Online order grabbing and dispatching system for online booked tour guide based on smart tourism

Abstract

The invention discloses an online order grabbing and dispatching system for an online tour guide based on smart tourism, relates to the technical field of smart tourism services, and aims to solve the problems of non-transparent information, low supply and demand matching efficiency, difficulty in guaranteeing service quality and the like in an existing tour guide service mode. The system comprises four core modules, namely a user side APP, a platform management background, an intelligent scheduling server and a big data and block chain supporting platform, and a three-stage hybrid scheduling model of intelligent matching pushing, tour guide limited order grabbing and system auxiliary order sending is constructed. And in combination with a multi-dimensional dynamic portrait matching algorithm, an LBS and context awareness whole-process supervision mechanism and a block chain credible credit system, accurate matching of a tour guide and tourists, effective control of service quality and upgrading of tourism experience are realized. According to the invention, the supply and demand matching efficiency and precision are improved, the service quality is guaranteed, the tourism resource configuration is optimized, the immersive and high-quality tour guide service is provided for tourists, and a benign growth ecology is constructed for excellent tour guides.

Description

Technical Field

[0001] This invention relates to the field of smart tourism services and online dispatching technology, specifically to an online order-grabbing and dispatching system for online tour guides based on smart tourism. Background Technology

[0002] Traditional tour guide services rely primarily on assignment by travel agencies or on-site selection by scenic spots, resulting in significant problems such as lack of information transparency, narrow selection range, inefficient supply-demand matching, and difficulty in guaranteeing service quality. With the development of smart tourism, massive amounts of tourism-related data (such as real-time visitor flow at scenic spots, tourist profiles, tour guide historical rating tags, and geographic location information) are constantly being generated. However, existing tour guide service platforms have failed to fully utilize this data, leading to numerous shortcomings in the service model.

[0003] In existing technologies, tour guide platforms based on simple lists and manual selection require tourists to spend a lot of time filtering information, resulting in high decision-making costs. Furthermore, information asymmetry makes it difficult for tourists to verify the true service level of tour guides, which can easily lead to malicious price competition. Tour guide apps that rely solely on order grabbing or order dispatching models either rely on response speed to match orders, neglecting service quality and matching accuracy, and thus failing to meet special needs orders. On the other hand, they deprive tour guides of their autonomy in selection, with opaque algorithms and low matching accuracy. Systems that are not deeply integrated with smart tourism data lack context awareness, cannot plan optimal tour routes, are difficult to monitor the service process, and cannot provide tour guides with intelligent auxiliary tools, resulting in a monotonous tourism experience.

[0004] Therefore, there is an urgent need for an online tour guide service dispatch system that deeply integrates smart tourism data, intelligently coordinates supply and demand, and effectively guarantees service quality, in order to address the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide an online order-grabbing and dispatching system for online tour guides based on smart tourism. The specific technical solution is as follows: System Composition This system comprises four core modules, which work together to achieve full-process tour guide service scheduling and management: User-side apps: divided into tourist apps and tour guide apps. The tourist app has functions such as order posting, order viewing, AR navigation, payment, reviews, and consumption referral recommendations; the tour guide app has functions such as order receiving, order grabbing, order acceptance, navigation, AR assistance, and anomaly reporting.

[0006] Platform management backend: includes order management module and credit and rating management module, responsible for the full life cycle management of orders and the maintenance of tour guide and tourist credit files.

[0007] Intelligent scheduling server: integrates an intelligent matching engine, which performs multi-dimensional matching calculations and hybrid mode scheduling logic.

[0008] Big Data and Blockchain Support Platform: The big data module is responsible for user profile construction, matching algorithm calculation, and smart tourism data integration; the blockchain module is responsible for key data storage and credit system construction; it also integrates third-party data interfaces, including map / LBS interfaces, scenic spot ticketing and visitor flow data interfaces.

[0009] Core technical features Hybrid intelligent scheduling model: It adopts a three-stage scheduling mode of "intelligent matching and push + limited order grabbing by tour guides + system-assisted order dispatch". First, the algorithm selects a set of candidate tour guides with high matching degree, then assigns dynamic priority to candidate tour guides to grab orders. If an order is not grabbed, the system will intelligently assign an order, which takes into account efficiency, fairness and quality.

[0010] Multi-dimensional dynamic profiling and matching algorithm: Construct dynamically updated "tourist profiles" and "tour guide profiles". The "tourist profile" includes information such as attraction preferences, time, number of people, language, budget, and personalized tags. The "tour guide profile" includes information such as qualifications, skill tags, historical reviews, real-time location, and price. Design a weighted matching algorithm to comprehensively consider multi-dimensional factors to calculate the matching degree and accurately achieve "person-job matching".

[0011] LBS and context-aware service monitoring and assistance throughout the entire process: Deeply integrate smart tourism data to plan and update the optimal tour route in real time; monitor service trajectories and issue warnings for abnormal behaviors such as deviation from the route and forced consumption; provide tour guides with auxiliary tools such as AR recognition and multilingual narration material generation.

[0012] Blockchain-based Trustworthy Evaluation and Credit System: Key service data (evaluation information, service duration, trajectory, anomaly records, etc.) are uploaded to the blockchain for storage, constructing an immutable and traceable credit profile, providing trusted data support for matching and platform governance.

[0013] Implementation steps Step 1: Order Posting and Profile Generation: Tourists post their needs through the APP, and the system generates a "tourist profile" by combining historical data; at the same time, it selects tour guides that meet the basic criteria from the "tour guide profile library".

[0014] Step 2: Smart Matching and Order Push: The smart matching engine calculates the matching degree between tour guides and tourists, and only pushes orders to tour guides whose matching degree is higher than a preset threshold.

[0015] Step 3: Hybrid mode scheduling execution: High-priority tour guides have priority to grab orders within the preset window period; if an order is not grabbed, the system will assign the order to the tour guide with the highest matching degree and explain the reason.

[0016] Step 4: Service process supervision and assistance: Plan the optimal route based on LBS and scenic area visitor flow data, monitor the service trajectory in real time and issue early warnings for anomalies; tour guides use AR auxiliary tools to improve the quality of their explanations.

[0017] Step 5: Service Completion and Trust Evaluation: After mutual evaluation, key data is uploaded to the blockchain for notarization, the system dynamically updates the user profile, and recommends nearby consumption projects to tourists.

[0018] The beneficial effects of this invention are as follows: This invention replaces manual screening with intelligent algorithms, significantly reducing tourists' decision-making costs, shortening order completion time, and achieving a high-satisfaction match between supply and demand. A hybrid scheduling model incentivizes tour guides to improve their professional skills; a full-process monitoring mechanism constrains abnormal tour guide behavior; and AR-assisted tools empower tour guides to enhance the depth and engagement of their explanations. Excellent tour guides receive more high-quality orders and higher compensation, fostering a healthy competitive ecosystem. Demand forecasting and intelligent scheduling alleviate the contradiction of tour guide shortages in popular scenic spots and underutilization of tour guides in less popular areas. It upgrades tour guide services from simply "leading the way" to in-depth cultural experiences based on real-time data and intelligent tools, meeting tourists' personalized and high-quality needs. A blockchain credit system ensures the authenticity and credibility of evaluations, solves the problem of information asymmetry, and provides data support for platform governance and industry standardization. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below. Detailed Implementation

[0021] The following combination Figure 1 The present invention will be further described in detail with reference to specific embodiments: Example 1: System Deployment and Data Integration The system's big data module connects to the scenic area's ticketing system, crowd monitoring system, and LBS location service platform to obtain real-time data such as visitor flow, attraction congestion, and road conditions. The third-party data interface integrates mainstream map service APIs to achieve high-precision positioning and route planning. The blockchain module adopts a consortium blockchain architecture, with the platform, scenic area management, and regulatory agencies participating as nodes to ensure the credibility of data storage.

[0022] Example 2: User Profile Construction and Matching Calculation A tourist posts a booking request, specifying the attraction as the Palace Museum, the time as Saturday morning, the number of people as two, the language requirement as English, the budget as 800 yuan, and the personalized tag as a "history enthusiast." The system, combining this tourist's previous bookings, generates a profile indicating a "history-loving, mid-to-high-end budget, and English-speaking" preference.

[0023] The system initially screened 15 tour guides from its database who held qualifications to guide visitors to the Palace Museum and were proficient in English. The intelligent matching engine used a weighted matching algorithm, setting matching factors with the following weights: skill tag compatibility 30%, historical evaluation 25%, real-time distance 20%, price reasonableness 15%, and historical order acceptance rate 10%. Calculations showed that Tour Guide A had a matching score of 91, Tour Guide B had a matching score of 85, and the matching scores of the remaining tour guides were all below 80. The system only pushed orders to Tour Guides A and B.

[0024] Example 3: Hybrid Scheduling Execution The system assigns tour guide A a 6-second priority booking right and tour guide B a 4-second priority booking right. If tour guide A completes the booking within the priority booking window, the system sends a notification to the tourists stating, "The best tour guide has been matched, and tour guide A will arrive at the South Gate of the Forbidden City within 30 minutes to meet you," and also pushes the optimal tour route to tour guide A.

[0025] If neither tour guide A nor tour guide B accepts the order within the window period, the system will automatically trigger the order assignment logic, assigning the order to tour guide A, who has a higher matching degree, and prompting the reason for the assignment through the tour guide's app: "You are a high-quality tour guide in the field of Ming and Qing history, and your needs are highly compatible with those of tourists."

[0026] Example 4: Service Process Monitoring and Assistance After tour guide A accepts an order, the system updates the tour route every 15 minutes based on real-time pedestrian flow data to avoid suddenly crowded attractions. The service trajectory monitoring module compares the tour guide's actual walking route with the planned route in real time. When the tour guide deviates from the route and goes to an unplanned shopping point, the system immediately sends an alert to the platform management backend and pushes a prompt message to the tourist's APP: "The tour guide's current route deviates from the planned route. Do you need assistance?"

[0027] During the tour, the tour guide uses the AR recognition function of the APP to scan the "Hall of Supreme Harmony" to obtain extended knowledge about the building's historical background, construction techniques, etc. When tourists request additional explanations in Japanese, the tour guide uses the one-click multilingual explanation material generation function to quickly generate Japanese explanations to meet the tourists' needs.

[0028] Example 5: Evaluation and Credit Record Keeping After the service, the tourist gave tour guide A a five-star rating, praising the tour guide for "professional explanations, reasonable route planning, and engaging AR assistance." Tour guide A gave the tourist a four-star rating, praising the tourist for "high cooperation and punctual meeting." The system uploaded data such as the service duration, actual tour route, evaluations from both parties, and no record of abnormal behavior by the tour guide to the blockchain for evidence storage.

[0029] Based on the service data, the system updated Tour Guide A's profile: the average historical evaluation score increased to 4.92 points, and the order acceptance rate for Ming and Qing history-related orders was updated to 93%; the system also updated the tourist profile: a new tag for "accepting supplementary multilingual explanations" was added, further increasing the weight of historical preferences. Simultaneously, the system recommended Ming and Qing themed restaurants and cultural and creative shops around the Forbidden City to tourists.

[0030] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0031] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A smart tourism-based online tour guide order-grabbing and dispatching system, characterized in that, The system includes a user-side app, a platform management backend, an intelligent scheduling server, and a big data and blockchain support platform. The intelligent scheduling server integrates an intelligent matching engine, and the big data and blockchain support platform includes a big data module and a blockchain module. The system performs the following operations: S1. Order Posting and Profile Generation: Tourists post their needs through the tourist app, and the system generates a dynamic "tourist profile" by combining the tourist's historical data; at the same time, it initially filters tour guides that meet the basic criteria from the "tour guide profile library"; S2, Intelligent Matching and Order Push: The intelligent matching engine performs multi-dimensional matching degree calculations on the initially screened tour guides and only pushes orders to tour guides whose matching degree is higher than the preset threshold; S3, Hybrid Mode Scheduling Execution: First, set dynamic priorities for the tour guides receiving the push notifications. High-priority tour guides will have priority to grab orders within a preset window period. If no one grabs an order within the window period or the matching degree is not optimal, the system will automatically assign the order to the tour guide with the highest matching degree. S4. Service Process Supervision and Assistance: Based on LBS and scenic area visitor flow data, plan and update the optimal tour route, monitor the service trajectory in real time and issue warnings for abnormal deviations; integrate AR recognition and multilingual narration material generation tools into the tour guide APP; S5. Service Completion and Credible Evaluation: After the service is completed, both parties are guided to give mutual evaluations. The evaluation information, service duration, route trajectory and other key data are uploaded to the blockchain for evidence storage, and the "tourist profile" and "tour guide profile" are dynamically updated.

2. The system according to claim 1, characterized in that, The factors used in the multi-dimensional matching calculation include tour guide rating, historical order acceptance rate, fit with tourist tags, real-time distance, and price reasonableness.

3. The system according to claim 1, characterized in that, The dynamic priority is determined based on the matching degree between tour guides and tourists. The higher the matching degree, the longer the priority booking period.

4. The system according to claim 1, characterized in that, The "tourist profile" includes attraction preferences, time arrangements, number of people, language requirements, budget range, personalized tags, and historical consumption and evaluation data; the "tour guide profile" includes qualification information, skill tags, historical evaluations, service history, order preferences, and price range.

5. The system according to claim 1, characterized in that, The key data stored in the blockchain module includes records of abnormal behavior during the service process, mutual evaluation results between the two parties, service duration, route trajectory, and tour guide qualification verification information, thus constructing an unalterable tour guide credit file.

6. The system according to claim 1, characterized in that, The service process supervision also includes early warning and intervention for abnormal behaviors such as forced consumption and failure to meet at the agreed time.

7. The system according to claim 1, characterized in that, The tourist app also features AR navigation, payment, and consumption recommendation functions, and can recommend nearby restaurants and entertainment projects based on tourist preferences.

8. The system according to claim 1, characterized in that, The intelligent matching engine uses a weighted matching algorithm, and the weight of each matching factor can be dynamically adjusted based on platform operation data.

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