Forest fire intelligent monitoring and early warning method and system based on multi-modal large model

Abstract

The invention discloses a forest fire intelligent monitoring and early warning method and system based on a multi-modal large model, and the method comprises the steps: carrying out the fire recognition and environment analysis based on a multi-modal data fusion mechanism of the multi-modal large model in combination with multi-source data collected by an unmanned plane; an auxiliary early warning solution is automatically generated for the fire by combining a multi-modal model with a forest ranger inspection report, an unmanned aerial vehicle inspection video and a fire analysis result, and emergency processing suggestions are provided; massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system is efficiently stored, managed and retrieved, the distributed training and reasoning process of a multi-modal large model is optimized, and it is ensured that the system can achieve rapid and accurate fire monitoring and early warning in a complex natural environment. According to the invention, full-process automatic management and comprehensive analysis from data acquisition, fire monitoring to analysis and decision making are realized, and problems of insufficient intelligent identification capability, high false alarm rate and low data integration analysis efficiency in the existing forest fire monitoring and early warning process are solved.

Description

Technical Field

[0001] The present invention relates to the field of multimodal artificial intelligence, and in particular to a forest fire intelligent monitoring and early warning method and system based on a multimodal large model. Background Art

[0002] Global climate change and intensified human activities. Currently, Xinjiang has 201 nature reserves with a total area of ​​about 376 million mu, which are crucial for ecological and environmental protection.

[0003] Xinjiang, China's most diverse region with the most borders, faces a far greater challenge in forest fire prevention than other provinces. For example, the complex topography of the Three Mountains makes sensor deployment and coverage extremely challenging. Currently, only some key forest areas in the front mountain belt have implemented fire monitoring platforms. However, due to terrain factors and limited visibility, coverage is suboptimal and false alarms are high. The intelligent fire monitoring and early warning network based on fire monitoring platforms has an early warning rate of less than 20% and a false alarm rate of up to 60%. Currently, drones are being used to address these monitoring shortcomings due to their maneuverability, air traffic control capabilities, and aerial perspective. The two basins are primarily desert terrain, with much of the forested area perennially at high risk for meteorological fires. Furthermore, because these forested areas are largely undeveloped, the investment in fire monitoring platforms is high, coverage is slow, and the coverage area of ​​intelligent monitoring systems is limited. Drones, with their unobstructed aerial data link, address network issues, while their long range alleviates power shortages, enabling efficient and cost-effective monitoring of forest areas.

[0004] However, the application of drones in forest fire monitoring is relatively new, and algorithms for classifying fire conditions using video and infrared imaging are largely lacking. For example, while burning wasteland and illegal use of fire are considered fires, these are not fires. Algorithms are needed to accurately classify these fire conditions and ensure the accuracy of fire information and the triggering conditions for emergency response plans. Due to this lack of algorithms, drone fire prevention patrols currently rely entirely on human monitoring. Given the large amount of video footage required for 24-hour continuous monitoring, humans cannot continuously monitor the system, leading to missed fires. Therefore, developing algorithms for drone-based fire classification is urgently needed.

[0005] With technological advancements, modern fire prevention methods have gradually incorporated advanced technologies, including ground-based monitoring, drone patrols, and satellite remote sensing. These technologies have also deployed a large number of environmental sensors, initially establishing an integrated "air-ground-air" monitoring network. Despite this, current fire warning systems still have significant room for improvement in terms of intelligent recognition capabilities and false alarm rates. This is particularly true when it comes to processing and integrating multi-source, multi-modal data, which traditional systems struggle to achieve efficient integration and analysis.

[0006] Therefore, how to provide a forest fire intelligent monitoring and early warning method and system based on a multimodal large model has become a technical problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0007] In response to the above-mentioned defects of the prior art, the purpose of the present invention is to provide a forest fire intelligent monitoring and early warning method and system based on a multimodal large model, which realizes the full process automated management and comprehensive analysis from data collection, fire monitoring to analysis and decision-making, aiming to solve the current problems of insufficient intelligent recognition capabilities, high false alarm rate and low efficiency of data integration and analysis in the forest fire monitoring and early warning process.

[0008] The present invention solves the technical problem by adopting the following technical solutions:

[0009] A multimodal large model-based intelligent forest fire monitoring and early warning method includes the following steps:

[0010] Step S1, fire identification and environmental analysis based on a multimodal large model: Fire identification and environmental analysis are performed based on a multimodal data fusion mechanism based on a multimodal large model, combined with multi-source data collected by drones;

[0011] Step S2: Automatically generate fire emergency plans based on domain knowledge: By combining a multimodal model with forest ranger inspection reports, drone inspection videos, and fire analysis results, an auxiliary warning solution is automatically generated for the identified fire and emergency response suggestions are provided.

[0012] Step S3, massive multi-source heterogeneous data management and system reasoning optimization: Efficiently store, manage, and retrieve massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimize the distributed training and reasoning process of multimodal large models to ensure that the system can achieve fast and accurate fire monitoring and early warning in complex natural environments.

[0013] Furthermore, in step S1, the specific method of fire identification and environment analysis based on the multimodal large model includes:

[0014] Step S11, multimodal data fusion and fire feature extraction: Based on the multimodal data fusion mechanism of the multimodal large model, combined with the multi-source data collected by the drone, the fire feature extraction and analysis are realized;

[0015] Step S12, developing a fire recognition algorithm in a complex environment: In order to cope with the challenges of complex natural environments to fire monitoring, a fire recognition algorithm with environmental perception capabilities will be developed, which mainly includes: (1) Environmental factor modeling: Comprehensively consider the environmental conditions when the fire occurs, use machine learning methods to model environmental characteristics, build a correlation model between fire and environmental conditions, and enhance the robustness of fire recognition; (2) Fire detection under occlusion: Study the impact of obstructions on fire monitoring, develop an algorithm that can still accurately detect fire under complex occlusion conditions, and reduce false alarms and missed alarms.

[0016] Furthermore, in step S11, the method for extracting and analyzing fire characteristics is: building an efficient cross-modal feature extraction framework based on a multimodal large model, combining visual features with infrared sensor features, performing joint representation learning, and mining key fire-related features from different modalities.

[0017] Furthermore, in step S2, the specific method for automatically generating a fire emergency plan in combination with domain knowledge includes:

[0018] Step S21: Domain knowledge integration and existing case library construction: Using knowledge graphs, rule-based reasoning, and machine learning technologies, a structured knowledge base containing forestry domain knowledge and past successful firefighting cases is constructed to achieve the integration and application of domain knowledge and fire data.

[0019] Step S22, emergency plan generation based on a large model of RAG technology: Develop a text generation large model based on RAG technology, use the text generation model in combination with RAG technology, and combine multi-source data to realize automatic analysis of fire data and plan generation.

[0020] Furthermore, through natural language processing technology, key information is extracted from forestry literature, ranger reports and historical fire data to construct a structured knowledge graph. This knowledge graph covers domain entities of forestry resources, fire characteristics, and emergency strategies, and defines the relationships between them to support complex queries and reasoning.

[0021] Furthermore, in step S3, the specific methods for managing massive multi-source heterogeneous data and optimizing system reasoning include:

[0022] Step S31: Efficient storage and management of massive multi-source heterogeneous data: First, a distributed file system is used to store structured data, and a time series database is used to manage sensor time series data, ensuring that the system can handle the storage needs of large-scale data. Second, the project will build a unified data management platform based on data lake technology to integrate structured, semi-structured, and unstructured data, supporting efficient processing and management of multiple data types. Finally, an efficient data indexing and retrieval mechanism will be developed, and a unified indexing system will be built to ensure rapid retrieval of multimodal data such as images, videos, and time series data, improving the system's query response speed and accuracy.

[0023] Step S32, distributed training and inference optimization of the multi-modal large model: first, use data parallelism and model parallelism techniques to realize multi-GPU architecture parallel training of large-scale deep learning models, ensuring training efficiency in large data scenarios; second, in the inference stage, use model quantization technology to convert high-precision floating-point numbers of the model into low-precision integer representations, reducing computational overhead; at the same time, implement model pruning technology to prune redundant network connections and reduce model size; on this basis, develop an inference acceleration engine to realize efficient inference process, ensuring that in edge devices and resource-constrained environments, the system can quickly respond to fire monitoring and early warning needs.

[0024] A forest fire intelligent monitoring and early warning system based on a multi-modal large model, comprising: a fire situation identification and environment analysis module, a fire emergency plan automatic generation module, and a massive multi-source heterogeneous data management and system inference optimization module.

[0025] The fire situation identification and environment analysis module, based on a multi-modal data fusion mechanism of a multi-modal large model, combines multi-source data collected by unmanned aerial vehicles to identify fire situations and analyze the environment.

[0026] The fire emergency plan automatic generation module automatically generates auxiliary warning solutions for the identified fire situations by combining the multi-modal model with forest ranger inspection reports, unmanned aerial vehicle inspection videos, and fire analysis results, and provides emergency handling suggestions.

[0027] The massive multi-source heterogeneous data management and system inference optimization module efficiently stores, manages, and retrieves massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimizes the distributed training and inference process of the multi-modal large model, ensuring that the system can quickly and accurately monitor and warn of fires in complex natural environments.

[0028] The beneficial effects of the present application are:

[0029] The present application constructs an intelligent, full-process integrated unmanned aerial vehicle patrol and ground monitoring forest fire intelligent monitoring and early warning system, realizing full-process automated management and comprehensive analysis from data acquisition, fire monitoring to analysis and decision-making. The system aims to solve the problems of insufficient intelligent recognition capability, high false alarm rate, and low data integration and analysis efficiency in the current forest fire monitoring and early warning process. Through the application of a multi-modal large model, the system can integrate image, video, infrared data, sensor data, and other multi-source information to achieve higher precision fire monitoring, early warning, and handling, and improve the intelligent level of forest and grass fire response. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is the construction process diagram of the forest fire intelligent monitoring and early warning method of the present application.

[0031] Figure 2is a principle block diagram of the present application.

[0032] Figure 3 is a contingency automatic generation flowchart of the present application.

[0033] Figure 4 is a system architecture diagram of the present application. DETAILED DESCRIPTION

[0034] For the purpose, technical solutions and advantages of the embodiments to be more clear, the technical solutions in the embodiments will be described clearly and completely below with reference to the drawings in the embodiments. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0035] With reference to Figure 1 The present application discloses a forest fire intelligent monitoring and early warning method based on a multi-modal large model, comprising the following steps:

[0036] Step S1, fire situation recognition and environment analysis based on a multi-modal large model: based on a multi-modal data fusion mechanism of the multi-modal large model, combined with multi-source data collected by a UAV, fire situation recognition and environment analysis are performed;

[0037] The method of fire situation recognition and environment analysis based on a multi-modal large model comprises:

[0038] Step S11, multi-modal data fusion and fire situation feature extraction: based on a multi-modal data fusion mechanism of the multi-modal large model, combined with multi-source data collected by a UAV, extraction and analysis of fire situation features are realized; based on the multi-modal large model, an efficient cross-modal feature extraction framework is constructed, visual features (such as flame, smoke shape) and infrared sensor features (such as heat source distribution) are combined for joint representation learning, and key features related to fire situation are mined from different modalities.

[0039] Step S12, development of fire situation recognition algorithm in complex environment: in order to cope with the challenges of complex natural environment to fire situation monitoring, a fire situation recognition algorithm with environment perception ability is developed, which mainly includes: (1) environment factor modeling: considering the environmental conditions (such as wind speed, humidity, terrain, etc.) when fire occurs, the environment features are modeled by using machine learning method, the correlation model between fire and environmental conditions is constructed, and the robustness of fire situation recognition is enhanced; (2) fire detection under occlusion: the influence of occlusion (such as trees, buildings) on fire monitoring is studied, an algorithm capable of accurately detecting fire under complex occlusion conditions is developed, and false positives and false negatives are reduced.

[0040] Step S2: Automatically generate fire emergency response plans based on domain knowledge. This involves combining a multimodal model with forest ranger inspection reports, drone inspection videos, and fire analysis results to automatically generate auxiliary warning solutions for the identified fires, covering the fire and environmental conditions and providing emergency response recommendations. The multimodal model supports voice or text input and optimizes report content and patrol strategies based on historical data.

[0041] The specific methods for automatically generating fire emergency plans based on domain knowledge include:

[0042] Step S21, domain knowledge integration and existing case library construction: Use knowledge graphs, rule reasoning, and machine learning related technologies to build a structured knowledge base that includes forestry domain knowledge and past successful firefighting cases, and realize the integration and application of domain knowledge and fire data; specifically, collect and integrate multi-source data such as forest ranger inspection reports, drone inspection videos, and fire analysis results, and use the Scrapy crawler framework and Neo4j graph database to build a structured knowledge base that includes forestry domain knowledge and past successful firefighting cases.

[0043] Through natural language processing technology, key information is extracted from forestry literature, ranger reports and historical fire data to construct a structured knowledge graph. This knowledge graph covers domain entities of forestry resources, fire characteristics, and emergency strategies, and defines the relationships between them to support complex queries and reasoning.

[0044] Step S22, Emergency Plan Generation Using a Large Model Based on RAG Technology: Develop a large text generation model based on RAG technology that can automatically generate fire emergency plans by combining retrieved domain knowledge and historical cases. Research will focus on optimizing retrieval algorithms to ensure the model can efficiently retrieve relevant information from the knowledge base and use it as contextual input to enhance the generative model's understanding of the fire situation. Simultaneously, we will explore how to improve the language model to more accurately integrate retrieved information and generate emergency response plans that meet actual needs. By combining a text generation model with RAG technology and multi-source data, we can achieve automatic analysis of fire data and plan generation.

[0045] Step S3, massive multi-source heterogeneous data management and system reasoning optimization: Efficiently store, manage, and retrieve massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimize the distributed training and reasoning process of multimodal large models to ensure that the system can achieve fast and accurate fire monitoring and early warning in complex natural environments.

[0046] Specific methods for managing massive multi-source heterogeneous data and optimizing system reasoning include:

[0047] Step S31, efficient storage and management of massive multi-source heterogeneous data: A highly scalable data management architecture will be designed and implemented to specifically handle multi-source heterogeneous data such as drones and sensors. First, a distributed file system (HDFS) will be used to store structured data such as images and videos, and a time series database (InfluxDB) will be used to manage the time series data of sensors to ensure that the system can handle the storage needs of large-scale data; secondly, the project will build a unified data management platform based on data lake technology to integrate structured, semi-structured and unstructured data, and support efficient processing and management of multiple data types; finally, an efficient data indexing and retrieval mechanism will be developed, and a unified indexing system will be built to ensure rapid retrieval of multimodal data such as images, videos, and time series data, thereby improving the system's query response speed and accuracy;

[0048] Step S32, Distributed Training and Inference Optimization of Multimodal Large Models: To address the high computational demands of multimodal large models, the project will implement distributed training and inference optimization. First, data parallelism and model parallelism will be used to enable multi-GPU architecture parallel training of large-scale deep learning models, ensuring training efficiency in big data scenarios. Second, during the inference phase, model quantization will be used to convert the model's high-precision floating-point numbers into low-precision integer representations, reducing computational overhead. Simultaneously, model pruning will be implemented to trim redundant network connections and reduce the size of the model. Furthermore, an inference acceleration engine will be developed to achieve efficient inference processes, ensuring rapid response to fire monitoring and early warning needs in environments with limited edge devices and resources.

[0049] The present invention also provides a forest fire intelligent monitoring and early warning system based on a multimodal large model, comprising: a fire situation identification and environment analysis module, a fire emergency plan automatic generation module, and a massive multi-source heterogeneous data management and system reasoning optimization module;

[0050] The fire identification and environment analysis module is based on the multimodal data fusion mechanism of the multimodal large model and combines the multi-source data collected by the drone to perform fire identification and environment analysis;

[0051] The fire identification and environmental analysis module includes a multimodal data fusion and fire feature extraction module and a fire identification module in complex environments;

[0052] (i) Multimodal data fusion and fire feature extraction module

[0053] like Figure 2As shown in the figure, this module processes multi-source data from drones, including images, videos, infrared data, and spatiotemporal data collected by sensors. First, the model needs to extract features from each modal data. This project uses Visual Transformer (ViT) to extract deep features from images, videos, and infrared data. ViT uses a block-by-block approach to divide the input image or video frame into several small blocks, and performs feature embedding and serialization on each block to obtain its local feature representation. Let the input be an image block sequence X = [x1, x2, ..., x n ], then the feature representation after Transformer is:

[0054] Z=ViT(X)

[0055] Among them, Z is the feature vector under this modality. Next, contrastive learning is used to efficiently fuse multimodal data. Suppose the features of image, video and infrared data are Z img ,Z vid ,Z ir ,Multimodal feature alignment is achieved through contrast loss function:

[0056]

[0057] Here, sim() represents the similarity function between features, and γ is the temperature coefficient. Through contrastive learning, the model can embed the features of each modality into a unified high-dimensional space, eliminating modal differences and achieving effective fusion of multimodal information.

[0058] (ii) Fire identification module in complex environments

[0059] This module aims to solve the problem of fire identification in complex natural environments, especially to improve the robustness of identification when fire identification is affected by wind speed, humidity, terrain and other environmental factors. We introduce support vector machine (SVM) as the main classifier and combine the multimodal features output by the above modules to classify fire. Let the feature vector after multimodal fusion be x i , the category label is y i ∈{-1,+1}, then the optimization goal of SVM is:

[0060]

[0061] Where w is the normal vector of the classification plane, b is the bias term, and C is the penalty parameter used to control the trade-off between maximizing the margin and the classification error rate. By solving this optimization problem, the model can determine the optimal hyperplane and achieve accurate fire identification in complex environments.

[0062] In implementation, the proposed feature fusion and alignment technology will be used on an existing multimodal large model to efficiently pre-train parameters, thereby constructing a multimodal large model for the forestry sector. On this basis, the proposed fire identification module will be integrated with the multimodal large model to ultimately form a fire identification and monitoring model.

[0063] The fire emergency plan automatic generation module automatically generates auxiliary warning solutions for identified fires and provides emergency response suggestions by combining forest ranger inspection reports, drone inspection videos, and fire analysis results through a multimodal model.

[0064] The fire emergency plan automatic generation module includes a domain knowledge integration and application module and an emergency plan generation module based on the RAG model;

[0065] (i) Domain knowledge integration and application module

[0066] like Figure 3 As shown, we first use the Scrapy crawler framework to crawl forestry-related data from data sources such as Wikidata and Hudong Baike, including forestry terminology, tree species information, forest distribution, and ecological environment. Then, we use named entity recognition (NER) technology and a customized forestry entity classification table, combined with deep learning models such as BERT, to accurately identify and classify entities in the data. Next, we represent the identified entities and relationships as RDF triples and store them in a Neo4j graph database for efficient storage and retrieval. Furthermore, we collect historical firefighting cases from the People's Court case library, fire rescue department reports, academic papers, and news reports, and annotate these cases in detail to extract key information. Finally, we use visualization tools such as ECharts to present the knowledge graph in an intuitive manner for users to understand and interact with. Through these steps, we will build two comprehensive and accurate databases, providing strong data support for fire warning and emergency response.

[0067] (ii) Emergency plan generation module based on RAG model

[0068] In this invention, a text generation model based on RAG (Retrieval-Augmented Generation) technology is developed, which will rely on the context learning and generation capabilities of a large language model (LLM). Under the RAG framework, the model first converts user queries and documents in the database into vector form through text embedding technology, and then retrieves the documents or paragraphs most similar to the query vector in the vector database. In order to further improve the performance of the model, we will introduce Few-Shot learning technology to provide additional contextual information to the model by recalling relevant entries in the past firefighting case library. These entries will serve as input to the generation model, and together with the retrieved domain knowledge, they will be used to enhance the model's understanding and answering capabilities for specific fire issues. In the generation stage, the large language model will use its powerful context understanding and text generation capabilities, combined with the retrieved information and few-shot recalled cases, to generate accurate and useful fire warning solutions and emergency response suggestions.

[0069] In terms of implementation, the project plans to train the relevant parameters of the RAG module with manually annotated data based on the existing large language model, so as to automatically and adaptively query relevant knowledge information from the knowledge graph, and use the query results as context to utilize the large language model to generate fire warning solutions and emergency treatment suggestions.

[0070] The massive multi-source heterogeneous data management and system reasoning optimization module efficiently stores, manages and retrieves massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimizes the distributed training and reasoning process of multimodal large models, ensuring that the system can achieve fast and accurate fire monitoring and early warning in complex natural environments.

[0071] The massive multi-source heterogeneous data management and system reasoning optimization module includes a multi-source heterogeneous data storage and management optimization module and a distributed training and reasoning optimization module;

[0072] (i) Multi-source heterogeneous data storage and management optimization module

[0073] like Figure 4 As shown in the figure, we will use HDFS (Hadoop Distributed File System) for large-scale storage of high-throughput data such as drone images, videos, and infrared data. For time series data generated by sensors, we will introduce the InfluxDB time series database to efficiently record and manage time series data. By combining the two in the system architecture, we can achieve seamless storage and access of massive amounts of data.

[0074] To bridge the gap between different types of data, this paper explores data lake technology. Data lakes allow for unified storage and querying of structured, semi-structured, and unstructured data. Therefore, we will employ the Apache Hudi and Delta Lake technology frameworks to store data of different modalities (such as images, videos, and time series data) in a single system, and implement efficient indexing and retrieval through a metadata layer.

[0075] To improve the query efficiency of multi-source data, we will develop an indexing mechanism based on vector retrieval. By introducing a vector retrieval library such as Faiss, we will use vectorization to perform fast indexing, thereby improving the real-time requirements of fire monitoring.

[0076] (ii) Distributed training and inference optimization module

[0077] To meet the demands of training large, multimodal models, we will employ both model and data parallelism. Data parallelism involves splitting training data and distributing it across different GPUs for parallel computing. Model parallelism involves distributing different layers or parts of a large model across multiple compute nodes to reduce the memory overhead of a single GPU. This implementation utilizes the Horovod and DeepSpeed ​​distributed training frameworks to optimize training efficiency.

[0078] During the inference phase, to accelerate model inference and reduce computing resource usage, we will employ model quantization technology. This technology reduces model parameters from high-precision (e.g., 32-bit floating-point numbers) to low-precision (e.g., 8-bit integers), significantly reducing the model's storage space and computational complexity. Furthermore, we will employ model pruning technology to further optimize model inference speed by removing redundant network connections and reducing the number of model parameters.

[0079] In addition, this invention will introduce inference acceleration frameworks such as TensorRT to deploy optimized models on edge devices for efficient inference. By optimizing the model through hardware acceleration, it ensures that the inference process can meet the real-time requirements of the fire warning system.

[0080] This invention will not only significantly advance domestic forest fire early warning technology, but will also promote international technical exchange and cooperation, contributing positively to global ecological and environmental protection. By achieving comprehensive automated management from data collection and intelligent analysis to command and dispatch, the project aims to establish a universal forest fire monitoring and early warning solution to meet the ecological protection needs of the new era.

[0081] The intelligent forest fire monitoring and early warning system, based on a multimodal large model, covers a single command and operation map, real-time monitoring, early warning notifications, fire prediction, reasoning optimization, plan generation, incident handling, incident archiving, intelligent scheduling, patrol monitoring, research and analysis, equipment management, and geographic information management. The system will be automated, intelligent, and responsive in real time, enabling its application in actual forest fire monitoring scenarios. The details are as follows:

[0082] (1) One map for command and operation: By integrating multiple data sources, such as geographic information, fire information, personnel distribution, and equipment distribution (drone equipment, ground monitoring equipment), an intuitive command map is generated. This map supports real-time updates and dynamic display, helping commanders quickly understand the overall situation and make accurate decisions.

[0083] (2) Real-time monitoring: Real-time monitoring of fire conditions in forest areas, using data collected by drone patrols and ground sensors to promptly detect and track fire sources;

[0084] (3) Early warning notification: Once a fire is discovered, the system can immediately issue an early warning notification, including text messages, phone calls, system pop-up reminders, etc., to ensure that relevant personnel can receive the information in time and take measures.

[0085] (4) Fire prediction: Fire prediction is conducted using the core technology of fire identification and environmental analysis based on a multimodal large model. By integrating multi-source information such as images, videos, infrared data, and sensor data, analyzing historical data, environmental factors (such as wind speed, humidity, temperature, terrain, etc.), and human activities, the possibility of fire occurrence and its possible impact range can be predicted, providing a scientific basis for prevention work.

[0086] (5) Reasoning and Optimization: Based on big data analysis and machine learning technology, the system can infer and predict the development of the fire and propose optimization solutions. It can analyze the changing trends of the fire and automatically adjust rescue resources and strategies to achieve the best rescue results.

[0087] (6) Plan Generation: By combining domain knowledge with the core technology of automatic generation of fire emergency plans, the corresponding emergency plan is automatically generated based on the fire prediction and actual situation. The plan includes rescue routes, personnel deployment, material preparation, etc., providing comprehensive emergency guidance to the command personnel.

[0088] (7) Incident Handling: Responsible for rapid response and handling of fires or other emergency incidents. The module supports real-time command and dispatch, information transmission, and resource coordination to ensure that rescue operations are carried out quickly and orderly.

[0089] (8) Event Archives: Used to store and manage historical records of fires and other emergency events. They support data classification, retrieval, and statistical analysis, providing basic data support for the core technologies of fire identification and environmental analysis based on multimodal large models, and providing experience and lessons for future emergency response work.

[0090] (9) Intelligent Dispatch: Through the integration of communication technologies, unified dispatch, departmental linkage, resource sharing, rapid response, and efficient disposal are achieved. Audio and video linkage across regions and devices is achieved to improve rescue efficiency.

[0091] (10) Patrol and Monitoring: Responsible for regular patrols and real-time monitoring of forest areas. Supporting remote monitoring, sensor data collection, and intelligent analysis, it can promptly detect potential fire risks and provide strong support for fire prevention work.

[0092] (11) Research and Analysis: Comprehensively analyze fire data and information to help commanders make scientific decisions.

[0093] (12) Equipment Management: Responsible for the registration and management of front-end data collection equipment. Support the entry and query of equipment information to ensure that the equipment is always in good condition and provide strong support for rescue operations.

[0094] (13) Data Management: As the data center of the entire system, it is responsible for data collection, storage, processing, and analysis. It supports multiple data formats and interfaces, can share and exchange data with other modules, and provides data support for the operation of the entire system. UAV Patrol: Using drones equipped with high-definition cameras and infrared sensors, it conducts high-altitude patrols of forest areas, collecting images and infrared data in real time. Ground Sensor Network: A sensor network is deployed in the forest area to monitor ground temperature, humidity, and other parameters, as well as fire characteristics such as smoke and flames in real time.

[0095] (14) Meteorological monitoring and management: used to monitor and record meteorological data, such as temperature, humidity, wind speed, wind direction, etc., to provide basic data support for the core technologies of fire identification and environmental analysis based on multimodal large models.

[0096] (15) Geographic Information Management: Manage and update geographic information related to forest fire monitoring, including administrative divisions, transportation networks, water source distribution, geographic coordinates, topography, vegetation distribution, etc. Utilize Geographic Information System (GIS) technology to achieve rapid query, update, and visualization of geographic information.

[0097] Based on this, the present invention is applied to forest and grassland fire monitoring in Xinjiang, which has important significance:

[0098] 1. Significantly improve fire monitoring capabilities: By applying multimodal large-scale modeling technology, data from multiple sources can be automatically processed and comprehensively analyzed, significantly improving the accuracy and real-time nature of fire monitoring. This approach is not only applicable to today's complex and ever-changing fire prevention needs, but also provides technical support for early detection and timely response to fires.

[0099] 2. Contribute to the sustainable economic and ecological development of Xinjiang. As a key ecological barrier in western my country, the stability of Xinjiang's ecosystem plays a vital role in regional economic development. This project will enhance fire prevention and control capabilities, reduce the ecological damage caused by forest and grassland fires, and protect economic activities such as agriculture, forestry, and tourism that rely on the ecological environment, thereby providing a solid foundation for sustainable development. By preventing fires and reducing ecosystem damage, the project will effectively support the development of a green economy in the region, promote the normal operation of industrial chains such as agriculture and tourism, and avoid long-term economic losses caused by fires.

[0100] 3. Supporting the advancement of enterprise technological capabilities: Leveraging big data and artificial intelligence technologies, this initiative will drive intelligent upgrades to emergency management through technological innovation, providing enterprises with an excellent opportunity to innovate and enhance their market competitiveness. Through the development and application of technologies within this project, enterprises will be able to create domestically advanced solutions in the field of forest and grassland fire monitoring and early warning, accumulate valuable technical experience, promote technological advancement in the industry, and contribute to the intelligent transformation of fire prevention and control nationwide, in line with the national digitalization strategy and enterprise technological innovation policies.

[0101] In recent years, a new generation of artificial intelligence technologies, represented by large models, has sparked a new round of industrial transformation. Multimodal large models, as a representative achievement, can break through modal limitations, allowing for more efficient and accurate processing and integration of information from different modalities, and have broad application prospects. In the field of forest fire detection, intelligent forest fire monitoring using multimodal large models offers the following advantages:

[0102] (1) Comprehensive analysis capability: Traditional forest fire monitoring is mostly limited to a certain modality. However, Xinjiang’s forestry environment is complex, resulting in a high false alarm rate for single-mode forest fire monitoring. However, multimodal large models can comprehensively process data from different sources (such as satellite images, meteorological data, geographic information, etc.), achieve data fusion, improve the accuracy of forest fire monitoring, and reduce false alarms and missed alarms, which is crucial for timely response and management of fires.

[0103] (2) Reduced labor costs: Traditional forest fire monitoring relies on human analysis. However, Xinjiang has a large forest area and a complex and changing environment, so labor costs are extremely high. Multimodal large models can achieve automated monitoring and analysis, reducing dependence on human resources, improving efficiency, and reducing costs, especially in vast and sparsely populated forest areas.

[0104] (3) Real-time analysis and response: Traditional forest fire monitoring relies on human analysis of data collected by machines, which often results in delayed analysis and slow response. Multi-modal large models can process large amounts of data and update analysis results in real-time, supporting real-time monitoring and response, and enabling early detection and response to reduce economic losses and ecological damage.

[0105] (4) Prediction and early warning: Thanks to its excellent learning and reasoning capabilities, multi-modal large models can combine historical data and real-time inputs to predict and warn of fire risks, helping relevant departments deploy resources in advance and reduce risks.

[0106] (5) Compatibility and scalability: Compared to manual monitoring and warning, multi-modal large models have higher compatibility and scalability, and can be integrated with other monitoring technologies and systems to form a comprehensive forest fire management system.

[0107] The application is applied to the project of forest and grassland fire monitoring in Xinjiang, which is expected to produce significant scientific, technological, industrial, social, economic and ecological benefits. The specific expected benefits are as follows:

[0108] 1. Scientific and technological value

[0109] Through the implementation of this project, significant progress can be made in multi-modal large model technology, fire monitoring algorithms, data fusion technology, and other fields. The multi-modal large model built by this project will become a basic technology platform for processing multi-source data fusion and fire identification in complex environments, and will provide reference for other fields such as urban monitoring, agricultural monitoring, and disaster warning. Breakthroughs in multi-modal data fusion and representation technology can promote the widespread application of artificial intelligence in processing complex environmental information. At the same time, the fire emergency plan generation model based on RAG technology is expected to improve the intelligent level of automated emergency response. The knowledge graph construction and emergency plan generation technology can not only be used in forest fire warning, but also can be expanded to other disaster management and emergency response systems, promoting technological innovation in related fields.

[0110] The forest fire identification and plan generation model developed in this project needs to rely on the large model base of external units, but needs to be redeveloped and built into a system to be accurately and efficiently deployed and applied. In this process, Xinjiang can bring the following benefits:

[0111] (1) Data and application knowledge: This project will accumulate a large amount of data and application experience related to Xinjiang's environment and needs. These materials will help us better understand how to use and optimize large models in local conditions and continuously improve the automation and intelligence level of related industries.

[0112] (2) Technology Accumulation: This project will integrate the developed large-scale model-based forest fire monitoring and early warning technology into a multimodal large-scale model-based intelligent forest fire monitoring and early warning system. This system will be led by Xinjiang Northwest Star Company and ultimately deployed to forestland regulatory agencies in Xinjiang. In addition, the relevant technology accumulation has the potential to be applied to other industries in Xinjiang.

[0113] (3) Intellectual Property: The scientific research results and corresponding intellectual property rights generated by the collaborative research and development of this project shall belong to the three parties involved, and the income from the transformation of related results shall also be jointly owned by the three parties.

[0114] (4) Talent team training: The implementation of this project will cultivate a team that is familiar with large-scale model operations and their applications. By participating in the project, Xinjiang technicians will receive training and practical opportunities to enhance their professional skills in the field of artificial intelligence.

[0115] (5) Cooperation Model: The implementation of this project will strengthen the cooperative relationship between local and external units, laying the foundation for future cooperation and technology transfer. This cooperation model will help local units obtain more technical support and resource sharing in the future, and further promote counterpart assistance to Xinjiang.

[0116] 2. Social benefits

[0117] The implementation of this project will have a profound social impact on my country's forest resource protection and disaster prevention and mitigation efforts. By improving the accuracy and response speed of fire monitoring and early warning systems, it will significantly reduce the damage caused by forest fires to forest resources and the ecological environment, minimize loss of life and property, and safeguard the safety of China's forest resources. Specific social benefits include:

[0118] Improving forest fire prevention and control capabilities: Intelligent forest fire monitoring and early warning systems can effectively reduce losses and damage after a fire occurs, helping relevant departments make quick decisions and respond. Through efficient fire identification and precise emergency plans, measures can be taken in the shortest possible time to protect forest resources and the safety of surrounding residents.

[0119] Promoting ecological protection and restoration: The widespread use of forest fire monitoring systems helps protect forest ecosystems, reduce environmental damage caused by fires, and thereby enhance the stability and biodiversity of regional ecosystems.

[0120] Promoting the development of smart forestry: This project will promote the development of digitalization and intelligentization in the forestry sector, providing technical support for smart forestry. This will not only improve forestry management efficiency but also provide new intelligent means for responding to other natural disasters.

[0121] 3. Ecological benefits

[0122] Through precise fire monitoring and rapid emergency response, this project will play a key role in protecting forest resources and reducing the damage of forest fires to the ecological environment:

[0123] Reducing the damage of forest fires to the ecological environment: The real-time monitoring and early warning functions of the system will significantly improve the timeliness of fire response, reduce the damage of fire spread to forest resources and the surrounding habitats of animals and plants, protect biodiversity, and maintain the balance and stability of the forest ecosystem.

[0124] Promoting sustainable development: By reducing forest fires and their damage to forest resources, the project makes a positive contribution to promoting ecological civilization construction and achieving the goal of sustainable development. The reduction of forest fires will help to exert the carbon sink function and is also of great significance in combating climate change.

[0125] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not limiting. Although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art will understand that they can still modify the technical solutions described in the foregoing examples, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A forest fire intelligent monitoring and early warning method based on a multimodal large model, characterized in that: The steps include: Step S1, fire identification and environmental analysis based on a multimodal large model: Fire identification and environmental analysis are performed based on a multimodal data fusion mechanism based on a multimodal large model, combined with multi-source data collected by drones; Step S2: Automatically generate fire emergency plans based on domain knowledge: By combining a multimodal model with forest ranger inspection reports, drone inspection videos, and fire analysis results, an auxiliary warning solution is automatically generated for the identified fire and emergency response suggestions are provided. Step S3, massive multi-source heterogeneous data management and system reasoning optimization: Efficiently store, manage, and retrieve massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimize the distributed training and reasoning process of multimodal large models to ensure that the system can achieve fast and accurate fire monitoring and early warning in complex natural environments.

2. The method for intelligent forest fire monitoring and early warning based on a multimodal large model according to claim 1 is characterized in that: Step S1, the specific method of fire identification and environment analysis based on the multimodal large model includes: Step S11, multimodal data fusion and fire feature extraction: Based on the multimodal data fusion mechanism of the multimodal large model, combined with the multi-source data collected by the drone, the fire feature extraction and analysis are realized; Step S12, developing a fire recognition algorithm in a complex environment: In order to cope with the challenges of complex natural environments to fire monitoring, a fire recognition algorithm with environmental perception capabilities will be developed, which mainly includes: (1) Environmental factor modeling: Comprehensively consider the environmental conditions when the fire occurs, use machine learning methods to model environmental characteristics, build a correlation model between fire and environmental conditions, and enhance the robustness of fire recognition; (2) Fire detection under occlusion: Study the impact of obstructions on fire monitoring, develop an algorithm that can still accurately detect fire under complex occlusion conditions, and reduce false alarms and missed alarms.

3. The method for intelligent forest fire monitoring and early warning based on a multimodal large model according to claim 2 is characterized in that: In step S11, the method for extracting and analyzing fire characteristics is as follows: an efficient cross-modal feature extraction framework is constructed based on a multimodal large model, visual features are combined with infrared sensor features, joint representation learning is performed, and key fire-related features are mined from different modalities.

4. The method for intelligent forest fire monitoring and early warning based on a multimodal large model according to claim 3 is characterized in that: In step S2, the specific method for automatically generating a fire emergency plan based on domain knowledge includes: Step S21: Domain knowledge integration and existing case library construction: Using knowledge graphs, rule-based reasoning, and machine learning technologies, a structured knowledge base containing forestry domain knowledge and past successful firefighting cases is constructed to achieve the integration and application of domain knowledge and fire data. Step S22, emergency plan generation based on a large model of RAG technology: Develop a text generation large model based on RAG technology, use the text generation model in combination with RAG technology, and combine multi-source data to realize automatic analysis of fire data and plan generation.

5. The method for intelligent forest fire monitoring and early warning based on a multimodal large model according to claim 4 is characterized in that: Through natural language processing technology, key information is extracted from forestry literature, ranger reports and historical fire data to construct a structured knowledge graph. This knowledge graph covers domain entities of forestry resources, fire characteristics, and emergency strategies, and defines the relationships between them to support complex queries and reasoning.

6. The method for intelligent forest fire monitoring and early warning based on a multimodal large model according to claim 5, characterized in that: In step S3, the specific methods for managing massive multi-source heterogeneous data and optimizing system reasoning include: Step S31: Efficient storage and management of massive multi-source heterogeneous data: First, a distributed file system is used to store structured data, and a time series database is used to manage sensor time series data, ensuring that the system can handle the storage needs of large-scale data. Second, the project will build a unified data management platform based on data lake technology to integrate structured, semi-structured, and unstructured data, supporting efficient processing and management of multiple data types. Finally, an efficient data indexing and retrieval mechanism will be developed, and a unified indexing system will be built to ensure rapid retrieval of multimodal data such as images, videos, and time series data, improving the system's query response speed and accuracy. Step S32, distributed training and inference optimization of multimodal large models: First, use data parallelism and model parallelism technologies to realize multi-GPU architecture parallel training of large-scale deep learning models to ensure training efficiency in big data scenarios; second, in the inference stage, use model quantization technology to convert the model's high-precision floating-point numbers into low-precision integer representations to reduce computing overhead; at the same time, implement model pruning technology to trim redundant network connections and reduce the model size; on this basis, develop an inference acceleration engine to achieve efficient inference process, ensuring rapid response to fire monitoring and early warning needs in edge devices and resource-constrained environments.

7. An intelligent forest fire monitoring and early warning system based on a multimodal large model, characterized by: include: Fire identification and environment analysis module, fire emergency plan automatic generation module, and massive multi-source heterogeneous data management and system reasoning optimization module; The fire identification and environment analysis module is based on the multimodal data fusion mechanism of the multimodal large model and combines the multi-source data collected by the drone to perform fire identification and environment analysis; The fire emergency plan automatic generation module automatically generates auxiliary warning solutions for identified fires and provides emergency response suggestions by combining forest ranger inspection reports, drone inspection videos, and fire analysis results through a multimodal model. The massive multi-source heterogeneous data management and system reasoning optimization module efficiently stores, manages and retrieves massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system, and optimizes the distributed training and reasoning process of multimodal large models, ensuring that the system can achieve fast and accurate fire monitoring and early warning in complex natural environments.

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