Seat reservation processing method, platform and system and storage medium

Abstract

The invention relates to the field of data management, and discloses a seat reservation processing method, platform and system and a storage medium, which are used for solving the problem that the system capacity cannot meet the requirements of all users when a large number of users enter the platform at the same time, and the method comprises the following steps: a user logs in or registers the platform by using an account password, collects real-time data of a seat reservation platform, and stores the real-time data of the seat reservation platform; real-time capacity monitoring is carried out according to real-time data of a seat reservation platform, early warning processing is carried out according to a detection result, after a user enters a reservation page, seat information is browsed, the user selects a needed seat according to the seat information and submits the seat to the platform for processing, the platform carries out conflict detection and time period locking operation on the seat, and if reservation succeeds, the user does not need to check the seat. And the platform generates the reservation record, stores the information in the database and sends a reservation success notification to the user, so that the user experience is effectively improved, and the seat reservation failure rate is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data management, and more particularly to a seat reservation processing method, platform, system and storage medium. Background Art

[0002] Seat reservation refers to a method by which a user makes a reservation for the location, quantity, and usage duration of a seat online through a platform. The system records the reservation data of the seat and updates the occupied, reserved, idle, and other statuses of the seat in real time, facilitating the user to enter the platform for reservation at any time and ensuring service quality.

[0003] Existing seat reservations are queued according to the order in which customers enter the system. However, this method may lead to a large number of customers pouring in during the peak period of the platform. Due to the capacity limitation of the platform system, it is unable to handle the requests of a large number of customers making reservations simultaneously, resulting in the user being unable to enter the reservation system page, increasing the user's reservation operation duration, reducing the user experience, and failing to achieve the expected service quality.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a seat reservation processing method, platform, system and storage medium to solve the problems existing in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A seat reservation processing method includes the following steps:

[0008] S001: The user logs in or registers on the platform using an account password for the platform to identify the user's identity for seat reservation operations;

[0009] S002: Collect real-time data of the seat reservation platform, perform real-time capacity monitoring based on the real-time data of the seat reservation platform, and perform warning processing according to the detection results;

[0010] S003: After the user enters the reservation page, browse the seat information, where the seat information includes the location, date, and available reservation duration information of the available seats. The user can select the required seat according to the seat information and submit it to the platform for processing. After receiving the request, the platform checks whether the seat is occupied and performs conflict detection and time period locking operations;

[0011] S004: If the reservation is successful, the platform will generate a reservation record, store the information in the database, and send a reservation success notification to the user.

[0012] Preferably, the step of performing real-time capacity monitoring according to the real-time data of the seat reservation platform is as follows:

[0013] By monitoring the number of requests sent by users within a unit time through the real-time monitoring platform, monitoring the response time from when the user requests to when the system returns within a unit time, and calculating the concurrent user number coefficient according to the M / M / 1 queue model, the formula is Where WP represents the concurrent user number coefficient, RU represents the number of requests sent by users, EL represents the average response time of the system, and the formula for calculating the average response time of the system is Where NUM represents the response time;

[0014] Real-time monitor the number of errors processed by the platform for the requests sent by users within a unit time period, and calculate the error rate coefficient using the error rate algorithm based on the monitored number of errors, the formula is Where DR represents the error rate coefficient, DP represents the number of user request errors processed by the platform within a unit time, and EP represents the total number of user requests processed by the platform within a unit time;

[0015] When users access the system, log data will be generated. Collect the log data of users, calculate the growth rate of user log data based on the log data of users, and calculate the growth coefficient of user log data based on the growth rate of user log data. The formula for calculating the growth rate of user log data is Where ZL represents the growth rate of log data, RZ D represents the current platform log quantity, RZ C represents the initial platform log quantity, T D represents the current time, T C represents the initial time;

[0016] Calculate the system capacity shortage index based on the concurrent user number coefficient, error rate coefficient, and user log data growth coefficient. The calculation formula is IC = a1×WP + a2×DR + a3×LG, where IC represents the system capacity shortage index, WP represents the concurrent user number coefficient, DR represents the error rate coefficient, LG represents the user log data growth coefficient, and a1, a2, and a3 represent the weight coefficients of the concurrent user number coefficient, error rate coefficient, and user log data growth coefficient.

[0017] Preferably, the step of the real-time monitoring platform monitoring the number of requests sent by users within a unit time is as follows:

[0018] Select the Prometheus tool as the tool for the monitoring platform to monitor the number of requests sent by users within a unit time;

[0019] Deploy the Prometheus tool to the platform to ensure its seamless integration with the application program;

[0020] Use the visualization function provided by the Prometheus monitoring tool to create a dashboard to display the real-time data of the number of requests sent.

[0021] Preferably, select the Prometheus tool as the monitoring tool;

[0022] Select key monitoring points in the system. These monitoring points can be the entry point where user requests reach the system or the key nodes where the system processes requests;

[0023] At each monitoring point, record the timestamp when the request arrives and the timestamp when the system returns a response, which can be achieved through logging in the code or the API provided by the monitoring tool;

[0024] According to the recorded timestamps, calculate the response time of each request. The response time is the time interval from when the request arrives at the system to when the system returns a response.

[0025] Preferably, the step of calculating the user log data growth coefficient according to the user log data growth rate is as follows:

[0026] Divide the detection time into n equal parts, and record each part of the time as a sub-detection time period;

[0027] Collect the user log data growth rate of each sub-detection time period, preset a user log data growth rate threshold, compare the user log data growth rate of each sub-detection time period with the preset threshold, and screen out the sub-detection time periods with a user log data growth rate greater than the preset threshold;

[0028] Calculate the average value of the user log data growth rate of the screened sub-detection time periods by the average value calculation method, and record it as the user log data growth coefficient. Its calculation formula is where LG represents the user log data growth coefficient, m represents the number of screened sub-detection time periods, and ZL i represents the user log data growth rate of the i-th screened sub-detection time period.

[0029] Preferably, the step of performing early warning processing according to the detection result is to compare the system capacity shortage index with the preset threshold. If the system capacity shortage index is less than the preset threshold, it is determined that the current ticket system capacity is sufficient and no capacity early warning is performed. If the system capacity shortage index is greater than the preset threshold, it is determined that the current platform system capacity is insufficient, which is likely to cause users to be unable to make seat reservations normally, and then a capacity early warning is issued to remind the platform staff that the current system capacity is insufficient.

[0030] Preferably, a seat reservation processing platform, the platform includes a terminal and a server, the terminal is used to upload user information and seat information to the processing system, and determine the user seat selection information, and the server is used for users to query seat information and send the seat information queried for the seat to the terminal.

[0031] Preferably, a seat reservation processing system, the system includes:

[0032] A user login module, which is used to provide a login and registration platform for users, so that users can enter the platform for reservation operations;

[0033] A system information collection module, which is used to collect system information, calculate the system capacity shortage index according to the system information, and perform early warning processing according to it;

[0034] A user seat selection module, which is used to perform conflict detection and time locking operations on the seats selected by users for reservation, and determine the certainty of the seats selected by users;

[0035] A reservation prompt module, which is used to prompt users who have successfully reserved.

[0036] Preferably, a seat reservation processing storage medium stores at least one instruction or at least one program segment, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the seat reservation processing method according to any one of claims 1-6.

[0037] The technical effects and advantages of the present invention:

[0038] Users log in or register on the platform using their account passwords, collect real-time data of the seat reservation platform, perform real-time capacity monitoring according to the real-time data of the seat reservation platform, and perform early warning processing according to the detection results. After users enter the reservation page, they browse the seat information. Users select the seats they need according to the seat information and submit them to the platform for processing. The platform performs conflict detection and time period locking operations on the seats. If the reservation is successful, the platform will generate a reservation record, store the information in the database, and send a reservation success notification to the user, effectively improving the user experience and reducing the seat reservation failure rate. Description of the Drawings

[0039] Figure 1 It is the overall flowchart of the present invention.

[0040] Figure 2 It is the flowchart of the seat reservation processing system of the present invention. Detailed Embodiments

[0041] The following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely examples. A seat reservation processing method, platform, system, and storage medium according to the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0042] The present invention provides a seat reservation processing method, including the following steps:

[0043] S001: The user logs in or registers on the platform using the account password, so that the platform can identify the user's identity for seat reservation operations;

[0044] S002: Collect real-time data of the seat reservation platform, perform real-time capacity monitoring based on the real-time data of the seat reservation platform, and perform warning processing according to the detection results;

[0045] S003: After the user enters the reservation page, browse the seat information. The seat information includes the location, date, and available reservation duration of the available seats. The user can select the required seat according to the seat information and submit it to the platform for processing. After receiving the request, the platform checks whether the seat is occupied and performs conflict detection and time period locking operations;

[0046] S004: If the reservation is successful, the platform will generate a reservation record, store the information in the database, and send a reservation success notification to the user.

[0047] The seat reservation system can help managers effectively manage the seat resources in the venue, including libraries, meeting rooms, offices, etc. Through the reservation system, the utilization of seats can be reasonably arranged, avoiding resource waste and congestion, thereby realizing the optimal utilization of resources. The seat reservation system can improve the user's reservation experience and service quality. Users can make seat reservations in advance, avoiding queuing or not being able to find a seat after arriving, improving the user's comfort and satisfaction. For people who need to use specific venues or facilities for work or study, the seat reservation system can help them effectively arrange time and resources and improve work and study efficiency.

[0048] The reservation system can reduce the time spent looking for seats, allowing users to focus more on work or study. In some special situations, such as during the epidemic, the seat reservation system can help control the flow of people and avoid crowd gathering, thereby reducing the risk of disease transmission. Through the reservation system, the number of people in the venue can be controlled, social distancing can be maintained, and epidemic prevention and control can be strengthened. The seat reservation system can record and analyze reservation data, providing data support and decision-making references for managers. By analyzing users' reservation behaviors and seat utilization, managers can better understand users' needs and optimize resource allocation and service strategies.

[0049] Seat reservation management can help institutions or venues manage seat resources more effectively. Through the reservation system, seat usage can be reasonably arranged according to demand, avoiding resource waste and overcrowding, and improving the utilization rate of seat resources. Managing seat reservations can improve the efficiency and quality of services. The reservation system enables users to arrange seats in advance, reducing waiting time, avoiding congestion and chaos, and enhancing the timeliness and convenience of services. Effective seat reservation management can enhance users' reservation experience and satisfaction.

[0050] Users can reserve seats according to their personal needs and preferences, avoiding the embarrassing situation of having no available seats after arriving, and improving users' comfort and satisfaction. Seat reservation management can generate a large amount of reservation data. Managers can analyze this data to understand information such as users' reservation behaviors and seat utilization rates, providing data support and basis for decision-making in resource allocation, service improvement, venue planning, etc. The reservation management system can record the information of reservation holders, which helps in security management and monitoring. For example, when applied to scenarios such as schools and enterprises, it can ensure that only appropriate personnel enter the venue, enhancing security.

[0051] In this embodiment, it should be specifically noted that the step of real-time capacity monitoring based on the real-time data of the seat reservation platform is as follows:

[0052] By real-time monitoring of the number of requests sent by users within a unit time on the real-time monitoring platform, and monitoring the response time from when the user requests to when the system returns within a unit time, the concurrent user number coefficient is calculated according to the M / M / 1 queue model, and its formula is Where WP represents the concurrent user number coefficient, RU represents the number of requests sent by users, EL represents the average system response time, and the formula for the average system response time is Where NUM represents the response time. When the concurrent user number coefficient is relatively high, it indicates that a large number of users enter simultaneously within a unit time. The unit time can be one minute or five minutes.

[0053] Real-time monitor the number of errors sent by users processed by the platform within a unit time period, and calculate the error rate coefficient using the error rate algorithm based on the monitored number of errors. The formula is Where DR represents the error rate coefficient, DP represents the number of user request errors processed by the platform per unit time, and EP represents the total number of user requests processed by the platform per unit time. When the error rate coefficient is relatively high, it indicates that the platform capacity is insufficient and cannot handle the access requests of the current large number of users.

[0054] When users access the system, log data will be generated. The log data of users is collected, the growth rate of user log data is calculated based on the user log data, and the growth coefficient of user log data is calculated based on the growth rate of user log data. The calculation formula for the growth rate of user log data is Where ZL represents the growth rate of log data, RZ D represents the current platform log quantity, RZ C represents the initial platform log quantity, T D represents the current time, T C represents the initial time. When a large number of users enter the platform per unit time, a large amount of user log data will be generated, resulting in a high growth rate of log data and occupying the platform capacity.

[0055] The system capacity shortage index is calculated based on the concurrent user number coefficient, error rate coefficient, and user log data growth coefficient. The calculation formula is IC = a1×WP + a2×DR + a3×LG, where IC represents the system capacity shortage index, WP represents the concurrent user number coefficient. When the concurrent user number coefficient is larger, the system capacity shortage index will increase because the proportion of time when the system is in a busy state increases, resulting in a decrease in the idle time in the system, that is, the possibility of system capacity shortage increases. Therefore, the larger the concurrent user number coefficient, the larger the system capacity shortage index. DR represents the error rate coefficient. When the system capacity is insufficient, since the system cannot meet all requests, it may lead to situations such as queuing, timeout, and resource competition during the request processing, thus increasing the probability of request failure or error. Therefore, in the case of system capacity shortage, the error rate often rises and shows a positive correlation with the system capacity shortage index. This situation is particularly obvious under high load. When the system is in an overloaded state, the error rate usually increases because the system cannot process all requests in time, resulting in some requests failing or making errors. Therefore, the larger the error rate coefficient, the larger the system capacity shortage index. LG represents the user log data growth coefficient. If the system capacity is insufficient, resulting in the system being unable to process user requests in time, the request processing time is extended and the user waiting time increases. This may lead to an increase in the user operation frequency or the user trying the same operation multiple times, thus generating more log data. Therefore, the larger the user log data growth coefficient, the larger the system capacity shortage index. a1, a2, and a3 represent the weight coefficients of the concurrent user number coefficient, error rate coefficient, and user log data growth coefficient, and in this embodiment, the specific values of a1, a2, and a3 are not specifically calculated.

[0056] The M / M / 1 queuing model is a classic model in queuing theory, which is used to describe the relationship between a single service channel (or server) and the customers (or requests) arriving at this service channel. In this model, "M" represents the arrival rate, "M" represents the service rate, and "1" represents that there is only one service channel (server).

[0057] In this embodiment, it should be specifically noted that the steps for the real-time monitoring platform to count the number of requests sent by users per unit time are as follows:

[0058] Select the Prometheus tool as the tool for the monitoring platform to count the number of requests sent by users per unit time. Prometheus is an open-source system monitoring and alerting toolkit, originally developed by SoundCloud and released as an open-source project in 2012. It focuses on real-time monitoring and alerting functions, aiming to help users collect time series data, execute queries, and send alerts;

[0059] Deploy the Prometheus tool to the platform to ensure seamless integration with the application;

[0060] Utilize the visualization function provided by the Prometheus monitoring tool to create dashboards to display the real-time request volume data. These dashboards can help you intuitively understand the operating status of the platform and quickly discover potential problems.

[0061] Understanding the user request volume can help the monitoring platform evaluate its performance status. By monitoring changes in the request volume, it can be timely discovered whether the system is working properly, whether there are performance bottlenecks, or whether it is necessary to expand resources to handle requests during peak periods. Based on the request volume per unit time, the monitoring platform can perform capacity planning. By analyzing the trends and changes in the request volume, server resources, network bandwidth, etc. can be reasonably adjusted to meet user needs and ensure the stable operation of the system under different loads. Abnormal request volumes may be signs of system failures or attacks. The monitoring platform can quickly discover system failures or abnormal traffic attacks through real-time monitoring of the request volume and take corresponding countermeasures to ensure the stability and security of the system. By understanding the patterns and frequencies of user requests, the monitoring platform can be optimized according to user behavior to improve the user experience. For example, adjust the system response speed according to the request volume during peak periods, or optimize the system design to improve the concurrent processing ability.

[0062] In this embodiment, it should be specifically noted that the steps for the monitoring platform to measure the response time from user requests to system returns are as follows:

[0063] Select the Prometheus tool as the monitoring tool;

[0064] Select key monitoring points in the system. These monitoring points can be the entry points where user requests reach the system, or key nodes where the system processes requests, such as backend services, database queries, etc.;

[0065] At each monitoring point, record the timestamp when the request arrives and the timestamp when the system returns a response. This can be achieved through logging in the code or the API provided by the monitoring tool;

[0066] Based on the recorded timestamps, calculate the response time for each request. The response time is the time interval from when the request reaches the system to when the system returns a response.

[0067] Monitoring the response time can help evaluate the performance of the system. A fast response time usually means that the system has good performance and response capabilities, while a slower response time may indicate performance bottlenecks or other problems in the system. The response time directly affects the user experience. A short response time can improve user satisfaction and loyalty, while a long wait will reduce the user experience and even lead to user churn. Therefore, monitoring the response time can help optimize the user experience, improve user satisfaction. An abnormal response time may be a sign of system failure or performance issues. By monitoring the response time, it is possible to quickly detect whether the system has failed or is abnormal and take corresponding measures to troubleshoot and repair. The response time can also be used for system capacity planning. By monitoring the response time and the expected load, it is possible to evaluate whether the system needs to expand resources to meet future demands and formulate corresponding expansion plans.

[0068] In this embodiment, it should be specifically noted that the step of calculating the user log data growth coefficient based on the user log data growth rate is as follows:

[0069] Divide the detection time into n equal parts, and record each part of the time as a sub-detection time period;

[0070] Collect the user log data growth rate for each sub-detection time period, preset a user log data growth rate threshold, compare the user log data growth rate for each sub-detection time period with the preset threshold, and filter out the sub-detection time periods where the user log data growth rate is greater than the preset threshold;

[0071] Calculate the average value of the user log data growth rates for the filtered sub-detection time periods through the mean calculation method, and denote it as the user log data growth coefficient. The calculation formula is where LG represents the user log data growth coefficient, m represents the number of filtered sub-detection time periods, and ZL i represents the user log data growth rate for the i-th filtered sub-detection time period.

[0072] In this embodiment, it should be specifically noted that the step of performing early warning processing according to the detection result is to compare the system capacity shortage index with a preset threshold. If the system capacity shortage index is less than the preset threshold, it is determined that the current receipt system capacity is sufficient and no capacity early warning is performed. If the system capacity shortage index is greater than the preset threshold, it is determined that the current platform system capacity is insufficient, which may easily cause users to be unable to make seat reservations normally, and then a capacity early warning is issued to remind the platform staff that the current system capacity is insufficient.

[0073] In this embodiment, it should be specifically noted that the step of performing conflict detection and time period locking operation is as follows:

[0074] Collect the seat reservation requests of users, including information such as the selected time period and seat location;

[0075] Check whether the time period selected by the user conflicts with the existing reservations in the database by querying the existing reservation records. If the selected time period already has reservations of other users or is occupied, the system returns an error message, notifies the user that the selected time period is unavailable, and requires the user to reselect;

[0076] If the selected time period is available, the system then checks whether the seat selected by the user has already been reserved or locked by other users. If the selected seat has already been reserved or locked by other users, the system returns an error message, notifies the user that the selected seat is unavailable, and requires the user to reselect the seat;

[0077] If the time period is available and the seat is also available, check the user's permissions. If the user's permissions are insufficient, the system returns the corresponding error message and notifies the user that the reservation cannot be made;

[0078] If the above checks pass, the system marks the selected time period as a locked state to prevent other users from making reservations during this time period. At the same time, the system marks the selected seat as a reserved state to ensure that other users cannot select the same seat again.

[0079] In this embodiment, it should be specifically noted that a seat reservation processing platform includes a terminal and a server. The terminal is used to upload user information and seat information to the processing system and determine the user's seat selection information. The server is used for users to query seat information and send the seat information queried to the terminal.

[0080] In this embodiment, it should be specifically noted that as Figure 2 shown, a seat reservation processing system includes:

[0081] A user login module, which is used to provide a login and registration platform for users to enable them to enter the platform for reservation operations;

[0082] A system information collection module, which is used to collect system information, calculate a system capacity shortage index based on the system information, and perform early warning processing based on it;

[0083] A user seat selection module, which is used to perform conflict detection and time locking operations on the seats selected by the user for reservation, and determine the certainty of the seats selected by the user;

[0084] A reservation prompt module, which is used to prompt users who have successfully reserved.

[0085] In this embodiment, it should be specifically noted that a seat reservation processing storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any of the above-mentioned methods are implemented;

[0086] First, the user logs in or registers on the platform through the client with a password, then the system information collection module collects the current system information, comprehensively analyzes the current system information, and issues an early warning according to the analysis result. Conflict detection and time locking are performed on the seats selected by the seat user selection module, and a successful reservation prompt is given for the locked seats according to the reservation prompt module.

[0087] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0088] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A seat reservation processing method, characterized in that: The following steps are involved: S001: The user logs in or registers on the platform using his / her account and password, which is used by the platform to identify the user and make seat reservations; S002: Collect real-time data from the seat reservation platform, conduct real-time capacity monitoring based on the real-time data from the seat reservation platform, and conduct early warning processing based on the detection results; S003: After the user enters the reservation page, he / she browses the seat information, which includes the location, date and duration of the seats that can be reserved. The user can select the required seat according to the seat information and submit it to the platform for processing. After receiving the request, the platform checks whether the seat is occupied, and performs conflict detection and time period locking operations; S004: If the reservation is successful, the platform will generate a reservation record, store the information in the database and send a successful reservation notification to the user.

2. A seat reservation processing method according to claim 1, characterized in that: The steps of performing real-time capacity monitoring based on the real-time data of the seat reservation platform are as follows: By monitoring the number of requests sent by users in a unit time on the platform in real time, monitoring the response time from user requests to system returns in a unit time, and calculating the concurrent user coefficient based on the M / M / 1 queue model, the formula is: Where WP represents the concurrent user coefficient, RU represents the number of requests sent by users, and EL represents the average system response time. The calculation formula of the average system response time is: Where NUM represents the response time; Real-time monitoring of the number of errors sent by users processed by the platform within a unit time period, and the error rate coefficient is calculated using the error rate algorithm based on the number of errors obtained through monitoring. The formula is: Where DR is the error rate coefficient, DP is the number of errors in user requests processed by the platform per unit time, and EP is the total number of user requests processed by the platform per unit time; When a user accesses the system, log data is generated. The user's log data is collected, and the user log data growth rate is calculated based on the user's log data. The user log data growth rate coefficient is calculated based on the user log data growth rate. The user log data growth rate calculation formula is: Where ZL represents the log data growth rate, RZ D Indicates the number of current platform logs, RZ C Expressed as the number of initial platform logs, T D Represents the current time, T C It is represented as the initial time; The system capacity shortage index is calculated according to the concurrent user number coefficient, error rate coefficient and user log data growth coefficient, and its calculation formula is IC=a1×WP+a2×DR+a3×LG, where IC represents the system capacity shortage index, WP represents the concurrent user number coefficient, DR represents the error rate coefficient, LG represents the user log data growth coefficient, and a1, a2, and a3 represent the weight coefficients of the concurrent user number coefficient, the error rate coefficient and the user log data growth coefficient.

3. A seat reservation processing method according to claim 2, characterized in that: The steps of real-time monitoring platform for the number of requests sent by users per unit time are as follows: Select Prometheus as the tool to monitor the number of requests sent by users per unit time on the platform; Deploy the Prometheus tool to the platform and ensure it can be seamlessly integrated with the application; Use the visualization function provided by the Prometheus monitoring tool to create a dashboard to display real-time request volume data.

4. A seat reservation processing method according to claim 2, characterized in that: The steps for monitoring the response time from user request to system return are: Choose Prometheus as the monitoring tool; Select key monitoring points in the system. These monitoring points can be the entry points where user requests arrive at the system or the key nodes where the system processes requests. At each monitoring point, record the timestamp of the request arrival and the timestamp of the system response. This can be achieved through logging in the code or the API provided by the monitoring tool. The response time of each request is calculated based on the recorded timestamp. The response time is the time interval from when the request arrives at the system to when the system returns a response.

5. A seat reservation processing method according to claim 2, characterized in that: The step of calculating the user log data growth coefficient according to the user log data growth rate is: Divide the detection time into n parts on average, and record each part as a sub-detection time period; Collect the user log data growth rate of each sub-detection time period, preset a user log data growth rate threshold, compare the user log data growth rate of each sub-detection time period with the preset threshold, and filter out the sub-detection time period with a user log data growth rate greater than the preset threshold; The average calculation method is used to calculate the growth rate of user log data in the selected sub-detection time period, and it is recorded as the user log data growth coefficient. The calculation formula is: Where LG represents the user log data growth coefficient, m represents the number of sub-detection time periods screened out, and ZL i It is represented as the growth rate of user log data in the i-th filtered sub-detection time period.

6. A seat reservation processing method according to claim 1, characterized in that: The step of performing early warning processing based on the detection results is to compare the system capacity shortage index with the preset threshold value. If the system capacity shortage index is less than the preset threshold value, it is determined that the current receipt system capacity is sufficient and no capacity warning is issued. If the system capacity shortage index is greater than the preset threshold value, it is determined that the current platform system capacity is insufficient, which may easily cause users to be unable to make seat reservations normally, and a capacity warning is issued to remind platform staff that the current system capacity is insufficient.

7. A seat reservation processing platform, characterized in that: The platform includes a terminal and a server. The terminal is used to upload user information and seat information to a processing system and determine user seat selection information. The server is used for users to query seat information and send the query seat information to the terminal.

8. A seat reservation processing system, characterized in that: The system comprises: The user login module is used to provide a login and registration platform for users, allowing them to enter the platform and make reservations; The system information collection module is used to collect system information, calculate the system capacity shortage index based on the system information, and perform early warning processing accordingly; The user seat selection module is used to perform conflict detection and time locking operations on the user's selected reserved seats to determine the certainty of the user's selected seats; The appointment reminder module is used to remind users who have successfully made an appointment.

9. A seat reservation processing storage medium, characterized in that: The storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the seat reservation processing method according to any one of claims 1 to 6.

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