An airport emergency response simulation system integrating a 3D sand table

Abstract

This invention relates to the field of teaching and demonstration tools for emergency drills, and discloses an airport emergency plan simulation system integrating a 3D sand table. The system includes an emergency plan management module, a 3D electronic sand table module, a simulation execution and integration module, and an evaluation and retrospection module. Through an instant teaching intervention mechanism, the system compares the model's dynamic data with preset core teaching constraints in real time during the simulation. When a deviation is detected, the system immediately executes a simulator pause command and forcibly activates the cognitive guidance interactive interface, compelling the user to make a cognitive choice among mutually exclusive teaching options before resuming the simulation. This invention solves the problem of delayed teaching feedback due to the separation of simulation and assessment. By providing instant intervention and cognitive guidance at the moment a student makes a mistake, it transforms the system from a passive assessment tool into an active teaching and tutoring tool, achieving a qualitative change from recording behavior to recording decision-making logic.

Description

Technical Field

[0001] This invention relates to an airport emergency response plan simulation system that integrates a three-dimensional sand table, belonging to the technical field of teaching and demonstration tools for emergency drills. Background Technology

[0002] Currently, using a 3D visualization environment to construct electronic sand tables has become a common technical approach. This method avoids the abstractness of traditional tabletop exercises by constructing virtual training scenarios, and also avoids the high costs, high risks, and constraints on normal operations brought about by large-scale live-fire exercises. However, existing such training systems are usually designed as passive process recording tools. Their core function is to record the movement trajectory of all models and the time taken at key nodes throughout the entire training process, and to generate a quantitative evaluation report after the training ends to point out the deviations of the trainees' operations from the standard plan.

[0003] This design approach, which separates the simulation process from the assessment and instruction, limits its functionality to that of a drill assessment tool or log analysis system, rather than a complete teaching and demonstration tool. When trainees deviate from the expected procedure, the system cannot provide immediate instructional feedback or error correction guidance; trainees can only obtain static assessment data afterward. This results in an interrupted teaching process and delayed feedback, making it difficult for trainees to establish a correct cognitive and operational loop at critical decision-making points. This post-drill assessment model, which separates drill execution from evaluation and analysis, is prevalent in existing technologies, even in systems used to optimize emergency plans. The system's technical approach has also failed to break free from this framework. For example, patent application CN115798294A discloses an emergency plan generation system and method for material reserves in response to emergencies. Although this solution also uses a three-dimensional electronic sand table simulation, its core purpose is to conduct post-event analysis of the simulation results through the exercise effect analysis module, thereby optimizing the emergency plan itself. In essence, it is still a post-exercise evaluation mechanism that serves the optimization of the plan. The simulation process itself is still passive for the trainees. The system cannot provide immediate teaching intervention or cognitive guidance when the trainees deviate from the operation.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a system that integrates a digital contingency plan 3D simulation environment with a teaching evaluation mechanism, provides immediate teaching intervention and cognitive guidance when trainees deviate from the expected operation, and achieves closed-loop management of the entire training process. Summary of the Invention

[0005] This invention provides an airport emergency response plan simulation system that integrates a three-dimensional sand table. Its main purpose is to solve the problems in the existing technology where the plan and simulation are separated, the simulation system lacks a real-time teaching intervention mechanism, and the exercise evaluation cannot delve into the trainees' cognitive process.

[0006] To achieve the above objectives, this invention provides an airport emergency response plan simulation system integrating a 3D electronic sand table. The system includes an emergency response plan management module, a 3D electronic sand table module, a simulation execution and integration module, and an evaluation and retrospective module, comprising:

[0007] The emergency response plan management module is used to define emergency response procedures as process node plans that include preset core teaching constraints.

[0008] The 3D electronic sand table module is used to build a visual simulation environment that integrates static resources and dynamic data. The visual simulation environment contains models; the dynamic data includes the model's dynamic data from user operations on the model in the visual simulation environment.

[0009] The evaluation and backtracking module is coupled with the deduction execution and fusion module through a real-time teaching intervention mechanism. This mechanism is used to: acquire dynamic model data in real time during the execution of the pre-planned process nodes, and compare the dynamic model data with the core teaching constraints to generate deviation results; automatically trigger a teaching intervention event when the deviation results exceed the core teaching constraints; respond to the teaching intervention event, control the deduction execution and fusion module to immediately execute the simulator pause command to freeze the time flow and model movement in the visual simulation environment, and simultaneously forcibly activate the cognitive guidance interactive interface; the cognitive guidance interactive interface is used to present users with multiple mutually exclusive teaching options based on the deviation results, and maintains the execution state of the simulator pause command until it receives a cognitive selection command from the user for the teaching options;

[0010] The assessment and retrospection module is used to record the cognitive choices made by users as core teaching data.

[0011] Preferably, the emergency response plan management module also provides a visual editing tool, which allows users to define emergency response nodes in the emergency response process as process node plans.

[0012] Preferably, the 3D electronic sand table module integrates the airport's static resources and dynamic data through emergency rescue one-map technology; static resources include fire hydrant locations and fixed monitoring locations; dynamic data also includes weather data, aircraft dynamic data, real-time personnel location information, and real-time vehicle location information.

[0013] Preferably, the simulation execution and fusion module is also used to: allow users to select and start one or more process node plans on the 3D electronic sand table module; after the simulation is started, display the process of the process node plan in real time on the 3D electronic sand table module, and highlight the node being executed.

[0014] Preferably, the simulation execution and fusion module is also used to: support users in setting up models on the 3D electronic sand table module by dragging and placing; the models include fire truck models and rescue personnel models; the simulation execution and fusion module is also used to support users in planning the model's travel route and assembly points.

[0015] Preferably, the simulation execution and fusion module is also used to: support participants in remotely controlling the model's movement via mobile devices; support participants in reporting task status via mobile devices; and dynamically update the execution status of process node plans in response to instructions from the mobile devices to remotely control the model's movement and the task status reported by the participants.

[0016] Preferably, the core teaching constraints are quantifiable physical parameters, including: the lateral deviation distance between the real-time trajectory represented by the model's dynamic data and the optimal route preset in the process node plan; and the time taken for the model to reach the preset assembly point in the process node plan.

[0017] Preferably, the real-time teaching intervention mechanism is also used to perform comparison operations at a preset frequency; when the real-time teaching intervention mechanism detects that the lateral deviation distance is greater than a preset distance threshold, or detects that the time consumed is greater than a time threshold. When a teaching intervention event is triggered, a time threshold is specified. Determined by the following formula: ,in, The preset optimal route length, The preset average velocity of the model, This is the preset time leeway factor for teaching purposes.

[0018] Preferably, the multiple mutually exclusive teaching options include: a first teaching option, which is used to allow users to acknowledge operational errors, and after the user selects the first teaching option, the deduction execution and fusion module highlights the optimal route preset in the process node plan on the three-dimensional electronic sand table module; and a second teaching option, which is used to allow users to explain that they have actively deviated from the plan due to observing unexpected events in the visual simulation environment, and after the user selects the second teaching option, the cognitive guidance interaction interface is also used to receive the reasons for the explanation input by the user.

[0019] Preferably, the evaluation and backtracking module is also used to: record in real time the model's motion trajectory, task execution status and key node time information represented by the model's dynamic data during the simulation execution and integration module; and provide a backtracking and review function after the simulation ends, which allows managers to replay the simulation process, including the model's motion trajectory process nodes, the execution flow of the plan, and the recording nodes of core teaching data.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. By deeply integrating the assessment function with the simulation execution process, the system can compare the dynamic operations of students with the preset teaching constraints in real time during the simulation. This operating mode enables the teaching assessment and simulation process to be carried out synchronously, and the teaching feedback no longer lags behind the operation process. The system is also transformed from a passive operation recording tool into an active tutoring tool that can run synchronously with students and discover teaching deviations in real time.

[0022] 2. When the system detects that a student's operation deviates from the teaching constraints, the mechanism of pausing the simulation environment immediately provides the student with a mandatory teaching node. This mechanism stops the student's subsequent operations on the wrong path and freezes the time and space state of the simulation, creating a teaching situation in which the student must stop operating and reflect immediately, providing the necessary preconditions for subsequent cognitive intervention and self-correction.

[0023] 3. The cognitive guidance interface activated after pausing the deduction exercise, especially the mutually exclusive teaching options provided by this invention, requires trainees to conduct an immediate self-analysis and confirmation of the motivation for their deviation behavior, such as operational errors or situational judgments, before the deduction exercise continues. The system records not only the trainee's physical operation trajectory, but also the trainee's decision-making logic at key nodes, which elevates the deduction review from traditional behavioral retrospection to a deep evaluation of the trainee's decision-making thinking process, making the teaching value more profound. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the real-time teaching intervention logic of the system of this invention;

[0025] Figure 2 This is a statistical chart showing the number of times the teaching intervention type of this invention is triggered;

[0026] Figure 3 This is a sequence diagram of the interactive events for real-time teaching intervention in this invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. However, those skilled in the art will understand that, in this invention, without conflict, the embodiments and features described herein can be combined with each other. The described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] This invention provides an airport emergency response plan simulation system integrating a 3D electronic sand table. In practical implementation, the system includes at least an emergency response plan management module, a 3D electronic sand table module, a simulation execution and integration module, and an evaluation and retrospective module. The system integrates the functions of the evaluation and retrospective modules into the simulation execution process through a real-time teaching intervention mechanism. This allows for immediate intervention when trainees deviate from the teaching simulation operation, enabling the system to proactively provide teaching guidance and addressing the technical problems of delayed teaching feedback and separation of practice and assessment in traditional drills. The emergency response plan management module transforms static document plans into executable teaching scripts. This module provides a visual editing tool that allows teaching administrators to deconstruct and define emergency response procedures into a series of process node plans. Furthermore, this tool allows administrators to bind a specific process node plan to each defined process node plan. There may be multiple pre-defined core instructional constraints. These core instructional constraints are quantifiable physical parameters used for instructional intervention. For example, for a fire truck dispatch process node plan, the instructional constraint can be specifically defined as the lateral deviation distance between the real-time trajectory of the fire truck model associated with that node and the pre-defined optimal route, as well as the time taken for the model to reach the pre-defined assembly point. For logical judgment constraints involved in the core instructional constraints, the emergency plan management module also provides a calibration tool based on a rule matrix. Instructional administrators can define the associated event attributes for specific process node plans, such as aircraft cable fires, such as fire type E, and further label the correspondence between the event attributes and emergency resources. This correspondence is stored as a two-dimensional lookup table, where rows are defined as resource types, such as water-based fire extinguishers and dry powder fire extinguishers, and lists are defined as event attributes. The values ​​of the intersection points of the Class E fire lookup table are marked as Boolean constraint results. The value corresponding to Class E fire for water-based fire extinguishers is marked as no. During the simulation, the real-time teaching intervention mechanism obtains the event attributes (Class E fire) of the current scenario while acquiring the dynamic data of the model of the trainee's operation model (e.g., selecting a water-based fire extinguisher). It then obtains the constraint results by querying the two-dimensional lookup table. When the result is no, the teaching intervention event is triggered.

[0029] The 3D electronic sand table module is used to construct a visual simulation environment that integrates static resources and dynamic data. In one implementation, this module uses emergency rescue one-map technology to integrate static resources within the airport area, such as fire hydrant locations and fixed monitoring locations, with dynamic data, such as weather data, aircraft dynamic data, real-time personnel location information, and real-time vehicle location information, into a single 3D visual scene. This visual simulation environment includes various models that trainees can manipulate, such as fire truck models and rescue personnel models. Trainees can drag and drop these models onto the simulation environment and plan their routes and assembly points. Trainees can then manipulate the models within the visual simulation environment. The dynamic data of the model consists of data such as the real-time position of the model changing over time. The simulation execution and fusion module is used to drive the execution of teaching simulations. This module allows users to select and start one or more process node plans on the 3D electronic sand table module. After the simulation is started, the module displays the process of the process node plan in real time on the 3D electronic sand table module and highlights the node being executed. This module also supports participants (students) to remotely control the movement of the model they are playing through a mobile terminal, or to report the task status through a mobile terminal. The 3D electronic sand table module will respond to these reported statuses and dynamically update the execution status of the process node plan. For example, after a student reports that the task has been completed, it will automatically flow to the next teaching node.

[0030] The evaluation and retrospection module is coupled with the deduction, execution, and integration module through a real-time teaching intervention mechanism. The specific operating procedure of this mechanism is as follows: During the execution of the process node plan, the processor of the evaluation and retrospection module acquires the dynamic model data on the 3D electronic sand table module in real time at a preset frequency, such as 10 times per second. It then compares and calculates the dynamic model data with the preset core teaching constraints in the current process node plan to generate deviation results. For example, when the teaching constraint is a time constraint, the time threshold of the constraint... It can be determined by the following formula: ,in, The preset optimal route length, The preset average velocity of the model, This is a preset time leeway for teaching; when the immediate teaching intervention mechanism detects deviations that exceed core teaching constraints, for example, when the actual time consumed exceeds a time threshold... If the system detects a lateral deviation distance greater than a preset distance threshold, it automatically triggers a teaching intervention event. In response to the teaching intervention event, the system control simulation execution and fusion module immediately executes a simulator pause command. This command freezes the time flow and model movement in the visualized simulation environment and simultaneously forces the activation of a cognitive guidance interaction interface on the student's control interface, such as a mobile device. The cognitive guidance interaction interface presents the user with multiple mutually exclusive teaching options based on the deviation result. For example, it includes a first teaching option for the user to acknowledge operational errors. After the user selects this option, the simulation execution and fusion module can highlight the preset optimal route on the 3D electronic sand table module. It also includes a second teaching option for the user to explain that they actively deviated due to observing a sudden event in the simulation environment. After the user selects this option, the interface also receives the user's explanation for the explanation.

[0031] Before the system receives a cognitive selection instruction from the user regarding the teaching options, the system maintains the simulator pause instruction execution state. Furthermore, when the deduction execution and fusion module initializes a deduction instance, it establishes an independent, active data communication link, such as a WebSocket connection, for each logged-in student's mobile device. When the real-time teaching intervention mechanism triggers a teaching intervention event, the deduction execution and fusion module sends a modal pop-up activation instruction containing mutually exclusive teaching option data to the specific student's mobile device that triggered the deviation through this data communication link. This instruction forces the student's mobile device to display the cognitive guidance interaction interface and blocks other operations. The cognitive selection instruction made by the student on this interface is transmitted back to the deduction execution and fusion module via the same data communication link. The fusion module, upon receiving the instruction, forwards the instruction data to the evaluation and retrospective module for recording, and only then releases the simulator pause on the model operated by the student. The evaluation and retrospective module records the cognitive choices made by the user on the cognitive guidance interface, as well as any possible reasons the user may input, as core teaching data. Furthermore, during the simulation, this module records in real-time the model's dynamic data, representing its trajectory, task execution, and key node time consumption. After the teaching simulation, the evaluation and retrospective module provides a retrospective review function, allowing teaching administrators to replay the entire simulation process, including the model's trajectory, the execution flow of the planned process nodes, and the recording nodes of core teaching data.

[0032] Example 1: In an emergency response training exercise simulating an aircraft fire at an airport, a participant's task is to operate a fire truck model to respond to the fire. The emergency plan management module has defined core teaching constraints for the fire dispatch process nodes in this exercise. These constraints include a preset optimal route and a constraint based on the length of that route. Model preset average speed With preset time margin Calculated time threshold After the simulation began, the student did not follow the preset optimal route highlighted by the system in the visualization simulation environment of the 3D electronic sand table module. Instead, at a key intersection, the student chose a crossing route that he judged to be shorter. When the lateral deviation distance of the real-time trajectory represented by the dynamic data of the model operated by the student exceeded the preset distance threshold of the core teaching constraint, the immediate teaching intervention mechanism was automatically triggered. The evaluation and backtracking module generated a deviation result and triggered the teaching intervention event.

[0033] In response to the teaching intervention event, the simulation execution and fusion module immediately executed the simulator pause command, freezing the time flow and movement of all models on the 3D electronic sand table module. The student's fire truck model stopped on the wrong crossing path. At the same time, the cognitive guidance interface was forcibly activated, presenting the student with mutually exclusive teaching options. The options included a first teaching option for acknowledging the operational error and requesting the optimal route to be marked, and a second teaching option for appealing that the student had actively deviated due to observing an unexpected event in the simulation environment. Under the forced pause teaching situation, the student made a cognitive selection command for the first teaching option. After receiving this command, the evaluation and backtracking module recorded this cognitive selection command itself as a core teaching data point. The simulation execution and fusion module highlighted the optimal route preset in the process node plan on the 3D electronic sand table module and lifted the simulator pause command, allowing the student to continue the simulation along the correct teaching path. In the backtracking and review function after the simulation, teaching administrators could not only replay the student's error trajectory but also clearly locate the node where the system executed the intervention and view the core teaching data of the student's acknowledgment of the operational error.

[0034] Example 2: In a comparative experiment to verify the teaching and demonstration effects, an experimental platform was set up. This platform was based on the airport emergency response plan simulation system integrating a 3D sand table, as described in the aforementioned specific implementation. Twenty airport emergency response trainees were recruited and randomly divided into a control group and an experimental group of the present invention, with 10 people in each group. The control group used the system, but its real-time teaching intervention mechanism was disabled, and the system was only used as a passive tool for recording the exercise process. The experimental group of the present invention used the system, and its real-time teaching intervention mechanism remained active. All trainees executed the same aircraft fire response process node plan. The core teaching constraint of this plan included a preset optimal route, and an easily confused error crossing route was set in the simulation environment. The experiment recorded and compared the deviations made by the two groups of trainees in the first simulation, and after a 24-hour interval... In the second simulation, the system quantified the retention of teaching cognition and skills by checking whether students repeatedly deviated from the same incorrect path. In the first simulation, more than 80% of the operators in both groups deviated from the correct path and entered the incorrect path. For the control group, the system only recorded their deviation trajectory, and the simulation continued. After the simulation, the students received an evaluation report containing the deviation record. For the sample group of students in this invention, the system immediately executed the simulator pause command the moment their model dynamic data broke through the core teaching constraints, and forcibly activated the cognitive guidance interaction interface. Students had to make a cognitive selection command between mutually exclusive teaching options of acknowledging the operational error or explaining the reason before they could continue the simulation. Data from 4 students in each of the two groups who deviated in the first simulation were selected for comparison. The comparison data of their teaching effects are shown in Table 1.

[0035] Table 1: Data from the Comparative Experiment on Teaching Effectiveness

[0036]

[0037] As shown in Table 1, among the four trainees in the control group, three (75%) repeated the same deviation error in the second simulation 24 hours later, indicating that the teaching feedback was lagging and the cognitive retention rate was low. In contrast, among the four trainees in the present invention sample group, only one (25%) repeated the deviation, indicating that the teaching cognitive retention was improved.

[0038] Example 3: This example sets up a comparative group to compare with the control group in Example 2 and the sample group of the present invention. The only difference between the system used in the comparative group and the sample group of the present invention is that its real-time teaching intervention mechanism is modified to a real-time alarm mechanism commonly used in the field of educational or demonstration tools. When the conventional mechanism detects that the dynamic data of the trainee's model exceeds the core teaching constraints, it only highlights and flashes an alarm prompt on the three-dimensional electronic sand table module, but does not execute the simulator pause command, nor does it forcibly activate the cognitive guidance interactive interface. Using the same experimental method as in Example 2, another 10 trainees in the initial training of airport emergency response were selected to form the comparative group, and their teaching effect was summarized and compared with the data of the control group that used post-performance evaluation and the sample group of the present invention that used real-time teaching intervention recorded in Example 2. The experimental data are shown in Table 2.

[0039] Table 2: Data from the Comparative Experiment of Teaching Intervention Mechanisms

[0040]

[0041] As shown in Table 2, the comparative group using the conventional immediate alarm mechanism showed no statistically significant improvement in the repeat deviation rate (77.8%) compared to the control group using post-performance evaluation (77.8%). This data indicates that providing only a negligible passive alarm during instructional simulations does not interrupt students' operational behavior under simulation pressure, and their cognitive process is not forcibly intervened. Therefore, this conventional improvement method cannot solve the problem of delayed instructional feedback.

[0042] Example 4: This example combines Figures 1 to 3 This document describes an airport emergency response simulation system that integrates a 3D sand table, such as... Figure 1 As shown, the emergency plan management module provides a process node plan containing preset core teaching constraints and provides a visual editing tool. The 3D electronic sand table module constructs a visual simulation environment. The simulation execution and fusion module outputs dynamic model data when the trainee starts the plan and operates the model. During the simulation, the simulation execution and fusion module sends dynamic data such as trajectory and time consumption to the evaluation and backtracking module for recording in real time. At the same time, the dynamic model data is compared with the core teaching constraints in real time. When the deviation result of the calculation does not exceed the core teaching constraints, the process returns to the simulation execution and fusion module and the simulation continues. When the result is yes, a teaching intervention event is triggered, which executes the simulator pause command to freeze the simulation environment and model movement, and forcibly activates the cognitive guidance interaction interface to present mutually exclusive teaching options. After the user makes a cognitive selection command, such as admitting a mistake or explaining the reasons, the evaluation and backtracking module records the command as core teaching data, then releases the pause and resumes the simulation. Finally, the evaluation and backtracking module also provides a backtracking and review function to replay the simulation process and decision nodes.

[0043] like Figure 2 As shown, the vertical axis represents the number of triggers, ranging from 0 to 30, and the horizontal axis represents the intervention type, specifically including trajectory deviation, timeout, route error, assembly failure, and resource misuse. The data shows that the exact number of triggers for trajectory deviation was 28, for timeouts 15, for route errors 12, for assembly failures 8, and for resource misuse 5. Figure 3 As shown, this process involves five participants: the student, the 3D electronic sand table module, the deduction execution and fusion module, the evaluation and backtracking module, and the real-time teaching intervention mechanism. The interaction begins with the student manipulating the model's movement on the 3D electronic sand table module. Subsequently, the real-time teaching intervention mechanism monitors the model's dynamic data from the 3D electronic sand table module at a preset frequency, such as 10 times per second, and receives its real-time position and trajectory. The real-time teaching intervention mechanism internally performs a comparison calculation with the core teaching constraints to generate deviation results. According to the loop path of not breaking the constraints shown in the figure, if the constraints are not broken, the system continues to deduce normally and maintains monitoring. However, when a deviation that breaks the teaching constraints is detected, such as exceeding the distance threshold or time threshold, the mechanism triggers a teaching intervention event to the deduction execution and fusion module. The deduction execution and fusion module then sends a simulator pause command to the 3D electronic sand table module, causing the 3D electronic sand table module to freeze the elapsed time and model movement. At the same time, the deduction execution and fusion module forcibly activates the cognitive guidance interaction interface on the student's interface, so that the student's side displays that the deduction is paused and enters a state of deduction pause, waiting for the student to make a selection.

[0044] Example 5: This example illustrates a reproducible, standardized engineering calibration procedure within the emergency response plan management module for defining process node plans and their core instructional constraints. This procedure aims to eliminate the arbitrariness in setting instructional parameters. In a course development scenario, an instructional administrator needs to create a new ambulance response process node plan. The challenge lies in setting an objective, reproducible, and meaningful core instructional constraint for this plan. The administrator first uses the visual editing tool provided by the emergency response plan management module, which loads airport map data from the 3D electronic sand table module. Within the tool interface, the administrator selects an ambulance model from the model library, and the system immediately loads the model's preset average speed. The set speed was 60 km / h. Subsequently, the administrators used the tool's route drawing function to draw a pre-defined optimal route from the medical center to the departure level of Terminal 2 on a 3D map through a series of clicks. Once the administrators confirmed the route drawing was complete, the system processor automatically summed the lengths of all line segments in the route based on the map's vector data, thus determining the length of the pre-defined optimal route. The calculated result is 2.5km.

[0045] Next, the teaching administrators set core teaching constraint parameters for the route in the property bar of the visual editing tool. Based on the teaching objectives, the administrators set the lateral deviation distance threshold to 15 meters. This threshold is used to determine whether the model trajectory has exceeded the teaching boundary in subsequent simulations. Simultaneously, the administrators set the time leeway coefficient for this teaching node. The coefficient is set to 0.2, which corresponds to a medium level of teaching difficulty. After obtaining these manually set parameters, the system immediately applies the formula described in the specific implementation method to automatically calculate and calibrate the time threshold of this process node plan. The calculation process is as follows: That is, 3 minutes; the system will eventually combine this set of data, which includes path vector data, distance threshold (15 meters), and time threshold. The parameters (3 minutes) are stored as a complete data structure as the core teaching constraint bound to the emergency response plan for the ambulance. After the trainee initiates the execution of this emergency response plan, the processor of the real-time teaching intervention mechanism executes the process judgment and quantification procedure at a preset frequency (e.g., 10 times per second): The processor obtains the dynamic data of the ambulance model operated by the trainee in real time, i.e., its current position coordinates, and calculates the shortest vertical distance between the coordinate point and the preset optimal route vector path through analytical geometry algorithm. The processor then compares the calculated real-time lateral deviation distance with the preset distance threshold (15 meters). At the same time, the processor compares the trainee's real-time time with the preset time threshold. (3 minutes) Comparison calculations are performed; when the deviation result of any comparison calculation exceeds its corresponding core teaching constraint, the system triggers a teaching intervention event, thereby ensuring that the execution of the teaching intervention is based on a parameter system that has been previously calibrated through standardized procedures and is reproducible.

[0046] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An airport emergency plan deduction system integrated with a three-dimensional sand table, the system comprising an emergency plan management module, a three-dimensional electronic sand table module, a deduction execution and integration module, and an evaluation and backtracking module, and characterized in that: the emergency plan management module is configured to define an emergency disposal process as a process node plan containing preset core teaching constraints; the three-dimensional electronic sand table module is configured to construct a visual simulation environment integrated with static resources and dynamic data, and the visual simulation environment is provided with models; the dynamic data includes model dynamic data of the models operated by a user on the visual simulation environment; the evaluation and backtracking module is coupled with the deduction execution and integration module through an instant teaching intervention mechanism, which is configured to: during execution of the process node plan, acquire model dynamic data in real time, and compare and operate the model dynamic data with the core teaching constraints to generate deviation results; when the deviation results break through the core teaching constraints, automatically trigger a teaching intervention event; in response to the teaching intervention event, control the deduction execution and integration module to immediately execute a simulator pause instruction to freeze time elapse of the visual simulation environment and movement of the models, and simultaneously forcibly activate a cognitive guidance interactive interface; the cognitive guidance interactive interface is configured to present a plurality of mutually exclusive teaching options based on the deviation results to the user, and maintain an execution state of the simulator pause instruction before receiving a cognitive selection instruction made by the user for the teaching options; the evaluation and backtracking module is configured to record the cognitive selection instruction made by the user as core teaching data; wherein the plurality of mutually exclusive teaching options include: a first teaching option for the user to admit an operation mistake, and after the user selects the first teaching option, the deduction execution and integration module highlights a preset optimal route in the process node plan on the three-dimensional electronic sand table module; and a second teaching option for the user to explain voluntary deviation due to observation of a sudden event in the visual simulation environment, and after the user selects the second teaching option, the cognitive guidance interactive interface is further configured to receive a user-input explanation reason. The emergency plan management module is further configured to provide a visual editing tool for allowing the user to define an emergency disposal node in an emergency disposal process as a process node plan. The three-dimensional electronic sand table module integrates static resources and dynamic data of the airport through an emergency rescue one-map technology; the static resources include fire hydrant locations and fixed monitoring locations; the dynamic data further includes weather data, aircraft dynamic data, real-time positioning information of personnel, and real-time positioning information of vehicles. The deduction execution and integration module is further configured to: allow the user to select and start one or more process node plans on the three-dimensional electronic sand table module; and after deduction is started, display a process of the process node plan on the three-dimensional electronic sand table module in real time, and highlight the node being executed. The deduction execution and integration module is further configured to: support the user to set models on the three-dimensional electronic sand table module through a drag-and-drop manner; the models include fire vehicle models and rescue personnel models; and support the user to plan a travel route and a rendezvous point of the models. ​ ​ 2. The airport emergency plan deduction system of fusing a three-dimensional sand table according to claim 1, characterized in that, ​ 3. The airport emergency plan deduction system of fusing a three-dimensional sand table according to claim 1, characterized in that, ​ 4. The airport emergency plan deduction system of fusing three-dimensional sand table according to claim 1, characterized in that, ​ 5. The airport emergency plan deduction system of fusing three-dimensional sand table according to claim 1, characterized in that, ​ 6. The airport emergency plan deduction system of fusing three-dimensional sand table according to claim 1, characterized in that, The deduction execution and fusion module is further configured to support the participants to remotely control the model movement through the mobile terminal, and support the participants to report the task status through the mobile terminal. The three-dimensional electronic sand table module dynamically updates the execution of the process node plan in response to the instruction of remotely controlling the model movement through the mobile terminal and the task status reported by the participants.

7. The airport emergency plan deduction system of fusing a three-dimensional sand table according to claim 1, characterized in that, The core teaching constraint is a quantifiable physical parameter, and the physical parameter includes a lateral deviation distance between a real-time trajectory represented by the model dynamic data and an optimal route preset in the process node plan, and a time consumption of the model to reach a rendezvous point corresponding to the process node plan.

8. The airport emergency plan deduction system of fusing a three-dimensional sand table according to claim 7, characterized in that, The instant teaching intervention mechanism is also configured to perform a comparison operation at a preset frequency; when the instant teaching intervention mechanism detects that the lateral deviation distance is greater than a preset distance threshold, or detects that the time consumption is greater than a time threshold , a teaching intervention event is triggered; wherein the time threshold is determined by the following formula: , wherein is the length of the preset optimal route, is the preset average speed of the model, is a preset time allowance coefficient for teaching.

9. The airport emergency plan deduction system of fusing a three-dimensional sand table according to claim 1, wherein, The evaluation and backtracking module is further configured to record the motion trajectory of the model represented by the model dynamic data, the task execution status and the key node time consumption information in real time during the deduction process of the deduction execution and fusion module, and provide a backtracking review function after the deduction is completed. The backtracking review function is configured to allow the management personnel to replay the deduction process, and the deduction process includes a motion trajectory of the model, an execution flow of the process node plan and a recording node of the core teaching data.

Images