Metacosm multi-scene interaction method and platform

Abstract

The invention provides a meta-universe multi-scene interaction method and a meta-universe multi-scene interaction platform, which relate to the technical field of virtual reality, and can perform simulation training based on VR equipment, adjust virtual environment elements in real time according to different scene characteristics and user behaviors, and improve the authenticity and continuity of scenes. Meanwhile, when the influence of external environment factors on the virtual scene is considered, the change of the scene under the complex external condition can be comprehensively and accurately simulated. Differentiation adjustment can be performed on the virtual scene according to the real-time environment data, so that the scene felt by the user in the interaction process is close to the real situation, and the practicability and attraction of meta-universe application are improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and in particular to a metaverse multi-scene interaction method and platform. Background Art

[0002] With the rise of the metaverse concept and the continuous development of technology, building highly realistic and immersive virtual scenes has become a research hotspot in many fields. In metaverse applications, multi-scene interaction methods are crucial for improving user experience and achieving a deep integration of virtual and real life. However, existing metaverse multi-scene interaction technologies still have many shortcomings in terms of scene simulation accuracy and interaction realism. Traditional metaverse scene interaction methods often struggle to precisely manage and dynamically control virtual spaces. When it comes to the division and updating of simulated spaces, there's a lack of effective strategies for adjusting virtual environment elements in real time based on scene characteristics and user behavior, resulting in poor scene realism and coherence. For example, when simulating complex environments, the dynamic changes in environmental elements cannot be accurately simulated, making it difficult to meet users' demands for an immersive interactive experience.

[0003] Therefore, how to combine user behavior and scene characteristics to achieve dynamic data interaction in virtual space has become an urgent problem that needs to be solved today. Summary of the Invention

[0004] The present invention provides a metaverse multi-scene interaction method and platform, which can realize dynamic data interaction in virtual space by combining user behavior and scene characteristics.

[0005] A first aspect of the present invention provides a metaverse multi-scenario interaction method, comprising: Delineating a plurality of reference domains including reference points and influence ranges in the simulation space, and updating the initial content in each of the reference domains at time intervals; When the airflow simulation is started, the current content of each reference domain is adjusted differently according to the wind condition data; Determining a target content corresponding to the user based on a positional relationship between the user position and the reference domain, the target content including a current content and a predicted content of the corresponding reference domain; The target content is converted into an interaction parameter and the simulation device is controlled to synchronously adjust the interaction parameter.

[0006] Optionally, in a possible implementation of the first aspect, updating the initial content in each reference domain according to a time interval includes: In the first time interval, a reference domain within the diffusion area of ​​the fault point is selected, and the initial content of the reference domain is set according to the fault level corresponding to the fault point; For the diffusion area with the set initial content, performing a regional diffusion operation according to the diffusion ratio, including: calculating the diffusion amount of the reference domain, allocating the diffusion amount to the adjacent diffusion area without the set initial content, and updating the initial content of the diffusion area; In subsequent time intervals, the reference domain in the newly added diffusion area is first selected according to the diffusion distance, and then the diffusion operation is performed on all diffusion areas with set initial contents, and the initial contents of each reference domain are obtained cyclically.

[0007] Optionally, in a possible implementation of the first aspect, calculating the diffusion amount of the reference domain, allocating the diffusion amount to adjacent diffusion areas for which no initial content is set, and updating the initial content of the diffusion areas includes: The diffusion amount is obtained according to the product of the initial content of the current diffusion area and the diffusion ratio; Allocating the diffusion amount to an adjacent diffusion area that is not set with an initial content, so that the diffusion amount of the diffusion area is increased; The remaining value after subtracting the diffusion amount from the initial content of the current diffusion area is used as the updated initial content.

[0008] Optionally, in a possible implementation of the first aspect, when the airflow simulation is started, differentially adjusting the current content of each reference domain according to the wind condition data includes: Taking the fault point as the center, the simulation space is divided into a plurality of directional diffusion zones according to wind direction and wind speed, wherein the directional diffusion zones are fan-shaped, and the wind condition data includes wind direction and wind speed; Determining the wind direction attributes of each directional diffusion zone according to the angle between the wind direction and the reference direction of each directional diffusion zone, wherein the wind direction attributes include a tailwind attribute, a headwind attribute, and a crosswind attribute, and each wind direction attribute is provided with a corresponding angle interval; The current content of each reference domain is dynamically adjusted based on the wind direction attribute of the directional diffusion area where the reference domain is located, the distance from the fault point, and the wind speed.

[0009] Optionally, in a possible implementation of the first aspect, the simulation space is divided into multiple directional diffusion zones based on wind direction and wind speed, with the fault point as the center, including: Determine the angle increment corresponding to the current wind speed based on the preset correspondence between wind speed and angle increment; Taking the direction vector of the wind direction as the central axis, dividing the area into two angular divisions at half the angle increment to obtain the first directional diffusion area; Taking the radius direction of the first directional diffusion zone as a reference, angular division is performed in sequence toward both sides according to the angle increment until the circumference range is covered, thereby obtaining a plurality of directional diffusion zones.

[0010] Optionally, in a possible implementation of the first aspect, dynamically adjusting the current content of each reference domain based on the wind direction attribute of the directional diffusion zone where the reference domain is located, the distance from the fault point, and the wind speed includes: For the directional diffusion area with downwind attributes, multiple grade areas are divided from large to small in the reference direction, the grade reduction period of each grade area is determined according to the wind speed, and the current content of each reference domain in the corresponding grade area is adjusted. The grade reduction period of each grade area is set with a corresponding reduction content; For the directional diffusion area with upwind properties, with the fault point as the starting point, the current content of the current reference domain is calculated according to the preset ratio of the current content of the previous reference domain for each preset distance increment; For the directional diffusion zone of the crosswind attribute, the offset direction from the fault point to each reference domain is determined, and the adjustment coefficient of the reference domain is determined according to the angle between the offset direction and the wind direction. The current content of the reference domain at the corresponding position is obtained by multiplying the current content at the same distance of the downwind attribute by the adjustment coefficient.

[0011] Optionally, in a possible implementation of the first aspect, determining the adjustment coefficient of the reference domain according to the angle between the offset direction and the wind direction includes: Obtaining a preset coefficient corresponding to a preset interval within which the included angle between the offset direction and the wind direction lies; An offset multiple of the wind speed to the preset coefficient is determined, and an adjustment coefficient is obtained according to the product of the preset coefficient and the offset multiple.

[0012] Optionally, in a possible implementation of the first aspect, determining a target content corresponding to the user based on a positional relationship between the user position and the reference domain, the target content including a current content and a predicted content of the corresponding reference domain, includes: Acquire a reference domain where the user is located as a target domain, and determine a current content of the target domain as the target content; When the user location is not located in any of the reference domains, the reference domains within the predicted range of the user location are obtained as reference domains, and the predicted content is calculated based on the current content of each of the reference domains.

[0013] Optionally, in a possible implementation of the first aspect, when the user location is not located in any of the reference domains, obtaining a reference domain within the predicted range of the user location as a reference domain, and calculating the predicted content based on the current content of each of the reference domains includes: Obtaining the interval distance between the user location and each of the reference domains, and determining the proportion of each of the interval distances; The predicted content is calculated based on the average value of the product of the proportion corresponding to each reference domain and the current content.

[0014] A second aspect of the present invention provides a metaverse multi-scenario interactive platform, comprising: A partitioning module is used to define a plurality of reference domains including reference points and influence ranges in the simulation space, and update the initial content in each of the reference domains according to time intervals; The airflow module is used to differentially adjust the current content of each reference domain according to wind condition data when the airflow simulation is started; a parameter module, configured to determine a target content corresponding to the user based on a positional relationship between the user's position and the reference domain, wherein the target content includes a current content and a predicted content of the corresponding reference domain; The control module is used to convert the target content into an interaction parameter and control the simulation device to synchronously adjust the interaction parameter.

[0015] The beneficial effects of the present invention are as follows: 1. This invention can achieve dynamic data interaction in virtual space by combining user behavior and scene characteristics. Specifically, by defining multiple reference domains within the simulated space, each containing a reference point and a range of influence, and updating the initial content within each reference domain at intervals, the accuracy of data interaction can be improved. By setting the initial content of the reference domain based on the fault level and performing regional diffusion operations according to the diffusion ratio, the distribution and changes of odors in space are more realistic, providing a precise data foundation for simulation training and greatly improving the authenticity and scene fidelity of odor simulation in simulation training.

[0016] 2. When airflow simulation is initiated, the current concentration of each reference zone is adjusted differentially based on wind data, significantly enhancing the realism and accuracy of the simulation. Centered around the fault point, multiple sector-shaped directional diffusion zones are divided according to wind direction and speed. Each zone's wind direction attribute (tailwind, headwind, and crosswind) is determined. Based on this, the current concentration of each reference zone is dynamically adjusted, taking into account the wind direction attribute of the directional diffusion zone within which the reference zone resides, its distance from the fault point, and wind speed. In the tailwind directional diffusion zone, the odor concentration of the reference zone at different distances is finely adjusted by dividing it into graded zones and determining a grade reduction cycle based on wind speed. In the headwind zone, the concentration of the reference zone is calculated using preset distance increments and ratios. In the crosswind zone, the concentration is calculated using an adjustment factor based on offset direction, wind angle, and wind speed. This comprehensive and detailed adjustment method simulates the odor diffusion process under different wind conditions, allowing trainees to experience a highly realistic experience in VR simulation.

[0017] 3. Based on the positional relationship between the user's location and the reference domain, the target concentration corresponding to the user is determined and converted into interactive parameters to control the simulation equipment, which then adjust the interactive parameters synchronously, improving interaction accuracy. When the user's location is within a reference domain, the current concentration in that reference domain is used as the target concentration, ensuring that the user experiences the actual odor concentration at their location in real time. For example, in a simulated fuel leak scenario, a user would detect a strong odor of fuel when moving to a reference domain near the leak point. When the user's location is not within any reference domain, a reference domain within the predicted user location is used as a reference domain. The predicted concentration is calculated based on the distance ratio and the current concentration in the reference domain, avoiding gaps in odor perception. Finally, the target concentration is converted into interactive parameters to control simulation equipment such as VR masks. This allows trainees to experience the corresponding odor environment based on their actual location, regardless of their location in the simulation space. This significantly enhances trainees' immersion in the simulation and significantly improves the realism and effectiveness of simulation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present invention; Figure 2 This is a flowchart of a metaverse multi-scenario interaction method provided by an embodiment of the present invention; Figure 3 This is a structural diagram of a metaverse multi-scenario interactive platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 , is a schematic diagram of an application scenario provided by an embodiment of the present invention. The present invention performs simulation training based on VR equipment, and can adjust virtual environment elements in real time according to the characteristics of different scenarios and user behaviors, thereby improving the authenticity and coherence of the scene. At the same time, when considering the impact of external environmental factors on the virtual scene, the changes in the scene under complex external conditions can be fully and relatively accurately simulated. The virtual scene can be differentially adjusted according to real-time environmental data, so that the scene perceived by the user during the interaction process is close to the real situation, thereby improving the practicality and attractiveness of the metaverse application.

[0021] See also Figure 2, is a flow chart of a metaverse multi-scenario interaction method provided by an embodiment of the present invention, Figure 2 The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDAs) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S4, as follows: S1, defining a plurality of reference domains including reference points and influence ranges in a simulation space, and updating the initial content in each of the reference domains at time intervals.

[0022] In simulated spaces such as indoor cabins or outdoor decks of simulated maritime vessels, dividing them into multiple reference domains and dynamically updating the content can finely manage the distribution and changes of odor in the space.

[0023] Taking an indoor cabin simulation scenario as an example, multiple reference domains are constructed by setting reference points and their influence ranges according to spatial layout patterns. For example, in a 50-square-meter ship cabin simulation, a reference point is set every 2-3 meters, and a circular area with a radius of 1 meter centered on the reference point is used as the influence range, resulting in 20 reference domains. When simulating scenarios such as fuel leaks, the initial odor content in the reference domain near the leak point is accurately set during the initial training phase. Subsequently, the odor content of each reference domain is continuously updated at different time stages according to established rules.

[0024] In outdoor simulations, reference zones are similarly arranged based on regional characteristics. For example, in a simulated offshore operation area the size of a football field, reference points are set every 5-10 meters, with a circular area with a radius of 3 meters representing the impact zone, resulting in 50 reference zones. At the initial stage of a simulated accident, an initial concentration is set in the reference zones surrounding the leak point, and the concentration is subsequently updated periodically. This approach allows for a high degree of reproducibility of the entire odor generation and diffusion process from the leak point in a real-world scenario. This allows for subsequent adjustments to odor concentration based on the trainee's real-time location and odor adjustments based on outdoor airflow conditions, laying the foundation for precise spatial management and ensuring the entire odor simulation process more closely resembles the real-world scenario of offshore ship operations.

[0025] The simulation space is a constructed virtual scene, such as an indoor cabin or outdoor deck area in marine ship simulation training. The reference point is a specific location point selected in the simulation space, which is used to determine the center position of the reference domain. The influence range is the area centered on the reference point, and the odor concentration and other properties in this area are affected by the reference point. The reference domain is the basic area unit composed of the reference point and its influence range. The time interval is a manually set time period used to periodically update the initial content in the reference domain, such as 5 minutes or 10 minutes. The initial content is the content of odor-related substances set for each reference domain at the beginning of a time interval, such as odor concentration.

[0026] It's worth noting that the reference domain is formed by setting virtual reference points and their influence ranges, eliminating the need for specific locations in physical space. For example, in a virtual cabin, multiple virtual areas are created based on the spatial layout. When simulating a fuel leak, the initial odor concentration is set in a specific virtual reference domain through program instructions. When the user reaches the corresponding location, the corresponding concentration of gas is released through the VR mask, simulating the odor at that location in the virtual space.

[0027] Based on the above embodiment, the specific implementation of "updating the initial content in each reference domain according to the time interval" in step S1 can be: S11, within the first time interval, a reference domain within the diffusion area of ​​the fault point is selected, and the initial content of the reference domain is set according to the fault level corresponding to the fault point.

[0028] At the start of the simulation, the concentration of the odor in the initial diffusion zone around the fault point can be accurately set based on the severity of the fault. For example, when simulating a fuel leak in a ship's interior compartment, by setting different fault levels (such as small, medium, and large leaks), the corresponding initial concentration in the reference zone around the fault point during the first time interval can be set. This provides the initial conditions for the subsequent simulated odor diffusion over time, enhancing the realism and scene fidelity of the simulation training.

[0029] The fault point is the location where a leak occurs in the simulated scenario, such as a ship's fuel pipeline or a chemical storage tank. The fault level is a classification of the severity of the leak at the fault point. For example, fuel leaks are categorized as small, medium, and large, corresponding to different initial odor release concentrations. The diffusion area is the range of odor diffusion outward from the fault point within each time interval. In the first time interval, it is a circular area centered on the fault point and defined by a preset diffusion distance. In subsequent time intervals, it is a circular area formed by expanding outward at the same distance from the outermost boundary of the previous diffusion as the inner boundary.

[0030] S12, performing regional diffusion operations on the diffusion areas with set initial contents according to the diffusion ratio, including: calculating the diffusion amount of the reference domain, allocating the diffusion amount to adjacent diffusion areas without set initial contents, and updating the initial contents of the diffusion areas.

[0031] It is understandable that in order to simulate the natural spread of odor from the initial diffusion area to the surrounding areas, by setting the diffusion ratio, the odor in the baseline domain with a set initial concentration is diffused to the adjacent new diffusion area without a set initial concentration. This can realistically reproduce the diffusion phenomenon of odor in space, allowing trainees to feel the dynamic changes of the gradual spread of odor, and enhance the scene coherence and realism of the simulation training.

[0032] The diffusion ratio is a coefficient used to quantify the degree to which the base domain diffuses odors into adjacent areas. The diffusion amount is the amount of odor diffused from the base domain to adjacent areas during the current time interval, calculated based on the diffusion ratio.

[0033] In some embodiments, the regional diffusion operation may be performed according to the diffusion ratio by the following steps: The diffusion amount is obtained by multiplying the initial content of the current diffusion area by the diffusion ratio; the diffusion amount is distributed to the adjacent diffusion area for which the initial content is not set, so that the diffusion area increases the diffusion amount; and the remaining value after subtracting the diffusion amount from the initial content of the current diffusion area is used as the updated initial content.

[0034] By multiplying the initial concentration of the baseline domain within the current diffusion area by the preset diffusion ratio, the amount of odor diffused outward from each diffusion area per unit time can be quantitatively calculated. Distributing the calculated diffusion amount to adjacent new diffusion areas that have not yet set an initial concentration simulates the natural spread of odor from the current diffusion area to surrounding areas. Updating the odor concentration of the baseline domain within the current diffusion area simulates the decrease in odor concentration within the original area after the odor diffuses outward.

[0035] S13 , in each subsequent time interval, first selecting a reference domain in the newly added diffusion area according to the diffusion distance, then performing the diffusion operation on all diffusion areas with set initial contents, and cyclically obtaining the initial contents of each reference domain.

[0036] Over time, the odor will continue to spread further. By expanding outward at the same diffusion distance at each subsequent time interval to form a new circular diffusion area, selecting a baseline domain within the area to set the initial concentration, and performing the diffusion operation on all areas with the set initial concentration, we can simulate a dynamic scenario where the odor continues to spread and the concentration constantly changes. This ensures that the odor environment in the simulation training matches the actual situation, enhancing the authenticity and effectiveness of the training. The diffusion distance is a fixed distance expanded outward from the fault point at each diffusion, which is used to determine the scope of the newly added diffusion area.

[0037] For example, in an indoor cabin simulation scenario, during the second 5-minute interval, a new diffusion area with a radius of 1-2 meters is formed, expanding outward from the initial diffusion area of ​​1 meter at a diffusion distance of 1 meter, centered on the fault point. The baseline domain within this area is selected and the initial concentration (assuming 40 ppm) is set. Diffusion operations are then performed on all baseline domains with established initial concentrations, including the initial diffusion area and the newly added area. The diffusion amount is calculated, and the concentration is assigned and updated. Subsequent time intervals follow this method, with new diffusion areas formed with a diffusion distance of 1 meter each, centered on the fault point, and the concentrations in each baseline domain continuously updated. In an outdoor work area simulation scenario, during the second 10-minute interval, a new diffusion area with a radius of 2-4 meters is formed, building on the initial diffusion area of ​​2 meters at a diffusion distance of 2 meters. The initial concentration setting and diffusion operations for the baseline domain are repeated, and this cycle continues.

[0038] Through cyclic operation, the dynamic process of odor diffusion in space over time is continuously simulated, accurately presenting the changing trend of odor diffusion range gradually expanding and concentration gradually decreasing, providing trainees with a real and continuous odor simulation experience.

[0039] S2, when the airflow simulation is started, the current content of each reference domain is adjusted differently according to the wind condition data.

[0040] In outdoor scenarios for maritime ship simulation training, wind has a significant impact on odor diffusion. Wind simulation is initiated to obtain wind speed and direction data. Based on this data, the reference domains for different wind direction attributes, such as downwind, headwind, and crosswind, are differentially adjusted with the leak point as the center, just as the wind on the real sea surface pushes the odor of the fuel leak in different directions. In this way, the simulated odor distribution can be made more consistent with actual conditions. For example, rapid odor diffusion and reduced concentration are simulated in the downwind direction, while obstructed odor diffusion and slow changes in concentration are simulated in the upwind direction. This enhances the realism and credibility of the simulation scene, allowing trainees to experience a more realistic experience in VR simulation.

[0041] Airflow simulation simulates airflow conditions within a simulated space and is used for outdoor wind simulation. Wind condition data describes the outdoor airflow state, primarily including wind direction and speed. The current concentration is the odor concentration in the reference domain at a specific moment and varies with time and factors such as outdoor airflow.

[0042] Based on the above embodiment, the specific implementation of step S2 may be: S21 , taking the fault point as the center, dividing the simulation space into a plurality of directional diffusion zones according to wind direction and wind speed, wherein the directional diffusion zones are fan-shaped, and the wind condition data includes wind direction and wind speed.

[0043] It is understandable that the simulation space is divided into fan-shaped directional diffusion zones in order to match the wind conditions in different areas and achieve accurate updates of the odor concentration in each area. Wind direction determines the direction of odor diffusion, wind speed affects the speed and range of diffusion, and areas in different locations are affected by wind differently. By dividing the simulation space into sectors, each fan-shaped directional diffusion zone can be regarded as a relatively independent odor diffusion unit, and the wind conditions within the area are similar. For example, in a southeast wind environment, in the fan-shaped area in the downwind direction, the odor diffuses rapidly under the push of the wind; in the fan-shaped area against the wind, the odor diffusion is hindered. By dividing the fan-shaped areas, the odor concentration can be updated in a targeted manner according to the unique wind conditions in each area.

[0044] The directional diffusion zone is a sector-shaped area centered on the fault point, divided according to wind direction and speed. It defines the odor diffusion range under specific wind conditions. Odor diffusion within each directional diffusion zone is influenced by the same wind direction and speed, resulting in similar diffusion characteristics.

[0045] In some embodiments, multiple directional diffusion regions may be defined by the following steps: According to the correspondence between the preset wind speed and the angle increment, the angle increment corresponding to the current wind speed is determined; with the direction vector of the wind direction as the central axis, the angle is divided to both sides by half of the angle increment to obtain the first directional diffusion zone; with the radius direction of the first directional diffusion zone as the reference, the angle is divided to both sides in turn according to the angle increment until the circumference range is covered, thereby obtaining multiple directional diffusion zones.

[0046] The preset correspondence between wind speed and angle increment is a pre-set correspondence rule between wind speed values ​​and angle increment values. For example, a wind speed of 1 m / s is set to correspond to an angle increment of 20°, a wind speed of 2 m / s is set to correspond to an angle increment of 30°, etc., which is an important basis for dividing the directional diffusion zone. The current wind speed is the wind speed data at the current moment obtained in real time by the virtual wind speed and wind direction instrument during the simulation training process. The angle increment is the angle interval value for dividing the directional diffusion zone determined according to the preset correspondence based on the current wind speed. The direction vector of the wind direction is a vector with direction and magnitude that represents the wind direction. In the simulation space, the angle division is performed with the direction vector of the wind direction as the central axis to determine the direction of the directional diffusion zone.

[0047] Assume that the virtual anemometer detects a current wind speed of 4 m / s from the southeast, and that the wind direction vector corresponds to an angle of 135°. Based on the preset relationship between wind speed and angle increments, for example, a wind speed of 4 m / s corresponds to an angle increment of 40°, the angle increment corresponding to the current wind speed is determined to be 40°. With the 135° direction vector as the central axis, divide the 40° angle increment into two 20° increments, creating the first directional diffusion zone with an angle range of 115° to 155° and a fan-shaped shape. Using the radius of the first directional diffusion zone as the reference, divide the angular area into two 40° increments. On the right side of the first directional diffusion zone, starting from 155°, directional diffusion zones of 155°-195°, 195°-235°, etc. are divided in sequence; on the left side, starting from 115°, directional diffusion zones of 75°-115°, 35°-75°, etc. are divided in sequence until the entire 360° circumference is covered, and finally multiple directional diffusion zones are obtained.

[0048] S22, determining the wind direction attribute of each directional diffusion zone according to the angle between the wind direction and the reference direction of each directional diffusion zone, wherein the wind direction attribute includes a tailwind attribute, a headwind attribute and a crosswind attribute, and each wind direction attribute is provided with a corresponding angle interval.

[0049] Under different wind direction attributes, the diffusion characteristics of odors in directional diffusion areas vary. By calculating the angle between the wind direction and the reference direction of each directional diffusion area, and determining the wind direction attribute (tailwind, headwind, crosswind) based on the preset angle range, the pattern of odor diffusion in each area can be further refined. For example, in a directional diffusion area with tailwind attributes, the odor will spread quickly to a distance; in areas with headwind attributes, the odor diffusion is hindered and the concentration changes relatively slowly. Clarifying the wind direction attributes of each area will help to differentiate the reference domain content according to different attributes, thereby more accurately simulating the diffusion of odors under the influence of wind in actual scenarios.

[0050] The reference direction is a set reference direction for each directional diffusion zone, used to calculate the angle between the wind direction and that zone. The wind direction attribute categorizes the directional diffusion zone based on the angle between the wind direction and the reference direction of the directional diffusion zone, and is divided into tailwind, headwind, and crosswind attributes. The angle interval is a pre-defined range of angles used to define different wind direction attributes. For example, a directional diffusion zone with an angle between 0° and 30° to the wind direction is designated as tailwind; 150° to 180° is designated as headwind; and 30° to 150° is designated as crosswind.

[0051] S23, dynamically adjusting the current content of each reference domain based on the wind direction attribute of the directional diffusion zone where the reference domain is located, the distance from the fault point, and the wind speed.

[0052] It's understandable that the diffusion of odors under wind pressure is not only affected by wind direction, but also closely related to the distance from the fault point and wind speed. By comprehensively considering the wind direction of the directional diffusion zone where the reference domain is located, the distance from the fault point, and the wind speed, and dynamically adjusting the current content of each reference domain, we can comprehensively and accurately simulate the diffusion process of odors under complex wind conditions. For example, in a reference domain with a tailwind and close to the fault point, the odor concentration will be higher due to wind propulsion and close diffusion; in a reference domain with a headwind and at a distance, the odor concentration will be lower.

[0053] In some embodiments, step S23 may be implemented by the following steps: For the directional diffusion zone with downwind properties, multiple grade areas are divided from large to small in the reference direction, the grade reduction period of each grade area is determined according to the wind speed, and the current content of each reference domain in the corresponding grade area is adjusted. The grade reduction period of each grade area is set with a corresponding reduction content.

[0054] In the directional diffusion zone with downwind properties, odors spread rapidly to distant locations driven by the wind, and the farther from the fault point, the more significantly the odor concentration decreases. To more realistically simulate this pattern of concentration variation with distance, the system divides the base direction into multiple graded areas and determines the grade reduction period based on wind speed. This allows for fine-tuning of the odor content in the base domain at different distances. For example, the greater the wind speed, the faster the odor diffuses, the shorter the grade reduction period, and the faster the concentration decreases. This allows trainees to experience more realistic odor concentration changes in the simulated scenario, enhancing the realism and immersion of the simulation training.

[0055] Level zones are divided into different areas in the downwind directional diffusion zone, from largest to smallest, based on their distance from the fault point. Each zone represents a different level of odor concentration change. The level reduction period is the time it takes for the odor concentration in each level zone to decrease by one level. It is determined by the current wind speed according to preset rules and reflects the rate of change of odor concentration over time.

[0056] For example, in a simulated training scenario, there is a directional diffusion zone with a southeast wind direction. Within this zone, starting from the fault point, three zones are divided into three levels, with distances of 5 meters each, from largest to smallest: Level 1 (0-5 meters), Level 2 (5-10 meters), and Level 3 (10-15 meters). The current wind speed is 5 m / s. Based on the preset relationship between wind speed and level reduction period (e.g., a wind speed of 5 m / s corresponds to a level reduction period of 10 minutes), the corresponding odor concentration drop every 10 minutes is determined for each level zone. For example, if the initial concentration in a baseline zone within the Level 1 zone is 100 ppm and drops to 80 ppm after 10 minutes, assuming the concentration in the Level 1 zone drops by 20 ppm, each level zone adjusts the current concentration of the baseline zone according to its own set reduction period within its corresponding reduction period. This approach enables dynamic adjustment of the current concentration of each baseline zone within the downwind directional diffusion zone.

[0057] For the directional diffusion area with headwind properties, with the fault point as the starting point, the current content of the current reference domain is calculated according to the preset ratio of the current content of the previous reference domain for each increase in the preset distance increment.

[0058] In the headwind directional diffusion zone, wind hinders odor diffusion, causing odor concentration to change relatively slowly and regularly over distance. Starting from the fault point, the current reference domain content is calculated based on a preset distance increment and a preset ratio of the previous reference domain content. This simulates the gradual decrease and relatively stable change of odor concentration in a headwind environment.

[0059] The preset distance increment is an artificially set distance interval used to divide different reference domain positions. The preset ratio is a pre-set coefficient used to calculate the ratio of change in odor content of the current reference domain relative to the previous reference domain. For example, if it is set to 90%, it means that the current reference domain content is 90% of the previous reference domain content.

[0060] For example, in a simulated outdoor scenario of a ship at sea, there is a directional diffusion zone with upwind properties. Starting from the fault point, the preset distance increment is set to 3 meters. Assuming the initial concentration of reference zone A at 3 meters from the fault point is 50 ppm, based on a preset ratio of 90%, the current concentration of reference zone B at 6 meters from the fault point is 45 ppm. The current concentration of reference zone C at 9 meters from the fault point is calculated as the current concentration of reference zone B × 90% = 40.5 ppm. Similarly, as the distance from the fault point increases, the current concentration of each reference zone is calculated according to the preset ratio, adjusting the odor concentration of the reference zones within the upwind directional diffusion zone. For the directional diffusion zone of the crosswind attribute, the offset direction from the fault point to each reference domain is determined, and the adjustment coefficient of the reference domain is determined according to the angle between the offset direction and the wind direction. The current content of the reference domain at the corresponding position is obtained by multiplying the current content at the same distance of the downwind attribute by the adjustment coefficient.

[0061] In the crosswind directional diffusion zone, odor diffusion is affected by both wind and not entirely headwind or tailwind, resulting in complex concentration variations. By determining the offset direction and wind angle from the fault point to each reference zone, the adjustment coefficient is determined. This is combined with the concentration at the same downwind distance to comprehensively consider the impact of crosswind direction and wind force on odor concentration, allowing for a reasonable simulation of the odor concentration in each reference zone under crosswind conditions.

[0062] The offset direction is the direction vector from the fault point to the reference domain, used to calculate the angle with the wind direction. The adjustment coefficient, determined based on the offset direction and the wind direction angle, is used to adjust the odor content in the reference domain of the crosswind directional diffusion zone, reflecting the impact of crosswind on odor concentration.

[0063] In some embodiments, the adjustment coefficient of the reference domain may be determined by the following steps: Obtain a preset coefficient corresponding to a preset interval where the angle between the offset direction and the wind direction is located; determine an offset multiple of the wind speed to the preset coefficient, and obtain an adjustment coefficient based on the product of the preset coefficient and the offset multiple.

[0064] In a directional diffusion zone with crosswind properties, the angle between the offset direction from the fault point to each reference zone and the wind direction varies, resulting in varying degrees of impact on the odor concentration within that reference zone. This impact can be quantified by pre-setting different angle intervals and assigning corresponding preset coefficients to each interval. For example, when the angle is small, the wind's influence on the odor is relatively large, and the preset coefficient may be large; when the angle is large, the wind's influence on the odor is relatively small, and the preset coefficient may be small.

[0065] The preset intervals are pre-set angle ranges, such as 0°-30°, 30°-60°, and 60°-90°, dividing the range of angle values. The preset coefficient is a value set for each preset interval, representing the degree to which the wind angle within that interval affects the odor concentration in the reference domain. Different preset intervals correspond to different preset coefficients.

[0066] S3, determining a target content corresponding to the user according to a positional relationship between the user position and the reference domain, wherein the target content includes a current content and a predicted content of the corresponding reference domain.

[0067] During simulation training, trainees move freely within the simulated space, whether indoor cabins or outdoor decks. To ensure trainees perceive realistic odor concentrations at any given location, target concentrations are determined based on the relationship between their location and the reference domain. For example, when a trainee is within a reference domain at a specific reference point in an indoor cabin, the current concentration of that reference domain is used as the target concentration, allowing the trainee to smell the odor concentration at that location. When a trainee is in an area without a set concentration, such as between two reference domains on an outdoor deck, the current concentrations of the closest reference domains are used to calculate a predicted concentration and determine the target concentration, thereby simulating the expected odor concentration at that location. This allows for a personalized odor experience, allowing trainees to experience the corresponding odor regardless of their location in the simulation space. This enhances trainees' immersion in the simulation, making the simulation training more realistic and authentic to real-world maritime vessel operations, and improving both its authenticity and effectiveness.

[0068] The target concentration is the amount of odor-related substances that the user should perceive at their current location, determined based on the user's location. This includes the current concentration in the user's reference domain and the predicted concentration when the user is not within the reference domain. The predicted concentration is an estimate calculated by calculating the current concentration in the surrounding relevant reference domains when the user is not within any reference domain.

[0069] Based on the above embodiment, the specific implementation of step S3 may be: S31 , obtaining a reference domain where the user is located as a target domain, and determining a current content of the target domain as the target content.

[0070] In maritime simulation training, in order to provide users with an odor experience that matches their location, it is necessary to accurately determine the odor content corresponding to the user in the simulated space. When the user's location is within a certain reference domain, the reference domain is directly used as the target domain, and its current odor content is set as the target content that the user should perceive. This allows the user to feel the actual odor concentration of their location in real time, enhancing the immersion and authenticity of the simulation training. For example, when simulating a fuel leak in a ship's cabin, if the user moves to a reference domain near the fuel leak point, the odor concentration of the reference domain is higher at this time. By using it as the target content, the user can smell the strong smell of fuel and experience the situation at the accident scene more realistically.

[0071] S32: When the user location is not located in any of the reference domains, obtain the reference domains within the predicted range of the user location as reference domains, and calculate the predicted content according to the current content of each of the reference domains.

[0072] In the simulated space, users are free to move and may find themselves in the blank area between two or more reference domains, meaning they are not located within any of the defined reference domains. To ensure that users can also experience a reasonable odor in these locations, the odor content at the user's location can be predicted based on the odor content of the surrounding related reference domains. By obtaining the reference domains within the predicted range of the user's location as reference domains and calculating the predicted content based on the current content of these reference domains, we can more accurately estimate the odor concentration that the user should perceive at that location, ensuring that the user can obtain a coherent and realistic odor experience throughout the entire simulated space, avoiding gaps or unreasonable situations in odor perception.

[0073] The prediction range is a spatial range set with the user's location as the center, which is used to determine which benchmark domains can be used as reference domains to calculate the predicted content. The size of the range can be set according to actual needs.

[0074] In some embodiments, the predicted content can be calculated by the following steps: Obtain the interval distance between the user location and each of the reference domains, and determine the proportion of each of the interval distances; and calculate the predicted content based on the average value of the product of the proportion corresponding to each of the reference domains and the current content.

[0075] When calculating the predicted odor content when the user's location is outside the reference domain, the distance between the user's location and each reference domain has a significant impact on the odor content at that location. Generally speaking, the closer the reference domain is to the user's location, the greater its impact on the user's location; the farther away, the smaller the impact. By obtaining the distance between the user's location and each reference domain and determining the proportion of these distances, the influence of each reference domain on the odor content at the user's location can be quantified.

[0076] After determining the proportion of the distance between each reference domain, the predicted content is calculated in combination with the current content of the reference domain. This is to comprehensively consider the distance factor and the odor concentration of each reference domain itself, and obtain a reasonable estimate of the odor content that the user should perceive at the current location. The current content of each reference domain represents the odor intensity of the area, while the proportion reflects its influence on the user's location. By multiplying the two and taking the average, the influence of each reference domain can be balanced, so that the predicted content more accurately reflects the actual odor concentration at the user's location, providing users with a more realistic and reliable odor simulation experience. S4, converting the target content into interaction parameters and controlling the simulation device to synchronously adjust the interaction parameters.

[0077] Converting the target concentration into interactive parameters and controlling the simulation device to adjust these parameters is a key step in transforming this data into a real, perceptible odor experience. For example, when a trainee is in an indoor cabin near a fuel leak and the calculated target concentration is high, the main control platform converts this target concentration into interactive parameters recognizable by the VR mask, such as the high-concentration fuel odor concentration value and component ratio data. The main control platform then sends instructions to the VR mask, which adjusts the internal gas release device to release a high-concentration fuel odor, allowing the trainee to actually smell the strong fuel odor. When the trainee moves to an area with lower odor concentration, the target concentration decreases, and the VR mask reduces the odor concentration accordingly, releasing a lighter odor. For example, the mask's built-in micro-air pump can be activated to inject an appropriate amount of clean air to dilute the original odor, while reducing the release of odorous gases and lowering the odor concentration, thereby achieving a switching of odor concentrations corresponding to different locations. The same is true in outdoor deck scenarios. Based on the target content at different locations and the concentration changes under the influence of outdoor airflow simulation, the VR mask releases realistic odors, allowing trainees to truly feel the odor environment at their location through their sense of smell throughout the entire simulation training process. This completes the entire process from data calculation to actual sensory experience, improving the quality and effectiveness of simulation training and allowing trainees to obtain a more realistic and effective training experience.

[0078] Interaction parameters are quantifiable indicators related to odor, such as odor concentration and composition ratios, used to describe the characteristics of the odor that a simulation device should emit. A simulation device, such as a VR mask, is a device that simulates odors and can release specific odors and adjust odor concentrations based on instructions. Interaction parameters are parameters related to the odor actually released by the simulation device, such as odor type and concentration, which directly impact the user's olfactory experience.

[0079] See also Figure 3 , is a schematic diagram of the structure of a metaverse multi-scenario interaction platform provided by an embodiment of the present invention, the metaverse multi-scenario interaction platform includes: A partitioning module is used to define a plurality of reference domains including reference points and influence ranges in the simulation space, and update the initial content in each of the reference domains according to time intervals; The airflow module is used to differentially adjust the current content of each reference domain according to wind condition data when the airflow simulation is started; a parameter module, configured to determine a target content corresponding to the user based on a positional relationship between the user's position and the reference domain, wherein the target content includes a current content and a predicted content of the corresponding reference domain; The control module is used to convert the target content into an interaction parameter and control the simulation device to synchronously adjust the interaction parameter.

[0080] Figure 3 The apparatus of the embodiment shown can be used to perform Figure 2 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A metaverse multi-scene interaction method, characterized in that: include: Delineating a plurality of reference domains including reference points and influence ranges in the simulation space, and updating the initial content in each of the reference domains at time intervals; When the airflow simulation is started, the current content of each reference domain is adjusted differently according to the wind condition data; Determining a target content corresponding to the user based on a positional relationship between the user position and the reference domain, the target content including a current content and a predicted content of the corresponding reference domain; The target content is converted into an interaction parameter and the simulation device is controlled to synchronously adjust the interaction parameter.

2. The method according to claim 1, characterized in that Updating the initial content in each of the reference domains at time intervals, including: In the first time interval, a reference domain within the fault point diffusion area is selected, and the initial content of the reference domain is set according to the fault level corresponding to the fault point; For the diffusion area with the set initial content, performing a regional diffusion operation according to the diffusion ratio, including: calculating the diffusion amount of the reference domain, allocating the diffusion amount to the adjacent diffusion area without the set initial content, and updating the initial content of the diffusion area; In subsequent time intervals, the reference domain in the newly added diffusion area is first selected according to the diffusion distance, and then the diffusion operation is performed on all diffusion areas with set initial contents, and the initial contents of each reference domain are obtained cyclically.

3. The method according to claim 2, characterized in that Calculating the diffusion amount of the reference domain, allocating the diffusion amount to adjacent diffusion areas that do not have an initial content set, and updating the initial content of the diffusion areas, including: The diffusion amount is obtained according to the product of the initial content of the current diffusion area and the diffusion ratio; Allocating the diffusion amount to an adjacent diffusion area that is not set with an initial content, so that the diffusion amount of the diffusion area is increased; The remaining value after subtracting the diffusion amount from the initial content of the current diffusion area is used as the updated initial content.

4. The method according to claim 1, wherein When the airflow simulation is started, the current content of each reference domain is adjusted differently according to the wind condition data, including: Taking the fault point as the center, the simulation space is divided into a plurality of directional diffusion zones according to wind direction and wind speed, wherein the directional diffusion zones are fan-shaped, and the wind condition data includes wind direction and wind speed; Determining the wind direction attributes of each directional diffusion zone according to the angle between the wind direction and the reference direction of each directional diffusion zone, wherein the wind direction attributes include a tailwind attribute, a headwind attribute, and a crosswind attribute, and each wind direction attribute is provided with a corresponding angle interval; The current content of each reference domain is dynamically adjusted based on the wind direction attribute of the directional diffusion area where the reference domain is located, the distance from the fault point, and the wind speed.

5. The method according to claim 4, characterized in that Taking the fault point as the center, the simulation space is divided into multiple directional diffusion zones according to wind direction and wind speed, including: Determine the angle increment corresponding to the current wind speed based on the preset correspondence between wind speed and angle increment; Taking the direction vector of the wind direction as the central axis, dividing the area into two angular divisions at half the angle increment to obtain the first directional diffusion area; Taking the radius direction of the first directional diffusion zone as a reference, angular division is performed in sequence toward both sides according to the angle increment until the circumference range is covered, thereby obtaining a plurality of directional diffusion zones.

6. The method according to claim 4, characterized in that Based on the wind direction attribute of the directional diffusion zone where the reference domain is located, the distance from the fault point, and the wind speed, the current content of each reference domain is dynamically adjusted, including: For the directional diffusion area with downwind attributes, multiple grade areas are divided from large to small in the reference direction, the grade reduction period of each grade area is determined according to the wind speed, and the current content of each reference domain in the corresponding grade area is adjusted. The grade reduction period of each grade area is set with a corresponding reduction content; For the directional diffusion area with upwind properties, with the fault point as the starting point, the current content of the current reference domain is calculated according to the preset ratio of the current content of the previous reference domain for each preset distance increment; For the directional diffusion zone of the crosswind attribute, the offset direction from the fault point to each reference domain is determined, and the adjustment coefficient of the reference domain is determined according to the angle between the offset direction and the wind direction. The current content of the reference domain at the corresponding position is obtained by multiplying the current content at the same distance of the downwind attribute by the adjustment coefficient.

7. The method according to claim 6, characterized in that The adjustment coefficient of the reference domain is determined based on the angle between the offset direction and the wind direction, including: Obtaining a preset coefficient corresponding to a preset interval within which the included angle between the offset direction and the wind direction lies; An offset multiple of the wind speed to the preset coefficient is determined, and an adjustment coefficient is obtained according to the product of the preset coefficient and the offset multiple.

8. The method according to claim 1, characterized in that Determining a target content corresponding to the user based on a positional relationship between the user position and the reference domain, wherein the target content includes a current content and a predicted content of the corresponding reference domain, including: Acquire a reference domain where the user is located as a target domain, and determine a current content of the target domain as the target content; When the user location is not located in any of the reference domains, the reference domains within the predicted range of the user location are obtained as reference domains, and the predicted content is calculated based on the current content of each of the reference domains.

9. The method according to claim 8, characterized in that When the user location is not located in any of the reference domains, obtaining the reference domains within the predicted range of the user location as reference domains, and calculating the predicted content according to the current content of each of the reference domains, including: Obtaining the interval distance between the user location and each of the reference domains, and determining the proportion of each of the interval distances; The predicted content is calculated based on the average value of the product of the proportion corresponding to each reference domain and the current content.

10. A metaverse multi-scenario interactive platform, characterized by: include: A partitioning module is used to define a plurality of reference domains including reference points and influence ranges in the simulation space, and update the initial content in each of the reference domains according to time intervals; The airflow module is used to differentially adjust the current content of each reference domain according to wind condition data when the airflow simulation is started; a parameter module, configured to determine a target content corresponding to the user based on a positional relationship between the user's position and the reference domain, wherein the target content includes a current content and a predicted content of the corresponding reference domain; The control module is used to convert the target content into an interaction parameter and control the simulation device to synchronously adjust the interaction parameter.

Images