Fire-fighting emergency response capability assessment method based on artificial intelligence

Abstract

The invention discloses a fire emergency response capability assessment method based on artificial intelligence, and the method employs an AI algorithm to automatically analyze and calculate assessment data, eliminates the subjectivity of manual assessment, and guarantees the objectivity and consistency of an assessment result. The performance of the trainee is monitored and evaluated in real time, so that feedback can be timely obtained and adjustment can be carried out in drilling and actual combat, the emergency response effect is improved, and the pertinence of training and drilling of the trainee in the later period is enhanced; according to the scheme, multiple factors such as physiological performance, action performance, voice communication and team cooperation are comprehensively considered, and the emergency response ability of individuals and teams is quantitatively evaluated from multiple dimensions; the comprehensive evaluation can reflect the real ability of the trainee more comprehensively, and provides a basis for personalized training; by introducing the concept of task matching degree, the evaluation result not only can reflect the individual ability, but also can perform adaptive adjustment for specific tasks and roles, thereby being beneficial to optimizing role allocation and improving the overall efficiency of a team.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence related technologies, and in particular to a fire emergency response capability assessment method based on artificial intelligence. Background Art

[0002] The assessment of emergency response capabilities is a key link in safety management and is directly related to the efficiency and effectiveness of emergency rescue. Existing assessment methods mainly rely on manual assessment and experience accumulation. Common assessment forms include expert scoring, questionnaire surveys, and case analysis. Although these traditional methods can reflect the capabilities of training personnel to a certain extent, they have the following defects:

[0003] Subjectivity and inconsistency: Traditional evaluations often rely on the experience and judgment of reviewers, resulting in highly subjective evaluation results that may differ significantly between reviewers. This inconsistency not only reduces the reliability of the evaluation, but may also lead to misjudgment of the true capabilities of trainees.

[0004] Lack of real-time performance: Traditional methods usually conduct post-event evaluations after an event occurs, which makes it difficult to reflect the performance of trainees in emergency situations in real time; this lag makes it impossible to use evaluation feedback to adjust tactics and strategies in an emergency.

[0005] Insufficient data utilization: Existing technologies usually only focus on a single dimension (such as physiological state or a single behavioral performance), and fail to comprehensively consider multiple factors such as physiology, psychology, and teamwork; this makes the evaluation results unable to fully reflect the true capabilities of the trainees and the team effectiveness.

[0006] Ignoring individual differences: There are significant differences in the performance of different trainees in emergency response, and traditional methods are difficult to dynamically adjust according to individual characteristics and specific task requirements; this leads to the potential of some trainees not being fully tapped, affecting the improvement of overall response capabilities. Summary of the invention

[0007] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] Therefore, in order to solve the above technical problems, the present invention provides the following technical solutions: a fire emergency response capability assessment method based on artificial intelligence, comprising the following specific steps: S1: installing team behavior data acquisition equipment in a training venue or virtual reality system, and configuring data acquisition equipment for individual trainees; S2: constructing a simulated fire emergency scenario, setting emergency tasks and team roles; S3: in the emergency scenario, real-time collection of trainees' physiological data, motion data and voice data, while collecting environmental data, and real-time monitoring of team collaboration behavior data; S4: by analyzing, processing and integrating the trainees' physiological data, motion data, voice and communication data and team collaboration data, a multi-dimensional comprehensive evaluation model is constructed through an AI algorithm, which takes into account the individual's independent performance and team collaboration; S5: combined with real-time data, the comprehensive evaluation model quantitatively evaluates the individual's emergency response capability from multiple angles, evaluates the team's overall emergency response capability, and optimizes the role allocation in future tasks in combination with the performance of individual roles.

[0009] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence described in the present invention, in which: in step S1, each trainee is equipped with a multimodal data acquisition device, including a physiological sensor (monitoring heart rate, respiratory rate, body temperature, etc.), a motion capture device (monitoring position, moving speed, posture, etc.), a voice recording device (collecting voice features) and an environmental sensor (monitoring the surrounding environment such as temperature and smoke concentration).

[0010] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence described in the present invention, in step S1, high-definition cameras, voice analysis equipment, Internet of Things devices, etc. are installed in the training ground or virtual reality system to capture interactive behaviors, command transmission, equipment use and rescue execution during the team collaboration process.

[0011] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence described in the present invention, in which: in step S2, according to real fire emergency events, virtual or physical simulation scenes are designed, covering different fire types (such as structural fires, forest fires, etc.), and setting a variety of complex conditions, including smoke, high temperature, trapped people, obstacles, etc., as close to the real environment as possible.

[0012] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence described in the present invention, in which: in step S2, the system assigns specific tasks (such as fire fighting, rescue, communication, medical treatment, etc.) and corresponding team roles (such as commander, rescuer, supporter) to each trainee, and stipulates the time requirements and goals for task completion.

[0013] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence described in the present invention, in which: in step S3, the command transmission, communication method and task execution status between team members are monitored through cameras and voice analysis equipment to collect team collaboration behavior data.

[0014] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence of the present invention, wherein: in step S4, the physiological performance data, action data, voice data and team behavior data of the trainees are analyzed to calculate the physiological emergency response score, action performance score, voice data score and team building collaboration performance score of the individual in the emergency scene;

[0015] Among them, the physiological performance data score is evaluated based on heart rate HR, respiratory rate BR, body temperature T, etc., and then the individual's physiological emergency response score is calculated; specifically, the calculation formula for the physiological emergency response score is as follows: ;

[0016] in, is the number of data points, and is a weight adjustment parameter that controls the sensitivity to fluctuations in heart rate and respiratory rate; and is the individual's resting state baseline value; It is the time window; It represents the physiological response score, which reflects the individual's physiological adaptability under stressful environment;

[0017] The action data score is evaluated based on the trainee's movement speed v, action accuracy A, etc., and then the individual's action reaction and execution efficiency are judged; specifically, the calculation formula for the action performance score is as follows: ; and They are the ideal values ​​for standard speed and movement accuracy respectively; , , and All are adjustment coefficients; It stands for Movement Performance Score, which is used to measure the agility and movement accuracy of the trainer;

[0018] The speech data score is evaluated by the natural language processing (NLP) algorithm, and the speech rate SR, intonation SP, emotional fluctuation EM, etc. are taken into account during the evaluation process; specifically, the calculation formula for the speech data score is as follows: ;

[0019] in, and are the optimal values ​​for speech speed and intonation, respectively; and To adjust the parameters; Score the verbal communication performance, which reflects the trainee's communication skills and emotional stability under stress;

[0020] The teamwork performance score is evaluated based on the quality of interaction between the individual and other team members (IQ), instruction execution effect (EX), task completion (TC), etc. Specifically, the calculation formula for the teamwork performance score is as follows: ;

[0021] in, The quality of interaction with other team members; To achieve the ideal instruction execution effect; To achieve the highest degree of task completion; Reflects individual performance in teamwork and is used to assess an individual's ability to communicate, execute and complete tasks in a team.

[0022] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence of the present invention, wherein: in step S5, the real-time data is input into the comprehensive assessment model to generate the individual emergency response capability comprehensive score; specifically, the individual emergency response capability comprehensive score The calculation formula is as follows: ;

[0023] in, , , , is the weight of each dimension, which is dynamically adjusted according to the needs of the task type and scenario; formula value range: ∈[0,1], The closer it is to 1, the stronger the emergency response capability of the trainee is.

[0024] As a preferred solution of the fire emergency response capability evaluation method based on artificial intelligence of the present invention, wherein: in step S5, in order to evaluate the overall performance of the team, a team emergency response capability report is generated; based on the individual evaluation results of each trainer, the overall team collaboration effectiveness is calculated. , the calculation formula is as follows: ;

[0025] in, is the comprehensive score of the i-th trainee; The task completion rate for each training personnel; To evaluate the overall team collaboration effectiveness and the team's overall task execution efficiency and coordination ability; formula value range: ∈[0,1], The closer it is to 1, the higher the overall collaboration efficiency of the team.

[0026] As a preferred solution of the fire emergency response capability assessment method based on artificial intelligence of the present invention, in which: in step S5, based on the individual assessment results and team performance, the system automatically generates a role allocation optimization plan, which is based on the individual role fitness and Task FitTM, to evaluate and determine the score of an individual's suitability for a role; the calculation formula for role fitness is as follows: ;

[0027] in, is the match between the individual and the current task; and are the scores of individuals in motor performance and communication performance, respectively; is the score of an individual's suitability for a role; the system Automatically optimize role allocation based on the team's overall task requirements to ensure maximum collaboration efficiency in future tasks; Formula range: ∈[0,1], The closer it is to 1, the more suitable the individual is for the current task role.

[0028] Beneficial effects of the present invention: 1. The present invention uses AI algorithms to automatically analyze and calculate evaluation data, eliminating the subjectivity of manual evaluation and ensuring the objectivity and consistency of evaluation results; this method improves the reliability of evaluation and provides a solid data foundation for scientific research. 2. The present invention can monitor and evaluate the performance of trainers in real time, so that timely feedback can be obtained and adjustments can be made during drills and actual combat; this real-time performance significantly improves the effect of emergency response and enhances the pertinence of later training and drills for trainers. 3. The present invention comprehensively considers multiple factors such as physiological performance, action performance, voice communication and teamwork, and quantitatively evaluates the emergency response capabilities of individuals and teams from multiple dimensions; this comprehensive evaluation can more comprehensively reflect the true capabilities of trainers and provide a basis for personalized training. 4. By introducing the concept of task matching, the present invention can not only reflect individual capabilities, but also make adaptive adjustments for specific tasks and roles; this feature helps to optimize role allocation and improve the overall efficiency of the team. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0030] Figure 1It is the overall workflow diagram of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1 , which is the first embodiment of the present invention, provides a fire emergency response capability assessment method based on artificial intelligence, comprising the following specific steps:

[0036] S1: Install team behavior data collection equipment in the training venue or virtual reality system, and configure data collection equipment for individual trainers;

[0037] Each trainee is equipped with multimodal data acquisition equipment, including physiological sensors (monitoring heart rate, respiratory rate, body temperature, etc.), motion capture devices (monitoring position, movement speed, posture, etc.), voice recording equipment (collecting voice features) and environmental sensors (monitoring temperature, smoke concentration and other surrounding conditions);

[0038] Install high-definition cameras, voice analysis equipment, Internet of Things devices, etc. in training venues or virtual reality systems to capture interactive behaviors, command transmission, equipment usage, and rescue execution during team collaboration.

[0039] S2: Construct a simulated fire emergency scenario and set emergency tasks and team roles;

[0040] Design virtual or physical simulation scenarios based on real fire emergency events, covering different fire types (such as structural fires, forest fires, etc.), setting up a variety of complex conditions, including smoke, high temperature, trapped people, obstacles, etc., as close to the real environment as possible;

[0041] The system assigns specific tasks (such as firefighting, rescue, communication, medical treatment, etc.) and corresponding team roles (such as commander, rescuer, supporter) to each trainee, and stipulates the time requirements and goals for task completion.

[0042] S3: In emergency scenarios, the training personnel’s physiological data, motion data, and voice data are collected in real time, while environmental data is collected and team collaboration behavior data is monitored in real time;

[0043] Through cameras and voice analysis equipment, the command transmission, communication methods and task execution between team members are monitored to collect team collaboration behavior data.

[0044] S4: By analyzing, processing and integrating the trainees’ physiological data, motion data, voice and communication data and team collaboration data, a multi-dimensional comprehensive evaluation model is constructed through AI algorithms, which takes into account the individual’s independent performance and team collaboration;

[0045] Analyze the trainees' physiological performance data, movement data, voice data and team behavior data, and calculate the individual's physiological emergency response score, movement performance score, voice data score and team building collaboration performance score in the emergency scenario;

[0046] Among them, the physiological performance data score is evaluated based on heart rate HR, respiratory rate BR, body temperature T, etc., and then the individual's physiological emergency response score is calculated; specifically, the calculation formula for the physiological emergency response score is as follows:

[0047] ;

[0048] in, is the number of data points, and is a weight adjustment parameter that controls the sensitivity to fluctuations in heart rate and respiratory rate; and is the individual's resting state baseline value; It is the time window; It represents the physiological response score, which reflects the individual's physiological adaptability under stressful environment;

[0049] The action data score is evaluated based on the trainee's movement speed v, action accuracy A, etc., and then the individual's action reaction and execution efficiency are judged; specifically, the calculation formula for the action performance score is as follows: ; and They are the ideal values ​​for standard speed and movement accuracy respectively; , , and All are adjustment coefficients; It stands for Movement Performance Score, which is used to measure the agility and movement accuracy of the trainer;

[0050] The speech data score is evaluated by the natural language processing (NLP) algorithm, and the speech rate SR, intonation SP, emotional fluctuation EM, etc. are taken into account during the evaluation process; specifically, the calculation formula for the speech data score is as follows: ;

[0051] in, and are the optimal values ​​for speech speed and intonation, respectively; and To adjust the parameters; Score the verbal communication performance, which reflects the trainee's communication skills and emotional stability under stress;

[0052] The teamwork performance score is evaluated based on the quality of interaction between the individual and other team members (IQ), instruction execution effect (EX), task completion (TC), etc. Specifically, the calculation formula for the teamwork performance score is as follows: ;

[0053] in, The quality of interaction with other team members; To achieve the ideal instruction execution effect; To achieve the highest degree of task completion; Reflects individual performance in teamwork and is used to assess an individual's ability to communicate, execute and complete tasks in a team.

[0054] S5: Combined with real-time data, the comprehensive evaluation model quantitatively evaluates the emergency response capabilities of individuals from multiple perspectives, evaluates the emergency response capabilities of the team as a whole, and optimizes the role allocation in future tasks based on the performance of individual roles;

[0055] The real-time data is input into the comprehensive evaluation model to generate the comprehensive score of the individual's emergency response capability; specifically, the comprehensive score of the individual's emergency response capability The calculation formula is as follows:

[0056] ;

[0057] in, , , , The weight of each dimension is dynamically adjusted according to the needs of the task type and scenario;

[0058] Formula value range: ∈[0,1], The closer it is to 1, the stronger the emergency response capability of the trainee is;

[0059] In order to evaluate the overall performance of the team, a report on the team's emergency response capability is generated; based on the individual evaluation results of each trainer, the overall team collaboration effectiveness is calculated , the calculation formula is as follows: ;

[0060] in, is the comprehensive score of the i-th trainee; The task completion rate for each training personnel; To evaluate the overall team collaboration effectiveness and the team's overall task execution efficiency and coordination ability;

[0061] Formula value range: ∈[0,1], The closer it is to 1, the higher the overall collaboration efficiency of the team;

[0062] Based on individual assessment results and team performance, the system automatically generates a role allocation optimization plan based on individual role fitness. and Task FitTM, to evaluate and determine the score of an individual's suitability for a role; the calculation formula for role fitness is as follows: ;

[0063] in, is the match between the individual and the current task; and are the scores of individuals in motor performance and communication performance, respectively; is the score of an individual's suitability for a role; the system Automatically optimize role allocation based on the team's overall task requirements to ensure maximum collaboration efficiency in future tasks;

[0064] Formula value range: ∈[0,1], The closer it is to 1, the more suitable the individual is for the current task role.

[0065] In this embodiment, by accurately evaluating the capabilities of individuals and teams, a scientific basis is provided for optimizing resource allocation and task allocation, so that rescue work can be carried out faster and more effectively in emergency situations; this evaluation method provides new ideas for the training and education of trainers, and the data-driven training strategy can enable trainers to quickly improve their emergency response capabilities in real scenarios; this embodiment helps to timely discover potential safety hazards through comprehensive evaluation capabilities, improve the overall level of safety management, and provide protection for social public safety;

[0066] The present invention forms a new evaluation model through the integration and innovation of multiple advanced technologies; key steps such as real-time data collection, dynamic feedback adjustment, comprehensive data analysis and personalized role matching make this method have obvious technical advantages in improving emergency response capabilities.

[0067] Example 2

[0068] This is the second embodiment of the present invention. This embodiment is different from the first embodiment in that: in order to verify the effectiveness of the fire emergency response capability assessment method based on artificial intelligence, an emergency response capability assessment test is designed to quantitatively assess the performance of trainees in emergency situations through artificial intelligence and multi-dimensional assessment models; in order to achieve this goal, this embodiment arranges a fire emergency scene in a virtual reality training site or a physical simulation scene. Set the fire type (such as structural fire, forest fire), and add complex elements such as smoke, high temperature, obstacles and trapped people to simulate the complexity of the real environment.

[0069] Data collection equipment configuration: Each trainee is equipped with multi-modal data collection equipment, including:

[0070] Physiological sensors: monitor heart rate (HR), breathing rate (BR), body temperature and other indicators.

[0071] Motion capture device: monitors data such as position, movement speed, and posture.

[0072] Voice recording equipment: collects voice features (such as speaking speed, intonation, emotions, etc.).

[0073] Environmental sensors: monitor environmental data such as temperature and smoke concentration.

[0074] Team tasks and role allocation: The system assigns specific tasks (firefighting, rescue, communication, medical treatment, etc.) and corresponding roles (commander, rescuer, supporter, etc.) to each trainee, and specifies the time requirements and goals for task completion.

[0075] The training personnel enter the site and perform the designated emergency tasks. During this process, physiological data, motion data, voice data and team collaboration data are collected in real time, and the transmission of instructions and task execution are recorded through high-definition cameras and voice analysis equipment.

[0076] Data processing and analysis;

[0077] Use AI algorithms to process and analyze the collected data and build a multi-dimensional comprehensive evaluation model.

[0078] Calculate the individual's physiological response score, action performance score, voice communication score and teamwork performance score to obtain the comprehensive score of each trainee's emergency response ability ;

[0079] Calculate the overall team collaboration effectiveness ;

[0080] Role optimization; based on individual role adaptability Optimize role allocation based on task matching;

[0081] The specific experimental results are shown in the following table: By analyzing the data in the table, we can know that: 1. Individual performance analysis:

[0082] From the comprehensive score point of view, the trainee No. 9 had the highest comprehensive score of 0.903, and the trainee No. 6 had the lowest comprehensive score of 0.790. This shows that there are significant differences in the performance of different individuals in task execution.

[0083] Trainees with high comprehensive scores (such as No. 3, 7, 9, and 12) scored high in action performance, language communication, and physiological performance, especially in teamwork scores, where they performed particularly well, indicating that they were able to cooperate effectively with the team during task execution.

[0084] The trainers with low scores (such as No. 6 and 4) scored below 0.8 in action performance, language communication and physiological performance, indicating that they may have inefficiency or communication barriers during task execution, affecting the overall team collaboration effectiveness.

[0085] 2. Comparison of task completion:

[0086] The highest value of task completion was 0.96, and the lowest value was 0.81, showing significant differences in task completion efficiency between individuals. No. 9 had the highest task completion, which was consistent with his high comprehensive score, indicating that he not only performed well in task execution, but also completed the task more efficiently.

[0087] Trainees with lower task completion (such as No. 6) also performed poorly in the comprehensive score, which further verified their shortcomings in the actual task execution.

[0088] 3. The relationship between the scoring indicators:

[0089] The data shows that there is a strong correlation between the teamwork score and the overall score. For example, No. 3 and No. 9 have high teamwork scores (0.95 and 0.94, respectively), and their overall scores are also high (0.895 and 0.903, respectively), which shows that teamwork is an important factor in improving overall performance.

[0090] The motor performance score and language communication score also have a significant impact on the overall score. Individuals with high scores usually have excellent performance in these two indicators (for example, No. 7 has a motor score of 0.88 and a language score of 0.88).

[0091] In the experiment, the present invention can accurately identify the strengths and weaknesses of trainees in emergency tasks through multimodal data collection and comprehensive scoring models. For example, trainee No. 9 performed well in multiple scores, and the system can recommend him to play a key role in future tasks, such as commander or rescuer. This data-based role optimization allocation can significantly improve the task completion efficiency of the entire team. Existing training systems usually rely on subjective evaluation or a single data source (such as action scores or physiological indicators), which cannot fully reflect the emergency response capabilities of individuals. The present invention uses a comprehensive scoring model, combined with multi-dimensional data such as action, language, and physiology, to more finely evaluate the performance of individuals and teams, which helps to improve training effects and actual combat performance.

[0092] For individuals with lower comprehensive scores (such as No. 6 and 4), the system can provide specific improvement suggestions (such as strengthening action performance training or language communication training) to improve their task execution capabilities. This personalized feedback mechanism is relatively rare in the prior art, and the usual training system cannot provide such detailed data analysis and optimization suggestions. Traditional role allocation is mostly based on experience or fixed division of labor, while the present invention dynamically adjusts role allocation in a data-driven manner to better adapt to different task requirements. This intelligent role optimization can significantly improve the team's task completion efficiency in complex environments.

[0093] The present invention can dynamically evaluate the physiological state and behavioral performance of trainees through real-time monitoring of multimodal data. For example, if a trainee's heart rate is found to be too high and his / her performance score decreases during training (such as the heart rate of trainee No. 6 is 87 and the action score is only 0.79), the system can suggest pausing the task to prevent excessive physical fatigue. This real-time emergency response adjustment function is an advantage that existing technologies cannot provide. The system can provide real-time feedback on the performance of trainees during task execution and dynamically adjust their training load and task allocation. This flexible training and task management mechanism is not common in existing training systems and provides significant innovation and practical value.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fire emergency response capability assessment method based on artificial intelligence, characterized by: The method comprises the following specific steps: S1: installing a team behavior data collection device in a training venue or a virtual reality system, and configuring a data collection device for individual trainers; S2: Build a simulated fire emergency scenario, set emergency tasks and team roles; S3: In the emergency scenario, collect the trainees’ physiological data, motion data and voice data in real time, collect environmental data, and monitor team collaboration behavior data in real time; S4: By analyzing, processing and integrating the trainees' physiological data, motion data, voice and communication data, and team collaboration data, a multi-dimensional comprehensive evaluation model is constructed through AI algorithm. This model takes into account the individual's independent performance and team collaboration. S5: Combined with real-time data, the comprehensive evaluation model quantitatively evaluates the individual's emergency response capabilities from multiple angles, evaluates the team's overall emergency response capabilities, and combines the performance of individual roles to optimize role allocation in future tasks.

2. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 1, characterized in that: In step S1, each trainee is equipped with a multimodal data acquisition device, including a physiological sensor, a motion capture device, a voice recording device, and an environmental sensor.

3. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 2, characterized in that: In step S1, high-definition cameras, voice analysis equipment, and Internet of Things devices are installed in the training venue or virtual reality system to capture interactive behaviors, command transmission, equipment use, and rescue execution during the team collaboration process.

4. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 3 is characterized by: In step S2, according to real fire emergency events, virtual or physical simulation scenarios are designed, covering different fire types and setting a variety of complex conditions, including smoke, high temperature, trapped people, and obstacles, to be as close to the real environment as possible.

5. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 4, characterized in that: In step S2, the system assigns specific tasks and corresponding team roles to each trainee, and specifies the time requirements and goals for task completion.

6. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 5, characterized in that: In step S3, the command transmission, communication method and task execution status between team members are monitored through cameras and voice analysis equipment to collect team collaboration behavior data.

7. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 6 is characterized by: In step S4, the physiological performance data, movement data, voice data and team behavior data of the trainees are analyzed to calculate the physiological emergency response score, movement performance score, voice data score and team building collaboration performance score of the individuals in the emergency scenario; Among them, the physiological performance data score is evaluated based on heart rate HR, respiratory rate BR, and body temperature T, and then the individual's physiological emergency response score is calculated; specifically, the calculation formula for the physiological emergency response score is as follows: ;in, is the number of data points, and is a weight adjustment parameter that controls the sensitivity to fluctuations in heart rate and respiratory rate; and is the individual's resting state baseline value; It is the time window; It represents the physiological response score, which reflects the individual's physiological adaptability under stressful environment. The action data score is evaluated based on the trainer's movement speed v and action accuracy A, and then judges the individual's action reaction and execution efficiency. Specifically, the calculation formula of the action performance score is as follows: ; and They are the ideal values ​​for standard speed and movement accuracy respectively; , , and All are adjustment coefficients; It represents the action performance score, which is used to measure the agility and action accuracy of the trainees. The voice data score is evaluated by the natural language processing algorithm, and the speech rate SR, tone SP, and emotional fluctuation EM are taken into account during the evaluation process. Specifically, the calculation formula for the voice data score is as follows: ;in, and are the optimal values ​​for speech speed and intonation, respectively; and To adjust the parameters; The voice communication performance score reflects the trainer's communication ability and emotional stability under pressure; the teamwork performance score is based on the quality of interaction IQ between the individual and other team members, the instruction execution effect EX, and the task completion TC. Specifically, the calculation formula for the teamwork performance score is as follows: ;in, The quality of interaction with other team members; To achieve the ideal instruction execution effect; To achieve the highest degree of task completion; Reflects individual performance in teamwork and is used to assess an individual's ability to communicate, execute and complete tasks within a team.

8. The fire emergency response capability assessment method based on artificial intelligence according to claim 7, characterized in that: In step S5, the real-time data is input into the comprehensive evaluation model to generate the individual's comprehensive emergency response capability score; specifically, the individual's comprehensive emergency response capability score The calculation formula is as follows: ;in, , , , is the weight of each dimension, which is dynamically adjusted according to the needs of the task type and scenario; formula value range: ∈[0,1], The closer it is to 1, the stronger the emergency response capability of the trainee is.

9. The fire emergency response capability assessment method based on artificial intelligence as claimed in claim 8, characterized in that: In step S5, in order to evaluate the overall performance of the team, a report on the team's emergency response capability is generated; based on the individual evaluation results of each trainer, the overall team collaboration effectiveness is calculated. , the calculation formula is as follows: ;in, is the comprehensive score of the i-th trainee; The task completion rate for each training personnel; To evaluate the overall team collaboration effectiveness, the team's overall task execution efficiency and coordination ability; formula value range: ∈[0,1], The closer it is to 1, the higher the overall collaboration efficiency of the team.

10. The fire emergency response capability assessment method based on artificial intelligence according to claim 9, characterized in that: In step S5, based on the individual evaluation results and team performance, the system automatically generates a role allocation optimization plan based on the individual role fitness. and Task FitTM, to evaluate and determine the score of an individual's suitability for a role; the calculation formula for role fitness is as follows: ;in, is the match between the individual and the current task; and are the scores of individuals in motor performance and communication performance, respectively; is the score of an individual's suitability for a role; the system Automatically optimize role allocation based on the team's overall task requirements to ensure maximum collaboration efficiency in future tasks; Formula value range: ∈[0,1], The closer it is to 1, the more suitable the individual is for the current task role.

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