A Context-Aware Adaptive Audio Narrative Method and System for Tourism

Abstract

This invention discloses a context-aware adaptive tourism audio narrative method and system, relating to the field of interactive entertainment technology, including the following steps: collecting multi-dimensional context data through a multi-dimensional perception module; calculating team aggregation degree, average team movement speed, and environmental status based on the multi-dimensional context data; constructing a preset script through a dynamic plot engine module, processing multi-dimensional context data, and making dynamic plot decisions; generating and broadcasting adaptive audio through an adaptive mixing module; and achieving real-time adaptive linkage between plot content and real-world scenes through deep integration of multi-dimensional environmental perception, dynamic plot decisions, and intelligent mixing technology. It can automatically select and broadcast matching narrative branches based on multiple context data such as weather, crowd flow, and team status. Simultaneously, through spatial audio rendering and intelligent fusion of ambient sound, it breaks down the auditory boundaries between the plot and reality, significantly enhancing tourists' immersion and emotional resonance.

Description

Technical Field

[0001] This invention relates to the field of interactive entertainment technology, and in particular to a context-aware adaptive travel audio narrative method and system. Background Technology

[0002] Immersive audio experiences, through multi-sensory fusion and contextualized storytelling, can significantly enhance tourists' emotional resonance and place attachment, thereby strengthening the differentiated competitiveness of tourism products and the depth of cultural dissemination.

[0003] In existing technologies, immersive audio experiences mainly include location-based audio triggering, playing fixed audio at fixed coordinates, wireless navigation systems, teams sharing the same channel and listening to the same content, and independent audio drama or radio drama apps, where the content is unrelated to the real geographical location.

[0004] First, the existing LBS audio experience is a linear playback strictly triggered by geographical location, which cannot dynamically adjust the plot according to environmental changes or group behavior, making it seem rigid and scripted. Second, the audio content is disconnected from the environmental sounds of the real world, such as the sounds of wind and rain or the noise of a market. The environmental sounds are not incorporated into the plot design as narrative elements, reducing the sense of immersion. Finally, the individual headphone listening mode results in a lack of shared experience based on the plot among tourists. When the group tour is underway, members are at different paces and cannot experience key plot points in sync, making it difficult for the tour guide to coordinate.

[0005] Therefore, a context-aware adaptive audio narrative method and system for tourism is provided to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a context-aware adaptive audio narrative method and system for tourism, which enables dynamic adaptation and integration of the plot with real-world scenes, thereby enhancing tourists' immersion, consistency of group experiences, and willingness to revisit.

[0007] To achieve the above objectives, this invention provides a context-aware adaptive tourism audio narrative method, comprising the following steps: S1: Collect multi-dimensional contextual data through the multi-dimensional perception module; S2: Calculate team cohesion degree based on multi-dimensional contextual data Team average movement speed and environmental conditions; S3: Constructs a pre-set script through the dynamic plot engine module, processes multi-dimensional context data, and makes dynamic plot decisions; S4: Adaptive audio generation and playback are performed through the adaptive mixing module.

[0008] Preferably, step S1 specifically includes the following steps: S11: Install temperature and humidity sensors on pillars in the open-air area of ​​the scenic spot. Collect relative temperature T and relative humidity RH through the temperature and humidity sensors. The collection frequency is set to 1 time / second. S12: Install a rain sensor on the top of the street light in the open area to collect the rainfall R and rainfall status through the rain sensor. The collection frequency is set to 1 time / 2 seconds. S13: Install light sensors on the surface of landscape facilities to collect ambient light intensity L, with the collection frequency set to 1 time / second; S14: Install a crowd density sensor above the entrance of the narrow passage. The crowd density sensor collects the number of people N and the direction of movement of people in the area. The collection frequency is set to 1 time / second. S15: Install UWB positioning sensors inside the tourists' headphones and at fixed points in the scenic area. Collect the tourists' three-dimensional coordinates (X,Y,Z) through the UWB positioning sensors, with the collection frequency set to 10 times / second. S16: Install a motion sensor inside the tourist's headphones to collect acceleration (α). x ,a y ,a z ) and angular velocity (ω x ,ω y ,ω z The sampling frequency was set to 50 times per second.

[0009] Preferably, step S2 specifically includes the following steps: S21: Calculate team cohesion degree If the team cohesion Less than the set degree threshold The team was deemed dispersed, indicating a low level of team cohesion. Specifically set as follows: ; ; ; ; in, The coordinates representing the team's center. Indicates the number of tourists. Indicates the visitor's serial number. Indicates tourists coordinates Indicates tourists Distance to the team center; S22: Calculate the team's average movement speed Team average movement speed Specifically set as follows: ; ; in, Indicates tourists speed, Indicates the location time. Indicates the positioning interval; S23: Determine the environmental status based on the combination of sensor data. If the rainfall R is not less than 0.1 mm / min, the environmental status is determined to be rainy. If the ambient light intensity L is less than 500 lx and the collection time is between 18:00 and 6:00, the environmental status is determined to be nighttime. If the number of people N in the area is greater than the area's rated capacity C, the environmental status is determined to be excessive population flow.

[0010] Preferably, step S3 specifically includes the following steps: S31: Construct a conditional-branch narrative graph using script editing tools. Each script has one initial node, no fewer than three key plot nodes, and no fewer than five branch nodes. Different branch nodes are linked through conditional expressions, and a priority weight W is set for each branch node. S32: Receive multi-dimensional context data, preprocess the multi-dimensional context data, remove outliers, smooth continuous data using a moving average method, and standardize the preprocessed multi-dimensional context data into a decision factor set F. The decision factor set F includes weather conditions, time period, location region, and team cohesion. Team average movement speed and the state of human flow; S33: Traverse all branch nodes of the current script through the dynamic plot engine module, filter out candidate branches that meet the conditions of the decision factor set F, and calculate the comprehensive score of the candidate branches. Overall score Specifically set as follows: ; in, This indicates the recommended movement speed for candidate branches. This indicates the maximum permissible movement speed within the scenic area. Indicates the environmental adaptability coefficient; S34: Overall Score The highest-scoring candidate branch is set as the currently executing branch, based on the combined score of multiple candidate branches. Similarly, randomly select a candidate branch as the current execution branch; if no candidate branch's decision factor set F meets the conditions, execute the default branch. S35: Send the plot content ID, playback duration, and mixing parameter suggestions corresponding to the candidate branch to the audio material library module and the tour guide collaborative control module, and perform collaborative control through the tour guide collaborative control module.

[0011] Preferably, in step S35, collaborative control is performed through the tour guide collaborative control module, specifically including the following steps: Step 1: The tour guide views the team status panel through the tour guide collaborative control module. The team status panel includes a real-time location distribution map of team members, the currently executing storyline node, and the team's aggregation level. Team average movement speed and environmental conditions; Step 2: When the preset key plot node is reached, the tour guide triggers the branch plot through the tour guide collaborative control module, inputs the trigger command, and sends the trigger command to the dynamic plot engine module through the MQTT protocol; Step 3: The dynamic story engine module receives the trigger command, pauses the matching of the current story branch, forcibly executes the story branch specified by the tour guide, and sends a synchronization signal to all tourist terminal modules at the same time; Step 4: The tour guide adjusts the mixing parameters through the tour guide collaborative control module and synchronizes the adjustment instructions to the adaptive mixing module in real time. The effective delay of the adjustment instructions is no more than 100ms.

[0012] Preferably, step S4 specifically includes the following steps: S41: Retrieve audio materials from the audio material library module based on the plot content ID. The audio material format is set to lossless WAV format. The audio materials include character dialogue, background sound effects and background music. S42: Acquire real-time ambient sound of the current area in the multi-dimensional perception module, extract ambient sound features through spectrum analysis algorithm, and calculate the average loudness of the ambient sound. Analyze the spectral distribution of ambient sounds, determine the main frequency ranges, and identify the type of ambient sound. S43: Calculate adaptive mixing parameters; S44: Based on adaptive mixing parameters, the mixed audio is mapped to 3D spatial audio through a spatial audio rendering algorithm. The 3D spatial audio is dynamically compressed and peaked, and output in AAC format. It is sent to the visitor terminal module via RTP protocol with a transmission delay of no more than 150ms.

[0013] Preferably, step S43 specifically includes the following steps: Step 1: Set the target loudness for the story audio. Calculate the mixing gain Mixing gain Specifically set as follows: ; If the mix gain Greater than 10dB, adjust the mixing gain. Reduced to 10dB, if the mix gain Less than 10dB, adjust the mixing gain. Reduced to 0dB; Step 2: Adjust the mixing gain based on the energy proportion of ambient sound within the frequency range. Adjustments were made to the frequency avoidance parameters. Specifically set as follows: ; in, Indicates ambient sound in the frequency range energy, Indicates the total energy across the entire frequency band; Step 3: Calculate the audio spatial parameters based on the relative position of the tourist's 3D coordinates (X,Y,Z) and the scene where the story takes place. The spatial parameters include the azimuth angle. Angle of elevation and distance decay azimuth Angle of elevation and distance decay Set them to: ; ; ; in, Indicates the coordinates of the center point of the plot scene. This indicates the effective propagation distance of the audio recording of the storyline.

[0014] A system for a context-aware adaptive tourism audio narrative method includes a multi-dimensional perception module for collecting environmental data, tourist location, and group status data; a dynamic plot engine module for processing context data, parsing script logic, and deciding plot branches; an audio material library module for storing plot audio materials and preset environmental sound templates, including dialogue, sound effects, and music; an adaptive mixing module for receiving real-time environmental sound and plot audio and performing intelligent mixing processing; a tour guide collaborative control module for displaying team status, receiving tour guide instructions, and triggering specific plots; and a tourist terminal module for receiving the mixed audio and providing feedback on the wearer's status.

[0015] Therefore, the present invention employs the above-mentioned context-aware adaptive tourism audio narrative method and system, which has the following beneficial effects: (1) This solution achieves deep adaptive integration of plot and real scene: by responding to the environment and group dynamics, the audio plot is no longer background sound, but a "live drama" that is linked with the real world. Each experience may be different due to weather, time and team behavior, which greatly improves the sense of immersion and revisit rate. (2) This solution creates a powerful environmental narrative capability: it incorporates real environmental sounds into the narrative system and breaks down the auditory boundary between "plot" and "reality" through intelligent mixing technology, making tourists feel that the story is happening around them. This is a "soundscape construction" effect that cannot be achieved by pure broadcasting art. (3) This scheme optimizes the group tour experience: by sensing the group status and broadcasting synchronously, it ensures the consistency of the experience of team members at key plot points. At the same time, by guiding within the plot, it naturally manages the order of the group, reduces the burden on the tour guide, and creates unique moments of group empathy. (4) This solution provides a flexible creative platform: it provides screenwriters and sound designers with a powerful creative tool that can write non-linear, interactive, immersive scripts, greatly expanding the content boundaries of tourism performances.

[0016] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a flowchart of a context-aware adaptive audio narrative method for tourism according to the present invention; Figure 2 This is a structural diagram of a system for a context-aware adaptive audio narrative method for tourism according to the present invention. Detailed Implementation

[0018] The method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the methodological or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Example like Figure 1 As shown, this invention provides a context-aware adaptive tourism audio narrative method, including the following steps: S1: Collect multi-dimensional contextual data through the multi-dimensional perception module; Step S1 specifically includes the following steps: S11: Install temperature and humidity sensors on pillars in the open-air area of ​​the scenic spot. Collect relative temperature T and relative humidity RH through the temperature and humidity sensors. The collection frequency is set to 1 time / second. S12: Install a rain sensor on the top of the street light in the open area to collect the rainfall R and rainfall status through the rain sensor. The collection frequency is set to 1 time / 2 seconds. S13: Install light sensors on the surface of landscape facilities to collect ambient light intensity L, with the collection frequency set to 1 time / second; S14: Install a crowd density sensor above the entrance of the narrow passage. The crowd density sensor collects the number of people N and the direction of movement of people in the area. The collection frequency is set to 1 time / second. S15: Install UWB positioning sensors inside the tourists' headphones and at fixed points in the scenic area. Collect the tourists' three-dimensional coordinates (X,Y,Z) through the UWB positioning sensors, with the collection frequency set to 10 times / second. S16: Install a motion sensor inside the tourist's headphones to collect acceleration (α). x ,a y ,a z ) and angular velocity (ω x ,ω y ,ωz The sampling frequency was set to 50 times per second.

[0022] S2: Calculate team cohesion degree based on multi-dimensional contextual data Team average movement speed and environmental conditions; Step S2 specifically includes the following steps: S21: Calculate team cohesion degree Team cohesion The larger the value, the higher the cohesion. If the team cohesion... Less than the set degree threshold The team was deemed dispersed, indicating a low level of team cohesion. Specifically set as follows: ; ; ; ; in, The coordinates representing the team's center. Indicates the number of tourists. Indicates the visitor's serial number. Indicates tourists coordinates Indicates tourists Distance to the team center; S22: Calculate the team's average movement speed Team average movement speed Specifically set as follows: ; ; in, Indicates tourists speed, Indicates the location time. This indicates the positioning interval; in this embodiment, the average moving speed is calculated based on three consecutive positioning data points. Positioning interval Set to 0.1s; S23: Determine the environmental status based on the combination of sensor data. If the rainfall R is not less than 0.1 mm / min, the environmental status is determined to be rainy. If the ambient light intensity L is less than 500 lx and the collection time is between 18:00 and 6:00, the environmental status is determined to be nighttime. If the number of people N in the area is greater than the area's rated capacity C, the environmental status is determined to be excessive population flow.

[0023] S3: Constructs a pre-set script through the dynamic plot engine module, processes multi-dimensional context data, and makes dynamic plot decisions; Step S3 specifically includes the following steps: S31: Construct a conditional-branch narrative graph using a script editing tool. Each script has one initial node, no fewer than three key plot nodes, and no fewer than five branch nodes. Different branch nodes are linked through conditional expressions. Set a priority weight W for each branch node. In this embodiment, the priority weight W is set to a range of 1-10. For example, the priority weight W for the branch "Ancient Alley on a Rainy Day" is 9, and the priority weight W for the branch "Square on a Sunny Day" is 7. S32: Receives multi-dimensional context data every 100ms, preprocesses the multi-dimensional context data, removes outliers such as positioning coordinates exceeding the scenic area boundary or sudden changes in sensor data, and smooths continuous data such as movement speed using the moving average method. The preprocessed multi-dimensional contextual data is standardized into a decision factor set F, which includes weather conditions, time period, location region, and team cohesion. Team average movement speed and the state of human flow; S33: Traverse all branch nodes of the current script through the dynamic plot engine module, filter out candidate branches that meet the conditions of the decision factor set F, and calculate the comprehensive score of the candidate branches. Overall score Specifically set as follows: ; in, This indicates the recommended movement speed for candidate branches. This indicates the maximum permissible movement speed within the scenic area; in this embodiment, it is set to 3 m / s. This represents the environmental adaptability coefficient, which is preset by the screenwriter. For example, the environmental adaptability coefficient for rainy days. Set to 1, environmental adaptability coefficient for sunny days. Set to 0.8; S34: Overall Score The highest-scoring candidate branch is set as the currently executing branch, based on the combined score of multiple candidate branches. Similarly, a candidate branch is randomly selected as the current execution branch. If no candidate branch's decision factor set F meets the conditions, the default branch is executed, and the priority weight W of the default branch is set to 5. S35: Send the plot content ID, playback duration, and mixing parameter suggestions corresponding to the candidate branch to the audio material library module and the tour guide collaborative control module, and perform collaborative control through the tour guide collaborative control module.

[0024] In step S35, collaborative control is performed through the tour guide collaborative control module, specifically including the following steps: Step 1: The tour guide views the team status panel through the tour guide collaborative control module. The team status panel includes a real-time location distribution map of team members, the currently executing storyline node, and the team's aggregation level. Team average movement speed and environmental conditions; Step 2: When the preset key plot node is reached, such as when the team needs to choose the plot direction together, the tour guide triggers the branch plot through the tour guide collaborative control module, inputs the trigger command, such as selecting branch A or playing the guiding script, and sends the trigger command to the dynamic plot engine module through the MQTT protocol; Step 3: The dynamic story engine module receives the trigger command, pauses the matching of the current story branch, forcibly executes the story branch specified by the tour guide, and sends a synchronization signal to all tourist terminal modules to ensure synchronized audio playback; Step 4: The tour guide adjusts the mixing parameters through the tour guide collaborative control module, such as increasing the dialogue volume and decreasing the background music volume, and synchronizes the adjustment commands to the adaptive mixing module in real time. The effective delay of the adjustment commands is no more than 100ms.

[0025] S4: Adaptive audio generation and playback are performed through the adaptive mixing module.

[0026] Step S4 specifically includes the following steps: S41: Retrieve audio materials from the audio material library module based on the plot content ID. The audio material format is set to lossless WAV format. The audio materials include character dialogue (mono, 48kHz sampling rate), background sound effects (stereo, 48kHz sampling rate), and background music (multi-channel, 96kHz sampling rate). S42: Acquire real-time ambient sound (48kHz sampling rate, mono) of the current area in the multi-dimensional perception module, extract ambient sound features through spectrum analysis algorithm, and calculate the average loudness of the ambient sound. Analyze the spectral distribution of ambient sound to determine the main frequency ranges, such as market noise concentrated in 500-2000Hz and rain noise concentrated in 200-1000Hz, and determine the type of ambient sound, such as dense human voices, natural sounds, and mechanical sounds. S43: Calculate adaptive mixing parameters; Step S43 specifically includes the following steps: Step 1: Set the target loudness for the story audio. The target loudness in this embodiment Set to 85dB and calculate the mixing gain. Mixing gain Specifically set as follows: ; If the mix gain Greater than 10dB, adjust the mixing gain. Reduce to 10dB to avoid excessive volume; if the mix gain... Less than 10dB, adjust the mixing gain. Reduced to 0dB, without lowering the story audio when ambient sound is too strong; Step 2: Adjust the mixing gain based on the energy proportion of ambient sound within the frequency range. Adjustments were made to the frequency avoidance parameters. Specifically set as follows: ; in, Indicates ambient sound in the frequency range energy, Indicates the total energy across the entire frequency band; Step 3: Calculate the audio spatial parameters based on the relative position of the tourist's 3D coordinates (X,Y,Z) and the scene where the story takes place. The spatial parameters include the azimuth angle. Angle of elevation and distance decay azimuth Angle of elevation and distance decay Set them to: ; ; ; in, Indicates the coordinates of the center point of the plot scene. This indicates the effective propagation distance of the audio, which is set to 50m in this embodiment.

[0027] S44: Based on adaptive mixing parameters, the mixed audio is mapped to 3D spatial audio through a spatial audio rendering algorithm. The 3D spatial audio is subjected to dynamic range compression (threshold -16dBFS, ratio 4:1) and peak limiting (≤0dBFS), and output in AAC format. It is sent to the visitor terminal module via RTP protocol with a transmission delay of no more than 150ms.

[0028] like Figure 2As shown, a system for a context-aware adaptive tourism audio narrative method includes a multi-dimensional perception module for collecting environmental data, tourist location, and group status data; a dynamic plot engine module for processing context data, parsing script logic, and deciding on plot branches; an audio material library module for storing plot audio materials and preset environmental sound templates, including dialogue, sound effects, and music; an adaptive mixing module for receiving real-time environmental sound and plot audio and performing intelligent mixing processing; a tour guide collaborative control module for displaying team status, receiving tour guide instructions, and triggering specific plots; and a tourist terminal module for receiving the mixed audio and providing feedback on the wearer's status.

[0029] This example selects an ancient street scenic area in Jinan (covering an area of ​​approximately 20,000 square meters). 2 The core tour route is 1500m long and includes six areas such as ancient alleys, squares, and shop clusters. The above system was deployed as a pilot project, with three groups of tourists (15 people in each group, with one tour guide) and a test period of one month (covering different scenarios such as sunny days, rainy days, weekdays, and weekends).

[0030] The accuracy rate of the adaptive plot trigger is 100% (number of times the corresponding branch is correctly triggered when the condition is met / total number of triggers), based on statistics of 1000 valid trigger events. 300 tourist questionnaires were distributed, and 286 valid questionnaires were returned. The average satisfaction score was taken. Key plot synchronization rate = Number of team members whose audio playback time difference at key plot points is ≤500ms / Total number of team members × 100%, based on statistics of 5 key plot points in each group; System latency was measured using professional testing equipment (audio latency tester). The time difference between when the sensor detects an environmental change and when the tourist's headphones output the corresponding adjusted audio was recorded, and the average value of 100 measurements was taken.

[0031] Table 1: Data Comparison between this Solution and Traditional LBS Audio Technology

[0032] As shown in Table 1, this solution achieves real-time perception and fusion of multi-dimensional data on environment, group, and location. The accuracy rate of plot branch decision-making exceeds 90%, and the system latency is controlled within 300ms, meeting the dynamic adaptation requirements of outdoor tourism scenarios. Tourists' satisfaction with the integration of audio and environment and the consistency of team experience is significantly improved, and the willingness to revisit is increased by more than 70% compared with traditional technology. The workload of tour guides is reduced by more than 70%, and there is no need to intervene in the team's progress. Scenic spots can enrich the tour content by flexibly adjusting the script branches and reduce the cost of content updates.

[0033] Therefore, the present invention adopts the above-mentioned context-aware adaptive tourism audio narrative method and system, which realizes dynamic adaptation of plot and intelligent audio mixing by integrating environmental, team and location data in real time, significantly improving immersion, team collaboration and willingness to revisit.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the method of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the method of the present invention, and these modifications or equivalent substitutions should not cause the modified method to deviate from the spirit and scope of the method of the present invention.

Claims

1. A context-aware adaptive audio narrative method for tourism, characterized in that, Includes the following steps: S1: Collect multi-dimensional contextual data through the multi-dimensional perception module; S2: Calculate team cohesion degree based on multi-dimensional contextual data Team average movement speed and environmental conditions; S3: Constructs a pre-set script through the dynamic plot engine module, processes multi-dimensional context data, and makes dynamic plot decisions; S4: Adaptive audio generation and playback are performed through the adaptive mixing module.

2. The context-aware adaptive tourism audio narrative method according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11: Install temperature and humidity sensors on pillars in the open-air area of ​​the scenic spot. Collect relative temperature T and relative humidity RH through the temperature and humidity sensors. The collection frequency is set to 1 time / second. S12: Install a rain sensor on the top of the street light in the open area to collect the rainfall R and rainfall status through the rain sensor. The collection frequency is set to 1 time / 2 seconds. S13: Install light sensors on the surface of landscape facilities to collect ambient light intensity L, with the collection frequency set to 1 time / second; S14: Install a crowd density sensor above the entrance of the narrow passage. The crowd density sensor collects the number of people N and the direction of movement of people in the area. The collection frequency is set to 1 time / second. S15: Install UWB positioning sensors inside the tourists' headphones and at fixed points in the scenic area. Collect the tourists' three-dimensional coordinates (X,Y,Z) through the UWB positioning sensors, with the collection frequency set to 10 times / second. S16: Install a motion sensor inside the tourist's headphones to collect acceleration (α). x ,a y ,a z ) and angular velocity (ω x ,ω y ,ω z The sampling frequency was set to 50 times per second.

3. The context-aware adaptive tourism audio narrative method according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21: Calculate team cohesion degree If the team cohesion Less than the set degree threshold The team was deemed dispersed, indicating a low level of team cohesion. Specifically set as follows: ； ； ； ； in, The coordinates representing the team's center. Indicates the number of tourists. Indicates the visitor's serial number. Indicates tourists coordinates Indicates tourists Distance to the team center; S22: Calculate the team's average movement speed Team average movement speed Specifically set as follows: ； ； in, Indicates tourists speed, Indicates the location time. Indicates the positioning interval; S23: Determine the environmental status based on the combination of sensor data. If the rainfall R is not less than 0.1 mm / min, the environmental status is determined to be rainy. If the ambient light intensity L is less than 500 lx and the collection time is between 18:00 and 6:00, the environmental status is determined to be nighttime. If the number of people N in the area is greater than the area's rated capacity C, the environmental status is determined to be excessive population flow.

4. The context-aware adaptive tourism audio narrative method according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31: Construct a conditional-branch narrative graph using script editing tools. Each script has one initial node, no fewer than three key plot nodes, and no fewer than five branch nodes. Different branch nodes are linked through conditional expressions, and a priority weight W is set for each branch node. S32: Receive multi-dimensional context data, preprocess the multi-dimensional context data, remove outliers, smooth continuous data using a moving average method, and standardize the preprocessed multi-dimensional context data into a decision factor set F. The decision factor set F includes weather conditions, time period, location region, and team cohesion. Team average movement speed and the state of human flow; S33: Traverse all branch nodes of the current script through the dynamic plot engine module, filter out candidate branches that meet the conditions of the decision factor set F, and calculate the comprehensive score of the candidate branches. Overall score Specifically set as follows: ； in, This indicates the recommended movement speed for candidate branches. This indicates the maximum permissible movement speed within the scenic area. Indicates the environmental adaptability coefficient; S34: Overall Score The highest-scoring candidate branch is set as the currently executing branch, based on the combined score of multiple candidate branches. Similarly, randomly select a candidate branch as the current execution branch; if no candidate branch's decision factor set F meets the conditions, execute the default branch. S35: Send the plot content ID, playback duration, and mixing parameter suggestions corresponding to the candidate branch to the audio material library module and the tour guide collaborative control module, and perform collaborative control through the tour guide collaborative control module.

5. The context-aware adaptive tourism audio narrative method according to claim 4, characterized in that, In step S35, collaborative control is performed through the tour guide collaborative control module, specifically including the following steps: Step 1: The tour guide views the team status panel through the tour guide collaborative control module. The team status panel includes a real-time location distribution map of team members, the currently executing storyline node, and the team's aggregation level. Team average movement speed and environmental conditions; Step 2: When the preset key plot node is reached, the tour guide triggers the branch plot through the tour guide collaborative control module, inputs the trigger command, and sends the trigger command to the dynamic plot engine module through the MQTT protocol; Step 3: The dynamic story engine module receives the trigger command, pauses the matching of the current story branch, forcibly executes the story branch specified by the tour guide, and sends a synchronization signal to all tourist terminal modules at the same time; Step 4: The tour guide adjusts the mixing parameters through the tour guide collaborative control module and synchronizes the adjustment instructions to the adaptive mixing module in real time. The effective delay of the adjustment instructions is no more than 100ms.

6. The context-aware adaptive tourism audio narrative method according to claim 5, characterized in that, Step S4 specifically includes the following steps: S41: Retrieve audio materials from the audio material library module based on the plot content ID. The audio material format is set to lossless WAV format. The audio materials include character dialogue, background sound effects and background music. S42: Acquire real-time ambient sound of the current area in the multi-dimensional perception module, extract ambient sound features through spectrum analysis algorithm, and calculate the average loudness of the ambient sound. Analyze the spectral distribution of ambient sounds, determine the main frequency ranges, and identify the type of ambient sound. S43: Calculate adaptive mixing parameters; S44: Based on adaptive mixing parameters, the mixed audio is mapped to 3D spatial audio through a spatial audio rendering algorithm. The 3D spatial audio is dynamically compressed and peaked, and output in AAC format. It is sent to the visitor terminal module via RTP protocol with a transmission delay of no more than 150ms.

7. A context-aware adaptive tourism audio narrative method according to claim 6, characterized in that, Step S43 specifically includes the following steps: Step 1: Set the target loudness for the story audio. Calculate the mixing gain Mixing gain Specifically set as follows: ； If the mix gain Greater than 10dB, adjust the mixing gain. Reduced to 10dB, if the mix gain Less than 10dB, adjust the mixing gain. Reduced to 0dB; Step 2: Adjust the mixing gain based on the energy proportion of ambient sound within the frequency range. Adjustments to frequency avoidance parameters. Specifically set as follows: ； in, Indicates ambient sound in the frequency range energy, Indicates the total energy across the entire frequency band; Step 3: Calculate the audio spatial parameters based on the relative position of the tourist's 3D coordinates (X,Y,Z) and the scene where the story takes place. The spatial parameters include the azimuth angle. Angle of elevation and distance decay azimuth Angle of elevation and distance decay Set them to: ； ； ； in, Indicates the coordinates of the center point of the scene. This indicates the effective propagation distance of the audio recording of the storyline.

8. A system for a context-aware adaptive tourism audio narrative method according to any one of claims 1-7, characterized in that, It includes a multi-dimensional perception module for collecting environmental data, tourist location and group status data; a dynamic plot engine module for processing context data, parsing script logic, and deciding plot branches; an audio material library module for storing plot audio materials and environmental sound preset templates, including dialogue, sound effects and music; an adaptive mixing module for receiving real-time environmental sound and plot audio and performing intelligent mixing processing; a tour guide collaborative control module for displaying team status, receiving tour guide instructions and triggering specific plots; and a tourist terminal module for receiving mixed audio and providing feedback on the wearer's status.

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