Artificial Intelligence-Based Training System and Method for Emergency Fire Response at Construction Sites

Abstract

This invention relates to the field of fire emergency response technology, specifically to an artificial intelligence-based training system and method for fire emergency response at construction sites. The AI-based training system includes a database for storing knowledge and information related to fire emergency response in different scenarios; an AI training subsystem, communicating with the database, for analyzing and extracting information from the database, generating theoretical training content, constructing challenge-based training games simulating different fire scenarios, and evaluating the training results of participants; and a virtual reality reinforcement subsystem, communicating with the AI ​​training subsystem, for providing immersive reinforcement training in different fire scenarios for participants and evaluating their training results. This invention utilizes artificial intelligence technology for simulated game training and virtual reality technology for reinforcement training, achieving a systematic training program for handling fire emergencies.

Description

Technical Field

[0001] This invention relates to the field of fire emergency response technology, specifically to an artificial intelligence-based training system and method for fire emergency response at construction sites. Background Technology

[0002] As companies rapidly expand their outsourced work teams, some employees lack sufficient understanding of safety regulations and have a weak awareness of safety risks. Furthermore, the safety skills of each employee vary considerably. Traditional training methods, limited to verbal lectures and video warnings, are monotonous and lack interaction, resulting in low efficiency and difficulty in ensuring training effectiveness. This is particularly true in the field of fire hazards, where the diverse conditions under which fires occur and their rapid spread necessitate that on-site personnel possess certain professional fire-fighting capabilities. In recent years, the rapid development and widespread application of virtual reality (VR) technology has effectively improved the effectiveness of related technical training. However, due to the increased cost and complexity of VR technology, its large-scale application in safety training has not yet been realized.

[0003] For the reasons mentioned above, this invention systematically establishes a training system for handling fire emergencies by combining artificial intelligence (AI) and VR technologies. This system fully utilizes AI and VR technologies to statistically analyze a large amount of fire-related knowledge and real-world cases, generating relevant data and simulated scenario examples. This provides trainees with a systematic and complete training process for responding to fire emergencies and further reduces the high cost of using VR technology. Summary of the Invention

[0004] The purpose of this invention is to provide an artificial intelligence-based training system for emergency response to fires at construction sites, in order to solve the problems of high cost and high training complexity of VR technology in the field of safety training.

[0005] To address the aforementioned problems, this invention proposes an artificial intelligence-based training system for emergency response to fires at construction sites. The technical solution adopted is as follows: An artificial intelligence-based training system for emergency response to fires at construction sites, comprising: The database is used to store knowledge and information related to fire emergency response in different scenarios; An artificial intelligence training subsystem is connected to the database to analyze and extract information from the database, generate theoretical training content, construct challenge-based training games simulating different fire scenarios, and evaluate the training results of the trainees. The virtual reality enhancement subsystem is communicatively connected to the artificial intelligence training subsystem. It is used to provide immersive fire scenario enhancement training for trainees and to evaluate the training results of the trainees.

[0006] Furthermore, the artificial intelligence training subsystem includes: The training content generation and theoretical evaluation module extracts and analyzes information from the database to generate text materials for trainees to learn and corresponding theoretical test questions, and evaluates the trainees' theoretical learning outcomes. The interactive scenario construction and gamification evaluation module is configured to extract and analyze knowledge information from the database to construct a challenge-based training game that simulates different fire scenarios, and to evaluate the completion results of the trainees in the training game. The comprehensive evaluation module is configured to comprehensively evaluate the overall training results of the trainees.

[0007] Furthermore, the artificial intelligence training subsystem also includes: An analytical model is used to analyze the knowledge information extracted from the database; Scene generation model, used to simulate different fire scenarios; The evaluation model is used to assess the participants' performance in training games, the results of their virtual reality intensive training, and the overall training outcomes.

[0008] Furthermore, the training process of the fire emergency response training system is configured as follows: The training consists of three phases: theoretical learning, game completion, and virtual reality intensive training. Participants must pass the assessment in each phase before they can proceed to the next.

[0009] Furthermore, the fire emergency response training system also includes: The process decision and adaptive control module analyzes and judges the particularity of the current fire scenario in real time based on the characteristic information of different fire scenarios constructed, and determines whether it is allowed to skip the steps of controlling the spread of fire and / or skip the steps of implementing isolation measures for flammable and explosive materials in the challenge-style training game or the virtual reality reinforcement training according to the preset decision rules. When it is determined that the conditions for direct fire extinguishing are met, the training process is controlled to directly switch to the execution of fire extinguishing operation.

[0010] Furthermore, the knowledge information includes basic knowledge of fire occurrence, basic knowledge of fire prevention, characteristics of fire occurrence, conventional methods for dealing with fire, relevant laws, regulations, and standards; the virtual reality enhancement subsystem creates a crisis atmosphere of a fire scene by integrating visual, auditory, and lighting elements, allowing trainees to roam in a realistic environment.

[0011] Furthermore, the disaster emergency response training system also includes: The knowledge base and model continuous evolution module is configured as follows: The system acquires the latest information on fire emergency response from external sources in real time or periodically, and updates it to the database automatically or after review. Based on newly entered data, user training behavior data, and training result feedback data, the analysis model, evaluation model, or scenario generation model in the artificial intelligence training subsystem are iteratively optimized.

[0012] This application also provides an artificial intelligence-based training method for emergency response to fires at construction sites, including the following steps: Step S1: Through the artificial intelligence training subsystem, provide trainees with text materials generated based on the database and organize theoretical knowledge learning tests; Step S2: After the trainees have completed the theoretical knowledge learning test, they are guided into a challenge-based training game simulating different fire scenarios through the artificial intelligence training subsystem. Step S3: After the trainees have completed all the levels of the training game, they will be provided with immersive fire scenario reinforcement training through the virtual reality reinforcement subsystem.

[0013] Furthermore, in the aforementioned challenge-based training game, the preset fire emergency response procedure includes the following steps to be performed sequentially: Ensure personnel safety; Control the spread of the fire; Implement isolation measures for flammable and explosive materials; Firefighting operations are underway.

[0014] Furthermore, based on the specific nature of the fire scenario, it is permissible to perform operations to control the spread of the fire and / or to implement isolation measures for flammable and explosive materials, and to directly execute fire extinguishing operations when conditions are met.

[0015] Compared with the prior art, this application has the following beneficial effects: This invention provides a systematic training program for handling fire emergencies by establishing a database for theoretical knowledge learning, utilizing artificial intelligence technology for simulated game training, and employing virtual reality technology for intensive training. The invention systematically establishes a training system for handling fire emergencies by combining artificial intelligence (AI) and VR technologies. This system fully utilizes AI and VR technologies to statistically analyze a large amount of fire-related knowledge and real-world cases, generating relevant data and simulated scenario examples. These provide trainees with a systematic and complete training process for responding to fire emergencies. Compared to traditional safety training or single virtual reality training, this significantly improves trainees' comprehensive emergency response capabilities and overall training efficiency, while effectively reducing the cost of virtual reality training.

[0016] This invention's training system, building upon existing VR technology, fully utilizes AI technology to digitize and generate a database of numerous fire scenarios, providing trainees with a quick opportunity to learn fire safety knowledge and emergency response procedures. Through theoretical learning, simulated game progression, and intensive VR training, participants receive more systematic training in handling fire emergencies. Compared to traditional safety training or single-faceted VR training, it significantly improves trainees' overall capabilities. Prior to VR training, participants have already completed theoretical learning and simulated games, reducing the difficulty of VR training and further enhancing its effectiveness.

[0017] The training process of the fire emergency response training system is configured as follows: The training program consists of three phases: theoretical learning, game completion, and intensive virtual reality training. Participants must pass the assessment at each phase before moving on to the next. This progressive training path ensures that trainees master the fundamental knowledge before engaging in practical exercises, avoiding the cognitive load and safety risks associated with skipping phases and enhancing the systematic nature and safety of the training.

[0018] The fire emergency response training system also includes: The process decision-making and adaptive control module analyzes and judges the specificity of the current fire scenario in real time based on the characteristic information of different constructed fire scenarios. According to preset decision rules, it determines whether it is permissible to skip the steps of controlling the fire spread and / or skipping the steps of isolating flammable and explosive materials in the challenge-based training game or the virtual reality reinforcement training. When it determines that the conditions for direct fire extinguishing are met, it controls the training process to directly switch to fire extinguishing operations. This achieves intelligent and adaptive training, simulates the dynamic decision-making process in a real fire, avoids rigid training processes, improves the realism and flexibility of training, and trains trainees' emergency judgment capabilities.

[0019] The knowledge and information mentioned includes basic knowledge about fire occurrence, basic knowledge about fire prevention, characteristics of fire occurrence, routine methods for dealing with fire, relevant laws, regulations, and standards; The virtual reality enhancement subsystem creates a crisis atmosphere of a fire scene by integrating visual, auditory, and lighting elements, allowing trainees to virtually roam through the environment. This multi-sensory immersive experience enhances the realism and sense of crisis, thus improving the effectiveness of the intensive training. Attached Figure Description

[0020] Figure 1 This invention relates to the fire emergency response training steps of the construction site fire emergency response training system based on artificial intelligence.

[0021] Figure 2 This is a flowchart of the training process for the artificial intelligence-based construction site fire emergency response training system of the present invention.

[0022] Figure 3 This invention relates to the fire emergency response process of the artificial intelligence-based construction site fire emergency response training system. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] The following description, with reference to the accompanying drawings, describes an AI-based training system and method for emergency response to fires at construction sites, based on an embodiment of this application.

[0026] The following is combined with Figure 1 This application provides a detailed description of the artificial intelligence-based construction site fire emergency response training system.

[0027] This application discloses an AI-based training system for emergency fire response at construction sites, comprising: a database for storing knowledge and information related to emergency fire response in different scenarios. This knowledge and information includes basic knowledge about fire occurrence, basic knowledge about fire prevention, characteristics of fire occurrence, standard procedures for responding to fires, relevant laws, regulations, and standards.

[0028] The artificial intelligence training subsystem communicates and connects with the database to analyze and extract information from the database, generate theoretical training content, construct challenge-based training games simulating different fire scenarios, and evaluate the training results of the participants.

[0029] Specifically, such as Figure 1 As shown, the AI ​​training subsystem includes: a training content generation and theoretical evaluation module, which extracts and analyzes information from the database to generate text materials for trainees to learn from and corresponding theoretical test questions, and evaluates the trainees' theoretical learning outcomes; an interactive scenario construction and gamified evaluation module, configured to extract and analyze knowledge information from the database to construct a challenge-based training game simulating different fire scenarios, and evaluate the trainees' completion results in the training game; and a comprehensive evaluation module, configured to comprehensively evaluate the overall training results of the trainees.

[0030] In another specific embodiment, the artificial intelligence training subsystem further includes: an analysis model for analyzing knowledge information extracted from the database; a scenario generation model for simulating different fire scenarios; and an evaluation model for assessing the completion results of trainees in training games, comprehensively evaluating the overall training results of trainees, and evaluating the results of virtual reality reinforcement training for trainees.

[0031] In one specific embodiment, the training process of the fire emergency response training system is configured as follows: The training consists of three phases: theoretical learning, game completion, and virtual reality intensive training. Participants must pass the assessment in each phase before they can proceed to the next.

[0032] Specifically, such as Figure 2As shown, the training process of the fire emergency response training system is configured as follows: First, the system analyzes information in the database to generate text materials for learning and conducts learning outcome tests. Only those who pass the test are considered to have passed the theoretical test and enter the game mode; if they fail the theoretical test, they continue learning theoretical knowledge. Second, a game simulating fire scenarios is created based on the information in the database. The game uses a level-based mode. Those who have passed the theoretical learning must complete fire response tests in different scenarios within the game. All participants must pass all levels to be considered qualified and enter VR training; if any fire response test in any scenario is failed, the game continues. Third, immersive fire scenario-based intensive training is provided, and the VR training results are evaluated to determine if the participants have achieved the expected training results. Only those who pass the evaluation are considered to have passed the VR training; if they fail, VR training continues. The evaluation of theoretical learning outcomes, game completion results, and VR training results are all handled by the artificial intelligence training subsystem.

[0033] In another specific embodiment, the fire emergency response training system further includes: The process decision and adaptive control module analyzes and judges the particularity of the current fire scenario in real time based on the characteristic information of different fire scenarios constructed. According to the preset decision rules, it determines whether it is allowed to skip the steps of controlling the spread of fire and / or skip the steps of implementing isolation measures for flammable and explosive materials in the challenge-style training game or the virtual reality reinforcement training. When it is determined that the conditions for direct fire extinguishing are met, the training process is controlled to directly switch to the execution of fire extinguishing operation.

[0034] The Virtual Reality Enhancement Subsystem, also known as VR Enhancement Training, communicates with the Artificial Intelligence Training Subsystem to provide trainees with immersive enhancement training in different fire scenarios and to evaluate their training results. The Virtual Reality Enhancement Subsystem creates a crisis atmosphere of a fire scene by integrating visual, auditory, and lighting elements, allowing trainees to virtually roam through the scene and experience the fire situation more comprehensively and intuitively. This near-realistic environment further enhances trainees' ability to handle fires.

[0035] In another specific embodiment, the fire emergency response training system further includes a knowledge base and model continuous evolution module, configured as follows: The system acquires the latest information on fire emergency response from external sources in real time or periodically, and updates it to the database automatically or after review. Based on newly entered data, user training behavior data, and training result feedback data, the analysis model, evaluation model, or scenario generation model in the AI ​​training subsystem are iteratively optimized to adapt to the cutting-edge development of AI technology and the need to improve actual training effectiveness. Here, external information sources include, but are not limited to, industry standard databases, accident case databases, and technical literature databases.

[0036] Specifically, such as Figure 3 As shown, the fire emergency response training system provides systematic training to trainees according to the following content and process: 1) When a fire occurs, first ensure the safety of personnel; 2) Then control the spread of the fire; 3) Conduct isolation to ensure that flammable and explosive materials will not pose new dangers due to the spread of the fire; 4) Extinguish the fire.

[0037] An AI-based training method for emergency response to fires at construction sites includes the following steps: Step S1: Through the artificial intelligence training subsystem, provide trainees with text materials generated based on the database and organize theoretical knowledge learning tests; Step S2: After the trainees pass the theoretical knowledge learning test, they are guided into a challenge-style training game simulating different fire scenarios through the artificial intelligence training subsystem. Step S3: After the trainees have completed all the levels of the training game, they will be provided with immersive fire scenario reinforcement training through the virtual reality reinforcement subsystem.

[0038] In one specific embodiment, such as Figure 3 As shown, the pre-set fire emergency response procedure in the challenge-based training game includes the following steps performed in sequence: ensuring personnel safety; controlling the spread of the fire; implementing isolation measures for flammable and explosive materials; and extinguishing the fire. Specifically, when a fire occurs, participants are systematically trained to handle different fire scenarios in the order of personnel safety, controlling the spread of the fire, implementing isolation measures, and extinguishing the fire. Ultimately, the trainees achieve the training objectives by passing theoretical knowledge tests, game-based training, and virtual reality-enhanced training.

[0039] In another specific embodiment, depending on the specific nature of the fire scenario, it is permissible to skip the "operation to control the spread of the fire" and / or the "operation to isolate flammable and explosive materials" in steps S2 and / or S3, and directly perform the fire extinguishing operation when the conditions are met.

[0040] The above-mentioned artificial intelligence-based training system and method for emergency response to fires at construction sites will be further explained using specific embodiments.

[0041] In one specific embodiment, such as Figure 1As shown, the training process consists of three steps: theoretical knowledge learning, game level training, and VR intensive training.

[0042] like Figure 2 As shown, all trainees first undergo theoretical learning. Those who complete the theoretical learning according to the syllabus can then take a test. The AI ​​subsystem generates exam questions to assess trainees' basic fire safety knowledge. If they fail the test, they continue with theoretical learning; if they pass the theoretical test, they can proceed to the game-based training phase.

[0043] Enter a fire scenario simulation game generated by an AI subsystem. In the retraining phase, trainees act as on-site personnel handling fire emergencies, using their learned basic fire safety knowledge as a guide to handle dangerous situations in different fire scenarios in a predetermined order. To complete the game, trainees must successfully handle all fire scenarios according to the expected objectives. Those who complete the game can proceed to the VR intensive training phase; those who do not complete the game fully will continue playing.

[0044] During the VR intensive training phase, trainees use VR technology to enter a virtual scenario, placing themselves in a fire situation and handling the emergency. The AI ​​subsystem evaluates the training results; if the evaluation is unsatisfactory, the VR intensive training continues; if the evaluation is satisfactory, the training ends.

[0045] It is worth noting that during the game completion training and VR intensive training sessions, participants need to consistently follow the instructions. Figure 3 The sequence shown is used to handle fire emergencies. However, considering that some scenarios may not require fire control or isolation steps, certain steps can be skipped to complete the firefighting operation, depending on the specific circumstances.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of patent protection of the present invention shall be determined by the claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention shall also be included within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based training system for emergency response to fires at construction sites, characterized in that: include: The database is used to store knowledge and information related to fire emergency response in different scenarios; An artificial intelligence training subsystem is connected to the database to analyze and extract information from the database, generate theoretical training content, construct challenge-based training games simulating different fire scenarios, and evaluate the training results of the trainees. The virtual reality enhancement subsystem is communicatively connected to the artificial intelligence training subsystem. It is used to provide immersive fire scenario enhancement training for trainees and to evaluate the training results of the trainees.

2. The artificial intelligence-based construction site fire emergency response training system according to claim 1, characterized in that, The artificial intelligence training subsystem includes: The training content generation and theoretical evaluation module extracts and analyzes information from the database to generate text materials for trainees to learn and corresponding theoretical test questions, and evaluates the trainees' theoretical learning outcomes. The interactive scenario construction and gamification evaluation module is configured to extract and analyze knowledge information from the database to construct a challenge-based training game that simulates different fire scenarios, and to evaluate the completion results of the trainees in the training game. The comprehensive evaluation module is configured to comprehensively evaluate the overall training results of the trainees.

3. The artificial intelligence-based construction site fire emergency response training system according to claim 2, characterized in that, The artificial intelligence training subsystem also includes: An analytical model is used to analyze the knowledge information extracted from the database; Scene generation model, used to simulate different fire scenarios; The evaluation model is used to assess the participants' performance in training games, the results of their virtual reality intensive training, and the overall training outcomes.

4. The artificial intelligence-based construction site fire emergency response training system according to claim 2, characterized in that, The training process of the fire emergency response training system is configured as follows: The training consists of three phases: theoretical learning, game completion, and virtual reality intensive training. Participants must pass the assessment in each phase before they can proceed to the next.

5. The artificial intelligence-based construction site fire emergency response training system according to claim 1, characterized in that, The fire emergency response training system also includes: The process decision and adaptive control module analyzes and judges the particularity of the current fire scenario in real time based on the characteristic information of different fire scenarios constructed, and determines whether it is allowed to skip the steps of controlling the spread of fire and / or skip the steps of implementing isolation measures for flammable and explosive materials in the challenge-style training game or the virtual reality reinforcement training according to the preset decision rules. When it is determined that the conditions for direct fire extinguishing are met, the training process is controlled to directly switch to the execution of fire extinguishing operation.

6. The artificial intelligence-based construction site fire emergency response training system according to claim 1, characterized in that, The knowledge and information mentioned includes basic knowledge about fire occurrence, basic knowledge about fire prevention, characteristics of fire occurrence, routine methods for dealing with fire, relevant laws, regulations, and standards; The virtual reality enhancement subsystem creates a crisis atmosphere at a fire scene by integrating visual, auditory, and lighting elements, allowing trainees to immerse themselves in the scene.

7. The artificial intelligence-based construction site fire emergency response training system according to claim 3, characterized in that, The fire emergency response training system also includes: The knowledge base and model continuous evolution module is configured as follows: The system acquires the latest information on fire emergency response from external sources in real time or periodically, and updates it to the database automatically or after review. Based on newly entered data, user training behavior data, and training result feedback data, the analysis model, evaluation model, or scenario generation model in the artificial intelligence training subsystem are iteratively optimized.

8. A training method for emergency response to fires at construction sites based on artificial intelligence, characterized in that, Includes the following steps: Step S1: Through the artificial intelligence training subsystem, provide trainees with text materials generated based on the database and organize theoretical knowledge learning tests; Step S2: After the trainees have completed the theoretical knowledge learning test, they are guided into a challenge-based training game simulating different fire scenarios through the artificial intelligence training subsystem. Step S3: After the trainees have completed all the levels of the training game, they will be provided with immersive fire scenario reinforcement training through the virtual reality reinforcement subsystem.

9. The training method for emergency response to fires at construction sites based on artificial intelligence according to claim 8, characterized in that, The pre-set fire emergency response procedure in the challenge-based training game includes the following steps to be performed sequentially: Ensure personnel safety; Control the spread of the fire; Implement isolation measures for flammable and explosive materials; Firefighting operations are underway.

10. The training method for emergency response to fires at construction sites based on artificial intelligence according to claim 8, characterized in that, Given the specific nature of the fire scenario, it is permissible to skip operations to control the spread of the fire and / or to isolate flammable and explosive materials, and to directly execute fire extinguishing operations when conditions are met.

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