CN112947746A - User experience management system based on VR equipment - Google Patents
User experience management system based on VR equipment Download PDFInfo
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- CN112947746A CN112947746A CN202110131733.9A CN202110131733A CN112947746A CN 112947746 A CN112947746 A CN 112947746A CN 202110131733 A CN202110131733 A CN 202110131733A CN 112947746 A CN112947746 A CN 112947746A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
Abstract
The invention discloses a user experience management system based on VR equipment, relates to the technical field of VR, and solves the technical problem that the user experience of VR equipment is poor because the user experience is not improved from the user in the existing scheme; the physical sign data analysis module is arranged, so that not only can the normal operation of VR equipment be ensured, but also the life safety of a user can be ensured; the invention is provided with an execution control module, and when the execution control module only receives the sign change curve, the controller adjusts the VR equipment according to the sign change curve; when the execution control module receives a user danger signal, the VR equipment is controlled to slowly stop through the controller; the execution control module controls the VR equipment through the received signals and the curve, so that the user safety is guaranteed, and the user experience can be improved.
Description
Technical Field
The invention belongs to the technical field of VR (virtual reality), and particularly relates to a user experience management system based on VR equipment.
Background
The virtual reality technology is a computer simulation system capable of creating and experiencing a virtual world, which utilizes a computer to generate a simulation environment, is a system simulation of multi-source information fusion and interactive three-dimensional dynamic visual and entity behaviors, and enables a user to be immersed in the environment.
The invention patent with publication number CN111145359A discloses a virtual reality mobile smart community experience system and method based on VR, which enter a virtual smart community according to the selected identity through a user identity selection module, trigger a VR device module through a control module to provide a scene holographic image of smart community experience for a user, acquire operation information of the user through a sensor module, acquire corresponding smart community experience data from a service module according to the operation information, and control VR equipment to provide a corresponding scene holographic image for the user.
The scheme improves the user experience to a great extent, and solves the technical problems that the popularization mode of the existing intelligent community is limited by the field, the propaganda cost is high, the popularization performance is poor and the user experience is poor; however, the above scheme does not solve the problem of user experience from the user, resulting in poor user experience of the VR device; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a user experience management system based on VR equipment.
The purpose of the invention can be realized by the following technical scheme: a user experience management system based on VR equipment comprises a processor, an internet of things communication module, a characteristic data analysis module, an execution control module, a publishing and printing module, an early warning management module and a data storage module;
the internet of things communication module is connected with the internet of things equipment and respectively sends data acquired by the internet of things equipment to the characteristic data analysis module and the data storage module; the Internet of things equipment comprises a temperature sensor, a pressure sensor, a heart rate sensor and a camera;
the characteristic data analysis module comprises a fault analysis unit and a physical sign analysis unit; the sign analysis unit is used for analyzing sign data of a user, and comprises:
acquiring physical sign data of a user through the Internet of things equipment; the physical sign data comprises real-time body temperature and real-time heart rate;
respectively marking the real-time body temperature and the real-time heart rate as ST and SX; by the formula
Obtaining physical signsEvaluating the coefficient TPX; wherein alpha 1 and alpha 2 are proportionality coefficients, alpha 1 is a real number greater than 0, and alpha 2 is a real number greater than 1;
when the sign evaluation coefficient TPX meets that the L1 is not more than TPX < L2, judging that the user is in a tension state, acquiring a facial image of the user through a camera, and analyzing the facial image of the user; when the sign evaluation coefficient TPX meets that L2 is not more than TPX, judging that the user is in a dangerous state, generating a user dangerous signal, and respectively sending the user dangerous signal to the execution control module and the early warning management module; wherein L1 and L2 are sign assessment coefficient thresholds, and L1> 0;
taking time as an independent variable and a sign evaluation coefficient as a dependent variable to carry out N-order polynomial fitting to establish a sign change curve; respectively sending the sign change curves to an execution control module and a data storage module;
sending the physical sign evaluation coefficient to a data storage module through a processor for storage;
the execution control module is electrically connected with a controller of the VR device; when the execution control module only receives the sign change curve, the controller adjusts the VR equipment according to the sign change curve; and when the execution control module receives the user danger signal, the VR equipment is controlled to slowly stop through the controller.
Preferably, the specific analysis step of the face image includes:
the method comprises the steps of marking a face image as an original image after image preprocessing is carried out on the face image; the image preprocessing comprises image segmentation, image denoising and gray level transformation;
obtaining a classification model through a data storage module;
inputting the original image into a classification model to obtain an output result; the output result is a face label corresponding to the original image;
when the output result is 1, generating and sending a user danger signal to the execution control module;
and sending the output result to a data storage module for storage through the processor.
Preferably, the early warning management module is used for receiving the early warning management signal and early warning workers through a loudspeaker and a warning lamp; the early warning management signal comprises a user danger signal, a communication abnormal signal and a communication early warning signal.
Preferably, the publishing and printing module is used for generating an experience report; the experience report comprises experience duration and sign change curves.
Preferably, the specific obtaining step of the classification model includes:
acquiring an image set; the image set is a facial image which is shot in the running process of the VR equipment and is allowed to be stored by a user;
constructing a fusion model; the fusion model is constructed by combining three baseline models of SVM, LR and GBDT with a fusion mode, wherein the fusion mode comprises a linear weighted fusion method, a cross fusion method, a waterfall fusion method, a characteristic fusion method and a prediction fusion method;
setting a face label for a face image in the image set; the face labels are 0 and 1, wherein when the face label is 1, the user in the corresponding face image is in a dangerous state, and when the face label is 0, the user in the corresponding face image is in a normal state;
dividing the image set and the corresponding face labels into a training set and a testing set according to a set proportion; the set ratio comprises 4:1, 3:2 and 5: 2;
inputting the training set and the test set into the fusion model for training and testing; when the training precision reaches the target precision, judging that the training of the fusion model is finished, and marking the trained fusion model as a classification model;
and sending the classification model to a data storage module for storage through the processor.
Preferably, the fault analysis unit is configured to analyze a fault of the VR device, and includes:
sending a first state signal to the communication equipment according to a set period through the fault analysis unit, and sending a second state signal to the fault analysis unit immediately after the communication equipment receives the first state signal; the communication equipment comprises a controller and an internet of things equipment, and the set period comprises one second, one minute and five minutes;
acquiring a time difference value between the sending time of the first state signal and the received time of the second state signal; when the time difference value of the three continuous moments is larger than or equal to the time difference value threshold value, judging that the communication state of the corresponding communication equipment is abnormal, and sending a communication abnormal signal to the early warning management module;
taking time as an independent variable and a time difference value as a dependent variable, and carrying out N-order polynomial fitting to establish a time difference value curve; wherein N is more than or equal to 3;
acquiring a first derivative of the time difference curve, acquiring a moment corresponding to the maximum value of the first derivative and marking the moment as a target moment; acquiring first derivative values of a previous moment and a target moment of the target moment and respectively marking the first derivative values as a reference value and a target value;
when the mean value of the reference value and the target value is larger than a set threshold value, judging that the communication state of the corresponding communication equipment is not good, and generating and sending a communication early warning signal to an early warning management module;
and sending the time difference curve to a data storage module for storage through a processor.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a sign data analysis module, and the setting comprises a fault analysis unit and a sign analysis unit; the fault analysis unit is used for detecting the communication state between VR devices in real time, and the sign analysis unit is used for analyzing sign data of a user; the sign data analysis module can not only ensure the normal operation of the VR equipment, but also ensure the life safety of a user;
2. the invention is provided with an execution control module, and when the execution control module only receives the sign change curve, the controller adjusts the VR equipment according to the sign change curve; when the execution control module receives a user danger signal, the VR equipment is controlled to slowly stop through the controller; the execution control module controls the VR equipment through the received signals and the curve, so that the user safety is guaranteed, and the user experience can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a user experience management system based on VR device includes a processor, an internet of things communication module, a characteristic data analysis module, an execution control module, a publishing and printing module, an early warning management module, and a data storage module;
the Internet of things communication module is connected with the Internet of things equipment and respectively sends data acquired by the Internet of things equipment to the characteristic data analysis module and the data storage module; the Internet of things equipment comprises a temperature sensor, a pressure sensor, a heart rate sensor and a camera;
the characteristic data analysis module comprises a fault analysis unit and a physical sign analysis unit; the sign analysis unit is used for analyzing sign data of a user, and comprises:
acquiring physical sign data of a user through the Internet of things equipment; the physical sign data comprises real-time body temperature and real-time heart rate;
respectively marking the real-time body temperature and the real-time heart rate as ST and SX; by the formula
Obtaining a physical sign evaluation coefficient TPX; wherein alpha 1 and alpha 2 are proportionality coefficients, alpha 1 is a real number greater than 0, and alpha 2 is a real number greater than 1;
when the sign evaluation coefficient TPX meets that the L1 is not more than TPX < L2, judging that the user is in a tension state, acquiring a facial image of the user through a camera, and analyzing the facial image of the user; when the sign evaluation coefficient TPX meets that L2 is not more than TPX, judging that the user is in a dangerous state, generating a user dangerous signal, and respectively sending the user dangerous signal to the execution control module and the early warning management module; wherein L1 and L2 are sign assessment coefficient thresholds, and L1> 0;
taking time as an independent variable and a sign evaluation coefficient as a dependent variable to carry out N-order polynomial fitting to establish a sign change curve; respectively sending the sign change curves to an execution control module and a data storage module;
sending the physical sign evaluation coefficient to a data storage module through a processor for storage;
the execution control module is electrically connected with a controller of the VR device; when the execution control module only receives the sign change curve, the controller adjusts the VR equipment according to the sign change curve; and when the execution control module receives the user danger signal, the VR equipment is controlled to slowly stop through the controller.
Further, the specific analysis step of the face image includes:
the method comprises the steps of marking a face image as an original image after image preprocessing is carried out on the face image; the image preprocessing comprises image segmentation, image denoising and gray level transformation;
obtaining a classification model through a data storage module;
inputting the original image into a classification model to obtain an output result; outputting a face label corresponding to the original image as an output result;
when the output result is 1, generating and sending a user danger signal to the execution control module;
and sending the output result to a data storage module for storage through the processor.
Further, the early warning management module is used for receiving the early warning management signal and early warning workers through a loudspeaker and a warning lamp; the early warning management signal comprises a user danger signal, a communication abnormal signal and a communication early warning signal.
Further, the publishing and printing module is used for generating an experience report; the experience report comprises experience duration and sign change curves.
Further, the specific obtaining step of the classification model comprises:
acquiring an image set; the image set is a facial image which is shot in the running process of the VR equipment and is allowed to be stored by a user;
constructing a fusion model; the fusion model is constructed by combining three baseline models of SVM, LR and GBDT with a fusion mode, wherein the fusion mode comprises a linear weighted fusion method, a cross fusion method, a waterfall fusion method, a characteristic fusion method and a prediction fusion method;
setting a face label for a face image in the image set; the face labels are 0 and 1, wherein when the face label is 1, the user in the corresponding face image is in a dangerous state, and when the face label is 0, the user in the corresponding face image is in a normal state;
dividing the image set and the corresponding face labels into a training set and a testing set according to a set proportion; the set ratios include 4:1, 3:2, and 5: 2;
inputting the training set and the test set into the fusion model for training and testing; when the training precision reaches the target precision, judging that the training of the fusion model is finished, and marking the trained fusion model as a classification model;
and sending the classification model to a data storage module for storage through the processor.
Further, the fault analysis unit is used for analyzing the fault of the VR device, and includes:
sending a first state signal to the communication equipment according to a set period through the fault analysis unit, and sending a second state signal to the fault analysis unit immediately after the communication equipment receives the first state signal; the communication equipment comprises a controller and an Internet of things device, and the set period comprises one second, one minute and five minutes;
acquiring a time difference value between the sending time of the first state signal and the received time of the second state signal; when the time difference value of the three continuous moments is larger than or equal to the time difference value threshold value, judging that the communication state of the corresponding communication equipment is abnormal, and sending a communication abnormal signal to the early warning management module;
taking time as an independent variable and a time difference value as a dependent variable, and carrying out N-order polynomial fitting to establish a time difference value curve; wherein N is more than or equal to 3;
acquiring a first derivative of the time difference curve, acquiring a moment corresponding to the maximum value of the first derivative and marking the moment as a target moment; acquiring first derivative values of a previous moment and a target moment of the target moment and respectively marking the first derivative values as a reference value and a target value;
when the mean value of the reference value and the target value is larger than a set threshold value, judging that the communication state of the corresponding communication equipment is not good, and generating and sending a communication early warning signal to an early warning management module;
and sending the time difference curve to a data storage module for storage through a processor.
Further, the processor is respectively in communication connection with the internet of things communication module, the characteristic data analysis module, the execution control module, the issuing and printing module, the early warning management module and the data storage module; the early warning management module is respectively in communication connection with the data storage module and the publishing and printing module; the characteristic data analysis module is respectively in communication connection with the internet of things communication module and the execution control module.
Further, the VR device includes a head mounted display device, a host system, a tracking system, and a controller.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
sending a first state signal to the communication equipment according to a set period through the fault analysis unit, and sending a second state signal to the fault analysis unit immediately after the communication equipment receives the first state signal; acquiring a time difference value between the sending time of the first state signal and the received time of the second state signal; when the time difference value of the three continuous moments is larger than or equal to the time difference value threshold value, judging that the communication state of the corresponding communication equipment is abnormal, and sending a communication abnormal signal to the early warning management module; taking time as an independent variable and a time difference value as a dependent variable, and carrying out N-order polynomial fitting to establish a time difference value curve; acquiring a first derivative of the time difference curve, acquiring a moment corresponding to the maximum value of the first derivative and marking the moment as a target moment; acquiring first derivative values of a previous moment and a target moment of the target moment and respectively marking the first derivative values as a reference value and a target value; when the mean value of the reference value and the target value is larger than a set threshold value, judging that the communication state of the corresponding communication equipment is not good, and generating and sending a communication early warning signal to an early warning management module;
acquiring physical sign data of a user through the Internet of things equipment; obtaining a physical sign evaluation coefficient TPX; when the sign evaluation coefficient TPX meets that the L1 is not more than TPX < L2, judging that the user is in a tension state, acquiring a facial image of the user through a camera, and analyzing the facial image of the user; when the sign evaluation coefficient TPX meets that L2 is not more than TPX, judging that the user is in a dangerous state, generating a user dangerous signal, and respectively sending the user dangerous signal to the execution control module and the early warning management module; taking time as an independent variable and a sign evaluation coefficient as a dependent variable to carry out N-order polynomial fitting to establish a sign change curve; and respectively sending the sign change curves to an execution control module and a data storage module.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. A user experience management system based on VR equipment is characterized by comprising a processor, an internet of things communication module, a characteristic data analysis module, an execution control module, a publishing and printing module, an early warning management module and a data storage module;
the internet of things communication module is connected with the internet of things equipment and respectively sends data acquired by the internet of things equipment to the characteristic data analysis module and the data storage module; the Internet of things equipment comprises a temperature sensor, a pressure sensor, a heart rate sensor and a camera;
the characteristic data analysis module comprises a fault analysis unit and a physical sign analysis unit; the sign analysis unit is used for analyzing sign data of a user, and comprises:
acquiring physical sign data of a user through the Internet of things equipment; the physical sign data comprises real-time body temperature and real-time heart rate;
respectively marking the real-time body temperature and the real-time heart rate as ST and SX; from the formula TPX ═ α 1 × ST
Obtaining a physical sign evaluation coefficient TPX; wherein alpha 1 and alpha 2 are proportionality coefficients, alpha 1 is a real number greater than 0, and alpha 2 is a real number greater than 1;
when the sign evaluation coefficient TPX meets that the L1 is not more than TPX < L2, judging that the user is in a tension state, acquiring a facial image of the user through a camera, and analyzing the facial image of the user; when the sign evaluation coefficient TPX meets that L2 is not more than TPX, judging that the user is in a dangerous state, generating a user dangerous signal, and respectively sending the user dangerous signal to the execution control module and the early warning management module; wherein L1 and L2 are sign assessment coefficient thresholds, and L1> 0;
taking time as an independent variable and a sign evaluation coefficient as a dependent variable to carry out N-order polynomial fitting to establish a sign change curve; respectively sending the sign change curves to an execution control module and a data storage module;
sending the physical sign evaluation coefficient to a data storage module through a processor for storage;
the execution control module is electrically connected with a controller of the VR device; when the execution control module only receives the sign change curve, the controller adjusts the VR equipment according to the sign change curve; and when the execution control module receives the user danger signal, the VR equipment is controlled to slowly stop through the controller.
2. The VR device based user experience management system of claim 1, wherein the facial image specific analysis step includes:
the method comprises the steps of marking a face image as an original image after image preprocessing is carried out on the face image; the image preprocessing comprises image segmentation, image denoising and gray level transformation;
obtaining a classification model through a data storage module;
inputting the original image into a classification model to obtain an output result; the output result is a face label corresponding to the original image;
when the output result is 1, generating and sending a user danger signal to the execution control module;
and sending the output result to a data storage module for storage through the processor.
3. The VR device based user experience management system of claim 1, wherein the pre-warning management module is configured to receive a pre-warning management signal and pre-warn staff through a speaker and a warning light; the early warning management signal comprises a user danger signal, a communication abnormal signal and a communication early warning signal.
4. The VR device-based user experience management system of claim 1, wherein the publishing printing module is to generate an experience report; the experience report comprises experience duration and sign change curves.
5. The VR device based user experience management system of claim 1, wherein the failure analysis unit is configured to analyze a failure of a VR device and comprises:
sending a first state signal to the communication equipment according to a set period through the fault analysis unit, and sending a second state signal to the fault analysis unit immediately after the communication equipment receives the first state signal; the communication equipment comprises a controller and an internet of things equipment, and the set period comprises one second, one minute and five minutes;
acquiring a time difference value between the sending time of the first state signal and the received time of the second state signal; when the time difference value of the three continuous moments is larger than or equal to the time difference value threshold value, judging that the communication state of the corresponding communication equipment is abnormal, and sending a communication abnormal signal to the early warning management module;
taking time as an independent variable and a time difference value as a dependent variable, and carrying out N-order polynomial fitting to establish a time difference value curve; wherein N is more than or equal to 3;
acquiring a first derivative of the time difference curve, acquiring a moment corresponding to the maximum value of the first derivative and marking the moment as a target moment; acquiring first derivative values of a previous moment and a target moment of the target moment and respectively marking the first derivative values as a reference value and a target value;
when the mean value of the reference value and the target value is larger than a set threshold value, judging that the communication state of the corresponding communication equipment is not good, and generating and sending a communication early warning signal to an early warning management module;
and sending the time difference curve to a data storage module for storage through a processor.
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