CN113808450A - External floating roof oil tank model accident handling training method, device and equipment - Google Patents
External floating roof oil tank model accident handling training method, device and equipment Download PDFInfo
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Abstract
The invention provides an accident handling training method, device and equipment for an external floating roof oil tank model in a virtual scene, wherein the method comprises the following steps: under a virtual scene, acquiring the state of each component in the external floating roof-based oil tank model, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; when the accident occurrence probability exceeds a preset threshold value, generating an operation task of the external floating roof oil tank model component for the accident in a virtual scene based on the real operation information of the trained object; and inputting the current state information of the outer floating roof oil tank model and the external environment information into a decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model. The training method is provided, the fire fighting level is greatly improved, and personal and property safety and clean and healthy environment of people are better guaranteed in actual accident handling.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to an accident handling training method, device and equipment for an external floating roof oil tank model in a virtual scene.
Background
With the steady advance of national economy, the explosive development of chemical industries such as petrochemical industry, coal chemical industry, fine chemical industry and the like in China drives the increase of storage requirements of chemical raw materials. Tanks containing large quantities of hazardous chemicals are present in the great south and north of our country. Due to the fact that the oil tank is difficult to fight fire, difficult in conditions and high in harmfulness, huge risks and rescue pressure are brought to emergency departments, particularly fire rescue departments. The accident probability of the external floating roof oil tank (crude oil) is higher. The oil tank fire-fighting training drill aims at enhancing the safety and fire-fighting awareness, further understanding and mastering the processing flow of fire, improving the coordination and coordination capacity in the process of handling emergency events and enhancing the capacity of fire fighters in handling accidents in the fire.
The existing training and drilling method mainly comprises the following steps:
the first and actual physical training mode: the fire-fighting training teaching mode mostly adopts a real tank or model tank mode to train on the spot. The on-site training has many advantages, but has the disadvantages of site limitation, high training cost, environmental pollution, no recurrence and great difficulty in repeated training.
The second video teaching mode is as follows: the method aims to know the structure of the oil tank and treat the accident of the oil tank by repeatedly watching videos of the treatment steps and the treatment method of the external floating roof oil tank (crude oil), but has the defects of being not intuitive enough, incapable of being operated in person and the like.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, and a device for training accident handling of an external floating roof tank model in a virtual scene, which can solve at least one of the above-mentioned technical problems. The specific scheme is as follows:
according to a specific implementation manner of the present disclosure, in a first aspect, the present disclosure provides an accident handling training method for an external floating roof tank model in a virtual scene, including:
under a virtual scene, acquiring the state of each component in the external floating roof-based oil tank model, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
when the accident occurrence probability exceeds a preset threshold value, generating an operation task of the external floating roof oil tank model component for the accident in a virtual scene based on the real operation information of the trained object;
and inputting the current state information of the outer floating roof oil tank model and the external environment information into a decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model.
Optionally, the calculating the normalization parameter of each component according to the state of each component, and determining the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component includes:
calculating the normalization parameters of each component according to the state of each component;
calculating normalization parameters of an external environment under the current environment, wherein the external environment comprises temperature, humidity, wind power and wind direction;
inputting the normalization parameters of all the components and the normalization parameters of the external environment into a probability model, and judging the accident occurrence probability of the current external floating roof oil tank model based on the calculation result of the probability model; wherein, the probability model satisfies the following conditions:
wherein i represents a natural number between 1 and n, n is a natural number greater than 1,
a normalized parameter representing an input floating-roof tank model component; w is aiRepresenting the weight coefficient corresponding to the floating roof tank model component;
a normalization parameter representing an external environment under the current environment; h isiAnd representing the weight coefficient corresponding to the external environment, and P is the calculated probability.
Optionally, the operation task includes at least one of the following operation tasks:
under a virtual scene, selecting a water tank fire truck, a city main war fire truck, a foam water tank fire truck or an emergency rescue vehicle, and assembling the water tank fire truck, the city main war fire truck, the foam water tank fire truck or the emergency rescue vehicle at a first distance from an outer floating roof oil tank model;
arranging an alert module at a second distance from the vehicle aggregation point, dividing an alert area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting an alert sign;
wearing the thermal suit assembly and the air respirator assembly in a virtual scene;
in a virtual scene, starting a foam gun to perform covering fire extinguishing, and ensuring that at least 50% of foam does not float outside a specified area;
in a virtual scenario, in a non-firing situation, closing the edge vent valve;
under a virtual scene, a main trunk water belt is laid from a foam truck to a walkway at the top of the oil tank and close to an external floating roof oil tank; and the two water distributors are connected, and the water belt extends to the windward direction of the top of the oil tank in the left and right directions.
Optionally, the generating an operation task of the external floating roof tank model component for an accident in a virtual scene based on the real operation information of the trained object includes:
capturing hand motion of a trained object through a hand motion capture device and generating a hand motion signal;
determining spatial location parameters of the hand motion by a positioning sensor;
the VR wearable device worn on the head of the trained object receives the hand motion signal and the spatial position parameter, and presents the operation task in the virtual scene.
Optionally, the hand motion capture device comprises a motion capture glove, a motion capture bracelet, and/or a motion capture handle.
Optionally, the decision tree model includes:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, and mkRepresenting the degree of matching of the execution of the kth operational task with the current state of the external floating-roof tank model, akWeight, n, representing the kth operation taskkRepresenting the degree of match of the kth external environment with the current state of the external floating roof tank model, bkRepresenting the weight of the kth external environment, L representing the training result obtained from the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model is, the more successful the training result is.
Optionally, the training result L calculated according to the decision tree model ranges from 0 to 0.3.
According to a specific embodiment of the present disclosure, in a second aspect, the present disclosure provides an accident disposal training apparatus for an external floating roof tank model in a virtual scene, including:
the acquiring unit is used for acquiring the state of each component in the external floating roof oil tank model in a virtual scene, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
the generating unit is used for generating an operation task of the external floating roof oil tank model component for the accident in the virtual scene based on the real operation information of the trained object when the accident occurrence probability exceeds a preset threshold;
and the judging unit is used for inputting the current state information of the external floating roof oil tank model and the external environment information into the decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model.
According to a third aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above.
According to a fourth aspect thereof, the present disclosure provides an electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a method as claimed in any preceding claim.
Compared with the prior art, the scheme of the embodiment of the disclosure at least has the following beneficial effects: this openly combines VR virtual reality technique, various disasters and dangerous situations that virtual hazardous chemicals oil tank probably appears, and the trainee can roam the detailed information of understanding the disasters with first person's visual angle in the scene, immerses in hazardous chemicals accident space environment, utilizes information such as position, gesture that computer sensor conveyed, deals with various accident scenes that appear, promotes the authenticity and the infectivity of virtual scene. Provides a realistic, safe, repeatable and low-consumption training means for fire-fighting officers and soldiers so as to improve the command and cooperative combat capability of large-scale fire field combined combat. The fire fighting level is greatly improved, and the personal and property safety and the environment cleanness and health of people are better guaranteed in the actual accident handling.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
FIG. 1 illustrates a flow diagram of an incident training method according to an embodiment of the present disclosure;
FIG. 2 illustrates an external floating roof tank junction construction schematic in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates an accident training apparatus scenario diagram, according to an embodiment of the present disclosure;
FIG. 4 illustrates an incident handling flow diagram according to an embodiment of the disclosure;
fig. 5 shows a schematic diagram of a training decision tree construction according to an embodiment of the present disclosure;
FIG. 6 illustrates an accident training apparatus frame configuration diagram, according to an embodiment of the present disclosure;
FIG. 7 illustrates a schematic diagram of an accident training apparatus, according to an embodiment of the present disclosure;
fig. 8 shows an electronic device connection structure schematic according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure clearer, the present disclosure will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present disclosure, rather than all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort, shall fall within the scope of protection of the present disclosure.
The terminology used in the embodiments of the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in the disclosed embodiments and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and "a plurality" typically includes at least two.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in the article or device in which the element is included.
Alternative embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, according to an embodiment of the present disclosure, the present disclosure provides an accident handling training method for an external floating roof tank model in a virtual scene, including the following method steps:
step S102: under a virtual scene, acquiring the state of each component in the external floating roof-based oil tank model, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
each part is modeled in a 3D mode by completely disassembling an outer floating roof oil tank model structure, and the outer floating roof oil tank comprises a tank shell 11, an edge vent valve 12, an edge sealing strip 13, a breathing device 14, a floating roof supporting foot 15, an emergency rainwater overflow pipe 16, an opening 17, a manhole 18, a sampling port 19, a floating roof anti-rotation upright post 20, a thermometer and a floating ball type liquid level meter 21, as shown in figure 2. Setting an incidence relation among the models to construct a static model; an operable motion model member is provided for the movable operation part, and for example, an edge vent valve, an edge seal, a thermometer, and a liquid level meter are provided as the operable motion model member.
Triggering the action of the external floating roof oil tank to construct a model according to the position, action and posture information in the real scene of the trained personnel, obtaining an operation result through model operation, and displaying through a first visual angle.
Optionally, the calculating the normalization parameter of each component according to the state of each component, and determining the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component includes:
step S1022: calculating the normalization parameters of each component according to the state of each component; for example, the normalized parameters for setting the edge vent valve, edge seal, thermometer, and level gauge are as follows: the edge vent valve is closed to be 0, and the value of the edge vent valve is 1-10 according to the opening size parameter when the edge vent valve is opened; the sealing of the edge sealing strip is 0, and the non-sealing can be set to be 1-10 according to the opening length; the temperature meter is 0 when the temperature is lower than 30 ℃ and 1 when the temperature is higher than 30 ℃, the parameter is increased by 1 when the temperature is 10 ℃, and the liquid level meter takes a value of 1-5 according to the height of the liquid level;
when the values of the edge vent valve, the edge sealing strip, the thermometer and the liquid level meter are respectively 2, 3, 1 and 4, the normalized parameter values are respectively 0.2, 0.3, 0.1 and 0.4.
Step S1024: calculating normalization parameters of an external environment under the current environment, wherein the external environment comprises temperature, humidity, wind power and wind direction;
the higher the temperature, the higher the value, for example, the temperature value is 1-10;
the higher the humidity, the lower the value, for example, the humidity value is 1-10;
the larger the wind power is, the higher the value is, for example, the temperature is 1-10;
the more the wind direction is matched with the opening, the higher the value is, for example, the temperature value is 1-10;
when the temperature, the humidity, the wind power and the wind direction respectively take values of 6, 2, 1 and 1, the normalized values are respectively 0.6, 0.2, 0.1 and 0.1.
Step S1026: inputting the normalization parameters of all the components and the normalization parameters of the external environment into a probability model, and judging the accident occurrence probability of the current external floating roof oil tank model based on the calculation result of the probability model; wherein, the probability model satisfies the following conditions:
wherein i represents a natural number between 1 and n, n is a natural number greater than 1,
a normalized parameter representing an input floating-roof tank model component; w is aiRepresenting a weight coefficient corresponding to the floating roof oil tank model assembly, wherein the weight coefficient can be preset according to the influence condition;
a normalization parameter representing an external environment under the current environment; h isiAnd representing a weight coefficient corresponding to the external environment, wherein the weight coefficient can be preset according to the influence condition, and P is the calculated probability.
Step S104: when the accident occurrence probability exceeds a preset threshold value, generating an operation task of the external floating roof oil tank model component for the accident in a virtual scene based on the real operation information of the trained object;
the preset threshold may be set to 0.3-0.5, for example, and training may be performed when the probability calculated as above is greater than the preset threshold.
Generating operation tasks of the external floating roof tank model component for accidents under the virtual scene based on the real operation information of the trained object, wherein the operation tasks comprise at least one of the following operation tasks:
under a virtual scene, selecting a water tank fire truck, a city main war fire truck, a foam water tank fire truck or an emergency rescue vehicle, and assembling the water tank fire truck, the city main war fire truck, the foam water tank fire truck or the emergency rescue vehicle at a first distance from an outer floating roof oil tank model;
arranging an alert module at a second distance from the vehicle aggregation point, dividing an alert area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting an alert sign;
wearing the thermal suit assembly and the air respirator assembly in a virtual scene;
in a virtual scene, starting a foam gun to perform covering fire extinguishing, and ensuring that at least 50% of foam does not float outside a specified area;
in a virtual scenario, in a non-firing situation, closing the edge vent valve;
under a virtual scene, a main trunk water belt is laid from a foam truck to a walkway at the top of the oil tank and close to an external floating roof oil tank; and the two water distributors are connected, and the water belt extends to the windward direction of the top of the oil tank in the left and right directions.
Optionally, the generating an operation task of the external floating roof tank model component for an accident in a virtual scene based on the real operation information of the trained object includes:
step S1042: capturing hand motion of a trained object through a hand motion capture device and generating a hand motion signal;
step S1044: determining spatial location parameters of the hand motion by a positioning sensor;
step S1046: the VR wearable device worn on the head of the trained object receives the hand motion signal and the spatial position parameter, and presents the operation task in the virtual scene.
Optionally, the hand motion capture device comprises a motion capture glove, a motion capture bracelet, and/or a motion capture handle.
Based on the real operation of the trained object, as shown in fig. 3, the method includes: a three-dimensional space comprising side surfaces and a top surface; a VR wearable device configured to be worn on an operator's head for presenting a virtual scene, the virtual scene including an external floating-roof tank model; the positioning device is arranged on the side face and/or the top face of the three-dimensional space, is in communication connection with the VR wearable equipment, and is used for positioning the position of the VR wearable equipment in the space to generate a positioning signal; the hand motion capture device is used for capturing hand motions of an operator and generating hand motion signals; and the upper computer is in communication connection with the VR wearable equipment, the positioning device and the hand motion capture equipment, is used for receiving the hand motion signals and the positioning signals, generating control signals according to the hand motion signals and the positioning signals, and sending the control signals to the VR wearable equipment. This system builds real disaster scene to make operating personnel immerse in the simulated environment through the wearable equipment of VR, make operating personnel can obtain the training effect with real environment basic unanimity.
As shown in fig. 3, the VR external floating roof tank accident handling teaching and training system includes: the device comprises a positioning device 1, a VR wearable device 2, a hand motion capture device 3, an upper computer 4 and a three-dimensional space 6.
The three-dimensional space 6 is used as an operation space for VR-based external floating roof oil tank accident handling teaching and training to implement VR-based external floating roof oil tank accident handling teaching and training. The three-dimensional space 6 can be closed or not, and the three-dimensional space 6 comprises a side surface and a top surface.
A VR wearable device 2 configured to be worn on an operator's head for presenting a virtual scene comprising an external floating roof tank model 5. The external floating roof tank model 5 is presented near the center of the three-dimensional space 6. The VR wearable device 2 includes, for example, a head-mounted display and/or a binocular omnidirectional display, and can realistically display an object in front of the eyes by a 3D rendering technique, thereby implementing a virtual reality function. The specific model of the VR wearable device 2 is not specifically described, and any VR wearable device 2 capable of virtual display can be applied to the present disclosure.
The positioning device 1 is disposed on a side surface and/or a top surface of the three-dimensional space 6, and is in communication connection, such as wireless or wired connection, with the VR wearable device 2, and is configured to position the VR wearable device 2 in a space, that is, a position of an operator wearing the VR wearable device 2, and generate a positioning signal. Positioner 1 adopts laser radar, infrared camera or body to feel in the camera one or more, and positioner is the basis that realizes that real space location to virtual reality space mapping, deploys positioner's quantity according to the space size to the realization is to operating personnel, for example training personnel, accurate location. The laser radar, the infrared camera or the somatosensory camera can be of any type and applied to the disclosure, and specific types are not introduced.
In some embodiments the monument 6 comprises a curved side and/or top surface and curves away from the external floating roof tank model 5, so that the operator has a wider operational active space.
In some embodiments, the number of the positioning devices 1 is multiple, and the multiple positioning devices 1 are distributed at intervals, as shown in fig. 1, and the multiple positioning devices 1 are arranged on the side of the three-dimensional space 6, for example.
In some embodiments, a plurality of positioning devices 1 are uniformly arranged along the arc-shaped surface, and the structure can ensure that the detection dead angle of the sensor is less or basically no dead angle, so that the trainees can perform simulation training within the maximum range of the working space.
The hand motion capture device 3 is used to capture hand motions of the operator and generate hand motion signals. The hand action signal corresponds to the specific operation of an operator, and can embody various specific operations of the operator in dealing with the accident of the external floating roof oil tank. The hand motion capture device 3 comprises a motion capture glove, a motion capture bracelet, or a motion capture handle. The motion capture pad is similar to a capture device such as a gamepad or joystick through which the hand movements of the trainee, e.g., left, right, front, back, determining, etc., can be captured. Specifically, the hand motion capture device 3 is, for example, a VR handle, which is used for interacting with an object in the virtual real world and has a built-in sensor that can be tracked by a locator; a laser generator and a photosensitive sensor are arranged in the positioner and used for determining the position of the VR handle. The specific type and structure of the hand motion capture device are not limited, and all hand motion capture devices capable of having the above functions can meet the requirements of the application. The hand motion capture device generates corresponding hand motion signals according to the hand motions of the trainees and transmits the hand motion signals to the upper computer 4.
Step S106: and inputting the current state information of the outer floating roof oil tank model and the external environment information into a decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model.
The decision tree model is constructed as shown in fig. 5, and includes:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, and mkThe degree of matching between the execution of the kth operation task and the current state of the external floating roof tank model is shown, for example, when the fire is found to be started and foam fire extinguishment is executed, the degree of matching is higher, and m is largerkThe smaller the value is, when the fire is found out, the higher the matching degree is, the higher m iskThe value is smaller, and the corresponding relation between the specific execution condition and the matching condition is set in the model data in a preset mode; a iskWeight, n, representing the kth operation taskkThe model of the kth external environment and the external floating roof tank is shownDegree of adaptation to the preceding state, e.g. rain in case of fire, is better, nkThe value is small, the matching degree is poor when the wind blows under the condition of fire, nkThe value is large; bkRepresenting the weight of the kth external environment, L representing the training result obtained from the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model is, the more successful the training result is. And the range of the training result L calculated according to the decision tree model is 0-0.3.
Specifically, as shown in fig. 4, the following operations are completed in the training and learning process:
1) force adjusting stage: VR simulation can go out and move water pitcher fire engine, city owner and fight against fire engine, foam water pitcher fire engine, rescue car, gather in the wind gap 1000 meters away from the accident vehicle.
2) And (3) warning around the accident, wherein VR simulation soft warning: setting up an alert 1000 meters outside the vehicle aggregation point, dividing an alert area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting up an alert sign; simulating hard guard: and (5) plugging the road surface by using a vehicle, and keeping the cooperation of two persons for warning.
3) And (4) performing short-distance detection, namely, performing VR simulation using an instrument or performing on-site observation and detection to grasp the leakage position, form, concentration and range of the tank car and the condition that personnel are trapped. And observing the environmental information around the accident to find out whether the shelter can be found.
4) And safety protection, namely VR simulation wearing thermal insulation clothes and an air respirator. If leakage occurs, heavy protective clothing and air respirators need to be worn.
5) Diluting, namely enabling the VR to be close to the tank body, and paving a main trunk water belt from the foam truck to a walkway on the top of the oil tank; and the two water distributors are connected, and the water belt extends to the windward direction of the top of the oil tank in the left and right directions.
6) And (5) closing the valve of the non-leakage tank under the condition of non-ignition simulation by VR. If the fire occurs, the valve of the non-fired tank is closed.
7) The foam guns are arranged on the water hoses in the left direction and the right direction and matched with the foam generators to extinguish fire downwards by the foam guns, and the bidirectional fighters can extinguish the fire and extend downwards in the downwind direction until all the fire is extinguished.
8) Prevent leakage and ensure that at least 50% of the foam does not float outside the designated area.
9) And judging whether the pre-positioning is finished or not, wherein the method comprises failure judgment and global judgment.
As shown in fig. 6, the upper computer 4 is, for example, a computer, and the computer includes a computing module, a teaching module, a single training module, a multi-training module, and a rendering module.
The computation module is used to undertake high-precision computation tasks and model rendering tasks that map data from the real world into virtual reality. The calculation module comprises a CPU calculation logic unit, a GPU rendering logic unit, a position tracking unit and a virtual reality projection unit. The computation module performs high-precision computation tasks and model rendering tasks that map data from the real environment into virtual reality.
The teaching module is used for providing basic general knowledge teaching, structure teaching and skill teaching, and comprises an oil tank structure teaching unit and an oil tank operable part teaching unit. The oil tank structure teaching unit covers structure teaching information such as a tank body, a frame, a tank wall, a manhole, a safety valve and the like; the teaching unit for the operable part of the oil tank mainly simulates operation technical and tactical teaching information of relevant parts such as force collection, warning, investigation, protection, dilution, valve closing, cooling, fire extinguishing and the like.
The single training module is used for single training, completes whole accident investigation and tactics in random or designated environment, and mainly comprises technical and tactical training units such as strength maneuvering, wearing equipment, detecting fire, laying a water hose, extending the water hose, arranging a foam pipe gun, fighting fire by foam, covering and extinguishing fire, preventing leakage and the like.
The multi-person training module is used for multi-person collaborative training, comprises units such as full scene establishment, fire scene establishment, task tree establishment and multi-person cooperation, establishes leakage, fire and other types of accident scenes of positions such as a three-dimensional manhole, a breather valve, a pipeline and the like by taking a domestic typical combat example as a background, is suitable for multi-person cooperation to complete a disposal task, and carries out real-time reasoning and resolving so as to improve the command and collaborative combat capability of large-scale fire field combined combat and greatly improve the fire fighting combat level.
The rendering module mainly comprises joint actions and displays of oil tank rendering, personnel rendering, equipment rendering and scene rendering, and a virtual simulation environment with strong immersion sense is created by comprehensively using a graphic image technology, a man-machine interaction technology and a mode recognition technology, so that a trainer is immersed in a virtual environment of an oil tank accident disposal site in a first person perspective mode, and a vivid, safe, repeatable and low-consumption training means is provided for fire-fighting officers and soldiers.
In some embodiments, the teaching and training implementation is as follows: training personnel get into the cubical space, and workspace promptly comes to the training position, wears the wearable equipment of VR, opens the host computer, and at this moment, the host computer provides different training scenes according to built-in training module, for example provides outer floating roof oil tank accident scene of catching a fire, and the accident scene passes through VR and shows in training personnel's the eye in front, and the display mode is a three-dimensional virtual image. At this time, the operator starts to deal with the external floating roof tank accident according to the accident scene, for example, the foam gun is operated to extinguish fire by the left and right operation handles in front, or various training units provided in the virtual scene are dragged by the handles, for example, water belts are laid, and the like, until the accident is handled.
This openly combines VR virtual reality technique, various disasters and dangerous situations that virtual hazardous chemicals oil tank probably appears, and the trainee can roam the detailed information of understanding the disasters with first person's visual angle in the scene, immerses in hazardous chemicals accident space environment, utilizes information such as position, gesture that computer sensor conveyed, deals with various accident scenes that appear, promotes the authenticity and the infectivity of virtual scene. Provides a realistic, safe, repeatable and low-consumption training means for fire-fighting officers and soldiers so as to improve the command and cooperative combat capability of large-scale fire field combined combat. The fire fighting level is greatly improved, and the personal and property safety and the environment cleanness and health of people are better guaranteed in the actual accident handling.
In addition, the present disclosure also provides an apparatus embodiment adapted to the above embodiment, for implementing the method steps described in the above embodiment, and the explanation based on the same name and meaning is the same as that of the above embodiment, and has the same technical effect as that of the above embodiment, and is not described again here.
As shown in fig. 7, according to an embodiment of the present disclosure, the present disclosure provides an accident handling training device for an external floating roof tank model in a virtual scene, including:
the acquisition unit 702: the method comprises the steps that the states of all components in an outer floating roof oil tank model are obtained in a virtual scene, normalization parameters of all the components are calculated according to the states of all the components, and the accident occurrence probability of the current outer floating roof oil tank model is judged based on the normalization parameters of all the components; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
optionally, the calculating the normalization parameter of each component according to the state of each component, and determining the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component includes:
calculating the normalization parameters of each component according to the state of each component; calculating normalization parameters of an external environment under the current environment, wherein the external environment comprises temperature, humidity, wind power and wind direction; inputting the normalization parameters of all the components and the normalization parameters of the external environment into a probability model, and judging the accident occurrence probability of the current external floating roof oil tank model based on the calculation result of the probability model; wherein, the probability model satisfies the following conditions:
wherein i represents a natural number between 1 and n, n is a natural number greater than 1,
a normalized parameter representing an input floating-roof tank model component; w is aiRepresenting a weight coefficient corresponding to the floating roof oil tank model assembly, wherein the weight coefficient can be preset according to the influence condition;
a normalization parameter representing an external environment under the current environment; h isiTo representAnd the weight coefficient corresponding to the external environment can be preset according to the influence condition, and P is the calculated probability.
The generation unit 704: generating an operation task of the external floating roof tank model component for the accident in the virtual scene based on the real operation information of the trained object when the accident occurrence probability exceeds a preset threshold;
the preset threshold may be set to 0.3-0.5, for example, and training may be performed when the probability calculated as above is greater than the preset threshold.
Generating operation tasks of the external floating roof tank model component for accidents under the virtual scene based on the real operation information of the trained object, wherein the operation tasks comprise at least one of the following operation tasks:
under a virtual scene, selecting a water tank fire truck, a city main war fire truck, a foam water tank fire truck or an emergency rescue vehicle, and assembling the water tank fire truck, the city main war fire truck, the foam water tank fire truck or the emergency rescue vehicle at a first distance from an outer floating roof oil tank model;
arranging an alert module at a second distance from the vehicle aggregation point, dividing an alert area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting an alert sign;
wearing the thermal suit assembly and the air respirator assembly in a virtual scene;
in a virtual scene, starting a foam gun to perform covering fire extinguishing, and ensuring that at least 50% of foam does not float outside a specified area;
in a virtual scenario, in a non-firing situation, closing the edge vent valve;
under a virtual scene, a main trunk water belt is laid from a foam truck to a walkway at the top of the oil tank and close to an external floating roof oil tank; and the two water distributors are connected, and the water belt extends to the windward direction of the top of the oil tank in the left and right directions.
Optionally, the generating an operation task of the external floating roof tank model component for an accident in a virtual scene based on the real operation information of the trained object includes:
capturing hand motion of a trained object through a hand motion capture device and generating a hand motion signal; determining spatial location parameters of the hand motion by a positioning sensor; the VR wearable device worn on the head of the trained object receives the hand motion signal and the spatial position parameter, and presents the operation task in the virtual scene.
Optionally, the hand motion capture device comprises a motion capture glove, a motion capture bracelet, and/or a motion capture handle.
The judging unit 706: and the method is configured to input the current state information of the external floating roof oil tank model and the external environment information into a decision tree model based on the execution result of the operation task, and judge the training state according to the decision tree model.
The decision tree model is constructed as shown in fig. 5, and includes:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, and mkThe degree of matching between the execution of the kth operation task and the current state of the external floating roof tank model is shown, for example, when the fire is found to be started and foam fire extinguishment is executed, the degree of matching is higher, and m is largerkThe smaller the value is, when the fire is found out, the higher the matching degree is, the higher m iskThe value is smaller, and the corresponding relation between the specific execution condition and the matching condition is set in the model data in a preset mode; a iskWeight, n, representing the kth operation taskkIndicating the degree of matching of the kth external environment to the current state of the external floating roof tank model, e.g. rain in case of fire, the degree of matching is better, nkThe value is small, the matching degree is poor when the wind blows under the condition of fire, nkThe value is large; bkRepresenting the weight of the kth external environment, L representing the training result obtained from the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model is, the more successful the training result is. And the range of the training result L calculated according to the decision tree model is 0-0.3.
This openly combines VR virtual reality technique, various disasters and dangerous situations that virtual hazardous chemicals oil tank probably appears, and the trainee can roam the detailed information of understanding the disasters with first person's visual angle in the scene, immerses in hazardous chemicals accident space environment, utilizes information such as position, gesture that computer sensor conveyed, deals with various accident scenes that appear, promotes the authenticity and the infectivity of virtual scene. Provides a realistic, safe, repeatable and low-consumption training means for fire-fighting officers and soldiers so as to improve the command and cooperative combat capability of large-scale fire field combined combat. The fire fighting level is greatly improved, and the personal and property safety and the environment cleanness and health of people are better guaranteed in the actual accident handling.
As shown in fig. 8, the present embodiment provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the method steps of the above embodiments.
The disclosed embodiments provide a non-volatile computer storage medium having stored thereon computer-executable instructions that may perform the method steps as described in the embodiments above.
Referring now to FIG. 8, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 801 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing apparatus 801, the ROM 802, and the RAM 803 are connected to each other via a bus 805. An input/output (I/O) interface 805 is also connected to bus 805.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 805 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, or the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 805. The communication means 805 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 8 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network through the communication device 805, or installed from the storage device 808, or installed from the ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as C #, Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
Claims (10)
1. An accident handling training method for an external floating roof oil tank model under a virtual scene is characterized by comprising the following steps:
under a virtual scene, acquiring the state of each component in the external floating roof-based oil tank model, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
when the accident occurrence probability exceeds a preset threshold value, generating an operation task of the external floating roof oil tank model component for the accident in a virtual scene based on the real operation information of the trained object;
and inputting the current state information of the outer floating roof oil tank model and the external environment information into a decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model.
2. The method of claim 1, wherein the calculating of the normalized parameters of the components according to the states of the components and the determining of the accident probability of the current external floating roof tank model based on the normalized parameters of the components comprises:
calculating the normalization parameters of each component according to the state of each component;
calculating normalization parameters of an external environment under the current environment, wherein the external environment comprises temperature, humidity, wind power and wind direction;
inputting the normalization parameters of all the components and the normalization parameters of the external environment into a probability model, and judging the accident occurrence probability of the current external floating roof oil tank model based on the calculation result of the probability model; wherein, the probability model satisfies the following conditions:
wherein i represents a natural number between 1 and n, n is a natural number greater than 1,
a normalized parameter representing an input floating-roof tank model component; w is aiRepresenting the weight coefficient corresponding to the floating roof tank model component;
a normalization parameter representing an external environment under the current environment; h isiAnd representing the weight coefficient corresponding to the external environment, and P is the calculated probability.
3. The method of claim 1, wherein the operational tasks include at least one of:
under a virtual scene, selecting a water tank fire truck, a city main war fire truck, a foam water tank fire truck or an emergency rescue vehicle, and assembling the water tank fire truck, the city main war fire truck, the foam water tank fire truck or the emergency rescue vehicle at a first distance from an outer floating roof oil tank model;
arranging an alert module at a second distance from the vehicle aggregation point, dividing an alert area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting an alert sign;
wearing the thermal suit assembly and the air respirator assembly in a virtual scene;
in a virtual scene, starting a foam gun to perform covering fire extinguishing, and ensuring that at least 50% of foam does not float outside a specified area;
in a virtual scenario, in a non-firing situation, closing the edge vent valve;
under a virtual scene, a main trunk water belt is laid from a foam truck to a walkway at the top of the oil tank and close to an external floating roof oil tank; and the two water distributors are connected, and the water belt extends to the windward direction of the top of the oil tank in the left and right directions.
4. The method according to claim 2, wherein the generating of the operation tasks of the external floating roof tank model component for the accident in the virtual scene based on the real operation information of the trained object comprises:
capturing hand motion of a trained object through a hand motion capture device and generating a hand motion signal;
determining spatial location parameters of the hand motion by a positioning sensor;
the VR wearable device worn on the head of the trained object receives the hand motion signal and the spatial position parameter, and presents the operation task in the virtual scene.
5. The method of claim 4, wherein the hand motion capture device comprises a motion capture glove, a motion capture bracelet, and/or a motion capture handle.
6. The method of claim 3, wherein the decision tree model comprises:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, and mkRepresenting the degree of matching of the execution of the kth operational task with the current state of the external floating-roof tank model, akWeight, n, representing the kth operation taskkRepresenting the degree of match of the kth external environment with the current state of the external floating roof tank model, bkRepresenting the weight of the kth external environment, L representing the training result obtained from the decision tree model.
7. The method of claim 6, wherein the smaller the training result L calculated from the decision tree model, the more successful the training result is.
8. The method of claim 7, wherein the training result L calculated from the decision tree model ranges from 0 to 0.3.
9. An outer floating roof oil tank model accident handling training device under a virtual scene is characterized by comprising:
the acquiring unit is used for acquiring the state of each component in the external floating roof oil tank model in a virtual scene, calculating the normalization parameter of each component according to the state of each component, and judging the accident occurrence probability of the current external floating roof oil tank model based on the normalization parameter of each component; wherein the components of the external floating roof tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
the generating unit is used for generating an operation task of the external floating roof oil tank model component for the accident in the virtual scene based on the real operation information of the trained object when the accident occurrence probability exceeds a preset threshold;
and the judging unit is used for inputting the current state information of the external floating roof oil tank model and the external environment information into the decision tree model based on the execution result of the operation task, and judging the training state according to the decision tree model.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1 to 8.
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