CN113808450B - 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 disclosure 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 states of all components in the external floating roof oil tank model, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; when the accident occurrence probability exceeds a preset threshold value, generating an operation task of the external floating roof oil tank model assembly for the accident under a virtual scene based on the real operation information of the trained object; based on the execution result of the operation task, the current state information of the external floating roof oil tank model and the external environment information are input into a decision tree model, and the training state is judged according to the decision tree model. The training method is provided, the fire fighting level is greatly improved, and the personal and property safety and the environmental cleaning and health of people are better ensured in actual accident disposal.
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 under a virtual scene.
Background
With the steady advancement of national economy, the chemical industries such as petrochemical industry, coal chemical industry, fine chemical industry and the like in China are vigorously developed, and the increase of storage requirements of chemical raw materials is driven. Large volumes of tanks loaded with hazardous chemicals are found in the north and south of the large river of the country. Because of the large difficulty of oil tank fire fighting, difficult conditions and large hazard, the oil tank fire fighting system brings huge risks and rescue pressure to emergency departments, especially fire rescue departments. Wherein the accident probability of the external floating roof oil tank (crude oil) is higher. The oil tank fire control training exercise is in order to strengthen the safety and fire protection consciousness, so that people can further know and master the processing flow of the fire disaster, the coordination and coordination capacity in the emergency processing process is improved, and the accident handling capacity of firefighters in the fire disaster is enhanced.
The existing training exercise method mainly comprises the following steps:
first, in-field physical training mode: the fire training teaching mode mostly adopts a real tank or model tank mode for training in the field. The field training has many advantages, but has the advantages of limited field, high training cost, environmental pollution, incapability of reproduction and great difficulty in repeated training.
Second, video teaching mode: the external floating roof oil tank (crude oil) treatment step and treatment method video can be repeatedly watched to achieve the purposes of knowing the oil tank structure and treating oil tank accidents, but the external floating roof oil tank (crude oil) treatment method has the defects of being not intuitive and incapable of being operated in person.
Disclosure of Invention
The disclosure aims to provide an external floating roof oil tank model accident handling training method, device and equipment in a virtual scene, which can solve at least one technical problem. The specific scheme is as follows:
according to a specific embodiment of the present disclosure, in a first aspect, the present disclosure provides an external floating roof tank model accident handling training method in a virtual scenario, including:
under a virtual scene, acquiring states of all components in the external floating roof oil tank model, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; the components of the outer floating roof oil 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 assembly for the accident under a virtual scene based on the real operation information of the trained object;
based on the execution result of the operation task, the current state information of the external floating roof oil tank model and the external environment information are input into a decision tree model, and the training state is judged according to the decision tree model.
Optionally, the calculating the normalized 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 normalized parameter of each component includes:
calculating 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 normalized parameters of each component and the normalized 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,
representing normalized parameters of the input floating roof oil tank model assembly; w (w) i Representing the weight coefficient corresponding to the floating roof oil tank model component; />
The normalization parameter of the external environment in the current environment is represented; h is a i And representing the weight coefficient corresponding to the external environment, wherein 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, an urban main warfare fire truck, a foam water tank fire truck or an emergency rescue truck, and gathering the water tank fire truck, the urban main warfare fire truck, the foam water tank fire truck or the emergency rescue truck at a first distance from an outer floating roof oil tank model;
Setting a warning module at a second distance from a vehicle collecting point, dividing a warning area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting a warning sign;
wearing a heat insulation clothing component and an air respirator component in a virtual scene;
under the virtual scene, starting a foam gun to conduct coverage fire extinguishment, and ensuring that at least 50% of foam does not float outside a designated area;
in a virtual scene, closing an edge vent valve under a non-firing condition;
under a virtual scene, the main water is paved from a foam vehicle to the top pavement of the oil tank near the outer floating roof oil tank; two water splitters are connected, and water is extended to the left and right directions to reach the windward direction at the top of the oil tank.
Optionally, the generating the 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 includes:
capturing the hand motions of the trained object through a hand motion capturing device and generating hand motion signals;
determining a spatial position parameter of the hand motion by a positioning sensor;
and receiving hand action signals and spatial position parameters through VR wearable equipment worn on the head of the trained object, and presenting operation tasks 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, m k Representing the matching degree of the execution of the kth operation task and the current state of the external floating roof oil tank model, a k Weights representing the kth operational task, n k B, representing the matching degree of the kth external environment and the current state of the external floating roof oil tank model k Representing the weight of the kth external environment, and L represents the training result obtained according to the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model, the more successful the training result.
Optionally, the training result L calculated according to the decision tree model ranges from 0 to 0.3.
According to a second aspect of the present disclosure, an external floating roof tank model accident handling training device under a virtual scenario includes:
the obtaining unit is used for obtaining states of all components in the external floating roof oil tank model under the virtual scene, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; the components of the outer floating roof oil 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 assembly for the accident under the virtual scene based on the real operation information of the trained object when the accident occurrence probability exceeds a preset threshold value;
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 of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as claimed in any one of the above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: one or more processors; storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of any of the preceding claims.
Compared with the prior art, the scheme of the embodiment of the disclosure has at least the following beneficial effects: the virtual dangerous chemical oil tank is combined with VR virtual reality technology, various disaster situations and dangerous situations possibly occur, a trainee can roam in the scene to know the detailed information of the disaster situations in the dangerous chemical accident space environment, the trainee is immersed in the dangerous chemical accident space environment, various occurring accident scenes are treated by utilizing the information such as the position, the gesture and the like transmitted by the computer sensor, and the reality and the infectivity of the virtual scene are improved. Provides a realistic, safe, repeatable and low-consumption training means for fire officers and soldiers, so as to improve the command and collaborative combat capability of the combined combat of a large fire scene. The fire fighting level is greatly improved, and the personal and property safety of people and the clean and healthy environment are better ensured in the actual accident disposal.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort. In the drawings:
FIG. 1 illustrates an accident training method flow diagram according to an embodiment of the present disclosure;
FIG. 2 illustrates an external floating roof tank node construction schematic in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates an accident training apparatus scenario schematic according to an embodiment of the present disclosure;
FIG. 4 illustrates an incident processing flow diagram according to an embodiment of the present disclosure;
FIG. 5 illustrates a training decision tree building schematic in accordance with an embodiment of the present disclosure;
FIG. 6 illustrates a schematic view of an accident training apparatus framework in accordance with an embodiment of the present disclosure;
FIG. 7 shows a schematic view of an accident training apparatus according to an embodiment of the present disclosure;
fig. 8 illustrates an electronic device connection structure schematic according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting an understanding of the principles and advantages of the disclosure, reference will now be made in detail to the drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the disclosure. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
The terminology used in the embodiments of the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure of 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, the "plurality" generally includes at least two.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are 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 phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product 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 product or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or device comprising such element.
Alternative embodiments of the present disclosure are described in detail below with reference to the drawings.
As shown in fig. 1, according to a specific 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 states of all components in the external floating roof oil tank model, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; the components of the outer floating roof oil tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
each part is 3D modeled by completely disassembling the outer floating roof tank model structure, and the outer floating roof tank comprises a tank shell 11, an edge vent valve 12, an edge sealing strip 13, a breathing device 14, a floating roof supporting leg 15, an emergency rainwater overflow pipe 16, an opening 17, a manhole 18, a sampling port 19, a floating roof anti-rotation column 20, a thermometer and a floating ball type liquid level meter 21, as shown in fig. 2. Setting association relations among the models to construct a static model; an operational action model member is provided for the actionable parts, for example an edge vent valve, an edge seal, a thermometer and a level gauge are provided as operational action model members.
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 view angle.
Optionally, the calculating the normalized 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 normalized parameter of each component includes:
step S1022: calculating normalization parameters of each component according to the state of each component; for example, the normalized parameters of the edge vent valve, edge seal, thermometer and level gauge are set as follows: the edge ventilation valve is closed to be 0, and the value is 1-10 according to the opening size parameter when the edge ventilation valve is opened; the sealing of the edge sealing strip is 0, and the unsealing can be set to be 1-10 according to the length of the opening; the thermometer is 0 below 30 degrees, 1 above 30 degrees, and 1 is added to the parameter every 10 degrees above, and the liquid level meter takes a value of 1-5 according to the liquid level height;
when the values of the edge ventilation valve, the edge sealing strip, the thermometer and the liquid level meter are respectively 2, 3, 1 and 4, the normalized parameter values are calculated to be 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, e.g., the temperature value of 1-10;
the higher the humidity, the lower the value, e.g., the humidity value of 1-10;
The larger the wind force, the higher the value, for example, the temperature value 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, humidity, wind power and wind direction are respectively 6, 2, 1 and 1, the normalized values are respectively 0.6, 0.2, 0.1 and 0.1.
Step S1026: inputting the normalized parameters of each component and the normalized 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,
representing normalized parameters of the input floating roof oil tank model assembly; w (w) i The weight coefficient corresponding to the floating roof oil tank model component is represented, and the weight coefficient can be preset according to the influence condition; />
The normalization parameter of the external environment in the current environment is represented; h is a i The weight coefficient corresponding to the external environment is represented, 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 assembly for the accident under a virtual scene based on the real operation information of the trained object;
The preset threshold may be set to, for example, 0.3-0.5, and training may be performed when the probability calculated by the model as described above is greater than the preset threshold.
Based on the real operation information of the trained object, generating operation tasks of the external floating roof oil tank model assembly for accidents in a virtual scene, wherein the operation tasks comprise at least one of the following operation tasks:
under a virtual scene, selecting a water tank fire truck, an urban main warfare fire truck, a foam water tank fire truck or an emergency rescue truck, and gathering the water tank fire truck, the urban main warfare fire truck, the foam water tank fire truck or the emergency rescue truck at a first distance from an outer floating roof oil tank model;
setting a warning module at a second distance from a vehicle collecting point, dividing a warning area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting a warning sign;
wearing a heat insulation clothing component and an air respirator component in a virtual scene;
under the virtual scene, starting a foam gun to conduct coverage fire extinguishment, and ensuring that at least 50% of foam does not float outside a designated area;
in a virtual scene, closing an edge vent valve under a non-firing condition;
under a virtual scene, the main water is paved from a foam vehicle to the top pavement of the oil tank near the outer floating roof oil tank; two water splitters are connected, and water is extended to the left and right directions to reach the windward direction at the top of the oil tank.
Optionally, the generating the 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 includes:
step S1042: capturing the hand motions of the trained object through a hand motion capturing device and generating hand motion signals;
step S1044: determining a spatial position parameter of the hand motion by a positioning sensor;
step S1046: and receiving hand action signals and spatial position parameters through VR wearable equipment worn on the head of the trained object, and presenting operation tasks 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.
Wherein based on the actual operation of the trained object, the method comprises the following steps as shown in fig. 3: a stereoscopic space including a side surface and a top surface; the VR wearable device is configured to be worn on the head of an operator and used for presenting a virtual scene, wherein the virtual scene comprises an external floating roof oil tank model; the positioning device is arranged on the side surface and/or the top surface of the three-dimensional space, is in communication connection with the VR wearable device and is used for positioning the position of the VR wearable device in the space and generating a positioning signal; the hand motion capturing 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 device, the positioning device and the hand motion capture device, and 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 device. The system builds a real disaster scene, and enables operators to be immersed in the simulation environment through the VR wearable device, so that the operators can obtain training effects basically consistent with the real environment.
As shown in fig. 3, the VR external floating roof tank accident handling teaching and training system includes: positioning device 1, VR wearable equipment 2, hand motion capture device 3, host computer 4 and three-dimensional space 6.
The three-dimensional space 6 serves as an operation space for VR-based external floating roof tank accident handling teaching and training to implement VR-based external floating roof tank accident handling teaching and training. The volume 6 may or may not be enclosed, and the volume 6 includes side surfaces and a top surface.
VR wearable device 2 is configured to be worn on the head of an operator for presenting a virtual scene comprising an external floating roof tank model 5. The outer floating roof tank model 5 is presented in a position close to the centre of the volume 6. The VR wearable device 2 includes, for example, a head-mounted display and/or a binocular omnidirectional display, and can realistically display objects in front of eyes through a 3D rendering technology, so as to implement 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 performing virtual display may 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, is in communication connection with the VR wearable device 2, for example, is in wireless or wired connection, and is used for positioning the VR wearable device 2 in the space, that is, the position of an operator wearing the VR wearable device 2, so as to generate a positioning signal. The positioning device 1 adopts one or more of a laser radar, an infrared camera or a somatosensory camera, and is a basis for realizing mapping from real space positioning to virtual real space positioning, and the number of the positioning devices is deployed according to the space size so as to realize accurate positioning on operators, such as training personnel. The laser radar, the infrared camera or the somatosensory camera can be applied to the present disclosure in any model, and specific models are not described.
In some embodiments, the volume 6 comprises sides and/or a top surface that is cambered and curved away from the outer floating roof tank model 5, so that the operator has a wider operational activity space.
In some embodiments, the number of positioning devices 1 is a plurality, and the plurality of positioning devices 1 are distributed at intervals, as shown in fig. 1, and the plurality of positioning devices 1 are disposed on a side surface of the three-dimensional space 6, for example.
In some embodiments, the 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 trained personnel can perform simulation training within the maximum range of the working space.
The hand motion capture device 3 is used to capture the hand motion of an operator and generate hand motion signals. The hand action signal corresponds to the specific operation of the operator, and various specific operations of the operator in coping with the accident of the external floating roof oil tank can be embodied. The hand motion capture device 3 comprises a motion capture glove, a motion capture bracelet or a motion capture handle. The motion capture handle is similar to a joystick or a collection device such as a joystick, through which the hand motion of a training person, e.g., left, right, front, back, determination, etc., may be obtained. Specifically, the hand motion capture device 3 is, for example, a VR handle, which is used to interact with an object in the virtual reality world, and has a built-in sensor that can be tracked by a positioner; the locator is internally provided with a laser generator and a photosensitive sensor for determining the position of the VR handle. The specific model and structure of the hand motion capture device are not limited, and all hand motion capture devices with the functions can meet the requirements of the application. The hand motion capturing device generates a corresponding hand motion signal from the hand motion of the training person, and transmits the hand motion signal to the host computer 4.
The upper computer 4 is in communication connection with the VR wearable device 2, the positioning device 1, and the hand motion capturing device 3, for example, wired and/or wireless connection, and is configured to receive the hand motion signal and the positioning signal, generate a control signal according to the hand motion signal and the positioning signal, and send the control signal to the VR wearable device 2. The host computer 4 controls the VR wearable device 2 to present the virtual scene and update in real time.
Step S106: based on the execution result of the operation task, the current state information of the external floating roof oil tank model and the external environment information are input into a decision tree model, and the training state is judged according to the decision tree model.
Construction of a decision tree model as shown in fig. 5, the decision tree model includes:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, m k Indicating the degree of matching between the execution of the kth operation task and the current state of the external floating roof tank model, e.g. when a fire is found to have occurred, executing a foam fire extinguishing, indicating a higher degree of matching, m k The smaller the value, when the fire is found, the valve is closed firstly and then foam fire extinguishing is carried out, the higher the matching degree is, and at the moment, m k The value is smaller, and the specific corresponding relation between the execution condition and the matching condition is set in the model data in a preset mode; a, a k Weights representing the kth operational task, n k The matching degree of the kth external environment and the current state of the external floating roof oil tank model is shown, for example, the external floating roof oil tank model rains under the condition of fire, the matching degree is good, and n k The value is smaller, the matching degree is poorer, n is lower when the fire is blown by wind k The value is larger; b k Representing the weight of the kth external environment, and L represents the training result obtained according to the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model, the more successful the training result. And the training result L calculated according to the decision tree model ranges from 0 to 0.3.
Specifically, as shown in fig. 4, the following operations are completed in the training learning process:
1) Force adjustment stage: VR simulation can go out of a water tank fire truck, an urban main warfare fire truck, a foam water tank fire truck and an emergency rescue truck, and is accumulated at an upper air port which is 1000 meters away from an accident vehicle.
2) Surrounding alert around accident VR simulated soft alert: setting an alert at 1000 meters outside the vehicle collecting point, dividing an alert area into a heavy danger area, a medium danger area, a light danger area and a safety area, and setting an alert mark; simulating hard warning: the road surface is blocked by the vehicle, and two people are kept for cooperation warning.
3) And (3) performing short-range investigation, namely performing VR simulation, using an instrument or observing and detecting the leakage position, shape, concentration and range of the tank truck in the field, and trapping personnel. And observing the surrounding information of the accident, and judging whether a shelter can be found.
4) Safety protection, namely VR simulation wearing heat insulation clothing and air respirator. If leakage occurs, heavy protective clothing and air respirators are worn.
5) Diluting, namely paving a trunk water from a foam vehicle to a pavement at the top of an oil tank when VR is close to the tank body; two water splitters are connected, and water is extended to the left and right directions to reach the windward direction at the top of the oil tank.
6) And closing the valve, namely closing the valve of the non-leakage tank by VR simulation under the non-ignition condition. If fire happens, the valve of the non-fire tank is closed first.
7) The water belts in the left direction and the right direction are provided with foam robs, the foam robs are matched with a foam generator to extinguish fire downwards, and the two-way fighters extend downwards in the wind direction while extinguishing fire until all the fire is extinguished.
8) Leakage is prevented, ensuring that at least 50% of the foam does not float outside the designated area.
9) Whether the preamble is completed or not is judged, and a failure judgment and a global judgment are provided.
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-person training module, and a rendering module.
The computing module is used to take charge of high-precision computing tasks and model rendering tasks that map data from the real world into virtual reality. The computing module comprises a CPU computing logic unit, a GPU rendering logic unit, a position tracking unit and a virtual reality projection unit. The computing module performs high-precision computing tasks and model rendering tasks that map data from the real environment into a virtual reality.
The teaching module is used for providing basic common sense 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 the structure teaching information such as a tank body, a frame, a tank wall, a manhole, a safety valve and the like; the teaching unit of the oil tank operable part mainly simulates the teaching information of the operation technology and tactics of relevant parts such as force adjustment, warning, investigation, protection, dilution, valve closing, cooling, fire extinguishing and the like.
The single training module is used for single training, and is used for completing whole accident investigation and tactics under random or appointed environments, and mainly comprises technical tactics exercise units for force mobilization, wearing equipment, reconnaissance of fire, laying of a water belt, extension of the water belt, setting of a foam pipe gun, foam robbery extinguishment, covering extinguishment, leakage prevention and the like.
The multi-person training module is used for multi-person collaborative training and comprises units of full scene establishment, fire scene establishment, task tree establishment, multi-person cooperation and the like, and is suitable for the cooperation of multiple persons to complete treatment tasks, push and understand calculation in real time by taking domestic typical combat cases as the background to construct leakage, fire and other accident scenes at positions of a three-dimensional manhole, a breather valve, a pipeline and the like, so that command and collaborative combat capability of large-scale fire scene combined combat is improved, and fire fighting level is greatly improved.
The rendering module mainly comprises combined actions and displays of oil tank rendering, personnel rendering, equipment rendering and scene rendering, and comprehensively utilizes a graphic image technology, a man-machine interaction technology and a pattern recognition technology to create a virtual simulation environment with strong immersion, so that training personnel are immersed in the virtual environment of the oil tank accident disposal site in a first person view angle mode, and a vivid, safe, repeatable and low-consumption training means is provided for fire officers and soldiers.
In some embodiments, the teaching and training is performed as follows: training personnel get into the three-dimensional space, namely working space, come to the training position, wear the wearable equipment of VR, open the host computer, 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 firing, and the accident scene passes through VR and shows in training personnel's front of the eye, and the display mode is a three-dimensional virtual image. At this time, the operator starts to process the accident of the external floating roof oil tank according to the accident scene, for example, the foam gun is operated to extinguish the fire by operating the left and right of the front operating handle, or various training units provided in the virtual scene, for example, a water belt is paved, etc., are dragged by the handle until the accident is processed.
The virtual dangerous chemical oil tank is combined with VR virtual reality technology, various disaster situations and dangerous situations possibly occur, a trainee can roam in the scene to know the detailed information of the disaster situations in the dangerous chemical accident space environment, the trainee is immersed in the dangerous chemical accident space environment, various occurring accident scenes are treated by utilizing the information such as the position, the gesture and the like transmitted by the computer sensor, and the reality and the infectivity of the virtual scene are improved. Provides a realistic, safe, repeatable and low-consumption training means for fire officers and soldiers, so as to improve the command and collaborative combat capability of the combined combat of a large fire scene. The fire fighting level is greatly improved, and the personal and property safety of people and the clean and healthy environment are better ensured in the actual accident disposal.
In addition, the disclosure further provides an embodiment of the apparatus adapted to the above embodiment, so as to implement the method steps described in the above embodiment, and the explanation based on the meaning of the same names is the same as that of the above embodiment, which has the same technical effects as those of the above embodiment, and will not be repeated herein.
As shown in fig. 7, according to a specific embodiment of the present disclosure, the present disclosure provides an external floating roof tank model accident handling training device under a virtual scene, including:
The acquisition unit 702: the method comprises the steps of under a virtual scene, acquiring states of components in an external floating roof oil tank model, calculating normalization parameters of the components according to the states of the components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of the components; the components of the outer floating roof oil tank model comprise an edge vent valve, an edge sealing strip, a thermometer and a liquid level meter;
optionally, the calculating the normalized 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 normalized parameter of each component includes:
calculating 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 normalized parameters of each component and the normalized 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,
representing normalized parameters of the input floating roof oil tank model assembly; w (w) i The weight coefficient corresponding to the floating roof oil tank model component is represented, and the weight coefficient can be preset according to the influence condition; />
The normalization parameter of the external environment in the current environment is represented; h is a i The weight coefficient corresponding to the external environment is represented, the weight coefficient can be preset according to the influence condition, and P is the calculated probability.
Generation unit 704: the external floating roof oil tank model assembly is configured to generate an operation task for an accident under a virtual scene based on real operation information of a trained object when the accident occurrence probability exceeds a preset threshold;
the preset threshold may be set to, for example, 0.3-0.5, and training may be performed when the probability calculated by the model as described above is greater than the preset threshold.
Based on the real operation information of the trained object, generating operation tasks of the external floating roof oil tank model assembly for accidents in a virtual scene, wherein the operation tasks comprise at least one of the following operation tasks:
under a virtual scene, selecting a water tank fire truck, an urban main warfare fire truck, a foam water tank fire truck or an emergency rescue truck, and gathering the water tank fire truck, the urban main warfare fire truck, the foam water tank fire truck or the emergency rescue truck at a first distance from an outer floating roof oil tank model;
Setting a warning module at a second distance from a vehicle collecting point, dividing a warning area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting a warning sign;
wearing a heat insulation clothing component and an air respirator component in a virtual scene;
under the virtual scene, starting a foam gun to conduct coverage fire extinguishment, and ensuring that at least 50% of foam does not float outside a designated area;
in a virtual scene, closing an edge vent valve under a non-firing condition;
under a virtual scene, the main water is paved from a foam vehicle to the top pavement of the oil tank near the outer floating roof oil tank; two water splitters are connected, and water is extended to the left and right directions to reach the windward direction at the top of the oil tank.
Optionally, the generating the 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 includes:
capturing the hand motions of the trained object through a hand motion capturing device and generating hand motion signals; determining a spatial position parameter of the hand motion by a positioning sensor; and receiving hand action signals and spatial position parameters through VR wearable equipment worn on the head of the trained object, and presenting operation tasks 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 judgment unit 706: and the external floating roof oil tank model is configured to input the current state information and the external environment information of the external floating roof oil tank model 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.
Construction of a decision tree model as shown in fig. 5, the decision tree model includes:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, m k Indicating the degree of matching between the execution of the kth operation task and the current state of the external floating roof tank model, e.g. when a fire is found to have occurred, executing a foam fire extinguishing, indicating a higher degree of matching, m k The smaller the value, when the fire is found, the valve is closed firstly and then foam fire extinguishing is carried out, the higher the matching degree is, and at the moment, m k The value is smaller, and the specific corresponding relation between the execution condition and the matching condition is set in the model data in a preset mode; a, a k Weights representing the kth operational task, n k The matching degree of the kth external environment and the current state of the external floating roof oil tank model is shown, for example, the external floating roof oil tank model rains under the condition of fire, the matching degree is good, and n k The value is smaller, the matching degree is poorer, n is lower when the fire is blown by wind k The value is larger; b k Representing the weight of the kth external environment, and L represents the training result obtained according to the decision tree model.
Optionally, the smaller the training result L calculated according to the decision tree model, the more successful the training result. And the training result L calculated according to the decision tree model ranges from 0 to 0.3.
The virtual dangerous chemical oil tank is combined with VR virtual reality technology, various disaster situations and dangerous situations possibly occur, a trainee can roam in the scene to know the detailed information of the disaster situations in the dangerous chemical accident space environment, the trainee is immersed in the dangerous chemical accident space environment, various occurring accident scenes are treated by utilizing the information such as the position, the gesture and the like transmitted by the computer sensor, and the reality and the infectivity of the virtual scene are improved. Provides a realistic, safe, repeatable and low-consumption training means for fire officers and soldiers, so as to improve the command and collaborative combat capability of the combined combat of a large fire scene. The fire fighting level is greatly improved, and the personal and property safety of people and the clean and healthy environment are better ensured in the actual accident disposal.
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 enable the at least one processor to perform the method steps described in the embodiments above.
The disclosed embodiments provide a non-transitory computer storage medium storing computer executable instructions that perform the method steps described in the embodiments above.
Referring now to fig. 8, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 8, the electronic device may include a processing means (e.g., a central processor, 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 required for the operation of the electronic device are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other by a bus 805. An input/output (I/O) interface 805 is also connected to the bus 805.
In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 805 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and 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 shows an electronic device having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 805, or installed from storage 808, or installed from ROM 802. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 801.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this 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 the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Claims (9)
1. An accident handling training method for an external floating roof oil tank model in a virtual scene is characterized by comprising the following steps:
under a virtual scene, acquiring states of all components in the external floating roof oil tank model, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; the components of the outer floating roof oil 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 assembly for the accident under a virtual scene based on the real operation information of the trained object;
inputting current state information of the external floating roof oil tank model and external environment information into a decision tree model based on an execution result of the operation task, and judging a training state according to the decision tree model;
The method for judging the accident occurrence probability of the current external floating roof oil tank model based on the normalized parameters of each component comprises the following steps:
calculating 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 normalized parameters of each component and the normalized 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,
representing normalized parameters of the input floating roof oil tank model assembly; w (w) i Representing the weight coefficient corresponding to the floating roof oil tank model component; />
The normalization parameter of the external environment in the current environment is represented; h is a i And representing the weight coefficient corresponding to the external environment, wherein P is the calculated probability.
2. The method of claim 1, wherein the operational tasks comprise at least one of the following operational tasks:
under a virtual scene, selecting a water tank fire truck, an urban main warfare fire truck, a foam water tank fire truck or an emergency rescue truck, and gathering the water tank fire truck, the urban main warfare fire truck, the foam water tank fire truck or the emergency rescue truck at a first distance from an outer floating roof oil tank model;
Setting a warning module at a second distance from a vehicle collecting point, dividing a warning area into a heavy-risk area, a medium-risk area, a light-risk area and a safety area, and setting a warning sign;
wearing a heat insulation clothing component and an air respirator component in a virtual scene;
under the virtual scene, starting a foam gun to conduct coverage fire extinguishment, and ensuring that at least 50% of foam does not float outside a designated area;
in a virtual scene, closing an edge vent valve under a non-firing condition;
under a virtual scene, the main water is paved from a foam vehicle to the top pavement of the oil tank near the outer floating roof oil tank; two water splitters are connected, and water is extended to the left and right directions to reach the windward direction at the top of the oil tank.
3. The method of claim 1, wherein generating the operational tasks for the accident-prone external floating roof tank model component in the virtual scenario based on the real operational information of the trained object comprises:
capturing the hand motions of the trained object through a hand motion capturing device and generating hand motion signals;
determining a spatial position parameter of the hand motion by a positioning sensor;
and receiving hand action signals and spatial position parameters through VR wearable equipment worn on the head of the trained object, and presenting operation tasks in the virtual scene.
4. The method of claim 3, wherein the hand motion capture device comprises a motion capture glove, a motion capture bracelet, and/or a motion capture handle.
5. The method of claim 2, wherein the decision tree model comprises:
wherein k represents a natural number between 1 and n, n is a natural number greater than 1, m k Representing the matching degree of the execution of the kth operation task and the current state of the external floating roof oil tank model, a k Weights representing the kth operational task, n k Represents the kth exteriorMatching degree of environment and current state of external floating roof oil tank model, b k Representing the weight of the kth external environment, and L represents the training result obtained according to the decision tree model.
6. The method of claim 5, wherein the smaller the training result L calculated from the decision tree model, the more successful the training result.
7. The method of claim 6, wherein the training result L calculated from the decision tree model ranges from 0 to 0.3.
8. An external floating roof oil tank model accident handling training device under virtual scene, which is characterized by comprising:
the obtaining unit is used for obtaining states of all components in the external floating roof oil tank model under the virtual scene, calculating normalization parameters of all components according to the states of all components, and judging accident occurrence probability of the current external floating roof oil tank model based on the normalization parameters of all components; the components of the outer floating roof oil 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 assembly for the accident under the virtual scene based on the real operation information of the trained object when the accident occurrence probability exceeds a preset threshold value;
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 a decision tree model based on the execution result of the operation task, and judging a training state according to the decision tree model;
the method for judging the accident occurrence probability of the current external floating roof oil tank model based on the normalized parameters of each component comprises the following steps:
calculating 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 normalized parameters of each component and the normalized 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,
representing normalized parameters of the input floating roof oil tank model assembly; w (w) i Representing the weight coefficient corresponding to the floating roof oil tank model component; />
The normalization parameter of the external environment in the current environment is represented; h is a i And representing the weight coefficient corresponding to the external environment, wherein P is the calculated probability.
9. 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 implement the method of any of claims 1 to 7.
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| CN110135741A (en) * | 2019-05-20 | 2019-08-16 | 中国船舶重工集团公司第七0三研究所 | Hot drill evaluating system and method based on carrier-borne aircraft fire |
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