Personnel training management system based on airport safety management

Abstract

The invention discloses a personnel training management system based on airport safety management, relates to the technical field of personnel training management, and solves the problems of low training efficiency, low pertinence and difficulty in quantitative evaluation of effects in the prior art. The method comprises the steps of obtaining training data related to airport safety management; constructing a knowledge graph based on the training data; obtaining the training content of the trainee based on the knowledge graph; the method comprises the following steps: acquiring training content of a corresponding post from a knowledge graph according to the post of a trainee and the responsibility of the corresponding post; the method comprises the steps of obtaining three-dimensional data of an airport management area; constructing a virtual reality three-dimensional model of the airport based on the three-dimensional data Performing analogue simulation assessment on the trainee based on the virtual reality three-dimensional model of the airport, and recording learning data of the trainee in real time in the assessment process; the training evaluation coefficient of the trainee is calculated through the learning data; and judging whether training is qualified or not based on the training evaluation coefficient and a preset threshold. The problem is solved.

Description

Technical Field

[0001] The invention belongs to the field of personnel training management, in particular to a personnel training management system based on airport safety management. Background Art

[0002] With the rapid development of the aviation industry, the importance of airport safety management has become increasingly prominent. As the key force to ensure the safe operation of the airport, the professional quality and safety awareness of airport staff are directly related to the overall safety level of the airport.

[0003] The existing training methods for airport safety management personnel mainly rely on paper textbooks, video teaching and field exercises, which have the problems of low training efficiency, lack of pertinence and difficulty in quantifying the effect. At the same time, since airport safety management training mostly relies on fixed teaching outlines and static training materials, it is impossible to flexibly adjust the training content according to the actual mastery of the trainees, resulting in uneven training results, which is difficult to meet the high requirements of modern airport safety management for personnel quality.

[0004] Therefore, the present invention proposes a personnel training management system based on airport safety management to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a personnel training management system based on airport safety management, which is used to solve the problems that the existing training methods for airport safety management personnel mainly rely on paper teaching materials, video teaching and field drills, and have low training efficiency, lack of pertinence, and difficulty in quantifying and evaluating the effect; at the same time, since airport safety management training mostly relies on fixed teaching outlines and static training materials, it is impossible to flexibly adjust the training content according to the actual mastery of the trainees, resulting in uneven training effects, and it is difficult to meet the technical problems of the high requirements of modern airport safety management on personnel quality.

[0006] To achieve the above objectives, the first aspect of the present invention provides a personnel training management system based on airport safety management, including: a data acquisition module, a data analysis module, a safety training module

[0007] Data acquisition module: used to obtain training data related to airport safety management; and to obtain three-dimensional data of the airport management area;

[0008] Data analysis module: building a knowledge graph based on training data; obtaining training content for trainees based on the knowledge graph; and,

[0009] Build a virtual reality 3D model of the airport based on 3D data;

[0010] Safety training module: Conducting simulation assessments on trainees based on the virtual reality 3D model of the airport, and recording the trainees’ learning data in real time during the assessment process; and

[0011] The training evaluation coefficient of the trainees is calculated based on the learning data; whether the training is qualified is judged based on the training evaluation coefficient and the preset threshold; if yes, the training is completed; if not, auxiliary training is provided to the trainees who fail the training.

[0012] Preferably, the data analysis module is communicated and / or electrically connected to the data acquisition module and the safety training module respectively.

[0013] Preferably, the obtaining of training data related to airport safety management includes:

[0014] The training data related to airport safety management is obtained based on the database; the training data includes: safety management process, safety cases, training materials, safety equipment operating procedures, and the job titles of trainees and corresponding job responsibilities.

[0015] It should be noted that the database refers to the information repository for airport safety management, which stores the training data to date and the basic data of airport staff; the safety management process refers to the accident and incident reporting procedures, including reporting procedures, investigation procedures, accident classification and follow-up actions; safety cases refer to airport safety accidents and incidents that have occurred in history, including the course of the accident, cause analysis, lessons learned and preventive measures; the safety equipment operating procedures refer to the formal operating procedures for relevant safety equipment, such as the use and maintenance standards of metal detectors, explosive detection equipment and fire extinguishers.

[0016] Preferably, the obtaining of three-dimensional data of the airport management area includes:

[0017] The three-dimensional data of the airport management area is collected by three-dimensional collection equipment; wherein the three-dimensional collection equipment includes a laser scanner, a camera or a depth camera; the three-dimensional data includes: environmental three-dimensional data and safety equipment three-dimensional data.

[0018] It should be noted that the three-dimensional environmental data includes: the airport's buildings, facilities, layout and roads; the three-dimensional safety equipment data includes: equipment parameters, equipment models, equipment materials, device specifications and the process flow of the corresponding equipment, as well as the working environment of the equipment; the working environment refers to the relative position relationship between the buildings and facilities around the equipment and other objects.

[0019] Preferably, the constructing of a knowledge graph based on training data includes:

[0020] S1: Extracting entities related to airport safety management from training data through natural language processing techniques;

[0021] S2: Extract the association relationship between entities through semantic analysis technology;

[0022] S3: Map entities and relationships into a graph using graph database technology, including Neo4j or Dgraph.

[0023] It should be noted that the entities include: steps in the security management process, accident types in safety cases, topics of training materials, names of safety equipment, and job titles and responsibilities of trainees; the association relationships refer to the various logical connections and interactions between entities, such as the "security inspection process" includes steps such as "baggage inspection" and "body scanning".

[0024] Preferably, the acquiring of training content for trainees based on the knowledge graph includes:

[0025] S1: Query and obtain the entity corresponding to the position title of the trainee in the knowledge graph;

[0026] S2: Get the job responsibilities associated with the job title entity;

[0027] S3: The entities based on job responsibilities obtain the training content of trainees through the knowledge graph.

[0028] It should be noted that the training content refers to the training data related to the job responsibilities of the trainees, including: safety management processes, safety cases, training materials and safety equipment operating procedures related to the job responsibilities of the trainees.

[0029] Preferably, the construction of a virtual reality three-dimensional model of an airport based on three-dimensional data includes:

[0030] S1: Constructing a virtual reality 3D model of the airport based on 3D data through virtual reality technology; wherein the virtual reality technology includes: digital twin technology or VR technology;

[0031] S2: Add logic control to the virtual reality 3D model of the airport through visual programming tools; the visual programming tools include: Node-RED or Blockly; the logic control includes: operation control of safety equipment, parameter adjustment and environmental response.

[0032] It should be noted that the environmental response refers to the security device performing corresponding actions according to specific events (such as button pressing, object detection or time reaching a preset value).

[0033] Preferably, the virtual reality three-dimensional model of the airport is used to conduct a simulation assessment on trainees, and the learning data of the trainees during the assessment is recorded in real time, including:

[0034] S1: Set up several safety management scenarios in the virtual reality 3D model of the airport; the safety management scenarios include: emergency evacuation, fire emergency, hazardous materials handling or passenger conflict response;

[0035] S2: Trainees handle several safety management scenarios;

[0036] S3: Record the learning data of trainees in handling each security management scenario; the learning data includes: the accuracy and completion time of the processing operation.

[0037] Preferably, the training evaluation coefficient of the trainee is calculated based on the learning data, including:

[0038] The correctness rate and completion time of the training personnel's processing operations for each safety management scenario are marked as Z and T respectively; where Z = [Z1, Z2, Z3, ..., Zn], T = [T1, T2, T3, ..., Tn];

[0039] The training evaluation coefficient is calculated by the formula: P = A × e^[Z / (Z+1)] + B × lnT / (1+lnT); where P is the training evaluation coefficient, P = [P1, P2, P3, …, Pn], A and B are proportional coefficients, and A>0, B<0, and ln(*) is a logarithmic function with the natural number e as the base.

[0040] It should be noted that P = [P1, P2, P3, ..., Pn] corresponds one-to-one to Z = [Z1, Z2, Z3, ..., Zn] and T = [T1, T2, T3, ..., Tn]; wherein n refers to the number of safety management scenarios set up in the virtual reality three-dimensional model of the airport, Zn and Tn refer to the accuracy and completion time of the processing operation of a certain safety management scenario, and Pn refers to the training evaluation coefficient corresponding to the processing operation of a certain safety management scenario; for example, Z1 and T1 represent the accuracy and completion time of the operation of dangerous goods handling, and then P1 represents the training evaluation coefficient of the operation of dangerous goods handling.

[0041] Preferably, judging whether the training is qualified based on the training evaluation coefficient and a preset threshold value includes:

[0042] The preset threshold is marked as Y; wherein Y = [Y1, Y2, Y3, ..., Yn], n refers to the number of security management scenes set in the virtual reality 3D model of the airport; the preset threshold is set according to the training requirements of the actual airport;

[0043] Determine whether P is greater than or equal to Y; if yes, the training is considered qualified; if not, the training is considered unqualified.

[0044] It should be noted that Y=[Y1, Y2, Y3, ..., Yn] and P=[P1, P2, P3, ..., Pn] are in one-to-one correspondence.

[0045] Preferably, the auxiliary training for trainees who fail the training includes:

[0046] S1: Obtain safety management scenarios where trainees fail to meet training standards;

[0047] S2: Extracting entities related to airport safety management from unqualified safety management scenarios through natural language processing technology;

[0048] S3: Based on the entities extracted in S2, relevant training content is obtained through the knowledge graph for review;

[0049] S4: Obtain relevant video tutorials, online course links or professional literature from the database to assist trainees in training.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention obtains training data related to airport safety management; constructs a knowledge graph based on the training data; obtains training content for trainees based on the knowledge graph; obtains training content for corresponding positions from the knowledge graph according to the positions of trainees and corresponding job responsibilities; solves the problems of non-personalized content, inaccurate resource allocation, slow knowledge updating, and rough effect evaluation in the training methods of the prior art, ensures that trainees can obtain the latest and most effective training materials closely related to their job responsibilities, and significantly improves the pertinence, efficiency and practical application value of training.

[0052] 2. The present invention obtains three-dimensional data of the airport management area; constructs a virtual reality three-dimensional model of the airport based on the three-dimensional data; conducts simulation assessment on trainees based on the virtual reality three-dimensional model of the airport, and records the trainees' learning data during the assessment in real time, thereby significantly improving the effectiveness and personalization of airport safety management training, and enhancing the trainees' practical operation capabilities and emergency response skills; it solves the problem that the training method of airport safety management personnel in the prior art mainly relies on paper teaching materials, video teaching and field drills, and has the problems of low training efficiency, lack of pertinence, and difficulty in quantifying and evaluating the results.

[0053] 3. The present invention calculates the training evaluation coefficient of the trainees through learning data; judges whether the training is qualified based on the training evaluation coefficient and the preset threshold; if yes, completes the training; if not, provides auxiliary training to the trainees who fail the training; solves the problem in the prior art that the airport safety management training mostly relies on fixed teaching outlines and static training materials, and the training content cannot be flexibly adjusted according to the actual mastery of the trainees, resulting in uneven training effects and difficulty in meeting the high requirements of modern airport safety management on personnel quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0055] Figure 1 A schematic diagram of a system module according to an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a specific process of an embodiment of the present invention;

[0057] Figure 3 Schematic diagram of auxiliary training steps according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 2 The first embodiment of the present invention provides a personnel training management system based on airport safety management, including: a data collection module, a data analysis module, a safety training module

[0060] Data acquisition module: used to obtain training data related to airport safety management; and to obtain three-dimensional data of the airport management area;

[0061] Data analysis module: build a knowledge graph based on training data; obtain the training content of trainees based on the knowledge graph;

[0062] Obtain training data related to airport security management, including:

[0063] The training data related to airport safety management is obtained based on the database; the training data includes: safety management process, safety cases, training materials, safety equipment operating procedures, and the job titles of trainees and corresponding job responsibilities.

[0064] Build a knowledge graph based on training data, including:

[0065] S1: Extracting entities related to airport safety management from training data through natural language processing techniques;

[0066] S2: Extract the relationship between entities through semantic analysis technology;

[0067] S3: Map entities and relationships into a graph using graph database technology, including Neo4j or Dgraph.

[0068] Obtain training content for trainees based on the knowledge graph, including:

[0069] S1: Query and obtain the entity corresponding to the position title of the trainee in the knowledge graph;

[0070] S2: Get the job responsibilities associated with the job title entity;

[0071] S3: The entities based on job responsibilities obtain the training content of trainees through the knowledge graph.

[0072] Example 1: Assume that an airport needs to provide airport safety management training to staff or new employees; the specific process is as follows:

[0073] 1. Data acquisition module;

[0074] Obtain training data related to airport safety management: Extract safety management processes, historical safety cases, training manuals, equipment operating procedures, etc. from the airport's internal database. For example, safety management processes include accident reporting procedures, emergency evacuation procedures, and dangerous goods handling guidelines. Safety cases cover all safety incidents that have occurred at domestic and foreign airports in the past ten years, such as runway incursions, passenger conflicts, and equipment failures. Equipment operating procedures include use and maintenance guidelines for equipment such as metal detectors, X-ray baggage scanners, and automated external defibrillators (AEDs).

[0075] Obtain 3D data of the airport management area: Use drone aerial photography, lidar scanning and other technologies to collect 3D terrain data of the airport, including high-precision models of areas such as terminal buildings, aprons, runways, and security check areas.

[0076] 2. Data analysis module;

[0077] Building a knowledge graph:

[0078] S1: Entity extraction: Through natural language processing technology, key entities such as "emergency evacuation", "runway intrusion", "metal detector", "security inspector", "operating procedures", etc. are identified from the training data.

[0079] S2: Association extraction: Use semantic analysis technology to determine the association between entities, such as the relationship between "emergency evacuation" and "evacuation route", "broadcasting system", and the relationship between "metal detector" and "security inspector", "operating procedures".

[0080] S3: Graph construction: Using graph database technology (such as Neo4j), these entities and their relationships are mapped into the knowledge graph to form a structured knowledge network.

[0081] Training content acquisition:

[0082] S1: Job title query: Assuming that the trainee is a newly hired "security inspector", query the entities related to the job title of "security inspector" in the knowledge graph.

[0083] S2: Obtaining job responsibilities: Extract the job description of the "security inspector" position, such as "performing security checks on passengers and luggage", "operating metal detectors", "identifying prohibited items", etc.

[0084] S3: Training content generation: Based on the job responsibilities of "security inspector", relevant training content is obtained from the knowledge graph, including security cases and training materials such as "passenger and baggage security inspection process", "metal detector operating procedures", and "common prohibited items identification".

[0085] Assume that the new security inspectors are undergoing training:

[0086] Based on the position of "security inspector", the system automatically extracts relevant training materials from the knowledge graph, such as the "Security Inspection Operation Manual", "Contraband Identification Guide", and historical security cases related to security inspection.

[0087] Through the above process, the present invention not only realizes the structured and intelligent management of airport safety management training data, but also can provide tailored training content according to the positions and responsibilities of trainees, which significantly improves the pertinence and effectiveness of training.

[0088] Example 2: Assume that the training content of airport firefighters needs to be obtained; the details are as follows:

[0089] 1. Data acquisition module;

[0090] Obtain airport safety management-related training data: Extract all fire safety-related training materials from the airport safety management database, including but not limited to fire emergency response procedures, fire case analysis, fire equipment manuals, and firefighters' job responsibilities and job requirements.

[0091] 2. Data analysis module;

[0092] Building a knowledge graph:

[0093] S1: Entity extraction: Through natural language processing technology, key entities such as "fire emergency response", "fire source control", "fire hydrant", "smoke detector", "firefighter", etc. are identified from the training data.

[0094] S2: Association extraction: Apply semantic analysis technology to identify the relationship between entities. For example, "fire emergency response" includes steps such as "fire assessment", "personnel evacuation", and "fire source control". "Firefighters" need to master the use of "fire hydrants" and be familiar with the working principles of "smoke detectors".

[0095] S3: Graph construction: Use graph database technology (such as Neo4j) to store entities and their relationships in a graph to form a structured knowledge network about fire safety.

[0096] 3. Obtain training content;

[0097] Training content for firefighters:

[0098] S1: Job title query: Search for the job title "firefighter" in the knowledge graph and locate its corresponding entity node.

[0099] S2: Job Responsibilities Acquisition: Extract job responsibilities associated with the "firefighter" entity, such as "initial fire response", "firefighting equipment operation", "emergency evacuation guidance", etc.

[0100] S3: Training content generation: Based on job responsibilities, obtain specific training content from the knowledge graph, including but not limited to "Detailed explanation of fire emergency response process", "Historical fire case analysis", "Fire hydrant and fire extinguisher operation specifications", etc.

[0101] 4. Specific Examples

[0102] Assuming that you are designing a training course for a new airport firefighter, the following are the specific implementation steps based on the above module process:

[0103] Data collection: Training materials directly related to fire safety are selected from the database, including but not limited to the operation manual of the airport fire department, fire accident reports in recent years, and instruction manuals for various fire-fighting equipment.

[0104] Knowledge graph construction:

[0105] Extract entities: "fire emergency response", "fire source isolation", "personnel evacuation", "fire hydrant", "smoke detector", "firefighter", etc.

[0106] Extracted relationships: "Firefighters" need to understand and be able to perform "fire emergency response", including "fire source isolation" and "personnel evacuation"; "firefighters" need to master the use of "fire hydrants" and "smoke detectors".

[0107] Customized training content:

[0108] Look up the position of "firefighter" to obtain the scope of its duties, such as "initial fire control", "fire equipment operation", etc.

[0109] According to the job responsibilities, select appropriate training materials from the knowledge graph, such as:

[0110] “Fire Emergency Response Process”: Introduces the entire process from fire alarm to fire source control.

[0111] "Fire Case Analysis": Select several typical fire cases, analyze the causes, summarize the lessons, and propose preventive measures.

[0112] "Firefighting Equipment Operation Guide": Details the correct use of fire hydrants, fire extinguishers, smoke masks and other equipment.

[0113] “Emergency Evacuation Drill”: Through simulated scenarios, firefighters are trained on how to guide people to evacuate the scene quickly and orderly.

[0114] Through the above steps, airport firefighters can not only fully understand their job responsibilities, but also master the various skills and knowledge required in actual work, thereby improving individual and team emergency response capabilities and ensuring airport fire safety.

[0115] Build a virtual reality 3D model of the airport based on 3D data;

[0116] Safety training module: Conduct simulation assessment of trainees based on the virtual reality 3D model of the airport, and record the trainees' learning data in real time during the assessment process; calculate the trainees' training evaluation coefficient based on the learning data; judge whether the training is qualified based on the training evaluation coefficient and the preset threshold; if yes, complete the training; if not, provide auxiliary training to trainees who fail the training.

[0117] Obtain 3D data of the airport management area, including:

[0118] The three-dimensional data of the airport management area is collected by three-dimensional collection equipment; wherein the three-dimensional collection equipment includes a laser scanner, a camera or a depth camera; the three-dimensional data includes: environmental three-dimensional data and safety equipment three-dimensional data.

[0119] Build a virtual reality 3D model of the airport based on 3D data, including:

[0120] S1: Constructing a virtual reality 3D model of the airport based on 3D data through virtual reality technology; wherein the virtual reality technology includes: digital twin technology or VR technology;

[0121] S2: Add logic control to the virtual reality 3D model of the airport through visual programming tools; the visual programming tools include: Node-RED or Blockly; the logic control includes: operation control of safety equipment, parameter adjustment and environmental response.

[0122] The trainees are assessed by simulation based on the virtual reality 3D model of the airport, and the learning data of the trainees during the assessment is recorded in real time, including:

[0123] S1: Set up several safety management scenarios in the virtual reality 3D model of the airport; the safety management scenarios include: emergency evacuation, fire emergency, hazardous materials handling or passenger conflict response;

[0124] S2: Trainees handle several safety management scenarios;

[0125] S3: Record the learning data of trainees in handling each security management scenario; the learning data includes: the accuracy and completion time of the processing operation.

[0126] The training evaluation coefficients of trainees are calculated based on the learning data, including:

[0127] The correctness rate and completion time of the training personnel's processing operations for each safety management scenario are marked as Z and T respectively; where Z = [Z1, Z2, Z3, ..., Zn], T = [T1, T2, T3, ..., Tn];

[0128] The training evaluation coefficient is calculated by the formula: P = A × e^[Z / (Z+1)] + B × lnT / (1+lnT); where P is the training evaluation coefficient, P = [P1, P2, P3, …, Pn], A and B are proportional coefficients, and A>0, B<0, and ln(*) is a logarithmic function with the natural number e as the base.

[0129] It should be noted that A>0, B<0 is because the higher the accuracy of the processing operation, the better the assessment result, which is directly proportional, so A>0; because the longer the completion time of the processing operation, the lower the assessment result, which is inversely proportional, so B<0.

[0130] Determine whether the training is qualified based on the training evaluation coefficient and the preset threshold, including:

[0131] The preset threshold is marked as Y; wherein Y = [Y1, Y2, Y3, ..., Yn], n refers to the number of security management scenes set in the virtual reality 3D model of the airport; the preset threshold is set according to the training requirements of the actual airport;

[0132] Determine whether P is greater than or equal to Y; if yes, the training is considered qualified; if not, the training is considered unqualified.

[0133] See also Figure 3 , provide supplementary training to trainees who fail the training, including:

[0134] S1: Obtain safety management scenarios where trainees fail to meet training standards;

[0135] S2: Extracting entities related to airport safety management from unqualified safety management scenarios through natural language processing technology;

[0136] S3: Based on the entities extracted in S2, relevant training content is obtained through the knowledge graph for review;

[0137] S4: Obtain relevant video tutorials, online course links or professional literature from the database to assist trainees in training.

[0138] For example, suppose an airport needs to conduct emergency evacuation training and assessment; the details are as follows:

[0139] 1. Obtain 3D data of the airport management area;

[0140] Use laser scanners and depth cameras to perform 3D scans of the airport and its surroundings, collecting precise 3D coordinates and size data of airport buildings and facilities (terminals, runways, aprons, etc.), layouts, roads, and safety equipment (such as automatic doors, fire hydrants, and evacuation signs).

[0141] 2. Build a virtual reality 3D model;

[0142] S1: Build the model: Use digital twin technology to build a virtual reality 3D model of the airport based on the collected 3D data, including the location information of all building details and safety equipment.

[0143] S2: Adding logical control: Use the Node-RED tool to add logical control to the safety devices in the virtual model, such as the opening and closing of automatic doors, the triggering mechanism of the fire alarm system, and the dynamic display function of the evacuation signboard to ensure that the model can respond to specific events.

[0144] 3. Safety training and assessment;

[0145] Set up safety management scenarios in a virtual reality 3D model, such as emergency evacuation drills, which include multiple exits, automatic doors, fire hydrants, and evacuation signs.

[0146] The trainees wore VR headsets to enter a virtual environment and simulated the emergency evacuation process in the event of a sudden fire, including the correct use of fire hydrants to extinguish small fires, and following evacuation signs to evacuate quickly.

[0147] The trainees’ operational data are recorded in real time, including the correct rate of operation (such as the number of successful uses of fire hydrants / total number of uses) and completion time (the time from fire alarm to complete evacuation).

[0148] 4. Calculate the training evaluation coefficient;

[0149] Assume that there are three safety management scenarios: emergency evacuation, fire hydrant use, and evacuation sign recognition, which are denoted as Z1, Z2, Z3 and T1, T2, T3 respectively.

[0150] Set the proportional coefficient A=0.6, B=0.4, and the preset threshold Y=[0.55, 0.5, 0.6].

[0151] Example data: Z = [0.9, 0.85, 0.95], T = [120, 110, 100] seconds (ideally, the shorter the better).

[0152] Calculate the training evaluation coefficient P:

[0153] The training evaluation coefficient is calculated by the formula: P = A × e^[Z / (Z+1)] + B × lnT / (1+lnT);

[0154] P1=0.6×e^(0.9 / (0.9+1))+0.4×ln(120) / (1+ln(120))≈0.493;

[0155] P2=0.6×e^(0.85 / (0.85+1))+0.4×ln(110) / (1+ln(110))≈0.468;

[0156] P3=0.6×e^(0.95 / (0.95+1))+0.4×ln(100) / (1+ln(100))≈0.506;

[0157] Determine whether the training is qualified;

[0158] Compare P with Y:

[0159] Since P = [0.493, 0.468, 0.506], Y = [0.55, 0.5, 0.6];

[0160] Obviously, all values ​​of P are less than Y, so it is judged that the training is unqualified.

[0161] 5. Auxiliary training

[0162] S1: Determine the unqualified scenarios: The trainees failed to meet the standards in the three scenarios of emergency evacuation, fire hydrant use, and evacuation sign recognition.

[0163] S2: Entity extraction and content acquisition: Through natural language processing technology, entities are extracted from unqualified scenarios, such as "emergency evacuation", "fire hydrant use", and "evacuation sign recognition".

[0164] S3: Review and training: Obtain training content related to these entities based on the knowledge graph, including review materials, video tutorials, and online course links, for targeted auxiliary training.

[0165] Through the above process, airport security managers can use virtual reality technology to effectively improve employees' emergency response capabilities and safety awareness, ensuring the safety and efficiency of airport operations.

[0166] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0167] Working principle of the present invention:

[0168] The present invention obtains training data related to airport safety management; and obtains three-dimensional data of the airport management area; constructs a knowledge graph based on the training data; obtains training content for trainees based on the knowledge graph; and constructs a virtual reality three-dimensional model of the airport based on the three-dimensional data; performs simulation assessment on trainees based on the virtual reality three-dimensional model of the airport, and records the learning data of the trainees during the assessment in real time; and calculates the training evaluation coefficient of the trainees based on the learning data; and determines whether the training is qualified based on the training evaluation coefficient and a preset threshold value, thereby solving the problems that the training method of airport safety management personnel in the prior art mainly relies on paper teaching materials, video teaching and on-site drills, and has low training efficiency, weak pertinence, and difficulty in quantitative evaluation of effects; at the same time, because airport safety management training mostly relies on fixed teaching outlines and static training materials, it is impossible to flexibly adjust the training content according to the actual mastery of the trainees, resulting in uneven training effects, and it is difficult to meet the high requirements of modern airport safety management on personnel quality.

[0169] The above embodiments are only used to illustrate the technical method 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Personnel training management system based on airport safety management, characterized by: include: Data collection module, data analysis module, safety training module Data collection module: used to obtain training data related to airport safety management; and, obtaining three-dimensional data of the airport management area; Data analysis module: build a knowledge graph based on training data; obtain the training content of trainees based on the knowledge graph; as well as, Build a virtual reality 3D model of the airport based on 3D data; Safety training module: Conduct simulation assessments on trainees based on the virtual reality 3D model of the airport, and record the trainees’ learning data in real time during the assessment process; as well as, The training evaluation coefficient of the trainee is calculated based on the learning data; whether the training is qualified is determined based on the training evaluation coefficient and the preset threshold; if yes, the training is completed; If not, supplementary training will be provided to trainees who fail the training.

2. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The acquisition of training data related to airport safety management includes: The training data related to airport safety management is obtained based on the database; the training data includes: safety management process, safety cases, training materials, safety equipment operating procedures, and the job titles of trainees and corresponding job responsibilities.

3. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The obtaining of three-dimensional data of the airport management area includes: The three-dimensional data of the airport management area is collected by three-dimensional collection equipment; wherein the three-dimensional collection equipment includes a laser scanner, a camera or a depth camera; the three-dimensional data includes: environmental three-dimensional data and safety equipment three-dimensional data.

4. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The construction of a knowledge graph based on training data includes: S1: Extracting entities related to airport safety management from training data through natural language processing techniques; S2: Extract the association relationship between entities through semantic analysis technology; S3: Map entities and relationships into a graph using graph database technology, including Neo4j or Dgraph.

5. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The obtaining of training content for trainees based on the knowledge graph includes: S1: Query and obtain the entity corresponding to the position title of the trainee in the knowledge graph; S2: Get the job responsibilities associated with the job title entity; S3: The entities based on job responsibilities obtain the training content of trainees through the knowledge graph.

6. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The method of constructing a virtual reality three-dimensional model of an airport based on three-dimensional data includes: S1: constructing a virtual reality 3D model of the airport based on 3D data through virtual reality technology; wherein the virtual reality technology includes: digital twin technology or VR technology; S2: Add logic control to the virtual reality 3D model of the airport through visual programming tools; the visual programming tools include: Node-RED or Blockly; the logic control includes: operation control of safety equipment, parameter adjustment and environmental response.

7. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The virtual reality three-dimensional model of the airport is used to simulate and assess the trainees, and the learning data of the trainees during the assessment is recorded in real time, including: S1: Set up several safety management scenarios in the virtual reality 3D model of the airport; the safety management scenarios include: emergency evacuation, fire emergency, hazardous materials handling or passenger conflict response; S2: Trainees handle several safety management scenarios; S3: Record the learning data of trainees in handling each security management scenario; the learning data includes: the accuracy and completion time of the processing operation.

8. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The training evaluation coefficient of the trainee is calculated based on the learning data, including: The correctness rate and completion time of the training personnel's processing operations for each security management scenario are marked as Z and T respectively; where Z = [Z1, Z2, Z3, ..., Zn], T = [T1, T2, T3, ..., Tn]; The training evaluation coefficient is calculated by the formula: P = A × e^[Z / (Z+1)] + B × lnT / (1+lnT); where P is the training evaluation coefficient, P = [P1, P2, P3, …, Pn], A and B are proportional coefficients, and A>0, B<0, and ln(*) is a logarithmic function with the natural number e as the base.

9. The personnel training management system based on airport safety management according to claim 1 is characterized in that: The determining whether the training is qualified based on the training evaluation coefficient and the preset threshold value includes: The preset threshold is marked as Y; wherein Y = [Y1, Y2, Y3, ..., Yn], n refers to the number of security management scenes set in the virtual reality 3D model of the airport; the preset threshold is set according to the training requirements of the actual airport; Determine whether P is greater than or equal to Y; if yes, the training is considered qualified; if not, the training is considered unqualified.

10. The personnel training management system based on airport safety management according to claim 1, characterized in that: The aforementioned supplementary training for trainees who fail the training includes: S1: Obtain safety management scenarios where trainees fail to meet training standards; S2: Extracting entities related to airport safety management from unqualified safety management scenarios through natural language processing technology; S3: Based on the entities extracted in S2, relevant training content is obtained through the knowledge graph for review; S4: Obtain relevant video tutorials, online course links or professional literature from the database to assist trainees in training.

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