MR and VR space synchronization system

Abstract

The invention relates to the technical field of spatial data synchronization, in particular to an MR and VR spatial synchronization system which comprises a frequency spectrum capturing module, a frequency matching module, a filtering adjusting module and a synchronous feedback module. According to the method, the spatial dynamic data in the environment are captured in real time, Fourier transform analysis is carried out, key frequency components are accurately recognized, and a detailed frequency map is constructed, so that the precision of synchronization between environment data and user operation is improved through fine data processing, and information delay between an entity and a virtual environment is reduced; a frequency similarity algorithm is adopted to select a key frequency, a self-adaptive filter is combined to adjust the frequency in real time, frequency response is optimized according to environmental changes, the response flexibility and adaptability of the system are remarkably enhanced, a synchronous feedback mechanism optimizes frequency adjustment based on user experience data, immersion and interactivity in MR and VR environments are improved, and the system performance is improved. And the user satisfaction and the practical value of the system are increased.

Description

Technical Field

[0001] The present invention relates to the field of spatial data synchronization technology, and in particular to an MR and VR spatial synchronization system. Background Art

[0002] Spatial data synchronization technology is an advanced computing technology used to achieve data consistency between the real world and the computationally generated virtual environment. This technology is mainly used in augmented reality and virtual reality systems to ensure that movements and changes in physical space can be reflected in the virtual environment in real time and accurately. This technology covers multiple aspects of data capture, processing and rendering, including spatial positioning, dynamic mapping and environmental interaction. The core of spatial data synchronization technology is to improve the continuity and immersion of user experience, and reduce the delay and inconsistency between reality and virtuality through efficient data processing algorithms and system design.

[0003] Among them, the MR and VR space synchronization system is a technical system specially designed for mixed reality (MR) and virtual reality (VR) platforms, with the purpose of synchronizing user operations and environmental data in these two different technical spaces. The main purpose of the system is to support complex interactive scenarios, such as virtual meetings, distance education, and collaborative work environments, where participants may be in different physical or virtual spaces. By synchronizing spatial data in MR and VR, the system can ensure interoperability and consistency between different devices and platforms, thereby providing a seamlessly integrated and highly interactive user experience, allowing users to interact and collaborate more naturally in virtual space.

[0004] The existing MR and VR spatial synchronization systems mainly focus on basic data capture and processing, but they still have limitations in terms of real-time and accuracy. Especially in complex augmented reality and virtual reality systems, existing technologies often suffer from data inconsistencies due to processing delays, affecting the continuity and immersion of user experience. Existing technologies lack effective mechanisms for real-time updating and accurate reflection of dynamic environmental data, which results in insufficient synchronization between user operations and environmental feedback in highly interactive virtual scenes, reducing interoperability and user satisfaction, which not only affects the user's interactive experience, but also limits the promotion and practicality of virtual and augmented reality technologies in a wider range of application scenarios. Summary of the invention

[0005] The present application provides an MR and VR space synchronization system. The existing MR and VR space synchronization system mainly focuses on basic data capture and processing, but still has limitations in real-time and accuracy. Especially in complex augmented reality and virtual reality systems, the existing technology often causes data inconsistency due to processing delays, affecting the continuity and immersion of user experience. The existing technology lacks an effective mechanism for real-time updating and accurate reflection of dynamic environmental data, which results in insufficient synchronization between user operations and environmental feedback in highly interactive virtual scenes, reducing interoperability and user satisfaction, which not only affects the user's interactive experience, but also limits the promotion and practicality of virtual and augmented reality technologies in a wider range of application scenarios.

[0006] In view of the above problems, the present application provides a MR and VR space synchronization system.

[0007] The present application provides a MR and VR space synchronization system, wherein the system includes:

[0008] The spectrum capture module collects spatial dynamic data in MR and VR environments based on real-time signal capture equipment, performs Fourier transform analysis, identifies and records key frequency components of activities, builds frequency maps based on key frequency components, and preliminarily classifies and organizes key frequency components to obtain a frequency component information set;

[0009] The frequency matching module compares similar frequency components in the MR and VR data based on the frequency component information set, evaluates and selects key frequencies required for synchronization using a frequency similarity algorithm, and generates matching frequency data;

[0010] The filter adjustment module uses the matching frequency data and an adaptive filter to enhance and suppress the key frequency in real time, dynamically adjusts the filter parameters according to the real-time environmental changes, optimizes the frequency response, adjusts the frequency threshold and maintains the synchronization accuracy, and obtains the adjusted frequency output result;

[0011] The synchronization feedback module implements a feedback mechanism based on the adjusted frequency output result, adjusts the frequency synchronization strategy according to the user experience data, automatically analyzes user feedback and optimizes the frequency adjustment, monitors user experience indicators, fine-tunes the synchronization strategy, and obtains the optimal frequency synchronization effect.

[0012] Preferably, the step of acquiring the spatial dynamic data is specifically as follows:

[0013] Configure real-time signal capture equipment to obtain initial signal data in MR and VR environments and generate original signal data sets;

[0014] Performing data processing operations on the original signal data set, including signal amplification and filtering, removing noise and outliers, and obtaining a purified signal data set;

[0015] The dynamic change information of the target space coordinates is extracted from the purification signal data set to obtain the spatial dynamic data, using the following formula:

[0016]

[0017] Among them, x(t) is the spatial dynamic data extracted at time point t, a(t) and b(t) represent the variables at the time point, d and e are adjustment coefficients, f is the normalization factor, g is a negative adjustment parameter, and h is a fixed non-zero parameter.

[0018] Preferably, the step of acquiring the frequency component information set is specifically:

[0019] Based on the spatial dynamic data, Fourier transform analysis is performed by complex frequency domain conversion using the formula:

[0020]

[0021] Generate a weighted frequency domain signal set, and perform, F(k) represents the complex frequency domain signal of frequency k, where N is the total number of data points, k is the frequency index, i is the imaginary unit, w(n) is the weighted window function, n is the sampling point index, e -i2πkn / N is the complex twiddle factor in the discrete Fourier transform;

[0022] Based on the weighted frequency domain signal set, identify and record the key frequency components of the activity, using the key frequency screening formula:

[0023]

[0024] Generate a set of filtered frequency components, where P(k) is the power spectrum of the key frequency, λ is the power normalization factor, τ is the set threshold, and |F(k)| 2 is the power value of frequency k;

[0025] Based on the filtered frequency component set, a frequency map is constructed through key frequency components and preliminarily classified and sorted, using the classification formula:

[0026]

[0027] A frequency component information set is obtained, where C(k) is the classified frequency component, P(j) is the filtered frequency component set, S is the set of frequency components satisfying P(j)>τ, j is the frequency index in the set, and α is the average classification enhancement coefficient.

[0028] Preferably, the comparison method of the same type of frequency components is:

[0029] Based on the frequency component information set, the same frequency components in the MR and VR data are compared to generate a frequency difference data set, and the frequency difference is calculated using the following formula:

[0030]

[0031] Where Δf represents the frequency difference, f MR and f VR denote the frequency components in MR and VR data, respectively, ∈ is the stability parameter;

[0032] The frequency difference data set is used to calculate the similarity score of each frequency component through a frequency similarity algorithm to obtain a frequency similarity score data set, using the following formula:

[0033]

[0034] Where S(f) represents the similarity score of the frequency component, α and β are adjustment coefficients, and ω is the normalization coefficient;

[0035] According to the frequency similarity scoring data set, the frequency components with similarity higher than the preset threshold are selected to generate matching frequency data, using the formula:

[0036] F match ={f|S(f)≥γ}

[0037] Among them, F match represents the matching frequency data, and γ is the preset threshold.

[0038] Preferably, the step of obtaining the adjusted frequency output result is specifically:

[0039] Based on the matching frequency data, an adaptive filter is used to enhance and suppress the key frequency in real time, and a frequency enhancement and suppression result adjusted in real time is obtained, and the formula used is as follows:

[0040]

[0041] Among them, f′ is the frequency enhancement suppression result adjusted in real time, f is the frequency component in the original matching frequency data, and f max is the maximum value among the frequency components, f avg is the average frequency value, λ and δ are adjustment coefficients, ∈ is the stability adjustment coefficient;

[0042] Based on the frequency enhancement suppression result adjusted in real time, the filtering parameters are dynamically adjusted according to the real-time environmental changes to obtain the optimized frequency response result, using the formula:

[0043]

[0044] Wherein, R(f) represents the optimized frequency response result, f′ is the frequency enhancement suppression result adjusted in real time, and ξ and η are parameters for adjusting stability and response smoothness;

[0045] Based on the optimized frequency response result, the frequency threshold is adjusted to maintain the synchronization accuracy and generate the adjusted frequency output result, using the formula:

[0046]

[0047] Among them, F out represents the adjusted frequency output result, and θ is the adjusted frequency threshold.

[0048] Preferably, the implementation method of the feedback mechanism is:

[0049] Based on the adjusted frequency output result, the current effect of the frequency synchronization strategy is evaluated according to the user experience data, and the difference between the current frequency and the expected frequency is calculated to obtain the frequency deviation, using the following formula:

[0050]

[0051] Where ΔF represents the frequency deviation, F c is the current frequency, F d is the expected frequency, ∈ is the stable deviation term, and μ is the high-order adjustment coefficient;

[0052] According to the frequency deviation, the adaptive adjustment algorithm is called to dynamically adjust the frequency synchronization strategy to obtain new synchronization strategy parameters, using the following formula:

[0053]

[0054] Among them, Θ n represents the new synchronization strategy parameter, Θ o is the old synchronization strategy parameter, κ is the adjustment coefficient used to control the amplitude of strategy adjustment, λ is the attenuation factor, and ω is the secondary adjustment coefficient;

[0055] The new synchronization strategy parameters are applied to monitor and optimize the frequency adjustment process and implement a feedback mechanism using the following formula:

[0056] M=Θ n ·ξ+v

[0057] Among them, M represents the feedback mechanism after application, A is the operation function of applying the new strategy, ξ is the fine-tuning coefficient, and v is the stability parameter.

[0058] Preferably, the method for monitoring the user experience indicator is:

[0059] Based on the feedback mechanism, user experience data is collected, the frequency synchronization strategy is adjusted, and the deviation between the user experience data and the ideal experience data is calculated to obtain the user experience deviation. The formula used is as follows:

[0060]

[0061] Among them, ΔU represents the user experience deviation, U c is the current user experience data, U i is the ideal experience data, α is the stable deviation term, and β is the logarithmic adjustment coefficient;

[0062] According to the user experience deviation, the adaptive adjustment algorithm is called to dynamically adjust the user experience strategy to obtain new user experience strategy parameters, using the formula:

[0063]

[0064] Among them, Θ UX represents the new user experience strategy parameter, Θ U is the old user experience strategy parameter, γ is the adjustment coefficient, δ is the attenuation factor, and ∈ is the exponential adjustment coefficient;

[0065] Apply the new user experience strategy parameters, execute the user experience optimization mechanism, monitor and optimize the user experience adjustment process, and obtain the user experience index, using the formula:

[0066] U metrics =Θ UX +ζ-θ

[0067] Among them, U metrics represents the user experience index, A UX is the operating function for applying the new strategy, ζ is the fine-tuning coefficient, and θ is the stability parameter.

[0068] Preferably, the step of obtaining the optimal frequency synchronization effect is specifically:

[0069] Based on the user experience index, the synchronization strategy is adjusted according to the user experience index, and the deviation between the real-time synchronization effect and the ideal synchronization effect is calculated to obtain the synchronization effect deviation, using the formula:

[0070]

[0071] Among them, ΔS represents the synchronization effect deviation, S a For the actual synchronization effect, S i is the ideal synchronization effect, ξ is the stable deviation term, and ρ is the periodic adjustment coefficient;

[0072] According to the synchronization effect deviation, the synchronization strategy is dynamically adjusted to obtain the synchronization effect strategy parameters, using the following formula:

[0073]

[0074] Among them, Θ S represents the synchronization effect strategy parameter, Θ P is the old synchronization effect strategy parameter, λ is the adjustment coefficient, μ is the attenuation factor, and v is the periodic adjustment coefficient;

[0075] Apply the synchronization effect strategy parameters, monitor and optimize the synchronization effect adjustment process, and obtain the optimal frequency synchronization effect.

[0076]

[0077] Among them, S optimal Indicates the optimal frequency synchronization effect, A S is the operating function for applying the new strategy, ω is the fine-tuning coefficient, φ is the stability parameter, and κ is the logarithmic adjustment coefficient.

[0078] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0079] By capturing the spatial dynamic data in the environment in real time and performing Fourier transform analysis, key frequency components can be accurately identified, thereby constructing a detailed frequency map. This sophisticated data processing improves the synchronization accuracy between environmental data and user operations and reduces information delays between physical and virtual environments. The frequency similarity algorithm is used to compare and select the key frequencies required for synchronization to further ensure the consistency and integrity of the data. The frequency is adjusted in real time through an adaptive filter, and the frequency response is dynamically optimized according to environmental changes. This strategy significantly enhances the system's response flexibility and environmental adaptability. Based on the synchronous feedback mechanism adjusted based on user experience data, the frequency adjustment process is optimized to achieve meticulous optimization of the user experience, ensuring the user's immersion and interactivity in the MR and VR environments, and improving the user satisfaction and practical value of the overall system.

[0080] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A module diagram of a MR and VR space synchronization system is proposed for the present invention;

[0082] Figure 2A flow chart of spatial dynamic data of a MR and VR spatial synchronization system is proposed for the present invention;

[0083] Figure 3 A flow chart of a frequency component information set of a MR and VR spatial synchronization system is proposed for the present invention;

[0084] Figure 4 A flow chart of similar frequency components of MR and VR spatial synchronization system is proposed for the present invention;

[0085] Figure 5 A flow chart of the frequency output result after the adjustment of the MR and VR spatial synchronization system is proposed for the present invention;

[0086] Figure 6 A flow chart of a feedback mechanism of MR and VR spatial synchronization system is proposed for the present invention;

[0087] Figure 7 A flow chart of user experience indicators of MR and VR space synchronization system is proposed for the present invention;

[0088] Figure 8 The present invention proposes a flow chart of the optimal frequency synchronization effect of the MR and VR spatial synchronization system. DETAILED DESCRIPTION

[0089] The present application provides a MR and VR space synchronization system.

[0090] Application Overview

[0091] The MR and VR spatial synchronization systems in the existing technologies mainly focus on basic data capture and processing, but they still have limitations in terms of real-time and accuracy. In particular, in complex augmented reality and virtual reality systems, the existing technologies often cause data inconsistencies due to processing delays, affecting the continuity and immersion of the user experience. The existing technologies lack effective mechanisms for real-time updating and accurate reflection of dynamic environmental data, which results in insufficient synchronization between user operations and environmental feedback in highly interactive virtual scenes, reducing interoperability and user satisfaction, which not only affects the user's interactive experience, but also limits the promotion and practicality of virtual and augmented reality technologies in a wider range of application scenarios.

[0092] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0093] like Figure 1 As shown, the present application provides a MR and VR space synchronization system, wherein the system includes:

[0094] The spectrum capture module collects spatial dynamic data in MR and VR environments based on real-time signal capture equipment, performs Fourier transform analysis, identifies and records key frequency components of activities, builds frequency maps based on key frequency components, and preliminarily classifies and organizes key frequency components to obtain a frequency component information set;

[0095] The frequency matching module compares similar frequency components in MR and VR data based on the frequency component information set, uses the frequency similarity algorithm to evaluate and select the key frequencies required for synchronization, and generates matching frequency data;

[0096] The filter adjustment module matches the frequency data and uses an adaptive filter to enhance and suppress the key frequencies in real time. It dynamically adjusts the filter parameters according to the real-time environmental changes, optimizes the frequency response, adjusts the frequency threshold and maintains the synchronization accuracy to obtain the adjusted frequency output result.

[0097] The synchronization feedback module implements a feedback mechanism based on the adjusted frequency output results, adjusts the frequency synchronization strategy according to user experience data, automatically analyzes user feedback and optimizes frequency adjustment, monitors user experience indicators, fine-tunes the synchronization strategy, and obtains the optimal frequency synchronization effect.

[0098] The frequency component information set includes key frequency components, frequency maps, and preliminary classification criteria. The matching frequency data includes key synchronization frequencies, similarity evaluation results, and screening criteria. The adjusted frequency output results include enhanced frequency points, suppressed frequency points, and filtering parameters. The optimal synchronization effect includes user feedback analysis results, experience indicator monitoring, and strategy fine-tuning results.

[0099] Specifically, if Figure 2 As shown, the steps for obtaining spatial dynamic data are as follows:

[0100] Configure real-time signal capture equipment to obtain initial signal data in MR and VR environments and generate original signal data sets;

[0101] Performing data processing operations on the original signal data set, including signal amplification and filtering, removing noise and outliers, and obtaining a purified signal data set;

[0102] The dynamic change information of the target space coordinates is extracted from the purified signal data set to obtain the spatial dynamic data. The formula is as follows:

[0103]

[0104] Among them, x(t) is the spatial dynamic data extracted at time point t, a(t) and b(t) represent the variables at the time point, d and e are adjustment coefficients, f is the normalization factor, g is a negative adjustment parameter, and h is a fixed non-zero parameter.

[0105] Spatial dynamic data formula:

[0106]

[0107] a(t) and b(t) are time series data extracted from the original signal data set. The data reflects the signal strength or position information at a specific time point t. For example, if a dynamic environment is monitored, a(t) may represent the X-axis position at time t, while b(t) represents the Y-axis position.

[0108] d and e are adjustment coefficients obtained by statistical analysis of historical data. Assume that in past experiments, it is found that the data contributions of the X-axis and the Y-axis are uneven. Through analysis, d = 1.2 and e = 0.8 are determined to balance the influence of the two axes.

[0109] f is the normalization factor, which is calculated based on the range of the entire data set. The maximum and minimum values ​​of all a(t)·d+b(t)·e in the data set are calculated, and the difference between the two values ​​is taken as the value of f to normalize the result.

[0110] g is a negative adjustment parameter, which is determined by data sensitivity analysis. Assuming g = -10, absolute value and square root operations are used to ensure that the denominator in the formula is non-negative and non-zero;

[0111] h is a fixed non-zero parameter set to adjust the formula output. Suppose analysis finds that adding 1 / h can effectively adjust the result, so set h=5.

[0112] Assume that at a specific time t, a(t) = 50 and b(t) = 30 are obtained from the signal acquisition device. According to the coefficient values ​​set above, the process of calculating x(t) is as follows:

[0113] Calculate a(t) d + b(t) e:

[0114] 50·1.2+30·0.8=60+24=84

[0115] Determine f based on the data range (set to 100, assuming this is the range value obtained from historical data analysis) and calculate the denominator:

[0116]

[0117] The final calculation x(t):

[0118]

[0119] This example shows how to use various parameters to calculate the spatial coordinates of dynamic data, thereby obtaining more accurate data output that is suitable for specific application scenarios.

[0120] Specifically, if Figure 3 As shown, the steps for obtaining the frequency component information set are specifically as follows:

[0121] Based on the spatial dynamic data, Fourier transform analysis is performed by complex frequency domain conversion using the formula:

[0122]

[0123] Generate a weighted frequency domain signal set, and perform, F(k) represents the complex frequency domain signal of frequency k, where N is the total number of data points, k is the frequency index, i is the imaginary unit, w(n) is the weighted window function, n is the sampling point index, e -i2πkn / N is the complex twiddle factor in the discrete Fourier transform;

[0124] Based on the weighted frequency domain signal set, identify and record the key frequency components of the activity, using the key frequency screening formula:

[0125]

[0126] Generate a set of filtered frequency components, where P(k) is the power spectrum of the key frequency, λ is the power normalization factor, τ is the set threshold, and |F(k)| 2 is the power value of frequency k;

[0127] Based on the filtered frequency component set, a frequency map is constructed through key frequency components and preliminarily classified and sorted, using the classification formula:

[0128]

[0129] A frequency component information set is obtained, where C(k) is the classified frequency component, p(j) is the filtered frequency component set, S is the set of frequency components satisfying p(j)>τ, j is the frequency index in the set, and α is the average classification enhancement coefficient.

[0130] Key frequency screening formula:

[0131]

[0132] x(n): the nth sample data point in the time series;

[0133] N: the total number of data points, i.e. the length of the time series;

[0134] k: frequency index, ranging from 0 to N-1;

[0135] i: imaginary unit;

[0136] w(n): weighted window function, usually used to reduce spectral leakage.

[0137] Initialize the sum F(k) to 0, and for each sample point n (from 0 to N-1), calculate the complex exponential part e -i2πkn / N , multiply x(n) by the window function w(n), multiply the result by the complex exponential part, and add it to F(k).

[0138] Assume there is a simple time series x = [1, -1, 1, -1] (i.e. N = 4), and use

[0139] A uniform window function w(n) = 0.5:

[0140] For k=0:

[0141] F(0)=0.5·1·1+0.5·(-1)·1+0.5·1·1+0.5·(-1)·1=0

[0142] Key frequency screening formula:

[0143]

[0144] |F(k)| 2 : The square of the amplitude of F(k), which represents the energy of frequency k;

[0145] λ: power normalization factor.

[0146] Calculate |F(k)| for each frequency k 2 , calculate the sum of all frequency energies For each k, calculate its power spectrum P(k).

[0147] The result using F(k), where F(0) = 0, F(1) = -2i, F(2) = 0, F(3) = 2i:

[0148] Total Energy

[0149] For k=1 or k=3,

[0150] Classification formula:

[0151]

[0152] S: the set of frequency components satisfying P(j)>τ;

[0153] α: average classification enhancement coefficient;

[0154] |S|: The size of the S set.

[0155] Determine the set S of frequency components satisfying P(j)>τ, calculate the average value of all P(j) in S, and multiply it by α to obtain the classified frequency component C(k).

[0156] Assuming τ = 0.45·λ, only P(k) for k = 1 and k = 3 exceeds τ:

[0157] S = {1, 3}, |S| = 2

[0158]

[0159] Through specific numerical calculations and parameter calls, the accuracy of understanding and application of formulas can be ensured.

[0160] Specifically, if Figure 4 As shown, the comparison method of similar frequency components is:

[0161] Based on the frequency component information set, the same frequency components in the MR and VR data are compared to generate a frequency difference data set. The frequency difference is calculated using the following formula:

[0162]

[0163] Where Δf represents the frequency difference, f MR and f VR denote the frequency components in MR and VR data, respectively, ∈ is the stability parameter;

[0164] Using the frequency difference data set, the similarity score of each frequency component is calculated through the frequency similarity algorithm to obtain the frequency similarity score data set. The formula used is as follows:

[0165]

[0166] Where S(f) represents the similarity score of the frequency component, α and β are adjustment coefficients, and ω is the normalization coefficient;

[0167] According to the frequency similarity scoring data set, the frequency components with similarity higher than the preset threshold are selected to generate matching frequency data. The formula used is:

[0168] F match ={f|S(f)≥γ}

[0169] Among them, F match represents the matching frequency data, and γ is the preset threshold.

[0170] Calculate the frequency difference Δf:

[0171] For the similar frequency components f in MR and VR data MR and f VR To extract, set the frequency component of an MR data to f MR =a, the frequency component of VR data is f VR= b, where a and b are the actual measured or simulated frequency values;

[0172] Calculate the difference between two frequency components, using the absolute value and square to ensure that the difference is non-negative, i.e. |ab| 2 ;

[0173] In order to avoid the calculated difference being zero when the two frequencies are exactly the same, thus affecting the stability of the subsequent logarithmic operation, a small positive number ∈ is introduced, assuming that ∈=0.001;

[0174] Calculate the final frequency difference Δf, the formula is For example, if a=100Hz, b=105Hz, then:

[0175]

[0176] Calculate the similarity score S(f) of the frequency components:

[0177] Using the calculated Δf, we set the adjustment coefficients α and β, where α = 0.1 and β = 2, to control the shape and position of the similarity curve. We use the normalization coefficient ω to ensure that the score is within a certain range, where ω = 1. We calculate the similarity score of the frequency component through an exponential function, using the formula:

[0178]

[0179] If Δf=5.0001, then:

[0180] Select frequency components whose similarity is higher than a preset threshold according to the frequency similarity score;

[0181] A preset threshold γ is set, where γ = 0.4 is taken, to screen the key frequencies. By comparing whether the similarity score S(f) of each frequency component is greater than or equal to γ, it is decided whether to include the frequency component in the matching frequency data. For example, the previously calculated S(f) = 0.426, because 0.426 ≥ 0.4, the frequency component will be selected into the matching frequency data F. match .

[0182] Specifically, if Figure 5 As shown, the steps for obtaining the adjusted frequency output result are specifically as follows:

[0183] Based on the matching frequency data, an adaptive filter is used to enhance and suppress the key frequency in real time, and the frequency enhancement and suppression results adjusted in real time are obtained. The formula used is as follows:

[0184]

[0185] Among them, f′ is the frequency enhancement suppression result adjusted in real time, f is the frequency component in the original matching frequency data, and f max is the maximum value among the frequency components, f avg is the average frequency value, λ and δ are adjustment coefficients, ∈ is the stability adjustment coefficient;

[0186] Based on the frequency enhancement suppression results adjusted in real time, the filtering parameters are dynamically adjusted according to the real-time environmental changes to obtain the optimized frequency response results. The formula used is:

[0187]

[0188] Wherein, R(f) represents the optimized frequency response result, f′ is the frequency enhancement suppression result adjusted in real time, and ξ and η are parameters for adjusting stability and response smoothness;

[0189] Based on the optimized frequency response result, the frequency threshold is adjusted to maintain the synchronization accuracy and generate the adjusted frequency output result. The formula used is:

[0190]

[0191] Among them, F out represents the adjusted frequency output result, and θ is the adjusted frequency threshold.

[0192] The formula for the frequency enhancement suppression result adjusted in real time is:

[0193]

[0194] Calculate the cosine part: Calculate f max -f gets the difference between the current frequency and the maximum frequency, using the cosine function Adjust the intensity of enhancement or suppression;

[0195] Calculate the exponential part: Calculate | ff avg |Get the absolute value of the difference between the current frequency and the average frequency, square it and add ∈, then take the square root to get the value under the square root, add ∈ to ensure stability, and pass Perform exponential decay to suppress the impact of excessive frequency gap;

[0196] Comprehensive adjustment coefficient: Multiply the cosine part and the exponential part, and multiply by λ: to obtain the final adjustment ratio. The final frequency f′ is the original frequency f multiplied by the above adjustment ratio.

[0197] Assumption: f = 100 Hz, f max =200Hz, f avg =120Hz, λ=0.5, δ=0.2, ∈=0.01;

[0198] First calculate:

[0199]

[0200] Then:

[0201]

[0202] at last:

[0203]

[0204] The optimized frequency response formula is:

[0205]

[0206] Calculate the numerator: directly use f′ obtained in step 1;

[0207] Calculate the denominator: square f′ and add ξ 2 and η 2 , and get the sum of the squares of the denominators.

[0208] Assumptions: ξ=1,η=1

[0209] f′=100.6475

[0210] f′ 2 +ξ 2 +η 2 =100.6475 2 +1 2 +1 2 =10129.739+1+1=10131.739

[0211]

[0212] The adjusted frequency output result formula is:

[0213]

[0214] Calculate the right side: Calculate And apply the sine function.

[0215] Comparison: Compare R(f) with value.

[0216] Assumption: θ = 0.95

[0217]

[0218] Compare:

[0219] R(f)=0.99991

[0220]

[0221] Therefore, f belongs to F out , because 0.99991 ≥ 0.707, so

[0222] Specifically, if Figure 6 As shown, the implementation method of the feedback mechanism is:

[0223] Based on the adjusted frequency output results, the current effect of the frequency synchronization strategy is evaluated according to the user experience data, and the difference between the current frequency and the expected frequency is calculated to obtain the frequency deviation. The formula used is as follows:

[0224]

[0225] Where ΔF represents the frequency deviation, F c is the current frequency, F d is the expected frequency, ∈ is the stable deviation term, and μ is the high-order adjustment coefficient;

[0226] According to the frequency deviation, the adaptive adjustment algorithm is called to dynamically adjust the frequency synchronization strategy to obtain new synchronization strategy parameters. The formula used is as follows:

[0227]

[0228] Among them, Θ n represents the new synchronization strategy parameter, Θ o is the old synchronization strategy parameter, k is the adjustment coefficient used to control the amplitude of strategy adjustment, λ is the attenuation factor, and ω is the secondary adjustment coefficient;

[0229] Apply new synchronization strategy parameters, monitor and optimize the frequency adjustment process, and implement a feedback mechanism using the following formula:

[0230] M=Θ n ·ξ+v

[0231] Among them, M represents the feedback mechanism after application, A is the operation function of applying the new strategy, ξ is the fine-tuning coefficient, and ν is the stability parameter.

[0232] Initialize frequency deviation ΔF and calculate current frequency F c With the expected frequency F d The difference between c =100Hz and F d =95Hz, then the difference is F c -F d =5Hz;

[0233] Calculate the square of the difference and use the square to handle the positive and negative nature of the difference to ensure that the result is non-negative, for example, (F c -F d ) 2 =25Hz;

[0234] The stable deviation term ∈ is introduced to avoid calculation instability when the frequency difference is extremely small. If ∈=0.1, then ∈ 2 =0.01;

[0235] Calculate the influence of the high-order adjustment coefficient and use the high-order adjustment coefficient μ to handle the frequency adjustment behavior when the deviation is large. If μ=0.01 is set, the high-order adjustment is μ·(F c -F d ) 4 =0.01×625=6.25.

[0236] Comprehensively calculate the frequency deviation ΔF, according to the formula:

[0237]

[0238] Substituting the above values ​​into

[0239] Calculate the new synchronization strategy parameter Θ n , first use the old synchronization strategy parameter Θ o , assuming its value is 1.0;

[0240] Calculate the effect of the adjustment coefficient κ, set κ = 0.5, then κ × ΔF = 0.5 × 5.59 = 2.795;

[0241] The influence of the attenuation factor λ, setting λ = 0.1, then 1 + λ × ΔF = 1 + 0.1 × 5.59 = 1.559;

[0242] The role of the secondary adjustment coefficient ω, set ω = 0.05, then ω × ΔF 2 =0.05×31.26=1.563;

[0243] The new strategy parameters after comprehensive adjustment are brought into the formula:

[0244]

[0245] Calculated according to the formula:

[0246]

[0247] Apply the new synchronization strategy parameters and set Θ n =0.1047;

[0248] The influence of fine-tuning coefficient ξ and stability parameter v, assuming ξ=0.2, v=0.3;

[0249] Finally, the feedback mechanism M is obtained, and the new strategy Θ is applied by the operation function A n , fine-tune the coefficient ξ and the stability parameter v to obtain M, M = Θ n ·ξ+ν=0.1047·0.2+0.3=0.32094.

[0250] Specifically, if Figure 7 As shown in the figure, the monitoring method of user experience indicators is:

[0251] Based on the feedback mechanism, user experience data is collected, the frequency synchronization strategy is adjusted, and the deviation between the user experience data and the ideal experience data is calculated to obtain the user experience deviation. The formula used is as follows:

[0252]

[0253] Among them, ΔU represents the user experience deviation, U c is the current user experience data, U i is the ideal experience data, α is the stable deviation term, and β is the logarithmic adjustment coefficient;

[0254] According to the user experience deviation, the adaptive adjustment algorithm is called to dynamically adjust the user experience strategy to obtain new user experience strategy parameters. The formula is:

[0255]

[0256] Among them, Θ UX represents the new user experience strategy parameter, Θ U is the old user experience strategy parameter, γ is the adjustment coefficient, δ is the attenuation factor, and ∈ is the exponential adjustment coefficient;

[0257] Apply new user experience strategy parameters, execute user experience optimization mechanism, monitor and optimize the user experience adjustment process, and obtain user experience indicators. The formula used is:

[0258] U metrics =Θ UX +ζ-θ

[0259] Among them, U metrics represents the user experience index, A UX is the operating function for applying the new strategy, ζ is the fine-tuning coefficient, and θ is the stability parameter.

[0260] User Experience Bias Formula:

[0261]

[0262] Uc and U i It is the current and ideal user experience data, which is obtained through user feedback or preset standards;

[0263] |U c -U i |Indicates the absolute value of the deviation between the current experience and the ideal experience;

[0264] α is a stable deviation term, which is used to ensure that c and U i When they are equal, the denominator is not zero to improve numerical stability, for example, α = 0.001;

[0265] β is the adjustment coefficient, which is used to control the sensitivity of logarithmic adjustment. For example, β = 0.1;

[0266] The calculation process first calculates |U c -U i | 2 + the square root of α, and then divided by 1+βln(1+|U c -U i |).

[0267] Assume U c =10,U i =8, then |U c -U i |=2, take α=0.001, β=0.1;

[0268]

[0269] 1+βln(1+|U c -U i |)=1+0.1ln(1+2)=1+0.1ln(3)≈1.10986

[0270]

[0271] New user experience strategy parameter formula:

[0272]

[0273] Θ U is the old user experience strategy parameter, obtained through previous analysis and strategy setting. For example, take Θ U =5;

[0274] γ, δ and ∈ are preset adjustment coefficients, for example, γ = 0.5, δ = 1, ∈ = 0.1;

[0275] Calculate 1+δe -∈ΔU, used to balance the adjustment impact of ΔU and calculate new user experience strategy parameters.

[0276] Given ΔU=1.80164, Θ U =5, γ=0.5, δ=1, ∈=0.1;

[0277] 1+δe -∈ΔU =1+1×e -0.1×1.80164 ≈1.835

[0278]

[0279] User experience indicator formula:

[0280] U metrics =Θ UX +ζ-θ

[0281] Θ UX is the new user experience strategy parameter, ζ and θ are fine-tuning coefficients and stability parameters, which are used to fine-tune and maintain the smoothness of the adjustment process. For example, ζ = 0.05, θ = 0.02. UX =4.509, ζ=0.05, θ=0.02;

[0282] U metrics =4.509+0.05-0.02=4.539

[0283] Specifically, if Figure 8 As shown, the steps for obtaining the optimal frequency synchronization effect are specifically as follows:

[0284] Based on the user experience indicators, adjust the synchronization strategy according to the user experience indicators, calculate the deviation between the real-time synchronization effect and the ideal synchronization effect, and get the synchronization effect deviation. The formula used is:

[0285]

[0286] Among them, ΔS represents the synchronization effect deviation, S a For the actual synchronization effect, S i is the ideal synchronization effect, ξ is the stable deviation term, and ρ is the periodic adjustment coefficient;

[0287] According to the synchronization effect deviation, the synchronization strategy is dynamically adjusted to obtain the synchronization effect strategy parameters. The formula used is as follows:

[0288]

[0289] Among them, Θ S represents the synchronization effect strategy parameter, Θ P is the old synchronization effect strategy parameter, λ is the adjustment coefficient, μ is the attenuation factor, and ν is the periodic adjustment coefficient;

[0290] Apply synchronization effect strategy parameters, monitor and optimize the synchronization effect adjustment process, and obtain the optimal frequency synchronization effect.

[0291]

[0292] Among them, S optimal Indicates the optimal frequency synchronization effect, A S is the operating function for applying the new strategy, ω is the fine-tuning coefficient, φ is the stability parameter, and κ is the logarithmic adjustment coefficient.

[0293] Synchronous effect deviation formula:

[0294]

[0295] S a : Actual synchronization effect, set to the example value 80;

[0296] S i : Ideal synchronization effect, set to the example value 95.

[0297] Calculate the difference: |S a -S i |The difference between actual and ideal, that is, |80-95|=15.

[0298] Calculate the power expression of the deviation: |S a -S i | 1.5 The difference to the power of 1.5, or 15 1.5 =58.094.

[0299] Calculate the periodic adjustment: Set ξ to 0.3 and ρ to 0.05 as an example;

[0300] tan(ρ·|S a -S i |): tangent adjustment, i.e. tan(0.05×15)=tan(0.75)=0.931596;

[0301] 1+ξ·tan(ρ·|S a -S i |): Denominator expression, that is, 1+0.3×0.931596=1.279479.

[0302] The final synchronization effect deviation ΔS is obtained:

[0303]

[0304] Formula for calculating synchronization effect strategy parameters:

[0305]

[0306] Set the old synchronization effect strategy parameters (Θ P ) is an example value of 50;

[0307] Calculate the periodically adjusted denominator:

[0308] Set λ=1.2, μ=0.1, v=0.01;

[0309] cos(v·ΔS): cosine adjustment, i.e. cos(0.01×45.4)=cos(0.454)≈0.8987;

[0310] 1+μ·cos(v·ΔS): denominator expression, that is, 1+0.1×0.8987=1.08987.

[0311] Get the synchronization effect strategy parameter Θ S :

[0312]

[0313] Calculate the optimal frequency synchronization effect formula:

[0314]

[0315] Operation function A that applies the new strategy S :

[0316] Assumption A S is a linear function, with ω=2, φ=0.5;

[0317] ω×Θ S +φ=2×0.02+0.5=0.54;

[0318] Calculate the log-adjusted denominator:

[0319] κ=0.4

[0320] ln(1+Θ S )=ln(1+0.02)=ln(1.02)=0.0198

[0321] 1+κln(1+Θ S )=1+0.4×0.0198=1.00792

[0322] Get the optimal frequency synchronization effect S optimal :

[0323]

[0324] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A MR and VR spatial synchronization system, characterized in that: The system comprises: The spectrum capture module collects spatial dynamic data in MR and VR environments based on real-time signal capture equipment, performs Fourier transform analysis, identifies and records key frequency components of activities, builds frequency maps based on key frequency components, and preliminarily classifies and organizes key frequency components to obtain a frequency component information set; The frequency matching module compares similar frequency components in the MR and VR data based on the frequency component information set, evaluates and selects key frequencies required for synchronization using a frequency similarity algorithm, and generates matching frequency data; The filter adjustment module uses the matching frequency data and an adaptive filter to enhance and suppress the key frequency in real time, dynamically adjusts the filter parameters according to the real-time environmental changes, optimizes the frequency response, adjusts the frequency threshold and maintains the synchronization accuracy, and obtains the adjusted frequency output result; The synchronization feedback module implements a feedback mechanism based on the adjusted frequency output result, adjusts the frequency synchronization strategy according to the user experience data, automatically analyzes user feedback and optimizes the frequency adjustment, monitors user experience indicators, fine-tunes the synchronization strategy, and obtains the optimal frequency synchronization effect.

2. The MR and VR spatial synchronization system according to claim 1, characterized in that: The steps for acquiring the spatial dynamic data are specifically as follows: Configure real-time signal capture equipment to obtain initial signal data in MR and VR environments and generate original signal data sets; Performing data processing operations on the original signal data set, including signal amplification and filtering, removing noise and outliers, and obtaining a purified signal data set; The dynamic change information of the target space coordinates is extracted from the purification signal data set to obtain the spatial dynamic data, using the following formula: Among them, x(t) is the spatial dynamic data extracted at time point t, a(t) and b(t) represent the variables at the time point, d and e is the adjustment coefficient, f is the normalization factor, g is a negative adjustment parameter, and h is a fixed non-zero parameter.

3. The MR and VR spatial synchronization system according to claim 2, characterized in that: The steps of acquiring the frequency component information set are specifically as follows: Based on the spatial dynamic data, Fourier transform analysis is performed by complex frequency domain conversion using the formula: Generate a weighted frequency domain signal set, and perform, F(k) represents the complex frequency domain signal of frequency k, where N is the total number of data points, k is the frequency index, i is the imaginary unit, w(n) is the weighted window function, n is the sampling point index, e -i2πkn / N is the complex twiddle factor in the discrete Fourier transform; Based on the weighted frequency domain signal set, identify and record the key frequency components of the activity, using the key frequency screening formula: Generate a set of filtered frequency components, where P(k) is the power spectrum of the key frequency, λ is the power normalization factor, τ is the set threshold, and |F(k)| 2 is the power value of frequency k; Based on the filtered frequency component set, a frequency map is constructed through key frequency components and preliminarily classified and sorted, using the classification formula: A frequency component information set is obtained, where C(k) is the classified frequency component, P(j) is the filtered frequency component set, S is the set of frequency components satisfying P(j)>τ, j is the frequency index in the set, and α is the average classification enhancement coefficient.

4. The MR and VR spatial synchronization system according to claim 3, characterized in that: The comparison method of the same frequency components is: Based on the frequency component information set, the same frequency components in the MR and VR data are compared to generate a frequency difference data set, and the frequency difference is calculated using the following formula: Where Δf represents the frequency difference, f MR and f VR Represent the frequency components in MR and VR data, ∈ is the stability parameter; The frequency difference data set is used to calculate the similarity score of each frequency component through a frequency similarity algorithm to obtain a frequency similarity score data set, using the following formula: Where S(f) represents the similarity score of the frequency component, α and β are adjustment coefficients, and ω is the normalization coefficient; According to the frequency similarity scoring data set, the frequency components with similarity higher than the preset threshold are selected to generate matching frequency data, using the formula: F match ={f|S(f)≥γ} Among them, F match represents the matching frequency data, and γ is the preset threshold.

5. The MR and VR spatial synchronization system according to claim 4, characterized in that: The steps for obtaining the adjusted frequency output result are specifically as follows: Based on the matching frequency data, an adaptive filter is used to enhance and suppress the key frequency in real time, and a frequency enhancement and suppression result adjusted in real time is obtained, and the formula used is as follows: Among them, f′ is the frequency enhancement suppression result adjusted in real time, f is the frequency component in the original matching frequency data, and f max is the maximum value among the frequency components, f avg is the average frequency value, λ and δ are adjustment coefficients, ∈ is the stability adjustment coefficient; Based on the frequency enhancement suppression result adjusted in real time, the filtering parameters are dynamically adjusted according to the real-time environmental changes to obtain the optimized frequency response result, using the formula: Among them, R(f) represents the optimized frequency response result, f ′ The frequency enhancement suppression results are adjusted in real time, ξ and η Parameters for adjusting stability and response smoothness; Based on the optimized frequency response result, the frequency threshold is adjusted to maintain the synchronization accuracy and generate the adjusted frequency output result, using the formula: Among them, F out represents the adjusted frequency output result, and θ is the adjusted frequency threshold.

6. The MR and VR spatial synchronization system according to claim 5, characterized in that: The implementation method of the feedback mechanism is: Based on the adjusted frequency output result, the current effect of the frequency synchronization strategy is evaluated according to the user experience data, and the difference between the current frequency and the expected frequency is calculated to obtain the frequency deviation, using the following formula: Where ΔF represents the frequency deviation, F c is the current frequency, F d is the expected frequency, ∈ is the stable deviation term, μ is the high-order adjustment coefficient; According to the frequency deviation, the adaptive adjustment algorithm is called to dynamically adjust the frequency synchronization strategy to obtain new synchronization strategy parameters, using the following formula: Among them, Θ n represents the new synchronization strategy parameter, Θ o is the old synchronization strategy parameter, κ is the adjustment coefficient used to adjust the control strategy, λ is the attenuation factor, and ω is the quadratic adjustment coefficient; The new synchronization strategy parameters are applied to monitor and optimize the frequency adjustment process and implement a feedback mechanism using the following formula: M=Θ n ·ξ+v Among them, M represents the feedback mechanism after application, A is the operation function of applying the new strategy, ξ is the fine-tuning coefficient, and v is the stability parameter.

7. The MR and VR spatial synchronization system according to claim 6, characterized in that: The monitoring method of the user experience indicator is: Based on the feedback mechanism, user experience data is collected, the frequency synchronization strategy is adjusted, and the deviation between the user experience data and the ideal experience data is calculated to obtain the user experience deviation. The formula used is as follows: Among them, ΔU represents the user experience deviation, U c is the current user experience data, U i is the ideal experience data, α is the stable deviation term, β is the logarithmic adjustment coefficient; According to the user experience deviation, the adaptive adjustment algorithm is called to dynamically adjust the user experience strategy to obtain new user experience strategy parameters, using the formula: Among them, Θ UX represents the new user experience strategy parameter, Θ U is the old user experience strategy parameter, γ is the adjustment coefficient, δ is the attenuation factor, and ∈ is the exponential adjustment coefficient; Apply the new user experience strategy parameters, execute the user experience optimization mechanism, monitor and optimize the user experience adjustment process, and obtain the user experience index, using the formula: U metrics =Θ UX +ζ-θ Among them, U metrics represents the user experience index, A UX is the operation function of applying the new strategy, ζ is the fine-tuning coefficient, θ is the stability parameter.

8. The MR and VR spatial synchronization system according to claim 7, characterized in that: The steps for obtaining the optimal frequency synchronization effect are specifically as follows: Based on the user experience index, the synchronization strategy is adjusted according to the user experience index, and the deviation between the real-time synchronization effect and the ideal synchronization effect is calculated to obtain the synchronization effect deviation, using the formula: Among them, ΔS represents the synchronization effect deviation, S a For the actual synchronization effect, S i For ideal synchronization effect, is the stable deviation term, ρ is the periodic adjustment coefficient; According to the synchronization effect deviation, the synchronization strategy is dynamically adjusted to obtain the synchronization effect strategy parameters, using the following formula: Among them, Θ S represents the synchronization effect strategy parameter, Θ P is the old synchronization effect strategy parameter, λ is the adjustment coefficient, μ is the attenuation factor, v is the periodic adjustment coefficient; Apply the synchronization effect strategy parameters, monitor and optimize the synchronization effect adjustment process, and obtain the optimal frequency synchronization effect. Among them, S optimal Indicates the optimal frequency synchronization effect, A S is the operating function for applying the new strategy, ω is the fine-tuning coefficient, φ is the stability parameter, and κ is the logarithmic adjustment coefficient.

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