters.

[0093] In one possible embodiment, the computing unit 703, in the aspect of strategy scheduling for the performance platform setup task of the humanoid robot, the robot dog, and the drone according to the first task strategy, the second task strategy, and the third task strategy to obtain the collaborative task strategy, is specifically used for: Based on the first operation strategy, the second operation strategy and the third operation strategy, the corresponding collaborative operation parameters for the humanoid robot, the robot dog and the drone are determined, and multiple third collaborative operation parameters are obtained. Based on the multiple third collaborative operation parameters, a task order queue is constructed on the target performance platform to obtain the first task queue; Based on the site image information, the areas where the humanoid robot, the robot dog, and the drone operate on the target performance platform are determined, resulting in multiple operating areas; Based on the first task queue and the multiple task areas, the humanoid robot, the robot dog, and the drone are scheduled to perform tasks, thereby obtaining a collaborative scheduling strategy. Control the humanoid robot, the robot dog, and the drone to execute the cooperative scheduling strategy and obtain multiple operational status parameters of the multiple robots; The first job queue is adjusted according to the multiple job status parameters to obtain the target job queue; The collaborative scheduling strategy is updated based on the target job queue to obtain the collaborative job strategy.

[0094] In one possible embodiment, before controlling the plurality of robots to execute the construction task of the target performance platform according to the collaborative operation strategy, the control unit 702 is specifically used for: Obtain the preset complete installation parameters and equipment installation parameters of the target performance platform; Determine the platform structure features and platform equipment installation features in the site image information; The integrity of the performance platform's structure is determined based on the platform's structural feature information, and the platform's completeness parameters are obtained. The installation integrity of the platform equipment is determined based on the installation feature information of the platform equipment, and the installation parameters of the platform equipment are obtained. Based on the complete installation parameters, the equipment installation parameters, the complete platform setup parameters, and the platform equipment installation parameters, determine the complete platform setup deviation parameters and the platform equipment installation deviation parameters; Based on preset compliance testing conditions, platform construction completeness deviation parameters, and platform equipment installation deviation parameters, the target performance platform is subjected to installation compliance testing to obtain the test results; If the detection result meets the compliance detection conditions, the system executes the steps of controlling the multiple robots to build the target performance platform according to the collaborative operation strategy; if the detection result does not meet the compliance detection conditions, an error message is sent to the control center device to prompt manual processing.

[0095] As can be seen, this application provides a performance platform construction device 700, applied to a control center device in a performance platform construction system. The performance platform construction system also includes multiple robots and drones communicating with the control center device. The performance platform construction device 700 acquires equipment parameters of the multiple robots and construction requirements information of the target performance platform; controls the drones to acquire site image information of the target performance platform; determines a collaborative operation strategy involving multiple robots based on the equipment parameters, construction requirements information, and site image information; and finally, controls the multiple robots to execute the construction task of the target performance platform according to the collaborative operation strategy. This improves the consistency of multi-robot collaborative operation and the accuracy of performance platform construction.

[0096] This application also provides a performance platform building system, wherein the performance platform building system can perform some or all of the steps of any of the methods described in the above method embodiments.

[0097] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes a control center device.

[0098] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer includes a control console device.

[0099] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0100] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0102] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0103] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0104] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method of setting up a stage platform, characterized by, A control platform device is applied to a performance platform setup system, the performance platform setup system further including multiple robots and drones communicating with the control platform device, the method comprising: Obtain the device parameters of the multiple robots and the construction requirements of the target performance platform; Control the drone to acquire site image information of the target performance platform; A collaborative operation strategy involving the multiple robots is determined based on the equipment parameters, the setup requirements, and the site image information. The multiple robots are controlled to perform the construction task of the target performance platform according to the collaborative operation strategy.

2. The method of claim 1, wherein, The plurality of robots includes robot dogs and humanoid robots. The step of determining a collaborative operation strategy for the plurality of robots based on the equipment parameters, the setup requirements, and the site image information includes: Based on the construction requirements information, the task stages for the multiple robots to build the target performance platform are determined, resulting in multiple task stages; the multiple task stages include a transportation stage, an assembly stage, and a disassembly stage. Obtain the task parameters corresponding to each of the multiple task stages to obtain multiple task parameters; Determine the first load capacity parameter of the humanoid robot and the second load capacity parameter of the robot dog in the equipment parameters; Based on the first load capacity parameter and the multiple task parameters, the operation strategy for the humanoid robot to perform the performance platform construction task is determined, and the first operation strategy is obtained. Based on the second load capacity parameter and the multiple task parameters, the operation strategy for the robot dog to perform the performance platform construction task is determined, and the second operation strategy is obtained. Based on the site image information and the multiple task parameters, the operation strategy for the UAV to perform the performance platform construction task is determined, resulting in a third operation strategy; Based on the first operation strategy, the second operation strategy, and the third operation strategy, the strategy scheduling for the humanoid robot, the robot dog, and the drone to perform the performance platform construction task is determined, thus obtaining the collaborative operation strategy.

3. The method of claim 2, wherein, The first operational strategy, determined based on the first load capacity parameter and the plurality of task parameters, for the humanoid robot to perform the performance platform construction task, is obtained, including: The task parameters of the humanoid robot corresponding to the handling stage, the assembly stage and the disassembly stage are extracted from the multiple task parameters to obtain the handling task parameters, the assembly task parameters and the disassembly task parameters. Based on the transport task parameters, multiple transport positions corresponding to the transport task performed by the humanoid robot are determined; and based on the preset transport path planning algorithm and the multiple transport positions, the transport path of the humanoid robot is calculated to obtain the transport path; The humanoid robot's handling operation strategy is generated based on the handling path; The platform assembly sequence is obtained by determining the platform assembly order of the performance platform construction task based on the assembly task parameters. Based on the first load capacity parameter, the task parameters that require collaborative operation for the platform assembly sequence are determined to obtain the first collaborative operation parameters for platform assembly; and the assembly operation strategy of the humanoid robot is generated according to the first collaborative operation parameters. The platform assembly sequence is reversed to obtain the platform disassembly sequence; Based on the first load capacity parameter, the task parameters that require collaborative operation for the platform disassembly sequence are determined, and the second collaborative operation parameters for platform disassembly are obtained; and the disassembly operation strategy for the humanoid robot is generated according to the second collaborative operation parameters. The first operation strategy is obtained by integrating the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy.

4. The method of claim 3, wherein, The first operation strategy is obtained by fusing the handling operation strategy, the assembly operation strategy, and the disassembly operation strategy, including: Obtain the operational constraint parameters of the humanoid robot; The transport operation strategy, the assembly operation strategy, and the disassembly operation strategy are integrated based on the platform construction task execution order to obtain the first operation sequence of the performance platform construction task; The first job sequence is subjected to platform building constraint processing based on the job constraint parameters to obtain the second job sequence; If the second job sequence has a conflict with the platform building job, the second job sequence is conflict-handled according to the preset conflict handling rules to obtain the target job sequence; the first job strategy is generated by the task execution library based on the target job sequence. If there is no conflict with the platform building task in the second task sequence, the first task strategy is generated by the task execution library based on the second task sequence.

5. The method of claim 2, wherein, The second operational strategy, determined based on the second load capacity parameter and the plurality of task parameters, for the robot dog to perform the performance platform setup task, is obtained by: Obtain the attribute parameters of the building components used to build the target performance platform; Filter out the supporting component attribute parameters corresponding to the components that need to be supported from the attribute parameters; Based on the second load capacity parameter and the support component attribute parameter, the task of the robot dog's support operation is determined, resulting in multiple support operation tasks; The support parameters and wiring parameters for the robot dog to perform the assembly of the performance platform are determined based on the multiple support tasks. The support pose parameters of the robot dog are determined based on the support parameters; and the support operation strategy of the robot dog is generated based on the support pose parameters. The wiring operation path of the robot dog is determined based on the wiring parameters, and the wiring operation strategy of the robot dog is generated based on the wiring operation path. The second operation strategy is obtained by merging the support operation strategy and the wiring operation strategy based on the operation sequence of the performance platform setup.

6. The method as described in claim 2, characterized in that, The step of determining the operational strategy for the drone to perform the performance platform construction task based on the site image information and the multiple task parameters yields a third operational strategy, including: The detection requirements parameters for the UAV during the handling, assembly, and disassembly phases are determined based on the multiple task parameters; the detection requirements parameters include the detection task type and the detection accuracy. Based on the site image information, the range parameters for the drone to detect the target performance platform are determined, and the detection range parameters are obtained. The flight detection parameters of the UAV are determined based on the detection range parameters; the flight detection parameters include flight altitude, flight path, and scan type. The third operational strategy of the UAV is generated based on the flight detection parameters and the detection requirement parameters.

7. The method as described in claim 2, characterized in that, The step of determining the strategy scheduling for the humanoid robot, the robot dog, and the drone to perform the performance platform construction task based on the first operation strategy, the second operation strategy, and the third operation strategy, to obtain the collaborative operation strategy, includes: Based on the first operation strategy, the second operation strategy, and the third operation strategy, the corresponding collaborative operation parameters for the humanoid robot, the robot dog, and the drone are determined, resulting in multiple third collaborative operation parameters. Based on the multiple third collaborative operation parameters, a task order queue for building the target performance platform is constructed to obtain the first task queue; Based on the site image information, the areas where the humanoid robot, the robot dog, and the drone operate on the target performance platform are determined, resulting in multiple operating areas; Based on the first task queue and the multiple task areas, the humanoid robot, the robot dog, and the drone are scheduled to perform tasks, thereby obtaining a collaborative scheduling strategy. The humanoid robot, the robot dog, and the drone are controlled to execute the cooperative scheduling strategy in order to obtain multiple operational status parameters of the multiple robots; The first job queue is adjusted according to the multiple job status parameters to obtain the target job queue; The collaborative scheduling strategy is updated based on the target job queue to obtain the collaborative job strategy.

8. The method according to any one of claims 1-7, characterized in that, Before controlling the plurality of robots to execute the construction task of the target performance platform according to the collaborative operation strategy, the method further includes: Obtain the preset complete installation parameters and equipment installation parameters of the target performance platform; Determine the platform structure features and platform equipment installation features in the site image information; The integrity of the performance platform's structure is determined based on the platform's structural feature information, and the platform's completeness parameters are obtained. The installation integrity of the platform equipment is determined based on the installation feature information of the platform equipment, and the installation parameters of the platform equipment are obtained. Based on the complete installation parameters, the equipment installation parameters, the complete platform setup parameters, and the platform equipment installation parameters, determine the complete platform setup deviation parameters and the platform equipment installation deviation parameters; Based on preset compliance testing conditions, platform construction completeness deviation parameters, and platform equipment installation deviation parameters, the target performance platform is subjected to installation compliance testing to obtain the test results; If the detection result meets the compliance detection conditions, the system executes the steps of controlling the multiple robots to build the target performance platform according to the collaborative operation strategy; if the detection result does not meet the compliance detection conditions, an error message is sent to the control center device to prompt manual processing.

9. A performance platform construction device, characterized in that, A control platform device is used in a performance platform setup system, the performance platform setup system also including multiple robots and drones communicating with the control platform device, the device comprising: The acquisition unit is used to acquire the equipment parameters of the multiple robots and the construction requirements of the target performance platform; The control unit is used to control the drone to acquire site image information of the target performance platform; The computing unit is used to determine a collaborative operation strategy including the multiple robots based on the equipment parameters, the setup requirements information, and the site image information. The control unit is also used to control the multiple robots to perform the construction task of the target performance platform according to the collaborative operation strategy.

10. A performance platform construction system, characterized in that, The performance platform building system performs the method described in any one of claims 1-8.

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