Emergency rescue simulation method and system, computer equipment and storage medium

Abstract

The invention relates to an emergency rescue simulation method and system, computer equipment and a storage medium, and relates to the technical field of computer simulation, and the method comprises the steps: obtaining multi-modal data of a plurality of users participating in simulation; for each user, based on the corresponding multi-modal data, calculating a cognitive-physiological state vector representing the internal state of the user in real time; gathering cognitive-physiological state vectors of all users, and analyzing and generating a group cognitive collaborative spectrum vector which contains indexes such as decision synchronization rate and information sharing entropy and represents the overall collaborative state of the team; a disaster regulation and control decision module adaptively decides and generates a disaster regulation and control action according to the spectrum vector; and the environment updating module updates a virtual environment according to the disaster regulation and control action. According to the invention, by constructing closed-loop feedback of team cognitive states and disaster environment evolution, objective quantitative evaluation and self-adaptive targeting training of team cooperation efficiency are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer simulation, in particular to an emergency rescue simulation method and system, a computer device and a storage medium. BACKGROUND

[0002] Emergency rescue simulation technology, as an important means to improve the combat capability of emergency response personnel and optimize rescue plans, has been widely applied and developed. Current simulation technology is mostly focused on building high-fidelity virtual physical environments, which can use advanced physical engines to accurately simulate the physical evolution process of disaster scenarios such as fire spread, smoke diffusion, building structure collapse, etc. Users immerse themselves in the virtual reality (VR) or augmented reality (AR) devices to practice operation skills and tactical processes.

[0003] However, at present, digital and intelligent means are gradually introduced in the field of emergency management and disaster rescue, and virtual environments are constructed for accident simulation and rescue drills to improve the ability and efficiency of responding to emergencies. Existing emergency rescue simulation technology mainly relies on finite element analysis, fluid dynamics simulation, and artificial neural network algorithms, but there are problems such as large consumption of computing resources, difficulty in balancing simulation accuracy and real-time performance.

[0004] In addition, the existing technology also exposes its shortcomings in the evaluation of team coordination effectiveness. Currently, the evaluation of team drilling effectiveness mostly relies on the subjective observation of instructors outside the scene or post-mortem discussions after the drill. This evaluation method is not only highly subjective and difficult to standardize, but also severely lacks process-oriented and objective quantitative data support. For deep-seated coordination problems such as whether team decisions are synchronized, whether key information is effectively transferred among members, and whether negative emotions are spreading in the team, the existing technology cannot provide real-time and quantifiable indicators for measurement, making the evaluation of team coordination capability often remain at a vague and superficial level, and unable to accurately identify specific weaknesses in team collaboration.

[0005] Finally, due to the lack of real-time and accurate insight into team coordination status, the existing simulation system also appears to be rigid in training mode. The triggering and evolution of disaster events are often based on pre-set scripts or fixed difficulty gradients, and cannot dynamically and specifically generate challenges according to the specific weaknesses exposed by the training team during the drill. For example, the system cannot adaptively generate a scenario to test distributed communication capabilities when it identifies communication bottlenecks in the team. This training mode is difficult to achieve targeted reinforcement training for different teams and different weaknesses, thereby limiting the utilization efficiency of training resources and the final training effect to some extent. SUMMARY

[0006] The application aims to provide an emergency rescue simulation method, system, computer device and storage medium, and solve the technical problem that the existing emergency rescue simulation technology cannot quantize the internal state of personnel and team coordination performance in real time, and dynamically couple them with disaster evolution, resulting in deviation between the simulation process and the real scene and lack of targeted training.

[0007] In a first aspect, the application provides an emergency rescue simulation method, which is realized by the following steps: First, the multi-modal data of multiple users participating in the simulation is obtained. Then, for each user, based on the corresponding multi-modal data, the cognitive-physiological state vector representing the internal state of the user is calculated in real time through a preset cognitive state conversion function.

[0008] After obtaining the individual cognitive-physiological state vectors of all users, the vectors are aggregated and calculated, and a group cognitive coordination spectrum vector representing the overall coordination state of the team is analyzed and generated.

[0009] Then, a disaster control decision module adaptively decides and generates a disaster control action according to the group cognitive coordination spectrum vector.

[0010] Finally, an environment update module updates the virtual environment state in the simulation according to the disaster control action, thereby completing a dynamic feedback loop.

[0011] As a specific implementation, the multi-modal data is obtained by: collecting real-time physiological indicators of the user through a biological sensor worn by the user to form a physiological data stream; extracting stress factors of the micro-environment in which the user is located from the physical engine of the simulation system to form an environmental data stream; and recording the operation interaction log of the user in the virtual environment to form a behavior data stream. The cognitive-physiological state vector is a data structure that at least includes one or more of the following quantitative indicators: cognitive load level, physiological stress level, or panic index.

[0012] As a specific implementation, the group cognitive coordination spectrum vector is a data structure that includes at least one or more of the following coordination indicators: decision synchronization rate, information sharing entropy, or emotional contagion index. These indicators quantize the coordination performance of the team from different dimensions.

[0013] In a specific embodiment, the decision synchronization rate is represented by symbol η dsr , which is used to quantify the consistency of the cognitive state response of team members after receiving critical instructions. This indicator can be calculated by the following formula: Where: Var(·) is a variance calculation function, ΔLcog,i the change of the cognitive load level of the i-th user within a preset time window after receiving the critical instruction, is a preset maximum variance value for normalization, and N is the total number of users.

[0014] In another specific embodiment, the information sharing entropy is denoted by a symbol H info , which is used to characterize the balance of the distribution of critical information among team members. This index can be calculated by the following formula: wherein p i (t) is the probability proportion of critical information initiated or transmitted by the i-th user within a specific time window t; and N is the total number of users.

[0015] In yet another specific embodiment, the emotional contagion index is denoted by a symbol κ emo , which is used to quantify the propagation intensity of negative emotions in the team network. This index is defined as the spectral radius of a correlation matrix R. The element p ij in the correlation matrix R represents the correlation of the panic index time series of user i and user j, which can be calculated by the following formula: wherein p ij is the correlation of the panic index time series of user i and user j; Cov(·,·) is a covariance calculation function; L pan,i (t ′ ) and L pan,j (t ′ ) are the panic index time series of user i and user j within a past time window t ′ ; and are the standard deviations of the corresponding panic index time series, respectively.

[0016] In a preferred embodiment, the step of adaptively deciding and generating disaster control actions specifically comprises: obtaining, by the disaster control decision module, a global state, wherein the data structure of the global state includes the cognitive-physiological state vectors of all users, the group cognitive synergy frequency spectrum vector, and the physical world state representing the current virtual environment physical state; based on the global state, the module outputs the disaster control actions through a preset decision model. Moreover, the disaster control actions are generated for the team cognitive synergy weaknesses revealed by the group cognitive synergy frequency spectrum vector.

[0017] In another preferred embodiment, the method further comprises a down-regulation step: based on the cognitive-physiological state vector of each user, real-time regulating the behavioral capability of the user in the virtual environment. The behavioral capability includes the operation accuracy or the virtual field of view range of the user. For example, when the physiological stress level or the cognitive load level of a user exceeds a preset threshold, the system processes the original input instruction of the user through a state-dependent transfer function to reduce the operation accuracy of the final action or to narrow the virtual field of view range of the user.

[0018] The second aspect of the present application provides an emergency rescue simulation system, comprising: a data acquisition module configured to acquire multi-modal data of a plurality of users participating in the simulation; an individual state solving module connected to the data acquisition module and configured to, for each user, based on the multi-modal data corresponding to the user, real-time solve a cognitive-physiological state vector representing the internal state of the user; a group spectrum analysis module connected to the individual state solving module and configured to aggregate the cognitive-physiological state vectors of all users, analyze and generate a group cognitive coordination spectrum vector representing the overall coordination state of the team; a disaster control decision module connected to the group spectrum analysis module and configured to, according to the group cognitive coordination spectrum vector, adaptively decide and generate a disaster control action; an environment updating module connected to the disaster control decision module and configured to, according to the disaster control action, update the virtual environment in the simulation.

[0019] The third aspect of the present application provides a computer device comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the method according to the first aspect of the present application.

[0020] The fourth aspect of the present application provides a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the method according to the first aspect of the present application.

[0021] In summary, the present application has at least one of the following beneficial technical effects: 1. The application characterizes the conduction relationship of risk in the building space by constructing an abstract weighted space adjacency graph, and quantifies the risk by using algebraic operation based on the hazard correlation matrix. The evaluation method based on graph theory and matrix operation has much lower computational complexity than the physical simulation method, so it can realize near real-time evaluation and spread path prediction of the risk situation in the whole building without relying on large-scale computing clusters, effectively avoiding the problem of balancing simulation accuracy and real-time performance in traditional technology, and providing a lightweight and efficient decision support tool for emergency command and rescue exercise; 2. The application can realize objective and quantitative evaluation of team coordination effectiveness, generate a group cognitive coordination spectrum vector containing specific indicators such as decision synchronization rate, information sharing entropy and emotional contagion index by real-time analysis of cognitive-physiological state vectors of all users, and convert the fuzzy team coordination performance difficult to measure in traditional exercise into a set of traceable, analyzable structured data, providing accurate data support for subsequent review, evaluation and capability improvement; 3. The application realizes adaptive and targeted training of rescue team, and the disaster control decision module can adaptively decide and generate disaster control actions corresponding to the team coordination short board revealed by the group cognitive coordination spectrum vector in real time, so as to guide the team to strengthen training in specific capability dimensions and improve the pertinence and efficiency of training when the system identifies that the team information sharing is not smooth. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a method flowchart of the application; Figure 2 is a system architecture diagram of the application; Figure 3 is a computer device structure schematic diagram of the application. DETAILED DESCRIPTION

[0023] The application is further described in detail in combination with the accompanying Figure 1 , drawings.

[0024] Embodiment: An emergency rescue simulation method, comprising the following steps: S1, acquiring multi-modal data of a plurality of users participating in simulation; In the specific implementation of the application, this step is a basic data collection process continuously performed in the simulation initialization stage and the entire running period. The core is to construct a comprehensive data portrait of each user participating in simulation, which contains his inherent characteristics and real-time dynamic reaction. The multi-modal data is composed of static data and dynamic data.

[0025] First, before the simulation task begins, the system will acquire or configure each user's static data. This part of data constitutes the baseline of user's ability and characteristics. Preferably, the static data is organized as a static profile vector. The vector provides personalized initial parameters or weight adjustment basis for subsequent cognitive-physiological state vector calculation. The static profile vector can include: experience level: this is a quantitative indicator representing the user's familiarity with the field of emergency rescue. For example, the user's experience level can be assigned according to the number of times the user has participated in related tasks, the total length of historical training, and other information.

[0026] psychological quality baseline: this is an indicator reflecting the user's psychological endurance and adjustment ability under normal stress. For example, the result of a standardized psychological assessment questionnaire can be pre-determined and input.

[0027] professional skill level: this is an indicator representing the user's mastery of the professional skills required for the role they play. The professional skill level can be set according to the user's historical examination results or certification level.

[0028] Second, after the simulation process begins, the system will continuously and real-time collect dynamic data for each user. Dynamic data is a direct reflection of the user's real-time interaction with the virtual environment, mainly including the following three parallel data streams: physiological data stream The data stream is acquired through wearable biological sensing devices configured for each user. Preferably, such sensing devices can be integrated into the user's virtual reality (VR) head-mounted display, interactive handle, or dedicated physiological monitoring wristband, etc. to complete data collection without disturbing the user's immersive experience. The data stream provides a series of quantitative indicators that can objectively reflect the user's autonomic nervous system activity and physiological stress response, which can include heart rate, heart rate variability, skin electrical response, etc. The physiological data stream is the most direct source of data for judging the user's internal physiological state changes.

[0029] The environmental data stream is dynamically extracted and generated by the physical simulation engine built into the simulation system according to the real-time position and state of each user in the virtual environment. It aims to quantify the objective stress exerted by the external environment on the user. The parameters in this data stream can include: environmental temperature, smoke concentration, environmental visibility, environmental noise decibel value, and real-time distance between the user and the preset danger source, etc. This data stream provides a key environmental stress basis for subsequent calculation of the user's cognitive load and physiological stress level.

[0030] The behavior data stream is a structured log generated by recording all the operation interaction events of the user in the virtual environment in real time. It reflects the user's explicit decision-making and execution ability under the driving of environmental information and internal state. The data stream can record in detail: the user's three-dimensional space movement path and instantaneous speed; the user's interaction frequency, duration and operation mode with virtual devices or tools; the user's start and end time, execution duration and success or failure of executing specific tasks; and the frequency and duration of voice or text communication initiated through the system communication module, etc.

[0031] In summary, step S1 integrates the static profile vector of each user, and simultaneously collects the physiological data stream, environmental data stream and behavior data stream in real time, which together constitute a complete and dynamically updated multi-modal data set. This data set provides comprehensive and reliable data support for the subsequent step S2 of accurately calculating the cognitive-physiological state vector of each user from four dimensions of inherent characteristics, internal physiological response, external environmental pressure and explicit behavior performance.

[0032] S2, for each user, based on the corresponding multi-modal data, a cognitive-physiological state vector representing the internal state of the user is calculated in real time; In the specific implementation of the present application, this step is performed in parallel by the individual state calculation module configured in the system and corresponding to each user. This design ensures that the state evaluation of each user is independent and real-time.

[0033] Specifically, the individual state calculation module receives the complete multi-modal data set related to the current user i from step S1. The data set includes the static profile vector of the user, as well as the real-time updated physiological data stream, environmental data stream and behavior data stream.

[0034] The core of the module is a preset cognitive state conversion function, denoted by f c . The function of this function is to establish a nonlinear mapping relationship between the above multi-dimensional input data and the user's internal cognitive physiological state. The cognitive state conversion function takes multi-modal data as input, and outputs the cognitive-physiological state vector C i (t) of the user at time t through calculation. Its function relationship can be represented as: C i (t) = f c (D phys,i (t), D env,i (t), D beh,i (t), U i ); Where: C i (t) represents the cognitive-physiological state vector of user i at time t; f c represents the cognitive state conversion function; D phys,i(t) represents the physiological data stream of user i at time t; D env,i (t) represents the environmental data stream of user i at time t; D beh,i (t) represents the behavioral data stream of user i at time t; U i represents the static profile vector of user i.

[0035] Preferably, the cognitive state transition function f c This can be achieved through at least one of the following technical paths: A pre-trained machine learning model, such as a recurrent neural network or its variant long short-term memory network, can be employed to construct the function. This model learns the complex mapping pattern from input data to output state through supervised learning training on a historical dataset containing multi-modal data and corresponding cognitive state labels.

[0036] A fuzzy logic system based on expert knowledge can be employed to construct the function. This system describes the relationship between different input data combinations and specific cognitive state levels by defining a series of rules.

[0037] By executing the cognitive state transition function, the system generates a cognitive-physiological state vector C i (t) for each user in real time. This vector is a multi-dimensional data structure that contains deep internal state indicators that cannot be directly measured by external devices. Preferably, this vector contains at least the following dimension quantification indicators: C i (t) = [L cog,i (t), L str,i (t), L pan,i (t)]. Where: Cognitive load level (L cog,i (t)): This indicator aims to quantify the psychological resources consumed by the user's brain in performing the current task. Its calculation process will consider factors such as task complexity (e.g. frequency of multi-target operations), communication data volume, and interference information (e.g. high decibel noise) in the environmental data stream.

[0038] Physiological stress level (L str,i (t)): This indicator aims to quantify the degree of tension and vigilance of the user's physiological system triggered by perceived threats or stress. Its calculation is mainly based on changes in physiological data stream indicators such as significant increase in heart rate, decrease in heart rate variability, and increase in galvanic skin response, combined with weighting of dangerous source information in the environmental data stream.

[0039] Panic index (L pan,i(t)): This index aims to assess the tendency of users to make irrational decisions or lose control of their behaviors. In addition to relying on extreme physiological stress indicators, it will also focus on analyzing abnormal patterns in the behavior data stream, such as aimless wandering, repetitive errors, or communication breakdowns.

[0040] In summary, step S2 successfully transforms the multi-source, heterogeneous raw data collected in step S1 into a set of standardized key indicators that can represent the users' internal cognitive and psychological states, through the individual state solving module and cognitive state transition function configured independently for each user. The set of cognitive-physiological state vectors {C1(t), C2(t),..., C N (t)} output by this step provides the necessary and high-quality data basis for step S3 to perform macro analysis of the team's overall collaborative state.

[0041] S3, aggregate the cognitive-physiological state vectors of all users, analyze and generate a group cognitive synergy spectrum vector representing the overall collaborative state of the team; In the specific implementation of the present application, this step is performed by a group spectrum analysis module within the system. This module serves as a centralized computing unit, with its data input connected to the output of the individual state solving modules of all users, to real-time aggregate the set of cognitive-physiological state vectors {C1(t), C2(t),..., C N (t)} of all users at time t.

[0042] The core function of this module is to perform statistical and dynamic analysis on the aggregated individual state data through a series of pre-set mathematical models and algorithms, thereby calculating and generating a structured data that can multi-dimensionally and quantitatively describe the current overall collaborative effectiveness of the team, i.e., the group cognitive synergy spectrum vector, denoted by S team (t).

[0043] Preferably, the group cognitive synergy spectrum vector contains a series of synergy indicators that reveal the internal operation state of the team from different dimensions. In a specific embodiment, this vector can contain at least one or more of the following synergy indicators: First, a key synergy indicator is the decision synchronization rate, denoted by η dsr . This index aims to quantify the consistency of the response speed and intensity of each member at the cognitive level when the team receives a unified key instruction. A high synchronization rate reflects the team's good instruction understanding and execution uniformity. The calculation process is as follows: the system monitors the communication data stream, and when a key instruction is identified to be issued at time t c , it then tracks and records the change ΔL cog,i of the cognitive load level L i of each user i within a very short pre-set time window Δt that follows.cog,i Subsequently, the decision synchronization rate is obtained by calculating the variance of the change amount of all users and performing normalization processing. The calculation formula can be represented as: wherein: η dsr (t c ) represents the decision synchronization rate at the instruction issuance time t c ; Var(·) represents the variance calculation function; ΔL cog,i (t c , Δt) represents the change amount of the cognitive load level of the i-th user within the time window from t c to t c + Δt; represents a preset maximum variance value for normalization, which is a fixed parameter set according to historical data or expert experience; and N represents the total number of users.

[0044] In addition, another important coordination index is the information sharing entropy, denoted by H info . This index aims to evaluate the balance degree of the distribution of key information among all members by analyzing the communication mode within the team. The calculation of this index is based on the statistics of effective communication events in the behavior data stream. Specifically, the system counts the number of key information initiated or successfully transmitted by each user i within a certain time window, and calculates the probability ratio p i (t) in the total amount of information of the team. The information sharing entropy is calculated by the Shannon entropy formula. This index helps to identify possible information bottlenecks or information overload problems in team communication. The calculation formula can be represented as: wherein: H info (t) represents the information sharing entropy at time t; p i (t) represents the probability ratio of key information initiated or transmitted by the i-th user within a certain time window; and N represents the total number of users.

[0045] Furthermore, a coordination index reflecting the emotional stability of the team is the emotional contagion index, denoted by κ emo . This index aims to quantify the propagation intensity and risk of negative emotions (represented by the panic index in the cognitive-physiological state vector) in the social network composed of team members. The calculation process is as follows: first, obtain the time series data of the panic index L pan of any two users i and j within a past time window t'. Then, calculate the Pearson correlation coefficient ρ ij of the two time series. Repeat this calculation for all possible user pairs in the team to construct an N x N correlation matrix R(t) = [ρ ij(t)].

[0046] The emotional contagion index is defined as the spectral radius (the largest eigenvalue of the modulus) of this correlation matrix, which can effectively capture the amplification effect of the strongest emotional transmission path in the network. The correlation coefficient ρ ij The calculation formula can be expressed as: Where: ρ ij (t) represents the correlation of the panic index time series of user i and user j at time t; Cov(·,·) represents the covariance calculation function; L pan,i (t ′ ) and L pan,j (t ′ ) represent the panic index time series of user i and user j in the past time window t ′ (length τ); σ Lpan,i and σ Lpan,j represent the standard deviation of the corresponding panic index time series.

[0047] In summary, step S3 uses the group spectrum analysis module to effectively refine and aggregate the massive, microscopic individual cognitive data generated in step S2 into a group of core indicators that can macroscopically and multidimensionally describe the team coordination state. The group cognitive coordination spectrum vector S team (t) output by this step provides a clear, quantitative team state snapshot for the disaster control decision module in step S4, enabling it to make more targeted intervention decisions that match the current cognitive weaknesses of the team.

[0048] S4, according to the group cognitive coordination spectrum vector, adaptively decides and generates a disaster control action; In the specific implementation of the present application, this step is performed by a disaster control decision module configured within the system. The function of this module is that the generated disaster event is no longer a preset, fixed script, but a real-time, dynamic response based on the current performance of the team.

[0049] Specifically, the decision of the disaster control decision module is not only based on the group cognitive coordination spectrum vector output by step S3, but also on a more comprehensive global state. This global state is represented by the symbol S(t), which integrates the core information of the entire simulation system at time t, and its composition can be represented as: S(t)=({C1(t),...,C N (t)},S team (t),W(t)); Where: {C1(t),...,C N(t) represents the set of individual cognitive-physiological state vectors of all users generated by step S2; S team (t) represents the group cognitive synergy spectrum vector characterizing the team's overall synergy state generated by step S3; W(t) represents a physical world state vector characterizing the current virtual environment physical state, which can include information such as fire spread range, remaining passable paths, building structure stability, etc., provided by the system's physical simulation engine.

[0050] By obtaining the global state S(t), the disaster regulation decision module can simultaneously perceive the internal state of each micro-individual, the synergy efficiency of the macro-team, and the physical reality of the objective environment in a unified data framework.

[0051] Internally, its decision-making process is executed through a pre-set decision strategy function, denoted by symbol π. The function of the strategy function is to map the currently obtained global state S(t) to a specific disaster regulation action A g (t) to be executed in the virtual environment. A g (t) = π(S(t)); wherein: A g (t) represents the disaster regulation action generated by the decision at time t; π represents the decision strategy function; S(t) represents the global state at the current time.

[0052] Preferably, the decision strategy function π can be obtained by offline training through deep reinforcement learning, etc.

[0053] The core of this step lies in its "adaptive" nature, i.e., the generated disaster regulation action A g (t) is directly related to the team cognitive synergy weaknesses revealed by the group cognitive synergy spectrum vector S team (t). For example: If the decision module receives S team (t) in which the decision synchronization rate η dsr indicator continuously falls below the pre-set threshold, indicating that the team has hesitation or inconsistency in command execution. At this time, the decision strategy π may generate a non-fatal challenge that requires precise collaboration among team members within a short time to overcome, such as a rescue passage gate that needs to be opened simultaneously by multiple people.

[0054] If the information sharing entropy H info in S team (t) is too low, indicating the emergence of information silos in the team. At this time, the decision strategy π may not increase physical danger, but generate an event simulating the temporary failure of a key communication node to prompt the team to establish new, distributed communication links.

[0055] Step S4 takes the team's cognitive synergy state as input, and outputs a disaster regulation action with explicit purpose through a strategic decision-making process, by means of a disaster regulation decision module. This step ensures that the evolution of the simulation environment is no longer random or preset, but becomes a dynamic tool that can purposefully detect, expose, and train the team's cognitive synergy capabilities. The disaster regulation action A g (t) generated by step S4 is a direct instruction for the environment update in step S5.

[0056] S5, update the virtual environment in the simulation according to the disaster regulation action.

[0057] In the specific implementation of the present application, this step is performed by an environment update module. The module serves as a bridge between the decision layer and the physical simulation layer in the system architecture. Its input end is connected to the output end of the disaster regulation decision module of step S4 to receive the disaster regulation action A g (t) generated by decision-making.

[0058] Specifically, the disaster regulation action A g (t) is not a simple trigger signal, but a structured data instruction containing explicit execution parameters. Preferably, the instruction can be a data object containing at least action type, target, location, intensity parameter, and duration fields, so as to ensure that the executed environment update is accurate and controllable.

[0059] After receiving the disaster regulation action instruction, the core task of the environment update module is to parse the instruction and convert it into a series of specific function calls or parameter modifications to the underlying physical simulation engine. The physical simulation engine is the basic component responsible for simulating all physical laws in the virtual environment.

[0060] To further illustrate the implementation of this step, the following examples of the specific execution process of the environment update module are given in combination with different disaster regulation actions that may be generated in step S4: If step S4 generates a disaster regulation action aimed at enhancing team coordination operation because it monitors that the team's decision synchronization rate η dsr is too low.

[0061] If step S4 generates a disaster regulation action aimed at interfering with key communication nodes because it monitors that the team's information sharing entropy H info is abnormal.

[0062] If step S4 generates a disaster regulation action aimed at interfering with key communication nodes because it monitors that the team's emotional contagion index κ emoRapidly rising, and generated a disaster control action aimed at raising environmental stress to test team emotional stability.

[0063] This step is the final execution end of the method of the application, which accurately materializes the upper decision into the dynamic evolution of the virtual environment through the environment updating module, ensuring that the simulation challenge is always closely associated with the real-time cognitive synergy state of the team.

[0064] In combination with the accompanying drawings Figure 2 Another embodiment of the present application provides an emergency rescue simulation system, comprising: A data acquisition module is configured to acquire multi-modal data of a plurality of users participating in the simulation. An individual state solving module is configured to, for each user, solve a cognitive-physiological state vector representing the user's internal state in real time based on the user's corresponding multi-modal data. A group spectrum analysis module is configured to aggregate cognitive-physiological state vectors of all users, analyze and generate a group cognitive synergy spectrum vector representing the overall synergy state of the team. A disaster control decision module is configured to adaptively decide and generate a disaster control action according to the group cognitive synergy spectrum vector; and an environment updating module is configured to update the virtual environment in the simulation according to the disaster control action.

[0065] The system of the embodiment can be used to execute the above-mentioned method embodiments, and has similar principles and technical effects, which will not be described here again.

[0066] In combination with the accompanying drawings Figure 3 The present application also provides a computer device, comprising a processor and a memory, the memory storing a computer program executable by the processor, and the computer program is executed by the processor to perform the above method.

[0067] The present application also provides a storage medium, the storage medium storing a computer program, and the computer program is executed by the processor to perform the above method.

[0068] The storage medium can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or an optical disk.

[0069] The embodiments of the present application are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. The same reference numerals are used to represent the same components in the embodiments of the present application. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be covered by the protection scope of the present application.

Claims

1. An emergency rescue simulation method, characterized by, The method comprises the following steps: acquiring multi-modal data of a plurality of users participating in simulation; for each of the users, calculating a cognitive-physiological state vector representing the user's internal state in real time based on the corresponding multi-modal data; aggregating the cognitive-physiological state vectors of all users, analyzing and generating a group cognitive synergy spectrum vector representing the overall team synergy state; adaptively deciding and generating a disaster control action according to the group cognitive synergy spectrum vector; updating the virtual environment in the simulation according to the disaster control action.

2. The method of claim 1, wherein, The acquisition of multi-modal data comprises: acquiring physiological data streams collected by biosensors; acquiring environmental data streams representing the micro-environmental stress of the user extracted from the virtual environment; acquiring behavior data streams recording the user's virtual operation log; The cognitive-physiological state vector comprises at least one of the following: cognitive load level, physiological stress level, or panic index.

3. The method of claim 1, wherein, The group cognitive synergy spectrum vector comprises at least one of the following synergy indicators: decision synchronization rate, information sharing entropy, or emotional contagion index.

4. The method of claim 3, wherein, The decision synchronization rate is calculated by the following formula: wherein: Var(·) is a variance calculation function, ΔL cog,i is the change amount of the cognitive load level of the i-th user within a preset time window after receiving the critical instruction, is a preset maximum variance value for normalization, and N is the total number of users.

5. The method of claim 3, wherein, Further comprising: based on the cognitive-physiological state vector of each user, adjusting the user's behavior ability in the virtual environment in real time, wherein the behavior ability includes the user's operation accuracy or virtual field of view range.

6. The method of claim 3, wherein, The emotional contagion index is the spectral radius of a correlation matrix whose elements p ij is calculated by the following equation: where: p ij is the correlation of the panic index time series of user i and user j; Cov(·, ·) is the covariance calculation function; L pan,i (t ′ ) and L pan,j (t ′ ) are the panic index time series of user i and user j in the past time window t ′ and are the standard deviations of the corresponding panic index time series, respectively.​ 7. The method of claim 1, wherein, The adaptive decision and generation of a disaster control action specifically comprises: acquiring a global state, which includes the cognitive-physiological state vectors of all users, the group cognitive synergy spectrum vector, and the physical world state representing the current virtual environment physical state; and based on the global state, deciding and generating the disaster control action.

8. An emergency rescue simulation system according to the method of any one of claims 1 to 7, characterized by Comprise: a data acquisition module for acquiring multi-modal data of a plurality of users participating in simulation; an individual state calculation module for calculating a cognitive-physiological state vector representing the internal state of each user in real time based on the corresponding multi-modal data; a group spectrum analysis module for aggregating the cognitive-physiological state vectors of all users, analyzing and generating a group cognitive synergy spectrum vector representing the overall team synergy state; a disaster control decision module for adaptively deciding and generating a disaster control action according to the group cognitive synergy spectrum vector; an environment update module for updating the virtual environment in the simulation according to the disaster control action.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-7.

10. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the method of any one of claims 1-7.

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