An internet APP reservation system

Abstract

This invention discloses an internet app reservation system, belonging to the field of internet application technology. It includes: a user interaction module for receiving user reservation requests, including service selection, reservation time slot confirmation, and user information submission, and for displaying users real-time updated service information, a list of available reservation time slots, and personalized recommendations; and a resource management module for real-time monitoring and aggregating the current availability of various service resources. This invention provides personalized recommendations by combining the user interaction module with a user preference model generated by the data analysis module, improving upon the limitations of single-function systems. The resource management module aggregates multi-source service resource data in real-time and models it uniformly, avoiding the coexistence of resource scarcity during peak periods and resource idleness during off-peak periods. Upon receiving a reservation cancellation or change request, the change processing module immediately triggers the resource management module to release or reclaim resources, solving the problem of resources not being reusable immediately after changes.

Description

Technical Field

[0001] This invention relates to the field of Internet application technology, specifically to an Internet APP reservation system. Background Technology

[0002] In the field of information technology, the widespread adoption of internet applications has dramatically changed the operating models of traditional service industries. Appointment systems, as a crucial link connecting users and service providers, have become an important technological direction for improving service efficiency and user experience. Appointment systems integrate service resources and user needs through online platforms, aiming to optimize resource allocation and facilitate convenient access to services.

[0003] Among these, internet-based mobile app reservation systems serve as a specific implementation method, with the core objective of providing users with convenient and efficient reservation services through mobile applications. These systems typically involve basic functional modules such as service display, time selection, user information submission, and reservation confirmation, striving to simplify the cumbersome process of traditional telephone or in-person reservations.

[0004] Existing internet app reservation systems generally suffer from limited functionality and insufficient intelligence. These systems typically only provide basic reservation information entry and storage, lacking the ability to monitor and dynamically adjust service resource status in real time, leading to a coexistence of resource shortages during peak periods and idle resources during off-peak periods. Furthermore, existing systems struggle to effectively handle user cancellations or last-minute changes, failing to provide immediate release and reallocation of reserved resources. In addition, their ability to analyze user behavior data is weak, making it impossible to provide personalized recommendations or predict changes in resource demand based on historical reservation patterns. During peak access times, system response delays and interface lag are frequent, severely impacting user experience and reservation success rates. Therefore, how to build an internet app reservation system capable of dynamic resource optimization, intelligent response to changing demands, and guaranteed system stability has become a pressing technical challenge in this field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an Internet APP reservation system that solves the problems of limited functionality, rigid resource scheduling, difficulty in timely resource reuse after reservation changes, weak data analysis, and system lag under high concurrency in existing technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an internet APP reservation system, comprising: The user interaction module is used to receive users' reservation requests, including service selection, reservation time slot confirmation, and user information submission. It is also used to display users real-time updated service information, a list of available reservation time slots, and personalized recommendation services. The resource management module is used to monitor and aggregate the current availability, occupancy, and future reservation information of various service resources in real time, and to maintain the detailed attributes and configuration parameters of service resources. The scheduling optimization module is used to dynamically evaluate and adjust the allocation strategy of reserved resources based on the service resource status data, user behavior data and preset scheduling rules provided by the resource management module. The change processing module is used to receive and process user-initiated appointment cancellation requests or appointment change requests, and to trigger the immediate release and reallocation process of the service resources based on the processing results. The data analysis module is used to collect, store, and analyze users' historical reservation behavior data, service resource usage data, and system operation data, and to build user preference models, predict future resource demand trends, and identify potential system bottlenecks. The performance assurance module is used to monitor the system's response time and throughput under high concurrency access, and to optimize system operation through load balancing, caching optimization, and asynchronous processing mechanisms.

[0007] Furthermore, the user interaction module is used to perform personalized filtering and priority sorting on the service item information and the list of available appointment time slots based on the user preference model generated by the data analysis module, and intelligently recommend service options that highly match the user's preferences; wherein, the personalized filtering and priority sorting includes dynamic adjustment based on the user's geographical location, historical browsing records and appointment frequency.

[0008] Furthermore, the resource management module is used to obtain data on the idle, occupied, maintained, and reserved status of service resources provided by third-party service providers through real-time data interaction via API interfaces; and the resource management module is also used to aggregate service resource data from multiple sources and to uniformly model and manage the capacity, duration, geographical location, and associated service personnel information of service resources.

[0009] Furthermore, the scheduling optimization module is used to automatically adjust the number of available time slots and the reservation interval, or provide intelligent alternatives, based on a preset dynamic scheduling strategy, when a surge in reservation demand or an excessively high idle rate is detected for a specific service resource; wherein, the dynamic scheduling strategy includes premium adjustments during peak hours, discount promotions during off-peak hours, and priority reservation rights configuration for VIP users.

[0010] Furthermore, the change processing module is used to immediately mark the corresponding service resources as available when it receives a user's appointment cancellation request, and send a resource release notification to users in the waiting list according to preset rules; and the change processing module is also used to first verify the availability of service resources for the new appointment time period when it receives a user's appointment change request, and if available, to perform an appointment update operation and immediately reclaim the resources for the original appointment time period.

[0011] Furthermore, the data analysis module is used to collect and store users' historical successful booking records, cancellation records, service evaluations, and in-app browsing behavior data. The data is then deeply mined using a collaborative filtering algorithm to construct a multi-dimensional user preference model that includes users' frequently used service types, preferred time periods, consumption habits, and cancellation tendencies. This user preference model is used to support the accuracy of personalized recommendation services.

[0012] Furthermore, the data analysis module is used to combine time series forecasting models, machine learning regression algorithms, and externally acquired data on holidays, major events, and weather changes to make refined predictions of the resource demand of specific service items in the future time period; wherein, the prediction results are used to guide the resource allocation decisions and early warnings of the scheduling optimization module.

[0013] Furthermore, the performance assurance module adopts a distributed deployment method based on a microservice architecture to achieve independent scaling and fault isolation of system components; and the performance assurance module is also used to integrate in-memory database and content delivery network (CDN) acceleration services to cope with high-concurrency read and write requests and shorten user data loading time.

[0014] Furthermore, the system also includes a notification management module, which is used to send customized status update notifications, appointment confirmation information and friendly reminders to users at key moments such as successful appointment, appointment change, appointment cancellation and service about to start, via in-app push, SMS, email or WeChat message.

[0015] Furthermore, the notification management module is used to intelligently adjust the text wording, sending timing, and sending frequency of notification content based on the user preference model and current service resource status provided by the data analysis module, so as to improve user attention to notifications and operation response rate; in addition, the notification management module is also used to proactively send personalized recommendation reminders to interested users when service resources are about to become idle.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides personalized recommendation services through a user preference model generated by a user interaction module combined with a data analysis module, overcoming the limitations of single-function systems. The resource management module aggregates multi-source service resource data in real time and models it uniformly, avoiding the coexistence of resource scarcity during peak periods and resource idleness during off-peak periods. Upon receiving a cancellation or change request, the change processing module immediately links with the resource management module to release or reclaim resources, solving the problem of resources not being reusable immediately after changes. The data analysis module constructs a user preference model and combines it with external data to predict resource demand, compensating for the weak data analysis capabilities of existing systems. The performance assurance module adopts a microservice architecture and CDN technologies to optimize system response under high concurrency, avoiding lag during peak periods. The scheduling optimization module dynamically adjusts resource allocation strategies and notifies the management module to promptly push key information, significantly improving overall resource utilization, user experience, and system stability. Attached Figure Description

[0017] Figure 1 This is a system structure diagram of the present invention; Figure 2 This is a flowchart illustrating the reservation and change processing of the present invention; Figure 3 This is a schematic diagram illustrating how the data analysis module of the present invention drives intelligent decision-making. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1-3 This invention provides an internet APP reservation system, comprising: The user interaction module is used to receive users' reservation requests, including service selection, reservation time slot confirmation, and user information submission. It is also used to display users real-time updated service information, a list of available reservation time slots, and personalized recommendation services. The resource management module is used to monitor and aggregate the current availability, occupancy, and future reservation information of various service resources in real time, and to maintain the detailed attributes and configuration parameters of service resources. The scheduling optimization module is used to dynamically evaluate and adjust the allocation strategy of reserved resources based on the service resource status data, user behavior data and preset scheduling rules provided by the resource management module. The change processing module is used to receive and process user-initiated appointment cancellation or appointment change requests, and to trigger the immediate release and reallocation of service resources based on the processing results. The data analysis module is used to collect, store, and analyze users' historical reservation behavior data, service resource usage data, and system operation data, and to build user preference models, predict future resource demand trends, and identify potential system bottlenecks. The performance assurance module is used to monitor the system's response time and throughput under high concurrency access, and to optimize system operation through load balancing, caching optimization, and asynchronous processing mechanisms.

[0020] Specifically, the user interaction module not only receives confirmation of the user's service selection and appointment time slot and user information submission requests, but also pulls the service information and available appointment time slot list synchronized by the resource management module in real time and dynamically displays them to the user. At the same time, it calls the preliminary user preference data generated by the data analysis module to recommend services with potential needs to the user.

[0021] The resource management module connects to the status collection nodes of various service resources in real time, continuously monitors and aggregates the current availability and occupancy of resources as well as future reservation information, and also maintains detailed attributes of resources such as service duration, service personnel qualifications, and configuration parameters, making the resource status transparent.

[0022] The scheduling optimization module uses the resource status data from the resource management module and the user behavior data from the data analysis module as its foundation, and combines preset scheduling rules to dynamically evaluate resource allocation. For example, when the demand for a certain type of service increases during weekday morning peak hours, it automatically determines whether to increase the number of available appointments.

[0023] After receiving a user's request to cancel or change a reservation, the change processing module will immediately link with the resource management module to trigger the corresponding resource to be marked as available in real time for cancellation requests. For change requests, the availability of the new time slot resource will be verified first, and after confirmation, the reservation will be updated and the original time slot resource will be reclaimed.

[0024] The data analysis module continuously collects historical user reservation service resource usage and system operation data. Through basic analysis, it builds a preliminary user preference model to identify resource usage patterns and potential system bottlenecks, providing data support for other modules.

[0025] The performance assurance module monitors system response time and throughput in real time. During high concurrency, it automatically starts the load balancing mechanism to distribute requests to different service nodes. It optimizes the temporary storage of frequently accessed data through caching to reduce the number of database queries. Non-real-time operations such as service evaluation submissions are processed asynchronously to avoid system lag and improve operational stability during peak periods.

[0026] In this embodiment, the user interaction module is used to perform personalized filtering and priority sorting of service item information and available appointment time slots based on the user preference model generated by the data analysis module, and intelligently recommend service options that highly match the user's preferences; wherein, personalized filtering and priority sorting includes dynamic adjustment based on the user's geographical location, historical browsing records and appointment frequency.

[0027] Specifically, when displaying service information and available time slots, the user interaction module deeply utilizes the user preference model generated by the data analysis module to personalize and prioritize information. If a user's geolocation data shows they frequently visit a certain area, the module will prioritize services within that area that have been viewed multiple times in the user's browsing history, placing these services at the top of the display list. Combined with the user's consistent booking frequency, such as booking every Friday afternoon, available resources for that time slot will be displayed first. This dynamic adjustment based on the user's geolocation history and booking frequency allows users to quickly find services that meet their needs without sifting through a large amount of irrelevant information, effectively improving selection efficiency, reducing app operation time, and further optimizing the booking experience.

[0028] In this embodiment, the resource management module is used to obtain data on the idle, occupied, maintained, and scheduled status of service resources provided by third-party service providers through real-time data interaction via API interfaces. Furthermore, the resource management module is also used to aggregate service resource data from multiple sources and to uniformly model and manage the capacity, duration, geographical location, and associated service personnel information of service resources.

[0029] Specifically, the resource management module establishes a real-time data interaction channel with third-party service providers through a pre-defined API interface. It periodically obtains data on the idle and occupied status of service resources, as well as their future planned status, ensuring that resource information is consistent with the actual situation and avoiding information lag. Simultaneously, the module aggregates data from different third parties and various types of service resources, integrating scattered data into a unified management platform. It then models each resource uniformly based on dimensions such as capacity, service duration, geographical location, and service personnel information (e.g., professional qualifications, work hours), forming a standardized resource information archive. This unified resource management model avoids scheduling chaos caused by information fragmentation, allowing the scheduling optimization module to obtain comprehensive and accurate resource data, providing reliable support for resource allocation decisions, and improving the efficiency and accuracy of resource management.

[0030] In this embodiment, the scheduling optimization module is used to automatically adjust the number of available time slots and the reservation interval, or provide intelligent alternatives, based on a preset dynamic scheduling strategy, when a surge in reservation demand or an excessively high idle rate is detected for a specific service resource. The dynamic scheduling strategy includes adjusting premiums during peak hours, offering discounts and promotions during off-peak hours, and configuring priority reservation rights for VIP users.

[0031] Specifically, the scheduling optimization module monitors changes in reservation demand for various service resources in real time. When it detects a surge in reservation demand for a specific resource, such as a rapid increase in ticket reservations for popular scenic spots before holidays, it automatically increases the number of available reservation slots for that resource based on a dynamic scheduling strategy, or appropriately shortens the reservation interval. If the quota is reached, it recommends similar services in the same area as intelligent alternatives to users. When it detects that the idle rate of a specific resource is too high, such as very few reservations for gyms on weekday mornings, the module will activate off-season discount promotions, lowering service prices for that time slot to attract users to make reservations. In addition, for certified VIP users, the module will assign priority reservation rights when resources are scarce, ensuring their convenient access to the services they need. This dynamic scheduling balances the supply and demand of resources, reduces overcrowding at popular resources and waste of idle resources, and significantly improves the overall utilization rate of resources.

[0032] In this embodiment, the change processing module is used to immediately mark the corresponding service resources as available when it receives a user's reservation cancellation request, and send a resource release notification to users in the waiting list according to preset rules; in addition, the change processing module is also used to first verify the availability of service resources for the new requested time period when it receives a user's reservation change request, and if available, to perform a reservation update operation and at the same time immediately reclaim the resources for the original reservation time period.

[0033] Specifically, upon receiving a user's reservation cancellation request, the change processing module immediately sends an instruction to the resource management module to mark the corresponding resource status as available, ensuring that other users can promptly obtain reservation permissions for that resource. Simultaneously, based on preset rules, it filters users who require the resource and meet the reservation conditions from the waiting list and sends a resource release notification to the notification management module. When receiving a user's reservation change request, the module first checks the resource availability for the newly requested time slot in the resource management module. If available, it immediately updates the reservation information, adjusting the user's reservation time slot to the new time slot. Simultaneously, it sends an instruction to the resource management module to reclaim the original reservation time slot and mark it as available. This instant change processing mechanism quickly reallocates resources, avoiding resource idleness caused by reservation cancellations or changes, improving user satisfaction when facing schedule changes, and reducing negative experiences.

[0034] In this embodiment, the data analysis module is used to collect and store users' historical successful booking records, cancellation records, service evaluations, and in-app browsing behavior data. The collaborative filtering algorithm is used to perform in-depth data mining to construct a multi-dimensional user preference model that includes users' frequently used service types, preferred time periods, consumption habits, and cancellation tendencies. The user preference model is used to support the accuracy of personalized recommendation services.

[0035] Specifically, the data analysis module continuously collects various user behavior data within the system, including historical successful booking records, booking cancellation records, service completion evaluations, and in-app browsing behavior. This data is then stored in a dedicated database. Next, the module uses collaborative filtering algorithms for in-depth data mining. Collaborative filtering, a current technology, primarily identifies frequently used service types by analyzing historical successful booking records, determines preferred time periods based on booking time distribution, summarizes user consumption habits based on service evaluations and spending amounts, and judges user cancellation tendencies based on the frequency and reasons for cancellations. Based on these analyses, a user preference model containing multi-dimensional information is constructed. This model is synchronized in real-time to the user interaction module, providing data support for personalized recommendation services, improving recommendation accuracy, and enhancing user stickiness and repurchase rates.

[0036] In this embodiment, the data analysis module is used to combine time series prediction models, machine learning regression algorithms, and externally acquired data on holidays, major events, and weather changes to make refined predictions of the resource demand of specific service items in the future time period; wherein, the prediction results are used to guide the resource allocation decisions and early warnings of the scheduling optimization module.

[0037] Specifically, when forecasting resource demand, the data analysis module first integrates various data resources, including historical service resource usage data and user behavior data from within the system, as well as information on holiday schedules, large-scale regional events, and future weather changes obtained from external platforms. The module then combines a time-series forecasting model to perform trend analysis on historical resource usage data, uses machine learning regression algorithms to perform correlation analysis on multi-dimensional data, and introduces a weighted forecasting formula to achieve refined demand calculation. The formula is as follows: ; in, This represents the projected resource demand for a specific service item over a certain period of time in the future. This represents the predicted trend value of historical data obtained based on a time series forecasting model. This represents the demand adjustment value corresponding to the impact factors of holidays and major events. If the future period includes holidays or major events, the value is positive, otherwise it is 0. This represents the demand adjustment value corresponding to the weather impact factor. The value is determined based on the degree of impact of the weather on the service type. For example, rainy days have a negative impact on the demand for outdoor services, so the value is negative, while sunny days are positive. They are respectively The weight coefficients are determined using the analytic hierarchy process (AHP), which involves constructing a judgment matrix to compare the importance of each influencing factor pairwise, thereby calculating the weight coefficients and satisfying the following conditions: For example, by combining information on upcoming holidays and large-scale events, the system can predict the growth in service demand during that period using formulas; by combining future weather data, the demand forecast for outdoor activities can be adjusted. The final forecast results are pushed to the scheduling optimization module in real time. Based on the forecast results, the scheduling optimization module adjusts resource allocation strategies in advance, such as increasing resource supply before peak periods and formulating promotional strategies before off-peak periods, and issuing warnings for possible resource shortages or idleness, thereby improving the rationality and foresight of resource allocation.

[0038] In this embodiment, the performance assurance module adopts a distributed deployment method based on a microservice architecture to achieve independent scaling and fault isolation of system components. Furthermore, the performance assurance module is also used to integrate in-memory database and content delivery network (CDN) acceleration services to cope with high-concurrency read and write requests and shorten user data loading time.

[0039] Specifically, the performance assurance module adopts a distributed deployment approach based on a microservice architecture, breaking down each functional module of the system into independent microservice components. Each component can scale independently according to access pressure. For example, when the access volume of the user interaction module surges, only the service node of that module is scaled up, without adjusting other modules. Simultaneously, fault isolation is achieved, ensuring that the failure of one component will not affect the operation of other components, thus guaranteeing the overall stability of the system. The module also integrates an in-memory database to store frequently accessed service information, such as appointment time lists, reducing the number of traditional database queries and accelerating data retrieval. It also integrates a Content Delivery Network (CDN) acceleration service, distributing static resources such as service introduction images and other app interface elements to CDN nodes in various locations. When users access the app, they obtain resources from the nearest node, significantly shortening data loading time. These technical measures effectively reduce system response latency under high concurrency, prevent interface lag, and significantly improve system stability and user experience.

[0040] In this embodiment, the system also includes a notification management module, which is used to send customized status update notifications, appointment confirmation information and warm reminders to users at key moments such as successful appointment, appointment change, appointment cancellation and service about to start, via in-app push, SMS, email or WeChat message.

[0041] Specifically, the notification management module presets key time nodes such as appointment success, appointment information change, appointment cancellation, and service start time. When the system detects that a user triggers a certain node, the module automatically generates notification content containing appointment details, status changes, or service reminders. Then, based on the user's pre-provided contact information and preferred notification methods, the module selects one or more methods—in-app push notification, SMS, email, or WeChat message—to send the notification. For example, after a user completes an appointment, an appointment confirmation message is immediately sent via app push notification; 24 hours before the service start time, a friendly reminder containing the service time and location is sent via SMS. This multi-channel, key node notification mechanism ensures that users receive appointment-related information in a timely manner, reducing missed appointments due to information delays and improving user control and satisfaction with the appointment service.

[0042] In this embodiment, the notification management module is used to intelligently adjust the text wording, sending timing, and sending frequency of notification content based on the user preference model provided by the data analysis module and the current service resource status, so as to improve user attention to notifications and operation response rate; in addition, the notification management module is also used to proactively send personalized recommendation reminders to interested users when service resources are about to become idle.

[0043] Specifically, before sending a notification, the notification management module first calls the user preference model from the data analysis module to obtain user preferences regarding the style, timing, and frequency of notification content reception. Simultaneously, it queries the resource management module to obtain the current service resource status. Based on this information, the module adjusts the wording of the notification content. For users who prefer a concise style, only core information is retained; for users who prefer detailed information, service details and precautions are added. The sending timing avoids inactive periods displayed in user history, selecting times when users frequently use the app. The sending frequency is controlled based on user acceptance to avoid frequent sending causing annoyance. Furthermore, when the resource management module detects that a service resource is about to become idle, the notification management module filters users interested in this type of service from the preference model and proactively sends personalized recommendation reminders containing information about the resource's idle time and related details. This intelligent adjustment increases user attention to notifications, promotes timely responses, and brings potential users to idle resources, improving resource utilization.

[0044] In summary, this invention provides personalized recommendation services through a user preference model generated by a user interaction module combined with a data analysis module, overcoming the limitations of single-function systems. The resource management module aggregates multi-source service resource data in real time and models it uniformly, avoiding the coexistence of resource scarcity during peak periods and resource idleness during off-peak periods. Upon receiving a cancellation or change request, the change processing module immediately links with the resource management module to release or reclaim resources, resolving the issue of resources not being reusable immediately after changes. The data analysis module constructs a user preference model and combines it with external data to predict resource demand, compensating for the weak data analysis capabilities of existing systems. The performance assurance module employs a microservice architecture and CDN technologies to optimize system response under high concurrency, avoiding lag during peak periods. The scheduling optimization module dynamically adjusts resource allocation strategies and notifies the management module to promptly push critical information, significantly improving overall resource utilization, user experience, and system stability.

[0045] 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.

[0046] 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. An internet-based mobile app reservation system, characterized in that, include: The user interaction module is used to receive users' reservation requests, including service selection, reservation time slot confirmation, and user information submission. It is also used to display users real-time updated service information, a list of available reservation time slots, and personalized recommendation services. The resource management module is used to monitor and aggregate the current availability, occupancy, and future reservation information of various service resources in real time, and to maintain the detailed attributes and configuration parameters of service resources. The scheduling optimization module is used to dynamically evaluate and adjust the allocation strategy of reserved resources based on the service resource status data, user behavior data and preset scheduling rules provided by the resource management module. The change processing module is used to receive and process user-initiated appointment cancellation requests or appointment change requests, and to trigger the immediate release and reallocation process of the service resources based on the processing results. The data analysis module is used to collect, store, and analyze users' historical reservation behavior data, service resource usage data, and system operation data, and to build user preference models, predict future resource demand trends, and identify potential system bottlenecks. The performance assurance module is used to monitor the system's response time and throughput under high concurrency access, and to optimize system operation through load balancing, caching optimization, and asynchronous processing mechanisms.

2. The Internet APP reservation system according to claim 1, characterized in that, The user interaction module is used to perform personalized filtering and priority sorting of the service item information and the list of available appointment time slots based on the user preference model generated by the data analysis module, and intelligently recommend service options that highly match the user's preferences; wherein, the personalized filtering and priority sorting includes dynamic adjustment based on the user's geographical location, historical browsing records and appointment frequency.

3. The Internet APP reservation system according to claim 1, characterized in that, The resource management module is used to obtain data on the availability, occupancy, maintenance, and reservation status of service resources provided by third-party service providers through real-time data interaction via API interfaces. Furthermore, the resource management module is also used to aggregate service resource data from multiple sources and to uniformly model and manage the capacity, duration, geographical location, and associated service personnel information of service resources.

4. The Internet APP reservation system according to claim 3, characterized in that, The scheduling optimization module is used to automatically adjust the number of available time slots and the reservation interval, or provide intelligent alternatives, based on a preset dynamic scheduling strategy, when a surge in reservation demand or an excessively high idle rate is detected for a specific service resource. The dynamic scheduling strategy includes adjusting premiums during peak hours, offering discounts and promotions during off-peak hours, and configuring priority reservation rights for VIP users.

5. The Internet APP reservation system according to claim 1, characterized in that, The change processing module is used to immediately mark the corresponding service resources as available when it receives a user's appointment cancellation request, and send a resource release notification to users in the waiting list according to preset rules. In addition, the change processing module is also used to first verify the availability of service resources for the new appointment time period when it receives a user's appointment change request. If available, it will perform an appointment update operation and immediately reclaim the resources for the original appointment time period.

6. The Internet APP reservation system according to claim 1, characterized in that, The data analysis module is used to collect and store users' historical successful booking records, cancellation records, service evaluations, and in-app browsing behavior data. It uses a collaborative filtering algorithm to perform in-depth mining on the data to construct a multi-dimensional user preference model that includes users' frequently used service types, preferred time periods, consumption habits, and cancellation tendencies. The user preference model is used to support the accuracy of personalized recommendation services.

7. The Internet APP reservation system according to claim 1, characterized in that, The data analysis module is used to combine time series forecasting models, machine learning regression algorithms, and externally acquired data on holidays, major events, and weather changes to make refined predictions of the resource demand of specific service items in the future time period; the prediction results are used to guide the resource allocation decisions and early warnings of the scheduling optimization module.

8. The Internet APP reservation system according to claim 1, characterized in that, The performance assurance module adopts a distributed deployment method based on a microservice architecture to achieve independent scaling and fault isolation of system components. Furthermore, the performance assurance module is also used to integrate in-memory database and content delivery network (CDN) acceleration services to cope with high-concurrency read and write requests and shorten user data loading time.

9. The Internet APP reservation system according to claim 1, characterized in that, The system also includes a notification management module, which sends customized status update notifications, appointment confirmation information, and friendly reminders to users at key moments such as successful appointment, appointment change, appointment cancellation, and service about to start, via in-app push, SMS, email, or WeChat message.

10. The Internet APP reservation system according to claim 9, characterized in that, The notification management module is used to intelligently adjust the text wording, sending timing, and sending frequency of notifications based on the user preference model and current service resource status provided by the data analysis module, so as to improve user attention to notifications and operation response rate; in addition, the notification management module is also used to proactively send personalized recommendation reminders to interested users when service resources are about to become idle.

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