CN117215415A - Multi-user collaborative virtual interaction method based on MR recording and broadcasting technology - Google Patents
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Abstract
The application discloses a multi-user collaborative virtual interaction method based on MR recording and broadcasting technology, in particular relates to the technical field of virtual reality, which is used for solving the problem that the prior MR recording and broadcasting can not reasonably arrange interactive users to conduct alternate dominant interaction in sequence in multi-user collaborative interaction, and reduces the participation enthusiasm of the interactive users; calculating a feedback ranking value according to the interaction feedback coefficient of each interaction user, and calculating a continuous ranking value according to the interaction persistence index of each interaction user; the feedback ranking value and the continuous ranking value of each interactive user are comprehensively processed, the dominant ranking value of each interactive user is calculated through a weighted solving method, and the dominant interaction sequence of each interactive user is distributed from large to small according to the dominant ranking value.
Description
Technical Field
The application relates to the technical field of virtual reality, in particular to a multi-user collaborative virtual interaction method based on an MR recording and broadcasting technology.
Background
MR is a technology integrating the real world and the virtual world, allowing users to interact with virtual objects in real time and coexist in the same environment, and MR recording and broadcasting is a field of user research for MR applications in which users' real-time interactions, experiences or operation processes in a mixed reality environment are recorded, saved and reproduced.
In the field of user research of MR application, a reasonable sequence arrangement mechanism for lack of collaborative interaction for multiple persons is adopted, a main server user is used as a master of the MR application in the existing MR system, and in the problem of collaborative interaction for multiple persons, other participants are arranged by a fixed master to carry out interactive expression output, so that the interactive enthusiasm of the participants is not improved, the system is limited in application authority control, and the output sequence of each participant cannot be reasonably distributed.
In order to solve the defects, a technical scheme is provided.
Disclosure of Invention
The application aims to provide a multi-user collaborative virtual interaction method based on an MR recording and broadcasting technology, so as to solve the defects in the background technology.
In order to achieve the above object, the present application provides the following technical solutions: the multi-user collaborative virtual interaction method based on the MR recording and broadcasting technology comprises the following steps of;
establishing an interaction feedback model according to the user behavior mode data, calculating an interaction feedback coefficient, and calculating an interaction persistence index according to the wearable equipment sensor data and the communication network state data;
calculating a feedback ranking value according to the interaction feedback coefficient of each interaction user, and calculating a continuous ranking value according to the interaction persistence index of each interaction user;
and comprehensively processing feedback ordering values and continuous ordering values of the interactive users, calculating dominant ordering values of the interactive users by a weighted solving method, and distributing the dominant interaction order of the interactive users from large to small according to the dominant ordering values.
In a preferred embodiment, the logic of establishing the interactive feedback model and the method for calculating the interactive feedback coefficient;
collecting sight focus data of the interaction users through an eye tracker built in the wearable equipment, collecting gesture action characteristics of the interaction users through a gyroscope, calculating speaking time ratio of each interaction user through speaking time, and establishing an interaction feedback model through interaction user behavior mode data;
calculating an interactive feedback coefficient according to the interactive feedback model, wherein the interactive feedback coefficient expression is as followsWherein Fp is the focus process perfection rate, st is the speaking time ratio, as is the triaxial angular acceleration step,>proportional coefficients of the sum of the focus process improvement rate, the speaking time length rate and the triaxial angular acceleration step respectively, and +.>Are all greater than 0.
In a preferred embodiment, the calculation method of the focus process perfection rate, the speaking duration ratio and the triaxial angular acceleration step;
extracting characteristics of the interactive analysis object, marking special points of the interactive analysis object, and calculating the perfection rate of the focus process as followsFp is the focus process perfection rate, sc is the focus scanning special point number, ta is the total special point number of the interactive analysis object, and Fp is a percentage form;
the speaking time length ratio is the ratio of the speaking time of each interactive user in the non-leading discussion link to the total speaking time, namely the speaking time length ratio expression is thatWherein Pe is the individual speaking duration, and Ov is the total speaking duration of each interactive user;
taking three axes of a gyroscope as an X axis, a Y axis and a Z axis, respectively calculating the angular acceleration of each axis, detecting the single-axis deflection angle theta, taking the time for the deflection angle theta as t0, and calculating the single-axis angular velocity asCalculating angular acceleration of single axisIn (1) the->For a period of time->Comparing the calculated single-axis angular acceleration with a set angular acceleration threshold value for the change rate of the single-axis angular velocity along the time, recording an angular acceleration step when the angular acceleration exceeds the angular acceleration threshold value once, wherein the three-axis angular velocity step is the total times that the angular acceleration measured values of the X axis, the Y axis and the Z axis exceed the angular acceleration threshold values of the X axis, the Y axis and the Z axis respectively, namely the three-axis angular acceleration step Cheng Biaoda is as followsWhere Xa is the number of times the X-axis measured angular acceleration exceeds the X-axis angular acceleration threshold, ya is the number of times the Y-axis measured angular acceleration exceeds the Y-axis angular acceleration threshold, za is the number of times the Z-axis measured angular acceleration exceeds the Z-axis angular acceleration threshold.
In a preferred embodiment, the method of calculating the interaction persistence index;
the interactive persistence index is expressed asWherein Sr is the area of the positioning range of the wearable equipment,>for the time sensor voltage, +.>For voltage monitoring period time, cd is network transmission delay, pl is packet loss rate, < ->The ratio of the positioning range of the wearable device to the voltage change rate of the sensor, the proportional coefficient of the product of the network transmission delay and the packet loss rate are respectively +.>Are all greater than 0.
In a preferred embodiment, a method of calculating a feedback ranking value and a persistence ranking value;
establishing a feedback ordering value p through the interactive feedback coefficient, wherein the larger the interactive feedback coefficient is, the larger the feedback ordering value p is; and establishing a continuous ranking value q through the interactive persistence index, wherein the larger the interactive persistence index is, the larger the continuous ranking value q is.
In a preferred embodiment, the logic of establishing the dominant ranking value and the method of assigning dominant interaction order;
the dominant ranking value is calculated according to the feedback ranking value p and the continuous ranking value q as expressed asWherein g is the dominant ranking value, +.>Proportional coefficients of feedback ranking value and continuous ranking value respectively, and +.>Are all greater than 0;
the hosting interaction orders of the interaction users are arranged according to the dominant ranking value g from large to small, the hosting interaction order numbers M of the n interaction users participating in virtual interaction are the positive integers with the value range of M being more than or equal to 1, and the larger the dominant ranking value, the smaller the number M.
In the technical scheme, the application has the technical effects and advantages that:
the application tracks the focus position of the interactive user through the eye tracker, detects the action gesture of the interactive user through the gyroscope, calculates the speaking time ratio of each interactive user through the speaking time without a leading discussion link, constructs an interactive feedback model, and calculates an interactive feedback coefficient; evaluating the sensor stability of each interactive user through the size of the positioning range and the voltage change rate of the wearable equipment sensor, evaluating the network state quality of each interactive user through the network transmission delay and the packet loss rate, calculating an interactive persistence index through the comprehensive sensor stability and the network state, evaluating the virtual interactive persistence of the interactive user in the objective environment dimension through the interactive persistence index, establishing a feedback ranking value and a persistence ranking value through the interactive feedback coefficient and the interactive persistence index, generating a dominant ranking value through the feedback ranking value and the persistence ranking value by using a weighted summation method, distributing the hosting interaction sequence of each interactive user according to the dominant ranking value from large to small,
the application can reasonably quantify the interaction enthusiasm of each interaction user, reflect the participation degree of each interaction user, solve the expression opportunity contradiction of the interaction participants in the interaction process, relieve the expression conflict of each interaction participant and improve the interaction experience feeling of each interaction participant.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the application relates to a multi-user collaborative virtual interaction method based on an MR recording and broadcasting technology, which comprises the following steps:
establishing an interaction feedback model according to the user behavior mode data, calculating an interaction feedback coefficient, and calculating an interaction persistence index according to the wearable equipment sensor data and the communication network state data;
calculating a feedback ranking value according to the interaction feedback coefficient of each interaction user, and calculating a continuous ranking value according to the interaction persistence index of each interaction user;
and comprehensively processing feedback ordering values and continuous ordering values of the interactive users, calculating dominant ordering values of the interactive users by a weighted solving method, and distributing the dominant interaction order of the interactive users from large to small according to the dominant ordering values.
In this embodiment, the user behavior data, the wearable device data and the communication network data participate in the model operation after data preprocessing, so as to ensure availability, applicability and quality of the data, and the method for preprocessing the data is as follows:
the data cleaning comprises the steps of processing missing values, processing abnormal values and solving repeated values, wherein the processing missing values are used for identifying and filling or deleting the missing values in the data, such as filling by using a mean value, a median value, a mode value and the like; the abnormal value is detected and processed, and can be identified and processed by using a statistical method or a visual method; solving the duplicate values refers to identifying and deleting duplicate records in the data to avoid adverse effects on the analysis results.
The data conversion comprises feature scaling, feature construction, feature selection and data dimension reduction, wherein the feature scaling scales the values of the features to a similar range, and common methods comprise normalization and standardization; the feature construction is to create new features based on existing features to improve model performance, such as polynomial features, cross features, etc.; feature selection refers to selecting the most significant or relevant features so as to reduce data complexity and improve model efficiency; the data dimension reduction is to reduce the dimension of the data by keeping main information, and common methods include principal component analysis and feature selection.
Data integration includes merging data sets into a single overall data set for better analysis and modeling, and resolving data conflicts; the data conflict is resolved for processing inconsistent, contradictory or repeated data, ensuring consistency and accuracy of the data set.
Data conventions include data aggregation, data sampling, data aggregation reducing the size of data by aggregating data, such as averages, sums, etc.; data sampling is the selection of a representative subset of samples to reduce the size of the data but preserve the representativeness of the data.
The continuous data is converted into discrete data by data discretization, so that the discrete data is convenient to process, and discretization can be performed based on methods such as frequency, equal-width interval, equal frequency and the like.
The time series data is processed by smoothing, differentiating, lagging and the like to adapt to the requirements of the model.
According to the embodiment, the data is subjected to unified normalization pretreatment, so that adverse effects on analysis results can be avoided, the complexity of data analysis can be reduced, and the speed and accuracy of model establishment can be improved.
Example 2: in this embodiment, the visual focus of the user is detected to analyze the user behavior data, taking an eye movement meter built in the wearable device as an example, applying an eye video analysis technology, using near infrared light to illuminate eyes, using a camera to perform interactive recording, and analyzing and estimating the position of the visual focus of the user through light and a back-end algorithm.
The gyroscope is a sensor for measuring the angular velocity of an object and is used for detecting the rotation state of the object, the gyroscope is based on the principle of gyroscopic effect, the effect refers to the trend of stable spin generated when the object rotates, the gyroscope determines the rotation velocity of the object by measuring the angular velocity of the object around a specific axis, and according to the measured angular velocity, the information such as the rotation direction, the angle and the motion track of the object can be deduced.
The eye tracker built in the wearable equipment is used for collecting sight focus data of the interaction users, the gyroscope is used for collecting gesture action characteristics of the interaction users, the speaking time ratio of each interaction user is calculated through speaking time, an interaction feedback model is built by integrating the three items of data, and the interaction feedback coefficient of each interaction user is calculated.
The interactive feedback coefficient is calculated asWherein Fp is the focus process perfection rate, st is the speaking time ratio, as is the triaxial angular acceleration step,>proportional coefficients of the sum of the focus process improvement rate, the speaking time length rate and the triaxial angular acceleration step respectively, and +.>Are all greater than 0.
Extracting features of the interactive analysis objects, marking analysis specific points of the interactive analysis objects, detecting visual focus positions of the interactive users through an eye tracker, and calculating the focus process perfection rate of each interactive user, wherein the calculation expression of the focus process perfection rate is as followsFp is the focus process perfection rate, sc is the focus scanning special point number, ta is the total special point number of the interactive analysis object, and Fp is a percentage form.
It should be noted that the interactive analysis object is not limited to the form of the multi-person virtual interactive study object for MR recording and playing, and the interactive analysis object may be a planar material such as a document view, or a three-dimensional material of a three-dimensional model, where the three-dimensional model includes industrial equipment, a building template, game rendering, or the like.
The speaking time length ratio is the ratio of the speaking time of each interactive user in the non-leading discussion link to the total speaking time, namely the speaking time length ratio expression is thatIn the formula, pe is the individual speaking time, ov is the total speaking time of each interactive user, and the longer the individual speaking time is, the higher the ratio of the individual speaking time is, the better the effect of user interaction feedback is, the higher the interaction participation initiative is, and the more remarkable the result of interaction discussion is.
The three-axis angular velocity step is used for detecting the action state of an interactive user according to a gyroscope built in the wearable device, taking three axes of the gyroscope as an X axis, a Y axis and a Z axis, respectively calculating the angular acceleration of each axis, taking the X axis as an example, and detecting the deflection angle of the X axis as an exampleDeflection angle->The time taken is t0, and the X-axis angular velocity is calculated to be +.>Calculating the angular acceleration of the X-axisWherein->For a period of time->Comparing the calculated X-axis angular acceleration with a set angular acceleration threshold value for the change rate of the X-axis angular velocity along time, recording an angular acceleration step when the angular acceleration exceeds the angular acceleration threshold value once, wherein the three-axis angular velocity step is the total times that the angular acceleration measured values of the X-axis, the Y-axis and the Z-axis exceed the angular acceleration threshold values of the X-axis, the Y-axis and the Z-axis respectively, namely the three-axis angular acceleration step Cheng Biaoda is as followsWherein Xa is the number of times that the X-axis measured angular acceleration exceeds the X-axis angular acceleration threshold, ya is the number of times that the Y-axis measured angular acceleration exceeds the Y-axis angle plusThe number of times of the speed threshold value Za is the number of times that the Z-axis measured angular acceleration exceeds the Z-axis angular acceleration threshold value.
It should be noted that, the angular acceleration thresholds of the three axes of the gyroscope are set by a person skilled in the art according to the specific model characteristics of the gyroscope and the actual activity range of the interactive user, and the angular acceleration thresholds of the respective axes may be the same or different.
The stability of the sensor and the network state data of each interaction user determine the sustainability of virtual interaction, the more stable the working state of the sensor is, the higher the credibility and accuracy of the sensor data are, the better the network state of the interaction user is, the more real and credible the interaction experience is, and the better the timeliness of the interaction effect is.
Calculating an interaction persistence index of each interaction user aiming at the sensor stability data and the interaction user network state data, wherein the expression of the interaction persistence index is as followsWherein Sr is the area of the positioning range of the wearable equipment,>for the time sensor voltage, +.>For voltage monitoring period time, cd is network transmission delay, pl is packet loss rate, < ->The ratio of the positioning range of the wearable device to the voltage change rate of the sensor, the proportional coefficient of the product of the network transmission delay and the packet loss rate are respectively +.>Are all greater than 0.
The positioning range of the wearable device is the sensing range of the MR system interactive input device, and the sensing area of the wearable device sensor is larger and better under the same MR output state parameter due to the requirement of the extending range of the action gesture of the interactive user on the sensing area;
the stability of the sensor voltage directly relates to the stability of the working state of the sensor, and the lower the rate of change of the voltage with time is, the higher the accuracy and reliability of the collected parameters in the working state of the sensor are;
the network transmission delay is delay time used for transmitting local network uploading data of the interactive user to the server and returning the local network uploading data, and the larger the delay time is, the higher the experience hysteresis of the interactive user is, and the worse the interactive experience is;
the packet loss rate is the ratio of the number of data packets uploaded by the interactive user to the total number of data packets transmitted, and the higher the packet loss rate is, the integrity of data transmission is affected, the communication quality is reduced, and the interactive experience of the interactive user is affected.
According to the embodiment, the focus position of the interactive user is tracked through the eye tracker, the action gesture of the interactive user is detected through the gyroscope, the speaking time ratio of each interactive user is calculated through the speaking time without a leading discussion link, an interactive feedback model is constructed, the interactive feedback coefficient is calculated, the interactive enthusiasm of each interactive user can be reasonably quantified, and the participation degree of each interactive user is reflected; the method comprises the steps of evaluating the stability of a sensor of each interactive user through the size of a positioning range and the voltage change rate of a wearable device sensor, evaluating the network state quality of each interactive user through network transmission delay and packet loss rate, calculating an interaction persistence index through the comprehensive sensor stability and the network state, and evaluating the virtual interaction persistence of the interactive user in the objective environment dimension through the interaction persistence index.
Example 3: establishing a feedback sorting value through interaction feedback coefficients of all interaction users, establishing a continuous sorting value through interaction persistence indexes of all interaction users, generating a dominant sorting value through the feedback sorting value and the continuous sorting value according to a weighted summation method, arranging all interaction users according to the dominant sorting value from big to small, and arranging the hosting interaction sequence of all interaction users according to the dominant arrangement sequence.
Establishing a feedback ordering value p through the interactive feedback coefficient, wherein the larger the interactive feedback coefficient is, the larger the feedback ordering value p is; and establishing a continuous ranking value q through the interactive persistence index, wherein the larger the interactive persistence index is, the larger the continuous ranking value q is.
The dominant ranking value is calculated according to the feedback ranking value p and the continuous ranking value q as expressed asWherein g is the dominant ranking value, +.>Proportional coefficients of feedback ranking value and continuous ranking value respectively, and +.>Are all greater than 0.
The hosting interaction order of each interaction user is arranged according to the dominant ranking value g from large to small, and the higher the dominant ranking value is, the higher the interaction participation enthusiasm and the equipment state stability of the interaction user are.
And the n interactive users participating in virtual interaction are subjected to hosting interaction sequence number M, wherein the value range of M is a positive integer greater than or equal to 1, and the number M is smaller as the dominant ordering value is larger.
In this embodiment, a feedback ranking value and a persistent ranking value are established for the interaction feedback coefficient and the interaction persistence index, a weighted summation method is used to generate a dominant ranking value through the feedback ranking value and the persistent ranking value, and the hosting interaction sequence of each interaction user is distributed according to the dominant ranking value from large to small, so that the contradiction of expression opportunities of interaction participants in the interaction process can be reasonably solved, the expression desire of each interaction participant is relieved, and the interaction experience feeling of each interaction participant is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functions, if implemented in the form of software functional units and sold or used as stand-alone goods, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of software goods stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. The multi-user collaborative virtual interaction method based on the MR recording and broadcasting technology is characterized by comprising the following steps of: the method comprises the following steps;
establishing an interaction feedback model according to the user behavior mode data, calculating an interaction feedback coefficient, and calculating an interaction persistence index according to the wearable equipment sensor data and the communication network state data;
calculating a feedback ranking value according to the interaction feedback coefficient of each interaction user, and calculating a continuous ranking value according to the interaction persistence index of each interaction user;
and comprehensively processing feedback ordering values and continuous ordering values of the interactive users, calculating dominant ordering values of the interactive users by a weighted solving method, and distributing the dominant interaction order of the interactive users from large to small according to the dominant ordering values.
2. The MR recording and broadcasting technology-based multi-user collaborative virtual interaction method according to claim 1, wherein the method comprises the following steps: establishing logic of the interactive feedback model and calculating method of the interactive feedback coefficient;
collecting sight focus data of the interaction users through an eye tracker built in the wearable equipment, collecting gesture action characteristics of the interaction users through a gyroscope, calculating speaking time ratio of each interaction user through speaking time, and establishing an interaction feedback model through interaction user behavior mode data;
calculating an interactive feedback coefficient according to the interactive feedback model, wherein the interactive feedback coefficient expression is as followsWherein Fp is the focus process perfection rate, st is the speaking time ratio, as is the triaxial angular acceleration step,>proportional coefficients of the sum of the focus process improvement rate, the speaking time length rate and the triaxial angular acceleration step respectively, and +.>Are all greater than 0.
3. The multi-user collaborative virtual interaction method based on the MR recording and broadcasting technology according to claim 2, wherein the method is characterized in that: the computing method of the focus process perfection rate, the speaking duration ratio and the triaxial angular acceleration step;
pair interaction analysis pairThe image is subjected to feature extraction, the special points of the interactive analysis objects are marked, and the calculation expression of the focus process perfection rate is as followsFp is the focus process perfection rate, sc is the focus scanning special point number, ta is the total special point number of the interactive analysis object, and Fp is a percentage form;
the speaking time length ratio is the ratio of the speaking time of each interactive user in the non-leading discussion link to the total speaking time, namely the speaking time length ratio expression is thatWherein Pe is the individual speaking duration, and Ov is the total speaking duration of each interactive user;
taking three axes of a gyroscope as an X axis, a Y axis and a Z axis, respectively calculating the angular acceleration of each axis, and detecting the single-axis deflection angle asDeflection angle->The time taken is t0, and the angular velocity of the single axis is calculated to be +.>Calculating the angular acceleration of the single axis +.>Wherein->For a period of time->Comparing the calculated single-axis angular acceleration with a set angular acceleration threshold value for the change rate of the single-axis angular velocity with time, and recording as an angular acceleration step when the angular acceleration exceeds the angular acceleration threshold value once, wherein the three-axis angular velocity stepNamely the total times that the angular acceleration measured values of the X axis, the Y axis and the Z axis exceed the angular acceleration threshold values of the X axis, the Y axis and the Z axis respectively, namely the three-axis angular acceleration step Cheng Biaoda is +.>Where Xa is the number of times the X-axis measured angular acceleration exceeds the X-axis angular acceleration threshold, ya is the number of times the Y-axis measured angular acceleration exceeds the Y-axis angular acceleration threshold, za is the number of times the Z-axis measured angular acceleration exceeds the Z-axis angular acceleration threshold.
4. The MR recording and broadcasting technology-based multi-user collaborative virtual interaction method according to claim 1, wherein the method comprises the following steps: a calculation method of interaction persistence index;
the interactive persistence index is expressed asWherein Sr is the area of the positioning range of the wearable equipment,>for the time sensor voltage, +.>For voltage monitoring period time, cd is network transmission delay, pl is packet loss rate, < ->The ratio of the positioning range of the wearable device to the voltage change rate of the sensor, the proportional coefficient of the product of the network transmission delay and the packet loss rate are respectively +.>Are all greater than 0.
5. The MR recording and broadcasting technology-based multi-user collaborative virtual interaction method according to claim 1, wherein the method comprises the following steps: a calculation method of a feedback ranking value and a continuous ranking value;
establishing a feedback ordering value p through the interactive feedback coefficient, wherein the larger the interactive feedback coefficient is, the larger the feedback ordering value p is; and establishing a continuous ranking value q through the interactive persistence index, wherein the larger the interactive persistence index is, the larger the continuous ranking value q is.
6. The MR recording and broadcasting technology-based multi-user collaborative virtual interaction method according to claim 1, wherein the method comprises the following steps: establishing logic of dominant sorting values and a dominant interaction order distribution method;
the dominant ranking value is calculated according to the feedback ranking value p and the continuous ranking value q as expressed asWherein g is the dominant ranking value, +.>Proportional coefficients of feedback ranking value and continuous ranking value respectively, and +.>Are all greater than 0;
the hosting interaction orders of the interaction users are arranged according to the dominant ranking value g from large to small, the hosting interaction order numbers M of the n interaction users participating in virtual interaction are the positive integers with the value range of M being more than or equal to 1, and the larger the dominant ranking value, the smaller the number M.
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