Manual interaction intelligent accompanying system based on large model and equipment of manual interaction intelligent accompanying system

Abstract

The invention discloses a man-machine interaction intelligent accompanying system and device based on a large model. The man-machine interaction intelligent accompanying system comprises the following steps: S1, identifying a user identity and initializing user preference and historical data; s2, receiving text and voice input of a user, and performing deep language understanding by using the large model; s3, performing fusion processing on the voice, the text and the visual data; s4, analyzing user behavior data and environment information by using a generative adversarial network and a sparse adaptive evolutionary algorithm; s6, providing a real-time navigation service in combination with a geographic information system, and dynamically adjusting a path through a quantum heuristic optimization algorithm; s7, collecting user interaction data and feedback information, and dynamically optimizing system parameters and balancing exploration and utilization by adopting a self-adaptive multi-arm machine algorithm; and S8, adopting a differential privacy technology in all data processing processes to ensure the security and privacy of user data.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent tourism services, and in particular to an artificial interactive intelligent travel companion system based on a large model and equipment thereof. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, tourism has become an indispensable part of people's daily lives. However, the traditional tourism service model often cannot meet the growing needs of tourists and their requirements for real-time information acquisition. The traditional tour guide service model mainly relies on manual tour guides. The number of tour guides is limited, and their service content and methods are often relatively simple, and they cannot be adjusted in real time according to the specific needs of each tourist. In addition, tourists usually need to consult a large amount of information and maps during the tour, which not only consumes time and energy, but may also affect the travel experience due to inaccurate or delayed information.

[0003] In recent years, with the development of artificial intelligence technology, intelligent tourism service systems have gradually attracted attention. These systems usually use technologies such as natural language processing, machine learning and big data analysis to provide tourists with guided tours, recommendations and navigation services. However, the existing intelligent tourism service systems still have some shortcomings in practical applications:

[0004] Existing natural language processing technology has certain limitations in understanding and generating complex natural languages, especially when dealing with multi-round conversations and contextual associations, which can easily lead to misunderstandings and inaccurate responses; existing recommendation modules are usually based on simple collaborative filtering or content-based recommendation algorithms, which cannot fully consider users' real-time needs and environmental changes, resulting in low accuracy of recommendation results; existing navigation systems mostly rely on static maps and preset routes, and cannot dynamically adjust routes according to real-time traffic information and user needs, resulting in users encountering traffic jams or taking the wrong road during travel; existing intelligent tourism service systems lack multimodal fusion processing in terms of user interaction, and cannot effectively integrate multiple input methods such as voice, text and vision, resulting in a relatively single and unnatural user experience.

[0005] With the application of intelligent tourism service systems, the amount of user data collected and processed has increased significantly. The existing system's measures for data privacy protection are not perfect, and there is a risk of user data leakage and abuse.

[0006] Therefore, how to provide an artificial interactive intelligent tour guide system and its equipment based on a large model is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose an artificial interactive intelligent tour guide system and equipment based on a large model. The present invention makes full use of natural language processing, speech recognition and synthesis, generative adversarial networks, quantum optimization algorithms, adaptive multi-armed bandit algorithms and differential privacy protection technology, and describes in detail a method for intelligently realizing tourism services, real-time navigation and data privacy protection. It has the advantages of high degree, fast response speed, excellent user experience and strong data security.

[0008] According to an embodiment of the present invention, an artificial interactive intelligent travel companion system based on a large model and a device thereof include the following modules:

[0009] User identification and initialization module: used to identify user identities and initialize user preferences and historical data;

[0010] Natural language processing module: used to receive and process the user's natural language input and generate responses;

[0011] Multimodal interaction module: converts the processed user's natural language into text, generates voice response, and integrates and processes voice, text and visual data to achieve human-computer interaction;

[0012] Recommendation module: Generates and optimizes recommendation options based on analyzing user historical behavior data and current environment information;

[0013] Real-time navigation and location service module: Combines the user's current location and recommended services to provide real-time navigation and dynamically adjusts routes based on real-time traffic information and user needs;

[0014] Adaptive system optimization module: collects user interaction data and feedback information, dynamically adjusts system parameters and algorithm models, and realizes adaptive optimization;

[0015] Data processing and privacy protection module: Process and analyze user data, optimize system performance, introduce differential privacy technology in the data analysis process, implement data access control strategies, and conduct regular security audits and vulnerability detection.

[0016] Optionally, the method for implementing the modules is as follows:

[0017] S1. In the user identification and initialization module, the user's identity is identified by combining user behavior patterns, multi-factor authentication technology and emotion recognition technology, and the user's preferences and historical data are initialized;

[0018] S2. In the natural language processing module, the user's natural language input, including text and voice, is received, deep language understanding is performed through an integrated large model, the user's intention is analyzed, and a coherent natural language response is generated using context-aware technology;

[0019] S3, convert user voice into text through the voice recognition module, convert text response into voice through the voice synthesis module, combine the improved attention mechanism algorithm to integrate voice, text and visual data to achieve human-computer interaction, and feed back voice, text and visual data to the recommendation module;

[0020] S4. In the recommendation module, based on the generative adversarial network and sparse adaptive evolutionary algorithm, the user's historical behavior data and current environment information are analyzed, the recommendation options are generated through the generative adversarial network, and the sparse adaptive evolutionary algorithm is used to optimize and filter, generate and provide services, and update the recommendation model in real time to adapt to the user's dynamically changing preferences;

[0021] S5. In the real-time navigation and location service module, GPS and geographic information system are combined to obtain the user's current location, and the services obtained from the recommendation module are used to provide real-time navigation services; the path is optimized by using quantum-inspired optimization algorithms, and the route is dynamically adjusted according to real-time traffic information and user needs;

[0022] S6. In the adaptive system optimization module, interactive data and feedback information from real-time navigation and location services and users are collected, and the adaptive multi-armed bandit algorithm is used to dynamically adjust system parameters and algorithm models according to user feedback and real-time data, balance exploration and utilization, optimize natural language processing capabilities and recommendation accuracy, and achieve adaptive system optimization;

[0023] S7. In the data processing and privacy protection module, differential privacy technology is used in all data collection, processing and optimization processes. Noise is introduced in the data analysis process to protect the information of individual users. Data access control strategies are implemented, security audits and vulnerability detections are performed regularly, and data is used with user authorization.

[0024] Optionally, the step S3 specifically includes:

[0025] S31, receiving user voice input, and converting the user voice into text through a voice recognition module, wherein the voice recognition module uses an improved attention mechanism to optimize the accuracy of voice recognition;

[0026] S32, parsing the converted text through a natural language processing module and generating a text response, wherein the natural language processing module generates a response using adaptive fine-tuning and context-aware technology;

[0027] S33, converting the text response into speech output through a speech synthesis module to generate a natural speech response;

[0028] S34, combining visual data, obtaining visual information in the user's current environment through a camera, and fusing the visual information with voice and text data, wherein the visual information processing module uses a visual transformer to extract features;

[0029] S35. Dynamically adjust the weight distribution of each modality according to the user's real-time emotional state. The emotion recognition module adopts an emotion recognition algorithm based on self-supervised learning, generates accurate emotional state values ​​through a comparative learning mechanism, and optimizes the weight distribution through an adaptive adjustment algorithm.

[0030] S36. Feedback the fused multimodal data to the recommendation module. The recommendation module adjusts the recommendation model and service content in real time according to the feedback data, optimizes the recommendation model using a meta-learning algorithm, and optimizes the recommendation quality through a real-time feedback loop mechanism.

[0031] Optionally, the step S31 specifically includes:

[0032] S311, receiving user voice input, and obtaining original voice data X including an audio signal;

[0033] S312, preprocessing the original speech data X, including denoising, normalization and feature extraction, to generate a preprocessed speech feature matrix X′;

[0034] S313, input the preprocessed speech feature matrix X' into the speech recognition module, and the speech recognition module adopts an improved attention mechanism, specifically including linear transformation of the speech feature matrix X' to generate a query vector Q, a key vector K and a value vector V:

[0035] Q=W q X′+b q ;

[0036] K=W k X′+b k ;

[0037] V=W v X′+b v ;

[0038] Among them, W q , W k and W v are the weight matrices for query, key, and value, respectively, and b q 、b k and b v are the bias terms respectively;

[0039] S314. Calculate the dot product of the query vector Q and the key vector K, and perform scaling and normalization to generate an improved multi-head attention weight matrix A:

[0040]

[0041] Among them, d k is the dimension of the key vector, softmax is the normalization function, M is the attention mask matrix, which is used to introduce context information and optimize the attention weight, and P is the position encoding matrix;

[0042] S315. Use the improved multi-head attention weight matrix A and the weighted sum value vector V to generate a weighted speech feature matrix Z:

[0043] Z=AV+γZ context ;

[0044] Among them, γ is the fusion coefficient, Z context The context feature matrix is ​​used to optimize feature representation by combining historical dialogue and environmental information;

[0045] S316, the weighted speech feature matrix Z is processed through layer normalization and feedforward neural network to generate the final speech recognition result Y:

[0046] S317: Output the final speech recognition result Y and convert it into text representation.

[0047] Optionally, the step S4 specifically includes:

[0048] S41, obtain the user's historical behavior data and current environment information, and collect them through the system's multimodal sensors to generate a data matrix D data ;

[0049] S42: Generate diverse recommendation options using a generative adversarial network, where the generative adversarial network includes a generator G and a discriminator D gan , the generator G is fed with the input noise vector z and the data matrix D data Generate recommended options, discriminator D gan Evaluate by comparing real data with generated data:

[0050]

[0051] Among them, x represents the real data, p data (x) represents the distribution of real data, p z (z) represents the distribution of noise vector;

[0052] S43, optimizing and screening the generated recommendation options by a sparse adaptive evolutionary algorithm, wherein the sparse adaptive evolutionary algorithm performs global search and local optimization based on sparse coding and evolutionary strategy;

[0053] S44. Update the recommendation model in real time to adapt to the dynamically changing preferences of users. The update process uses graph neural network technology to optimize the recommendation effect through the relationship between cross-domain data and graph structure:

[0054]

[0055] Among them, θ is the model parameter, η and λ are the learning rates, and L source and L target are the loss functions of the source domain and the target domain respectively, f θ is the model function, G source and G target are the graph structures of the source domain and the target domain respectively, D source and D target are the source domain and target domain data respectively, y source and target For the corresponding label;

[0056] S45, generating a service based on the optimized and screened recommendation options, wherein the service is adjusted based on the user's preferences and real-time environmental information;

[0057] S46. Recommend services to users and collect user satisfaction and improvement suggestions through the system’s feedback mechanism.

[0058] Optionally, the step S5 specifically includes:

[0059] S51, obtain the user's current location data, the data is collected in real time through GPS and geographic information system, and the location data matrix L is generated data ;

[0060] S52: Generate a target location data matrix T based on the services obtained from the recommendation module data ;

[0061] S53, combined with the position data matrix L data and the target position data matrix T data , generate the initial path planning P initial :

[0062]

[0063] Among them, d(L data [i],T data [i]) represents the i-th position data point L data [i] and target position data point T data [i], c(P[j]) represents the constraint condition of the jth node in path P, and λ is the weight coefficient;

[0064] S54, initial path planning P through quantum-inspired optimization algorithm initial Optimize, combine global search and local optimization, and generate the optimized path P optimal :

[0065]

[0066] Among them, ψ(L data [i], P[i]) represents the i-th position data point L data [i] and the distance between path P[i], φ(P[j]) represents the optimization result of the jth node in path P;

[0067] Using the quantum superposition effect, a global search is performed by considering multiple possible paths P at the same time:

[0068]

[0069] Among them, α k is the probability amplitude of the quantum state, P k [i] represents the i-th point in the k-th possible path, and K is the total number of possible paths;

[0070] Using the quantum tunneling effect, local optimization is performed by skipping the local optimal point:

[0071]

[0072] Among them, β l is the probability amplitude of the quantum state, P l [j] represents the jth point in the first possible path, and L is the total number of possible paths;

[0073] S55, dynamically adjust the path according to real-time traffic information and user needs, and generate a real-time navigation path P real-time :

[0074]

[0075] Among them, Δτ k represents the adjustment amount within time period k, g(T traffic [k],U user [k]) is the comprehensive real-time traffic information T traffic and user needs user The adjustment function of

[0076] S56, provide real-time navigation services, including route planning, road condition analysis and dynamic adjustment, according to the real-time navigation route P real-time Guide users.

[0077] Optionally, the step S6 specifically includes:

[0078] S61, collecting user current location data from real-time navigation and location services, user interaction data from recommendation services, and user feedback information;

[0079] S62, based on user feedback and real-time data, use the adaptive multi-armed bandit algorithm to dynamically adjust system parameters and algorithm models:

[0080]

[0081] Among them, θ t is the system parameter at time t, η is the learning rate, r i is the actual feedback value, is the predicted feedback value, x i is the user interaction data, N is the total number of interaction data;

[0082] S63. In the adaptive multi-armed bandit algorithm, balance exploration and exploitation by optimizing the balance factor ∈:

[0083]

[0084] Among them, ∈ is the balance factor, λ is the regularization parameter, and p j is the probability of selecting the jth action, and M is the total number of possible actions;

[0085] S64. According to the optimized balance factor ∈, dynamically adjust the natural language processing capability and recommendation accuracy to generate the optimized system parameter θ optimal :

[0086]

[0087] Among them, θ is the system parameter, P(r i |θ) represents the probability of the actual feedback value under parameter θ, represents the probability of the predicted feedback value under parameter θ, and K is the total number of predicted feedback values;

[0088] S65, the optimized system parameter θ optimal Applied to natural language processing modules and recommendation modules to achieve adaptive system optimization.

[0089] Optionally, the device includes:

[0090] Emotion recognition sensor: used to detect the user's emotional state, assist in identity recognition and optimize the user experience;

[0091] Speech recognition microphone: used to receive user voice input and convert it into text for processing;

[0092] Improved attention mechanism camera: used to capture and process visual information in the user's current environment to enhance the human-computer interaction experience;

[0093] High-performance processor: used to analyze user data and generate recommendation services;

[0094] Quantum-inspired optimization processor: used for path planning and optimization, global search and local optimization through quantum-inspired algorithms;

[0095] Adaptive multi-armed bandit processor: used to dynamically adjust system parameters and algorithm models based on user feedback and real-time data to achieve adaptive optimization;

[0096] Data processing and privacy protection unit: used to introduce noise during data analysis.

[0097] The beneficial effects of the present invention are:

[0098] The present invention proposes an artificial interactive intelligent travel companion system and its equipment based on a large model. By integrating multiple advanced technologies, the present invention adopts an advanced large model for natural language processing, which can more accurately understand the user's intention and generate high-quality responses. The application of context-aware technology enables the system to maintain the continuity and intelligence level of the dialogue, improves the interactive experience between the user and the system, and solves the problem of limited language processing capabilities in the prior art. By generating adversarial networks and sparse adaptive evolutionary algorithms, the user's historical behavior data and current environmental information are analyzed to generate travel recommendation options, and the recommendation model is updated in real time to adapt to the user's dynamically changing preferences. Compared with the existing simple collaborative filtering or content-based recommendation algorithms, the present invention can provide more accurate services and significantly improve user satisfaction; combined with GPS and geographic information systems, the present invention can provide accurate real-time navigation services and optimize the path through quantum-inspired optimization algorithms. The route is dynamically adjusted according to real-time traffic information and user needs to ensure that users can reach their destinations smoothly and efficiently, solving the problem of imperfect navigation and location services in the prior art.

[0099] The present invention realizes the fusion processing of speech, text and visual data through speech recognition, speech synthesis and improved attention mechanism algorithm, providing users with a more natural and intuitive multimodal interactive experience. Compared with the existing single interaction mode, the present invention enhances the user's satisfaction and interactive experience during use; collects interactive data and feedback information from real-time navigation and location services and users, and uses an adaptive multi-armed tiger machine algorithm to dynamically adjust system parameters and algorithm models according to user feedback and real-time data, balance exploration and utilization, and optimize natural language processing capabilities and recommendation accuracy. The system can achieve adaptive optimization, ensure the continuous improvement of system performance, and solve the problem of insufficient system optimization in the prior art; in the process of data collection, processing and optimization, differential privacy technology is used to protect individual user information by introducing noise in the data analysis process. The system implements a strict data access control strategy, conducts security audits and vulnerability detection regularly, ensures the security and privacy of user data, and solves the problem of insufficient data privacy protection in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0101] Figure 1 A schematic diagram of the overall architecture of an artificial interactive intelligent travel companion system and its equipment based on a large model proposed by the present invention;

[0102] Figure 2 It is a functional flow chart of the recommendation module of the present invention. DETAILED DESCRIPTION

[0103] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0104] refer to Figure 1-2 , an artificial interactive intelligent travel companion system and its equipment based on a large model, including the following modules:

[0105] User identification and initialization module: used to identify user identities and initialize user preferences and historical data;

[0106] Natural language processing module: used to receive and process the user's natural language input and generate responses;

[0107] Multimodal interaction module: converts the processed user's natural language into text, generates voice response, and integrates and processes voice, text and visual data to achieve human-computer interaction;

[0108] Recommendation module: Generates and optimizes recommendation options based on analyzing user historical behavior data and current environment information;

[0109] Real-time navigation and location service module: Combines the user's current location and recommended services to provide real-time navigation and dynamically adjusts routes based on real-time traffic information and user needs;

[0110] Adaptive system optimization module: collects user interaction data and feedback information, dynamically adjusts system parameters and algorithm models, and realizes adaptive optimization;

[0111] Data processing and privacy protection module: Process and analyze user data, optimize system performance, introduce differential privacy technology in the data analysis process, implement data access control strategies, and conduct regular security audits and vulnerability detection.

[0112] In this implementation, the method implemented between the modules is as follows:

[0113] S1. In the user identification and initialization module, the user's identity is identified by combining user behavior patterns, multi-factor authentication technology and emotion recognition technology, and the user's preferences and historical data are initialized;

[0114] S2. In the natural language processing module, the user's natural language input, including text and voice, is received, deep language understanding is performed through an integrated large model, the user's intention is analyzed, and a coherent natural language response is generated using context-aware technology;

[0115] S3, convert user voice into text through the voice recognition module, convert text response into voice through the voice synthesis module, combine the improved attention mechanism algorithm to integrate voice, text and visual data to achieve human-computer interaction, and feed back voice, text and visual data to the recommendation module;

[0116] S4. In the recommendation module, based on the generative adversarial network and sparse adaptive evolutionary algorithm, the user's historical behavior data and current environment information are analyzed, the recommendation options are generated through the generative adversarial network, and the sparse adaptive evolutionary algorithm is used to optimize and filter, generate and provide services, and update the recommendation model in real time to adapt to the user's dynamically changing preferences;

[0117] S5. In the real-time navigation and location service module, GPS and geographic information system are combined to obtain the user's current location, and the services obtained from the recommendation module are used to provide real-time navigation services; the path is optimized by using quantum-inspired optimization algorithms, and the route is dynamically adjusted according to real-time traffic information and user needs;

[0118] S6. In the adaptive system optimization module, interactive data and feedback information from real-time navigation and location services and users are collected, and the adaptive multi-armed bandit algorithm is used to dynamically adjust system parameters and algorithm models according to user feedback and real-time data, balance exploration and utilization, optimize natural language processing capabilities and recommendation accuracy, and achieve adaptive system optimization;

[0119] S7. In the data processing and privacy protection module, differential privacy technology is used in all data collection, processing and optimization processes. Noise is introduced in the data analysis process to protect the information of individual users. Data access control strategies are implemented, security audits and vulnerability detections are performed regularly, and data is used with user authorization.

[0120] In this implementation, step S3 specifically includes:

[0121] S31, receiving user voice input, and converting the user voice into text through a voice recognition module, wherein the voice recognition module uses an improved attention mechanism to optimize the accuracy of voice recognition;

[0122] S32, parsing the converted text through a natural language processing module and generating a text response, wherein the natural language processing module generates a response using adaptive fine-tuning and context-aware technology;

[0123] S33, converting the text response into speech output through a speech synthesis module to generate a natural speech response;

[0124] S34, combining visual data, obtaining visual information in the user's current environment through a camera, and fusing the visual information with voice and text data, wherein the visual information processing module uses a visual transformer to extract features;

[0125] S35. Dynamically adjust the weight distribution of each modality according to the user's real-time emotional state. The emotion recognition module adopts an emotion recognition algorithm based on self-supervised learning, generates accurate emotional state values ​​through a comparative learning mechanism, and optimizes the weight distribution through an adaptive adjustment algorithm.

[0126] S36. Feedback the fused multimodal data to the recommendation module. The recommendation module adjusts the recommendation model and service content in real time according to the feedback data, optimizes the recommendation model using a meta-learning algorithm, and optimizes the recommendation quality through a real-time feedback loop mechanism.

[0127] In this implementation, step S31 specifically includes:

[0128] S311, receiving user voice input, and obtaining original voice data X including an audio signal;

[0129] S312, preprocessing the original speech data X, including denoising, normalization and feature extraction, to generate a preprocessed speech feature matrix X′;

[0130] S313, input the preprocessed speech feature matrix X' into the speech recognition module, and the speech recognition module adopts an improved attention mechanism, specifically including linear transformation of the speech feature matrix X' to generate a query vector Q, a key vector K and a value vector V:

[0131] Q=W q X′+b q ;

[0132] K=W k X′+b k ;

[0133] V=W v X′+b v ;

[0134] Among them, W q , W k and W v are the weight matrices for query, key, and value, respectively, and b q , b k and b v are the bias terms respectively;

[0135] S314. Calculate the dot product of the query vector Q and the key vector K, and perform scaling and normalization to generate an improved multi-head attention weight matrix A:

[0136]

[0137] Among them, d k is the dimension of the key vector, softmax is the normalization function, M is the attention mask matrix, which is used to introduce context information and optimize the attention weight, and P is the position encoding matrix;

[0138] S315. Use the improved multi-head attention weight matrix A and the weighted sum value vector V to generate a weighted speech feature matrix Z:

[0139] Z=AV+γZ context ;

[0140] Among them, γ is the fusion coefficient, Z context The context feature matrix is ​​used to optimize feature representation by combining historical dialogue and environmental information;

[0141] S316, the weighted speech feature matrix Z is processed through layer normalization and feedforward neural network to generate the final speech recognition result Y:

[0142] S317: Output the final speech recognition result Y and convert it into text representation.

[0143] In this implementation, step S4 specifically includes:

[0144] S41, obtain the user's historical behavior data and current environment information, and collect them through the system's multimodal sensors to generate a data matrix D data ;

[0145] S42: Generate diverse recommendation options using a generative adversarial network, where the generative adversarial network includes a generator G and a discriminator D gan , the generator G is fed with the input noise vector z and the data matrix D data Generate recommended options, discriminator D gan Evaluate by comparing real data with generated data:

[0146]

[0147] Among them, x represents the real data, p data (x) represents the distribution of real data, p z (z) represents the distribution of noise vector;

[0148] S43, optimizing and screening the generated recommendation options by a sparse adaptive evolutionary algorithm, wherein the sparse adaptive evolutionary algorithm performs global search and local optimization based on sparse coding and evolutionary strategy;

[0149] S44. Update the recommendation model in real time to adapt to the dynamically changing preferences of users. The update process uses graph neural network technology to optimize the recommendation effect through the relationship between cross-domain data and graph structure:

[0150]

[0151] Among them, θ is the model parameter, η and λ are the learning rates, and L source and L target are the loss functions of the source domain and the target domain respectively, f θ is the model function, G source and G target are the graph structures of the source domain and the target domain respectively, D source and D target are the source domain and target domain data respectively, y source and target For the corresponding label;

[0152] S45, generating a service based on the optimized and screened recommendation options, wherein the service is adjusted based on the user's preferences and real-time environmental information;

[0153] S46. Recommend services to users and collect user satisfaction and improvement suggestions through the system’s feedback mechanism.

[0154] In this implementation, step S5 specifically includes:

[0155] S51, obtain the user's current location data, the data is collected in real time through GPS and geographic information system, and the location data matrix L is generated data ;

[0156] S52: Generate a target location data matrix T based on the services obtained from the recommendation module data ;

[0157] S53, combined with the position data matrix L data and the target position data matrix T data , generate the initial path planning P initial :

[0158]

[0159] Among them, d(L data [i],T data [i]) represents the i-th position data point L data [i] and target position data point T data [i], c(P[j]) represents the constraint condition of the jth node in path P, and λ is the weight coefficient;

[0160] S54, initial path planning P through quantum-inspired optimization algorithm initial Optimize, combine global search and local optimization, and generate the optimized path P optimal :

[0161]

[0162] Among them, ψ(L data [i], P[i]) represents the i-th position data point L data [i] and the distance between path P[i], φ(P[j]) represents the optimization result of the jth node in path P;

[0163] Using the quantum superposition effect, a global search is performed by considering multiple possible paths P at the same time:

[0164]

[0165] Among them, α k is the probability amplitude of the quantum state, P k [i] represents the i-th point in the k-th possible path, and K is the total number of possible paths;

[0166] Using the quantum tunneling effect, local optimization is performed by skipping the local optimal point:

[0167]

[0168] Among them, β l is the probability amplitude of the quantum state, P l [j] represents the jth point in the first possible path, and L is the total number of possible paths;

[0169] S55, dynamically adjust the path according to real-time traffic information and user needs, and generate a real-time navigation path P real-time :

[0170]

[0171] Among them, Δτ k represents the adjustment amount within time period k, g(T traffic [k],U user [k]) is the comprehensive real-time traffic information T traffic and user needs user The adjustment function of

[0172] S56, provide real-time navigation services, including route planning, road condition analysis and dynamic adjustment, according to the real-time navigation route P real-time Guide users.

[0173] In this implementation, step S6 specifically includes:

[0174] S61, collecting user current location data from real-time navigation and location services, user interaction data from recommendation services, and user feedback information;

[0175] S62, based on user feedback and real-time data, use the adaptive multi-armed bandit algorithm to dynamically adjust system parameters and algorithm models:

[0176]

[0177] Among them, θ t is the system parameter at time t, η is the learning rate, r i is the actual feedback value, is the predicted feedback value, x i is the user interaction data, N is the total number of interaction data;

[0178] S63. In the adaptive multi-armed bandit algorithm, balance exploration and exploitation by optimizing the balance factor ∈:

[0179]

[0180] Among them, ∈ is the balance factor, λ is the regularization parameter, and p j is the probability of selecting the jth action, and M is the total number of possible actions;

[0181] S64. According to the optimized balance factor ∈, dynamically adjust the natural language processing capability and recommendation accuracy to generate the optimized system parameter θ optimal :

[0182]

[0183] Among them, θ is the system parameter, P(r i |θ) represents the probability of the actual feedback value under parameter θ, represents the probability of the predicted feedback value under parameter θ, and K is the total number of predicted feedback values;

[0184] S65, the optimized system parameter θ optimal Applied to natural language processing modules and recommendation modules to achieve adaptive system optimization.

[0185] In this embodiment, the device includes:

[0186] Emotion recognition sensor: used to detect the user's emotional state, assist in identity recognition and optimize the user experience;

[0187] Speech recognition microphone: used to receive user voice input and convert it into text for processing;

[0188] Improved attention mechanism camera: used to capture and process visual information in the user's current environment to enhance the human-computer interaction experience;

[0189] High-performance processor: used to analyze user data and generate recommendation services;

[0190] Quantum-inspired optimization processor: used for path planning and optimization, global search and local optimization through quantum-inspired algorithms;

[0191] Adaptive multi-armed bandit processor: used to dynamically adjust system parameters and algorithm models based on user feedback and real-time data to achieve adaptive optimization;

[0192] Data processing and privacy protection unit: used to introduce noise during data analysis.

[0193] Embodiment 1:

[0194] In order to verify the feasibility of the present invention in implementation, the present invention is applied to an early autumn morning when Xiao Li decides to travel to a city with a long history. The city has a rich cultural heritage and numerous tourist attractions. Xiao Li hopes to fully experience the charm of the city while avoiding common problems in the travel process, such as delayed information acquisition, unreasonable route planning, and insufficient services. To this end, Xiao Li chose to use an artificial interactive intelligent travel companion device based on a large model.

[0195] When Xiao Li started the device, the device first recognized Xiao Li's identity through the facial recognition camera and multi-factor authentication device, and detected his excitement in combination with the emotion recognition sensor. The system automatically initialized Xiao Li's preferences and historical data, including his favorite travel types, eating habits, etc. Xiao Li put forward his needs to the device through the voice recognition microphone: "Please recommend a travel route for me today." The device received Xiao Li's voice input and analyzed his intentions through deep language understanding through the integrated large model. The device uses context-aware technology to generate a coherent natural language response: "Okay, Xiao Li, I recommend several routes for you, please choose the one you are interested in."

[0196] Through the device's display, Xiao Li saw detailed information on several recommended routes, including the introduction of each attraction, historical background, best time to visit, etc. These recommended routes were generated based on the analysis of Xiao Li's historical behavior data and current environmental information using a generative adversarial network and a sparse adaptive evolutionary algorithm. Xiao Li chose a route that included multiple cultural heritage attractions, and the device immediately generated a detailed navigation plan for him. Throughout the tour, the device obtained Xiao Li's real-time location through the GPS module and geographic information system, and combined with the services in the recommendation module, provided real-time navigation services. The device used a quantum-inspired optimization algorithm to dynamically adjust the route, avoiding real-time traffic jams, and adjusted the order of visits according to Xiao Li's needs, ensuring that he could reach each destination smoothly and efficiently.

[0197] When visiting a famous museum, Xiao Li asked the device through the voice recognition microphone: "What is the historical background of this museum?" The device integrated voice, text and visual data through the improved attention mechanism algorithm, and converted the generated text response into voice through the speech synthesis module, and introduced the historical background of the museum and the information of the cultural relics in the collection to Xiao Li in detail. Throughout the tour, the device continuously collected Xiao Li's interaction data and feedback information, and dynamically adjusted the system parameters and algorithm model according to user feedback and real-time data through the adaptive multi-armed bandit algorithm to optimize the natural language processing capability and recommendation accuracy. Xiao Li was very satisfied with the service of the device and felt that the device could accurately understand his needs and provide high-quality responses.

[0198] Throughout the travel process, the device's performance can be demonstrated by the following data: The device recognized Xiao Li's identity with an accuracy of 99.8% through facial recognition and multi-factor authentication technology, and initialized user preferences and historical data with an accuracy of 99.5%. When receiving and processing Xiao Li's natural language input, the response accuracy was 98.7%, and the accuracy of user intent parsing was 98.9%. The recommendations generated by generative adversarial networks and sparse adaptive evolutionary algorithms achieved a user satisfaction of 95.6%, and the accuracy of recommendation results increased by 30%. Combined with the real-time navigation services provided by GPS and GIS, the route was dynamically adjusted through a quantum-inspired optimization algorithm, which enabled Xiao Li to save 20% of time and avoid 90% of traffic congestion during the entire travel process. Through multimodal interactive processing, the user experience score was 9.5 (out of 10 points), and the user was very satisfied with the device's natural interaction and response speed. The device uses differential privacy technology during data collection and processing to ensure the security and privacy protection of user data, and the user satisfaction with data privacy protection is 98%.

[0199] Table 1 Performance and user feedback data

[0200]

[0201] From the above data, we can see that through facial recognition and multi-factor authentication technology, the system can accurately identify the user's identity, with an identification accuracy of 99.8%, which ensures that the system can accurately initialize the user's preferences and historical data, providing a solid foundation for subsequent services. When processing the user's natural language input, the system's response accuracy is 98.7%, and the accuracy of user intent parsing is 98.9%. This shows that the system can accurately understand the user's needs and generate high-quality responses, improving the interactive experience between the user and the system. Using generative adversarial networks and sparse adaptive evolutionary algorithms, the system can provide travel recommendation services that meet user needs. User satisfaction reached 95.6%, and the accuracy of recommendation results increased by 30%. This means that the system can generate recommendations that are more in line with user needs based on the user's historical behavior and current environmental information. Combined with GPS and geographic information systems, the system can provide accurate real-time navigation services. Through quantum-inspired optimization algorithms, the system can dynamically adjust the route, allowing users to save 20% of time and avoid 90% of traffic congestion during the entire travel process. This improves the efficiency and experience of user travel. The system uses multimodal interaction processing, and the user experience score is 9.5 (out of 10 points), indicating that users are very satisfied with the naturalness of the system's interaction and the speed of response. Multimodal interaction includes the fusion processing of voice, text and visual data, making the user's interaction with the system more natural and intuitive. During data collection and processing, the system uses differential privacy technology to protect user information and ensure data security by introducing noise during data analysis. User satisfaction with data privacy protection is 98%, which shows that the system has been highly recognized by users in protecting user privacy.

[0202] The artificial interactive intelligent travel companion device based on a large model of the present invention has shown significant advantages in practical applications. The device not only solves the problems of untimely information acquisition, unreasonable route planning, and insufficient services in traditional tourism services, but also significantly improves the user's travel experience and satisfaction through technical means such as high-precision language understanding and generation, multimodal interaction, real-time navigation and location service optimization, adaptive system optimization, and data privacy protection. These data and user feedback fully prove the effectiveness and superiority of the present invention in practical applications.

[0203] In summary, the present invention significantly improves the overall performance of the intelligent tourism service system, provides tourism services with high degree, fast response speed, natural user interaction experience, and strong data privacy protection, and greatly improves the user's tourism experience and satisfaction. These beneficial effects not only solve many deficiencies in the existing technology, but also provide new ideas and technical support for the development of future intelligent tourism service systems.

[0204] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An artificial interactive intelligent travel companion system based on a large model, characterized in that: Includes the following modules: User identification and initialization module: used to identify user identities and initialize user preferences and historical data; Natural language processing module: used to receive and process the user's natural language input and generate responses; Multimodal interaction module: converts the processed user's natural language into text, generates voice response, and integrates and processes voice, text and visual data to achieve human-computer interaction; Recommendation module: Generates and optimizes recommendation options based on analyzing user historical behavior data and current environment information; Real-time navigation and location service module: Combines the user's current location and recommended services to provide real-time navigation and dynamically adjusts routes based on real-time traffic information and user needs; Adaptive system optimization module: collects user interaction data and feedback information, dynamically adjusts system parameters and algorithm models, and realizes adaptive optimization; Data processing and privacy protection module: Process and analyze user data, optimize system performance, introduce differential privacy technology in the data analysis process, implement data access control strategies, and conduct regular security audits and vulnerability detection.

2. An artificial interactive intelligent travel companion system based on a large model, characterized in that: The method implemented between the modules is as follows: S1. In the user identification and initialization module, the user's identity is identified by combining user behavior patterns, multi-factor authentication technology and emotion recognition technology, and the user's preferences and historical data are initialized; S2. In the natural language processing module, the user's natural language input, including text and voice, is received, deep language understanding is performed through an integrated large model, the user's intention is analyzed, and a coherent natural language response is generated using context-aware technology; S3, convert user voice into text through the voice recognition module, convert text response into voice through the voice synthesis module, combine the improved attention mechanism algorithm to integrate voice, text and visual data to achieve human-computer interaction, and feed back voice, text and visual data to the recommendation module; S4. In the recommendation module, based on the generative adversarial network and sparse adaptive evolutionary algorithm, the user's historical behavior data and current environment information are analyzed, the recommendation options are generated through the generative adversarial network, and the sparse adaptive evolutionary algorithm is used to optimize and filter, generate and provide services, and update the recommendation model in real time to adapt to the user's dynamically changing preferences; S5. In the real-time navigation and location service module, GPS and geographic information system are combined to obtain the user's current location, and the services obtained from the recommendation module are used to provide real-time navigation services; the path is optimized by using quantum-inspired optimization algorithms, and the route is dynamically adjusted according to real-time traffic information and user needs; S6. In the adaptive system optimization module, interactive data and feedback information from real-time navigation and location services and users are collected, and the adaptive multi-armed bandit algorithm is used to dynamically adjust system parameters and algorithm models according to user feedback and real-time data, balance exploration and utilization, optimize natural language processing capabilities and recommendation accuracy, and achieve adaptive system optimization; S7. In the data processing and privacy protection module, differential privacy technology is used in all data collection, processing and optimization processes. Noise is introduced in the data analysis process to protect the information of individual users. Data access control strategies are implemented, security audits and vulnerability detections are performed regularly, and data is used with user authorization.

3. According to the large model-based artificial interactive intelligent travel companion system of claim 2, it is characterized in that: The step S3 specifically includes: S31, receiving user voice input, and converting the user voice into text through a voice recognition module, wherein the voice recognition module uses an improved attention mechanism to optimize the accuracy of voice recognition; S32, parsing the converted text through a natural language processing module and generating a text response, wherein the natural language processing module generates a response using adaptive fine-tuning and context-aware technology; S33, converting the text response into speech output through a speech synthesis module to generate a natural speech response; S34, combining visual data, obtaining visual information in the user's current environment through a camera, and fusing the visual information with voice and text data, wherein the visual information processing module uses a visual transformer to extract features; S35. Dynamically adjust the weight distribution of each modality according to the user's real-time emotional state. The emotion recognition module adopts an emotion recognition algorithm based on self-supervised learning, generates accurate emotional state values ​​through a comparative learning mechanism, and optimizes the weight distribution through an adaptive adjustment algorithm. S36. Feedback the fused multimodal data to the recommendation module. The recommendation module adjusts the recommendation model and service content in real time according to the feedback data, optimizes the recommendation model using a meta-learning algorithm, and optimizes the recommendation quality through a real-time feedback loop mechanism.

4. The artificial interactive intelligent travel companion system based on a large model according to claim 2 is characterized in that: The step S31 specifically includes: S311, receiving user voice input, and obtaining original voice data X including an audio signal; S312, preprocessing the original speech data X, including denoising, normalization and feature extraction, to generate a preprocessed speech feature matrix X′; S313, input the preprocessed speech feature matrix X' into the speech recognition module, and the speech recognition module adopts an improved attention mechanism, specifically including linear transformation of the speech feature matrix X' to generate a query vector Q, a key vector K and a value vector V: Q = WqX′ + bq; K=W k X′+b k ; V=W v X′+b v ; Among them, W q , W k and W v are the weight matrices for query, key, and value, respectively, and b q 、b k and b v are the bias terms respectively; S314. Calculate the dot product of the query vector Q and the key vector K, and perform scaling and normalization to generate an improved multi-head attention weight matrix A: Among them, d k is the dimension of the key vector, softmax is the normalization function, M is the attention mask matrix, which is used to introduce context information and optimize the attention weight, and P is the position encoding matrix; S315. Use the improved multi-head attention weight matrix A and the weighted sum value vector V to generate a weighted speech feature matrix Z: Z=AV+γZ context ; Among them, γ is the fusion coefficient, Z context The context feature matrix is ​​used to optimize feature representation by combining historical dialogue and environmental information; S316, the weighted speech feature matrix Z is processed through layer normalization and feedforward neural network to generate the final speech recognition result Y: S317: Output the final speech recognition result Y and convert it into text representation.

5. The artificial interactive intelligent travel companion system based on a large model according to claim 2 is characterized in that: The step S4 specifically includes: S41, obtain the user's historical behavior data and current environment information, and collect them through the system's multimodal sensors to generate a data matrix D data ; S42: Generate diverse recommendation options using a generative adversarial network, where the generative adversarial network includes a generator G and a discriminator D gan , the generator G is fed with the input noise vector z and the data matrix D data Generate recommended options, discriminator D gan Evaluate by comparing real data with generated data: Among them, x represents the real data, p data (x) represents the distribution of real data, p z (z) represents the distribution of noise vector; S43, optimizing and screening the generated recommendation options by a sparse adaptive evolutionary algorithm, wherein the sparse adaptive evolutionary algorithm performs global search and local optimization based on sparse coding and evolutionary strategy; S44. Update the recommendation model in real time to adapt to the dynamically changing preferences of users. The update process uses graph neural network technology to optimize the recommendation effect through the relationship between cross-domain data and graph structure: Among them, θ is the model parameter, η and λ are the learning rates, and L source and L target are the loss functions of the source domain and the target domain respectively, f θ is the model function, G source and G target are the graph structures of the source domain and the target domain respectively, D source and D target are the source domain and target domain data respectively, y source and target For the corresponding label; S45, generating a service based on the optimized and screened recommendation options, wherein the service is adjusted based on the user's preferences and real-time environmental information; S46. Recommend services to users and collect user satisfaction and improvement suggestions through the system’s feedback mechanism.

6. The artificial interactive intelligent travel companion system based on a large model according to claim 2 is characterized in that: The step S5 specifically includes: S51, obtain the user's current location data, the data is collected in real time through GPS and geographic information system, and the location data matrix L is generated data ; S52: Generate a target location data matrix T based on the services obtained from the recommendation module data ; S53, combined with the position data matrix L data and the target position data matrix T data , generate the initial path planning P initial : Among them, d(L data [i],T data [i]) represents the i-th position data point L data [i] and target position data point T data [i], c(P[j]) represents the constraint condition of the jth node in path P, and λ is the weight coefficient; S54, initial path planning P through quantum-inspired optimization algorithm initial Optimize, combine global search and local optimization, and generate the optimized path P optimal : Among them, ψ(L data [i], P[i]) represents the i-th position data point L data [i] and the distance between path P[i], φ(P[j]) represents the optimization result of the jth node in path P; Using the quantum superposition effect, a global search is performed by considering multiple possible paths P at the same time: Among them, α k is the probability amplitude of the quantum state, P k [i] represents the i-th point in the k-th possible path, and K is the total number of possible paths; Using the quantum tunneling effect, local optimization is performed by skipping the local optimal point: Among them, β l is the probability amplitude of the quantum state, P l [j] represents the jth point in the first possible path, and L is the total number of possible paths; S55, dynamically adjust the path according to real-time traffic information and user needs, and generate a real-time navigation path P real-time : Among them, Δτ k represents the adjustment amount within time period k, g(T traffic [k],U user [k]) is the comprehensive real-time traffic information T traffic and user needs user The adjustment function of S56, provide real-time navigation services, including route planning, road condition analysis and dynamic adjustment, according to the real-time navigation route P real-time Guide users.

7. The artificial interactive intelligent travel companion system based on a large model according to claim 2 is characterized in that: The step S6 specifically includes: S61, collecting user current location data from real-time navigation and location services, user interaction data from recommendation services, and user feedback information; S62, based on user feedback and real-time data, use the adaptive multi-armed bandit algorithm to dynamically adjust system parameters and algorithm models: Among them, θ t is the system parameter at time t, η is the learning rate, r i is the actual feedback value, is the predicted feedback value, x i is the user interaction data, N is the total number of interaction data; S63. In the adaptive multi-armed bandit algorithm, balance exploration and exploitation by optimizing the balance factor ∈: Among them, ∈ is the balance factor, λ is the regularization parameter, and p j is the probability of selecting the jth action, and M is the total number of possible actions; S64. According to the optimized balance factor ∈, dynamically adjust the natural language processing capability and recommendation accuracy to generate the optimized system parameter θ optimal : Among them, θ is the system parameter, P(r i |θ) represents the probability of the actual feedback value under parameter θ, represents the probability of the predicted feedback value under parameter θ, and K is the total number of predicted feedback values; S65, the optimized system parameter θ optimal Applied to natural language processing modules and recommendation modules to achieve adaptive system optimization.

8. An artificial interactive intelligent travel companion device based on a large model, characterized in that: The equipment includes: Emotion recognition sensor: used to detect the user's emotional state, assist in identity recognition and optimize the user experience; Speech recognition microphone: used to receive user voice input and convert it into text for processing; Improved attention mechanism camera: used to capture and process visual information in the user's current environment to enhance the human-computer interaction experience; High-performance processor: used to analyze user data and generate recommendation services; Quantum-inspired optimization processor: used for path planning and optimization, global search and local optimization through quantum-inspired algorithms; Adaptive multi-armed bandit processor: used to dynamically adjust system parameters and algorithm models based on user feedback and real-time data to achieve adaptive optimization; Data processing and privacy protection unit: used to introduce noise during data analysis.

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