A digital twin intelligent tourism management system and method

Abstract

The application discloses a kind of for digital twin wisdom travel management system and method, it is related to travel interactive technical field, including the following steps: establish multi-projection unified phase baseline and energy distribution audit model, the phase difference of the multiple projection light wave in virtual-real superposition area and the brightness change curve are nanosecond level synchronous sampling, generate corresponding initial light field energy coupling matrix;Based on initial light field energy coupling matrix, interference trajectory inversion network is constructed, and the light wave superposition sequence is replayed using time reversal algorithm.The application realizes the accurate monitoring and active regulation of light field energy by unified phase baseline and energy audit, effectively eliminates interference focusing and brightness mutation, improves visual stability and immersion experience;And through phase conjugate driving and spectral inverse diffusion gating, energy adaptive balance and dynamic closed-loop control are realized, to ensure that light field operation is safe, brightness is soft and stable, and a comfortable and healthy digital twin travel light environment is created.

Description

Technical Field

[0001] This invention relates to the field of cultural and tourism interactive technology, specifically to a digital twin smart cultural and tourism management system and method. Background Technology

[0002] The Digital Twin Smart Cultural Tourism Management System is a comprehensive intelligent service and immersive experience platform for cultural tourism built upon digital twin, artificial intelligence, and multimodal interaction technologies. This system collects geographic information, visitor behavior, environmental data, and cultural resource data from physical spaces such as scenic spots, museums, and intangible cultural heritage blocks to establish a digital twin model that operates synchronously with the real-world environment, achieving virtual-real mapping and dynamic interaction. Using BIM+GIS as its spatial foundation, the system integrates AR / VR immersive displays, intelligent guide recommendations, digital human explanations, visitor profile analysis, and dynamic monitoring of energy consumption and visitor flow, forming a closed loop of perception, cognition, and feedback. Visitors can participate in multi-dimensional immersive activities such as virtual tours, intangible cultural heritage experiences, interactive storylines, and intelligent Q&A through mobile phones, wearable devices, or interactive terminals. Meanwhile, management can monitor the operational status, visitor flow trends, and cultural dissemination effects in real time through the twin's backend, achieving digital governance and intelligent operation of cultural scenarios, thereby constructing an intelligent cultural tourism ecosystem that combines cultural dissemination, experiential innovation, and management decision support.

[0003] The existing technology has the following shortcomings:

[0004] During multi-projection fusion rendering, the accumulation of phase differences and energy imbalances in the virtual-real overlay area caused by multiple light sources lead to nonlinear interference focusing in the light field distribution. When the energy density at a local interference point instantaneously exceeds the visual safety threshold, an oversaturated, high-brightness flickering area is formed, causing abrupt changes in light intensity and a flickering effect. This results in visual afterimages, glare, and sensory illusions for viewers. This phenomenon not only disrupts the visual stability of the virtual-real fusion scene but may also trigger individual photosensitivity reactions or even induce photosensitive epilepsy, thus seriously impacting system safety and user experience.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a digital twin smart cultural tourism management system and method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for digital twin-based smart cultural tourism management, comprising the following steps:

[0008] Step 1: Establish a unified phase baseline and energy distribution audit model for multiple projections, and perform nanosecond-level synchronous sampling of the phase difference and brightness variation curves of multiple projection light waves in the virtual-real superposition area to generate the corresponding initial light field energy coupling matrix.

[0009] Step 2: Construct an interferometric trajectory inversion network based on the initial light field energy coupling matrix, replay the light wave superposition sequence using a time inversion algorithm, extract the transient energy focusing trajectory from the replayed sequence, and generate the corresponding high-risk interferometric kernel;

[0010] Step 3: Based on the high-risk interferometric kernel, establish an energy transition gradient prediction model, generate a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculate the real-time energy adjustment coefficient, and feed the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy regulation of the light source.

[0011] Step 4: Construct a multi-source phase conjugate drive chain based on the energy adjustment coefficient, perform a breathing-type energy traction operation in the light wave superposition path, dynamically migrate and disperse local energy peaks, and achieve adaptive equilibrium of light field energy distribution.

[0012] Step 5: After obtaining the adaptive equilibrium state of the light field energy, a spectral inverse diffusion gating mechanism is introduced based on the energy distribution data to perform write-back and time window locking operations on the energy residual density, continuously reducing potential energy backflow and eliminating high-brightness flicker.

[0013] Preferably, the steps for establishing a multi-projection unified phase baseline and energy distribution audit model include:

[0014] The phase characteristics and spatial distribution features of multiple projection light sources are uniformly calibrated. By deploying a multi-point high-sensitivity optical detection array in the virtual-real superposition area, the light waves of each light source are sampled in both the time and spatial domains to determine the phase starting point, propagation path difference and incident angle variation range of each light source under a unified reference coordinate system, and to establish the phase baseline of the light waves.

[0015] After obtaining the phase baseline, the light field of the virtual and real superposition area is audited in multiple dimensions for brightness and energy. The light intensity distribution and reflected brightness of each light source are matched synchronously with the phase baseline in the form of time series to form a group of brightness change curves.

[0016] Based on the phase baseline and brightness change curve, joint auditing and energy mapping are performed to calculate the transient energy distribution of multiple light sources in the superimposed area and identify constructive interference regions;

[0017] An initial light field energy coupling matrix is ​​generated based on the coupling relationship between the phase baseline and the brightness curve, which characterizes the energy superposition intensity and interference relationship between the light sources.

[0018] Preferably, during the generation of the initial light field energy coupling matrix, the phase difference and energy superposition intensity of each light source at the same spatial position are compared point by point, and a continuous energy distribution surface is formed by interpolation expansion, so that the matrix reflects the phase shift between light waves, energy flow direction and energy density changes in the local interference focusing area.

[0019] Preferably, the steps for constructing the interferometric trajectory inversion network based on the initial light field energy coupling matrix include:

[0020] After obtaining the initial light field energy coupling matrix, the energy distribution information in the matrix is ​​extended in time series and correlated with spatial location. The energy state points are arranged in time order to form a time-series energy chain of light wave superposition, and a continuous trajectory of light wave energy changing with time is established.

[0021] Based on the formation of the temporal energy chain of the light field, the evolution process of the energy state is spatially replayed. The time-continuous energy states are spatially mapped and superimposed along the light wave propagation path to obtain the dynamic energy distribution evolution diagram of the light field.

[0022] Based on the energy distribution evolution map, local high-energy regions are continuously tracked to identify the dynamic trend of energy from a dispersed state to a concentrated state and form an energy focusing trajectory;

[0023] Based on the energy density peak, duration, and spatial concentration range of the energy focusing trajectory, a classification is established to generate corresponding high-risk interferometric nuclei, which are then fed back to the light field energy control process.

[0024] Preferably, in the step of generating a high-risk interferometric nucleus, the energy density peak, brightness fluctuation frequency, and spatial aggregation range of the energy focusing trajectory are jointly determined. When the energy density exceeds the visual safety threshold and the brightness fluctuation frequency is within the photosensitive response range, the energy focusing trajectory is identified as a high-risk interferometric trajectory, and its spatial position and energy characteristic data are synchronously fed back for subsequent light field energy regulation.

[0025] Preferably, the steps for establishing an energy transition gradient prediction model based on high-risk interferometric nuclei include:

[0026] After obtaining the high-risk interferometric nucleus, the energy density distribution, spatial diffusion range and duration are quantitatively analyzed to extract the transition trend of light energy at different time points and form an energy change gradient model.

[0027] A light intensity prediction function is generated based on the energy change gradient, and a suppression threshold curve is plotted according to visual safety requirements to determine the safety boundary of light intensity changes.

[0028] The real-time energy adjustment coefficient is calculated based on the light intensity prediction function and the suppression threshold curve. The energy adjustment factor is generated by comparing the difference between the light intensity change rate and the safety boundary, thereby achieving real-time balance of the light field energy.

[0029] The energy adjustment coefficient is fed back to the light source drive control unit, and the light source brightness, exposure duration and emission angle are adjusted synchronously according to the change of the adjustment coefficient to realize adaptive closed-loop control of light field energy.

[0030] Preferably, the process of feeding back the energy adjustment coefficient to the light source drive control unit includes: performing graded control of the brightness output of the light source based on the real-time energy adjustment coefficient; automatically reducing the output power of the corresponding light source when the energy adjustment coefficient changes negatively; restoring the brightness of the light source when the energy adjustment coefficient changes positively; and maintaining a stable output when the light field energy tends to be balanced.

[0031] Preferably, the step of constructing a multi-source phase conjugate drive chain based on the energy regulation coefficient includes:

[0032] After obtaining the energy adjustment coefficient, the phase state and spatial distribution of each light source are synchronously mapped, the phase shift is corrected according to the energy adjustment coefficient, a multi-light source phase conjugate driving chain is formed and an energy traction relationship is established.

[0033] Based on the phase conjugate driving chain, the energy flow direction in the light wave superposition path is adjusted in a breathing manner. When the energy density exceeds the balance threshold, the energy is caused to diffuse to the low density region. After the brightness decreases, the energy is returned through phase reverse compensation.

[0034] During the breathing energy traction process, the energy peak migration state is monitored in real time. When energy accumulation is detected, the phase delay is adjusted to make the energy migrate along the low energy gradient direction to disperse the light energy.

[0035] After the energy is dynamically migrated and dispersed, a global equilibrium assessment of the energy distribution in the virtual-real superposition region is performed, and the traction frequency is automatically adjusted according to the steady state to maintain the adaptive balance of the light field energy.

[0036] Preferably, the steps for introducing a spectral inverse diffusion gating mechanism based on energy distribution data include:

[0037] After achieving adaptive equilibrium of the light field energy, the spectral characteristics of the virtual-real superposition region are mapped in layers to identify regions where energy density fluctuations exceed the upper limit of the average deviation and to determine the energy residual formation region.

[0038] A spectral reverse diffusion gated interface is established based on the energy residual formation region. When the light energy continues to rise and approaches the set upper limit, the energy is reversed from the high-density region to the low-density region by adjusting the phase delay and emission sequence of the light source to weaken the aggregation trend.

[0039] After inverse diffusion gating, an energy residual density write-back operation is performed to update the energy density change after inverse diffusion into the original energy distribution data to maintain the temporal continuity of the control.

[0040] After energy write-back, a time window locking operation is performed to rhythmically constrain the energy change cycle, ensuring that the light field energy change only occurs within the set time window.

[0041] A digital twin smart cultural tourism management system includes a phase baseline establishment module, an interferometric trajectory inversion module, an energy gradient prediction module, a phase conjugation driving module, and a spectral inverse diffusion gating module.

[0042] The phase baseline establishment module establishes a unified phase baseline and energy distribution audit model for multiple projections, performs nanosecond-level synchronous sampling of the phase difference and brightness change curves of multiple projected light waves in the virtual-real superposition area, and generates the corresponding initial light field energy coupling matrix.

[0043] The interferometric trajectory inversion module constructs an interferometric trajectory inversion network based on the initial light field energy coupling matrix, uses a time inversion algorithm to replay the light wave superposition sequence, extracts the transient energy focusing trajectory from the replayed sequence, and generates the corresponding high-risk interferometric kernel;

[0044] The energy gradient prediction module establishes an energy transition gradient prediction model based on a high-risk interferometer kernel, generates a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculates the real-time energy adjustment coefficient, and feeds the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy regulation of the light source.

[0045] The phase conjugate drive module constructs a multi-source phase conjugate drive chain based on the energy adjustment coefficient, performs a breathing-style energy traction operation in the light wave superposition path, dynamically migrates and disperses local energy peaks, and achieves adaptive equilibrium of light field energy distribution.

[0046] The spectral inverse diffusion gating module, after obtaining the adaptive equilibrium state of the light field energy, introduces a spectral inverse diffusion gating mechanism based on energy distribution data to perform write-back and time window locking operations on the energy residual density, continuously reducing potential energy backflow and eliminating high-brightness flicker.

[0047] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0048] This invention establishes a unified phase baseline and energy distribution audit mechanism during multi-projection fusion rendering, enabling precise quantification and dynamic monitoring of the spatiotemporal characteristics of the light field. This allows for real-time control of the phase difference and energy superposition relationship of light waves. By synchronously sampling and modeling the energy coupling of light waves within the virtual-real superposition region, the evolution trend of energy anomalies can be identified before interference focusing occurs, giving the system the ability to proactively sense and adjust in advance. This method effectively suppresses nonlinear focusing and brightness abrupt changes in the superposition process of multi-source energy, maintaining a continuous and smooth light field distribution. It fundamentally eliminates the visual instability caused by oversaturation flicker, significantly improving image consistency and visual comfort in immersive environments.

[0049] This invention constructs a multi-source phase conjugate driving chain and a spectral anti-diffusion gating mechanism to achieve an adaptive balance of light field energy in both spatial and temporal dimensions, realizing dynamic energy traction and stable return. Energy is promptly transferred to high-brightness areas and periodically balanced through time window locking, resulting in a flexible rhythmic characteristic of light field brightness changes and avoiding the stimulation of the human eye caused by sudden changes in light intensity. Through this closed-loop control process, the safety of light field operation is significantly enhanced, and the energy distribution tends to be stable over a long period. The system can maintain the safety and reliability of visual output even under high load and multi-interaction conditions, providing a durable, comfortable, and healthy light environment for the immersive experience of digital twin cultural tourism scenarios. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a flowchart of a digital twin-based smart cultural tourism management method according to the present invention.

[0052] Figure 2 This is a schematic diagram of a module for a digital twin smart cultural tourism management system according to the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0054] This invention provides, for example Figure 1 The method for digital twin-based smart cultural tourism management shown includes the following steps:

[0055] Step 1: Establish a unified phase baseline and energy distribution audit model for multiple projections. Perform nanosecond-level synchronous sampling on the phase difference and brightness variation curves of multiple projection light waves in the virtual-real superposition area to generate the corresponding initial light field energy coupling matrix, which is used to characterize the energy superposition and interference relationship between each light source.

[0056] The specific implementation process of this step is as follows:

[0057] The phase characteristics and spatial distribution features of multiple projection light sources are uniformly calibrated. Specifically, a multi-point high-sensitivity optical detection array is deployed on the surface of the display medium in the virtual-real superposition area to sample the light waves emitted by each projection light source in both the time and spatial domains. Based on the sampling results, the phase starting point, propagation path difference, and incident angle variation range of each light source in the same reference coordinate system are determined, thereby establishing a phase baseline for the light waves in a unified reference system. At this point, the phase baseline of each light source not only reflects its initial optical state but also serves as the basic reference for subsequent phase difference calculations. To ensure the synchronization between multiple light waves, a nanosecond-level time-resolved sampling method is used during the calibration process to capture the minute phase drift and intensity fluctuations of the light waves during propagation, thus ensuring the temporal consistency and spatial correspondence of the phase baseline data. Through this step, an initial light wave phase distribution map with time as the vertical axis and space as the horizontal axis can be obtained, providing a precise optical field reference for subsequent energy distribution analysis.

[0058] After establishing a unified phase baseline, a multi-dimensional audit of the brightness and energy of the light field in the virtual-real superposition region is performed. Specifically, this involves point-by-point measurement of the light intensity distribution, reflected brightness, and ambient light interference of each light source at different locations within the superposition region, and synchronously matching the measurement results with the corresponding phase baseline in the form of a time series. In this way, the correspondence between the phase change and energy output of each light source can be obtained, thus forming a group of brightness variation curves. Each brightness variation curve reflects the light energy output trend and spatial distribution of a specific light source within a specific time window. By normalizing these curves, errors caused by differences in equipment or surface reflectivity can be eliminated, thereby ensuring the comparability of energy data. This process not only reveals the dynamic characteristics of the light intensity of each light source with phase change but also lays the foundation for quantitative analysis of the light energy superposition law.

[0059] After acquiring the phase baseline and brightness variation curves, a joint audit and energy mapping are performed on both. At this point, the phase and brightness data of each light source within the same time slice are spatially superimposed to calculate the transient energy distribution of multiple light sources within the virtual-real fusion region. By comparing the phase differences of different light sources at the same spatial location point by point, potential constructive interference and anti-interference regions can be identified, thus deriving the initial outline of the energy coupling relationship. To ensure the spatial continuity of the data, interpolation expansion is performed on all light source sampling points, allowing the light field energy distribution to be represented as a continuous surface. This yields a complete light energy distribution audit image in three-dimensional space, accurately describing the energy gradient, phase consistency, and interference concentration regions of the light field after multi-source superposition. This process not only reveals the local nonlinear characteristics of light energy superposition but also enables a corresponding mapping between the light field distribution and the phase baseline, providing data for interference trajectory inversion.

[0060] After obtaining complete light energy audit results, an initial light field energy coupling matrix is ​​generated based on the coupling relationship between the phase baseline and the brightness curve. This matrix uses the number of light sources as a dimension and spatial points of the light field as nodes, forming a multi-dimensional coupling structure by recording the energy superposition intensity and phase difference amplitude between each light source at different spatial locations. Each matrix unit contains numerical information on phase shift, energy flow direction, and superposition intensity between light waves, which can accurately reflect the energy coupling state between light sources. When there is an energy concentration trend or local light field anomalous enhancement in the virtual-real superposition region, the energy density value of the corresponding region in the matrix will show a significant abrupt change, thus indicating a potential risk of interference focusing. Through global and local analysis of this matrix, the uneven distribution of light energy and time drift characteristics can be further identified, providing a scientific basis for subsequent light field manipulation.

[0061] Through the above steps, not only is accurate modeling of the energy distribution of multiple projection light sources under a unified phase baseline achieved, but a comprehensive characterization mechanism capable of dynamically auditing light field energy is also established. This enables the system to identify energy superposition imbalances caused by phase difference accumulation in the virtual-real superposition region. By combining nanosecond-level synchronous sampling with multi-dimensional energy auditing, dynamic tracking and full-field coupled modeling of the relationship between light wave phase and energy are achieved, thus providing sufficient basic data support for subsequent energy balance control and visual safety adjustment. This method can effectively prevent local energy overload and nonlinear interference focusing problems caused by phase inconsistency in multi-projection fusion rendering, improving the visual stability and audience comfort of digital twin cultural tourism scenes.

[0062] Step 2: Construct an interferometric trajectory inversion network based on the initial light field energy coupling matrix, use the time inversion algorithm to replay the light wave superposition sequence, extract the transient energy focusing trajectory from the replayed sequence, and generate the corresponding high-risk interferometric kernel to provide input parameters for subsequent energy gradient prediction;

[0063] The specific implementation process of this step is as follows:

[0064] After obtaining the initial light field energy coupling matrix, the energy distribution information contained in the matrix is ​​extended temporally and correlated spatially. Specifically, each data unit in the aforementioned matrix is ​​regarded as an energy state point of the light field at a specific spatial location and time slice, and they are arranged sequentially on the time axis to form a temporal energy chain of superimposed light waves. To ensure the continuity of energy states, the rate of energy change between adjacent time slices is smoothed to avoid abrupt errors caused by sampling time differences. Through this process, a continuous trajectory of light wave energy change over time can be established, allowing for an accurate description of the dynamic evolution characteristics of the light field. The key to this step is to transform the static energy coupling relationship into a temporally continuous energy evolution sequence, providing a continuous foundation for subsequent energy focusing path identification.

[0065] Based on the formation of a temporal energy chain for the light field, the evolution of the energy state is spatially reenacted. Specifically, based on the spatial coordinates and propagation direction information of each light source within the virtual-real superposition region, the temporally continuous energy state is spatially mapped along the light wave propagation path. By layer-by-layer superposition of the energy distribution at each time point, a superimposed image of the gradually evolving energy field in space can be obtained. At this time, the phase difference between different light sources will manifest as a periodic change in energy intensity during the superposition process, while the constructive interference and anti-interference of light waves will appear as the aggregation and dispersion of energy peaks in the spatial mapping. By continuously reconstructing the superposition results of multiple time slices in chronological order, a dynamic energy distribution evolution diagram of the light field within the virtual-real superposition region can be formed, intuitively reflecting the energy accumulation and attenuation process caused by phase changes during light wave propagation. This process not only reconstructs the propagation and interference paths of light waves in space but also reveals the spatiotemporal laws of energy exchange between different light sources.

[0066] After obtaining the spatial evolution map of the light field energy, the local high-energy regions are continuously tracked to identify transient energy focusing trajectories. Specifically, by analyzing the spatial location and migration direction of energy peaks at each time point, the dynamic trend of energy shifting from a dispersed to a concentrated state can be determined. When the energy density of a certain region shows a cumulative increase over multiple consecutive time slices, that region can be identified as a potential interference focusing area. Furthermore, by analyzing the spatial extension morphology of these energy focusing regions, continuous energy focusing trajectory lines can be plotted to describe the main path of energy flow in the light field. Each trajectory line represents the complete process of light waves from initial superposition to interference focusing within the virtual-real superposition region. The key to this stage is to reorganize the temporally discrete energy peaks into continuous spatial paths, enabling the quantitative representation of the transient characteristics of light wave interference.

[0067] After identifying energy focusing trajectories, these trajectories are categorized and risk-classified to generate corresponding high-risk interferometric nuclei. Specifically, the energy focusing phenomenon is quantitatively assessed based on the peak energy density, duration, and spatial aggregation range corresponding to different trajectories. When the energy density of a focusing trajectory exceeds the visual safety threshold, or its duration and brightness fluctuation frequency fall within the sensitive range of human photosensitivity, the trajectory is determined to be a high-risk interferometric trajectory. By performing spatial overlap analysis on all high-risk trajectories, the region with the most frequent energy focusing and the most intense brightness fluctuations can be extracted, forming a high-risk interferometric nucleus. This interferometric nucleus, as a concentrated manifestation of energy imbalance, includes multi-dimensional parameters such as the superposition of light wave phase differences, energy peak aggregation, and temporal persistence, and can accurately reflect the instability source of the light field in the virtual-real superposition region. Subsequently, the spatial location and energy characteristic data of the high-risk interferometric nucleus are fed back to the light field energy control process to guide energy gradient prediction and light source driving adjustment, thereby achieving closed-loop management from identification to control.

[0068] Through the above steps, not only was the dynamic inversion and visual reconstruction of the light field energy distribution achieved, but also, for the first time, an interferometric focusing identification mechanism based on energy trajectories was proposed in a virtual-real superposition environment. By transforming the static energy relationship in the initial light field energy coupling matrix into a spatiotemporal dynamic process, the nonlinear energy concentration phenomenon caused by light wave superposition can be quantitatively revealed. At the same time, through the identification of transient energy focusing trajectories and the generation of high-risk interferometric kernels, the system can achieve predictive judgment and early intervention before energy anomalies form, thereby effectively avoiding the problems of high brightness flicker and visual stimulation in virtual-real fusion scenes.

[0069] Step 3: Based on the high-risk interferometric kernel, establish an energy transition gradient prediction model, generate a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculate the real-time energy adjustment coefficient, and feed the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy control of the light source.

[0070] The specific implementation process of this step is as follows:

[0071] After obtaining the high-risk interferometric nucleus, gradient quantization and energy transition analysis are performed on the energy focusing characteristics it represents. Specifically, the energy density distribution, spatial diffusion range, and duration in the high-risk interferometric nucleus are used as the main observation parameters. The rate of change of energy density over time is continuously calculated to extract the transition trend of light energy at different time points. By comparing the energy growth rates between different focusing trajectories, the critical stage of energy transition from low to high levels can be identified, and the starting and ending regions of energy transition can be marked in the light field space. In this way, a set of transition curves with time as the vertical axis and energy density as the horizontal axis can be established, forming a three-dimensional energy change gradient model. This step transforms the high-risk interferometric nucleus obtained in the previous stage into energy gradient data that can be used for dynamic prediction, enabling the energy evolution trend of the light field to be expressed in a quantitative form, providing a basis for subsequent light intensity prediction.

[0072] After establishing the energy transition gradient, a light intensity prediction function is generated based on the continuous trend of energy density change to characterize the variation law of light field intensity within a future time window. Specifically, each energy transition curve is regarded as the evolution trajectory of light field intensity, and the continuous distribution interval of light intensity over time is determined by extending the energy change rate over time. By comparing the light intensity change patterns corresponding to different trajectories, the overall trend of light field energy in spatial distribution can be extracted, including local enhancement regions, diffusion regions, and attenuation regions, thereby obtaining the dynamic distribution characteristics of light intensity. Furthermore, based on the light intensity prediction function, a suppression threshold curve is plotted according to the visual safety requirements and photobiological safety limits of the light wave superposition region to define the safety boundary of light intensity change. When the light field intensity prediction result is about to exceed this threshold, the system can enter the suppression state in advance to preventively intervene in potential high-brightness flicker. The core of this step is to transform the static high-risk interference kernel into a dynamic light intensity prediction mechanism, so that energy changes can be perceived and assessed before they occur.

[0073] After obtaining the light intensity prediction function and the suppression threshold curve, a real-time energy adjustment coefficient is calculated to guide the fine control of the light source output. Specifically, based on the transient rate of change of light field energy in the light intensity prediction curve and the safety boundary of the suppression threshold curve, the difference between the two is converted into an energy adjustment factor, thus forming a real-time adjustment reference driven by the light source. By continuously updating the energy adjustment factor, the balance of the light field can be maintained during light intensity changes. When the energy density in a certain region of the light field shows a continuous upward trend, the energy adjustment coefficient will provide negative feedback to reduce the light source output in that region; conversely, when the energy density is in the decay stage, the energy adjustment coefficient will provide positive feedback to restore the balance of the light field brightness. Through this dynamic balance mechanism, the energy output between light sources can form a flexible complementary relationship, thereby avoiding local energy overload and light field instability. This step realizes real-time linkage from prediction to control, ensuring that the light field energy distribution remains stable in both time and space.

[0074] The real-time energy adjustment coefficient is fed back to the light source drive control unit to guide the dynamic energy output adjustment of the light source. Specifically, based on the real-time changes in the adjustment coefficient, the brightness, exposure duration, and emission angle of each light source are synchronously adjusted to ensure that the output power of different light sources in the superimposed area is always in a coordinated state. When the energy superposition intensity of a light source increases due to a phase difference change, the adjustment signal will immediately trigger the light source output attenuation, thereby reducing the local light intensity; when the interference state is resolved or the energy distribution tends to be balanced, the adjustment signal will gradually restore the light source output, allowing the light field to return to a stable state. In the continuous feedback and response process, the energy output of the light source is always dynamically constrained by the real-time adjustment coefficient, thus forming an adaptive closed loop of energy change-predictive analysis-output control. This process ensures that the superposition state of light waves in the virtual and real superimposed area is always maintained within a safe energy range, preventing oversaturation and high-brightness flicker caused by energy focusing, and effectively improving the visual stability and audience comfort of the digital twin cultural tourism scene.

[0075] Through the implementation of the above steps, not only is dynamic prediction and real-time control of light field energy realized, but also an innovative mechanism for feedforward adjustment based on energy transition characteristics is proposed. This allows for predictive intervention before energy anomalies are formed, resulting in higher stability and continuity of light field changes and effectively avoiding energy superposition imbalance caused by the accumulation of light wave phase differences.

[0076] Step 4: Construct a multi-source phase conjugate drive chain based on the energy adjustment coefficient, and perform a breathing-type energy traction operation in the light wave superposition path to dynamically migrate and disperse local energy peaks, so as to achieve adaptive balance of light field energy distribution and reduce energy focusing accumulation.

[0077] The specific implementation process of this step is as follows:

[0078] After obtaining the energy adjustment coefficients, the phase states and spatial distributions of each light source in a unified reference frame are synchronously mapped to construct the initial structure of a multi-source phase conjugate driving chain. Specifically, the phase baseline, energy output intensity, and propagation direction of each light source are used as input conditions, and their phase offsets are corrected accordingly based on the energy adjustment coefficients, enabling the light sources to form a phase conjugate relationship in both the temporal and spatial domains. At this point, the phase difference between different light sources is constrained in real time by the energy adjustment coefficients, thereby ensuring that each light wave maintains a dynamically coordinated state during propagation and superposition. By establishing this conjugate driving relationship, an energy traction effect between light sources can be formed, allowing energy changes from a single light source to be transmitted to other light sources through the conjugate chain structure, achieving a coordinated response in energy distribution. The key to this step is to unify the originally independently controlled light sources into a phase conjugate system through the energy adjustment coefficients, thus providing the basic conditions for subsequent energy migration.

[0079] After establishing a multi-source phase conjugate drive chain, the energy flow direction in the superposition path is adjusted in a breathing-like manner based on the spatial distribution characteristics of the light field. Specifically, the spatial path with the most significant change in light field energy density within the virtual-real superposition area is selected as the main energy traction channel, and energy expansion and contraction operations are periodically performed in this channel. When the energy density of a certain region in the light field exceeds a preset equilibrium threshold, the light energy in that region is diffused to adjacent low-density regions by controlling the phase delay in the phase conjugate chain; when the energy diffusion causes the local brightness to decrease to a safe lower limit, the energy is redirected through phase reversal compensation to maintain the visual continuity of the overall brightness. This process, like breathing, forms a reciprocating rhythm of energy flow in the light field, which can both weaken the concentration of energy peaks and maintain the brightness balance of the overall light field. Through this breathing-like energy traction method, the light field energy no longer exists in a static focused form in space, but exhibits a dynamic equilibrium characteristic of continuous flow.

[0080] During the implementation of the breathing-style energy traction operation, the migration and dispersion of local energy peaks are monitored and rebalanced in real time. Specifically, the energy change trend of each spatial node along the light wave superposition path is continuously tracked to identify the movement direction and diffusion speed of the energy focusing area. When energy is found to be continuously accumulating in a certain area, the phase delay of the corresponding light source in that area is immediately adjusted to make the energy peak migrate along the direction of low energy gradient, thereby dispersing the local light energy density. When the energy diffusion process leads to insufficient brightness in the edge area, the energy is guided back in the opposite direction to avoid light field discontinuities or dark areas. Through this continuous energy migration regulation, the energy peaks in the light field no longer form stable dwell points, but rather migrate periodically in space, significantly weakening the nonlinear interference phenomenon of energy superposition. This step not only balances the spatial distribution of light energy but also reduces the risk of visual stimulation caused by sudden changes in local light intensity, thereby effectively improving the stability and comfort of the virtual-real fusion image.

[0081] Based on the dynamic migration and dispersion of light field energy, a global equilibrium assessment of the energy distribution across the entire virtual-real superposition region is performed to achieve adaptive balance and long-term stability of the light field. Specifically, by continuously monitoring the phase synchronization state and energy output ratio of each light source in the conjugate drive chain, when the overall energy distribution tends to stabilize and the output of each light source is within a balanced range, the frequency of the breathing energy traction is gradually reduced, allowing the light field to enter a slow-stabilized state. When the external environment changes or a new energy focusing trend emerges, the traction frequency is automatically increased to re-establish the balance cycle. In this way, the light field energy forms adaptive oscillations in the time dimension and maintains a smooth transition in the spatial dimension, thereby achieving a true energy self-balancing mechanism. Through this process, the light field can continuously maintain a stable brightness distribution and visual consistency in complex virtual-real superposition environments, avoiding problems such as high-brightness flicker, glare abrupt changes, and visual fatigue.

[0082] Through the above steps, a multi-source phase conjugate driving mechanism based on energy regulation coefficients was realized, and with breathing-like energy traction as the core, dynamic migration and spatial dispersion of light field energy were achieved. This step extends energy regulation from static intensity compensation to dynamic phase driving, enabling the formation of a self-organizing energy balance structure within the light field. This not only addresses energy unevenness caused by phase difference accumulation in real time but also achieves self-healing stability of the light field through a continuous energy migration mechanism, thereby improving the visual safety and immersive experience quality of virtual-real fusion scenes.

[0083] Step 5: After obtaining the adaptive equilibrium state of the light field energy, a spectral inverse diffusion gating mechanism is introduced based on the aforementioned energy distribution data to perform write-back and time window locking operations on the energy residual density, thereby continuously reducing potential energy backflow and eliminating high-brightness flicker, achieving dynamic stability of the light field and visual safety closed-loop control.

[0084] The specific implementation process of this step is as follows:

[0085] After achieving adaptive equilibrium of the optical field energy, the spectral characteristics of the virtual-real superposition region are mapped hierarchically based on energy distribution data to identify energy frequency bands and spatial zones that still exhibit slight fluctuations under equilibrium conditions. Specifically, by spectrally deconstructing the energy adjustment results from the previous stage, the entire optical field is divided into several continuous energy bands according to wavelength ranges, with each energy band corresponding to a specific spatial brightness layer and phase retardation layer. Synchronous statistical analysis of the brightness fluctuation amplitude and time response of these energy bands is performed to identify regions where the energy density change exceeds the upper limit of the average deviation, and these regions are used as candidates for spectral anti-diffusion gating. In this process, not only the continuity of the spectrum in spatial distribution is considered, but the energy change rate in the time dimension is also included in the comparison to ensure that the selected energy fluctuation region indeed has a potential backflow tendency. Through this step, the formation region of the energy residual can be accurately located under the premise of overall optical field stability, thus providing a clear target for subsequent anti-diffusion control.

[0086] After identifying energy fluctuation regions, spectral anti-diffusion gating interfaces are established for these regions to regulate energy flow and weaken local aggregation trends. Specifically, based on the average brightness and phase difference distribution of each energy band in equilibrium, energy inflow and outflow thresholds are set. When the light energy in a certain region shows an upward trend in a continuous time slice and approaches the set upper limit, the spectral anti-diffusion operation is triggered. At this time, by adjusting the phase delay and emission order of each light source in the energy diffusion path, energy diffuses from high-density areas to low-density areas, thereby forming a reverse energy flow within the light field. Simultaneously, the gradual change in energy diffusion is maintained, allowing energy migration to proceed gradually and avoiding new brightness abrupt changes. The core of this process lies in utilizing the reverse gradient distribution between spectra to achieve energy redistribution, fundamentally weakening the cumulative effect of local energy peaks, and maintaining a continuous dynamic balance of the light field at the microscopic scale.

[0087] In the spectral backdiffusion gating process, to prevent residual energy from re-accumulating in a specific region after multiple diffusions, this invention introduces an energy residual density write-back mechanism after the gating operation. Specifically, the change in energy density after backdiffusion is re-recorded into the original energy distribution data, allowing the new energy distribution to reflect the current true state. Through this write-back operation, the energy distribution data is no longer a statically stored result, but a dynamic reference reflecting the instantaneous state of the light field. When the light field environment or external illumination conditions change, the system can directly use the written-back energy density information as the initial condition for a new round of balance calculations, thus achieving a seamless transition. This step not only prevents data lag after energy diffusion but also ensures the continuity of light field control in the time dimension, enabling the light field energy to participate in subsequent adjustments with the latest distribution state in each cycle, avoiding the cumulative effect of energy backflow.

[0088] After the energy residual density is written back, a time window locking operation is introduced to further stabilize the energy output of the light field and prevent visual flicker caused by high-frequency fluctuations, thereby rhythmically constraining the changes in the light field within the time domain. Specifically, through statistical analysis of the energy change cycle, a time window boundary for the light field is set, ensuring that light energy changes only occur within a controllable time slice. When the light field energy fluctuation is within the set time window, minor adjustments are allowed to maintain the breathing dynamic characteristics; when the energy change exceeds the time window boundary, a delay locking mechanism is immediately triggered, postponing the energy adjustment to the next cycle, thus avoiding high-brightness flicker caused by excessively frequent light intensity changes. Simultaneously, at the end of each time window, the energy change data within the window is converged to ensure that the energy baseline for the next cycle is re-established from a stable starting point. Through this time domain locking mechanism, the changes in the light field energy exhibit rhythmicity and predictability, eliminating random flicker caused by multi-projection superposition errors or light source response delays, ensuring a smooth and stable visual presentation.

[0089] Through the implementation of the above steps, a complete self-healing closed loop of spectral inverse diffusion, residual write-back, and time window locking is formed in the final stage of energy regulation. This ensures that the light field energy is not only evenly distributed in space but also dynamically stable in the temporal dimension. This step, by introducing a spectral inverse diffusion mechanism, extends energy balance from a single spatial level to the spectral dimension, achieving synergistic suppression of multi-frequency energy. Through energy residual write-back, the light field data possesses real-time updating and adaptive characteristics, forming a continuously evolving energy cognition system. Time window locking establishes a temporal constraint boundary for light field changes, avoiding visual risks caused by flicker and energy rebound. Through these steps, the light field in the virtual-real fusion scene can maintain steady-state equilibrium during long-term operation, significantly improving the visual safety and sensory comfort of the immersive environment.

[0090] This invention establishes a unified phase baseline and energy distribution audit mechanism during multi-projection fusion rendering, enabling precise quantification and dynamic monitoring of the spatiotemporal characteristics of the light field. This allows for real-time control of the phase difference and energy superposition relationship of light waves. By synchronously sampling and modeling the energy coupling of light waves within the virtual-real superposition region, the evolution trend of energy anomalies can be identified before interference focusing occurs, giving the system the ability to proactively sense and adjust in advance. This method effectively suppresses nonlinear focusing and brightness abrupt changes in the superposition process of multi-source energy, maintaining a continuous and smooth light field distribution. It fundamentally eliminates the visual instability caused by oversaturation flicker, significantly improving image consistency and visual comfort in immersive environments.

[0091] This invention constructs a multi-source phase conjugate driving chain and a spectral anti-diffusion gating mechanism to achieve an adaptive balance of light field energy in both spatial and temporal dimensions, realizing dynamic energy traction and stable return. Energy is promptly transferred to high-brightness areas and periodically balanced through time window locking, resulting in a flexible rhythmic characteristic of light field brightness changes and avoiding the stimulation of the human eye caused by sudden changes in light intensity. Through this closed-loop control process, the safety of light field operation is significantly enhanced, and the energy distribution tends to be stable over a long period. The system can maintain the safety and reliability of visual output even under high load and multi-interaction conditions, providing a durable, comfortable, and healthy light environment for the immersive experience of digital twin cultural tourism scenarios.

[0092] This invention provides, for example Figure 2 The digital twin smart cultural tourism management system shown includes a phase baseline establishment module, an interferometric trajectory inversion module, an energy gradient prediction module, a phase conjugation driving module, and a spectral inverse diffusion gating module.

[0093] The phase baseline establishment module establishes a unified phase baseline and energy distribution audit model for multiple projections, performs nanosecond-level synchronous sampling of the phase difference and brightness change curves of multiple projected light waves in the virtual-real superposition area, and generates the corresponding initial light field energy coupling matrix.

[0094] The interferometric trajectory inversion module constructs an interferometric trajectory inversion network based on the initial light field energy coupling matrix, uses a time inversion algorithm to replay the light wave superposition sequence, extracts the transient energy focusing trajectory from the replayed sequence, and generates the corresponding high-risk interferometric kernel;

[0095] The energy gradient prediction module establishes an energy transition gradient prediction model based on a high-risk interferometer kernel, generates a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculates the real-time energy adjustment coefficient, and feeds the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy regulation of the light source.

[0096] The phase conjugate drive module constructs a multi-source phase conjugate drive chain based on the energy adjustment coefficient, performs a breathing-style energy traction operation in the light wave superposition path, dynamically migrates and disperses local energy peaks, and achieves adaptive equilibrium of light field energy distribution.

[0097] The spectral inverse diffusion gating module, after obtaining the adaptive equilibrium state of the light field energy, introduces a spectral inverse diffusion gating mechanism based on energy distribution data to perform write-back and time window locking operations on the energy residual density, continuously reducing potential energy backflow and eliminating high-brightness flicker.

[0098] The present invention provides a method for digital twin smart cultural tourism management, which is implemented through the above-mentioned digital twin smart cultural tourism management system. For details of the specific method and process of the digital twin smart cultural tourism management system, please refer to the above-mentioned embodiment of the method for digital twin smart cultural tourism management, which will not be repeated here.

[0099] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for digital twin-based smart cultural tourism management, characterized in that, Includes the following steps: Step 1: Establish a unified phase baseline and energy distribution audit model for multiple projections, and perform nanosecond-level synchronous sampling of the phase difference and brightness variation curves of multiple projection light waves in the virtual-real superposition area to generate the corresponding initial light field energy coupling matrix. Step 2: Construct an interferometric trajectory inversion network based on the initial light field energy coupling matrix, replay the light wave superposition sequence using a time inversion algorithm, extract the transient energy focusing trajectory from the replayed sequence, and generate the corresponding high-risk interferometric kernel; Step 3: Based on the high-risk interferometric kernel, establish an energy transition gradient prediction model, generate a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculate the real-time energy adjustment coefficient, and feed the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy regulation of the light source. Step 4: Construct a multi-source phase conjugate drive chain based on the energy adjustment coefficient, perform a breathing-type energy traction operation in the light wave superposition path, dynamically migrate and disperse local energy peaks, and achieve adaptive equilibrium of light field energy distribution. Step 5: After obtaining the adaptive equilibrium state of the light field energy, a spectral inverse diffusion gating mechanism is introduced based on the energy distribution data to perform write-back and time window locking operations on the energy residual density, continuously reducing potential energy backflow and eliminating high-brightness flicker.

2. The method for digital twin-based smart cultural tourism management according to claim 1, characterized in that, The steps for establishing a multi-projection unified phase baseline and energy distribution audit model include: The phase characteristics and spatial distribution features of multiple projection light sources are uniformly calibrated. By deploying a multi-point high-sensitivity optical detection array in the virtual-real superposition area, the light waves of each light source are sampled in both the time and spatial domains to determine the phase starting point, propagation path difference and incident angle variation range of each light source under a unified reference coordinate system, and to establish the phase baseline of the light waves. After obtaining the phase baseline, the light field of the virtual and real superposition area is audited in multiple dimensions for brightness and energy. The light intensity distribution and reflected brightness of each light source are matched synchronously with the phase baseline in the form of time series to form a group of brightness change curves. Based on the phase baseline and brightness change curve, joint auditing and energy mapping are performed to calculate the transient energy distribution of multiple light sources in the superimposed area and identify constructive interference regions; An initial light field energy coupling matrix is ​​generated based on the coupling relationship between the phase baseline and the brightness curve, which characterizes the energy superposition intensity and interference relationship between the light sources.

3. The method for digital twin-based smart cultural tourism management according to claim 2, characterized in that, In the process of generating the initial light field energy coupling matrix, the phase difference and energy superposition intensity of each light source at the same spatial position are compared point by point, and a continuous energy distribution surface is formed by interpolation expansion, so that the matrix reflects the phase shift between light waves, energy flow direction and energy density changes in the local interference focusing area.

4. A method for digital twin-based smart cultural tourism management according to claim 2, characterized in that, The steps for constructing an interferometric trajectory inversion network based on the initial light field energy coupling matrix include: After obtaining the initial light field energy coupling matrix, the energy distribution information in the matrix is ​​extended in time series and correlated with spatial location. The energy state points are arranged in time order to form a time-series energy chain of light wave superposition, and a continuous trajectory of light wave energy changing with time is established. Based on the formation of the temporal energy chain of the light field, the evolution process of the energy state is spatially replayed. The time-continuous energy states are spatially mapped and superimposed along the light wave propagation path to obtain the dynamic energy distribution evolution diagram of the light field. Based on the energy distribution evolution map, local high-energy regions are continuously tracked to identify the dynamic trend of energy from a dispersed state to a concentrated state and form an energy focusing trajectory; Based on the energy density peak, duration, and spatial concentration range of the energy focusing trajectory, a classification is established to generate corresponding high-risk interferometric nuclei, which are then fed back to the light field energy control process.

5. A method for digital twin-based smart cultural tourism management according to claim 4, characterized in that, In the step of generating a high-risk interferometric nucleus, the energy density peak, brightness fluctuation frequency, and spatial aggregation range of the energy focusing trajectory are jointly determined. When the energy density exceeds the visual safety threshold and the brightness fluctuation frequency is within the photosensitive response range, the energy focusing trajectory is identified as a high-risk interferometric trajectory, and its spatial position and energy characteristic data are synchronously fed back for subsequent light field energy regulation.

6. The method for digital twin-based smart cultural tourism management according to claim 1, characterized in that, The steps for establishing an energy transition gradient prediction model based on high-risk interfering nuclei include: After obtaining the high-risk interferometric nucleus, the energy density distribution, spatial diffusion range and duration are quantitatively analyzed to extract the transition trend of light energy at different time points and form an energy change gradient model. A light intensity prediction function is generated based on the energy change gradient, and a suppression threshold curve is plotted according to visual safety requirements to determine the safety boundary of light intensity changes. The real-time energy adjustment coefficient is calculated based on the light intensity prediction function and the suppression threshold curve. The energy adjustment factor is generated by comparing the difference between the light intensity change rate and the safety boundary, thereby achieving real-time balance of the light field energy. The energy adjustment coefficient is fed back to the light source drive control unit, and the light source brightness, exposure duration and emission angle are adjusted synchronously according to the change of the adjustment coefficient to realize adaptive closed-loop control of light field energy.

7. A method for digital twin-based smart cultural tourism management according to claim 6, characterized in that, The process of feeding back the energy regulation coefficient to the light source drive control unit includes: performing graded control of the brightness output of the light source based on the real-time energy regulation coefficient; automatically reducing the output power of the corresponding light source when the energy regulation coefficient changes negatively; restoring the brightness of the light source when the energy regulation coefficient changes positively; and maintaining a stable output when the light field energy tends to be balanced.

8. A method for digital twin-based smart cultural tourism management according to claim 6, characterized in that, The steps for constructing a multi-source phase conjugate drive chain based on the energy regulation coefficient include: After obtaining the energy adjustment coefficient, the phase state and spatial distribution of each light source are synchronously mapped, the phase shift is corrected according to the energy adjustment coefficient, a multi-light source phase conjugate driving chain is formed and an energy traction relationship is established. Based on the phase conjugate driving chain, the energy flow direction in the light wave superposition path is adjusted in a breathing manner. When the energy density exceeds the balance threshold, the energy is caused to diffuse to the low density region. After the brightness decreases, the energy is returned through phase reverse compensation. During the breathing energy traction process, the energy peak migration state is monitored in real time. When energy accumulation is detected, the phase delay is adjusted to make the energy migrate along the low energy gradient direction to disperse the light energy. After the energy is dynamically migrated and dispersed, a global equilibrium assessment of the energy distribution in the virtual-real superposition region is performed, and the traction frequency is automatically adjusted according to the steady state to maintain the adaptive balance of the light field energy.

9. A method for digital twin-based smart cultural tourism management according to claim 8, characterized in that, The steps for introducing a spectral inverse diffusion gating mechanism based on energy distribution data include: After achieving adaptive equilibrium of the light field energy, the spectral characteristics of the virtual-real superposition region are mapped in layers to identify regions where energy density fluctuations exceed the upper limit of the average deviation and to determine the energy residual formation region. A spectral reverse diffusion gated interface is established based on the energy residual formation region. When the light energy continues to rise and approaches the set upper limit, the energy is reversed from the high-density region to the low-density region by adjusting the phase delay and emission sequence of the light source to weaken the aggregation trend. After inverse diffusion gating, an energy residual density write-back operation is performed to update the energy density change after inverse diffusion into the original energy distribution data to maintain the temporal continuity of the control. After energy write-back, a time window locking operation is performed to rhythmically constrain the energy change cycle, ensuring that the light field energy change only occurs within the set time window.

10. A digital twin smart tourism management system, used to implement the digital twin smart tourism management method according to any one of claims 1-9, characterized in that, It includes a phase baseline establishment module, an interferometric trajectory inversion module, an energy gradient prediction module, a phase conjugation driving module, and a spectral inverse diffusion gating module; The phase baseline establishment module establishes a unified phase baseline and energy distribution audit model for multiple projections, performs nanosecond-level synchronous sampling of the phase difference and brightness change curves of multiple projected light waves in the virtual-real superposition area, and generates the corresponding initial light field energy coupling matrix. The interferometric trajectory inversion module constructs an interferometric trajectory inversion network based on the initial light field energy coupling matrix, uses a time inversion algorithm to replay the light wave superposition sequence, extracts the transient energy focusing trajectory from the replayed sequence, and generates the corresponding high-risk interferometric kernel; The energy gradient prediction module establishes an energy transition gradient prediction model based on a high-risk interferometer kernel, generates a light intensity prediction function and a suppression threshold curve based on trajectory changes, calculates the real-time energy adjustment coefficient, and feeds the energy adjustment coefficient back to the light source drive control unit to guide the dynamic energy regulation of the light source. The phase conjugate drive module constructs a multi-source phase conjugate drive chain based on the energy adjustment coefficient, performs a breathing-style energy traction operation in the light wave superposition path, dynamically migrates and disperses local energy peaks, and achieves adaptive equilibrium of light field energy distribution. The spectral inverse diffusion gating module, after obtaining the adaptive equilibrium state of the light field energy, introduces a spectral inverse diffusion gating mechanism based on energy distribution data to perform write-back and time window locking operations on the energy residual density, continuously reducing potential energy backflow and eliminating high-brightness flicker.

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