CN116524778A - Intelligent simulation manufacturing method based on virtual reality - Google Patents
Intelligent simulation manufacturing method based on virtual reality Download PDFInfo
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- CN116524778A CN116524778A CN202310489811.1A CN202310489811A CN116524778A CN 116524778 A CN116524778 A CN 116524778A CN 202310489811 A CN202310489811 A CN 202310489811A CN 116524778 A CN116524778 A CN 116524778A
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- 239000000463 material Substances 0.000 claims abstract description 8
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- 238000012423 maintenance Methods 0.000 claims description 14
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Abstract
The invention discloses an intelligent simulation manufacturing method based on virtual reality, which belongs to the technical field of intelligent manufacturing, and comprises the following steps: constructing and gradually perfecting various teaching virtual devices; when teaching is needed, starting the teaching virtual equipment corresponding to the application, and operating the teaching virtual equipment according to the needs by a user to generate corresponding operation data; analyzing the obtained operation data, controlling the teaching virtual equipment to run based on the analysis result, and processing the corresponding materials; displaying the corresponding processing process to a user in real time; displaying the corresponding processing process to a user in real time; the teaching effect is further improved, more real operation simulation is realized, corresponding functions such as sound, light and vibration are supplemented, simulation operation is enabled to be more fit with reality, and when a user is applying real equipment, the teaching effect can be improved, various actual conditions can be met, and follow-up real face time and busyness are avoided.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to an intelligent simulation manufacturing method based on virtual reality.
Background
The current design and manufacture have the characteristics of high information integration level and complex system in the teaching process, real equipment is adopted for experimental teaching, a manufacturing flow field exists, the required modern precision manufacturing equipment is various, the price is high, and the early investment is large; in the running process, the consumption of electricity, liquid, gas and other resources is large, the later maintenance cost of equipment is high, and some equipment has high operation risk; the practical capability culturing requirement of students cannot be met due to the limitation of expenses and sites, and open, autonomous design and innovation experiments are more difficult to develop; even if some enterprises provide space for first-line practice of students, the provided practice space is small, and the expected effect is difficult to achieve in practice; therefore, in order to solve the current teaching problem, the invention provides an intelligent simulation manufacturing method based on virtual reality.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an intelligent simulation manufacturing method based on virtual reality.
The aim of the invention can be achieved by the following technical scheme:
an intelligent simulation manufacturing method based on virtual reality, the method comprises the following steps:
constructing and gradually perfecting various teaching virtual devices; when teaching is needed, starting the teaching virtual equipment corresponding to the application, and operating the teaching virtual equipment according to the needs by a user to generate corresponding operation data; analyzing the obtained operation data, controlling the teaching virtual equipment to run based on the analysis result, and processing the corresponding materials; and displaying the corresponding processing process to a user in real time.
Further, the construction method of the teaching virtual device comprises the following steps:
determining target equipment, collecting target data corresponding to the target equipment, determining a corresponding core structure based on the target data, and constructing corresponding teaching virtual equipment according to each core structure and virtual reality technology.
Further, the method for determining the core structure according to the target data comprises the following steps:
analyzing the target data through a preset core evaluation model to obtain functional values and teaching values corresponding to all structures of the target equipment, calculating corresponding core values according to the obtained functional values and teaching values, and marking the structure with the core value larger than the threshold value X1 as a core structure.
Further, the method for calculating the core value includes:
calculating a corresponding core value based on a core evaluation formula hx=b1×gn+b2×jx;
wherein HX is a core value; GN is a functional value; JX is a teaching value; b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
Further, the teaching virtual equipment associates the corresponding material library.
Further, in the operation application process of the teaching virtual model, the expression form of the operation flow is reflected in real time.
Further, the method for embodying the expression form of the operation flow in real time comprises the following steps:
establishing an auxiliary library based on historical operating data of the target equipment; and establishing a fault discrimination model to evaluate the real-time operation process of the teaching virtual model, obtaining corresponding operation problems, matching corresponding expression forms from an auxiliary library according to the obtained operation problems, and synchronously reflecting in the teaching virtual model based on the obtained expression forms.
Further, the method for perfecting the teaching virtual equipment comprises the following steps:
establishing a problem collection library, wherein the problem collection library is used for collecting feedback problems of users in real time and sorting the stored feedback problems; and the same problem of the same user only remains one in the problem collection library;
and analyzing each feedback problem to obtain a corresponding maintenance value, marking the feedback problem as a correction problem when the maintenance value is larger than a threshold value X2, and improving and perfecting the teaching virtual equipment according to the correction problem.
Further, the method for analyzing the feedback problem comprises the following steps:
corresponding problem values and adjustment coefficients are set according to the feedback problems and the corresponding feedback quantity, the obtained problem values and adjustment coefficients are marked as WT and c respectively, and corresponding maintenance values are calculated according to a maintenance evaluation formula WH=c×WT.
Wherein both threshold X1 and threshold X2 are set by the expert group for discussion.
Compared with the prior art, the invention has the beneficial effects that:
displaying the corresponding processing process to a user in real time; the teaching effect is further improved, more real operation simulation is realized, corresponding functions such as sound, light and vibration are supplemented, the possible phenomena such as sound, light and vibration are analyzed in real time, and the analyzed phenomena such as sound, light and vibration are synchronously displayed, so that the simulation operation is more fit and reality, a user can conveniently face various actual conditions from the face when applying real equipment, and the follow-up real face is prevented from being busy; by evaluating the feedback problem in real time, the dynamic updating and perfecting of the teaching virtual equipment are realized, and the teaching virtual equipment is more and more suitable for teaching and training along with the increase of application time.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a virtual reality-based intelligent simulation manufacturing method includes:
step one: constructing and gradually perfecting various teaching virtual devices;
according to teaching requirements and application equipment in the current actual manufacturing industry, equipment which needs to be applied in the teaching process is determined to be target equipment, such as a numerical control machine tool of a certain version; constructing corresponding teaching virtual equipment according to the determined target equipment; and then dynamically updating and perfecting the teaching virtual equipment according to the application feedback data.
In one embodiment, for the construction of the teaching virtual device, the corresponding construction is performed by combining the functions of the corresponding target device according to the existing virtual reality technology in a manual mode, that is, the construction of the teaching virtual device is performed by using the existing technology in a manual mode.
In another embodiment, for the construction of the teaching virtual equipment, the core component of the target equipment is determined by combining the mode provided by the invention, the construction of the corresponding equipment is focused, and the construction efficiency of the teaching virtual equipment is improved; the specific process is as follows:
acquiring target data such as an operation mode, a corresponding operation purpose, an implementation process and a structure of target equipment, wherein the target data is preset with a corresponding acquisition template, each data item needing data acquisition is set in the acquisition template in a manual mode, the target data is acquired according to the corresponding acquisition template, and the target data in a uniform format is adopted, so that the analysis and the evaluation of a subsequent core evaluation model are facilitated; establishing a corresponding core evaluation model based on a CNN network or a DNN network, establishing a corresponding training set by a manual mode for training, and analyzing target data of each target device through the core evaluation model after successful training to obtain a functional value and a teaching value corresponding to each structure in the target device; the function value is set according to the function of the structure for the target equipment, and the teaching value is set according to the meaning of the application, operation and the like of the structure function for teaching; for example, the numerical control machine tool has extremely high function value corresponding to the programming function, and the programming is one of the most important teaching purposes for teaching, so that the corresponding teaching value is extremely high; the structure can also be a module, such as a module corresponding to a programming function, and the structure is uniformly replaced for representation; because neural networks are prior art in the art, the specific setup and training process in this invention is not described in detail.
Respectively marking the obtained functional value and teaching value as GN and JX, and calculating a corresponding core value HX according to a core evaluation formula HX=b1×GN+b2×JX, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1; structures with core values greater than the threshold value X1 are marked as core structures.
And constructing the teaching virtual equipment according to the obtained core structure. The core structure is built based on the virtual reality technology in an artificial mode, and other non-core structures are simply arranged; the construction flow is simplified, and the construction workload is reduced.
For the constructed teaching virtual equipment, a corresponding material library is arranged in the construction process, namely various raw materials for processing the target equipment are arranged in a follow-up mode, and the corresponding material library is arranged in a correlated mode according to the properties of the raw materials and the processing principle of the target equipment.
Step two: when teaching is needed, starting the teaching virtual equipment corresponding to the application, and operating the teaching virtual equipment according to the needs by a user to generate corresponding operation data;
step three: analyzing the obtained operation data, controlling the teaching virtual equipment to run based on the analysis result, and processing the corresponding materials;
analyzing the obtained operation data, namely identifying corresponding operation steps, programming and the like to control teaching virtual equipment to run, performing simulation running according to original real target equipment, and training a corresponding artificial intelligent model by combining the existing neural network technology and performing dynamic control through the artificial intelligent model; in the step, the teaching virtual equipment can run according to the corresponding operation data and the actual target equipment by utilizing the prior art; therefore, in the setting process of the teaching virtual device, a large number of historical operation data, cases, and the like of the target device need to be summarized for setting.
Step four: and displaying the corresponding processing process to a user in real time.
In one embodiment, in order to further improve the teaching effect, more realistic operation simulation is realized, corresponding functions such as sound, light, vibration and the like are supplemented, namely, according to the operation process of the teaching virtual equipment, phenomena such as sound, light, vibration and the like possibly generated are analyzed in real time, and the analyzed phenomena such as sound, light, vibration and the like are synchronously displayed, so that the simulation operation is more realistic, and the simulation operation is convenient to be carried out, so that when a user is applying real equipment, the user can face various actual conditions from the container, and the follow-up real face is avoided.
The specific process is as follows:
according to a large amount of historical operation data of target equipment, various expression forms possibly occurring in the operation process of the target equipment and operation problems corresponding to the expression forms are acquired, and a corresponding auxiliary library is built in a summarizing mode; establishing a corresponding fault discrimination model based on a CNN network or a DNN network, establishing a corresponding training set in a manual mode for training, evaluating the real-time operation process of the teaching virtual model through the fault discrimination model after successful training to obtain corresponding operation problems, matching corresponding expression forms from an auxiliary library according to the obtained operation problems, and synchronously reflecting in the teaching virtual model based on the obtained expression forms.
In one embodiment, the method for gradually perfecting various teaching virtual devices comprises the following steps:
establishing a problem collection library, when a user feels that the problem needs to be improved in the application process, sending the corresponding problem to the problem collection library for storage, wherein the same problem of the same user only remains one in the problem collection library;
identifying the problems in the problem collection library in real time, marking the problems in the problem collection library as feedback problems, classifying the feedback problems for feeding back the same problem into one class, and identifying and classifying the feedback problems by utilizing the existing data identification processing technology, wherein the feedback descriptions of different users are different because of the same problem, so that the feedback problems are classified by utilizing the corresponding existing data processing technology, namely, the real problems of the problems are extracted, and the description data fed back by other users are uniformly classified under the feedback problem classification as the feedback problems of the class, thereby realizing the arrangement of the problem collection library;
analyzing each feedback problem in real time, specifically, obtaining a problem value from the specific problem analysis of the feedback problem, and then analyzing the feedback quantity of the feedback problem to obtain an adjustment coefficient, thereby comprehensively analyzing the maintenance value of the feedback problem; the method specifically comprises the steps of establishing a corresponding problem assessment model based on a CNN (computer numerical network) or a DNN (digital network), establishing a corresponding training set by a manual mode for training, analyzing each feedback problem and the feedback quantity corresponding to the feedback problem through the problem assessment model after the training is successful to obtain a corresponding problem value and an adjustment coefficient, respectively marking the obtained feedback problem and the adjustment coefficient as WT and c, calculating a corresponding maintenance value according to a maintenance assessment formula WH=c×WT, marking the feedback problem as a correction problem when the maintenance value is larger than a threshold X2, improving and perfecting teaching virtual equipment according to the correction problem, and deleting the feedback problem from a problem collection library after the correction is finished; when the maintenance value is not greater than the threshold value X2, no operation is performed.
By evaluating the feedback problem in real time, the dynamic updating and perfecting of the teaching virtual equipment are realized, and the teaching virtual equipment is more and more suitable for teaching and training along with the increase of application time.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (9)
1. The intelligent simulation manufacturing method based on virtual reality is characterized by comprising the following steps of:
constructing and gradually perfecting various teaching virtual devices; when teaching is needed, starting the teaching virtual equipment corresponding to the application, and operating the teaching virtual equipment according to the needs by a user to generate corresponding operation data; analyzing the obtained operation data, controlling the teaching virtual equipment to run based on the analysis result, and processing the corresponding materials; and displaying the corresponding processing process to a user in real time.
2. The intelligent simulation manufacturing method based on virtual reality according to claim 1, wherein the construction method of the teaching virtual device comprises the following steps:
determining target equipment, collecting target data corresponding to the target equipment, determining a corresponding core structure based on the target data, and constructing corresponding teaching virtual equipment according to each core structure and virtual reality technology.
3. The virtual reality-based intelligent simulation manufacturing method of claim 2, wherein the method of determining the core structure from the target data comprises:
analyzing the target data through a preset core evaluation model to obtain functional values and teaching values corresponding to all structures of the target equipment, calculating corresponding core values according to the obtained functional values and teaching values, and marking the structure with the core value larger than the threshold value X1 as a core structure.
4. A virtual reality-based intelligent simulation manufacturing method according to claim 3, wherein the core value calculation method comprises:
calculating a corresponding core value based on a core evaluation formula hx=b1×gn+b2×jx;
wherein HX is a core value; GN is a functional value; JX is a teaching value; b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
5. The intelligent simulation manufacturing method based on virtual reality according to claim 1, wherein the teaching virtual equipment is associated with a corresponding material library.
6. The virtual reality-based intelligent simulation manufacturing method according to claim 1, wherein the expression form of the operation flow is embodied in real time in the operation application process of the teaching virtual model.
7. The virtual reality-based intelligent simulation manufacturing method of claim 6, wherein the method for embodying the representation of the operation flow in real time comprises:
establishing an auxiliary library based on historical operating data of the target equipment; and establishing a fault discrimination model to evaluate the real-time operation process of the teaching virtual model, obtaining corresponding operation problems, matching corresponding expression forms from an auxiliary library according to the obtained operation problems, and synchronously reflecting in the teaching virtual model based on the obtained expression forms.
8. The intelligent simulation manufacturing method based on virtual reality according to claim 1, wherein the method for perfecting the teaching virtual equipment comprises the following steps:
establishing a problem collection library, wherein the problem collection library is used for collecting feedback problems of users in real time and sorting the stored feedback problems; and the same problem of the same user only remains one in the problem collection library;
and analyzing each feedback problem to obtain a corresponding maintenance value, marking the feedback problem as a correction problem when the maintenance value is larger than a threshold value X2, and improving and perfecting the teaching virtual equipment according to the correction problem.
9. The virtual reality based intelligent simulation manufacturing method of claim 8, wherein the method for analyzing feedback problems comprises:
corresponding problem values and adjustment coefficients are set according to the feedback problems and the corresponding feedback quantity, the obtained problem values and adjustment coefficients are marked as WT and c respectively, and corresponding maintenance values are calculated according to a maintenance evaluation formula WH=c×WT.
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| CN202310489811.1A CN116524778A (en) | 2023-05-04 | 2023-05-04 | Intelligent simulation manufacturing method based on virtual reality |
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| CN202310489811.1A CN116524778A (en) | 2023-05-04 | 2023-05-04 | Intelligent simulation manufacturing method based on virtual reality |
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Application publication date: 20230801 |