Task processing method, device and system

Abstract

The present application provides a task processing method, device and system, the task processing method comprising: a task scheduling device sends a task trigger instruction to a first device, the first device sends a first task to multiple second devices according to the instruction, each second device corresponds to the first task in a different region. Subsequently, the second device sends a second task to multiple third devices according to its corresponding first task, each third device corresponds to a different self-pickup point in the same region, and the second task is used to trigger the third device to count the logistics statistics of the target self-pickup point in the target region corresponding to the third device. The above method enables multiple devices to concurrently count the logistics data of self-pickup points in various regions through multi-level task distribution, thereby improving the efficiency of logistics statistics and providing data support for route planning at the logistics transportation end.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a task processing method, device, and system. Background Art

[0002] Community group buying is an online and offline shopping activity conducted by resident groups within a real residential community. It is a regionalized, niche, localized, and networked group buying model based on real communities. Consumers can select group-purchased items through the community group buying service system provided by the information platform, such as a standalone application or a mini-program or lightweight application running within other applications. When placing an order, they can select a pickup point, such as a local supermarket or courier service outlet. Items ordered to the same pickup point can be delivered together to improve delivery efficiency.

[0003] With the rapid development of community group buying and the increasing volume of orders, the cargo computing performance of the original information platform could no longer meet long-term needs. Slow SQL, high memory and CPU consumption could severely impact all aspects of the community group buying business, even causing business unavailability and impacting the efficiency, stability, and accuracy of the information platform's logistics data (including cargo volume). Summary of the Invention

[0004] The embodiments of the present application provide a task processing method, device and system to improve the efficiency of logistics data statistics.

[0005] A first aspect of an embodiment of the present application provides a task processing method, applied to a first device, the method comprising:

[0006] Periodically receiving task triggering instructions from a task scheduling device;

[0007] Sending the first task to multiple second devices according to the task triggering instruction;

[0008] The first task is used to trigger the second device to send the second task to multiple third devices, and the second task is used to trigger the third device to collect logistics statistics of the target pick-up point in the target area corresponding to the third device.

[0009] In an optional embodiment of the first aspect of the present application, sending the first task to multiple second devices according to the task triggering instruction includes:

[0010] Randomly selecting the plurality of second devices from the device cluster according to the task triggering instruction;

[0011] sending the first task to each second device of the plurality of second devices;

[0012] The first task includes a database identifier and a data table identifier of a target region, and the first tasks of different second devices correspond to different target regions.

[0013] A second aspect of the present application provides a task processing method, applied to a second device, the method comprising:

[0014] receiving a first task from a first device;

[0015] A second task is sent to multiple third devices according to the first task, where the second task is used to trigger the third devices to collect logistics statistics of target pick-up points in the target area corresponding to the third devices.

[0016] In an optional embodiment of the second aspect of the embodiment of the present application, the first task includes a database identifier and a data table identifier of the target area;

[0017] The sending of the second task to the plurality of third devices according to the first task includes: obtaining the total number of self-pickup points in the target area according to the database identifier and the data table identifier of the target area;

[0018] Determining the number of self-pickup point groups according to the total number of self-pickup points and the number of self-pickup points in the preset self-pickup point group, where the number of self-pickup point groups is equal to the number of the third devices;

[0019] The plurality of third devices are randomly selected from the device cluster according to the number of the third devices, and the second task is sent to each of the plurality of third devices.

[0020] In an optional embodiment of the second aspect of the present application, the second task includes a database identifier, a data table identifier, and a self-pickup point identifier of the target area, and different third devices correspond to different self-pickup points in the same target area.

[0021] A third aspect of an embodiment of the present application provides a task processing method, applied to a third device, the method comprising:

[0022] receiving a second task from a second device, and obtaining, according to the second task, logistics order data associated with a target pickup point in a target area within a first preset time period;

[0023] Determining logistics statistics of the target self-pickup point within the first preset time period based on the logistics order data, the logistics statistics including at least one of a total number of logistics orders and a total number of items in all logistics orders;

[0024] Determine whether to update the logistics statistics of the target self-pickup point within a second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period; the second preset time period includes multiple consecutive first preset time periods.

[0025] In an optional embodiment of the third aspect of the present application, the second task includes a database identifier, a data table identifier, and a target self-pickup point identifier of the target area;

[0026] The step of obtaining logistics order data associated with a target pickup point in a target area within a first preset time period according to the second task includes:

[0027] According to the database identifier and data table identifier of the target area, a data table corresponding to the target area is obtained from a first database cluster; the first database cluster is used to store logistics order data of different areas and logistics statistics of self-pickup point groups;

[0028] According to the target self-pickup point identifier, the logistics order data associated with the target self-pickup point in the target area within the first preset time period is obtained from the data table corresponding to the target area.

[0029] In an optional embodiment of the third aspect of the present application, the logistics order data includes the identifiers of all logistics orders associated with the target pick-up point in the target area within the first preset time period, and the number of items in each logistics order.

[0030] In an optional embodiment of the third aspect of the present application, obtaining, from a data table corresponding to the target area, logistics order data associated with the target self-pickup point in the target area within the first preset time period based on the identifier of the target self-pickup point includes:

[0031] According to the identifier of the target self-pickup point, obtaining, from the logistics order item list corresponding to the target area, the identifiers of all logistics orders associated with the target self-pickup point in the target area within the first preset time period, and the quantity of items on each logistics order;

[0032] The logistics order item list includes the logistics order identification, the quantity of items and the pick-up point identification.

[0033] In an optional embodiment of the third aspect of the present application, determining whether to update the logistics statistics of the target self-pickup point within the second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period includes:

[0034] Obtaining historical logistics statistics of the target self-pickup point within the second preset time period from the self-pickup point group information statistics table corresponding to the target area; the self-pickup point group information statistics table includes historical logistics statistics of each group of self-pickup points;

[0035] If the logistics statistical data of the target self-pickup point within the first preset time period is not 0, the logistics statistical data of the target self-pickup point within the second preset time period is updated according to the logistics statistical data of the target self-pickup point within the first preset time period and the historical logistics statistical data of the target self-pickup point within the second preset time period.

[0036] In an optional embodiment of the third aspect of the present application, the method further includes: if it is determined to update the logistics statistical data of the target pick-up point within the second preset time period, recording the update information in the binary log file of the third device.

[0037] In an optional embodiment of the third aspect of the present application, the method further comprises: at the end of the second preset time period, obtaining final logistics statistical data of the target self-pickup point within the second preset time period;

[0038] The final logistics statistics are sent to a message middleware, and the message middleware is used to forward the final logistics statistics to a logistics transportation end.

[0039] A fourth aspect of the present application provides a task processing device, including:

[0040] A receiving module, configured to periodically receive task triggering instructions from a task scheduling device;

[0041] The sending module is used to send the first task to multiple second devices according to the task trigger instruction; the first task is used to trigger the second device to send the second task to multiple third devices, and the second task is used to trigger the third device to count the logistics statistical data of the target pick-up point in the target area corresponding to the third device.

[0042] A fifth aspect of an embodiment of the present application provides a task processing device, including:

[0043] A receiving module, configured to receive a first task from a first device;

[0044] A sending module is used to send a second task to multiple third devices based on the first task, and the second task is used to trigger the third device to collect logistics statistics of the target pick-up point in the target area corresponding to the third device.

[0045] A sixth aspect of the present application provides a task processing device, including:

[0046] A receiving module, configured to receive a second task from a second device;

[0047] an acquisition module, configured to acquire, according to the second task, logistics order data associated with a target pickup point in a target area within a first preset time period;

[0048] a processing module, configured to determine, based on the logistics order data, logistics statistics of the target self-pickup point within the first preset time period, the logistics statistics including at least one of a total number of logistics orders and a total number of items in all logistics orders;

[0049] Determine whether to update the logistics statistics of the target self-pickup point within a second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period; the second preset time period includes multiple consecutive first preset time periods.

[0050] A seventh aspect of an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program; the computer program is stored in the memory and is configured to be executed by the processor to implement a method as described in any one of the first aspects of the present application, or a method as described in any one of the second aspects of the present application, or a method as described in any one of the third aspects of the present application.

[0051] An eighth aspect of the embodiments of the present application provides a task processing system, comprising: a task scheduling device and a device cluster, wherein the device cluster comprises a plurality of devices, and the task scheduling device is connected to each device in the device cluster;

[0052] The task scheduling device is used to periodically send a task trigger instruction to a first device, where the first device is a device randomly selected by the task scheduling device from the device cluster;

[0053] The first device is used to perform the method according to any one of the first aspects of this application;

[0054] The second device is used to perform the method according to any one of the second aspects of this application;

[0055] The third device is used to execute the method as described in any one of the third aspects of this application.

[0056] A ninth aspect of the embodiments of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method as described in any one of the first aspects of the present application, or the method as described in any one of the second aspects of the present application, or the method as described in any one of the third aspects of the present application.

[0057] The tenth aspect of the embodiments of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any one of the first aspects of the present application, or the method described in any one of the second aspects of the present application, or the method described in any one of the third aspects of the present application.

[0058] The embodiments of the present application provide a task processing method, device, and system, wherein the task processing method includes: a task scheduling device sends a task trigger instruction to a first device, and the first device sends a first task to multiple second devices according to the instruction, and each second device corresponds to the first task in a different region. Subsequently, the second device sends a second task to multiple third devices according to its corresponding first task, and each third device corresponds to a different self-pickup point in the same region. The second task is used to trigger the third device to count the logistics statistics of the target self-pickup point in the target region corresponding to the third device. The above method realizes the parallel statistics of the logistics data of the self-pickup points in each region by multiple devices through multi-level task distribution, thereby improving the efficiency of logistics statistics and providing data support for route planning at the logistics transportation end. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the architecture of a task processing system provided in an embodiment of the present application;

[0060] Figure 2 Schematic diagram of the sub-library and sub-table of the first database cluster and the second database cluster provided in the embodiment of the present application;

[0061] Figure 3 A schematic diagram of downstream applications of the task processing method provided in an embodiment of the present application;

[0062] Figure 4 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 1 ;

[0063] Figure 5 A flowchart of a method for processing a task executed by a first device according to an embodiment of the present application;

[0064] Figure 6 A flowchart of a method for processing a task executed by a second device provided in an embodiment of the present application;

[0065] Figure 7 A flowchart of a method for processing a task executed by a third device provided in an embodiment of the present application;

[0066] Figure 8 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 2 ;

[0067] Figure 9 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 3 ;

[0068] Figure 10 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 1 ;

[0069] Figure 11 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 2 ;

[0070] Figure 12 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 3 ;

[0071] Figure 13 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0073] The terms "first", "second", etc. in the specification, claims, and above-mentioned drawings of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than as illustrated or described herein. It should be understood that the terms "including" and "having" used herein and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0074] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and being indicated, configuration and being configured, etc.

[0075] Community group buying differs from traditional e-commerce in that it is a group-buying model, a new business model that has emerged in the era of social e-commerce. Especially during the severe pandemic, community group buying has also changed the shopping habits of some people, proving its value.

[0076] As community group buying orders increase, logistics and distribution will become one of the core competitive advantages of community group buying. Improving logistics and distribution efficiency is an urgent issue that needs to be explored. Logistics and distribution require refined data analysis, such as accurately counting the delivery volume of self-pickup points in different regions and optimizing transportation routes based on the delivery volume of self-pickup points, thereby improving logistics and distribution efficiency across the region.

[0077] At this stage, the cargo volume calculation performance of the original information platform can no longer meet long-term needs. Although core order data can be retained for, for example, 10 days, the daily increase in order volume, for example, 20 million, will also put pressure on the platform's data capacity and affect computing performance. Frequent SQL fullness has seriously affected the efficiency, stability and accuracy of the platform's logistics statistics, including cargo volume statistics.

[0078] To address the above issues, the present invention provides a task processing solution that can improve the efficiency, stability, and accuracy of logistics statistics. The main inventive concepts are as follows:

[0079] First, by building a device cluster, the logistics statistics tasks of self-pickup points in different regions can be distributed to multiple devices in the device cluster. Multiple devices process their respective logistics statistics tasks in parallel, thereby improving logistics statistics efficiency.

[0080] Second, by building a computing database cluster, each database in the computing database cluster stores the logistics order data of the self-pickup points in its own area. For example, the logistics order data is stored in the form of logistics order table, logistics order item table, self-pickup point group information statistics table, etc. Each device in the equipment cluster accesses the logistics order data of different databases in the database cluster respectively to perform logistics statistics, thereby improving the stability of logistics statistics.

[0081] Third, by periodically triggering devices in the device cluster to execute logistics statistics tasks, the latest logistics statistics for each region's pick-up points can be obtained in a timely manner, thereby accurately ensuring the accuracy of logistics statistics. Furthermore, to ensure the accuracy of logistics statistics obtained by the logistics transportation end at the end of each day, the final logistics statistics can be updated to the logistics transmission end through a comprehensive distribution method.

[0082] In order to facilitate the understanding of the technical solution of this application, first combine Figure 1 The task processing system according to the embodiment of the present application is described.

[0083] Figure 1 This is a schematic diagram of the architecture of the task processing system provided in the embodiment of the present application. Figure 1As shown, the task processing system includes a task scheduling device, a device cluster, a first database cluster, and a second database cluster. The task scheduling device can communicate with each device in the device cluster, and the devices in the device cluster can communicate with each other. Each device in the device cluster can access each database in the first database cluster and obtain data from the database. Data synchronization can be performed between the first and second database clusters.

[0084] It should be noted that the total amount of data in all databases in the first database cluster is less than the total amount of data in all databases in the second database cluster. Figure 1 Here m1<m2.

[0085] The databases in the first database cluster primarily store logistics-related data. Optionally, the various databases in the first database cluster include, but are not limited to, a logistics order table, a logistics order item table, and a self-pickup point group information statistics table. The first database cluster can be considered a computational library, containing relevant data for calculating logistics statistics for self-pickup points in different regions.

[0086] The second database cluster stores not only logistics-related data, but also data on orders, exchanges, returns, and after-sales service. This second database cluster can be considered a central repository, containing all data from the entire platform process, from order generation to after-sales service.

[0087] As an example, the task scheduling device periodically sends a message to any device in the device cluster, such as Figure 1 Device 1 in the task scheduler sends a task trigger instruction. Device 1 is a device randomly selected from the device cluster by the task scheduler. The task scheduler sends the task trigger instruction at a preset interval, for example, every 5 minutes. This means that the task scheduler sends the task trigger instruction once every 5 minutes. The task trigger instruction can be considered a first-level task of the logistics statistics task.

[0088] As an example, device 1 sends a first task to multiple devices in the device cluster according to the task trigger instruction, such as Figure 1 Device 1 sends a first task to each of devices 2 through 8. Devices 2 through 8 are randomly selected by device 1 from the device cluster. The first task can be considered a secondary task of the logistics statistics task, indicating the identifier of a database in the first database cluster. For example, the first task sent by device 1 to device 2 includes the identifier of database 1 in the first database cluster, which stores logistics data for a specific region (such as a province or city). It should be understood that the specific instructions of the first task received by devices 2 through 8 are different, meaning that devices 2 through 8 correspond to secondary tasks for different regions.

[0089] As an example, device 2 sends a second task to multiple devices in the device cluster based on the first task, such as Figure 1 Device 2 sends a second task to each of devices 9 to 15. Devices 9 to 15 are randomly selected by device 2 from the device cluster. The second task can be regarded as a third-level task of the logistics statistics task. In addition to indicating the identifier of the database in the first database cluster, it also indicates the identifier of the self-pickup point. It should be understood that the second task received by devices 9 to 15 includes the same database identifier, such as the identifier of library 1 in the first database cluster, but the self-pickup point identifier is different, that is, devices 9 to 15 correspond to logistics statistics tasks for different self-pickup points (groups) in the same area.

[0090] It should be noted that, under normal circumstances, the second task indicates the identifications of multiple pick-up points, and these multiple pick-up points are relatively concentrated in the geographical location of the region. For example, the distance between any two of the multiple pick-up points is less than the preset distance. These multiple pick-up points can be regarded as a pick-up point group.

[0091] As an example, the first database cluster can complete data synchronization with the second database cluster through the message middleware ( Figure 1 (Not shown). Based on the above description, the data synchronization between the first database cluster and the second database cluster mainly synchronizes the data related to the logistics order.

[0092] As an example, the first database cluster is used to store logistics order data for different regions and logistics statistics for self-pickup point groups. The second database cluster is used to store logistics order data for different regions and data related to other business links, such as return and exchange data and after-sales service data.

[0093] Figure 2 This is a schematic diagram of the sub-databases and sub-tables of the first database cluster and the second database cluster provided in the embodiment of the present application. Figure 2 As shown, the first database cluster includes, for example, 8 databases, respectively recorded as compute_0000 to compute_0007. Each database includes data tables for multiple regions. For example, the compute_0000 database contains data tables for 16 regions, and the data tables are identified as 0000 to 0015. According to the specific content of the data table, it can be further subdivided into three types of tables, namely, the logistics order table, the logistics order item table, and the self-pickup point group information statistics table. For example, the data table of region A is identified as 0000, which includes the logistics order table, the logistics order item table, and the self-pickup point group information statistics table of region A.

[0094] Similar to the first database cluster, the second database cluster includes, for example, 32 databases, designated center_0000 through center_0031. Each database also includes data tables for multiple regions. Considering that each database contains data for other business processes in addition to regional logistics order data, the number of regional data tables contained in each database is smaller than that contained in each database in the first database cluster. For example, center_0000 contains only data tables for four regions. Each regional data table is also divided into three categories based on its specific content, and the labeling method is the same as above.

[0095] It should be understood that the total number of data tables related to logistics order data in the first database cluster and the second database cluster is the same. For example, the first database cluster includes 128 tables, and the second database cluster also includes 128 tables.

[0096] As an example, data in the data tables related to logistics data in the first database cluster and the second database cluster are synchronized according to the database configuration information, wherein the database configuration information includes the database-table mapping relationship (sub-database and sub-table routing rules) between the first database cluster and the second database cluster.

[0097] based on Figure 1 The system architecture shown below is combined with Figure 3 Describe the downstream applications of the equipment in the equipment cluster that performs the third-level tasks of logistics statistics tasks.

[0098] Figure 3 Schematic diagram of downstream applications of the task processing method provided in the embodiment of this application. Figure 3 As shown, the devices performing the third-level logistics statistics task are devices 9 through 15. Each of these devices is connected to a message-based middleware, which in turn is connected to the logistics transport end. For example, the message-based middleware can obtain logistics statistics from any of these devices (this data could be new or updated) and send it to the logistics transport end. The logistics transport end then orchestrates transportation routes based on the device's logistics statistics.

[0099] For example, Figure 3 Device 9 calculates the total cargo volume for a particular pickup point in a certain area for the day. The message middleware retrieves this total from device 9 and sends it to the logistics and transportation end. Similarly, the logistics and transportation end obtains the total cargo volume counted by devices 10 through 15. Since different devices correspond to different pickup points in the same area, the logistics and transportation end can determine the transportation route based on the order of the devices' total cargo volume, determining which pickup points to visit first and which to visit later.

[0100] Optionally, the message middleware obtains logistics statistics through the following two possible implementation methods:

[0101] In one possible implementation, if any device from device 9 to device 15 determines logistics statistics data (or updated data of logistics statistics data), the logistics statistics data of the device (or updated data of logistics statistics data) is sent to the message middleware.

[0102] In a possible implementation, the message middleware monitors devices 9 to 15, and if it is determined that any of the devices has updated logistics statistics data, it obtains updated logistics statistics data from the device.

[0103] In this embodiment, devices 9 through 15 all have logistics data statistics and guaranteed delivery capabilities. Logistics data statistics refer to the number of logistics orders and total shipments at several pick-up points in a designated area within a preset time period, such as a single day. The guaranteed delivery function requires that after the preset time period, the device sends its final logistics statistics to the message-based middleware, which then synchronizes them with the logistics transport end, preventing missed orders and ensuring the accuracy of transportation and fulfillment data.

[0104] The following describes in detail the task processing solution provided by the embodiments of the present application through several specific embodiments. It should be noted that the technical solution provided by the embodiments of the present application may include part or all of the following contents, and the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0105] Figure 4 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 1 The task processing method of this embodiment involves the interaction between the first device, the second device and the third device, wherein the first device may correspond to Figure 1 The device 1 shown, the second device may correspond to Figure 1 The example shown is device 2, of course the second device can also be Figure 1 One of the devices 3 to 8, the third device can correspond to Figure 1 One of the devices 9 to 15 shown.

[0106] like Figure 4 As shown, the task processing method includes the following steps:

[0107] Step 101: A task scheduling device sends a task triggering instruction to a first device.

[0108] In this embodiment, the task scheduling device sends a task trigger instruction to the first device according to a preconfigured task scheduling period. The first device is any device in the device cluster. Those skilled in the art can appropriately set the preconfigured task scheduling period based on actual needs. For example, setting it to 5 minutes means that the task scheduling device sends a task trigger instruction to the first device every 5 minutes. This embodiment of the present application does not impose any limitations on this.

[0109] Step 102: The first device sends the first task to the second device according to the task triggering instruction.

[0110] It should be noted that, typically, a first device sends a first task to multiple second devices based on a task trigger instruction. The first tasks of these multiple second devices correspond to different regions, and the number of second devices matches the number of regions. For example, if logistics data comes from 20 regions, the first device will send the first task to 20 second devices based on the task trigger instruction.

[0111] For the convenience of description, this step is described based on only one of the multiple second devices.

[0112] In this embodiment, the first task is used to trigger the second device to send the second task to multiple third devices. The second device is any device in the device cluster except the first device.

[0113] Step 103: The second device sends the second task to the third device according to the first task.

[0114] It should be noted that, typically, the second device sends the second task to multiple third devices based on the first task. The second tasks of these multiple third devices correspond to different pickup point groups in the target area, and the number of third devices matches the number of pickup point groups in the target area. For example, if target area A includes three pickup point groups, each with ten closely spaced pickup points, the second device will send the second task to the three third devices based on the first task.

[0115] For the convenience of description, this step is described based on only one of the multiple third devices.

[0116] In this embodiment, the second task triggers a third device to collect logistics statistics for the target pickup points in the target region corresponding to the third device. The third device is any device in the device cluster other than the first and second devices. The number of target pickup points can be one or more.

[0117] Step 104: The third device obtains logistics order data associated with the target pick-up point in the target area within the first preset time period according to the second task.

[0118] It should be noted that the duration of the first preset period is the same as the task scheduling period. For example, if the task scheduling period is 5 minutes, the duration of the first preset period is 5 minutes. This means that the third device, based on the second task, obtains the logistics order data associated with the target pickup point in the target area for the previous 5 minutes.

[0119] Specifically, the third device obtains logistics order data associated with the target pick-up point in the target area within the first preset time period from the first database cluster according to the second task.

[0120] Step 105: The third device determines the logistics statistics of the target self-pickup point within the first preset time period based on the logistics order data. In this embodiment, the logistics statistics include at least one of the total number of logistics orders and the total number of items in all logistics orders.

[0121] Step 106: The third device determines whether to update the logistics statistics of the target self-pickup point within the second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period.

[0122] The first preset period includes multiple consecutive first preset periods. For example, if the first preset period is 5 minutes long and the second preset period is from 9:00 AM to 9:00 PM every day (720 minutes), then the second preset period includes 144 consecutive first preset periods. That is, logistics data is collected every 5 minutes starting from 9:00 AM every day.

[0123] Specifically, if the third device determines that the logistics statistics data of the target self-pickup point within the first preset time period is not 0, the logistics statistics data of the target self-pickup point within the second preset time period are updated.

[0124] In the task processing method illustrated in this embodiment, a task scheduling device sends a task trigger instruction to a first device. The first device, based on the instruction, sends a first task to multiple second devices, each of which corresponds to a first task in a different region. Subsequently, the second device sends a second task to multiple third devices based on its corresponding first task. Each third device corresponds to a different pickup point in the same region. The second task is used to trigger the third device to collect logistics statistics for the target pickup point in the target region corresponding to the third device. The above method, through multi-level task issuance, enables multiple devices to collect logistics data from pickup points in various regions in parallel, thereby improving logistics statistics efficiency and providing data support for route planning at the logistics transportation end.

[0125] Based on the above embodiments, the specific processes of the first device, the second device, and the third device executing the task processing method are described in detail below through several embodiments.

[0126] Figure 5 Schematic diagram of the process of executing a task by a first device according to an embodiment of the present application. Figure 5As shown, the task processing method of this embodiment includes the following steps:

[0127] Step 201: Periodically receive a task trigger instruction from a task scheduling device.

[0128] Step 202: According to the task triggering instruction, a plurality of second devices are randomly selected from the device cluster.

[0129] In this embodiment, after receiving the task trigger instruction, the first device first obtains the database identifier and the data table identifier of each database from the first database cluster. Based on the database identifier and the data table identifier of each database, it determines the number of regions. Then, based on the number of regions, it randomly selects multiple second devices from the device cluster. The number of regions is the number of second devices selected.

[0130] Step 203: Send the first task to each second device of the plurality of second devices.

[0131] In this embodiment, the first task includes the database identifier and data table identifier of the target region, that is, the first device sends the database identifier and data table identifier of the target region as a task list to the second device. It should be understood that the first tasks of different second devices correspond to different target regions.

[0132] The task processing method shown in this embodiment mainly involves the first device. Under the triggering of the task scheduling device, the first device generates a first-level task for the logistics statistics task. The first-level task includes a database identifier and a data table identifier of the target area, and sends the corresponding first-level task to multiple second devices.

[0133] Figure 6 Schematic diagram of the process of executing a task by a second device according to an embodiment of the present application. Figure 6 As shown, the task processing method of this embodiment includes the following steps:

[0134] Step 301: Receive a first task from a first device, where the first task includes a database identifier and a data table identifier of a target region.

[0135] Step 302: Obtain the total number of pick-up points in the target area based on the database identifier and data table identifier of the target area.

[0136] In an optional embodiment of this embodiment, the second device obtains the logistics order item table of the target area from the corresponding database table of the first database cluster based on the database identifier and data table identifier of the target area. The logistics order item table includes the logistics order identifier, the quantity of items and the pick-up point identifier, and can count the total number of pick-up points in the target area.

[0137] Step 303: Determine the number of self-pickup point groups based on the total number of self-pickup points and the number of self-pickup point groups in the preset self-pickup point groups. The number of self-pickup point groups is equal to the number of third devices.

[0138] In an optional embodiment of this embodiment, the second device can unify all the pick-up point identifiers from the logistics order item table into a list, and then divide them into multiple pick-up point groups according to the number of pick-up points in the preset pick-up point groups, and determine the pick-up point identifier of each pick-up point group.

[0139] For example, the total number of self-pickup points in a city is 2,000. If they are divided into groups of 50, the number of self-pickup point groups can be determined to be 40.

[0140] Step 304: randomly select multiple third devices from the device cluster according to the number of third devices.

[0141] Step 305: Send the second task to each third device of the plurality of third devices.

[0142] In this embodiment, the second task includes the database identifier, data table identifier, and pickup point identifier of the target region. That is, the second device sends the database identifier, data table identifier, and pickup point identifier of the target region as a task list to the third device. It should be understood that second tasks on different second devices may correspond to different pickup points in the same region.

[0143] Based on the example of step 303, the second device randomly selects 40 third devices from the device cluster, and each third device calculates the logistics statistics of a certain pick-up point group in the city.

[0144] The task processing method shown in this embodiment mainly involves the second device. Under the triggering of the first device, the second device generates a secondary task of the logistics statistics task. The secondary task includes the database identifier, data table identifier and self-pickup point identifier of the target area, and sends the corresponding secondary tasks to multiple third devices.

[0145] Figure 7 This is a flow chart of a method for processing a task performed by a third device provided in an embodiment of the present application. Figure 7 As shown, the task processing method of this embodiment includes the following steps:

[0146] Step 401: Receive a second task from a second device, where the second task includes a database identifier, a data table identifier, and a self-pickup point identifier of a target area.

[0147] Step 402: According to the database identifier and data table identifier of the target region, obtain the data table corresponding to the target region from the first database cluster.

[0148] The first database cluster is used to store logistics order data of different regions and logistics statistics of self-pickup point groups. The data tables corresponding to the target region include three categories, namely logistics order table, logistics order item table and self-pickup point group information statistics table. The logistics order table includes the identification of the logistics order of the target region, the logistics order item table includes the identification of the logistics order of the target region, the number of items and the self-pickup point identification. The self-pickup point group information statistics table includes the historical logistics statistics of each group of self-pickup points in the target region.

[0149] Step 403: Based on the target pickup point identifier, obtain the logistics order data associated with the target pickup point in the target area within the first preset period from the data table corresponding to the target area. The logistics order data includes the identifiers of all logistics orders and the quantity of items in each logistics order.

[0150] In an optional embodiment of this embodiment, according to the target pick-up point identifier, the identifiers of all logistics orders associated with the target pick-up point in the target area within the first preset time period and the number of items in each logistics order are obtained from the logistics order item table corresponding to the target area.

[0151] Step 404: Determine the logistics statistics of the target pick-up point within the first preset time period based on the logistics order data.

[0152] The logistics statistics include the total number of logistics orders and the total number of items in all logistics orders. It should be noted that the logistics statistics of the target self-pickup point within the first preset time period refer to the logistics statistics of the logistics orders newly generated by the target self-pickup point within the first preset time period.

[0153] Step 405: Obtain historical logistics statistics of the target pick-up point within the second preset time period.

[0154] In an optional embodiment of this embodiment, historical logistics statistics of the target self-pickup point within the second preset time period are obtained from the self-pickup point group information statistics table corresponding to the target area.

[0155] Step 406: If the logistics statistics of the target pickup point during the first preset period are not zero, the logistics statistics of the target pickup point during the second preset period are updated based on the logistics statistics of the target pickup point during the first preset period and the historical logistics statistics of the target pickup point during the second preset period. The second preset period includes multiple consecutive first preset periods.

[0156] Specifically, if the logistics statistical data of the target pick-up point in the first preset time period is not 0, the sum of the logistics statistical data of the target pick-up point in the first preset time period and the historical logistics statistical data of the target pick-up point in the second preset time period will be used as the latest logistics statistical data of the target pick-up point in the second preset time period.

[0157] For example, the second preset time period is from 9 am to 9 pm every day, the duration of the first preset time period is 5 minutes, the historical logistics statistics of the target pick-up point as of the current moment are 12 logistics orders and 30 items, and the logistics statistics within 5 minutes after the current moment are 1 logistics order and 2 items. Then, the logistics statistics of the target pick-up point 5 minutes after the current moment are updated to 13 logistics orders and 32 items.

[0158] The task processing method shown in this embodiment mainly involves the third device. Under the triggering of the second device, the third device executes the third-level task of the logistics statistics task, and counts the logistics statistical data of the target self-pickup point in the target area within the first preset time period. If the data is not 0, it indicates that the logistics data of the target self-pickup point in the target area has been added, and the logistics statistical data of the target self-pickup point in the second preset time period needs to be updated in time.

[0159] Based on the above embodiments, Figure 8 and Figure 9 The downstream application scheme of the third device is described in detail.

[0160] Figure 8 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 2 The task processing method of this embodiment involves the interaction between the third device, the message middleware and the logistics transport end. Figure 8 As shown, the task processing method includes the following steps:

[0161] Step 501: If the third device determines to update the logistics statistics of the target pick-up point within the second preset time period, the updated information is recorded in the binary log file of the third device.

[0162] In this embodiment, the binary log file may be a binlog log file.

[0163] Step 502: The message middleware obtains updated information of logistics statistics by detecting the binary log file.

[0164] Step 503: The message middleware sends the latest logistics statistics of the target pick-up point within the second preset time period to the logistics transportation end.

[0165] The task processing method shown in this embodiment forwards the latest logistics statistics to the logistics transportation end in a timely manner through the monitoring function of the message middleware, thereby greatly reducing data delay.

[0166] Figure 9 Interaction diagram of the task processing method provided in the embodiment of the present application Figure 3 The task processing method of this embodiment involves the interaction between the third device, the message middleware and the logistics transport end. Figure 9As shown, the task processing method includes the following steps:

[0167] Step 601: At the end of the second preset time period, the third device obtains final logistics statistical data of the target pick-up point within the second preset time period.

[0168] Step 602: The third device sends the final logistics statistics of the target pick-up point within the second preset time period to the message middleware.

[0169] Optionally, the third device sends the final logistics statistics of the target pick-up point within the second preset time period to the message middleware via a metaQ message.

[0170] Step 603: The message middleware sends the final logistics statistics of the target pick-up point within the second preset time period to the logistics transportation end.

[0171] For example, the second preset time period is from 9 am to 9 pm every day. After 9 pm every day, the third device obtains the logistics statistical data from 9 am to 9 pm that day and sends it as a backup.

[0172] The task processing method shown in this embodiment is that the third device sends the full statistical data of the target pick-up point within the second preset time period to the logistics transportation end through the message middleware to ensure the accuracy of the transportation and fulfillment data.

[0173] The above describes the task processing method provided by the embodiment of the present application. The following describes the task processing device provided by the embodiment of the present application.

[0174] In the embodiment of the present application, the task processing device can be divided into functional modules according to the above method embodiment. For example, each functional module can be divided into different functional modules corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0175] It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. In actual implementation, there may be other division methods. The following uses the example of dividing each functional module according to each function to illustrate.

[0176] Figure 10 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 1 .like Figure 10 As shown, the task processing device 700 of this embodiment includes: a receiving module 701 , a sending module 702 and a processing module 703 .

[0177] The receiving module 701 is used to periodically receive a task trigger instruction from a task scheduling device;

[0178] A sending module 702 is configured to send the first task to multiple second devices according to the task triggering instruction;

[0179] The first task is used to trigger the second device to send the second task to multiple third devices, and the second task is used to trigger the third device to collect logistics statistics of the target pick-up point in the target area corresponding to the third device.

[0180] In an optional embodiment of this embodiment, the processing module 702 is configured to randomly select the plurality of second devices from the device cluster according to the task triggering instruction;

[0181] A sending module 702 is configured to send the first task to each of the plurality of second devices;

[0182] The first task includes a database identifier and a data table identifier of a target region, and the first tasks of different second devices correspond to different target regions.

[0183] The task processing device provided in this embodiment can execute the technical solution of the first device in any of the aforementioned embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0184] Figure 11 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 2 .like Figure 11 As shown, the task processing device 800 of this embodiment includes: a receiving module 801 , a sending module 802 , an acquiring module 803 and a processing module 804 .

[0185] A receiving module 801 is configured to receive a first task from a first device;

[0186] The sending module 802 is used to send a second task to multiple third devices according to the first task, and the second task is used to trigger the third device to collect logistics statistics of the target pick-up point in the target area corresponding to the third device.

[0187] In an optional embodiment of this embodiment, the first task includes a database identifier and a data table identifier of the target area;

[0188] An acquisition module 803 is configured to acquire the total number of pick-up points in the target area according to the database identifier and the data table identifier of the target area;

[0189] Processing module 804 is configured to determine the number of self-pickup point groups based on the total number of self-pickup points and the number of self-pickup points in the preset self-pickup point group, where the number of self-pickup point groups is equal to the number of third devices; and randomly select the plurality of third devices from the device cluster based on the number of third devices;

[0190] The sending module 802 is configured to send the second task to each third device of the plurality of third devices.

[0191] In an optional embodiment of this embodiment, the second task includes a database identifier, a data table identifier, and a self-pickup point identifier of the target area, and different third devices correspond to different self-pickup points in the same target area.

[0192] The task processing device provided in this embodiment can execute the technical solution of the second device in any of the aforementioned embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0193] Figure 12 Schematic diagram of the structure of the task processing device provided in the embodiment of the present application Figure 3 .like Figure 12 As shown, the task processing device 900 of this embodiment includes: a receiving module 901 , an acquiring module 902 , a processing module 903 , an updating module 904 , a storage module 905 and a sending module 906 .

[0194] Receiving module 901, configured to receive a second task from a second device;

[0195] An acquisition module 902 is configured to acquire, according to the second task, logistics order data associated with a target pickup point in a target area within a first preset time period;

[0196] Processing module 903, configured to determine logistics statistics of the target self-pickup point within the first preset time period based on the logistics order data, where the logistics statistics include at least one of the total number of logistics orders and the total number of items in all logistics orders;

[0197] Determine whether to update the logistics statistics of the target self-pickup point within a second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period; the second preset time period includes multiple consecutive first preset time periods.

[0198] In an optional embodiment of this embodiment, the second task includes a database identifier, a data table identifier, and a target self-pickup point identifier of the target area; the acquisition module 902 is used to:

[0199] According to the database identifier and data table identifier of the target area, a data table corresponding to the target area is obtained from a first database cluster; the first database cluster is used to store logistics order data of different areas and logistics statistics of self-pickup point groups;

[0200] According to the target self-pickup point identifier, the logistics order data associated with the target self-pickup point in the target area within the first preset time period is obtained from the data table corresponding to the target area.

[0201] In an optional embodiment of this embodiment, the logistics order data includes the identifiers of all logistics orders associated with the target pick-up point in the target area within the first preset time period, and the quantity of items in each logistics order.

[0202] In an optional embodiment of this embodiment, the acquisition module 902 is configured to:

[0203] According to the identifier of the target self-pickup point, obtaining, from the logistics order item list corresponding to the target area, the identifiers of all logistics orders associated with the target self-pickup point in the target area within the first preset time period, and the quantity of items on each logistics order;

[0204] The logistics order item list includes the logistics order identification, the quantity of items and the pick-up point identification.

[0205] In an optional embodiment of this embodiment, the acquisition module 902 is configured to:

[0206] Obtaining historical logistics statistics of the target self-pickup point within the second preset time period from the self-pickup point group information statistics table corresponding to the target area; the self-pickup point group information statistics table includes historical logistics statistics of each group of self-pickup points;

[0207] Update module 904 is used to update the logistics statistics of the target self-pickup point within the second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period and the historical logistics statistics of the target self-pickup point within the second preset time period if the logistics statistics of the target self-pickup point within the first preset time period is not 0.

[0208] In an optional embodiment of this embodiment, the storage module 905 is used to record the updated information in the binary log file of the third device if it is determined to update the logistics statistical data of the target pick-up point within the second preset time period.

[0209] In an optional embodiment of this embodiment, the acquisition module is used to obtain final logistics statistical data of the target self-pickup point within the second preset time period at the end of the second preset time period;

[0210] The sending module 906 is used to send the final logistics statistics to the message middleware, and the message middleware is used to forward the final logistics statistics to the logistics transportation end.

[0211] The task processing device provided in this embodiment can execute the technical solution of the third device in any of the aforementioned embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0212] Figure 13 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 13 As shown, the electronic device 1000 provided in this embodiment includes: a memory 1001, a processor 1002 and a computer program; wherein the computer program is stored in the memory 1001 and is configured to be executed by the processor 1002 to implement the technical solution of the first device, or the second device, or the third device in any of the aforementioned method embodiments. The implementation principles and technical effects are similar and will not be repeated here.

[0213] Optionally, the memory 1001 may be independent or integrated with the processor 1002. When the memory 1001 is a device independent of the processor 1002, the electronic device 1000 further includes a bus 1003 for connecting the memory 1001 and the processor 1002.

[0214] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. The computer program is executed by the processor 1002 to implement the technical solution of the first device, or the second device, or the third device in any of the aforementioned method embodiments.

[0215] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the technical solution of the first device, the second device, or the third device in any of the aforementioned method embodiments.

[0216] An embodiment of the present application provides a chip, including: a processing module and a communication interface, wherein the processing module can execute the technical solution of the first device, or the second device, or the third device in any of the aforementioned method embodiments.

[0217] Optionally, the chip also includes a storage module (such as a memory), the storage module is used to store instructions, the processing module is used to execute the instructions stored in the storage module, and the execution of the instructions stored in the storage module enables the processing module to execute the technical solution of the first device, or the second device, or the third device in any of the aforementioned method embodiments.

[0218] The embodiment of the present application also provides a task processing system, including: a task scheduling device and a device cluster, the device cluster includes multiple devices, and the task scheduling device is connected to each device in the device cluster. Figure 1 .

[0219] The task scheduling device is used to periodically send a task trigger instruction to the first device, and the first device is a device randomly selected by the task scheduling device from the device cluster.

[0220] The first device is used to trigger the second device to send the first task to the third device, and the second device is a device randomly selected by the first device from the device cluster. There can be multiple second devices.

[0221] The second device is used to trigger a third device to collect logistics statistics for a target pickup point in a target area corresponding to the third device. The third device is randomly selected by the second device from the device cluster. There can be multiple third devices.

[0222] It should be understood that the processor described above may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASICs). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0223] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0224] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0225] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0226] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an application-specific integrated circuit (ASIC). Of course, the processor and storage medium can also exist as discrete components in an electronic device.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A task processing method, characterized in that: Applied to a first device, the method includes: Periodically receiving task triggering instructions from a task scheduling device; Sending the first task to the plurality of second devices according to the task triggering instruction; wherein the first tasks of the plurality of second devices correspond to different regions, and the number of the second devices is consistent with the number of regions; The first task is used to trigger the second device to send the second task to multiple third devices, and the second task is used to trigger the third device to count the logistics statistical data of the target pick-up point in the target area corresponding to the third device; wherein, the second tasks of multiple third devices respectively correspond to different pick-up point groups in the target area, and the number of third devices is consistent with the number of pick-up point groups in the target area.

2. The method according to claim 1, characterized in that The sending the first task to the plurality of second devices according to the task triggering instruction includes: Randomly selecting the plurality of second devices from the device cluster according to the task triggering instruction; sending the first task to each second device of the plurality of second devices; The first task includes a database identifier and a data table identifier of the target area.

3. A task processing method, characterized in that: Applied to the second device, the method includes: Receiving a first task from a first device; wherein the first tasks of a plurality of second devices correspond to different regions, and the number of the second devices is consistent with the number of regions; A second task is sent to multiple third devices according to the first task, and the second task is used to trigger the third devices to count the logistics statistical data of the target pick-up points in the target area corresponding to the third devices, wherein the second tasks of the multiple third devices respectively correspond to different pick-up point groups in the target area, and the number of third devices is consistent with the number of pick-up point groups in the target area.

4. The method according to claim 3, characterized in that The first task includes database identification and data table identification of the target area; The sending of the second task to the plurality of third devices according to the first task includes: obtaining the total number of self-pickup points in the target area according to the database identifier and the data table identifier of the target area; Determining the number of self-pickup point groups according to the total number of self-pickup points and the number of self-pickup points in the preset self-pickup point group, where the number of self-pickup point groups is equal to the number of the third devices; The plurality of third devices are randomly selected from the device cluster according to the number of the third devices, and the second task is sent to each of the plurality of third devices.

5. The method according to claim 4, characterized in that The second task includes a database identifier, a data table identifier, and a self-pickup point identifier of the target area.

6. A task processing method, characterized in that: Applied to a third device, the method includes: Receive a second task from a second device, and obtain, based on the second task, logistics order data associated with a target pickup point in a target area within a first preset time period; wherein the first tasks of the plurality of second devices correspond to different areas, and the number of the second devices matches the number of areas; and wherein the second tasks of the plurality of third devices correspond to different pickup point groups in the target area, and the number of the third devices matches the number of pickup point groups in the target area; Determining logistics statistics of the target self-pickup point within the first preset time period based on the logistics order data, the logistics statistics including at least one of a total number of logistics orders and a total number of items in all logistics orders; Determine whether to update the logistics statistics of the target self-pickup point within a second preset time period based on the logistics statistics of the target self-pickup point within the first preset time period; the second preset time period includes multiple consecutive first preset time periods.

7. The method according to claim 6, characterized in that The second task includes a database identifier, a data table identifier, and a target pick-up point identifier for the target area; The step of obtaining logistics order data associated with a target pickup point in a target area within a first preset time period according to the second task includes: According to the database identifier and data table identifier of the target area, a data table corresponding to the target area is obtained from a first database cluster; the first database cluster is used to store logistics order data of different areas and logistics statistics of self-pickup point groups; According to the target self-pickup point identifier, the logistics order data associated with the target self-pickup point in the target area within the first preset time period is obtained from the data table corresponding to the target area.

8. The method according to claim 6, characterized in that The logistics order data includes the identifiers of all logistics orders associated with the target pick-up point in the target area within the first preset time period, and the quantity of items in each logistics order.

9. The method according to claim 7, characterized in that The acquiring, according to the identifier of the target self-pickup point, from a data table corresponding to the target area, logistics order data associated with the target self-pickup point in the target area within the first preset time period includes: According to the identifier of the target self-pickup point, obtaining, from the logistics order item list corresponding to the target area, the identifiers of all logistics orders associated with the target self-pickup point in the target area within the first preset time period, and the quantity of items on each logistics order; The logistics order item list includes the logistics order identification, the quantity of items and the pick-up point identification.

10. The method according to claim 6, characterized in that The determining whether to update the logistics statistics of the target self-pickup point within the second preset time period according to the logistics statistics of the target self-pickup point within the first preset time period includes: Obtaining historical logistics statistics of the target self-pickup point within the second preset time period from the self-pickup point group information statistics table corresponding to the target area; the self-pickup point group information statistics table includes historical logistics statistics of each group of self-pickup points; If the logistics statistical data of the target self-pickup point within the first preset time period is not 0, the logistics statistical data of the target self-pickup point within the second preset time period is updated according to the logistics statistical data of the target self-pickup point within the first preset time period and the historical logistics statistical data of the target self-pickup point within the second preset time period.

11. The method according to any one of claims 6 to 10, characterized in that The method further includes: if it is determined to update the logistics statistics data of the target self-pickup point within the second preset time period, recording the update information in the binary log file of the third device.

12. The method according to any one of claims 6 to 10, characterized in that The method further includes: at the end of the second preset time period, obtaining final logistics statistical data of the target self-pickup point within the second preset time period; The final logistics statistics are sent to a message middleware, and the message middleware is used to forward the final logistics statistics to a logistics transportation end.

13. An electronic device, characterized in that: include: A memory, a processor, and a computer program; the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to claim 1 or 2, or the method according to any one of claims 3 to 5, or the method according to any one of claims 6 to 12.

14. A task processing system, characterized in that: include: A task scheduling device and a device cluster, wherein the device cluster includes a plurality of devices, and the task scheduling device is connected to each device in the device cluster; The task scheduling device is used to periodically send a task trigger instruction to a first device, where the first device is a device randomly selected by the task scheduling device from the device cluster; The first device is used to perform the method according to claim 1 or 2; The second device is used to perform the method according to any one of claims 3 to 5; The third device is configured to execute the method according to any one of claims 6 to 12.

Images