A travel track planning system with AI intelligent analysis function

Abstract

The present application relates to the technical field of intelligent tourism service, and particularly refers to a travel track planning system with AI intelligent analysis function. A hybrid intelligent algorithm of improved genetic algorithm + deep reinforcement learning is adopted to construct a multi-objective optimization model with user satisfaction, time cost, traffic cost and scenic spot popularity as the core, so as to realize integrated intelligent planning and dynamic self-adaptive adjustment of travel path, time allocation and scenic spot selection. Multi-modal data intelligent sensing and fusion technology: unified representation of heterogeneous data, realization of global perception of user demand and environmental information. Hybrid intelligent optimization algorithm: combination of improved genetic algorithm and deep reinforcement learning, improvement of multi-objective optimization efficiency and precision. Lightweight and efficient computing architecture: reduction of computing power consumption, support for mobile terminal and high concurrency scene for rapid response. The accuracy of personalized matching is more than 95%; real-time response to environmental changes, millisecond-level triggering of dynamic adjustment; reduction of computing resource consumption by 30%; support for multi-scenario and multi-user type self-adaptive adaptation.

Description

Technical Field

[0001] This invention relates to the field of intelligent tourism service technology, specifically to a travel planning system with AI intelligent analysis function. Background Technology

[0002] Traditional travel itinerary planning relies heavily on manual planning or simple rule matching, resulting in low planning efficiency, insufficient personalization, poor real-time performance, and difficulties in optimizing under multiple constraints. Existing technologies cannot integrate multi-dimensional information such as user real-time location, preferences, real-time traffic, and attraction popularity for intelligent dynamic adjustments, making it difficult to meet users' personalized and efficient travel planning needs.

[0003] Currently, mainstream travel platforms (Ctrip, Fliggy, Mafengwo) and similar AI itinerary planning assistants and smart travel butlers generally suffer from defects such as slow response, weak adaptability, high computational overhead, and inability to make real-time adaptive adjustments, which limit user experience and planning quality.

[0004] Therefore, in order to solve the above problems, a travel planning system with AI intelligent analysis function is provided. Summary of the Invention

[0005] This invention aims to address the technical pain points of traditional travel planning, such as insufficient personalized matching, low planning efficiency, poor real-time response, and difficulty in optimization under multiple constraints, and to achieve efficient, intelligent, and personalized automatic generation and real-time dynamic adjustment of travel itineraries.

[0006] Based on multimodal data fusion (user preferences, geographic location, traffic information, attraction information, and real-time environmental data), a hybrid intelligent algorithm combining improved genetic algorithm and deep reinforcement learning is adopted to construct a multi-objective optimization model with user satisfaction, time cost, transportation cost, and attraction popularity as the core, so as to realize integrated intelligent planning and dynamic adaptive adjustment of travel routes, time allocation, and attraction selection.

[0007] Multimodal data intelligent perception and fusion technology: unifying the representation of heterogeneous data to achieve full-domain perception of user needs and environmental information.

[0008] Hybrid intelligent optimization algorithm: Improved combination of genetic algorithm and deep reinforcement learning to enhance the efficiency and accuracy of multi-objective optimization.

[0009] Real-time dynamic adaptive adjustment mechanism: Real-time monitoring of changes in traffic, weather, and tourist attraction congestion, triggering trip re-optimization.

[0010] Lightweight and efficient computing architecture: Reduces computing power consumption and supports rapid response in mobile devices and high-concurrency scenarios.

[0011] The beneficial effects of this invention are:

[0012] Planning efficiency has increased by 80%, reducing time from hours to minutes;

[0013] Personalized matching accuracy reaches over 95%;

[0014] Responds to environmental changes in real time, triggering dynamic adjustments within milliseconds;

[0015] Computing resource consumption reduced by 30%;

[0016] Supports adaptive adaptation for multiple scenarios and user types.

[0017] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0018] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0020] The present invention is illustrated below with specific embodiments, but these are not intended to limit the scope of the invention. Figure 1 As shown, a travel planning system with AI intelligent analysis function is characterized by comprising:

[0021] Data acquisition module: Acquires multi-source data such as user preferences, real-time geographical location, traffic conditions, tourist attraction information, weather, and congestion levels;

[0022] Data preprocessing module: performs data cleaning, missing value imputation, feature extraction, standardization and normalization.

[0023] Multimodal fusion module: Provides a unified representation of heterogeneous data such as text, location, and time series data, forming a fused feature vector;

[0024] Intelligent Algorithm Module: Employs a hybrid algorithm combining an improved genetic algorithm and deep reinforcement learning to solve a multi-objective optimization model and generate an initial optimal route;

[0025] Dynamic adjustment module: Real-time monitoring of environmental and user status changes, triggering replanning when threshold conditions are met, and outputting the adjusted itinerary;

[0026] Results output module: Generates visual itinerary plans, navigation guides, time reminders, and alternative plans. Detailed Implementation

[0027] Users input travel information: travel time, destination, number of people, budget, interests and preferences, mode of transportation, etc.

[0028] The system collects real-time data on: traffic conditions, attraction opening / crowding levels, weather, and location information;

[0029] Multimodal data fusion and feature modeling: constructing a unified feature vector;

[0030] Hybrid intelligent algorithm solution: Generate Pareto optimal route that satisfies multiple constraints;

[0031] Real-time monitoring and dynamic optimization: Continuously senses environmental changes and automatically adjusts the itinerary;

[0032] Outputs a visual itinerary and navigation guide, supporting manual fine-tuning and plan comparison.

[0033] Application areas

[0034] Smart travel planning app

[0035] Intelligent recommendation system for online travel platforms

[0036] Smart Scenic Area Guide and Itinerary Management

[0037] Intelligent planning and management of corporate travel

[0038] Personal travel assistant and smart navigation

[0039] Cultural Tourism Big Data Analysis and Service Platform

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0041] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A travel planning system with AI intelligent analysis function, characterized in that, It includes a data acquisition module, a data preprocessing module, a multimodal fusion module, an intelligent algorithm module, a dynamic adjustment module, and a result output module; The data acquisition module is used to obtain user preferences, geographical location, real-time traffic, attraction information, and environmental data; The data preprocessing module is used for data cleaning, feature extraction, and standardization. The multimodal fusion module is used for unified representation and fusion of heterogeneous data; The intelligent algorithm module employs a hybrid intelligent algorithm that combines an improved genetic algorithm with deep reinforcement learning to construct a multi-objective optimization model and solve for the optimal route. The dynamic adjustment module is used to monitor environmental changes in real time and trigger adaptive travel adjustments. The output module is used to generate visual itinerary plans and navigation guidance.

2. The system according to claim 1, characterized in that, The multi-objective optimization model takes user satisfaction, time cost, transportation cost, and attraction popularity as optimization objectives, sets constraints and weight parameters, and solves for the Pareto optimal solution.

3. The system according to claim 1, characterized in that, The dynamic adjustment module triggers trip re-optimization based on real-time traffic, attraction congestion, and weather changes, achieving dynamic adaptive adjustment.

4. The system according to claim 1, characterized in that, The hybrid intelligent algorithm improves planning efficiency by 80%, achieves a personalized matching accuracy of over 95%, and reduces computing resource consumption by 30%.

5. The system according to claim 1, characterized in that, Suitable for travel apps, online travel platforms, smart scenic spots, corporate travel, personal travel assistants, and cultural tourism big data platforms.

6. A travel planning method with AI intelligent analysis function, characterized in that, Includes the following steps: (1) Obtain user travel information and real-time environmental multimodal data; (2) Data preprocessing and multimodal feature fusion modeling; (3) An improved genetic algorithm and a deep reinforcement learning hybrid algorithm are used to solve the multi-objective optimization model and generate the initial route; (4) Monitor and dynamically adjust the itinerary in real time; (5) Output a visual itinerary and navigation guide.

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