Grain storage-oriented localized deployment AI agent decision support method

Abstract

The invention relates to the technical field of grain storage, in particular to a localized deployment AI agent decision support method for grain storage, which comprises the following steps: collecting unstructured data and structured / time series data, integrating the collected unstructured data and structured / time series data, and adopting an AI pre-processing technology to obtain an AI agent decision support; a multi-source information data set is converted into an attribute layer multi-dimensional database and a semantic layer bridge structure, a structured report including conclusion answers, warehouse general situations and risk analysis is automatically generated through a query result, a user uses a natural language to ask questions, through mobile terminal adaptation and complete local deployment, in combination with an equipment linkage function, a multi-source database is established, and a multi-source database is established. The intelligent control of grain condition measurement and control, temperature and humidity control and 3D material level intelligent clearance is realized, and a safe and convenient mobile management and equipment control scheme is obtained. According to the method, the problems of difficulty in multi-source heterogeneous data fusion processing, slow intelligent query response and inaccuracy in grain storage management are solved.

Description

Technical Field

[0001] This invention belongs to the field of grain storage technology, specifically relating to a localized AI agent decision support method for grain storage. Background Technology

[0002] In the field of grain storage technology, traditional management methods often rely on manual inspections and experience-based judgment, making it difficult to achieve efficient and accurate decision support. With the development of the Internet of Things (IoT), big data, and artificial intelligence (AI) technologies, intelligent management has become a key approach to improving the efficiency and quality of grain storage. However, existing technologies mostly focus on processing single data sources or rely on cloud services for data analysis, leading to problems such as data silos, slow response times, and data security risks in practical applications. Specifically, the data generated during grain storage is multi-source and heterogeneous, including unstructured grain condition knowledge bases and expert experience documents, as well as structured time-series data such as temperature, humidity, and gas concentration. How to effectively integrate this data to achieve intelligent querying and decision support is a major challenge currently facing intelligent grain storage. At the same time, given the sensitivity of grain storage data, localized deployment to ensure data security and privacy has become an urgent need.

[0003] Existing technologies have shortcomings such as insufficient ability to fuse and process multi-source heterogeneous data, reliance on cloud services leading to slow response speeds and data security risks, inability to effectively provide localized and accurate decision support, and lack of natural language question-and-answer interaction and convenient mobile management solutions. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a localized AI agent decision support method for grain storage, which can solve the problems of difficult multi-source heterogeneous data fusion and processing, and slow and inaccurate intelligent query response in grain storage management. To achieve the above objectives, this invention adopts the following technical solution: The proposed localized AI intelligent agent decision support method for grain storage includes the following steps: First, by collecting unstructured and structured / time-series data, the data is integrated to obtain a multi-source information dataset. Second, using AI preprocessing technology, the multi-source information dataset is transformed into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture. Third, through semantic-layer vector positioning of intent and attribute-layer SQL precise retrieval, the combined use of vector and SQL engines yields query results from fuzzy questions to precise answers. Fourth, based on the query results, a structured report including conclusive answers, warehouse overview, and risk analysis is automatically generated, providing customized decision support information. Fifth, by having users ask questions in natural language, semantic understanding provides users with traceable answers within tens of seconds, resulting in a question-and-answer interaction experience. Sixth, through mobile adaptation and fully local deployment, combined with equipment linkage functions, intelligent control of grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen-filled atmosphere regulation, and 3D intelligent material level clearing is achieved, resulting in a safe and convenient mobile management and equipment control solution.

[0005] Furthermore, the process of collecting unstructured data and structured / time-series data, and then integrating the collected unstructured data and structured / time-series data to obtain a multi-source information dataset, includes the following steps: employing a multi-channel collection strategy, extracting unstructured data and structured time-series data through the Grain Cloud Knowledge Base; the unstructured data includes text and image materials from the Grain Condition Knowledge Base, Ventilation Knowledge Base, Temperature Control Knowledge Base, and Controlled Atmosphere Knowledge Base, as well as articles from the Grain Intelligence WeChat Official Account and expert knowledge base; the structured time-series data includes grain condition data on temperature, humidity, water, air, insects, and mold monitored in real time by grain condition scouts, removing duplicate information, establishing correlations, and obtaining a multi-source information dataset.

[0006] Furthermore, the AI ​​preprocessing technology is used to transform multi-source information datasets into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture. This includes the following steps: Using AI processing technology, deep learning algorithms are used to analyze the multi-source information datasets, extracting key features such as temperature, humidity, and pests from the grain condition data, and constructing an attribute-layer multidimensional database to store structured indicators; natural language processing technology is used to transform textual knowledge into semantic vectors, forming a semantic-layer bridge structure connecting user intent and data attributes; through the organic integration of the attribute layer and the semantic layer, a three-layer intelligent data architecture comprising a raw data layer, an attribute database, and a semantic network is obtained.

[0007] Furthermore, the process of obtaining query results from fuzzy questions to precise answers through semantic layer vector positioning of intent and attribute layer SQL precise retrieval, utilizing the collaboration of vector and SQL dual engines, includes the following steps: A dual-engine collaborative query mechanism is adopted, using the semantic layer vector engine to perform intent parsing and semantic positioning of the user's fuzzy questions, extracting the core elements of the question and converting them into query vectors; using the attribute layer SQL engine, the semantic positioning results are automatically mapped into structured query statements, and precise retrieval and association calculations are performed in the attribute database; the semantic understanding capability of the vector engine and the data processing capability of the SQL engine are deeply integrated to obtain precise answers containing tracing evidence and data support.

[0008] Furthermore, the process of automatically generating a structured report, including conclusive answers, warehouse overview, and risk analysis, based on the query results to obtain customized decision support information includes the following steps: Using automated report generation technology, the query result data is deeply analyzed to extract conclusions as key answer points; related data is automatically integrated based on warehouse identifiers to generate a warehouse overview module including grain temperature and humidity indicators; a built-in analysis model is used to perform risk rating on abnormal data, forming a risk analysis unit including cause analysis and disposal suggestions; the conclusive answers, basic warehouse information, and risk warning content are arranged according to management needs to obtain a customized structured report containing current situation assessment, risk warnings, and action guidelines.

[0009] Furthermore, the user asks questions using natural language, and through semantic understanding, provides the user with a source-tracing answer within tens of seconds, resulting in a question-and-answer interaction experience. This includes the following steps: using natural language processing technology, the user initiates a question in everyday language; the semantic understanding module analyzes the question's intent and extracts key elements; relevant data is quickly located in the knowledge graph and structured database; a source-tracing algorithm is used to trace the source of the answer; the query results are linked and integrated with the original data, generating feedback information including a conclusive answer and data source explanation within tens of seconds. The user will receive an interactive experience that is both accurate and interpretable.

[0010] Furthermore, the aforementioned solution, through mobile adaptation and fully local deployment, combined with device linkage functions, enables intelligent control of grain condition monitoring, temperature and humidity control, off-grid photovoltaic nitrogen-filled atmosphere regulation, and 3D intelligent material level clearing, resulting in a safe and convenient mobile management and device control solution. This solution includes the following steps: Adopting a mobile adaptation design concept, an interactive interface for mobile device screens is developed, featuring fragmented viewing capabilities, allowing users to access early warning information and grain condition dynamics at any time; A fully local deployment architecture is adopted, enclosing data processing and storage within the device, ensuring normal use of core functions even in offline environments; Through device linkage functions, more efficient intelligent analysis and decision-making are integrated into grain condition monitoring, temperature and humidity control, off-grid photovoltaic nitrogen-filled atmosphere regulation, and 3D intelligent material level clearing, achieving intelligent device control; Hardware-level security encryption and a self-locking mechanism for offline data transmission ensure data transmission security against cloud risks, resulting in an intelligent mobile management and device control solution.

[0011] Furthermore, the method of using natural language processing (NLP) technology to transform textual knowledge into semantic vectors and form a semantic layer bridge structure connecting user intent and data attributes includes the following steps: using NLP technology to extract entity and relational elements from textual knowledge through lexical analysis, and using a deep learning model to transform unstructured text into high-dimensional semantic vectors; constructing semantic space mapping rules, calculating the similarity between the user's intent vector and the data attribute vector, forming a dynamically associated semantic network, and obtaining a semantic layer bridge structure that includes intent parsing, attribute matching, and association tracing functions.

[0012] Furthermore, the process of using a built-in analysis model to assess the risk of abnormal data and form a risk analysis unit that includes cause analysis and handling suggestions includes the following steps: using the built-in analysis model to automatically capture abnormal fluctuations by monitoring data streams in real time, and using threshold comparison and trend prediction algorithms to extract numerical characteristics and frequency indicators of abnormal data; classifying and assessing anomalies based on a risk assessment matrix to identify high, medium, and low risk levels; combining a grain situation knowledge base and a historical case database to deeply analyze the causes of anomalies and generate targeted handling plans; and integrating risk levels, cause explanations, and operational suggestions into a structured risk analysis unit.

[0013] In the technical solution provided by this invention, unstructured data and structured / time-series data are collected and integrated to obtain a multi-source information dataset. AI preprocessing technology is used to transform the multi-source information dataset into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture. Semantic-layer vector positioning of intent and attribute-layer SQL precise retrieval are achieved through the collaboration of vector and SQL dual engines, yielding query results from fuzzy questions to precise answers. Based on the query results, a structured report including conclusive answers, warehouse overview, and risk analysis is automatically generated, providing customized decision support information. Users ask questions using natural language, and through semantic understanding, a traceable answer is provided within tens of seconds, resulting in a question-and-answer interaction experience. Through mobile adaptation and fully local deployment, combined with equipment linkage functions, intelligent control of grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen atmosphere regulation, and 3D intelligent material level clearing is achieved, resulting in a safe and convenient mobile management and equipment control solution. This invention solves the problems of difficult multi-source heterogeneous data fusion and processing, slow and inaccurate intelligent query response in grain storage management. It also provides natural language question-and-answer interaction and customized decision support, and ensures offline availability and data security through fully local deployment. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0015] Figure 1 This is a schematic diagram of the first embodiment of a localized deployment AI agent decision support method for grain storage according to an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of a second embodiment of a localized deployment AI agent decision support method for grain storage according to an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of a third embodiment of a localized deployment AI agent decision support method for grain storage according to an embodiment of the present invention.

[0018] Figure 4 This is a schematic diagram of the fourth embodiment of a localized deployment AI agent decision support method for grain storage according to the present invention.

[0019] Figure 5 This is a schematic diagram of the fifth embodiment of a localized deployment AI agent decision support method for grain storage according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] A localized AI agent decision support method for grain storage, such as... Figure 1 As shown, the process includes the following steps: First, by collecting unstructured and structured / time-series data, the collected data is integrated to obtain a multi-source information dataset. Second, using AI preprocessing technology, the multi-source information dataset is transformed into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture. Third, through semantic-layer vector positioning of intent and attribute-layer SQL precise retrieval, the combined use of vector and SQL engines yields query results from fuzzy questions to precise answers. Fourth, based on the query results, a structured report including conclusive answers, warehouse overview, and risk analysis is automatically generated, providing customized decision support information. Fifth, users ask questions using natural language, and through semantic understanding, a traceable answer is provided within tens of seconds, resulting in a question-and-answer interaction experience. Sixth, through mobile adaptation and fully local deployment, combined with equipment linkage functions, intelligent control of grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen-filled atmosphere regulation, and 3D intelligent material level clearing is achieved, resulting in a safe and convenient mobile management and equipment control solution.

[0023] like Figure 2 As shown, in this embodiment, a multi-channel collection strategy is adopted, extracting unstructured data and structured time-series data through the Grain Cloud Knowledge Base. The unstructured data includes text and image materials from the Grain Condition Knowledge Base, Ventilation Knowledge Base, Temperature Control Knowledge Base, and Controlled Atmosphere Knowledge Base, as well as articles from the Grain Intelligence WeChat Official Account and expert knowledge base. The structured time-series data includes grain condition data such as temperature, humidity, water, air, insects, and mold monitored in real time by the Grain Condition Scouts. Duplicate information is removed, and correlations are established to obtain a multi-source information dataset.

[0024] This invention employs a multi-channel collection strategy to effectively integrate unstructured data from the Grain Cloud Knowledge Base (such as text and image data from grain condition and ventilation knowledge bases, as well as articles from WeChat official accounts and expert knowledge bases) with structured time-series data (grain condition data such as temperature and humidity) monitored in real time by grain condition scouts. By removing duplicate information and establishing correlations, a multi-source information dataset is formed. This invention not only enriches the data dimensions and improves data quality but also provides a solid data foundation for subsequent AI preprocessing and dual-engine querying, thereby enhancing the accuracy and comprehensiveness of decision support.

[0025] like Figure 3 As shown, in this embodiment, AI processing technology is used to analyze multi-source information datasets through deep learning algorithms, extract key features of temperature, humidity, and pests from grain condition data, and construct an attribute layer multidimensional database to store structured indicators; natural language processing technology is used to transform text knowledge into semantic vectors, forming a semantic layer bridge structure connecting user intent and data attributes; through the organic integration of the attribute layer and the semantic layer, an intelligent data three-layer architecture including the original data layer, the attribute database, and the semantic network is obtained.

[0026] Employing AI processing technology, key grain condition features are accurately extracted through deep learning, constructing a multi-dimensional attribute-layer database to achieve efficient storage of structured indicators. Simultaneously, natural language processing technology transforms textual knowledge into semantic vectors, building a semantic layer bridge connecting user intent and data attributes. This organic integration of the attribute and semantic layers forms a three-layer intelligent data architecture, which not only improves the efficiency of data processing and analysis but also provides robust data support for dual-engine queries, thereby ensuring the accuracy and reliability of decision support.

[0027] like Figure 4 As shown, in this embodiment, a dual-engine collaborative query mechanism is adopted. The semantic layer vector engine performs intent parsing and semantic positioning on the user's fuzzy question, extracts the core elements of the question and transforms them into query vectors. The attribute layer SQL engine is used to automatically map the semantic positioning results into structured query statements and perform precise retrieval and association calculations in the attribute database. The semantic understanding capability of the vector engine and the data processing capability of the SQL engine are deeply integrated to obtain accurate answers that include traceability evidence and data support.

[0028] Employing a dual-engine collaborative query mechanism, the system uses a semantic layer vector engine to accurately analyze the user's ambiguous query intent, transforming it into query vectors for semantic localization. Then, an attribute layer SQL engine automatically converts the localization results into structured query statements, performing precise retrieval and correlation calculations. This mechanism deeply integrates semantic understanding and data processing capabilities, significantly improving query efficiency and accuracy. It also generates precise answers containing traceability evidence and data support, providing reliable and efficient decision support for grain and oil storage managers.

[0029] like Figure 5 As shown, in this embodiment, automated report generation technology is used to extract conclusions as key points of the answer by deeply analyzing the query results data. Based on the warehouse identification, related data is automatically integrated to generate a warehouse overview module including grain temperature and humidity indicators. The built-in analysis model is used to perform risk rating on abnormal data to form a risk analysis unit including cause analysis and disposal suggestions. The conclusive answer, basic warehouse information and risk warning content are arranged according to management needs to obtain a customized structured report that includes current status judgment, risk warning and action guidelines.

[0030] This invention employs automated report generation technology to deeply analyze query results, accurately extract key answer points, and integrate related data based on warehouse identification to generate a warehouse overview module containing indicators such as grain temperature and humidity. Simultaneously, a built-in analysis model is used to perform risk rating on abnormal data, providing cause analysis and handling suggestions, forming a risk analysis unit. The conclusive answers, basic warehouse information, and risk warning content are scientifically arranged to obtain a customized structured report. This invention effectively improves the efficiency and accuracy of report generation, providing managers with comprehensive and intuitive decision-making support.

[0031] In this embodiment, natural language processing technology is used. Users ask questions in everyday language, and the semantic understanding module analyzes the intent of the question and extracts elements. Related data is quickly located in the knowledge graph and structured database, and the source tracing algorithm is used to track the source of the answer. The query results are linked and integrated with the original data, and feedback information including conclusive answers and data source explanations is generated within tens of seconds. Users will get an interactive experience that is both accurate and interpretable.

[0032] Employing natural language processing technology, the system enables users to easily ask questions in everyday language. A semantic understanding module accurately analyzes the question's intent and extracts key elements, quickly locating related data in knowledge graphs and structured databases. Using a source tracing algorithm, the system can track the source of the answer, ensuring the credibility of the results. By linking and integrating the query results with the original data, it generates feedback information containing conclusive answers and data source explanations within tens of seconds.

[0033] In this embodiment, a mobile-adaptive design concept is adopted to develop an interactive interface for mobile device screens, featuring fragmented viewing capabilities, allowing users to access early warning information and grain condition dynamics at any time. A fully local deployment architecture is employed, enclosing data processing and storage within the device, ensuring core functions remain operational even in offline environments. Through device linkage functions, more efficient intelligent analysis and decision-making are integrated into grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen-filled atmosphere regulation, and 3D intelligent material level clearing, achieving intelligent equipment control. Hardware-level security encryption and a self-locking mechanism for offline data transmission mitigate risks associated with cloud data transmission, resulting in a mobile management and equipment control solution for intelligent control.

[0034] Adopting a mobile-optimized design, it allows users to view information in short bursts and stay informed about early warnings and grain conditions anytime. Its fully local deployment architecture ensures core functions remain operational even during network outages, enhancing system stability and reliability. Device linkage integrates intelligent analysis and decision-making across multiple grain storage stages, enabling intelligent equipment control and improving management efficiency. Hardware-level security encryption and a self-locking mechanism for network outages effectively reduce the risks associated with data transmission to the cloud, providing a comprehensive, efficient, secure, and stable intelligent mobile management and equipment control solution for grain storage management.

[0035] In this embodiment, natural language processing technology is used to extract entity and relational elements from text knowledge through lexical analysis. A deep learning model is used to transform unstructured text into high-dimensional semantic vectors. Semantic space mapping rules are constructed to calculate the similarity between the user's intent vector and the data attribute vector, forming a dynamically associated semantic network. This results in a semantic layer bridge structure that includes intent parsing, attribute matching, and association tracing functions.

[0036] By employing natural language processing techniques and leveraging lexical analysis to accurately extract text entities and relational elements, and utilizing deep learning models to transform unstructured text into high-dimensional semantic vectors, this approach constructs semantic space mapping rules to calculate the similarity between user query intent vectors and data attribute vectors, forming a dynamic semantic network. This dynamic semantic layer bridge structure not only achieves accurate intent parsing and attribute matching but also effectively traces related information.

[0037] In this embodiment, a built-in analysis model is used to automatically capture abnormal fluctuations by monitoring the data stream in real time. Threshold comparison and trend prediction algorithms are used to extract the numerical characteristics and frequency indicators of abnormal data. Based on the risk assessment matrix, the abnormalities are classified and assessed to identify high, medium and low risk levels. Combined with the grain situation knowledge base and historical case database, the causes of the abnormalities are analyzed in depth and targeted handling plans are generated. The risk level, cause explanation and operation suggestions are integrated into a structured risk analysis unit.

[0038] Utilizing a built-in analytical model, it can monitor data streams in real time and automatically capture abnormal fluctuations, accurately extracting abnormal data indicators through threshold comparison and trend prediction algorithms. Based on a risk assessment matrix, it categorizes and evaluates abnormal risks, and combines a grain situation knowledge base and historical case database to deeply analyze the causes and generate targeted response plans. It integrates risk levels, cause explanations, and operational suggestions into structured risk analysis units, providing comprehensive and accurate risk warnings and decision support for grain storage management.

[0039] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for localized deployment of AI intelligent agents for decision support in grain storage, characterized in that, The aforementioned method for localized deployment of AI agents for decision support in grain storage includes the following steps: By collecting unstructured data and structured / time-series data, and integrating the collected unstructured data and structured / time-series data, a multi-source information dataset is obtained; By employing AI preprocessing technology, multi-source information datasets are transformed into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture. By using semantic layer vectors to locate intent and attribute layer SQL for precise retrieval, and by leveraging the synergy of vector and SQL dual engines, query results are obtained from fuzzy questions to precise answers. Based on the query results, a structured report is automatically generated, including conclusive answers, warehouse overview, and risk analysis, providing customized decision support information. Users ask questions using natural language, and through semantic understanding, the system provides users with tracing answers within tens of seconds, resulting in a question-and-answer interaction experience. By adapting to mobile devices and deploying them entirely locally, combined with equipment linkage functions, intelligent control of grain condition monitoring, temperature and humidity control, off-grid photovoltaic nitrogen charging and atmosphere regulation, and 3D intelligent material level clearing is achieved, resulting in a safe and convenient mobile management and equipment control solution.

2. The method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 1, characterized in that, The process of collecting unstructured data and structured / time-series data, and then integrating the collected unstructured data and structured / time-series data to obtain a multi-source information dataset includes the following steps: A multi-channel collection strategy was adopted to extract unstructured data and structured time-series data through the Grain Cloud knowledge base; The unstructured data includes text and image data from grain condition knowledge base, ventilation knowledge base, temperature control knowledge base, and controlled atmosphere knowledge base, as well as articles from the Grain Intelligence WeChat official account and expert knowledge base; The structured time-series data includes real-time grain condition data such as temperature, humidity, water, air, insects, and mold monitored by grain condition scouts. Duplicate information is removed, and correlations are established to obtain a multi-source information dataset.

3. The method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 1, characterized in that, The method employs AI preprocessing technology to transform multi-source information datasets into an attribute-layer multidimensional database and a semantic-layer bridge structure, resulting in a three-layer intelligent data architecture, which includes the following steps: AI processing technology is used to analyze multi-source information datasets through deep learning algorithms, extract key features of temperature, humidity and pests in grain condition data, and construct an attribute-layer multidimensional database to store structured indicators. By using natural language processing technology, textual knowledge is transformed into semantic vectors, forming a semantic layer bridge structure that connects user intent and data attributes; By organically integrating the attribute layer and the semantic layer, a three-layer intelligent data architecture is obtained, which includes the raw data layer, the attribute database, and the semantic network.

4. The method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 1, characterized in that, The process of locating intent through semantic layer vectors and performing precise SQL retrieval at the attribute layer, utilizing the synergy of vector and SQL dual engines to obtain query results from fuzzy questions to precise answers, includes the following steps: A dual-engine collaborative query mechanism is adopted. The semantic layer vector engine performs intent parsing and semantic localization on the user's fuzzy question, extracts the core elements of the question and transforms them into query vectors. Using an attribute-layer SQL engine, semantic location results are automatically mapped into structured query statements, enabling precise retrieval and association calculations to be performed in the attribute database; By deeply integrating the semantic understanding capabilities of the vector engine with the data processing capabilities of the SQL engine, accurate answers containing traceability evidence and data support are obtained.

5. The method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 1, characterized in that, The process of automatically generating a structured report based on the query results, including conclusive answers, warehouse overview, and risk analysis, to obtain customized decision support information includes the following steps: Using automated report generation technology, the system deeply analyzes query results data, extracts conclusions as key points for the answer, and automatically integrates related data based on warehouse identification to generate a warehouse overview module that includes grain temperature and humidity indicators. The built-in analysis model is used to perform risk rating on abnormal data, forming a risk analysis unit that includes cause analysis and handling suggestions. The conclusive answers, basic warehouse information, and risk warnings are arranged according to management needs to obtain a customized, structured report that includes current situation assessment, risk warnings, and action guidelines.

6. The method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 1, characterized in that, The user asks a question using natural language, and through semantic understanding, the system provides the user with a source-based answer within tens of seconds, resulting in a question-and-answer interaction experience. This includes the following steps: Using natural language processing technology, users ask questions in everyday language, and the semantic understanding module analyzes the intent of the question and extracts key elements. Quickly locate related data in knowledge graphs and structured databases, and use source tracing algorithms to track the source of answers; By integrating query results with the original data links, feedback information including conclusive answers and data source explanations is generated within tens of seconds, providing users with an interactive experience that is both accurate and interpretable.

7. The method for localized deployment of AI intelligent agents for grain storage decision support according to claim 1, characterized in that, The aforementioned solution, through mobile terminal adaptation and fully local deployment, combined with equipment linkage functions, enables intelligent control of grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen charging and atmosphere regulation, and 3D intelligent material level clearing, resulting in a safe and convenient mobile management and equipment control solution. This solution includes the following steps: Adopting a mobile-optimized design concept, an interactive interface for mobile device screens has been developed, featuring fragmented viewing capabilities, allowing users to access early warning information and grain situation updates at any time. It adopts a fully local deployment architecture, which encloses data processing and storage inside the device, and the core functions can still be used normally in the absence of network. Through the equipment linkage function, more efficient intelligent analysis and intelligent decision-making are integrated into grain condition monitoring and control, temperature and humidity control, off-grid photovoltaic nitrogen charging and atmosphere regulation, and 3D material level intelligent clearing, so as to realize intelligent control of equipment; By employing hardware-level security encryption and a self-locking mechanism when the network is disconnected, the risk of data transmission to the cloud is mitigated, resulting in a smart mobile management and device control solution.

8. A method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 3, characterized in that, The process of using natural language processing technology to transform textual knowledge into semantic vectors, forming a semantic layer bridge structure connecting user intent and data attributes, includes the following steps: Using natural language processing technology, entity and relation elements in text knowledge are extracted through lexical analysis, and unstructured text is transformed into high-dimensional semantic vectors using a deep learning model; By constructing semantic space mapping rules, the similarity between the user's intent vector and the data attribute vector is calculated to form a dynamically associated semantic network, resulting in a semantic layer bridge structure that includes intent parsing, attribute matching, and association tracing functions.

9. A method for localized deployment of AI intelligent agents for decision support in grain storage according to claim 5, characterized in that, The process of using a built-in analysis model to perform risk rating on abnormal data and forming a risk analysis unit that includes cause analysis and handling suggestions includes the following steps: Using a built-in analysis model, it automatically captures abnormal fluctuations by monitoring data streams in real time, and uses threshold comparison and trend prediction algorithms to extract numerical features and frequency indicators of abnormal data. Anomalies are classified and assessed based on a risk assessment matrix, clearly defining high, medium, and low risk levels. By combining a grain situation knowledge base and a historical case database, the causes of anomalies are analyzed in depth, and targeted response plans are generated. The risk level, cause explanation, and operational suggestions are integrated into a structured risk analysis unit.

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