Travel itinerary planning auxiliary system and method

Abstract

The invention relates to the technical field of intelligent travel and computer application, in particular to a travel itinerary planning auxiliary system and method, and the method comprises an initial travel itinerary planning module which is used for receiving a travel demand of a user and generating an initial travel itinerary plan; the user state sensing module is used for continuously acquiring and processing the physiological state data and the real-time geographic position of the user; the multi-source data acquisition module is used for acquiring dynamic data from an external data source according to the real-time geographic position; the travel planning module is used for performing travel re-planning according to the collected data and generating one or more optimized travel schemes; the dynamic adjustment and execution module is used for calling the travel route planning module according to a preset evaluation model; and receiving the optimized journey scheme generated by the journey planning module, and pushing the optimized journey scheme to a user. According to the method, the problems of poor experience and high health risk caused by the fact that the existing static travel planning cannot adapt to dynamic change in travel are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent tourism and computer application, and particularly relates to a travel itinerary planning auxiliary system and method. BACKGROUND

[0002] With the improvement of people's living standards, travel has become a common way of leisure. At present, tourists usually make detailed travel plans before traveling through various tourism APPs or websites, including destination, transportation, accommodation and scenic spot arrangement. However, the applicant found through personal experience that the existing itinerary planning tools have significant defects: 1. Rigid planning, lack of adaptability: the existing tools generate an itinerary that is usually static and cannot be changed once it is made. However, the travel process is full of variables, such as the "one-day-one-city" high-intensity itinerary experienced by the applicant, which can easily lead to physical fatigue (such as foot pain), but the system cannot perceive and actively suggest adjustments.

[0003] 2. Ignoring the user's real-time state: the existing system completely relies on the preference settings before traveling and cannot obtain the user's physiological state (such as fatigue level, health indicators) during the travel process, so it cannot intelligently suggest slowing down the itinerary when the user needs to rest.

[0004] 3. Not deeply integrated with real-time data: Although some APPs can provide ticket information, they do not deeply integrate ticket purchase difficulty, real-time accommodation prices and inventory, best season for scenic spots and real-time passenger flow, sudden weather, etc. into the dynamic adjustment logic of the itinerary. When tickets are sold out or it is raining, users can only passively and manually re-plan, which is time-consuming and laborious.

[0005] 4. High adjustment cost: When users discover that the original plan is not feasible, temporary adjustments often face the dilemma of high-priced accommodation and no tickets to buy, and the system cannot provide an overall optimization solution that takes into account the cost of cancellation and rebooking and the additional cost.

[0006] Therefore, there is an urgent need for a travel planning solution that can understand the user's "current" situation and actively and intelligently provide personalized adjustment solutions. SUMMARY

[0007] One of the purposes of the present application is to provide a travel itinerary planning auxiliary system to solve the problem that the existing static travel planning cannot adapt to the dynamic changes during the journey, resulting in poor experience and high health risk.

[0008] In order to achieve the above purpose, a travel itinerary planning auxiliary system is provided, comprising: An initial travel itinerary planning module for receiving a user's travel destination, time budget, cost budget and interest preferences, and generating an initial travel itinerary plan based on a preset template; User state awareness module: for continuously acquiring and processing the physiological state data and real-time geographic location of the user during the execution of the initial travel itinerary planning; Multi-source data acquisition module: for acquiring dynamic data from external data sources according to the real-time geographic location, including traffic data, accommodation data, scenic spot passenger flow data and weather data; Itinerary planning module: for itinerary re-planning according to the user's initial preferences, dynamic data provided by the user state awareness module and the multi-source data acquisition module, and generating one or more optimized itinerary schemes; Dynamic adjustment and execution module: for comprehensive evaluation of real-time data provided by the data awareness and acquisition module according to a preset evaluation model, and generating a re-planning trigger signal to the itinerary planning module when the evaluation result meets the preset condition; receiving the optimized itinerary scheme generated by the itinerary planning module and pushing it to the user; after the user confirms to select a certain optimization scheme, updating the itinerary planning and providing scheme execution assistance to the user, while comparing and displaying the update details, including traffic rescheduling, scenic spot change recommendation and new accommodation reservation information.

[0009] Further, the user state awareness module includes: Data interface with user smart wearable devices for acquiring heart rate, step count, sleep quality and body fatigue indicators; User manual input interface for receiving user-initiated reports of physical discomfort or fatigue state.

[0010] Further, the evaluation model continuously runs during the execution of the itinerary and generates a re-planning trigger signal based on multi-dimensional evaluation factors; the multi-dimensional evaluation factors include: User state factor: based on physiological data acquired from user smart wearable devices, a comprehensive physiological fatigue score is calculated; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be in a trigger state; External environment factor: based on weather data of the next destination and / or real-time passenger flow data of the next scenic spot, when the weather level reaches an unsuitable level or the real-time passenger flow exceeds a comfort threshold, the factor is determined to be in a trigger state; Itinerary feasibility factor: based on traffic ticket data for the next segment of the itinerary and the user's budget, when the tickets are sold out or the remaining ticket price exceeds the budget threshold, the factor is determined to be in a trigger state; Wherein, when at least one of the multi-dimensional evaluation factors is determined to be in a trigger state, the evaluation model generates the re-planning trigger signal to enable the itinerary planning module to start the itinerary re-planning process.

[0011] Further, the itinerary planning module generates optimization schemes by one or more of the following strategies: Itinerary relaxation strategy: reduce the intensity of the original planned itinerary by extending the total itinerary or inserting rest days locally; Attraction reorganization strategy: reassign the timing and duration of visiting attractions to avoid external environmental risks or match the user's real-time state; Resource substitution strategy: find and recommend alternative attractions or activities that are similar in experience but more feasible or comfortable in terms of user's current geographic location.

[0012] One of the purposes of the present application is to provide a travel itinerary planning assistance method, comprising the following steps: Initial travel itinerary planning step: receive the user's travel destination, time budget, cost budget and interest preferences, and generate an initial travel itinerary plan based on a preset template; User state awareness step: continuously acquire and process the user's physiological state data and real-time geographic location during the user's execution of the initial travel itinerary plan; Multi-source data acquisition step: acquire dynamic data from external data sources according to real-time geographic location, including traffic data, accommodation data, attraction passenger flow data and weather data; Itinerary planning step: according to the user's initial preferences, dynamic data provided by the user state awareness module and the multi-source data acquisition module, re-plan the itinerary to generate one or more optimized itinerary schemes; Dynamic adjustment and execution step: according to a preset evaluation model, comprehensively evaluate the real-time data provided by the data awareness and acquisition module, and when the evaluation result meets the preset condition, generate a re-planning trigger signal to the itinerary planning module; receive the optimized itinerary scheme generated by the itinerary planning module and push it to the user; after the user confirms to select a certain optimization scheme, update the itinerary plan and provide scheme execution assistance to the user, while comparing and displaying the update details, including traffic rescheduling, attraction change recommendation and new accommodation reservation information.

[0013] Further, the user state awareness step includes: Data interface with user's smart wearable device for acquiring heart rate, step count, sleep quality, and body fatigue indicators; User manual input interface for receiving user-initiated reports of physical discomfort or fatigue state.

[0014] Further, the evaluation model continuously runs during the execution of the itinerary and generates a re-planning trigger signal based on multi-dimensional evaluation factors; the multi-dimensional evaluation factors include: User status factor: Based on physiological data obtained from the user's smart wearable device, a comprehensive physiological fatigue score is calculated; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be in a triggered state. External environmental factors: Based on weather data of the next destination and / or real-time passenger flow data of the next attraction, this factor is determined to be triggered when the weather level reaches the unsuitable level or the real-time passenger flow exceeds the comfort threshold. Trip Feasibility Factor: Based on the transportation ticketing data for the next leg of the trip and the user's budget, this factor is triggered when tickets are sold out or the remaining ticket price exceeds the budget threshold. When at least one of the multi-dimensional evaluation factors is determined to be in a triggered state, the evaluation model generates the replanning trigger signal, causing the trip planning module to start the trip replanning process.

[0015] Furthermore, the trip planning step generates an optimization scheme through one or more of the following strategies: Slow-down itinerary strategy: Reduce the intensity of the original itinerary by extending the total itinerary or inserting rest days in certain sections; Attraction reorganization strategy: By reallocating the timing and duration of attraction visits, external environmental risks can be avoided or the user's real-time status can be matched. Resource substitution strategy: Based on the current geographical location, find and recommend alternative attractions or activities that are similar in experience but better in terms of feasibility or comfort.

[0016] Principles and advantages: 1. It has achieved a leap from static blueprints to dynamic companions, and the journey has full-cycle adaptability.

[0017] Existing technologies generate itineraries that are one-time, static "blueprints" before travel, rendering them ineffective in the face of changes. This invention, however, constructs a closed-loop control system through the continuous operation of a data sensing and acquisition module, the real-time evaluation of a dynamic adjustment and control module, and the rapid response of a dynamic itinerary planning engine. This transforms travel planning from a rigid document into a "dynamic companion" that runs throughout the entire journey. It proactively senses changes in user status (such as fatigue) and external environment (such as ticket availability and weather), triggering timely replanning, thus truly solving the core pain point of existing planning tools' inability to adapt to the various uncertainties during travel.

[0018] 2. By incorporating physiological indicators into the decision-making loop, proactive protection of user health is achieved, fundamentally improving the quality of the travel experience.

[0019] The application innovatively takes physiological data (such as heart rate, step count, sleep) obtained by the user state perception unit as the core evaluation factor. This enables the system to start from the actual feeling of the user's body, issue a warning before the occurrence of health risks such as excessive fatigue (such as foot pain), and actively adjust the itinerary, for example, insert a rest day or recommend light activities. This raises the focus of the travel experience from simply "completing the task" to a higher level of "ensuring the physical and mental health of the user", effectively avoiding the decline in experience and health risks caused by unreasonable "training type" travel, and fundamentally improving the quality of travel.

[0020] 3. Deeply integrate multi-source real-time data to greatly improve the feasibility and composure of the itinerary.

[0021] The application deeply integrates dynamic information such as traffic tickets, accommodation, scenic spot passenger flow, and weather into the decision logic through a multi-source data acquisition unit. Instead of being based on ideal conditions, the system plans and adjusts based on the real-time state of the real world. When it detects that the next high-speed rail ticket has sold out, there is a rainstorm at the destination, or the scenic spot passenger flow is full, the system can provide a feasible alternative solution. This enables the user to calmly deal with various unexpected situations, changing from passive "tired of dealing with" to active "enjoying leisurely", greatly reducing anxiety and uncertainty during travel.

[0022] 4. Introduce a cost optimization algorithm in dynamic adjustment to achieve a balance between experience and economic benefits.

[0023] The optimization scheme of the application is not cost-free. When re-planning, the itinerary dynamic planning engine will consider additional accommodation fees, transportation change fees, and other costs generated by changes, and compare them with the original scheme. The dynamic adjustment and control module clearly shows the user the details of the scheme update, including the changes in various costs. This makes the system's recommendations not only "the best solution", but also "the solution with the highest cost performance", helping users make more economical and rational decisions at critical moments, avoiding unnecessary economic losses in panic, and achieving the dual goals of travel experience and budget control. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A logic block diagram of a travel itinerary planning assistance system according to an embodiment of the application. DETAILED DESCRIPTION

[0025] The following will be further described in detail through specific embodiments: EMBODIMENT A travel itinerary planning assistance system, substantially as Figure 1As shown, including servers, user end (mobile phone, tablet computer) and wearable devices, the wearable devices are connected with the mobile phone through Bluetooth communication, the mobile phone is remotely connected with the server, and the server includes the following modules: Initial travel itinerary planning module: for receiving travel destination, time budget, cost budget and interest preference of a user, and generating initial travel itinerary planning based on preset template; initial planning: before going out, user A inputs 4-day holiday, medium budget and liking historical sites in the APP. The system generates the above-mentioned "one city a day" compact plan. User A wants to travel alone in Yunnan, and the original plan is "Kunming 1 day -> Dali 1 day -> Lijiang 1 day -> Shangri-La 1 day".

[0026] User state perception module: for continuously acquiring and processing physiological state data and real-time geographic position of a user during execution of the initial travel itinerary planning; the user state perception module includes: Data interface with user intelligent wearable device, for acquiring heart rate, step count, sleep quality and body fatigue index; User manual input interface, for receiving body discomfort or fatigue state actively reported by the user.

[0027] If user A has walked nearly 30,000 steps in Kunming for half a day, the intelligent bracelet monitors that the "fatigue" index is significantly increased.

[0028] Multi-source data acquisition module: for acquiring dynamic data from external data sources (weather software, tourism app, etc.) according to real-time geographic position, including traffic data, accommodation data, scenic spot passenger flow data and weather data; the multi-source data acquisition module finds that the high-speed rail ticket from Kunming to Dali tomorrow has been basically sold out, and it will rain in Dali tomorrow. The one city a day compact plan is disrupted, and even if the physical factor is excluded, the itinerary of user A also needs to be changed.

[0029] Itinerary planning module: for re-planning itinerary according to initial preference of a user, dynamic data provided by the user state perception module and the multi-source data acquisition module, and generating one or more optimized itinerary schemes; the itinerary planning module generates optimized schemes through one or more of the following strategies: Itinerary relaxation strategy: for reducing the intensity of the original plan by prolonging the total itinerary or inserting a rest day in the local; the itinerary relaxation strategy specifically includes: Identifying the date when the physiological fatigue of a user exceeds the threshold value in the itinerary, or the date of subsequent continuous high-intensity travel; Automatically inserting a rest day after the date, and moving the original subsequent itinerary sequence backward; For the rest day, it recommends leisure activities near the user's location, including but not limited to: SPA, cafe, cinema, bookstore, city park walk.

[0030] Attraction reorganization strategy: by reassigning the timing and duration of attraction tours to avoid external environmental risks or match the user's real-time state; for example, splitting a high-intensity multi-attraction tour planned for a day into two days; the attraction reorganization strategy specifically includes: Timing splitting: split and assign multiple attractions planned to be visited in a single day to be completed in two consecutive days or more; Logical reorganization: when it is detected that a certain attraction in the plan is not suitable for visiting due to weather, passenger flow, or closure, etc., automatically exchange its order with the attractions suitable for visiting in the subsequent itinerary.

[0031] Resource substitution strategy: according to the current geographical location, find and recommend alternative attractions or activities that are similar in experience but more optimal in feasibility or comfort. For example, replace it with an attraction near the current geographical location that is similar in experience but less intense or less affected by weather / passenger flow. The resource substitution strategy specifically includes: Build an attraction attribute knowledge graph, including attraction type, sightseeing intensity, indoor / outdoor, popularity; When the original planned attraction is not suitable for visiting, the system finds one or more attractions near the current geographical location that are similar in "attraction type" to the original attraction, but lower in "sightseeing intensity", and / or more adaptable to the current weather in the "indoor / outdoor" attribute, as a substitute recommendation.

[0032] After generating any optimization scheme, the system also performs a cost impact assessment: Calculate and compare the differences in additional accommodation fees, transportation change fees, and the estimated cost of the original scheme caused by scheme changes; Use the cost impact assessment results as the basis for scheme recommendation priority ranking, or directly show them to the user.

[0033] Dynamic adjustment and execution module: used to comprehensively evaluate real-time data provided by the data perception and collection module according to a preset evaluation model, and generate the re-planning trigger signal to the itinerary planning module when the evaluation result meets the preset conditions; receive the optimized itinerary scheme generated by the itinerary planning module and push it to the user; after the user confirms to select a certain optimization scheme, update the itinerary plan and provide scheme execution assistance to the user, while comparing and displaying the update details, including transportation change, attraction change recommendation, and new accommodation reservation information.

[0034] The evaluation model runs continuously during the trip execution and generates a re-planning trigger signal based on multi-dimensional evaluation factors; the multi-dimensional evaluation factors include: User state factor: based on physiological data obtained from the user's smart wearable device, a comprehensive physiological fatigue score is calculated; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be a trigger state; the calculation of the comprehensive physiological fatigue score is based at least in part on the user's daily cumulative step count, heart rate, and / or sleep quality data.

[0035] External environment factor: based on weather data for the next destination and / or real-time passenger flow data for the next scenic spot, when the weather level reaches an unsuitable level or the real-time passenger flow exceeds a comfort threshold, the factor is determined to be a trigger state; Trip feasibility factor: based on traffic ticket data for the next segment of the trip and the user's budget, when the tickets are sold out or the remaining ticket price exceeds the budget threshold, the factor is determined to be a trigger state; the judgment of the trip feasibility factor further considers the purchase difficulty of the ticket, which is determined by the remaining ticket trend and price fluctuation trend in the future period. By introducing the concept of "trend prediction", it goes beyond the simple "yes / no" or "expensive / cheap" judgment. For example, the system may trigger an adjustment in advance if it finds that there are tickets available for tomorrow but tickets for the next day are sold out and the price is soaring. This reflects a higher level of intelligence.

[0036] When at least one of the multi-dimensional evaluation factors is determined to be a trigger state, the evaluation model generates the re-planning trigger signal, causing the trip planning module to start the trip re-planning process. For users with excellent physical fitness or other needs, the generation of the re-planning trigger signal requires at least two evaluation factors to be determined as trigger states simultaneously. For example, the system only suggests a rest when "user fatigue exceeds standard" and "heavy rain tomorrow". This "and" logic can reduce unnecessary interference and is suitable for scenarios where decision accuracy is pursued. Further, a weight can be assigned to each evaluation factor for the trigger state, and a comprehensive trigger score is calculated by weighting, and when the comprehensive trigger score exceeds a second preset threshold, the re-planning trigger signal is generated. For example, the weight of "user sudden illness" can be set very high, and once triggered, it almost certainly leads to re-planning; while the weight of "slight over-standard of scenic spot passenger flow" is lower, and it may need to be combined with other factors to trigger. This makes the model's decision closer to human complex judgment.

[0037] When the evaluation model receives "high fatigue", "ticket shortage", and "poor weather", the comprehensive score far exceeds the threshold, and the system determines that "urgent adjustment" is needed. The trip planning engine starts.

[0038] Scenario 1 (relaxation): Suggests the user to stay one more night in Kunming, and recommends several light cultural districts or SPA in Kunming for the next day, and postpones the Dali trip one day. The engine calculates that this scenario will increase one night of accommodation in Kunming, but the weather in Dali will be sunny the next day, and the experience will be better.

[0039] Scenario 2 (replacement): Suggests the user to give up Dali and go directly from Kunming to Lijiang, and arrange three days of in-depth tour in Lijiang, and recommends several light scenic spots around Lijiang. This scenario avoids the influence of rainy days, and the itinerary is more relaxed.

[0040] User decision and execution: User A feels that the feet are indeed sore, and chooses scenario 1. The APP immediately: Pops up an interface to guide the user to change the original high-speed rail ticket to the next day.

[0041] Recommends and assists the user to renew the current hotel at the agreed price.

[0042] Synchronizes the new "Kunming rest day" itinerary and the updated Dali and Lijiang itinerary to the user's itinerary table.

[0043] Through the above process, the present application successfully converts a tiring and chaotic trip into a comfortable, comfortable and personalized experience.

[0044] A travel itinerary planning assistance method, characterized in that it comprises the following steps: Initial travel itinerary planning step: receiving the user's travel destination, time budget, cost budget and interest preferences, and generating an initial travel itinerary plan based on a preset template; User state sensing step: continuously acquiring and processing the user's physiological state data and real-time geographic location during the user's execution of the initial travel itinerary plan; the user state sensing step comprises: Data interface with the user's smart wearable device, for acquiring heart rate, step count, sleep quality, and body fatigue indicators; User manual input interface for receiving user-initiated reports of physical discomfort or fatigue status.

[0045] Multi-source data acquisition step: acquiring dynamic data from external data sources according to real-time geographic location, including traffic data, accommodation data, scenic spot passenger flow data and weather data; Itinerary planning step: re-planning the itinerary according to the user's initial preferences, dynamic data provided by the user state sensing module and the multi-source data acquisition module, and generating one or more optimized itinerary plans; the itinerary planning step generates optimized plans through one or more of the following strategies: Itinerary relaxation strategy: to reduce the intensity of the original itinerary by extending the total itinerary or inserting a rest day locally; the itinerary relaxation strategy specifically includes: Identifying in the itinerary the date when the user's physiological fatigue exceeds a threshold, or the date of subsequent consecutive high-intensity travel; Automatically inserting a rest day after that date and moving the original subsequent itinerary sequence backward; Recommending light activities near the location of the rest day for the rest day, including but not limited to: SPA, cafe, cinema, bookstore, city park stroll.

[0046] Attraction reorganization strategy: to avoid external environmental risks or match the user's real-time state by reallocating the timing and duration of attraction visits; for example, splitting the original plan of high-intensity multiple attraction visits in one day into two days; the attraction reorganization strategy specifically includes: Timing splitting: splitting and assigning multiple attractions originally planned to be visited in a single day to be completed in two consecutive days or more; Logical reorganization: when detecting that a certain attraction in the plan is not suitable for visiting due to weather, passenger flow, or closure, etc., automatically exchanging its sequence with the attractions suitable for visiting in the subsequent itinerary.

[0047] Resource substitution strategy: according to the current geographical location, find and recommend alternative attractions or activities that are similar in experience but more optimal in feasibility or comfort. For example, substitute with attractions near the current geographical location that are similar in experience but lower in intensity or less affected by weather / passenger flow. The resource substitution strategy specifically includes: Building an attraction attribute knowledge graph, including attraction type, sightseeing intensity, indoor / outdoor, popularity; When the original planned attraction is not suitable for visiting, the system finds one or more attractions near the current geographical location that are similar in "attraction type" to the original attraction, but lower in "sightseeing intensity", and / or more adaptable to the current weather in the "indoor / outdoor" attribute, as a substitute recommendation.

[0048] After generating any optimization scheme, the system also performs cost impact assessment: Calculate and compare the difference between the additional accommodation fees, transportation change fees caused by the scheme change and the original scheme estimated cost; Use the cost impact assessment results as the basis for scheme recommendation priority ranking, or directly show them to the user.

[0049] Dynamic adjustment and execution step: according to the preset evaluation model, the real-time data provided by the data perception and collection module is comprehensively evaluated, and when the evaluation result meets the preset condition, the re-planning trigger signal is generated to the trip planning module; receiving the optimized trip plan generated by the trip planning module, and pushing it to the user; after the user confirms to select a certain optimization scheme, updating the trip plan, and providing scheme execution assistance to the user, while comparing and displaying the update details, the update details including traffic rescheduling, scenic spot change recommendation and new accommodation reservation information.

[0050] The evaluation model continuously runs during the trip execution process and generates a re-planning trigger signal based on multi-dimensional evaluation factors; the multi-dimensional evaluation factors include: User state factor: based on the physiological data obtained from the user's smart wearable device, a comprehensive physiological fatigue score is calculated; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be in a trigger state; External environment factor: based on weather data of the next destination and / or real-time passenger flow data of the next scenic spot, when the weather level reaches an unsuitable level or the real-time passenger flow exceeds a comfort threshold, the factor is determined to be in a trigger state; Trip feasibility factor: based on traffic ticket data of the next trip and user's expense budget, when the ticket has been sold out or the remaining ticket price exceeds the budget threshold, the factor is determined to be in a trigger state; Wherein, when at least one of the multi-dimensional evaluation factors is determined to be in a trigger state, the evaluation model generates the re-planning trigger signal to make the trip planning module start the trip re-planning process. For users with excellent physical fitness or other needs, the generation of the re-planning trigger signal requires at least two evaluation factors to be determined as trigger states simultaneously. For example, only when "user fatigue exceeds standard" and "heavy rain tomorrow", the system will suggest to rest. This "and" logic can reduce unnecessary interference and is suitable for scenarios that pursue decision accuracy. Further, a weight can be assigned to each evaluation factor of the trigger state, and a comprehensive trigger score is calculated by weighting, and when the comprehensive trigger score exceeds a second preset threshold, the re-planning trigger signal is generated. For example, the weight of "user sudden illness" can be set to be very high, and once triggered, it almost inevitably leads to re-planning; while the weight of "slight over-standard of scenic spot passenger flow" is low, it may need to be combined with other factors to trigger. This makes the model's decision closer to human complex judgment.

[0051] The above-mentioned are only embodiments of the present application, and the common knowledge of the specific structure and characteristics in the scheme is not described too much, the ordinary skilled in the art knows all the ordinary technical knowledge in the field of the present application before the application date or the priority date, can know all the prior art in the field, and has the ability to apply the conventional experimental means before the date, the ordinary skilled in the art can improve and implement the present scheme under the inspiration given by the present application, and some typical known structure or known method should not become the obstacle for the ordinary skilled in the art to implement the present application. It should be pointed out that for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the patent. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. A travel itinerary planning assistance system characterized by comprising: Comprise: An initial travel itinerary planning module for receiving a user's travel destination, time budget, cost budget and interest preferences, and generating an initial travel itinerary plan based on preset templates; A user state perception module for continuously acquiring and processing the user's physiological state data and real-time geographic location during the user's execution of the initial travel itinerary plan; A multi-source data acquisition module for acquiring dynamic data from external data sources according to the real-time geographic location, including traffic data, accommodation data, scenic spot passenger flow data and weather data; An itinerary planning module for itinerary re-planning based on the user's initial preferences, dynamic data provided by the user state perception module and the multi-source data acquisition module, and generating one or more optimized itinerary plans; A dynamic adjustment and execution module for comprehensively evaluating real-time data provided by the data perception and acquisition module according to a preset evaluation model, and generating a re-planning trigger signal to the itinerary planning module when the evaluation result meets the preset conditions; receiving the optimized itinerary plan generated by the itinerary planning module and pushing it to the user; After the user confirms to select a certain optimization plan, the itinerary plan is updated, and the user is provided with plan execution assistance, while the update details are compared and displayed, including traffic rescheduling, scenic spot change recommendation and new accommodation reservation information.

2. The travel itinerary planning assistance system of claim 1, wherein: The user state perception module comprises: A data interface with the user's smart wearable device for acquiring heart rate, step count, sleep quality and body fatigue indicators; A user manual input interface for receiving the user's active report of physical discomfort or fatigue state.

3. The travel itinerary planning assistance system of claim 2, wherein: The evaluation model continuously runs during the execution of the itinerary and generates a re-planning trigger signal based on multi-dimensional evaluation factors; The multi-dimensional evaluation factors include: A user state factor based on physiological data acquired from the user's smart wearable device to calculate a comprehensive physiological fatigue score; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be in a trigger state; An external environment factor based on weather data for the next destination and / or real-time passenger flow data for the next scenic spot; when the weather level reaches an unsuitable level or the real-time passenger flow exceeds a comfort threshold, the factor is determined to be in a trigger state; An itinerary feasibility factor based on traffic ticket data for the next segment of the itinerary and the user's cost budget; when the tickets are sold out or the remaining ticket price exceeds the budget threshold, the factor is determined to be in a trigger state; When at least one of the multi-dimensional evaluation factors is determined to be in a trigger state, the evaluation model generates the re-planning trigger signal to enable the itinerary planning module to start the itinerary re-planning process.

4. The travel itinerary planning assistance system of claim 3, wherein: The itinerary planning module generates optimized plans through one or more of the following strategies: An itinerary relaxation strategy by extending the total itinerary or inserting a rest day locally to reduce the intensity of the original plan; A scenic spot reorganization strategy by reallocating the timing and duration of scenic spot tours to avoid external environmental risks or match the user's real-time state; A resource substitution strategy for finding and recommending alternative scenic spots or activities that are similar in experience but more optimal in feasibility or comfort near the current geographic location for the user.

5. A travel itinerary planning assistance method characterized by comprising: The method comprises the following steps: An initial travel itinerary planning step: receiving the user's travel destination, time budget, cost budget and interest preferences, and generating an initial travel itinerary plan based on preset templates; A user state awareness step: continuously acquiring and processing the user's physiological state data and real-time geographic location during the user's execution of the initial travel itinerary plan; A multi-source data collection step: acquiring dynamic data from external data sources based on the real-time geographic location, including traffic data, accommodation data, attraction flow data and weather data; An itinerary planning step: according to the user's initial preferences, dynamic data provided by the user state awareness module and the multi-source data collection module, itinerary re-planning is performed to generate one or more optimized itinerary plans; A dynamic adjustment and execution step: according to a preset evaluation model, the real-time data provided by the data awareness and collection module is comprehensively evaluated, and when the evaluation result meets the preset condition, a re-planning trigger signal is generated to the itinerary planning module; receiving the optimized itinerary plan generated by the itinerary planning module and pushing it to the user; After the user confirms to select a certain optimization scheme, the itinerary plan is updated, and the user is provided with scheme execution assistance, while the update details are compared and displayed, including traffic rescheduling, attraction change recommendation and new accommodation reservation information.

6. The travel itinerary planning assistance method of claim 5, wherein: The user state awareness step comprises: A data interface with the user's smart wearable device for acquiring heart rate, step count, sleep quality and body fatigue indicators; A user manual input interface for receiving the user's active report of physical discomfort or fatigue state.

7. The travel itinerary planning assistance method of claim 6, wherein: The evaluation model continuously runs during the itinerary execution process and generates a re-planning trigger signal based on multi-dimensional evaluation factors; The multi-dimensional evaluation factors include: User state factor: based on the physiological data obtained from the user's smart wearable device, a comprehensive physiological fatigue score is calculated; when the comprehensive physiological fatigue score exceeds a first preset threshold, the factor is determined to be in a trigger state; External environment factor: based on the weather data of the next destination and / or the real-time flow data of the next attraction, when the weather level reaches an unsuitable level or the real-time passenger flow exceeds a comfortable threshold, the factor is determined to be in a trigger state; Itinerary feasibility factor: based on the traffic ticket data of the next itinerary and the user's cost budget, when the tickets are sold out or the remaining ticket price exceeds the budget threshold, the factor is determined to be in a trigger state; When at least one of the multi-dimensional evaluation factors is determined to be in a trigger state, the evaluation model generates the re-planning trigger signal to enable the itinerary planning module to start the itinerary re-planning process.

8. The travel itinerary planning assistance method of claim 7, wherein: In the itinerary planning step, the optimized scheme is generated by one or more of the following strategies: Itinerary relaxation strategy: by extending the total itinerary or inserting a rest day locally, to reduce the intensity of the original plan; Attraction reorganization strategy: by reallocating the timing and duration of attraction visits to avoid external environmental risks or match the user's real-time state; Resource substitution strategy: according to the current geographic location, find and recommend alternative attractions or activities that are similar in experience but more optimal in feasibility or comfort.

Images