Community group purchase server ordering method and device, equipment and storage medium

Abstract

The application provides a community group purchase server ordering method, device and equipment and a storage medium. The community group purchase server ordering method comprises the following steps: determining a server for ordering operation of a user based on a current delivery address of the user; providing an automatically selected delivery time in a delivery time option in response to the determined server meeting a set congestion condition; and causing the determined server to receive a payment request of the user for a current order in response to the user's payment request based on the current delivery address and the delivery time being the automatically selected delivery time, so that the determined server judges whether there is delivery capacity at a delivery station corresponding to the delivery address, and if there is delivery capacity, an order corresponding to the payment request is confirmed. The application avoids a large number of users simultaneously ordering, which causes the server load to be too high, thereby improving the user ordering experience.

Description

Technical Field

[0001] This invention relates to the field of computer applications, and in particular to a method, apparatus, device, and storage medium for placing orders on a community group-buying server. Background Technology

[0002] Community group buying is an online and offline shopping and consumption behavior of residents within a real residential community. It is a regionalized, niche, localized, and networked group buying form based on a real community.

[0003] In community group buying scenarios, when placing an order, users need to first select their desired delivery time and then choose a payment method. However, when a large number of users simultaneously make payment selections, it can cause server congestion. When users finally select a delivery time and try to pay, that delivery time is often unavailable, ultimately leading to order failure and a very poor user experience.

[0004] This situation can be effectively alleviated by expanding server capacity, but the frequency of high load is usually low, the utilization rate of expansion is low, and the cost is too high.

[0005] Therefore, how to avoid a large number of users placing orders at the same time, causing excessive server load and resulting in order failure when users select the expected delivery time and make payment, but the delivery time is no longer available, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] In order to overcome the defects of the above-mentioned related technologies, the present invention provides a community group buying server ordering method, apparatus, equipment and storage medium, thereby avoiding the situation where a large number of users place orders at the same time, resulting in excessive server load and order failure after the user selects the expected delivery time and makes payment, as the delivery time is no longer available, thus improving the user ordering experience.

[0007] According to one aspect of the present invention, a method for placing an order on a community group-buying server is provided, comprising:

[0008] Based on the user's current shipping address, determine the server that will place the order for the user.

[0009] In response to the determined server meeting the set congestion conditions, an automatic selection of delivery time is provided in the delivery time options;

[0010] In response to the user's payment request based on the current delivery address, and with the delivery time set to automatic selection, the designated server receives the user's payment request for the current order. The server then determines whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed.

[0011] In some embodiments of this application, the setting of congestion conditions includes one or more of the following congestion conditions:

[0012] The real-time load of the determined server is greater than the set load threshold.

[0013] The number of active users on the identified server exceeds the set threshold.

[0014] The time difference between the current time and the determined server product sales time and / or capacity opening time is less than the first set time period threshold.

[0015] In some embodiments of this application, the active user includes:

[0016] Users currently operating the community group buying client; and / or

[0017] Users who have engaged in communication with the designated server within a second set time period threshold.

[0018] In some embodiments of this application, providing automatic selection of delivery time among the delivery time options includes:

[0019] Cancel the option for the original delivery time and automatically select the delivery time as the automatic selection delivery time; or

[0020] The options for the delivery time include the original time and automatic selection of the delivery time.

[0021] In some embodiments of this application, the determination by the server of whether the delivery station corresponding to the delivery address has delivery capacity further includes:

[0022] In response to the user's order placement time, if it falls within a third predetermined time period threshold after the server meets the set congestion conditions, the server determines whether the corresponding delivery station has delivery capacity based on whether the current accumulated order volume of the delivery station meets the set total order volume.

[0023] If the current cumulative order volume of a delivery station meets the set total order volume, then the corresponding delivery station has no delivery capacity; if the current cumulative order volume of a delivery station is less than the set total order volume, then the corresponding delivery station has delivery capacity.

[0024] In some embodiments of this application, the third set time period threshold is preset or determined based on the duration during which the server transitions from meeting set congestion conditions to meeting set idle conditions.

[0025] In some embodiments of this application, within a fourth set time period threshold after the user completes the payment operation for the current order, the determined server adds the user's current order to the current cumulative order quantity of the delivery station. The fourth set time period threshold is preset or determined according to the third set time period threshold.

[0026] In some embodiments of this application, after a fifth preset time period threshold following the user's completion of the payment operation for the current order, the method further includes:

[0027] The determined server calculates delivery routes based on multiple orders from the delivery station; and

[0028] The designated server allocates delivery times to each order according to the calculated delivery routes and order order sequence.

[0029] Wherein, the fifth set time period threshold is greater than or equal to the fourth set time period threshold.

[0030] In some embodiments of this application, the fifth set time period threshold is preset or determined based on the third set time period threshold.

[0031] According to another aspect of this application, a community group-buying server ordering device is also provided, comprising:

[0032] The server determination module is used to determine the server that will place the order for the user based on the user's current delivery address.

[0033] An automatic delivery time selection module is provided to automatically select a delivery time from the options in response to a determined server meeting set congestion conditions;

[0034] The payment confirmation module, in response to the user's payment request based on the current delivery address and with the delivery time set to automatic selection, enables the server to receive the user's payment request for the current order. The server then determines whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed.

[0035] According to another aspect of the present invention, an electronic device is also provided, the electronic device comprising: a processor; and a storage medium having a computer program stored thereon, the computer program being executed by the processor to perform the steps described above.

[0036] According to another aspect of the present invention, a storage medium is also provided, on which a computer program is stored, the computer program being executed by a processor to perform the steps described above.

[0037] Compared with the prior art, the advantages of this invention are:

[0038] When the server processing orders is congested, an automatic delivery time selection option can be provided in the order placement settings. This allows the server to directly determine the corresponding delivery station based on the address and perform delivery capacity assessment and payment processing without needing to differentiate between delivery times. This avoids excessive server load caused by a large number of simultaneous orders, preventing order failures where users select and pay for an unavailable delivery time, thus improving the user experience. Attached Figure Description

[0039] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0040] Figure 1 A flowchart illustrating a community group-buying server order placement method according to an embodiment of the present invention is shown.

[0041] Figure 2 The diagram shows the order placement interface of a community group-buying platform according to an embodiment of the present invention.

[0042] Figure 3 A flowchart illustrating how a server determines delivery capacity according to an embodiment of the present invention is shown.

[0043] Figure 4 A flowchart illustrating the server updating delivery capacity after a user successfully places and pays for an order, according to an embodiment of the present invention, is shown.

[0044] Figure 5 A flowchart illustrating the server's calculation of delivery routes and delivery times according to an embodiment of the present invention is shown.

[0045] Figure 6 A block diagram of a community group-buying server order placement device according to an embodiment of the present invention is shown.

[0046] Figure 7 The schematic diagram illustrates a computer-readable storage medium according to an exemplary embodiment of the present invention.

[0047] Figure 8 The schematic diagram illustrates an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0049] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0050] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined. Therefore, the actual execution order may change depending on the specific circumstances.

[0051] Figure 1 A flowchart illustrating a community group-buying server order placement method according to an embodiment of the present invention is shown. The community group-buying server order placement method includes the following steps:

[0052] Step S110: Based on the user's current shipping address, determine the server that will place the order for the user.

[0053] Specifically, multiple servers are used to place orders for users, forming a server cluster or distributed server. Each server can be responsible for executing order operations for users in different regions. In some embodiments, regions can be pre-divided to determine the corresponding server for each user based on the region where their delivery address is located. In other embodiments, each server can be responsible for one or more delivery stations, thereby determining the server for executing the order operation for that user based on the delivery range of the delivery station where the user's current delivery address is located.

[0054] Step S120: In response to the determined server meeting the set congestion conditions, provide automatic selection of delivery time in the delivery time options.

[0055] Specifically, whether a server meets the set congestion criteria can be determined based on real-time server monitoring. This allows for flexible adjustments to the server to handle congestion, ensuring the original community group-buying shopping experience for users when not congested. For example, when the server's real-time load exceeds a set load threshold, the server is considered to meet the congestion criteria. Similarly, when the number of active users on the server exceeds a set threshold, the server is considered to meet the congestion criteria. Active users can include users currently operating the community group-buying client (these users may not yet have communicated with the designated server but are potential candidates). Active users can also include users who have interacted with the designated server within a second set time period threshold (backend users). Therefore, the more active users there are, the greater the probability of excessive server load; thus, the number of active users can also be used to determine whether the server meets the congestion criteria.

[0056] Furthermore, in some embodiments, due to limitations in delivery capacity or the quantity of goods, community group-buying platforms may set sales times and / or delivery opening times. When these times arrive, the server often experiences excessive load, leading to congestion. In such embodiments, real-time monitoring of server load and the number of active users may not be necessary; instead, time-based matching can be used to determine if the server meets the congestion criteria. Furthermore, a lead time can be set for the server to ensure that when the current time arrives at the sales time and / or delivery opening time, the server is prepared and adjusted for congestion. In this case, the congestion criteria can be set to ensure that the time difference between the current time and the determined server sales time and / or delivery opening time is less than a first set time period threshold (lead time). For example, if the sales time is 8:00 AM and the first set time period is 15 minutes, the server meets the congestion criteria starting at 7:45 AM.

[0057] In some embodiments, when the determined server is in a congested state, the option for the original delivery time can be canceled, and the delivery time can be automatically selected as the automatic selection delivery time. In this embodiment, only the automatic selection delivery time option exists in the delivery time, and users cannot select other delivery time periods, thereby making the server more efficient in performing order placement operations and requiring less load.

[0058] In other embodiments, the delivery time options include a raw time and an automatically selected delivery time. In this embodiment, automatically selected delivery time is the default option, thus allowing users who do not have specific delivery time requirements to avoid selecting a delivery time. This improves order placement efficiency for users and also makes the server's order placement operation based on the automatically selected delivery time more efficient and requires less load. In this embodiment, users can still switch the automatically selected delivery time to their desired delivery time period to provide convenient service to users who need to receive their goods within a specific time frame.

[0059] Step S130: In response to the user's payment request based on the current delivery address, and the delivery time is automatically selected, the determined server receives the user's payment request for the current order, so that the determined server can determine whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed.

[0060] Specifically, the user's order interface (200) can be referenced. Figure 2 ( Figure 2 (This is merely illustrative; the order interface in this application is not intended to be limiting.) When the server associated with the user's delivery address that performs the order placement operation meets congestion conditions, the delivery time can be automatically set to the "Automatically select delivery time" option when the user places an order. When the user selects the immediate payment control on interface 200, a payment request is sent to the determined server. After receiving the payment request, the determined server finds the delivery station corresponding to the delivery address and determines whether the delivery station has delivery capacity. Based on the delivery capacity, it determines whether to call the payment service to complete the user's order. In some other embodiments, in step S130, after the user completes the order payment operation, the determined server confirms the validity of the user's paid order based on the delivery capacity. If the delivery capacity is insufficient, some orders can be invalidated (some orders can be canceled).

[0061] Specifically, the delivery capacity in step S130 can be the number of delivery orders at the delivery station. The number of delivery orders can be calculated, for example, by the number of delivery personnel at the delivery station and the historical number of delivery orders. Furthermore, the delivery capacity can also be determined by comprehensively calculating the delivery route planning of placed orders, the weight and volume of goods, as well as the number of delivery personnel and delivery vehicles at the delivery station.

[0062] Specifically, community group buying delivery times are generally updated daily (same day or next day). Under normal server conditions, users can choose a specific time slot within a day for delivery. For example, users can be provided with delivery time options of 6:00-8:00; 8:00-10:00; 10:00-12:00; 12:00-14:00; 14:00-16:00; 16:00-18:00; 18:00-20:00; and 20:00-22:00 (from 22:00 to 6:00 the next day, inventory checks can be conducted, and deliveries are suspended). The server allocates the delivery station's capacity to each delivery time slot. When a user requests payment for an order, the server determines whether there is still available capacity for that time slot based on the user's selected delivery time slot. If so, the payment service is invoked; otherwise, the order fails, the server recalculates the available time slots, provides them to the user for selection, and the user places another order, sending a payment request to the server. Therefore, on the one hand, after receiving a user's payment request, the service needs to first check the current delivery capacity for the corresponding delivery time slot before it can perform a capacity assessment; on the other hand, since the delivery station's capacity is allocated across multiple time slots, and the delivery capacity for each time slot is relatively small, congestion is more likely to occur when too many people choose that delivery time slot, and the probability of order failure is higher; furthermore, since users will choose other delivery time slots to place orders again after an order fails, the number of payment requests received by the server will further increase, putting greater pressure on the server's load.

[0063] This application, by setting up automatic selection of delivery time slots, eliminates the need to query the delivery capacity for the corresponding delivery time slot when a user's payment request is received, reducing query steps; it also integrates the delivery capacity of delivery stations, reducing the probability of user order failure and improving the user ordering experience; furthermore, when a user's order fails, it indicates that the delivery station's capacity is insufficient for the day, so the user will not place a new order, thus reducing the number of payment requests the server needs to receive and lowering the server load pressure.

[0064] In one variation of this application, in addition to providing users with the option to automatically select a delivery time, the intervals between multiple delivery time slots can be adjusted in real time based on the server's congestion level. The more congested the server, the longer the intervals between delivery time slots. In a specific implementation, congestion parameters can be set for the server. When the congestion parameter (such as CPU utilization load parameters and / or the number of active users) reaches the congestion condition, the option to automatically select a delivery time is provided to the user; when the congestion parameter does not reach the congestion condition, the higher the congestion parameter, the longer the intervals between delivery time slots are provided. For example, the congestion parameter is the server's CPU utilization, and multiple segments can be set for the server's CPU utilization, such as CPU utilization below 60%; 60%-75%; 75%-90%; and above 90%. When CPU utilization is below 60%, delivery times can be divided into one-hour intervals (e.g., 6:00-7:00; 7:00-8:00, and so on). When CPU utilization is between 60% and 75%, delivery times can be divided into two-hour intervals (e.g., 6:00-8:00; 8:00-10:00, and so on). When CPU utilization is between 75% and 90%, delivery times can be divided into four-hour intervals (e.g., 6:00-10:00; 10:00-14:00, and so on). When CPU utilization is above 90%, users are provided with the option to automatically select a delivery time. Since longer intervals allow for more delivery capacity within the same time slot, reducing server load, this phased, real-time adjustment of delivery time intervals can alleviate server load while also catering to users and products with time-sensitive delivery requirements, thereby improving the user's community group-buying shopping experience.

[0065] In some embodiments, when the server is congested (e.g., the server is congested from 8:00 AM when orders open), the backend can delay order settlement for a set period of time (e.g., 5 to 10 minutes). During settlement, the number of users placing orders and delivery routes in each set time interval (e.g., 10 to 20 seconds) can be counted sequentially, and the availability of delivery capacity can be determined based on the statistical results. If the delivery capacity is sufficient, the number of users placing orders and delivery routes in the next set time interval can be calculated. If the delivery capacity is insufficient, the availability of delivery capacity can be determined by accumulating the number of orders placed by users in the next set time interval. If the delivery capacity is sufficient, the availability of delivery capacity can be determined by accumulating the number of orders placed by users in the next set time interval. If the delivery capacity is insufficient, orders can be randomly assigned to users who placed orders in the current set time interval, or orders from users with fewer orders can be prioritized, and orders for special categories (such as fresh produce) can be confirmed only in a limited manner. This application is not limited to these limitations.

[0066] Furthermore, within a certain time threshold after a user completes payment, the user's access requests to the community group-buying platform can be prohibited to further reduce server load.

[0067] In the community group-buying server ordering method provided by this invention, when the server executing orders for users is congested, an automatic selection of delivery time is provided in the delivery time option when the user places an order. Therefore, when a user places an order based on their current delivery address and the delivery time is set to automatic selection, the determined server can directly determine the corresponding delivery station based on the delivery address to assess delivery capacity and perform payment for the current order, without needing to differentiate delivery time and perform delivery capacity assessment based on delivery time. This avoids a large number of users placing orders simultaneously, leading to excessive server load and order failure where the delivery time is no longer available after the user selects and pays, thus improving the user ordering experience.

[0068] See below. Figure 3 , Figure 3 A flowchart illustrating how a server determines delivery capacity according to an embodiment of the present invention is shown. Figure 3 The following steps are shown:

[0069] Step S311: The determined server receives the user's payment request.

[0070] Step S312: In response to the user's order placement time, which falls within the third set time period threshold after the determined server meets the set congestion conditions, the determined server determines whether the corresponding delivery station has delivery capacity based on whether the current cumulative order volume of the delivery station meets the set total order volume.

[0071] If the current cumulative order volume of a delivery station meets the set total order volume, then the corresponding delivery station has no delivery capacity; if the current cumulative order volume of a delivery station is less than the set total order volume, then the corresponding delivery station has delivery capacity.

[0072] Specifically, the total order volume can be set as the estimated total order volume for the delivery station. The estimated total order volume can be calculated based on the delivery station's historical delivery data. For example, the average daily order volume of the delivery station can be calculated based on its historical delivery data, and this average daily order volume can be used as the estimated order volume. This application is not limited to this; the calculated average daily order volume can be adjusted to account for changes in the number of people and time (weekdays, holidays, promotional days, etc.). In other embodiments, the number of people at the delivery station, the means of transport, the date, etc., can also be input into a trained artificial intelligence model to predict the estimated total order volume for the delivery station. The artificial intelligence model can be trained using the delivery station's historical delivery data.

[0073] Specifically, the current cumulative order volume can be determined based on the cumulative number of orders already placed. See details below. Figure 4 and Figure 4 The explanation will not be repeated here.

[0074] Specifically, compared to real-time route planning and delivery time allocation based on orders, determining capacity based on order volume requires less computing power. Therefore, by setting a third set time period threshold, a less computationally intensive capacity determination method can be used during periods of server congestion, thereby reducing server load. This third set time period threshold can be preset. For example, it can be set as needed or determined based on historical server congestion duration. In some variations, server congestion status can be monitored in real time; when the server remains congested, a less computationally intensive capacity determination method is used. This application can implement many more variations, which will not be elaborated here.

[0075] Step S313: In response to the availability of delivery capacity at the delivery station, invoke the payment service to execute the payment operation for the current order.

[0076] See below. Figure 4 , Figure 4 A flowchart illustrating the server updating delivery capacity after a user successfully places and pays for an order, according to an embodiment of the present invention, is shown. Figure 4 The steps following step S313 are shown:

[0077] Step S314: In response to the user completing the payment operation for the current order, the determined server adds the user's current order to the current cumulative order quantity of the delivery station. Within the fourth set time period threshold after the user completes the payment operation for the current order, the determined server does not calculate the delivery route for the current order.

[0078] Specifically, to coordinate with the server's order volume statistics, when a user completes payment for the current order, the order can be added to the current cumulative order volume of the delivery station without calculating a delivery route for that order. Delivery routes will only be calculated for the order four pre-defined time intervals after the user completes payment, thus reducing server load during peak hours.

[0079] Specifically, the fourth time period threshold can be preset. For example, it can be set to half an hour, one hour, etc., as needed. The fourth time period threshold can also be determined based on the third time period threshold. For example, the fourth time period threshold can be equal to the third time period threshold. The fourth time period threshold can be obtained by subtracting the current time from the third time period threshold after server congestion. Many other variations are possible with this application, but will not be elaborated upon here.

[0080] See below. Figure 5 , Figure 5 A flowchart illustrating the server's calculation of delivery routes and delivery times according to an embodiment of the present invention is shown. Figure 5 The following steps are shown:

[0081] Step S315: After the user completes the payment operation for the current order, at the fifth set time period threshold, the server determines the delivery route based on the multiple orders of the delivery station.

[0082] Specifically, orders with similar delivery addresses can be assigned to the same delivery person, and delivery routes can be calculated for the delivery person based on the assigned orders. Other delivery route algorithms can be used in this application, which will not be elaborated here.

[0083] Step S316: The determined server allocates delivery time to each order according to the calculated delivery route and order order.

[0084] Specifically, the fifth set time period threshold is greater than or equal to the fourth set time period threshold. Further, the fifth set time period threshold can be preset. The fifth set time period threshold can also be determined based on the third set time period threshold. For example, the fifth time period threshold can be equal to the third time period threshold (or the sum of the third time period threshold and a buffer time period). The fifth time period threshold can be calculated by subtracting the current time from the time of the third time period threshold after server congestion (or the sum of that time difference and a buffer time period).

[0085] This allows for the calculation of delivery routes and allocation of delivery times for each order when the server is not congested, so that users can know the estimated delivery time and delivery status of their orders.

[0086] Furthermore, considering that even after the delivery capacity has been allocated, some users who were unable to place an order may still attempt to do so, therefore, the above-mentioned... Figures 3 to 5 In this implementation, for a certain period after a user completes payment, the system does not assign or select a delivery time in the background; it only calculates the total number of orders for the corresponding delivery station. After reaching the fifth time period threshold, the background then begins to calculate delivery routes and calculates and prompts delivery times according to the routes and order order. At this time, due to the delivery routes and the volume and weight of the goods in the orders, there may still be surplus delivery capacity. In this case, after completing the delivery capacity calculation, the released delivery capacity can be pushed to the user. Specifically, when delivery capacity is released, a delivery capacity release reminder can be sent to users whose orders failed, so that users can be informed of the delivery capacity release status and place orders again.

[0087] In some embodiments of this application, when the server is congested, if the ordered goods are out of stock but there is available delivery capacity, the user can directly initiate a payment request without confirming the order. This reduces the time required for user order confirmation, improves order efficiency, reduces the probability of order failure, and decreases the number of times the user needs to send a payment request to the server again.

[0088] The above are merely several specific implementations of the community group-buying server order placement method of the present invention. Each implementation can be implemented independently or in combination, and the present invention is not intended to limit it. Furthermore, the flowchart of the present invention is merely illustrative, and the execution order between the steps is not limited thereto. The splitting, merging, sequential exchange, and other synchronous or asynchronous execution methods of the steps are all within the protection scope of the present invention.

[0089] See below. Figure 6 , Figure 6 A block diagram of a community group-buying server ordering device according to an embodiment of the present invention is shown. The community group-buying server ordering device 400 includes a server determination module 410, an automatic delivery time selection and provision module 420, and a payment determination module 430.

[0090] The server determination module 410 is used to determine the server for placing an order for the user based on the user's current delivery address.

[0091] The automatic delivery time selection module 420 is used to provide automatic selection of delivery time among the delivery time options in response to the determined server meeting the set congestion conditions;

[0092] The payment confirmation module 430 is used to respond to the user's payment request based on the current delivery address, and the delivery time is automatically selected. The server receives the user's payment request for the current order so that the server can determine whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed.

[0093] In the community group-buying server ordering device of the exemplary embodiment of the present invention, when the server for executing orders for users is congested, an automatic selection of delivery time is provided in the option for the delivery time required when the user places an order. Thus, when a user places an order based on the current delivery address and the delivery time is set to automatic selection, the determined server can directly determine the corresponding delivery station based on the delivery address to assess delivery capacity and perform payment for the current order, without needing to distinguish delivery time and perform delivery capacity assessment based on delivery time. This avoids a large number of users placing orders simultaneously, leading to excessive server load and order failure where the user selects an expected delivery time and makes payment only to find that the delivery time is no longer available, thus improving the user ordering experience.

[0094] Figure 6 This illustration of the community group-buying server ordering device 400 provided by the present invention is merely schematic. Without departing from the inventive concept, the splitting, merging, and addition of modules are all within the scope of protection of this invention. The community group-buying server ordering device 400 provided by the present invention can be implemented by software, hardware, firmware, plugins, and any combination thereof; the present invention is not limited thereto.

[0095] In exemplary embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by, for example, a processor, can implement the steps of the community group-buying server order placement method described in any of the above embodiments. In some possible embodiments, various aspects of the present invention can also be implemented as a program product comprising program code, which, when run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the above-described community group-buying server order placement method section of this specification.

[0096] refer to Figure 7 As shown, a program product 700 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0097] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0098] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0099] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the tenant's computing device, partially on the tenant's device, as a standalone software package, partially on the tenant's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the tenant's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0100] In an exemplary embodiment of the present invention, an electronic device is also provided, which may include a processor and a memory for storing executable instructions of the processor. The processor is configured to perform the steps of the community group-buying server order placement method described in any of the above embodiments by executing the executable instructions.

[0101] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0102] The following reference Figure 8 To describe an electronic device 500 according to this embodiment of the present invention. Figure 8 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0103] like Figure 8 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), a display unit 540, etc.

[0104] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the above-described community group-buying server order placement method section of this specification, according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform, as follows: Figure 1 The steps are shown.

[0105] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 5201 and / or a cache storage unit 5202, and may further include a read-only memory unit (ROM) 5203.

[0106] The storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0107] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0108] Electronic device 500 can also communicate with one or more external devices 600 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable tenants to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. Network adapter 560 can communicate with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0109] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the community group-buying server order placement method described above according to the embodiments of the present invention.

[0110] Compared with the prior art, the advantages of this invention are:

[0111] When the server processing orders is congested, an automatic delivery time selection option can be provided in the order placement settings. This allows the server to directly determine the corresponding delivery station based on the address and perform delivery capacity assessment and payment processing without needing to differentiate between delivery times. This avoids excessive server load caused by a large number of simultaneous orders, preventing order failures where users select and pay for an unavailable delivery time, thus improving the user experience.

[0112] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the appended claims.

Claims

1. A method for placing orders on a community group-buying server, characterized in that, include: Based on the user's current shipping address, determine the server that will place the order for the user. In response to the determined server meeting the set congestion conditions, an automatic selection of delivery time is provided in the delivery time options; In response to the user's payment request based on the current delivery address, and the delivery time is set to automatically select the delivery time, the determined server receives the user's payment request for the current order, so that the determined server can determine whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed. The server determines whether the delivery station corresponding to the delivery address has delivery capacity by including: In response to the user's order placement time, if it falls within a third predetermined time period threshold after the server meets the predetermined congestion conditions, the server determines whether the corresponding delivery station has delivery capacity based on whether the current accumulated order volume of the delivery station meets the predetermined total order volume. If the current cumulative order volume of a delivery station meets the set total order volume, then the corresponding delivery station has no delivery capacity; if the current cumulative order volume of a delivery station is less than the set total order volume, then the corresponding delivery station has delivery capacity.

2. The community group-buying server ordering method as described in claim 1, characterized in that, The congestion conditions set include one or more of the following congestion conditions: The real-time load of the determined server is greater than the set load threshold. The number of active users on the identified server exceeds the set threshold. The time difference between the current time and the determined server product sales time and / or capacity opening time is less than the first set time period threshold.

3. The community group-buying server ordering method as described in claim 2, characterized in that, The active users include: Users currently operating the community group buying client; and / or Users who have engaged in communication with the designated server within a second set time period threshold.

4. The community group-buying server ordering method as described in claim 1, characterized in that, The option to automatically select a delivery time includes: Cancel the option for the original delivery time and automatically select the delivery time as the automatic selection delivery time; or The options for the delivery time include the original time and automatic selection of the delivery time.

5. The community group-buying server ordering method as described in claim 1, characterized in that, The third set time period threshold is preset or determined based on the duration during which the server transitions from meeting the set congestion conditions to meeting the set idle conditions.

6. The community group-buying server ordering method as described in claim 1, characterized in that, Within a fourth set time period threshold after the user completes the payment operation for the current order, the server will add the user's current order to the current cumulative order quantity of the delivery station. The fourth set time period threshold is preset or determined according to the third set time period threshold.

7. The community group-buying server ordering method as described in claim 6, characterized in that, After the fifth set time period threshold following the user's completion of the payment operation for the current order, the following is also included: The determined server calculates delivery routes based on multiple orders from the delivery station; and The designated server allocates delivery times to each order according to the calculated delivery routes and order order sequence. Wherein, the fifth set time period threshold is greater than or equal to the fourth set time period threshold.

8. The community group-buying server ordering method as described in claim 7, characterized in that, The fifth set time period threshold is preset or determined based on the third set time period threshold.

9. A community group-buying server ordering device, characterized in that, include: The server determination module is used to determine the server that will place the order for the user based on the user's current delivery address. An automatic delivery time selection module is provided to automatically select a delivery time from the options in response to a determined server meeting set congestion conditions; The payment confirmation module, in response to the user's payment request based on the current delivery address and the delivery time being automatically selected, enables the server to receive the user's payment request for the current order, so that the server can determine whether the delivery station corresponding to the delivery address has delivery capacity. If delivery capacity exists, the order corresponding to the payment request is confirmed. The determination by the server of whether the delivery station corresponding to the delivery address has delivery capacity includes: In response to the user's order placement time, if it falls within a third predetermined time period threshold after the server meets the predetermined congestion conditions, the server determines whether the corresponding delivery station has delivery capacity based on whether the current accumulated order volume of the delivery station meets the predetermined total order volume. If the current cumulative order volume of a delivery station meets the set total order volume, then the corresponding delivery station has no delivery capacity; if the current cumulative order volume of a delivery station is less than the set total order volume, then the corresponding delivery station has delivery capacity.

10. An electronic device, characterized in that, The electronic device includes: processor; A memory, on which a computer program is stored, is executed by the processor during runtime: The community group buying server ordering method as described in any one of claims 1 to 8.

11. A storage medium, characterized in that, The storage medium stores a computer program, which is executed by a processor: The community group buying server ordering method as described in any one of claims 1 to 8.

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