CN114881179B - Intelligent experiment method based on intention understanding - Google Patents
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Abstract
The invention relates to the technical field of data processing, and provides an intelligent experiment method based on intention understanding. The method has the advantages of accurately understanding the user intention and greatly improving the learning concentration degree and efficiency of learners.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent experiment method based on intention understanding.
Background
With the development of virtual reality technology, the virtual reality technology is increasingly applied to production, life and scientific research and education activities of people. In the teaching, the virtual reality technology has interactive characteristics and an intelligent three-dimensional dynamic vision and virtual behavior simulation system, so that learners can be introduced into different situations more quickly, and the limitation of many real teaching can be overcome.
For example, many chemical experimental designs are currently performed on a virtual-real fused experimental platform, and in a virtual platform, a real operation process and a virtual experimental phenomenon display can be performed by using a fused real-object interaction suite.
According to the heart flow theory, the heart flow experience is a completely immersed feeling of forgetting fatigue, time and other things except the current things when doing something, if students can reach such a state in the experimental process, the students with different bases are guided to find confidence and places of whereabouts of learning by creating reasonable scenes, the learning targets of the students are clear and accord with the abilities of the students, the learning interests of the students are stimulated to the maximum extent, the students can gradually obtain the heart flow experience, and the experience becomes the power of the students for continuously meeting challenges and realizing self-growth.
In practical use, the interactive system collects various static information and dynamic information of an operator by using multiple channels, but how to fuse various multi-channel information data and more accurately predict the intention of the operator and apply the result of the intention prediction to the interesting learning mode design, designs an interesting learning strategy by fusing the heart flow theory, improves the learning interest and the learning efficiency of students, and is an application hotspot of the current virtual reality in chemical experiment application.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent experiment method based on intention understanding according to a model for obtaining the heart fluid experiment learning.
The invention is realized through the following technical scheme, and provides an intelligent experiment method based on intention understanding, which is characterized in that multi-mode fusion intention understanding is firstly carried out, and then a weak point capturing feedback mechanism, an individualized dynamic experiment mode matching strategy and a strategy of a man-machine competition answering strategy are adopted;
the multimodal fusion is intended to be understood to include the following steps:
(1) processing multi-channel data;
firstly, setting initial probability, and establishing a state transition probability model based on the operation behavior of an experimenter
The transition probability from the j-state to the i-state is recorded as
；
Establishing a behavior intention library, collecting information of a scene channel and a voice channel, processing the information and then carrying out quantitative matching;
for the calculation of the position information, the intention probability of the current position channel is obtained through a formula (1) according to the hand coordinates captured in real time and the coordinates of each apparatus in the scene,
wherein,
r is an operation intention probability surge region set according to a large number of experiments as the probability of the ith intention of the position lane, S1, S2, S3 represent distances,
is the probability weight in the R region;
for the calculation of the motion direction, the moving track of the hand is captured in real time, the direction relation between the motion direction of the hand and each apparatus in the scene is calculated to obtain a probability value, the specific calculation method is as formula (2),
wherein,
the probability of the ith intention of a directional channel,
the distance between the ith equipment and the hand movement track;
for voice information, firstly, a voice database is established according to intentions of different experiment intention sets
(2) Multi-channel information fusion;
firstly, fusing the obtained dynamic information of each channel, and calculating the intention probability variance of each channel to obtain the weight of each channel, wherein the specific formula is as follows,
wherein,
representing three channels of position, direction and voice,
for the weight of each channel it is desirable to,
is a first
The probability of the ith intention of an individual channel,
is a first
Average probability of n intents in each channel;
final dynamic information prediction probability of each intention
Is composed of
Combining the prediction probability obtained by the static information with the probability obtained by the dynamic information to obtain the intention probability after fusion, wherein the formula is as follows
Wherein,
as the probability of the predicted ith intention,
to state transition probabilities from the jth intent in the jth intent library to the ith intent,
probability of the ith intention obtained by fusing the dynamic information;
selecting
The largest one corresponds to the predicted current intent.
Preferably, a vulnerability capturing and feedback mechanism is set in the steps of monitoring fluency and operation of user experiment operation, and the vulnerability capturing and feedback relearning process comprises the following steps:
(1) recording the knowledge of the suspicious weaknesses: monitoring the operation process of an experimenter in real time, and recording the points of the experimenter which are guided and warned;
(2) relearning the vulnerability knowledge: testing the question matched with the question with the highest conformity with the captured weak point knowledge in the question removing library, and if the guide and the warning are finished and the experimenter already masters the knowledge point, continuing to perform the next experiment; otherwise, the computer explains the knowledge point by voice or video, extracts the question from the question bank again after the completion of the explanation and tests the question until the answer is correct, which means that the experimenter has completely mastered the knowledge point, and after the experiment is finished, the computer returns a summary of the weak point knowledge point to help the students to more intuitively see the weak points and to review the weak points.
Preferably, the individual experiment mode is set according to different experiment states of each experimenter by monitoring the operation time and the accuracy of the experimenter, and comprises the following strategies,
(1) timely excitation: when an experimenter well completes a certain step, the system can issue corresponding rewards according to the completion time and the operation specification, wherein the rewards adopt a virtual gold coin form;
(2) intelligent navigation: the system monitors the operation process of an experimenter in real time, and when the fact that the experimenter stays in a certain step for too long time and cannot quickly perform the next step is detected, the system can conduct timely guidance; meanwhile, through intention understanding, the computer predicts that an experimenter is about to perform an error operation, and the system also warns;
(3) the barrier is established to intelligence: the hidden level is set, when the experimenter operates smoothly, when the whole experiment process reaches a certain condition, the corresponding hidden level can be unlocked, the setting of the hidden level is the expansion and the pull-out of the current basic experiment knowledge point, and the experimenter can also obtain extra gold coin rewards when learning more knowledge.
In conclusion, the invention can evaluate multi-channel dynamic information, automatically endow different weights to each channel according to the value content of effective information, can acquire static information according to the relationship between human behaviors and motivations, and fuse the static information and the dynamic information by adopting a fusion strategy, thereby improving the accuracy of intention prediction. The method has the advantages of accurately understanding the user intention and greatly improving the learning concentration degree and efficiency of learners.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the intelligent experimental method based on the intention understanding of the invention;
fig. 2 is a schematic view of a schematic flow structure of a vulnerability capture feedback mechanism in an intelligent experimental method based on intention understanding.
Detailed Description
In order to clearly illustrate the technical features of the present invention, the present invention is further illustrated by the following detailed description with reference to the accompanying drawings.
As shown in fig. 1 to 2, the invention provides an intelligent experiment method based on intention understanding, which first performs multi-modal fusion intention understanding, and then adopts a vulnerability capture feedback mechanism, a personalized dynamic experiment mode matching strategy and a man-machine competition question answering strategy;
the multimodal fusion is intended to be understood to include the following steps:
(1) processing multi-channel data;
firstly setting initial probability, inviting a plurality of classmates of different grades to operate concentrated sulfuric acid dilution experiments in sequence, recording the operation sequence and selected instruments of the experiments, analyzing and establishing a state transition probability model based on the operation behaviors of experimenters, wherein the model is one
The transition probability from the j-state to the i-state is recorded as
；
The dynamic information comprises a plurality of channels, so that information supplement can be better realized, and the precision of a prediction result is improved, so that the robustness of a prediction model is improved;
for the calculation of the position information, the intention probability of the current position channel is obtained through a formula (1) according to the hand coordinates captured in real time and the coordinates of each equipment in the scene,
wherein,
r is an operation intention probability surge region set according to a large number of experiments as the probability of the ith intention of the position lane, S1, S2, S3 represent distances,
is the probability weight in the R region;
for the calculation of the motion direction, the moving track of the hand is captured in real time, the direction relation between the motion direction of the hand and each apparatus in the scene is calculated to obtain a probability value, the specific calculation method is as formula (2),
wherein,
the probability of the ith intention of a directional channel,
the distance between the ith equipment and the hand movement track;
for voice information, firstly, a voice database is established according to intentions of different experiment intention sets
(2) Multi-channel information fusion;
firstly, fusing the obtained dynamic information of each channel, and calculating the intention probability variance of each channel to obtain the weight of each channel, wherein the specific formula is as follows,
wherein,
representing position, squareThree channels of direction and voice are provided,
for the weight of each channel it is desirable to,
is as follows
The probability of the ith intention of an individual channel,
is as follows
Average probability of n intents in each channel;
final per-intent dynamic information prediction probability
Is composed of
Combining the prediction probability obtained by the static information with the probability obtained by the dynamic information to obtain the intention probability after fusion, wherein the formula is as follows
Wherein,
as the probability of the predicted ith intention,
the state of the jth intention to the ith intention from the jth intention in the ith intention libraryThe probability of the transition is,
the probability of the ith intention obtained by fusing the dynamic information;
selecting
The largest one corresponds to the predicted current intent.
Based on the intention understanding fusion strategy, the next operation intention of the user can be obtained, the operation of the current user is interfered according to the correct experiment process, meanwhile, interesting learning elements are added in the experiment process, interesting learning strategies are set, different interesting learning strategies are matched with students of different levels, the user is better assisted to efficiently learn, finally, the system gives learning evaluation feedback, the user can learn after thinking resistance and summarization conveniently, in the embodiment, the fluency and the operation steps of the experiment operation of the user are monitored, a weakness capturing feedback mechanism is set, and the weakness capturing and feedback relearning process is as follows:
(1) recording the knowledge of the suspicious weaknesses: monitoring the operation process of an experimenter in real time, and recording the points of the experimenter which are guided and warned;
(2) relearning the vulnerability knowledge: testing the question matched with the question with the highest conformity with the captured weak point knowledge in the question removing library, and if the guide and the warning are finished and the experimenter already masters the knowledge point, continuing to perform the next experiment; otherwise, the computer explains the knowledge point by voice or video, extracts the question from the question bank again after the completion of the explanation and tests the question until the answer is correct, which means that the experimenter has completely mastered the knowledge point, and after the experiment is finished, the computer returns a summary of the weak point knowledge point to help the students to more intuitively see the weak points and to review the weak points.
In order to adapt to students with different bases and enable a computer to effectively and efficiently cooperate with the students to perform correct and ordered experiments, in the interesting learning mode in the embodiment, by monitoring the operation time and the accuracy of experimenters and according to different experimental states of each experimenter, a personalized experiment mode is set, which comprises the following strategies,
(1) timely excitation: when an experimenter well completes a certain step, the system can issue corresponding rewards according to the completion time and the operation specification, wherein the rewards adopt a virtual gold coin form;
(2) intelligent navigation: the system monitors the operation process of an experimenter in real time, and when the fact that the experimenter stays in a certain step for too long time and cannot quickly perform the next step is detected, the system can conduct timely guidance; meanwhile, through intention understanding, the computer predicts that an experimenter is about to perform an error operation, and the system also warns;
(3) the barrier is established to intelligence: the hidden level is set, when the experimenter operates smoothly, when the whole experiment process reaches a certain condition, the corresponding hidden level can be unlocked, the setting of the hidden level is the expansion and the pull-out of the current basic experiment knowledge point, and the experimenter can also obtain extra gold coin rewards when learning more knowledge.
In the learning process, it is very important to provide timely feedback to learners, and for experimenters, positive feedback can show the effectiveness of the current learning behaviors of the learners, so that the control feeling of the learners on the application can be ensured, and meanwhile, the learners can have a more definite target in the learning process. Therefore, after the experiment is finished in the embodiment, a knowledge answering module is arranged, the computer and the experimenter perform questions at the same time, the computer is provided with reaction time, the experimenter and the machine perform answering in a rush mode, the correct answering can be added with points, the wrong answering can be deducted, if the answering is abandoned, the points are not added or deducted, and if the experimenter finally outputs the data to the computer, all previous gold coins are rewarded and cleared; on the contrary, if the experimenter defeats the computer, different titles are awarded according to the situation of the gold coins obtained by the experimenter in the whole experiment process, including 'bad feeling', 'good feeling', 'surplus' and 'pure green' of the fire.
In this implementation, an algorithm description is also given as follows:
the algorithm name is as follows: multimode fusion intention probability acquisition algorithm (VCSIG algorithm) based on static information and dynamic information fusion
Inputting: voice information (V), scene position information (C) captured by a camera, state transition probability (S), knowledge base information (D)
And (3) outputting: user's current operation intention (I), interesting learning strategy (G)
1.While（C）：
2.Computer 
using the formula (1);
3.Computer 
using the formula (2);
4.
（
）
5.Computer 
using the formula (3);
6.Computer 
using the formula (4);
7.
was obtained through a large number of experimental statistics
8.ComputerP using the formula (5);
9.
/*Takethe intention corresponding to 
as the final result*/
10.
：
11.Recording time t；
12.
13.Record current operation process
,Executegame strategy
/*warning*/
14.
15.Record current operation process
,Executegame strategy
/*guide*/
16.
17.Unlock hidden level/*Hide levels to improve current knowledge*/
18.ReturnO
End
The main difficulty of multi-mode fusion intention understanding lies in that for the processing problem of multi-channel information, some channel information is incomplete, and the information of some channels has redundancy, which all affect the accuracy of intention understanding, and effective information needs to be correctly distinguished and utilized to obtain more accurate intention. Meanwhile, the design of many current virtual simulation experiment systems is too streamlined and not flexible enough, and the design of interesting learning modes is to enable users to be concentrated in the learning process and improve the learning efficiency of the users.
In the embodiment, the multi-modal fusion intention probability obtaining algorithm based on static information and dynamic information fusion has the advantages that (1) multi-channel dynamic information can be evaluated, and different weights are automatically given to each channel according to the value content of effective information; (2) acquiring static information according to the relationship between the behavior of a person and the motivation, and fusing the static information and the dynamic information by adopting a fusion strategy to improve the accuracy rate of intention prediction; (3) interesting learning elements are added, and targeted guiding warning, barrier setting and the like can be performed according to the experimental conditions of different students, so that the students can more easily reach the state of mind.
Finally, it should be further noted that the above examples and descriptions are not limited to the above embodiments, and technical features of the present invention that are not described may be implemented by or using the prior art, and are not described herein again; the above embodiments and drawings are only for illustrating the technical solutions of the present invention and not for limiting the present invention, and the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that changes, modifications, additions or substitutions within the spirit and scope of the present invention may be made by those skilled in the art without departing from the spirit of the present invention, and shall also fall within the scope of the claims of the present invention.
Claims (3)
1. An intelligent experiment method based on intention understanding is characterized in that multi-mode fusion intention understanding is firstly carried out, and then a weak point capturing feedback mechanism, a personalized dynamic experiment mode matching strategy and a man-machine competition answering strategy are adopted;
the multimodal fusion is intended to be understood to include the following steps:
(1) processing multi-channel data;
firstly, setting initial probability, and establishing a state transition probability model based on the operation behavior of an experimenter
Matrix, transition probability from j state to i state is noted
；
Establishing a behavior intention library, collecting information of a scene channel and a voice channel, processing the information and then carrying out quantitative matching;
for the calculation of the position information, the intention probability of the current position channel is obtained through a formula (1) according to the hand coordinates captured in real time and the coordinates of each apparatus in the scene,
wherein,
r is an operation intention probability surge region set according to a large number of experiments as the probability of the ith intention of the position lane, S1, S2, S3 represent distances,
probability weight in the R region;
for the calculation of the motion direction, the moving track of the hand is captured in real time, the direction relation between the motion direction of the hand and each apparatus in the scene is calculated to obtain a probability value, the specific calculation method is as formula (2),
wherein,
the probability of the ith intention of a directional channel,
the distance between the ith equipment and the hand movement track;
for voice information, firstly, a voice database is established according to intentions of different experiment intention sets
(2) Multi-channel information fusion;
firstly, the dynamic information of each channel is fused, the intention probability variance of each channel is calculated to obtain the weight of each channel, the concrete formula is as follows,
wherein,
representing three channels of position, direction and voice,
for the weight of each channel it is desirable to,
is as follows
The probability of the ith intention of an individual channel,
is as follows
Average probability of n intents in each channel;
final per-intent dynamic information prediction probability
Is composed of
Combining the prediction probability obtained by the static information with the probability obtained by the dynamic information to obtain the intention probability after fusion, wherein the formula is as follows
Wherein,
as the probability of the predicted ith intention,
from the jth intention to the ith intention in the intention libraryThe probability of a state transition of (a),
probability of the ith intention obtained by fusing the dynamic information;
selecting
The largest one corresponds to the predicted current intent.
2. The intelligent experiment method based on intention understanding, as claimed in claim 1, is characterized in that a vulnerability capturing and feedback mechanism is provided in the steps of monitoring fluency and operation of the user experiment, and the vulnerability capturing and feedback relearning process is as follows:
(1) recording the knowledge of the suspicious weaknesses: monitoring the operation process of an experimenter in real time, and recording the points of the experimenter which are guided and warned;
(2) relearning the vulnerability knowledge: testing the question matched with the question with the highest conformity with the captured weak point knowledge in the question removing library, and if the guide and the warning are finished and the experimenter already masters the knowledge point, continuing to perform the next experiment; otherwise, the computer explains the knowledge point by voice or video, extracts the question from the question bank again after the completion of the explanation and tests the question until the answer is correct, which means that the experimenter has completely mastered the knowledge point, and after the experiment is finished, the computer returns a summary of the weak point knowledge point to help the students to more intuitively see the weak points and to review the weak points.
3. The intelligent experiment method based on intention understanding, according to claim 1, is characterized in that, by monitoring the operation time and accuracy of experimenters, personalized experiment modes are set according to different experiment states of each experimenter, including the following strategies,
(1) timely excitation: when an experimenter well completes a certain step, the system can issue corresponding rewards according to the completion time and the operation specification, wherein the rewards adopt a virtual gold coin form;
(2) intelligent navigation: the system monitors the operation process of an experimenter in real time, and when the fact that the experimenter stays in a certain step for too long time and cannot quickly perform the next step is detected, the system can conduct timely guidance; meanwhile, through intention understanding, the computer predicts that an experimenter is about to perform an error operation, and the system also warns;
(3) the barrier is established to intelligence: the hidden level is set, when the experimenter operates smoothly, when the whole experiment process reaches a certain condition, the corresponding hidden level can be unlocked, the setting of the hidden level is the expansion and the pull-out of the current basic experiment knowledge point, and the experimenter can also obtain extra gold coin rewards when learning more knowledge.
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| CN110286763A (en) * | 2019-06-21 | 2019-09-27 | 济南大学 | A kind of navigation-type experiment interactive device with cognitive function |
| CN110554774A (en) * | 2019-07-22 | 2019-12-10 | 济南大学 | AR-oriented navigation type interactive normal form system |
| CN114582030A (en) * | 2022-05-06 | 2022-06-03 | 湖北工业大学 | Behavior recognition method based on service robot |
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| CN110286763A (en) * | 2019-06-21 | 2019-09-27 | 济南大学 | A kind of navigation-type experiment interactive device with cognitive function |
| CN110554774A (en) * | 2019-07-22 | 2019-12-10 | 济南大学 | AR-oriented navigation type interactive normal form system |
| CN114582030A (en) * | 2022-05-06 | 2022-06-03 | 湖北工业大学 | Behavior recognition method based on service robot |
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| 多通道人机交互信息融合的智能方法;杨明浩等;《中国科学:信息科学》;20180420(第04期);79-94页 * |
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