An industrial AI intelligent interaction system and an interaction method based on a wood-based board industry

Abstract

The application discloses an industrial AI intelligent interaction system and method based on a wood-based board industry, relates to the technical field of industrial intelligent man-machine interaction, and comprises the following: a data acquisition module, which is used for collecting production data of a wood-based board production line and manual active voice instructions of a wood-based board production site in real time; a multi-modal industrial knowledge base, which is used for storing multi-source wood-based board production industrial knowledge data; an end-side industrial AI interaction module, which is used for performing semantic understanding on the manual active voice instructions, performing intelligent reasoning in combination with the production data of the wood-based board production line and / or the multi-source wood-based board production industrial knowledge data, and generating a structured response strategy; and an audio frequency interaction output module, which is used for generating a response voice according to the structured response strategy and outputting the same. The application breaks away from a passive broadcasting mode, has the ability of semantic understanding, logical reasoning, intelligent decision-making and autonomous question answering, and realizes a technical leap from 'equipment mechanical playing' to 'AI intelligent thinking response'.

Description

Technical Field

[0001] This application relates to the field of industrial intelligent human-machine interaction technology, and in particular to an industrial AI intelligent interaction system and interaction method based on the wood-based panel industry. Background Technology

[0002] Engineered wood products (such as medium-density fiberboard (MDF), particleboard (PB), and oriented strand board (OSB) are the mainstream choice in the furniture and building decoration industries. Their production lines are characterized by long process flows, high equipment coupling, and continuous high-speed operation. Currently, the intelligent transformation of engineered wood production lines mainly focuses on the automation control of equipment and basic voice alarms and text prompts.

[0003] Existing industrial voice alarm systems and human-machine interface (HMI) prompting systems are generally mechanical, passive output devices. They can only play fixed text alarms or prompts based on preset equipment signals or trigger thresholds, lacking the capabilities of artificial intelligence understanding, intent reasoning, and two-way question-and-answer. In actual production operations, this traditional technology exhibits significant limitations: (1) Lack of intelligent semantic understanding: Traditional equipment cannot recognize spoken and fragmented voice commands initiated by humans, and cannot understand the specific questions of on-site maintenance personnel.

[0004] (2) Operation and maintenance are highly dependent on human experience: The production process of artificial boards is extremely complex. Once an abnormal operation occurs, the causes of the failure are often diverse and overlapping. The existing automatic control equipment cannot reason about the fault mechanism and provide intelligent answers to human questions, which leads to the troubleshooting of anomalies being highly dependent on the personal experience of senior employees and lacking standardized auxiliary decision support.

[0005] (3) The data query mode is primitive and outdated: the existing HMI system can only passively display or store historical data, and operators cannot directly query through natural language and obtain the system's real-time summary and intelligent analysis feedback of the data. Summary of the Invention

[0006] The purpose of this application is to provide an industrial AI intelligent interaction system and interaction method based on the wood-based panel industry, which can completely break through the limitations of traditional mechanical output and have true industrial AI intelligent interaction capabilities.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides an industrial AI intelligent interaction system based on the engineered wood products industry, including: The data acquisition module is used to collect production data from the engineered wood production line and active voice commands from the engineered wood production site in real time. A multimodal industrial knowledge base for storing multi-source knowledge data on the production of engineered wood panels; The edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands, and to perform intelligent reasoning by combining production data from the artificial board production line and / or multi-source artificial board production industrial knowledge data to generate structured response strategies. The audio interaction output module is used to generate and output response speech based on the structured response strategy.

[0008] Secondly, this application provides an interaction method for an industrial AI intelligent interaction system based on the wood-based panel industry, including: Real-time collection of production data from the engineered wood production line and active voice commands from the engineered wood production site; Semantic understanding of human-initiated voice commands is performed, and intelligent reasoning is conducted by combining production data from the wood-based panel production line and / or multi-source industrial knowledge data on wood-based panel production to generate structured response strategies. The response speech is generated and output according to the structured response strategy.

[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described interaction method of the industrial AI intelligent interaction system based on the wood-based panel industry.

[0010] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described interaction method of the industrial AI intelligent interaction system based on the wood-based panel industry.

[0011] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described interaction method of the industrial AI intelligent interaction system based on the wood-based panel industry.

[0012] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides an industrial AI intelligent interaction system and method for the wood-based panel industry. The system includes: a data acquisition module for real-time acquisition of production data from the wood-based panel production line and active voice commands from the production site; a multimodal industrial knowledge base for storing multi-source industrial knowledge data on wood-based panel production; an edge-side industrial AI interaction module for semantic understanding of active voice commands and intelligent reasoning based on production data from the wood-based panel production line and / or multi-source industrial knowledge data on wood-based panel production to generate a structured response strategy; and an audio interaction output module for generating and outputting response voice based on the structured response strategy. This application breaks away from the passive broadcasting mode, possessing semantic understanding, logical reasoning, intelligent decision-making, and autonomous question-and-answer capabilities. It achieves a technological leap from "mechanical equipment playback" to "AI intelligent thinking and response," effectively filling the technological gap in industrial-grade AI intelligent two-way interaction in the wood-based panel industry, significantly reducing on-site maintenance difficulty and labor costs, and improving the level of intelligence in wood-based panel production. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A schematic diagram of the overall architecture of an industrial AI intelligent interaction system based on the wood-based panel industry provided in one embodiment of this application; Figure 2 A schematic diagram of the workflow of an industrial AI intelligent interaction system for the wood-based panel industry provided in an embodiment of this application; Figure 3 A flowchart illustrating an interaction method for an industrial AI intelligent interaction system based on the wood-based panel industry, provided as an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] In one exemplary embodiment, such as Figure 1 As shown, an industrial AI intelligent interaction system based on the engineered wood products industry is provided. It aims to construct an edge-side offline AI architecture for the engineered wood products vertical sector, completely changing the traditional mechanical passive information output mode and endowing industrial equipment with advanced intelligent interaction capabilities that can "understand, think, answer questions, and provide assistance." Specifically, the system includes: a data acquisition module, a multimodal industrial knowledge base, an edge-side industrial AI interaction module, and an audio interaction output module.

[0018] The data acquisition module is used to collect production data from the engineered wood production line and active voice commands from the engineered wood production site in real time.

[0019] Production data includes operating condition data, quality inspection data, and output statistics. The data acquisition module is also used to acquire data from the management information system in real time. This management information system data includes stored information from Enterprise Resource Planning (ERP) systems, Manufacturing Execution System (MES) systems, Office Automation (OA) systems, and Human Resources (HR) systems.

[0020] A multimodal industrial knowledge base for storing multi-source knowledge data on the production of engineered wood panels.

[0021] The edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands, and to perform intelligent reasoning by combining production data from the wood-based panel production line and / or multi-source wood-based panel production industrial knowledge data to generate structured response strategies.

[0022] As an optional implementation, the edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands and to perform intelligent reasoning by combining production data from the wood-based panel production line to generate structured response strategies.

[0023] As another optional implementation, the edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands and combine multi-source artificial board production industrial knowledge data for intelligent reasoning to generate structured response strategies.

[0024] As an alternative implementation, an edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands and, combined with production data from the wood-based panel production line and multi-source industrial knowledge data on wood-based panel production, to perform intelligent reasoning and generate structured response strategies. This approach generates more accurate response strategies from both production data and multi-source industrial knowledge data on wood-based panel production.

[0025] As another optional implementation, the edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands, and based on the production data of the wood-based panel production line and multi-source wood-based panel production industrial knowledge data, it further combines management information system data to perform intelligent reasoning and generate structured response strategies.

[0026] The audio interaction output module is used to generate and output response speech based on a structured response strategy. It applies text-to-speech (TTS) technology to generate the response speech.

[0027] In another exemplary embodiment of this application, the data acquisition module includes: a non-invasive data acquisition unit and a sound pickup unit.

[0028] The non-intrusive data acquisition unit is used to collect production data from the engineered wood production line in real time. The non-intrusive data acquisition unit connects to the programmable logic controllers (PLCs) and human-machine interface (HMI) systems of the entire engineered wood production line via a bypass read-only protocol. The entire production line includes all equipment systems such as the material preparation and screening system, hot grinding / shaving system, energy plant, drying system, glue application and mixing system, paving and forming system, hot press, diagonal saw, raw board line, intermediate storage system, sanding and sawing system, board inspection and packaging system, finished product transfer system, and warehousing and loading system.

[0029] The microphone unit is used to collect active voice commands from workers at the engineered wood production site in real time. The microphone unit employs a high signal-to-noise ratio microphone array, such as an industrial microphone array.

[0030] In another exemplary embodiment of this application, the multimodal industrial knowledge base includes: a static technical specification database and a dynamically iterative expert experience database. The data stored within the multimodal industrial knowledge base can be stored in the form of a knowledge graph, and therefore can also be referred to as a multimodal industrial knowledge graph.

[0031] The technical specifications database stores equipment component manuals, operation manuals, maintenance manuals, inspection and maintenance procedures, quality control requirements, and production process specifications. The technical specifications database corresponds to... Figure 1 The database of static equipment technical documents in China; The expert experience database includes: failure mechanisms, causes, and troubleshooting logic for wood-based panel production equipment; historical failure handling records; and failure handling experience data from technicians. The technician failure handling experience data includes experience rules summarized by senior technicians and experience data verbally recorded by frontline operators, maintenance personnel, and managers for various operating conditions. The expert experience database corresponds to... Figure 1 The dynamic expert processing experience database in the middle.

[0032] In another exemplary embodiment of this application, the edge-side industrial AI interaction module includes: an audio preprocessing unit, a wood-based panel vertical domain NLP unit, an intent recognition unit, and an intelligent decision scheduling unit.

[0033] The audio preprocessing unit is used to perform industrial background noise reduction and filtering on the active voice commands from the artificial board production site to obtain preprocessed voice commands.

[0034] The NLP unit for the wood-based panel vertical industry is used to perform semantic understanding (word segmentation and entity recognition) on pre-processed voice commands based on the process semantic library and equipment terminology library of the wood-based panel industry, and to extract wood-based panel professional entities and command semantic features from the pre-processed voice commands.

[0035] The intent recognition unit is used to classify pre-processed semantic instructions based on the semantic features of artificial board professional entities and instructions, and to determine the scene intent category to which the pre-processed semantic instructions belong. As an example, scene intent categories include: fault operation and maintenance consultation scenarios, production data inquiry scenarios, equipment operation guidance scenarios, and process consultation scenarios. The identification of scene intent categories can accurately identify human needs.

[0036] The intelligent decision-making and scheduling unit is used to retrieve and intelligently reason based on the scenario intent category to which the preprocessed semantic instructions belong, and to generate a structured response strategy by calling production data from the artificial board production line and / or multi-source artificial board production industrial knowledge data.

[0037] If the scenario intent category is a fault operation and maintenance consultation scenario, the intelligent decision scheduling unit infers the fault mechanism and retrieves the optimal solution, outputting standardized troubleshooting steps and handling guidance; if the scenario intent category is a production data inquiry scenario, the intelligent decision scheduling unit synchronously summarizes real-time production data and intelligently organizes the response; if the scenario intent category is a process operation consultation scenario, the intelligent decision scheduling unit retrieves standard process specifications to complete intelligent guidance.

[0038] Current general-purpose speech or language models heavily rely on cloud networks and lack deep adaptation to the specialized terminology, process logic, and fault mechanisms of the wood-based panel industry. Furthermore, they cannot operate stably under harsh working conditions such as network outages, strong interference, and high noise levels in workshops. To address these issues, this application introduces a multimodal industrial knowledge base, a wood-based panel vertical domain NLP unit, an intent recognition unit, and an intelligent decision-making and scheduling unit. Moreover, the semantic understanding, intent reasoning, and intelligent decision-making in this application are performed offline, thus resolving the aforementioned problems.

[0039] In another exemplary embodiment of this application, in scenarios involving foreign production, cross-border joint venture operation and maintenance, and intelligent training for new operators, traditional equipment cannot achieve intelligent and accurate cross-language Q&A and lacks multilingual interaction capabilities. To address this, the edge-side industrial AI interaction module further includes a multilingual intelligent adaptation unit; this unit can achieve semantic-level bidirectional intelligent conversion and intelligent response adaptation across languages.

[0040] The multilingual intelligent adaptation unit is used to identify the language type of the human-initiated voice commands in the artificial board production site, convert non-target language types into target language types, and output them to the audio preprocessing unit.

[0041] The multilingual intelligent adaptation unit is also used to generate response voices that match the language type of the human-initiated voice commands in the artificial board production site according to the structured response strategy, and output them to the audio interaction output module.

[0042] In another exemplary embodiment of this application, the industrial AI intelligent interaction system based on the wood-based panel industry further includes a self-learning iteration module, which is used to store historical interaction data into a multimodal industrial knowledge base for self-learning iteration, such as automatically optimizing semantic understanding accuracy and intelligent response adaptability, so as to realize the autonomous upgrading of AI intelligent capabilities.

[0043] The semantic understanding, intent reasoning, intelligent decision-making, and language conversion operations involved in this application are all completed offline on the local edge device, without relying on external cloud computing power or external network support. Advanced administrators can choose whether to utilize external cloud computing power and external network support based on specific circumstances, thereby further improving the level of intelligence.

[0044] To fully demonstrate the technological advancements of the industrial AI intelligent interaction system presented in this application, a multi-dimensional comparison of its features with traditional HMI and voice alarm systems is conducted below:

[0045] Based on the comparison of the above characteristics, the beneficial effects of this application include: (1) Completely breaks through the limitations of traditional mechanical output and possesses true industrial AI intelligent interaction capabilities. The system of this application breaks away from the passive broadcast mode and has semantic understanding, logical reasoning, intelligent decision-making and autonomous question answering capabilities, realizing a technological leap from "mechanical playback by equipment" to "AI intelligent thinking and response".

[0046] (2) Build an industry-specific AI-powered intelligent operation and maintenance system to break free from reliance on human experience. Relying on knowledge reasoning and intelligent question-and-answer capabilities, standardize the answers to various equipment, process, and fault problems, reduce the learning curve for newcomers, and reduce human error and downtime losses.

[0047] (3) It has the ability to generalize and adapt to industrial semantics, fits the conversational interaction scenarios in industrial sites, and its recognition accuracy and adaptability far exceed those of traditional fixed text devices, making it extremely practical for industrial use.

[0048] (4) Fully offline AI autonomous computing, adaptable to harsh industrial conditions, free from cloud network dependence, and meets the intelligent auxiliary needs of 24-hour continuous production.

[0049] (5) It realizes multilingual semantic-level intelligent interaction, fills the technical gap in foreign-related production, multinational operation and maintenance, and intelligent training in the industry, and has extremely strong system scalability.

[0050] like Figure 2 As shown below, taking the cross-language intelligent fault diagnosis and auxiliary decision-making scenario as an example, the application process of the industrial AI intelligent interaction system based on the wood-based panel industry is illustrated: In a large-scale continuous flat-press particleboard production line overseas, the workshop environment is noisy (approximately 85 decibels) and accompanied by strong electromagnetic interference. When uneven paving occurs in some areas of the laying and forming section, and abnormal pressure fluctuations occur in the subsequent hot press, a foreign operator proactively asked the industrial AI intelligent interaction system in Russian: "The pressure in section 3 of the hot press is constantly fluctuating. How can we troubleshoot this?" The specific execution process and processing mechanism of the industrial AI intelligent interaction system are as follows: (1) Voice signal acquisition and noise reduction: The high signal-to-noise ratio pickup array captures the Russian voice stream signal, and the audio preprocessing unit uses an end-side adaptive filtering algorithm to quickly filter out industrial background noise from the on-site hot mill, exhaust fan, etc.

[0051] (2) Semantic parsing and language adaptation: The multilingual intelligent adaptation unit identifies the input language as Russian, and the NLP unit of the artificial board vertical field parses the voice command and identifies professional entities, accurately locking the core industrial entities such as "hot press No. 3 segment" and "pressure fluctuation".

[0052] (3) Intent cognition and data fusion: The intent cognition unit autonomously classifies the voice command as a "fault operation and maintenance consultation scenario". The intelligent decision scheduling unit immediately retrieves the actual pressure change curve of the hot press section 3, the current steel belt speed, and the real-time dynamic material ratio data of the upstream adhesive application system in the current HMI system through the non-intrusive data acquisition unit in a bypass read-only manner.

[0053] (4) Knowledge graph retrieval and reasoning: Combining the real-time multi-factor operating data collected above, the intelligent decision scheduling unit performs graph path retrieval and correlation probability reasoning in the expert experience database of the multimodal industrial knowledge base. The analysis concludes that the most likely cause is: the fluctuation of the moisture content of the upstream dried wood shavings leads to uneven density of local paving, which in turn causes closed-loop oscillation of the local hot pressing closed-loop control system.

[0054] (5) Response strategy generation and on-site output: retrieve the "Standard Operating Procedure for Troubleshooting Pressure Anomalies of Hot Press" from the multimodal industrial knowledge base, generate structured multi-step troubleshooting guidance, convert it into standard Russian voice through the multilingual intelligent adaptation unit, broadcast it on-site through the audio interaction output unit, and automatically record the interaction log and store it in the local database for self-learning iteration.

[0055] In another exemplary embodiment of this application, such as Figure 3 As shown, an interaction method for an industrial AI intelligent interaction system based on the wood-based panel industry is provided, including: S1: Real-time collection of production data from the engineered wood production line and active voice commands from the engineered wood production site.

[0056] S2: Perform semantic understanding on human-initiated voice commands, and combine production data from the wood-based panel production line and / or multi-source industrial knowledge data on wood-based panel production to perform intelligent reasoning and generate structured response strategies.

[0057] S3: Generate and output the response speech based on the structured response strategy.

[0058] In another exemplary embodiment of this application, in step S2, semantic understanding is performed on the artificial active voice command, and intelligent reasoning is performed by combining the production data of the wood-based panel production line and / or multi-source wood-based panel production industrial knowledge data to generate a structured response strategy, specifically including: (1) Perform industrial background noise reduction and filtering on the active voice commands of artificial board production site to obtain pre-processed voice commands.

[0059] (2) Based on the process semantic library and equipment professional terminology library of the wood-based panel industry, semantic understanding is performed on the pre-processed voice commands, and wood-based panel professional entities and command semantic features are extracted from the pre-processed voice commands.

[0060] (3) Based on the professional entity and instruction semantic features of artificial board, the preprocessed semantic instructions are classified according to scene intent to determine the scene intent category to which the preprocessed semantic instructions belong.

[0061] (4) Based on the scenario intent category to which the preprocessed semantic instructions belong, retrieve and intelligently reason using the production data of the artificial board production line and / or multi-source artificial board production industrial knowledge data to generate a structured response strategy.

[0062] In another exemplary embodiment of this application, the industrial AI intelligent interaction method based on the wood-based panel industry further includes: When collecting human-generated voice commands from the artificial board production site in real time, the system identifies the language type of the human-generated voice commands and converts non-target language types into target language types to perform semantic understanding of the human-generated voice commands.

[0063] After generating a structured response strategy, a response voice that matches the language type of the human-initiated voice commands in the wood-based panel production site is generated based on the structured response strategy.

[0064] For specific limitations in the provided method embodiments, please refer to the limitations of the industrial AI intelligent interaction system based on the wood-based panel industry above, which will not be repeated here.

[0065] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores interactive data for the industrial AI intelligent interactive system. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an interactive method for an industrial AI intelligent interactive system based on the wood-based panel industry.

[0066] Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0067] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0068] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An industrial AI intelligent interactive system for the engineered wood products industry, characterized in that, include: The data acquisition module is used to collect production data from the engineered wood production line and active voice commands from the engineered wood production site in real time. A multimodal industrial knowledge base for storing multi-source knowledge data on the production of engineered wood panels; The edge-side industrial AI interaction module is used to perform semantic understanding of human-initiated voice commands, and to perform intelligent reasoning by combining production data from the artificial board production line and / or multi-source artificial board production industrial knowledge data to generate structured response strategies. The audio interaction output module is used to generate and output response speech based on the structured response strategy.

2. The industrial AI intelligent interaction system for the wood-based panel industry according to claim 1, characterized in that, The data acquisition module includes: A non-intrusive data acquisition unit is used to collect production data from the engineered wood production line in real time. The voice pickup unit is used to collect active voice commands from people at the artificial board production site in real time.

3. The industrial AI intelligent interaction system for the wood-based panel industry according to claim 1, characterized in that, The multimodal industrial knowledge base includes: a technical specifications database and an expert experience database; Technical specifications database, used to store equipment component manuals, operation manuals, maintenance manuals, inspection and maintenance specifications, quality inspection requirements, and production process specifications; The expert experience database includes: the failure mechanism, failure cause, and failure troubleshooting logic of wood-based panel production equipment, historical information on failure handling, and failure handling experience data of technical personnel.

4. The industrial AI intelligent interaction system for the wood-based panel industry according to claim 1, characterized in that, The edge-side industrial AI interaction module includes: The audio preprocessing unit is used to perform industrial background noise reduction and filtering on the active voice commands from the artificial board production site to obtain preprocessed voice commands. The NLP unit for the vertical field of wood-based panels is used to perform semantic understanding on pre-processed voice commands based on the process semantic library and equipment terminology library of the wood-based panel industry, and to extract wood-based panel professional entities and command semantic features from the pre-processed voice commands. The intent recognition unit is used to classify the scene intent of preprocessed semantic instructions based on the professional entities and semantic features of artificial boards and instructions, and to determine the scene intent category to which the preprocessed semantic instructions belong. The intelligent decision-making and scheduling unit is used to retrieve and intelligently reason based on the scenario intent category to which the preprocessed semantic instructions belong, and to generate a structured response strategy by calling production data from the artificial board production line and / or multi-source artificial board production industrial knowledge data.

5. The industrial AI intelligent interaction system for the wood-based panel industry according to claim 4, characterized in that, The edge-side industrial AI interaction module also includes: a multilingual intelligent adaptation unit; The multilingual intelligent adaptation unit is used to identify the language type of the human-initiated voice commands in the artificial board production site, and convert non-target language types into target language types and output them to the audio preprocessing unit. The multilingual intelligent adaptation unit is also used to generate response voices that match the language type of the human-initiated voice commands in the artificial board production site according to the structured response strategy, and output them to the audio interaction output module.

6. An interaction method for an industrial AI intelligent interaction system based on any one of claims 1 to 5 in the wood-based panel industry, characterized in that, include: Real-time collection of production data from the engineered wood production line and active voice commands from the engineered wood production site; Semantic understanding of human-initiated voice commands is performed, and intelligent reasoning is conducted by combining production data from the wood-based panel production line and / or multi-source industrial knowledge data on wood-based panel production to generate structured response strategies. The response speech is generated and output according to the structured response strategy.

7. The industrial AI intelligent interaction method for the wood-based panel industry according to claim 6, characterized in that, Semantic understanding of human-initiated voice commands is performed, and intelligent reasoning is conducted by combining production data from the wood-based panel production line and / or multi-source industrial knowledge data on wood-based panel production to generate structured response strategies, specifically including: Industrial background noise reduction and filtering are performed on the active voice commands from the artificial board production site to obtain pre-processed voice commands; Based on the process semantic library and equipment terminology library of the wood-based panel industry, semantic understanding is performed on the pre-processed voice commands to extract wood-based panel professional entities and command semantic features from the pre-processed voice commands. Based on the professional entity and instruction semantic features of artificial board, the preprocessed semantic instructions are classified according to scene intent to determine the scene intent category to which the preprocessed semantic instructions belong. Based on the scenario intent category to which the preprocessed semantic instructions belong, the system retrieves and intelligently reasons by calling production data from the artificial board production line and / or multi-source artificial board production industrial knowledge data, and generates a structured response strategy.

8. The industrial AI intelligent interaction method for the wood-based panel industry according to claim 6, characterized in that, The industrial AI intelligent interaction method based on the wood-based panel industry also includes: When collecting human-initiated voice commands from the artificial board production site in real time, the language type of the human-initiated voice commands from the artificial board production site is identified, and non-target language types are converted into target language types to perform semantic understanding of the human-initiated voice commands. After generating a structured response strategy, a response voice that matches the language type of the human-initiated voice commands in the wood-based panel production site is generated based on the structured response strategy.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the interaction method of the industrial AI intelligent interaction system based on the wood-based panel industry as described in any one of claims 6-8.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the interaction method of the industrial AI intelligent interaction system based on the wood-based panel industry as described in any one of claims 6-8.

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