A speech-driven construction site task intelligent decomposition and dynamic monitoring system

Abstract

The application provides a speech-driven construction site task intelligent decomposition and dynamic supervision method and system, relates to the technical field of intelligent management of construction sites, and is used for improving the intelligent level of construction task decomposition and site supervision. The method comprises the following steps: converting speech into text, performing semantic analysis and knowledge retrieval, automatically decomposing to generate a structured construction process list, reporting images during process execution, comparing the pre-retrieved process acceptance standard, and outputting problem identification results and rectification suggestions. The application realizes speech-driven automatic decomposition of construction tasks and closed-loop dynamic supervision, and improves the efficiency, accuracy and standardization level of construction site task management.

Description

Technical Field

[0001] This invention relates to the field of intelligent supervision technology, and in particular to a voice-driven method and system for intelligent decomposition and dynamic supervision of construction site tasks. Background Technology

[0002] In construction site management, task assignment and supervision primarily rely on manual methods. Construction supervisors typically assign tasks verbally or via telephone, with on-site workers executing tasks based on experience. Quality and safety at each stage depend on on-site inspections by supervisors. This traditional approach has several shortcomings: First, task decomposition lacks standardization; verbal instructions are often vague, requiring manual breakdown of specific steps, which can easily lead to misunderstandings or omissions of key processes. Second, the execution process lacks real-time recording and tracking; voice instructions and execution status are usually not documented, making process tracing and accountability difficult. Third, quality and safety supervision is inefficient; supervisors must conduct repeated on-site inspections, relying on visual inspection and experience to determine compliance with specifications, which is time-consuming, labor-intensive, and prone to subjective oversights. Furthermore, there is currently no unified platform integrating voice recognition, knowledge retrieval, and image analysis to support closed-loop management of construction tasks from assignment to verification.

[0003] Therefore, there is an urgent need to develop a voice-driven method for intelligent decomposition and dynamic monitoring of construction site tasks. Summary of the Invention

[0004] The purpose of this invention is to provide a voice-driven intelligent decomposition and dynamic monitoring method for construction site tasks, in order to solve the problems existing in the prior art.

[0005] The technical solution adopted to achieve the purpose of this invention is as follows: a voice-driven intelligent decomposition and dynamic monitoring method for construction site tasks, comprising the following steps:

[0006] S1. Receive voice or text commands from construction personnel. The voice recognition module then transcribes the voice commands into structured text command data.

[0007] S2. The semantic parsing module deployed in the cloud performs semantic parsing on the structured text instruction data. Combined with the retrieval enhancement generation mechanism, the reference data corresponding to the construction task is retrieved from the construction domain knowledge base, the construction intention is identified, and structured construction task data is generated.

[0008] S3. Based on the structured construction task data, automatically decompose and generate a structured construction process list consisting of multiple process nodes. Set the corresponding execution order and completion judgment conditions for each process node.

[0009] S4. During the execution of the construction process, receive the construction site image data corresponding to the current process node uploaded by the construction personnel, use the image analysis module to extract the construction elements in the image, compare the construction elements with the acceptance standard data corresponding to the process node, and output the process problem diagnosis results and rectification suggestions.

[0010] S5. Based on the process problem diagnosis results, the intelligent agent module controls the construction process flow. By updating the status flags corresponding to the process nodes, it achieves automatic switching between different execution states of the current process node. When the current process is determined to meet the acceptance requirements, it automatically advances to the next process node. When a problem is determined to exist in the current process, it maintains the current process status and guides the construction personnel to complete the rectification.

[0011] Furthermore, in step 2), the construction domain knowledge base includes a vector index of pre-built construction specification clauses, construction method information, and quality acceptance standards.

[0012] Furthermore, in step 3), the task breakdown is made traceable. Each process node obtained from the breakdown is associated with and archived with the corresponding original voice commands, semantic parsing results, and user confirmation information to achieve traceability of the entire construction task breakdown process.

[0013] Furthermore, in step S4, the construction site image data consists of images of the construction process or results captured by various on-site acquisition devices. These on-site acquisition devices include mobile terminals, wearable devices, fixed monitoring equipment, or drones. When a problem is identified in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form via the terminal.

[0014] Furthermore, in step S4, the construction elements include the component layout status, component dimensional parameters, and the compliance or safety protection status of the work activities. The acceptance criteria are retrieved from the construction domain knowledge base through a retrieval-enhanced generation mechanism and correspond one-to-one with the current process node.

[0015] Furthermore, in step S5, when a problem is determined to exist in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form.

[0016] Furthermore, in step S5, the intelligent agent is used to coordinate semantic parsing, process decomposition, image analysis, and process flow in a unified manner, so as to realize closed-loop dynamic supervision of construction tasks.

[0017] Furthermore, following step S5, there is a data archiving step. The voice command texts, process lists, image data, process judgment results, and rectification records generated during the construction process are uniformly archived for subsequent querying, verification, or management analysis.

[0018] This invention also discloses a voice-driven intelligent task decomposition and dynamic monitoring system for construction sites, comprising:

[0019] The voice processing module is used to receive voice commands from construction workers and transcribe them into structured text command data.

[0020] The semantic parsing module is used to perform semantic parsing on the structured text instruction data and generate structured construction task data by combining it with a construction domain knowledge base.

[0021] The process decomposition module is used to decompose the structured construction task data into a list of construction processes containing multiple process nodes.

[0022] The image analysis module is used to analyze image data from the construction site and output the process verification results corresponding to the process nodes.

[0023] The intelligent agent module is used to control the state flow of construction process nodes based on the process verification results, so as to realize closed-loop dynamic supervision of construction processes.

[0024] The storage module is used to store voice command data, construction task data, process node data, image data, and process execution results.

[0025] Each module is controlled collaboratively through the intelligent agent module to achieve automatic disassembly of construction tasks, image verification, and closed-loop flow of process status.

[0026] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0027] The technical effects of this invention are beyond doubt:

[0028] Voice-driven automated decomposition: By introducing speech recognition and large language model technology, this invention realizes the automatic conversion of construction site instructions from voice to text and then to work order list, which significantly improves the efficiency and accuracy of task decomposition and avoids misunderstandings and omissions that may occur with manual communication.

[0029] Knowledge-integrated guidance: Utilizing LLM (Limited Management Model) trained in the construction field combined with the RAG (Research and Analysis Group) retrieval mechanism, this ensures that the task breakdown process is based on authoritative standards and knowledge, with each step being well-founded. This knowledge-driven analysis makes the decomposed task list more comprehensive and standardized, meeting engineering quality and safety requirements.

[0030] Intelligent Image Acceptance and Feedback: This invention applies image recognition technology to construction quality and safety inspection, automatically extracts key elements on site and compares them with acceptance standards, enabling timely and objective identification of problems in the construction process and providing rectification suggestions. This reduces the subjectivity and risk of missed inspections associated with manual visual inspection, and improves the real-time performance and accuracy of quality and safety supervision.

[0031] Closed-loop monitoring throughout the entire process: Intelligent agents coordinate voice command processing, task assignment, construction verification, and result feedback, achieving closed-loop management from task release to completion and acceptance. Each process can only proceed to the next step after completion and compliance with requirements, ensuring an orderly construction process and preventing substandard work from entering the next stage. This dynamic monitoring mechanism effectively improves the standardization of construction management.

[0032] Traceable Data Archives: The system retains all process data, including voice commands, task breakdowns, execution status, image evidence, and processing results, enabling digital recording and traceability of the entire construction process. In the event of quality or safety issues, relevant stages can be quickly traced back to clarify responsibilities and improvement directions, providing a basis for construction management decisions and continuous optimization.

[0033] Through the above innovations, this invention significantly improves the intelligence and automation level of on-site task management and supervision, effectively improves construction efficiency, ensures project quality and safety, and provides strong technical support for the informatization and digital supervision of the construction field. Attached Figure Description

[0034] Figure 1 A flowchart for a voice-driven intelligent task decomposition and dynamic monitoring method for construction sites;

[0035] Figure 2 This is a schematic diagram of the system architecture;

[0036] Figure 3 This is a schematic diagram of the process verification and feedback procedure;

[0037] Figure 4 This is a schematic diagram illustrating the closed-loop supervision of construction process status control and dynamic flow. Detailed Implementation

[0038] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0039] Example 1:

[0040] See Figure 1This embodiment provides a voice-driven method for intelligent decomposition and dynamic monitoring of construction site tasks, including the following steps:

[0041] S1. Receive voice or text commands from construction personnel. The voice recognition module then transcribes the voice commands into structured text command data.

[0042] S2. The semantic parsing module deployed in the cloud performs semantic parsing on the structured text instruction data. Combined with the retrieval enhancement generation mechanism, the reference data corresponding to the construction task is retrieved from the construction domain knowledge base, the construction intention is identified, and structured construction task data is generated.

[0043] S3. Based on the structured construction task data, automatically decompose and generate a structured construction process list consisting of multiple process nodes. Set the corresponding execution order and completion judgment conditions for each process node.

[0044] S4. During the execution of the construction process, receive the construction site image data corresponding to the current process node uploaded by the construction personnel, use the image analysis module to extract the construction elements in the image, compare the construction elements with the acceptance standard data corresponding to the process node, and output the process problem diagnosis results and rectification suggestions.

[0045] S5. Based on the process problem diagnosis results, the intelligent agent module controls the construction process flow. By updating the status flags corresponding to the process nodes, it achieves automatic switching between different execution states of the current process node. When the current process is determined to meet the acceptance requirements, it automatically advances to the next process node. When a problem is determined to exist in the current process, it maintains the current process status and guides the construction personnel to complete the rectification.

[0046] This embodiment can automatically convert voice commands into structured construction plans and dynamically and in a standardized manner monitor the construction process, thereby improving construction efficiency and ensuring project quality and safety.

[0047] Example 2:

[0048] The main content of this embodiment is the same as that of embodiment 1. In step 2), the construction field knowledge base includes a vector index of pre-constructed construction specification clauses, construction method information and quality acceptance standards.

[0049] Example 3:

[0050] The main content of this embodiment is the same as that of embodiment 1 or 2, except that in step 3), the task decomposition is processed for traceability. Each process node obtained from the decomposition is associated with and archived with the corresponding original voice command, semantic parsing result, and user confirmation information to achieve traceability of the entire construction task decomposition process.

[0051] Example 4:

[0052] The main content of this embodiment is the same as any one of embodiments 1 to 3. In step S4, the construction site image data consists of images of the construction process or results captured by various on-site acquisition devices. These on-site acquisition devices include mobile terminals, wearable devices, fixed monitoring equipment, or drones. When a problem is determined to exist in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form via the terminal.

[0053] Example 5:

[0054] The main content of this embodiment is the same as any one of embodiments 1 to 4. In step S4, the construction elements include the component layout status, component size parameters, and the compliance or safety protection status of the work behavior. The acceptance criteria are retrieved from the construction domain knowledge base through a retrieval-enhanced generation mechanism and correspond one-to-one with the current process node.

[0055] Example 6:

[0056] The main content of this embodiment is the same as any one of embodiments 1 to 5. In step S5, when it is determined that there is a problem in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form.

[0057] Example 7:

[0058] The main content of this embodiment is the same as any one of embodiments 1 to 6. In step S5, the intelligent agent is used to coordinate semantic parsing, process decomposition, image analysis and process flow in a unified manner to realize closed-loop dynamic supervision of construction tasks.

[0059] Example 8:

[0060] The main content of this embodiment is the same as any one of embodiments 1 to 7, except that after step S5, there is also a data archiving step. The voice command text, process list, image data, process judgment results and rectification records generated during the construction process are uniformly archived for subsequent query, verification or management analysis.

[0061] Example 9:

[0062] The main content of this embodiment is the same as any one of embodiments 1 to 8, except that the specific implementation methods of speech transcription, knowledge retrieval and process flow control are further explained.

[0063] The speech-to-text step is performed by a speech processing module, which includes a speech acquisition unit and a speech-to-text unit. The speech processing module is used to receive speech signals from the construction site and convert them into structured text instruction data. The structured text instruction data includes at least a construction task identifier and a task description.

[0064] The semantic parsing and knowledge retrieval steps are performed by the semantic parsing module, which is configured as follows:

[0065] Based on the structured text instruction data, the construction task is semantically parsed, and based on the parsing results, the process template data and acceptance standard data corresponding to the construction task are retrieved from the construction domain knowledge base. The process template data and acceptance standard data are stored in the form of structured data.

[0066] The process decomposition module generates a construction process list containing multiple process nodes based on the process template data, and assigns a unique process identifier and a corresponding status identifier to each process node. The status identifier is used to characterize the execution status of the process node.

[0067] The process flow and status control are executed by the intelligent agent module, which is configured as follows:

[0068] After receiving the process verification results output by the image analysis module, the status identifier corresponding to the current process node is automatically updated according to the preset process status transition rules, and the activation or blocking of subsequent process nodes is controlled, thereby realizing the automatic flow and closed-loop control of construction processes.

[0069] Through the above methods, this embodiment realizes the automatic control of the entire process of construction tasks, from voice input, process generation, image verification to process status flow, so that the construction process has the technical characteristics of being executable, verifiable and traceable.

[0070] Example 10:

[0071] Figure 1 It illustrates the process of parsing voice commands and breaking down procedures. Figure 2 It demonstrates the collaborative relationship between modules such as speech recognition, language model, knowledge base, image analysis, and intelligent agent in the cloud. Figure 3 The demonstration showcased a closed-loop process for uploading construction site images and conducting intelligent comparison and acceptance.

[0072] like Figure 1 As shown in the figure, an embodiment of the present invention provides a voice-driven intelligent decomposition and dynamic monitoring method for construction site tasks, comprising the following steps:

[0073] S1. Voice Intent Acquisition and Transcription: The construction supervisor issues voice commands to the construction site equipment or the back-end system microphone via a mobile terminal application. For example, in one instance, the supervisor gives the voice command: "Perform column foundation rebar tying construction." The system's voice recognition module calls the Whisper model to transcribe the voice command into the text: "Perform column foundation rebar tying construction." The recognized text command can be displayed on the terminal interface for the supervisor to confirm before proceeding to the next step. Thanks to the high accuracy of the Whisper model, this step can accurately capture construction commands and key points, laying the foundation for subsequent understanding.

[0074] S2. Semantic Parsing and Process Decomposition: Confirmed text instructions are submitted to the semantic parsing module. This module incorporates a Large Language Model (LLM) finely tuned for the construction domain, capable of understanding construction-related terminology and contextual meaning. For example, for the instruction "Perform column foundation rebar tying construction," the LLM recognizes the user's intent as completing the column foundation rebar tying work and decomposes it into a series of sub-processes required to achieve this intent. Combined with the RAG retrieval mechanism, the LLM extracts relevant specification requirements for column foundation rebar engineering (such as rebar lap length, spacing standards, and construction process specifications) from the backend construction knowledge base. With this knowledge support, the LLM automatically refines the task into a structured process list, for example: Process 1—Prepare rebar and tying tools; Process 2—Place and fix the foundation rebar cage; Process 3—Tie rebar joints according to specifications and set protective layer spacers; Process 4—Self-inspect the rebar tying quality and report for inspection. Each generated process includes clear operational points or quality standard descriptions. The system presents this process list to the responsible person for confirmation or adjustment. If the person in charge finds any omissions or modifications needed in the generated process list, they can edit and adjust it on the interface. After the adjustments are submitted, LLM can further optimize the process list by incorporating new inputs. The final confirmed process list will be locked and used to guide on-site construction.

[0075] S3. Process Binding and Archiving: Once the process list is confirmed, the system enters the task execution and recording phase. The system generates a unique identifier for each process in the list and binds it to the source voice command fragment, LLM breakdown results, and the interaction records of the responsible person during the confirmation process. For example, for processes 1 to 4 above, the system records the corresponding original voice content (edited from the overall voice command), the initial description generated by the LLM, the final description confirmed by the responsible person, and information such as the confirmation time and the identity of the confirmer. This data is systematically stored in the project archive in the cloud database. As the construction task progresses, this archive will be continuously enriched, including the completion status and verification results of each process. Through this complete record, project managers can later query the origin and development of any process, such as which voice command generated a certain process, what rules it was broken down according to, and when and by whom it was confirmed. This traceability is extremely important in case of disputes or debriefing.

[0076] S4. Image-Based Process Verification and Feedback: When on-site personnel begin construction according to the process checklist, the system enters a dynamic monitoring phase. For each process, the system requires a quality inspection and confirmation upon completion. In the above example, assume that process 2, "Placing and fixing the foundation steel reinforcement cage," has been completed by workers. On-site supervisors use mobile terminals to take photos of the completed column foundation steel reinforcement cage as evidence and upload the photos for acceptance. The cloud-based image analysis module then intervenes: First, it uses a computer vision model to process the photo, identifying key construction elements such as the layout of the steel reinforcement cage, details of the tying points, and the location of the spacers; then, the system retrieves the acceptance standards related to process 2 from the knowledge base, such as clauses like "the steel reinforcement cage should be fixed in the design position, the protective layer thickness should meet the specifications, and the lap length of the steel reinforcement joints should be ≥ the specifications," and compares the information identified in the photo with the standards item by item. If the analysis results show that the reinforcement cage is correctly positioned and the rebar specifications and lap splices meet the requirements, the system judges the quality of process 2 to be qualified. If problems are found (such as identifying that some main reinforcement bars are off-center from the design axis, or that the number of spacers is insufficient, resulting in the protective layer thickness possibly not meeting the standard), the system will generate a "unqualified" judgment and provide a prompt: "Please adjust the position of the rebar cage to center it and align it with the foundation pit axis; add spacers to ensure that the protective layer thickness meets the design requirements." This problem feedback and rectification suggestions are notified to the on-site personnel in real time via mobile terminal. For processes judged as unqualified, the system allows on-site rectification followed by re-photographing and uploading, repeating the above analysis process until it is qualified. Through intelligent image analysis and knowledge comparison, the system can objectively and accurately control the quality of each process, transforming manual experience judgment into data-driven intelligent acceptance.

[0077] S5. Process Flow and Dynamic Monitoring: Combining Figure 2The system architecture shown depicts an intelligent agent module that continuously monitors the status of each process step and controls the workflow based on the acceptance results. After process 2 is verified as successful, the intelligent agent updates its status to "complete" and automatically triggers the execution notification for process 3 (e.g., pushing an instruction to relevant construction personnel via the application: "Next step can proceed: rebar splice tying"). This ensures that only completed and accepted processes can trigger subsequent tasks, achieving strict sequential control. If a process fails to pass acceptance for an extended period, the intelligent agent will also issue a warning to the project management team, indicating a delay or quality risk in that stage. For any anomalies during process execution (such as unrecognized voice commands or delayed image uploads), the intelligent agent can coordinate with various modules to take emergency measures, such as requesting the user to retry voice input or checking network connectivity, to ensure uninterrupted workflow. Through the coordination of the intelligent agent, the pace and quality status of on-site construction tasks are under system monitoring, ensuring that the construction project proceeds steadily and in control according to the established plan.

[0078] S6. System Implementation Architecture and Deployment: The system of this invention adopts a modular cloud architecture, with each functional module connected through an intelligent agent to form a closed-loop solution. The speech recognition module, LLM semantic parsing module, knowledge base and RAG retrieval module, image analysis module, and process control agent module can be deployed on a cloud server as microservices. The knowledge base can pre-integrate national regulations, industry standards, and internal enterprise regulations, and achieve high-speed retrieval through vector databases to support LLM RAG calls. Due to their large computational demands, the Whisper model and large visual model are also deployed in the cloud to utilize powerful computing resources, while terminal devices only need to perform lightweight operations such as recording, taking photos, and displaying results. The system supports user access via a web interface or mobile app, and voice commands and image data are uploaded through an encrypted channel to ensure data security. With this architectural design, the system has good scalability and reliability, and can serve the concurrent needs of large-scale construction projects. In addition, in practical applications, other sensor data access (such as environmental monitoring data provided by IoT devices) can be added as needed for intelligent agents to conduct comprehensive analysis, thereby further enriching the dimensions of construction site supervision.

[0079] It should be noted that the collaboration between the modules of this invention is not limited to the above order. Without departing from the principles of this invention, some steps can be executed in parallel or slightly adjusted. For example, relevant standard data can be pre-loaded during speech transcription to accelerate subsequent retrieval. Furthermore, the method and system of this invention can be customized and extended according to different construction scenarios, such as applying them to different sub-fields like civil construction and safety inspection. Their core idea is to achieve intelligent decomposition and dynamic monitoring of construction tasks through the fusion of voice, knowledge, and vision.

[0080] As described in the above embodiments, this invention effectively integrates technologies such as speech recognition, large language models, knowledge retrieval, and computer vision, providing a new intelligent management model for construction sites. It uses voice as the driving entry point, efficiently transforming the manager's intentions into actionable plans, and rigorously controlling quality and safety through data-driven methods during execution, forming a closed-loop management system. In practice, this invention will significantly reduce the reliance on manual experience in construction task management, improve work efficiency and project quality, and has broad application prospects.

[0081] Example 11:

[0082] This embodiment provides a voice-driven intelligent task decomposition and dynamic monitoring system for construction sites, including: a voice processing module, a semantic parsing module, a process decomposition module, an image analysis module, an intelligent agent module, and a storage module.

[0083] The voice processing module is used to receive voice commands from construction workers and transcribe them into text.

[0084] The semantic parsing module is used to understand the semantics of text instructions based on a large language model and generate construction tasks.

[0085] The process decomposition module is used to break down construction tasks into multiple process nodes;

[0086] The image analysis module is used to analyze images of the construction site and determine the execution status of the work process;

[0087] The intelligent agent module is used to control the flow of construction procedures and the closed loop of rectification.

[0088] The storage module is used to store voice commands, process information, image data, and process execution results;

[0089] The semantic parsing module, image analysis module, and intelligent agent module are deployed on a cloud server, and the system is configured to execute the method described in any one of Examples 1 to 10.

[0090] Example 12:

[0091] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any one of embodiments 1 to 10.

Claims

1. A voice-driven intelligent task decomposition and dynamic monitoring method for construction sites, characterized in that, Includes the following steps: S1. Receive voice or text commands input by construction personnel; wherein, the voice command is transcribed into structured text command data using a voice recognition module; S2. Use the semantic parsing module deployed in the cloud to perform semantic parsing on the structured text instruction data, and combine it with the retrieval enhancement generation mechanism to retrieve the reference data corresponding to the construction task from the construction domain knowledge base, identify the construction intention and generate structured construction task data. S3. Based on the structured construction task data, automatically decompose and generate a structured construction process list consisting of multiple process nodes; set the corresponding execution order and completion judgment conditions for each process node; S4. During the execution of the construction process, receive the construction site image data corresponding to the current process node uploaded by the construction personnel, use the image analysis module to extract the construction elements in the image, compare the construction elements with the acceptance standard data corresponding to the process node, and output the process problem diagnosis results and rectification suggestions. S5. Based on the process problem diagnosis results, the intelligent agent module controls the flow of construction processes. By updating the status flags corresponding to the process nodes, the current process node is automatically switched between different execution states. When the current process is determined to meet the acceptance requirements, it is automatically advanced to the next process node. When a problem is identified in the current process, maintain the current process status and guide the construction personnel to complete the rectification.

2. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step 2), the construction domain knowledge base includes a vector index of pre-built construction specification clauses, construction method information, and quality acceptance standards.

3. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step 3), the task breakdown is made traceable; each process node obtained from the breakdown is associated with the corresponding original voice command, semantic parsing result and user confirmation information and archived to achieve traceability of the entire construction task breakdown process.

4. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step S4, the construction site image data consists of images of the construction process or results captured by various on-site acquisition devices; the on-site acquisition devices include mobile terminals, wearable devices, fixed monitoring devices, or drones; when a problem is determined to exist in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form through the terminal.

5. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step S4, the construction elements include the component layout status, component size parameters, and compliance or safety protection status of the work behavior; the acceptance criteria are retrieved from the construction domain knowledge base through a retrieval enhancement generation mechanism and correspond one-to-one with the current process node.

6. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step S5, when a problem is determined to exist in the current process, the generated rectification suggestions are fed back to the construction personnel in text and / or voice form.

7. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: In step S5, the intelligent agent is used to coordinate semantic parsing, process decomposition, image analysis, and process flow in a unified manner, so as to realize closed-loop dynamic supervision of construction tasks.

8. The method for intelligent decomposition and dynamic monitoring of construction site tasks based on voice-driven approach as described in claim 1, characterized in that: After step S5, there is also a data archiving step; the voice command text, process list, image data, process judgment results and rectification records generated during the construction process are archived in a unified manner for subsequent query, verification or management analysis.

9. A voice-driven intelligent task decomposition and dynamic monitoring system for construction sites, characterized in that, include: The voice processing module is used to receive voice commands from construction workers and transcribe them into structured text command data. The semantic parsing module is used to perform semantic parsing on the structured text instruction data and generate structured construction task data by combining it with a construction domain knowledge base. The process decomposition module is used to decompose the structured construction task data into a construction process list containing multiple process nodes; The image analysis module is used to analyze image data from the construction site and output the verification results of the corresponding process nodes. The intelligent agent module is used to control the state flow of construction process nodes based on the process verification results, so as to realize closed-loop dynamic supervision of construction processes. The storage module is used to store voice command data, construction task data, process node data, image data, and process execution results; Each module is controlled collaboratively through the intelligent agent module to achieve automatic disassembly of construction tasks, image verification, and closed-loop flow of process status.

10. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method of any one of claims 1 to 8.

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