Travel itinerary navigation method, apparatus and device, and storage medium

Abstract

The invention discloses a travel itinerary navigation method, device and equipment and a storage medium, and the method comprises the steps: collecting user biological rhythm parameters, cultural heritage activation indexes and multi-dimensional data of microclimate evolution prediction in real time, carrying out the feature weight dynamic distribution of the fusion of the multi-dimensional data through a quantum annealing algorithm, and constructing a four-dimensional decision space; based on a four-dimensional decision space, the tourist density, the cultural relic protection critical value and the commercial facility bearing capacity are mapped to an Isin spin system, a spin state evolution path is optimized in combination with a quantum walking algorithm, and an optimal solution set containing multiple candidate paths is generated; bio-rhythm parameters, emotional changes and scenic spot environment data of tourists are collected in real time, a quantum annealing algorithm and a quantum walking algorithm are adopted to perform deep fusion and optimization on multi-dimensional data, and tourism recommendation routes, information pushing frequency and feedback intensity are dynamically adjusted according to instant demands of the tourists. And the tourism experience of tourists is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of tourism navigation, and in particular to a tourism itinerary navigation method, device, equipment and storage medium. Background Art

[0002] With the booming modern tourism industry, tourists' demands for travel experiences are increasingly intelligent, personalized, and refined. However, traditional travel itinerary navigation systems often rely solely on static geographic locations and time schedules, failing to meet tourists' diverse and personalized needs in a dynamic environment. These systems lack in-depth analysis of individual differences and real-time feedback mechanisms, making them unable to accurately grasp tourists' physiological states, emotional changes, and the multiple factors that influence their travel experiences.

[0003] Currently, most tourism navigation systems use traditional location-based methods to make recommendations, ignoring tourists' emotional reactions, cognitive load, and subtle changes in their interactions with cultural heritage. With the rapid development of big data, quantum computing, and intelligent sensing technologies, traditional methods are unable to effectively integrate and process such vast amounts of multidimensional data in real time. Furthermore, environmental variations in tourism scenarios, such as climate, visitor density, and cultural heritage protection needs, are often not fully considered. This can lead to tourists encountering problems such as overcrowding and unsuitable environments during their visits, compromising the quality of their travel experience. Furthermore, the conflict between cultural heritage protection and commercial interests poses significant challenges to the display and revitalization of cultural heritage. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the prior art and provide a travel itinerary navigation method, device, equipment and storage medium to solve the problem that the travel navigation system in the prior art lacks real-time perception of tourists' emotional fluctuations and environmental changes, which affects the tourists' travel experience.

[0005] The first aspect of the present invention is achieved as follows: a travel itinerary navigation method, comprising the following steps:

[0006] 1) Real-time collection of multi-dimensional data on user biorhythm parameters, cultural heritage activation index, and microclimate evolution predictions, and the use of quantum annealing algorithms to dynamically assign feature weights to the multi-dimensional data fusion to construct a four-dimensional decision space;

[0007] 2) Based on the four-dimensional decision space, the tourist density, cultural relic protection threshold, and commercial facility carrying capacity are mapped to the Ising spin system. The spin state evolution path is optimized using a quantum walk algorithm to generate an optimal solution set containing multiple candidate paths.

[0008] 3) Based on the optimal solution set, obtain eye attention maps, social network emotion polarity, and wearable device physiological flow data, perform data alignment processing through a cross-modal attention mechanism, and generate dynamic cognitive load assessment results;

[0009] 4) adjusting the display density and tactile feedback intensity in real time based on the dynamic cognitive load assessment results, and using geomagnetic anomaly detection to trigger resonance compensation of the tactile feedback device to optimize the user's travel experience;

[0010] 5) Construct a dynamic Bayesian network with multiple influencing factors and verify the historical and cultural experience integrity of the path plan through counterfactual simulation;

[0011] 6) Introducing environmental sensor data, generating multi-physics field coupling feedback through fuzzy logic controllers to optimize the overall user experience of the travel itinerary.

[0012] Furthermore, the calculation of the cultural heritage activation index in step 1) includes:

[0013] The cross-modal correlation between the length of stay of tourists and the temperature and humidity fluctuation data on the surface of cultural relics is analyzed through a spatiotemporal convolutional network to generate analysis results.

[0014] Construct a dual-objective optimization model for cultural heritage activation and commercial benefits. Based on the analysis results, use the quantum particle swarm algorithm to solve the equilibrium point and optimize the balance between cultural heritage activation and commercial benefits.

[0015] The update formula of quantum particle swarm optimization algorithm is:

[0016] v j (t+1)=ωv j (t)+c1r1(p best -γ j )+c2r2(g best -γ j )

[0017] Where, v j (t+1) is the velocity of the particle, p best and g best are the individual optimal and global optimal positions, ω is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers.

[0018] Furthermore, the quantum annealing algorithm formula in step 1) is:

[0019]

[0020] Where E(x) is the energy function, f i (x) is the function of the i-th feature, w i is the weight of the feature.

[0021] Furthermore, the quantum walk algorithm formula in step 2) is:

[0022]

[0023] Where ψ(a,t) is the quantum state at time t, and a and b are the positions in space.

[0024] Furthermore, the calculation of the sentiment fluctuation index of the social network sentiment polarity in step 3) includes:

[0025] By combining facial expression recognition with physiological signals, real-time analysis of tourists' emotional changes in scenic areas;

[0026] Based on real-time sentiment changes and scenic spot feedback data, a genetic algorithm is used to optimize the recommendation strategy;

[0027] A quantum genetic algorithm is introduced to balance tourists' emotional comfort and cultural heritage display effects according to the recommendation strategy.

[0028] Furthermore, the formula of the cross-modal attention mechanism in step 3) is:

[0029]

[0030] Where Q is the query vector, K is the key vector, V is the value vector, and d k is the dimension of the key vector.

[0031] The second aspect of the present invention is achieved as follows: a travel itinerary navigation device includes a biological perception module, a heterogeneous computing module, an adaptive feedback module, a virtual tour guide engine and an environmental data fusion module;

[0032] The biosensing module is used to collect real-time biosignals and environmental data to provide data support for travel itinerary navigation;

[0033] The heterogeneous computing module is used to perform large-scale data parallel processing and deep learning model reasoning using a hybrid computing platform based on a graphics processing unit and quantum computing architecture;

[0034] The adaptive feedback module is used to dynamically adjust the intensity and method of feedback based on the tourists' emotional state and cognitive load to optimize the user's travel experience; dynamically adjust the presentation mode of cultural heritage content based on the tourists' emotional fluctuations and cognitive state to optimize the tourists' cultural immersion; introduce eye tracking technology to dynamically adjust the focus and details of the displayed content based on the tourists' visual focus;

[0035] The virtual tour guide engine is used to generate a recommended travel route based on user preferences and physiological status in real time;

[0036] The environmental data fusion module is used to collect environmental data of the scenic area in real time and perform multimodal analysis and fusion of biological feedback and environmental impacts; through physical field monitoring and data analysis, it uses machine learning algorithms to predict changes in tourist flow in the scenic area, and adjusts tourist guidance strategies in advance based on tourists' behavioral preferences.

[0037] Furthermore, it also includes a biosensing terminal, which performs the following operations: through skin electrical response and body temperature data, it evaluates the psychological stress and physiological load of tourists in real time and generates evaluation results; according to the evaluation results, it adjusts the information push frequency and feedback intensity of the virtual guide engine to optimize the pertinence and accuracy of information push.

[0038] The third aspect of the purpose of the present invention is achieved as follows: a travel itinerary navigation device includes a memory, a processor and a computer program stored in the memory and running on the processor, and the above-mentioned travel itinerary navigation method is implemented when the computer program is run on the processor.

[0039] The fourth aspect of the purpose of the present invention is achieved as follows: a travel itinerary navigation guide storage medium, the guide storage medium is a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned travel itinerary navigation method.

[0040] The present invention adopts the above technical solution, and compared with the existing technology, the beneficial effects are as follows: the present invention collects tourists' biorhythm parameters, emotional changes and scenic area environment data in real time, and uses quantum annealing algorithm and quantum walk algorithm to deeply integrate and optimize multi-dimensional data, dynamically adjusts travel recommendation routes, information push frequency and feedback intensity according to tourists' immediate needs, and optimizes tourists' travel experience; by introducing dynamic Bayesian network, genetic algorithm and quantum particle swarm algorithm, it balances tourists' emotional comfort, cognitive load and cultural heritage display effect, thereby providing a more personalized, comfortable and interesting travel experience. In addition, the present invention also effectively resolves the conflict between cultural heritage protection and commercial interests, ensures the integrity and activation effect of cultural heritage display, and helps to enhance tourists' cultural immersion. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the travel itinerary navigation method of the present invention.

[0042] Figure 2 This is a framework diagram of the travel itinerary navigation device of the present invention. DETAILED DESCRIPTION

[0043] like Figure 1 A travel itinerary navigation method is shown, comprising the following steps:

[0044] 1) Real-time collection of multi-dimensional data on user biorhythm parameters, cultural heritage activation index, and microclimate evolution predictions, and the use of quantum annealing algorithms to dynamically assign feature weights to the multi-dimensional data fusion to construct a four-dimensional decision space;

[0045] The calculation of the cultural heritage activation index involves: using a spatiotemporal convolutional network to analyze the cross-modal correlation between visitor dwell time and surface temperature and humidity fluctuation data of cultural relics to generate analysis results; constructing a dual-objective optimization model for cultural heritage activation and commercial benefits, and using a quantum particle swarm algorithm to solve the equilibrium point based on the analysis results to optimize the balance between cultural heritage activation and commercial benefits;

[0046] The update formula of quantum particle swarm optimization algorithm is:

[0047] v j (t+1)=ωv j (t)+c1r1(p best -γ j )+c2r2(g best -γ j )

[0048] Where, v j (t+1) is the velocity of the particle, p best and g best are the individual optimal and global optimal positions, ω is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers.

[0049] The quantum annealing algorithm formula is:

[0050]

[0051] Where E(x) is the energy function, f i (x) is the function of the i-th feature, w i is the weight of the feature.

[0052] 2) Based on a four-dimensional decision space, the tourist density, cultural relic protection threshold, and commercial facility carrying capacity are mapped to the Ising spin system. The quantum walk algorithm is then used to optimize the spin state evolution path, generating an optimal solution set containing multiple candidate paths.

[0053] The quantum walk algorithm formula is:

[0054]

[0055] Where ψ(a,t) is the quantum state at time t, and a and b are the positions in space.

[0056] 3) Based on the optimal solution set, we obtain eye attention maps, social network emotion polarity, and wearable device physiological flow data, perform data alignment processing through a cross-modal attention mechanism, and generate dynamic cognitive load assessment results;

[0057] The calculation of the emotional fluctuation index of social network sentiment polarity includes: combining facial expression recognition with physiological signals to analyze tourists' emotional changes in real time within the scenic area; using genetic algorithms to optimize recommendation strategies based on real-time emotional changes and scenic spot feedback data; and introducing quantum genetic algorithms to balance tourists' emotional comfort and cultural heritage display effects according to the recommendation strategy.

[0058] The formula of the cross-modal attention mechanism is:

[0059]

[0060] Where Q is the query vector, K is the key vector, V is the value vector, and d k is the dimension of the key vector.

[0061] 4) Real-time adjustment of display density and tactile feedback intensity based on dynamic cognitive load assessment results, and use of geomagnetic anomaly detection to trigger resonance compensation of the tactile feedback device to optimize the user's travel experience;

[0062] 5) Construct a dynamic Bayesian network with multiple influencing factors and verify the historical and cultural experience integrity of the path plan through counterfactual simulation;

[0063] 6) Introducing environmental sensor data, generating multi-physics field coupling feedback through fuzzy logic controllers to optimize the overall user experience of the travel itinerary.

[0064] like Figure 2 As shown, a travel itinerary navigation device includes a biological perception module, a heterogeneous computing module, an adaptive feedback module, a virtual tour engine and an environmental data fusion module;

[0065] The biosensing module is used to collect real-time biological signals and environmental data to provide data support for travel itinerary navigation. It uses skin galvanic response and body temperature data to assess tourists' psychological stress and physiological load in real time and generate assessment results. Based on the assessment results, the frequency of information push and feedback intensity of the virtual tour engine are adjusted to optimize the relevance and accuracy of information push.

[0066] Heterogeneous computing module for performing large-scale data parallel processing and deep learning model inference using a hybrid computing platform based on graphics processing units and quantum computing architecture;

[0067] The adaptive feedback module dynamically adjusts the intensity and method of feedback based on the visitor's emotional state and cognitive load, optimizing the user's travel experience. It also dynamically adjusts the presentation of cultural heritage content based on the visitor's emotional fluctuations and cognitive state, optimizing the visitor's cultural immersion. Eye-tracking technology is introduced to dynamically adjust the emphasis and details of the displayed content based on the visitor's gaze focus.

[0068] A virtual tour engine that generates recommended travel routes based on user preferences and physiological status in real time;

[0069] The environmental data fusion module is used to collect environmental data of the scenic area in real time and conduct multimodal analysis and fusion of biological feedback and environmental impacts. Through physical field monitoring and data analysis, it uses machine learning algorithms to predict changes in tourist flow within the scenic area, and adjust tourist guidance strategies in advance based on tourists' behavioral preferences.

[0070] It also includes a bio-sensing terminal, which performs the following operations: evaluates the psychological stress and physiological load of tourists in real time through skin electrical response and body temperature data, and generates evaluation results; adjusts the information push frequency and feedback intensity of the virtual guide engine according to the evaluation results, and optimizes the pertinence and accuracy of information push.

[0071] Example 1:

[0072] Application example: Smart cultural tourism navigation assistant based on travel itinerary navigation method

[0073] Mr. Li, a tourist, plans a three-day immersive tour of a historic city. The city boasts numerous cultural heritage sites, and visitor density varies significantly at different times of the year. Mr. Li hopes to receive personalized, intelligent itinerary guidance from the Smart Cultural Tourism Navigation Assistant.

[0074] 1. Application function implementation:

[0075] (1) Personalized data collection and integration:

[0076] The biosensing module is activated: the smart bracelet (biosensing terminal) worn by Mr. Li begins to collect his heart rate, body temperature, cadence and other biorhythm parameters in real time.

[0077] Environmental data fusion: The smart cultural tourism navigation assistant obtains real-time microclimate data (temperature, humidity, light intensity, etc.), visitor density of each scenic spot, carrying capacity of cultural relics protection areas, and other information through the sensor network deployed in the scenic area.

[0078] Cultural heritage activation assessment: The system uses a spatiotemporal convolutional network to analyze data such as the length of time visitors stay in the cultural heritage area and fluctuations in temperature and humidity on the surface of cultural relics, dynamically calculates the cultural heritage activation index, and evaluates the display effect of cultural heritage and visitor interaction.

[0079] Multi-dimensional data fusion: Using the quantum annealing algorithm, the system integrates multi-dimensional data such as Mr. Li's biorhythm parameters, cultural heritage activation index, microclimate data, and tourist density of scenic spots, dynamically assigns feature weights, and constructs a four-dimensional decision space to provide a data basis for personalized route planning.

[0080] (2) Intelligent path planning and optimization:

[0081] Ising spin system mapping: The system maps constraints such as tourist density, cultural relic protection threshold, and commercial facility carrying capacity to the Ising spin system.

[0082] Quantum walk algorithm optimization: Incorporating a quantum walk algorithm, the system optimizes the spin state evolution path in a four-dimensional decision space, generating multiple candidate paths. These paths take into account factors such as Mr. Li's personal preferences (obtained through historical behavioral data analysis), the historical and cultural value of the attraction, real-time visitor traffic, and cultural relic protection requirements.

[0083] Dynamic Bayesian network verification: For the generated candidate paths, the system constructs a dynamic Bayesian network and verifies the historical and cultural experience integrity of each path plan through counterfactual simulation, ensuring that Mr. Li can fully experience the historical value and cultural connotation of cultural heritage.

[0084] Optimal route recommendation: The system comprehensively considers factors such as route length, tour time, integrity of historical and cultural experience, and tourist comfort, selects the optimal route from the candidate routes and recommends it to Mr. Li, and displays it on AR glasses or mobile phone APP.

[0085] (3) Real-time emotion and cognitive load management:

[0086] Eye tracking and sentiment analysis: Mr. Li's AR glasses are equipped with eye tracking technology, capturing his eye attention in real time. Simultaneously, the system analyzes social network sentiment polarity to understand Mr. Li's real-time feedback on attractions and his experience.

[0087] Physiological flow data monitoring: The smart bracelet continuously monitors Mr. Li’s physiological flow data (such as skin conductivity, brain waves, etc.) to assess his cognitive load and emotional state.

[0088] Cross-modal attention mechanism: The system uses a cross-modal attention mechanism to align multimodal data such as eye attention maps, social network emotional polarity, and physiological flow data to generate dynamic cognitive load assessment results.

[0089] Adaptive feedback adjustment: Based on the cognitive load assessment results, the system adjusts the AR display density (for example, reducing the amount of AR information displayed when cognitive load is high) and tactile feedback intensity in real time (for example, providing gentle tactile reminders through the smart bracelet to guide Mr. Li to less crowded areas when visitor density is too high). Furthermore, the system can also use geomagnetic anomaly detection technology to trigger resonance compensation in the tactile feedback device, further enhancing Mr. Li's visitor experience.

[0090] (4) Multi-physics field coupling feedback:

[0091] Environmental sensor data access: Continuously introduce environmental sensor data within the scenic area, including temperature, humidity, light intensity, air quality, etc.

[0092] Fuzzy logic controller: Based on environmental sensor data and Mr. Li’s real-time status, the fuzzy logic controller generates multi-physics field coupling feedback. For example:

[0093] Response to weather changes: If the weather changes suddenly (such as rain), the system will promptly adjust the recommended route, suggesting Mr. Li go to the indoor exhibition hall or provide rain gear rental information.

[0094] Tourist density control: If the tourist density at a certain attraction is too high, the system will recommend alternative attractions with fewer tourists to Mr. Li through AR glasses or mobile APP, or provide diversion suggestions to guide him to visit other areas to improve the overall tour experience.

[0095] Microclimate comfort optimization: Based on real-time microclimate data, the brightness, contrast and other parameters of the AR display content are dynamically adjusted to ensure that Mr. Li can tour in a comfortable environment.

[0096] 2. Application effect:

[0097] With the guidance of the Smart Cultural Tourism Navigation Assistant, Mr. Li gained the following high-quality experience:

[0098] Personalized itinerary customization: A travel itinerary was tailored based on his personal interests and real-time status, avoiding crowded attractions and allowing him to experience the charm of cultural heritage more deeply.

[0099] Intelligent route guidance: During the tour, the route is optimized in real time to ensure that he can visit the attractions in the most comfortable and efficient way and fully experience the historical and cultural atmosphere.

[0100] Emotional and cognitive load management: Pay attention to his emotional state and cognitive load in real time, and through adaptive feedback regulation, always keep his tour experience at the best state, avoiding information overload and fatigue.

[0101] Multi-physics field coupling experience: Taking into account multiple factors such as weather, tourist density, microclimate, etc., it provides him with a safe, comfortable and convenient travel environment, allowing him to fully devote himself to the exploration of the historical and cultural city.

[0102] The application of the smart cultural and tourism navigation assistant not only enhances the tourists' travel experience, but also provides strong support for the protection and revitalization of cultural heritage, achieving dual optimization of tourist experience and cultural heritage protection.

[0103] A travel itinerary navigation device comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the computer program runs on the processor, the travel itinerary navigation method is implemented.

[0104] A travel itinerary navigation guide storage medium is provided. The guide storage medium is a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the computer-readable storage medium controls the device containing the computer-readable storage medium to execute the various steps of the above-described travel itinerary navigation method, achieving the same technical effect. To avoid repetition, the description is omitted here. The computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0105] The present invention provides a tourist itinerary navigation method, device, equipment, and storage medium. By collecting multi-dimensional data such as user biorhythms, cultural heritage activation index, and microclimate evolution in real time, and combining advanced technologies such as quantum annealing algorithms and spatiotemporal convolutional networks, this method achieves precise perception and dynamic optimization of tourist behavior and the environment, optimizing the balance between cultural heritage activation and commercial benefits, and improving the quality of the tourist experience. By combining a four-dimensional decision space, a quantum walk algorithm, and a cross-modal attention mechanism, this method dynamically perceives and optimizes tourist behavior, emotional fluctuations, and physiological states. By accurately modeling visitor density, cultural heritage protection, and commercial facility capacity, and assessing visitor emotions and cognitive load in real time, this method generates personalized travel route recommendations and optimizes the balance between visitor emotional comfort and cultural heritage display effectiveness, thereby enhancing the visitor experience and scenic area management efficiency. By dynamically adjusting display density and tactile feedback intensity, the experience is optimized in real time based on the visitor's cognitive load. Geomagnetic anomaly detection is used to compensate for tactile feedback resonance, enhancing comfort and interactivity. Furthermore, by incorporating environmental sensor data and a fuzzy logic controller to generate multi-physics field coupled feedback, this method further optimizes the overall tourist itinerary and provides a more personalized and immersive travel experience.

[0106] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solutions disclosed in the present invention, those skilled in the art can make some substitutions and modifications to some of the technical features therein according to the disclosed technical content without creative labor, and these substitutions and modifications are all within the protection scope of the present invention.

Claims

1. A travel itinerary navigation method, characterized in that: The following steps are involved: 1) Real-time collection of multi-dimensional data on user biorhythm parameters, cultural heritage activation index, and microclimate evolution predictions, and the use of quantum annealing algorithms to dynamically assign feature weights to the multi-dimensional data fusion to construct a four-dimensional decision space; 2) Based on the four-dimensional decision space, the tourist density, cultural relic protection threshold, and commercial facility carrying capacity are mapped to the Ising spin system. The spin state evolution path is optimized using a quantum walk algorithm to generate an optimal solution set containing multiple candidate paths. 3) Based on the optimal solution set, obtain eye attention maps, social network emotion polarity, and wearable device physiological flow data, perform data alignment processing through a cross-modal attention mechanism, and generate dynamic cognitive load assessment results; 4) adjusting the display density and tactile feedback intensity in real time based on the dynamic cognitive load assessment results, and using geomagnetic anomaly detection to trigger resonance compensation of the tactile feedback device to optimize the user's travel experience; 5) Construct a dynamic Bayesian network with multiple influencing factors and verify the historical and cultural experience integrity of the path plan through counterfactual simulation; 6) Introducing environmental sensor data, generating multi-physics field coupling feedback through fuzzy logic controllers to optimize the overall user experience of the travel itinerary.

2. The travel itinerary navigation method according to claim 1, characterized in that: The calculation of the cultural heritage revitalization index in step 1) includes: The cross-modal correlation between the length of stay of tourists and the temperature and humidity fluctuation data on the surface of cultural relics is analyzed through a spatiotemporal convolutional network to generate analysis results. Construct a dual-objective optimization model for cultural heritage activation and commercial benefits. Based on the analysis results, use the quantum particle swarm algorithm to solve the equilibrium point and optimize the balance between cultural heritage activation and commercial benefits. The update formula of quantum particle swarm optimization algorithm is: v j (t+1)=ωv j (t)+c1r1(p best -c j )+c2r2(g best -c j ) Where, v j (t+1) is the velocity of the particle, p best and g best are the individual optimal and global optimal positions, ω is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers.

3. The travel itinerary navigation method according to claim 1, characterized in that: The quantum annealing algorithm formula in step 1) is: Where E(x) is the energy function, f i (x) is the function of the i-th feature, w i is the weight of the feature.

4. The travel itinerary navigation method according to claim 1, characterized in that: The quantum walk algorithm formula in step 2) is: Where ψ(a,t) is the quantum state at time t, and a and b are the positions in space.

5. The travel itinerary navigation method according to claim 1, characterized in that: The calculation of the sentiment fluctuation index of the social network sentiment polarity in step 3) includes: By combining facial expression recognition with physiological signals, real-time analysis of tourists' emotional changes in scenic areas; Based on real-time sentiment changes and scenic spot feedback data, a genetic algorithm is used to optimize the recommendation strategy; A quantum genetic algorithm is introduced to balance tourists' emotional comfort and cultural heritage display effects according to the recommendation strategy.

6. The travel itinerary navigation method according to claim 1, characterized in that: The formula of the cross-modal attention mechanism in step 3) is: Where Q is the query vector, K is the key vector, V is the value vector, and d k is the dimension of the key vector.

7. A travel itinerary navigation device, characterized in that: It includes biological perception module, heterogeneous computing module, adaptive feedback module, virtual tour engine and environmental data fusion module; The biosensing module is used to collect real-time biosignals and environmental data to provide data support for travel itinerary navigation; The heterogeneous computing module is used to perform large-scale data parallel processing and deep learning model reasoning using a hybrid computing platform based on a graphics processing unit and quantum computing architecture; The adaptive feedback module is used to dynamically adjust the intensity and method of feedback based on the tourists' emotional state and cognitive load to optimize the user's travel experience; dynamically adjust the presentation mode of cultural heritage content based on the tourists' emotional fluctuations and cognitive state to optimize the tourists' cultural immersion; introduce eye tracking technology to dynamically adjust the focus and details of the displayed content based on the tourists' visual focus; The virtual tour guide engine is used to generate a recommended travel route based on user preferences and physiological status in real time; The environmental data fusion module is used to collect environmental data of the scenic area in real time and perform multimodal analysis and fusion of biological feedback and environmental impacts; through physical field monitoring and data analysis, it uses machine learning algorithms to predict changes in tourist flow in the scenic area, and adjusts tourist guidance strategies in advance based on tourists' behavioral preferences.

8. The travel itinerary navigation device according to claim 7, characterized in that: It also includes a biosensing terminal, which performs the following operations: evaluates the psychological stress and physiological load of tourists in real time through skin electrical response and body temperature data to generate evaluation results; adjusts the information push frequency and feedback intensity of the virtual guide engine according to the evaluation results to optimize the pertinence and accuracy of information push.

9. A travel itinerary navigation device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein when the computer program is run on the processor, the method for navigating a travel itinerary as claimed in any one of claims 1 to 6 is implemented.

10. A travel itinerary navigation guide storage medium, characterized in that: The boot storage medium is a computer-readable storage medium, which stores a computer program. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the travel itinerary navigation method according to any one of claims 1 to 6.

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