CN105046388A - Simulating deduction method for electric power transmission line operating risk training - Google Patents
Simulating deduction method for electric power transmission line operating risk training Download PDFInfo
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Abstract
The invention discloses a simulating deduction method for electric power transmission line operating risk training. The simulating deduction method for electric power transmission line operating risk training comprises the steps: abstracting and establishing a simulating deduction model according to the risk training content of electric power transmission line operation; extracting the information required by the electric power transmission line operating risk training, instantiating the elements in the simulating deduction model according to the information, and generating a simulating deduction plan on a time axis; and constructing a virtual scene of the electric power transmission line and displaying the operation staff and the related behaviors according to the described information of each element in the simulating deduction plan. Through the simulating deduction method provided by the invention, the operation staff can know well about the task environment, and the capability of the working staff for dealing with the practical emergency can be improved; and at the same time, the simulating deduction method is beneficial to examination and improvement of a risk preparedness plan, and can continuously enlighten new risk response ideas.
Description
Technical field
The present invention relates to a kind of emulation deduction method, particularly relate to a kind of emulation deduction method for the training of electric power transmission line operating risk, belong to Simulating technique in Electric Power System field.
Background technology
Operating risk management be with engineering, system, enterprise etc. for object, implement identification of dangerous source, venture analysis, risk assessment, risk control respectively, thus reach control risk, prevent accident, safing object.Wherein, the educational training of operating risk management is the important step during electric system Risk Management System is built.
Current, electric system advances Risk Management System construction, mainly use for reference the working experiences such as safety evaluatio, venture analysis and control in the past, set up corresponding working stamndard and working mechanism, and by KXG software, identify, the various risk factors of typing, by this system software from data base querying, show the corresponding control measure that gear to actual circumstances and method, to take precautions against the generation of personnel's security incident in operation process.
But the Educational Training Service of electric power transmission line operating risk management in current electric system, in view of cost and safety factor, much all cannot accomplish on-the-spot real training, can only be that mode is inculcated based on theory, is described as main with text in form.In the training process of reality, no matter be by Information software, or drilled by collective, electric power transmission line job content is all that static data in the form of text shows, and does not possess intuitively the dynamically form of expression.Therefore task and scope are relatively-stationary, and to perform flow process be also relatively single, lack attractive force.Final result of training often becomes a mere formality, and cannot reach the object of prediction scheme rehearsal.And along with the development of science and technology, although some electrical hazards simulation training systems add the display of the data such as picture, video, but generally lack feeling of immersion, sentience and naturality, the feature of risk in real electric operating scene cannot be embodied in training and learning environment as far as possible truly.
Therefore, existing electric power transmission line operating risk emulation training mode can not meet the demand for experience of trainer well, and have impact on the whole structure of training, hidden danger has been buried in the safety in production for future.
Summary of the invention
For the deficiencies in the prior art, technical matters to be solved by this invention is to provide a kind of emulation deduction method for the training of electric power transmission line operating risk.
For achieving the above object, the present invention adopts following technical scheme:
For an emulation deduction method for electric power transmission line operating risk training, comprise the steps:
According to the risk training contents of electric power transmission line operation, abstract and foundation emulates deduces model;
Extract the information that electric power transmission line operating risk nursery needs, and carry out instantiation according to described information to the element that described emulation is deduced in model, plan is deduced in the emulation on rise time axle; Wherein, described element comprise training entity, operation behaviour, artificial tasks and risk mutual;
Deduce in the works to the descriptor of each element according to described emulation, build the virtual scene of electric power transmission line and show operating personnel and corelation behaviour.
Wherein more preferably, generate emulation deduction plan to comprise the steps:
According to the whole description of risk training contents, build emulation and deduce program introduction;
If desired introduce the background of this operating risk training, then build task context; Otherwise, do not build task context;
Extract the emulation related in this operating risk training process and deduce model element, the composition according to each element organizes input content, builds the formalized description of element.
Wherein more preferably, deduce in the works to the descriptor of each element according to described emulation, display operating personnel and corelation behaviour comprise the steps:
The descriptor that plan obtains certain artificial tasks is deduced from emulation;
Learner sequentially performs the behavior of described artificial tasks at described virtual scene, and shows the operation behaviour of corresponding training entity;
According to the descriptor of described artificial tasks judge the training entity that learner operates and operation behaviour whether correct: if correct, read the descriptor that described risk is mutual, otherwise perform next behavior;
In described virtual scene, risk animation is shown according to the descriptor that described risk is mutual.
Wherein more preferably, deduce the training entity information of plan according to emulation, build the virtual scene of electric power transmission line: wherein,
The classification information of described training entity is judged according to entity class;
In scene library or personage storehouse, corresponding three-dimensional model is searched according to identity property;
According to space attribute, behavior property and perception properties information described three-dimensional model can be arranged at virtual scene and show.
Wherein more preferably, the entity class of described training entity comprises operating environment, leader's entity and behavior entity.
Wherein more preferably, in described virtual scene, show operation behaviour to comprise the steps:
In behavior storehouse, corresponding three-dimensional model is obtained according to behavior mark, behavior title;
Described three-dimensional model shows by the place occurred according to behavior and behavior conditional information in virtual scene.
Wherein more preferably, obtain according to the behavior sequence in described artificial tasks the training entity and operation behaviour that current artificial tasks comprises.
Wherein more preferably, the descriptor of described artificial tasks comprises risk relations, obtains the mutual descriptor of corresponding risk according to the risk identification in described risk relations.
Wherein more preferably, read the descriptor that described risk is mutual, and in described virtual scene, show risk animation comprise the steps:
According to mutual sending entity information, judge whether consistent with the training entity of artificial tasks: if consistent, continue to perform; Otherwise this time risk terminates alternately;
In risk storehouse, corresponding three-dimensional animation is searched according to described risk classifications, the impact on active entities, the impact on passive entity;
At the three-dimensional animation of the described impact on passive entity of mutual receiving entity display or the three-dimensional animation in the described impact on active entities of mutual active entities display.
Emulation deduction method for the training of electric power transmission line operating risk provided by the present invention has following beneficial effect: carry out simulation reply for contingent risk situation in future; Operating personnel is enable to be familiar with task environment; Check and improve risk prediction scheme, and demonstration assessment carried out to prediction scheme and supplements perfect, constantly inspiring new risk resolution thought.By the present invention, the ability that the operational capability of prediction scheme and the familiarity of the aftertreatment flow process that has an accident and operating personnel tackle actual burst situation effectively can be promoted.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the emulation deduction method of electric power transmission line operating risk provided by the present invention training;
The emulation deduction illustraton of model of Fig. 2 for adopting in the present invention;
Fig. 3 is the illustraton of model emulating deduction plan in the present invention;
Fig. 4 is the workflow diagram building emulation deduction plan in the present invention;
Fig. 5 is the process flow diagram performing emulation deduction plan in the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, technology contents of the present invention is described in detail.
In order to solve in electric power transmission line operating risk emulation training the problem lacking the visual and prediction scheme of risk and deduce, the present invention deduces model by the emulation of setting up towards risk management and control, build the visual virtual scene of electric power transmission line, finally realize the workflow that operating risk management emulation is deduced.See Fig. 1, the emulation deduction method of electric power transmission line operating risk provided by the present invention training, comprises the steps: according to electric power transmission line risk training contents, and abstract and foundation emulates deduces model; Element in model is deduced to emulation and carries out formalized description, generate emulation and deduce plan; Deduce the descriptor of plan according to emulation, perform operating risk emulation and deduce flow process.Be described in detail below.
Step 1, according to electric power transmission line risk training contents, abstract and foundation emulates deduces model.
When setting up emulation and deducing model, usually adopt the systematic analytic method of entity-oriented, namely EATI (Entities, Actions, Tasks, Interactions, entity, action, task, mutual) method carries out the definition of deduction information.Then according to the description of the risk operation training contents form of implementation of reality, so that the identification of computing machine and process.Possess self descriptiveness, extensibility, standardization, the feature such as cross-platform based on extend markup language (XML), the present invention adopts XMLSchema to deduce the formalized description method of model as emulation.See Fig. 2, it is mutual that the element that model is deduced in emulation in the present embodiment comprises training entity, operation behaviour, artificial tasks and risk.Below the definition of each element is described in detail.
First, training entity is introduced.In electric power transmission line operating risk emulation training, training entity to refer in operation process behavior and interactional main body thereof or object, specifically comprises leader entity, behavior entity, operating environment and load-bearing capacity etc.Wherein, leader's entity is leader with specific duties at different levels and department, is the entity behavior entity of its subordinate being implemented to commander's command, as safety monitor Quality Mgmt Dept, work responsible official; Behavior entity is the main body of carrying out operation, such as line inspection personnel, circuit operations staff, line maintenance personnel etc.; Operating environment comprises equipment, the facility and weather condition etc. of operation.Training entity needs to be described the equipment, environment, personnel etc. that relate in electric power transmission line operating risk emulation training by correlation attribute information.Training entity in the present embodiment need the information described comprise identity property (ID), entity class (Type), space attribute (Space), behavior property (Behaviors), can perception properties (Perception) etc.
Secondly, operation behaviour is introduced.Operation behaviour in the present embodiment is the dynamic moving process with certain hour span that training entity performs.Such as, climb shaft tower, Work tool test, change insulator chain etc.Operation behaviour is described training entity task activity that may perform in operation process simulation by correlation attribute information, specifically needs the place (Place), behavior condition (Condition) etc. that the attribute information described comprises behavior identifier (ID), behavior title (Name), behavior occur.
Then, artificial tasks is introduced.Training entity (such as: with the erection of shaft tower double-circuit line, leap, parallel erection etc.) under certain environment condition adopts certain mode, strategy etc. to take precautions against contingent risk, to realize the action process of target (such as: strain section wire is changed).Artificial tasks has the main body of action executing, the target of action effect and realizing intention requirement, is the instantiation of action step.Such as, " maintainer changes power failure new Hangzhoupro 2Q98 circuit #8-#18 strain section wire ".Artificial tasks needs the attribute information described to comprise: the condition (Condition) of task identification (ID), task names (Name), mission statement (Description), tasks carrying, behavior sequence (JobSequence), risk relations (Relationship).
Finally, risk is introduced mutual.Risk refers to alternately executes the task or interacts because of the existence of a certain risk between the entity of behavior and target entity.Initiatively produce risk and active entities is called to the entity that other entity is exerted one's influence, accepting risk and affected entity is called passive entity.The mutual process of risk is divided into three phases: first, active entities sends alternately: secondly, and passive entity accepts mutual; Finally, the impact and control measure that produce because risk exists between interworking entity is calculated according to interaction parameter.Risk needs the information described to comprise alternately: the risk (Risk) of mutual mark (ID), type of interaction (Type), mutual sending entity (Source), mutual receiving entity (Target), existence, the impact (Affected) on active entities, the impact (Affect) on passive entity.
Step 2, carries out formalized description to the element that emulation is deduced in model, generates emulation and deduces plan.
The generative process of emulation deduction plan is in fact the instantiation of emulation being deduced to element in model, namely EATI method is utilized, extract the information needed for electric power transmission line operating risk emulation training, and combined with concrete emulation training content by the element that emulation is deduced in model, and then generate an emulation deduction plan deduced on a timeline.
See Fig. 3, the model element of emulation deduction plan comprises training entity, operational action, artificial tasks and the risk interactive elements deduced in program introduction, task context and emulation deduction model.Below for the training of an electric power transmission line operating risk, the detailed process setting up emulation deduction plan is described.Suppose that the content of a certain electric power transmission line operating risk training is as follows:
220kV Hang Xin 2Q97, new Hangzhoupro 2Q98 line, with the erection of shaft tower double-circuit line, wherein Hang Xin 2Q97 circuit runs, and the 2Q98 line outage of new Hangzhoupro is overhauled.Power failure new Hangzhoupro 2Q98 circuit #8-#18 strain section wire is changed.Wherein, line route and friendship are across situation: cross over 110kV in (1) 220kV Hang Xin 2Q97, new Hangzhoupro 2Q98 line #9-#10 tower shelves and build paulownia line 1735 line #22-#23 shelves.(2) Hang Xin scape highway is crossed in 220kV Hang Xin 2Q97, new Hangzhoupro 2Q98 line #14-#15 tower shelves.
See Fig. 4, the detailed process setting up emulation deduction plan comprises the steps:
Step 201, builds according to the whole description content of electric power transmission line operating risk training and deduces program introduction.According to present example, using following key entry and as this deduction program introduction: shaft tower double-circuit line sets up, Hang Xin 2Q97 circuit runs, and the 2Q98 line outage of new Hangzhoupro is overhauled.Power failure new Hangzhoupro 2Q98 circuit #8-#18 strain section wire is changed.
If the current related introduction that need carry out operation background to this operating risk training, then organize input content to build task context; As need not, can build.Such as, the task context of the present embodiment is: cross over 110kV in 220kV Hang Xin 2Q97, new Hangzhoupro 2Q98 line #9-#10 tower shelves and build paulownia line 1735 line #22-#23 shelves; Hang Xin scape highway is crossed in 220kV Hang Xin 2Q97, new Hangzhoupro 2Q98 line #14-#15 tower shelves.
Step 202, extracts the entity related in this operating risk training process, then according to the descriptor of the training entity of definition, organizes input content, builds training entity.Such as, in the present embodiment, specifically organize input content as follows:
<Entity>
<ID>#9</ID>
<Type> operating environment </Type>
<Space>220kV Hang Xin 2Q97</Space>
<Behavior> crosses over </Behavior>
<Perception>
<Source>220kV Hang Xin 2Q97</Source>
<Target>110kV builds paulownia line 1735 line </Target>
<Source>220kV Hang Xin 2Q97</Source>
<Target> Hang Xin scape highway </Target>
Step 203, extracts the behavior related in this operating risk training process, then according to the descriptor of the operation behaviour of definition, organizes input content, builds operation behaviour.Such as, relate to multiple behavior in the present embodiment, specifically organize input content as follows:
<Behavior>
<ID>001</ID>
<Name> changes insulator <Name>
<Place>220kV Hang Xin 2Q97#9<Place>
<Condition> power failure <Condition>
<Behavior>
<ID>002</ID>
<Name> climbing tower <Name>
<Place>220kV Hang Xin 2Q97#9<Place>
<Condition> <Condition> on daytime
Step 204, extracts relating in this operating risk training process of task, according to the descriptor of the artificial tasks of definition, organizes input content, builds artificial tasks.Such as, specifically organize input content as follows in the present embodiment:
<Task>
<ID>001</ID>
<Name> power failure restringing </Name>
<Description> changes power failure new Hangzhoupro 2Q98 circuit #8-#18 strain section wire.
</Description>
<Condition> makes an inspection tour </Condition>
<JobSequence>
<SequenceItem>
<EntityID>003</EntityID>
<BehaviorID>002</BehaviorID>
</SequenceItem>
<SequenceItem>
<EntityID>005</EntityID>
<BehaviorID>001</BehaviorID>
</SequenceItem>
</JobSequence>
<Relationship>
<RiskID>001</RiskID>
</Relationship>
Wherein, behavior sequence is the behavior set for emulating deduction plan based on time sequencing.When reality is deduced, distinguish training entity and operation behaviour according to EntityID and BehaviorID, by corresponding ID in acquisition descriptor.
Step 205, extracts the risk point related in this operating risk training process, according to the mutual descriptor of risk of definition, organizes input content, builds risk mutual.Such as, specifically organize input content as follows in the present embodiment:
<Interaction>
<ID>001<ID>
<Type> electric shock </Type>
<TaskName> power failure restringing </TaskName>
<Source> trackman </Source>
<Target>220kV Hang Xin 2Q97</Target>
Whether the shaft tower during <Risk> uses meets the requirement of operation electrical safety distance on bar.
</Risk>
<Affected> electric shock </Affected>
<Affect> damages </Affect>
</Interaction>
Step 3, deduces the descriptor of plan according to emulation, perform operating risk emulation and deduce flow process.The specific implementation process of emulation deduction is introduced below in conjunction with Fig. 5.
Step 301, reads in the description deducing program introduction and task context, and shows in interface;
Step 302, deduces plan training entity description information according to emulation, builds electric power transmission line virtual scene.Specific as follows according to the process of training entity description information architecture electric power transmission line virtual scene:
Obtain the entity class of training entity, be judged as that training entity is leader's entity, behavior entity or operating environment:
If be leader's entity or behavior entity, then in personage storehouse, search corresponding three-dimensional model according to the identity property in descriptor, and be all placed in the three-dimensional scenic of the blank of generation; According to space attribute, behavior property, perception properties can arrange in three-dimensional scenic and put corresponding model;
If be as environment, then in scene library, search corresponding three-dimensional model according to the identity property in descriptor, and be all placed in the blank three-dimensional virtual scene of generation; According to space attribute, behavior property with perception properties can put in three-dimensional scenic and arrange corresponding model.Such as, the behavior property of 220kV Hang Xin 2Q97 crosses over, that 110kV builds paulownia line 1735 line and Hang Xin scape highway crossing object can be also illustrated in perception properties, so when model is put in three-dimensional scenic, in #9-#10 tower shelves of 220kV Hang Xin 2Q97, below should be that 110kV builds paulownia line 1735 line, and in #14-#15 tower shelves, below should be Hang Xin scape highway.
Step 303, deduces from emulation the descriptor that plan obtains certain artificial tasks;
Emulation deduction plan comprises the descriptor of several artificial tasks.First the task identification (ID) obtaining the descriptor of artificial tasks describes, and can determine next artificial tasks that will perform according to this task identification.Then the executive condition (Condition) obtaining current artificial tasks describes, and whether can continue to perform according to this condition judgment current task, if can, obtain and show task names (Name) and the mission statement (Description) of this artificial tasks, otherwise, obtain the task identification of next artificial tasks.
Step 304, learner sequentially performs the behavior of artificial tasks at virtual scene, and show the operation behaviour of corresponding training entity, according to the descriptor of artificial tasks judge the training entity that learner operates and operation behaviour whether correct: if correct, read the descriptor that risk is mutual, otherwise perform next behavior.
After learner obtains current artificial tasks, its operation behaviour of training entity is set in virtual scene.Such as, learner by keyboard and mouse operatively current potential electrician virtual portrait carry out pole-climbing etc.According to the operation behaviour of learner's setting, obtain its descriptor, in virtual scene, show corresponding three-dimensional animation.Specific as follows according to the process of the descriptor display three-dimensional animation of operation behaviour:
Read emulation and deduce calculated operation behaviour descriptor, and the attributes such as the place occurred according to behavior identifier, behavior condition, behavior title and behavior, in behavior storehouse, search corresponding behavioral animation, and be placed in three-dimensional scenic.Such as, the place that behavior name is called " climbing shaft tower ", behavior occurs is the behavior of " 220kV Hang Xin 2Q97 ", be then extracted in the behavioral animation subordinate act storehouse " climbing 220kV steel tower ", and be applied in generated three-dimensional scenic.
According to the behavior sequence information of artificial tasks judge the training entity that learner operates and operation behaviour whether correct: if correct, read the descriptor that risk is mutual, otherwise perform next behavior.
The behavior sequence (JobSequence) obtaining current artificial tasks describes, and can obtain the training entity and operation behaviour that comprise in current artificial tasks according to the descriptor of behavior sequence.Behavior sequence comprises some act of execution, and the training entity of the behavior that contains of each behavior and operation behaviour.After the descriptor obtaining behavior sequence, need sequentially read the content of each behavior according to time order and function order and perform corresponding operating.According to by identification information in behavior sequence, distinguish training entity and operation behaviour.Wherein, training entity identification is EntityID, and operation behaviour is designated BehaviorID.When after its corresponding identification information of acquisition, according to this identification information training entity and operation behaviour information.
When after the training entity obtained in behavior sequence and operation behaviour, judge that whether the training entity that learner operates is consistent with behavior sequence with operation behaviour.Such as, obtaining training entity in artificial tasks is earth potential electrician, acquisition operation behaviour is pole-climbing, if learner by keyboard and mouse operatively current potential electrician virtual portrait carry out pole-climbing, then be considered as meeting, then obtain risk descriptor, otherwise provide miscue and obtain next behavior in behavior sequence.
Step 305, reads the descriptor that described risk is mutual, and shows risk animation in described virtual scene.
From artificial tasks, obtain corresponding risk relations (Relationship) describe, and judge whether risk relations has all read in; If completed, then the next item down in act of execution sequence.
From the formal definitions of risk relations, obtain risk identification (RiskID), and then obtain corresponding risk and describe alternately (ID by corresponding Risk);
After reading in the mutual descriptor of risk, obtain corresponding mutual sending entity (Source), whether the training entity judged whether and obtain in behavior sequence is consistent, if consistent, read corresponding risk and (Risk) and risk classifications (Type) are described, and show; Otherwise this time risk performs alternately and terminates and the next item down obtained in risk relations.
Concrete, the process generating risk animation according to the mutual descriptor of risk is as follows:
According to attributes such as type of interaction, the impact (Affected) on active entities, the impacts (Affect) on passive entity, in risk storehouse, search corresponding risk phenomenon and animation, and be all placed in the three-dimensional scenic of generation.At the three-dimensional animation (such as because equipotential electrician get an electric shock cause shaft tower damage) of mutual receiving entity (Target) display on the impact (Affect) of passive entity; At the three-dimensional animation (such as equipotential electrician get an electric shock) of mutual active entities (Source) display on the impact (Affected) of active entities.
Such as, task names is the artificial tasks of " the two 5465 line electric discharge defect eliminations of 500kV good fortune ", the risk existed is " discharging gap is too small; there is the phenomenon of electric discharge ", on the impact of active entities is get an electric shock, on the impact of passive entity be damage, so " 500kV electric discharge ", " staff's electric shock " and the risk animation of " steel tower discharging gap " are extracted from risk storehouse, and be applied in generated three-dimensional scenic.
In sum, emulation deduction method for the training of electric power transmission line operating risk provided by the present invention, according to the task object and the Initial situation that prefer middle setting in advance, according to production plan order and process, the still nonevent potential various risks of each sessions are comprehensively identified, identification, the process such as assessment and analysis drill, so that in actual job, corresponding preventive measure can be taked in time, risk is reduced to acceptable limit.
Compared with prior art, the present invention, before transmission line operation task-cycle, provides three-dimensional task environment intuitively true to nature by computing machine, carries out simulation reply process for contingent risk situation; Enable operating personnel be familiar with task environment, check and improve risk prediction scheme, and demonstration assessment is carried out to prediction scheme and supplements perfect, thus constantly inspire new risk resolution thought.By emulation deduction method of the present invention, the ability that the operational capability of prediction scheme and the familiarity of the aftertreatment flow process that has an accident and operating personnel tackle actual burst situation effectively can be promoted.
Above the emulation deduction method for the training of electric power transmission line operating risk provided by the present invention is described in detail.For one of ordinary skill in the art, to any apparent change that it does under the prerequisite not deviating from connotation of the present invention, all by formation to infringement of patent right of the present invention, corresponding legal liabilities will be born.
Claims (9)
1., for an emulation deduction method for electric power transmission line operating risk training, it is characterized in that comprising the steps:
According to the risk training contents of electric power transmission line operation, abstract and foundation emulates deduces model;
Extract the information that electric power transmission line operating risk nursery needs, and carry out instantiation according to described information to the element that described emulation is deduced in model, plan is deduced in the emulation on rise time axle; Wherein, described element comprise training entity, operation behaviour, artificial tasks and risk mutual;
Deduce in the works to the descriptor of each element according to described emulation, build the virtual scene of electric power transmission line and show operating personnel and corelation behaviour.
2. emulate deduction method as claimed in claim 1, it is characterized in that generating emulation deduction plan comprises the steps:
According to the whole description of risk training contents, build emulation and deduce program introduction;
If desired introduce the background of this operating risk training, then build task context; Otherwise, do not build task context;
Extract the emulation related in this operating risk training process and deduce model element, the composition according to each element organizes input content, builds the formalized description of element.
3. emulate deduction method as claimed in claim 1, it is characterized in that deducing in the works to the descriptor of each element according to described emulation, display operating personnel and corelation behaviour comprise the steps:
The descriptor that plan obtains certain artificial tasks is deduced from emulation;
Learner sequentially performs the behavior of described artificial tasks at described virtual scene, and shows the operation behaviour of corresponding training entity;
According to the descriptor of described artificial tasks judge the training entity that learner operates and operation behaviour whether correct: if correct, read the descriptor that described risk is mutual, otherwise perform next behavior;
In described virtual scene, risk animation is shown according to the descriptor that described risk is mutual.
4. emulate deduction method as claimed in claim 1, it is characterized in that:
Deduce the training entity information of plan according to emulation, build the virtual scene of electric power transmission line: wherein,
The classification information of described training entity is judged according to entity class;
In scene library or personage storehouse, corresponding three-dimensional model is searched according to identity property;
According to space attribute, behavior property and perception properties information described three-dimensional model can be arranged at virtual scene and show.
5. emulate deduction method as claimed in claim 4, it is characterized in that:
The entity class of described training entity comprises operating environment, leader's entity and behavior entity.
6. emulate deduction method as claimed in claim 3, it is characterized in that in described virtual scene, show operation behaviour comprises the steps:
In behavior storehouse, corresponding three-dimensional model is obtained according to behavior mark, behavior title;
Described three-dimensional model shows by the place occurred according to behavior and behavior conditional information in virtual scene.
7. emulate deduction method as claimed in claim 3, it is characterized in that:
The training entity and operation behaviour that current artificial tasks comprises is obtained according to the behavior sequence in described artificial tasks.
8. emulate deduction method as claimed in claim 3, it is characterized in that:
The descriptor of described artificial tasks comprises risk relations, obtains the mutual descriptor of corresponding risk according to the risk identification in described risk relations.
9. emulate deduction method as claimed in claim 3, it is characterized in that reading the mutual descriptor of described risk, and in described virtual scene, show risk animation comprise the steps:
According to mutual sending entity information, judge whether consistent with the training entity of artificial tasks: if consistent, continue to perform; Otherwise this time risk terminates alternately;
In risk storehouse, corresponding three-dimensional animation is searched according to described risk classifications, the impact on active entities, the impact on passive entity;
At the three-dimensional animation of the described impact on passive entity of mutual receiving entity display or the three-dimensional animation in the described impact on active entities of mutual active entities display.
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| CN112561276A (en) * | 2020-12-08 | 2021-03-26 | 珠海优特电力科技股份有限公司 | Operation risk demonstration method and device, storage medium and electronic device |
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