Logistics order fulfillment detection method and delayed task processing method

Abstract

The present application discloses a logistics order fulfillment detection method, a delayed task processing method and a device. The method adopts a sparse storage method to store a large number of detection tasks within each second. Even when there is no detection task, a detection task record will be generated, so that the starting position and the end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics of the amount of data supported by non-relational databases that is not limited by memory, and the whole block data reading characteristics, it is possible to quickly read tens of millions of detection tasks within a second. This method of exchanging the storage space of the detection task for the reading time of the detection task optimizes the batch processing of detection tasks in seconds, and can trigger tens of millions of detection tasks in seconds; therefore, the logistics order fulfillment detection performance under the condition of large concurrent logistics orders can be effectively improved, and the detection task data volume indicator can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of logistics management technology, and specifically to a logistics order fulfillment detection method and device, a delayed task processing method and device, a delayed queue system, and an electronic device. Background Art

[0002] The delay queue system is a system that can execute detection tasks at a specified time in the future. The delay queue system can be widely used in various fields to detect tasks with completion time requirements in the corresponding fields, and detect whether the tasks are completed on time. The number of tasks to be detected per second can range from 0 to a certain level.

[0003] A typical application area is the logistics order management field. The logistics industry is extremely sensitive to fulfillment timeliness and abnormal orders, which prompts logistics parties to pay attention to the estimated arrival time of packages and expect to be aware of packages that have exceeded fulfillment time so as to intervene and solve problems in a timely manner. For this reason, a delay queue system is usually used to calculate the timeout of the package based on the timeliness. The existing delay queue system mainly adopts the following implementation methods: delay queue implementation based on Redis ordered sets, delay queue implementation based on relational database and timed scheduling, and delay queue implementation based on message middleware. With the popularity of online shopping, the logistics orders generated by multiple product lines of the logistics management platform have reached a high concurrency of tens of thousands per second. Due to the special detection rules in the logistics field, the delay queue system will be responsible for the fulfillment detection tasks of tens of millions of logistics orders per second and the overtime detection calculation of hundreds of millions of logistics orders.

[0004] However, in the process of implementing the present invention, the inventors found that when the number of concurrent logistics orders increases sharply, the existing delay queue system has at least the following problems: it only supports thousands of logistics order fulfillment detection requests per second, and does not support tens of thousands of detection requests per second when the number of concurrent logistics orders increases sharply; the number of triggered tasks at the same time only reaches tens of thousands, and cannot support millions or even tens of millions of triggered tasks concentrated in a single second when the number of concurrent logistics orders increases sharply. Therefore, when the number of concurrent logistics orders increases sharply, it is impossible to detect on time whether the goods in the logistics order have been fulfilled before the expected time, and thus it is impossible to promptly learn about the logistics orders that have not been fulfilled on time. In summary, when the number of concurrent logistics orders is large, the existing delayed task system has the problem of poor performance. Summary of the invention

[0005] The present application provides a logistics order fulfillment detection method to solve the problem of poor detection performance in the prior art when there is a large number of concurrent logistics orders. The present application also provides a logistics order fulfillment detection device, a delayed task processing method and device, a delayed queue system, and an electronic device.

[0006] The present application provides a logistics order fulfillment detection method, comprising:

[0007] Store in a non-relational database a logistics order fulfillment detection task corresponding to a trigger time in seconds and having a number that is a processing quantity threshold in seconds, wherein the detection task includes a detection task sequence number;

[0008] Allocate an unoccupied inspection task sequence number corresponding to its target fulfillment time to the logistics order, and store the logistics order information in the inspection task corresponding to the unoccupied inspection task sequence number;

[0009] According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and which correspond to the target second-level trigger time are read from the database;

[0010] Execute the inspection tasks corresponding to the logistics order among the read inspection tasks to check whether the logistics order is fulfilled on time.

[0011] Optionally, also include:

[0012] According to the logistics order detection rules, the target fulfillment time of multiple logistics orders received at different times is set to the same time.

[0013] Optionally, receive the logistics order detection request through the streaming computing service;

[0014] The step of allocating an unoccupied inspection task sequence number corresponding to the target fulfillment time to the logistics order includes:

[0015] Store the occupied detection task sequence numbers of the second-level trigger time into the cache;

[0016] Determine the unoccupied detection task sequence number according to the occupied detection task sequence number in the cache;

[0017] The executing of the inspection task corresponding to the logistics order among the inspection tasks read includes:

[0018] Through the streaming computing service, the detection task corresponding to the logistics order is executed within the second-level trigger time.

[0019] Optionally, the step of allocating an unoccupied inspection task sequence number corresponding to the target fulfillment time to the logistics order includes:

[0020] If the generation sequence number of the detection task in the current second-level time is greater than the second-level processing quantity threshold, the unoccupied detection task sequence number is determined according to the next second time and the re-counted sequence number.

[0021] Optionally, according to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, reading from the database the detection task records whose number corresponding to the target second-level trigger time is the second-level processing number threshold, includes:

[0022] Through the second-level timing scheduler, according to the starting and ending positions of the detection tasks corresponding to the target second-level trigger time, the detection task records corresponding to the target second-level trigger time and the number of detection task records corresponding to the second-level processing quantity threshold are read from the database.

[0023] Optionally, the duration of the target fulfillment time relative to the generation time of the detection task includes: month-level duration and year-level duration.

[0024] The present application also provides a method for detecting an order timeout payment, comprising:

[0025] Store in a non-relational database order timeout payment detection tasks corresponding to a second-level trigger time and having a number equal to a second-level processing quantity threshold, wherein the detection task includes a detection task sequence number;

[0026] Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number;

[0027] According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and which correspond to the target second-level trigger time are read from the database;

[0028] Execute the inspection task corresponding to the order among the read inspection tasks to check whether the order is paid on time.

[0029] The present application also provides a method for detecting an order timeout, comprising:

[0030] Store in a non-relational database an order timeout acceptance detection task corresponding to a trigger time in seconds and having a number equal to a processing quantity threshold in seconds, wherein the detection task includes a detection task sequence number;

[0031] Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number;

[0032] According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and which correspond to the target second-level trigger time are read from the database;

[0033] Execute the inspection task corresponding to the order among the read inspection tasks to check whether the order is accepted on time.

[0034] The present application also provides a delayed task processing method, comprising:

[0035] storing, in a non-relational database, a number of detection tasks corresponding to a second-level trigger time and a second-level processing quantity threshold, wherein the detection task includes a detection task sequence number;

[0036] Allocating an unoccupied detection task sequence number corresponding to the target completion time of the task to be executed, and storing the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number;

[0037] According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and which correspond to the target second-level trigger time are read from the database;

[0038] Execute the detection tasks corresponding to the tasks to be executed among the read detection tasks to detect whether the tasks to be executed are completed on time.

[0039] The present application also provides a delayed task processing device, comprising:

[0040] A detection task storage unit, used to store detection tasks corresponding to a trigger time in seconds and having a number equal to a second-level processing quantity threshold in a non-relational database, wherein the detection task includes a detection task sequence number;

[0041] A task sequence number allocation unit, used to allocate an unoccupied detection task sequence number corresponding to the target completion time of the task to be executed, and store the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number;

[0042] A detection task reading unit, used to read detection tasks corresponding to the target second-level trigger time and having a number equal to a second-level processing number threshold from a database according to a start position and an end position of the detection task corresponding to the target second-level trigger time;

[0043] The detection task execution unit is used to execute the detection tasks corresponding to the tasks to be executed among the read detection tasks, so as to detect whether the tasks to be executed are completed on time.

[0044] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned various methods.

[0045] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the above-mentioned various methods.

[0046] Compared with the prior art, this application has the following advantages:

[0047] The logistics order fulfillment detection method provided in the embodiment of the present application adopts a sparse storage method to store a large number of detection tasks within each second. Even if there is no detection task, a detection task record will be generated, so that the starting position and the end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics that the amount of data supported by non-relational databases is not limited by memory and the whole block data reading characteristics, it can quickly read tens of millions of detection tasks within a second, that is, the storage space of the detection task is exchanged for the reading time of the detection task, and the detection task batch processing in seconds is optimized. It can trigger tens of millions of detection tasks in seconds; therefore, it can effectively improve the logistics order fulfillment detection performance under the condition of large concurrent logistics orders, and effectively improve the detection task data volume indicator. In addition, this processing method fully considers the capacity and persistent storage issues of the detection task, and therefore supports the triggering of detection tasks far away from the current time (such as several months), and there is no upper limit to the support for future time.

[0048] The delayed task processing method provided by the embodiment of the present application adopts a sparse storage method for a large number of detection tasks within each second, and a detection task record will be generated even when there is no detection task, so that the starting position and the end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics that the amount of data supported by the non-relational database is not limited by memory and the whole block data reading characteristics, it can quickly read tens of millions of detection tasks within a second, that is, the storage space of the detection task is exchanged for the reading time of the detection task, and the detection task batch processing in seconds is optimized, and tens of millions of detection tasks can be triggered in seconds; therefore, in the case of a large number of concurrent tasks to be executed with completion time requirements, the detection performance of detecting whether the detection of the tasks to be executed is completed on time can be effectively improved, and the detection task data volume index can be effectively improved. In addition, this processing method fully considers the capacity and persistent storage problems of the detection task, and therefore supports the triggering of detection tasks far away from the current time (such as several months), and there is no upper limit to the support for future time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a flow chart of an embodiment of a logistics order fulfillment detection method provided in the present application;

[0050] Figure 2 Schematic diagram of application scenarios of an embodiment of the logistics order fulfillment detection method provided in this application;

[0051] Figure 3 A schematic diagram of device interaction in an embodiment of a logistics order fulfillment detection method provided in the present application;

[0052] Figure 4 A schematic diagram of a specific implementation of an embodiment of a logistics order fulfillment detection system provided in the present application. DETAILED DESCRIPTION

[0053] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0054] In the present application, a logistics order fulfillment detection method and device, a delayed task processing method and device, a delayed queue system, and an electronic device are provided. In the following embodiments, various solutions are described in detail one by one.

[0055] First embodiment

[0056] The present application provides a logistics order fulfillment detection method, and the execution subject of the method includes but is not limited to a delay queue system, and can also be any device that can implement the method.

[0057] Please refer to Figure 1 , which is a flow chart of an embodiment of the logistics order fulfillment detection method of the present application. In this embodiment, the method may include the following steps:

[0058] Step S101: storing logistics order fulfillment detection tasks corresponding to a trigger time in seconds and a quantity that is a second-level processing quantity threshold in a non-relational database, wherein the detection task includes a detection task sequence number.

[0059] In order to facilitate intuitive understanding of the logistics order fulfillment detection method provided in the embodiment of the present application, the following briefly describes its application scenario. Figure 2 , which is a schematic diagram of the application scenario of the method embodiment provided by the present application. In this embodiment, the method is applied in a logistics order fulfillment detection system. The system can generate logistics orders at the level of 10,000 or more per second through the logistics management platform; through the delayed task processing server, whether these logistics orders are completed on time can be detected, and the abnormal logistics orders that are not completed on time can be sent to the logistics management platform. The logistics management platform sends alarm information of logistics orders that are not completed on time to the client of the management user, so that the management user can be informed of the logistics orders that are not completed on time in time, so as to intervene in time to solve the problem. In specific implementation, a logistics order production device can be deployed in the server of the logistics management platform, and a logistics order fulfillment detection device can be deployed in the server of the delayed task processing.

[0060] With the popularity of online shopping, the number of logistics orders generated by multiple product lines of logistics management platforms can reach 10,000 per second. For example, in the e-commerce industry, big promotions often occupy a large amount of traffic, with the characteristics of concentrated packages and concentrated time. The number of logistics orders generated by big promotions can reach 10,000 per second.

[0061] The logistics order fulfillment detection device executes the method and is responsible for the logistics order fulfillment detection tasks at the level of tens of millions per second and the logistics order fulfillment detection calculations at the level of hundreds of millions. The logistics scenario has special detection rules. Taking the order outbound detection as an example, it is required to notify the warehouse of the packaging orders before 18:00 every day, and the orders must be shipped out before 20:00 on the same day. This means that all orders for multiple hours in a day are concentrated at 20:00 to perform outbound detection. This is why the logistics orders generated at the level of tens of thousands per second will result in the logistics order fulfillment detection tasks at the level of tens of millions per second. Therefore, it is necessary to support a large number of logistics order timeout detection tasks generated at the same time.

[0062] like Figure 3 As shown, the logistics order fulfillment detection device is used to store logistics order fulfillment detection tasks corresponding to the second-level trigger time and the second-level processing quantity threshold in a non-relational database, and the detection task includes a detection task sequence number, and the sequence number includes the second-level trigger time and the number within the second; the logistics order production device is used to generate logistics order information, send a logistics order detection request to the logistics order fulfillment detection device, and send the logistics order information to the logistics order fulfillment detection device; the logistics order fulfillment detection device is used to assign an unoccupied detection task sequence number corresponding to its target fulfillment time to the logistics order, and store the logistics order information in the detection task corresponding to the unoccupied detection task sequence number; according to the starting position and end position of the detection task corresponding to the target second-level trigger time, read the detection task corresponding to the second-level processing quantity threshold from the database; execute the detection task corresponding to the logistics order in the read detection task to detect whether the logistics order is fulfilled on time. The logistics order fulfillment detection device can also be used to send the logistics order that is not completed on time to the abnormal logistics order processing device, and the abnormal logistics order processing device can display the abnormal logistics order to the user for viewing.

[0063] The logistics order may be a logistics order for a logistics sub-link in a logistics chain, such as a logistics order for trunk transportation from abroad to domestic in a cross-border logistics chain. The logistics order may also be a delivery order for online shopping directly to consumers, such as a takeout order, a Taobao order, etc. The logistics order information includes a logistics order identifier and a target fulfillment time, and may also include common logistics information such as a delivery address. The target fulfillment time may be the package delivery time, or the time from the package entering the warehouse to leaving the warehouse, the time for customs clearance to be completed, etc. The target fulfillment time may be a time in minutes, a time in hours, or a time in other time units.

[0064] The method provided in the embodiment of the present application can convert each received detection request into a detection task. The detection task is a detection task for a logistics order, which detects whether the logistics order has been completed at the expected fulfillment time (target fulfillment time). In this embodiment, the detection task is stored in a non-relational database. The non-relational database can be a distributed, column-oriented database, which can also be called a big data storage engine, a distributed storage system for structured data, such as an Hbase database that supports large throughput and big data. The detection tasks stored in the database may include key-value pairs, with the detection task sequence number (detection task identifier) ​​as the key and the logistics order information (text) as the value. The detection task identifier is used to query the detection task, and the logistics order information is used to execute the detection task.

[0065] In logistics scenarios, a huge number of logistics order fulfillment inspection tasks need to be executed every minute. In order to effectively execute these inspection tasks, a batch processing method of inspection tasks in seconds is usually adopted to concentrate on triggering inspection tasks within one second. Therefore, the trigger time of the logistics order fulfillment inspection task is in seconds.

[0066] Since there are multiple detection tasks within one second, the detection task sequence number includes the second-level trigger time and the second-level number. Among them, the second-level trigger time indicates the second at which the task is triggered, which can be a timestamp of the second-level moment, which can be used to read all the detection tasks of the second from the database when the second-level time is reached. The second-level number indicates the number of the detection task in this second, which can determine the position of this detection task, and also makes all the detection task identifiers within one second unique. The first number in the second indicates the starting position of all detection tasks in this second, and the last number in the second indicates the end position of all detection tasks in this second.

[0067] The second-level trigger time can be determined according to the target fulfillment time of the logistics order. The logistics order fulfillment detection device is insensitive to logistics order fulfillment detection tasks with target fulfillment times of minutes or above, and does not require the logistics order fulfillment detection task to be triggered precisely at a certain second. In other words, the logistics order fulfillment detection task can correspond to any second-level trigger time within its target fulfillment time, as long as the detection task can be read within the target fulfillment time (such as 2021 / 10 / 15 15:30) for execution.

[0068] In this embodiment, a logistics order fulfillment detection task corresponding to the second-level trigger time is stored in a non-relational database, and the number of the second-level processing quantity threshold is stored. In other words, the number of detection task records corresponding to one second in the database is a fixed number. The second-level processing quantity threshold can be set according to the order of magnitude of detection tasks that can be centrally read per second by the system. For example, if 10 million detection tasks can be read per second, the second-level processing quantity threshold is 10 million. The method provided in the embodiment of the present application makes the number of detection tasks within one second a fixed number, so the starting position and the ending position of all detection tasks within this second can be directly determined, without the need to read the detection task serial number one by one to determine whether all detection tasks within the second have been read.

[0069] It can be seen that the system provided in the embodiment of the present application adopts a method of presetting the detection task sequence number (key) when considering the storage structure of the detection task trigger time. In this storage method, the key space is sparse, and the key range will be scanned even if there is no task. In this way, it will lead to a waste of computing resources when the detection tasks are sparse, and it is necessary to manage the occupancy of keys. However, in scenarios with a large number of concurrent logistics orders, the distribution of detection tasks in each second is relatively dense, and batch optimization makes it possible to process a large number of logistics order detection tasks. Table 1 shows the detection task data in this embodiment.

[0070] Detection task identification Logistics order identification 1634567172 00000000 AX0215463165 1634567172 00000001 AX0215463165 1634567172 00000002 Asf466464656 1634567172 00000003 645665116316 … 163456717299999999 641166889436 1634567173 00000000 189156165166 1634567173 00000001 null 1634567173 00000002 null … 163456717399999999 null 163456717400000000 444215463165 163456717400000001 545566464656 163456717400000002 789665116316 …

[0071] Table 1. Detection task data in this embodiment

[0072] As can be seen from Table 1, the detection task sequence number includes the timestamp of the second-level trigger time and the number within the second. The starting position and the ending position of the detection task within each second are hidden in the detection task sequence number. The starting position corresponds to the sequence number 00000000, and the ending position corresponds to the sequence number 99999999. The starting position and the ending position can be directly determined without more calculations. In this way, the entire block of data corresponding to the second can be quickly read, supporting tens of millions of trigger tasks in a single second.

[0073] In another implementation, the ordered set of redis is used to store the detection task trigger time. The ordered set of redis uses sequential storage. In this storage method, the key space is compact, and no key is generated when there is no delayed detection task. Although this implementation can reduce storage space or processing time, it often costs more processing time because it cannot perform batch processing optimization. Table 2 shows the detection task data of another implementation.

[0074] Detection task identification Logistics order identification 1634567172 00000000 AX0215463165 1634567172 00000001 AX0215463165 1634567172 00000002 Asf466464656 1634567172 00000003 645665116316 … 163456717201000000 641166889436 1634567173 00000000 189156165166 1634567173 00000001 564464646666 1634567173 00000002 444456775757 … 163456717300000100 785778575757 163456717400000000 444215463165 163456717400000001 545566464656 163456717400000002 789665116316 …

[0075] Table 2. Detection task data in the prior art

[0076] As can be seen from Table 2, compared with the compact storage solution adopted by another implementation method, assuming that 1 million detection tasks are stored in the second 1634567172, and the numbers in the second are 1 to 1000000, when it reaches the second 1634567172, to determine which detection tasks there are in this second, it is necessary to check whether each data from 1 to 1000000 belongs to this second, that is: in order to determine how large a range of detection task data is to be obtained, it is necessary to frequently read the database. 1 million data in one second may need to be read 1 million times. Frequent reading of the database makes it impossible to support tens of millions of trigger tasks concentrated in a single second.

[0077] Step S103: Allocate an unoccupied inspection task sequence number corresponding to its target fulfillment time to the logistics order, and store the logistics order information in the inspection task corresponding to the unoccupied inspection task sequence number.

[0078] The method adopts the method of pre-setting the inspection task sequence number. After receiving the logistics order inspection request, an unoccupied inspection task sequence number corresponding to its target fulfillment time can be allocated to the logistics order, and the logistics order information can be stored in the inspection task corresponding to the unoccupied inspection task sequence number.

[0079] In this embodiment, the logistics order fulfillment detection device will receive tens of thousands of logistics orders with high concurrency within one second, and correspondingly can generate tens of millions of detection tasks. These detection tasks can be numbered according to the order in which they are generated within one second, that is, the intra-second number in the detection task serial number is the generation order number of the detection task within seconds.

[0080] For example, the target fulfillment time of logistics order A is 2021 / 10 / 18 15:28, the time of receiving logistics order A is 2021 / 10 / 15 5:15:23, and it is the first logistics order received within the second. It is set that the fulfillment of 10 million logistics orders can be detected within one second, that is, the second-level processing quantity threshold is 10 million, then the detection task sequence number can be: 202110181528 23 00000001. Among them, 202110181528 represents the target fulfillment time, 23 represents the 23rd second within the minute, and 00000001 represents that the detection task is the first detection task within the 23rd second of 2021 / 10 / 18 15:28.

[0081] In this embodiment, the generation sequence number is less than or equal to the second-level processing quantity threshold; the detection task sequence number can be allocated in the following manner: if the generation sequence number of the detection task in the current second time is greater than the second-level processing quantity threshold, the unoccupied detection task identifier is determined according to the next second moment and the re-counted sequence number. In this way, when the sequence number of the current second is full, it is shifted to the next second to ensure that the detection task per second does not exceed the quantity threshold, and all tasks within one second are quickly read, thereby ensuring that tens of millions of detection tasks per second are triggered in a centralized manner.

[0082] In one example, step S203 may include the following sub-steps: storing the occupied detection task sequence numbers of the second-level trigger time into a cache, such as a Redis cache system, etc.; determining the unoccupied detection task sequence numbers based on the occupied detection task sequence numbers in the cache. In this way, the usage status of the sequence numbers within seconds can be cached, and the remaining space of the current second is recorded by caching the used sequence numbers, which simplifies the determination method of the detection task sequence numbers, effectively improves the determination speed of the detection task sequence numbers, and ensures that the sequence numbers will not be occupied repeatedly, thereby ensuring the performance of logistics order fulfillment detection under high concurrency.

[0083] In this embodiment, the method forms a detection task in the form of "detection task sequence number + logistics order information" for each logistics order reception message, and the detection task sequence number is stored as the key of the big data storage engine (such as Hbase), and the logistics order information is used as the storage content of the big data storage engine. In terms of the generation of the detection task sequence number, the time when the logistics order triggers the detection in the future is recorded, and the sparse storage structure conforms to the Hbase query structure to facilitate range queries, and the number of detection tasks that can be stored in the same second is limited, so the number of occupied sequence numbers in this second can be recorded, and after the sequence number of this second is full, it will be postponed to the next second. To this end, the method can use redis to record the situation of occupied sequence numbers to ensure that there will be no repeated occupation and fullness.

[0084] In this embodiment, the delayed processing duration of the target fulfillment time relative to the generation time of the detection task can be a month-level duration, such as the target fulfillment time of the logistics order is three months later. The delayed processing duration can also be a year-level duration, such as the target fulfillment time of the logistics order is one year later. In this way, logistics orders with a long fulfillment time can be supported to meet the application requirements in the field of logistics management that logistics orders take a long time to complete fulfillment. For example, in the logistics merchant stocking scenario, trunk line transportation often takes weeks or even months, so the supported future time should have a longer span.

[0085] The method provided in the embodiment of the present application includes the second-level trigger time of the detection task and the generation sequence number of the detection task within the second time in the detection task sequence number, stores the detection task identifier of this composite structure in a non-relational database, and generates a fixed number (second-level processing quantity threshold) of detection tasks per second. It can provide a data basis for supporting batch processing optimization that supports centralized processing of tens of millions of detection tasks in seconds.

[0086] So far, the generation method of the logistics order fulfillment detection task has been described. The following describes the triggering and execution method of the detection task.

[0087] Step S105: according to the starting position and the ending position of the detection task corresponding to the target trigger time in seconds, the detection tasks whose number is the second-level processing quantity threshold and which correspond to the target trigger time in seconds are read from the database.

[0088] The method provided in the embodiment of the present application can check every second whether the logistics order with the target fulfillment time of the current minute has been completed on time. In order to be able to timely detect whether tens of millions of logistics orders per second are completed on time when the concurrent volume of logistics orders is large, the method directly reads a fixed number of detection tasks corresponding to the current second-level moment from a non-relational database in blocks when detecting the fulfillment of logistics orders. In this way, tens of millions of detection tasks within a second can be quickly read, which may include detection tasks with empty logistics order information. In this way, 0 to multiple detection tasks with a trigger time of the current second moment can be read every second, and tens of millions of detection tasks can be collectively triggered in units of seconds.

[0089] As mentioned above, the starting and ending positions of the detection task within each second can be hidden in the detection task sequence number. For example, the starting position corresponds to the sequence number 00000000, and the ending position corresponds to the sequence number 99999999. The starting and ending positions can be directly determined without further calculations. In this way, the entire block of data corresponding to the second can be quickly read, supporting tens of millions of trigger tasks in a single second.

[0090] In one example, step S105 can be implemented as follows: through a second-level timing scheduler, according to the starting position and ending position of the detection task corresponding to the target second-level trigger time, the detection task records corresponding to the target second-level trigger time and the number of detection task records corresponding to the second-level processing quantity threshold are read from the non-relational database. In this way, it can be ensured that no detection task of one second will be missed, and the accuracy of logistics order fulfillment detection can be effectively improved.

[0091] In specific implementation, a distributed task scheduling service (such as schedulerx) can be used to trigger a pulse per second, and a streaming consumption service (such as blink) can be used to receive the pulse and start scanning. The principle is that after the passage of time, the "future tense" of the logistics order message produced by the logistics order production end (logistics order production device) may have become the "present tense" at present. All the detection task sequence numbers before the pulse will be taken out and sent to the message processing end. This processing ensures that no second will be missed.

[0092] Step S107: Execute the inspection task corresponding to the logistics order among the read inspection tasks to detect whether the logistics order is fulfilled on time.

[0093] After obtaining tens of millions of detection tasks within one second, the logistics order fulfillment detection device can execute these detection tasks and promptly detect the logistics orders that have not been fulfilled on time. In the method provided in this embodiment, the detection tasks within one second read from the non-relational database include detection tasks corresponding to logistics orders, and also include detection tasks when the logistics orders are empty. Only the detection tasks corresponding to the logistics orders need to be executed.

[0094] In one example, the logistics order fulfillment detection device can receive a logistics order detection request through a streaming computing service (such as a blink streaming service, etc.); accordingly, the detection task within one second can be executed through the streaming computing service. In this way, tens of millions of logistics orders triggered per second can be detected in parallel in a distributed manner through a server cluster, and dynamic adjustment of performance parameters can be supported, thereby effectively improving the concurrency index.

[0095] like Figure 4As shown, in this embodiment, the logistics management platform (logistics order production device) sends a large number of concurrent logistics order detection requests to the logistics order fulfillment detection device through a message middleware (such as MetaQ). The logistics order fulfillment detection device receives a large number of concurrent logistics orders through a streaming computing service (such as blink), generates a detection task corresponding to the logistics order, and writes tens of millions of detection tasks per second to a big data storage engine (such as Hbase). Among them, the detection task sequence number is a key in a non-relational database, the logistics order information is a value in a non-relational database, and the detection task sequence number includes the second-level trigger time and the generation sequence number of the logistics order fulfillment detection task. Then, a pulse trigger is generated per second through a timing scheduler (such as Schedulerx timing scheduling, etc.), and the blink streaming consumption service will receive the pulse and start scanning. According to the starting position and end position of the detection task corresponding to the target second-level trigger time, the detection task corresponding to the target second-level trigger time is read from the database. The number is the second-level processing quantity threshold. Finally, the detection task is executed through the streaming computing service. Experiments have proven that this configuration can support the consumption of 10 million data compressed to the same second within 6 minutes, and ensure that none of them are lost. In this way, a delay queue based on a big data storage engine and streaming computing services can be implemented to support large concurrency, large data volumes, and high-order moment triggering tasks, effectively improving the performance of logistics order fulfillment detection.

[0096] As can be seen from the above embodiments, the logistics order fulfillment detection method provided by the embodiment of the present application adopts a sparse storage method for a large number of detection tasks within each second. Even if there is no detection task, a detection task record will be generated, so that the starting position and end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics that the amount of data supported by non-relational databases is not limited by memory and the whole block data reading characteristics, it can quickly read tens of millions of detection tasks within a second, that is, the storage space of the detection task is exchanged for the reading time of the detection task, and the detection task batch processing in seconds is optimized. It can trigger tens of millions of detection tasks in seconds; therefore, it can effectively improve the logistics order fulfillment detection performance under the condition of large concurrent logistics orders, and effectively improve the detection task data volume indicator. In addition, this processing method fully considers the capacity and persistent storage problems of the detection task, and therefore supports the triggering of detection tasks far away from the current time (such as several months), and there is no upper limit to the support for future time.

[0097] Second embodiment

[0098] In the above-mentioned embodiment, a logistics order fulfillment detection method is provided. Correspondingly, the present application also provides a logistics order fulfillment detection device. The device corresponds to the embodiment of the above-mentioned method. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple. For relevant parts, please refer to the partial description of the method embodiment. The device embodiment described below is only illustrative.

[0099] The present application provides a logistics order fulfillment detection device, comprising:

[0100] A detection task storage unit, used to store in a non-relational database logistics order fulfillment detection tasks corresponding to a trigger time in seconds and having a number that is a second-level processing quantity threshold, wherein the detection task includes a detection task sequence number;

[0101] A task sequence number allocation unit is used to allocate an unoccupied detection task sequence number corresponding to its target fulfillment time to the logistics order, and store the logistics order information in the detection task corresponding to the unoccupied detection task sequence number;

[0102] A detection task reading unit, used to read detection tasks corresponding to the target second-level trigger time and having a number equal to a second-level processing number threshold from a database according to a start position and an end position of the detection task corresponding to the target second-level trigger time;

[0103] The detection task execution unit is used to execute the detection tasks corresponding to the logistics order among the read detection tasks to detect whether the logistics order is fulfilled on time.

[0104] Third embodiment

[0105] In the above-mentioned embodiment, a logistics order fulfillment detection method is provided. Correspondingly, the present application also provides a delayed task processing method, which is not limited to a specific application scenario. This method corresponds to the embodiment of the above-mentioned method. Since this embodiment is basically similar to the method embodiment 1, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiment 1. The method embodiment described below is only illustrative.

[0106] In this embodiment, the delayed task processing method may include the following steps:

[0107] Step 1: storing, in a non-relational database, detection tasks corresponding to a trigger time in seconds and having a number equal to a second-level processing quantity threshold, wherein the detection tasks include detection task sequence numbers;

[0108] Step 2: assigning an unoccupied detection task sequence number corresponding to the target completion time to the task to be executed, and storing the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number;

[0109] Step 3: According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and corresponding to the target second-level trigger time are read from the database;

[0110] Step 4: Execute the detection task corresponding to the task to be executed among the read detection tasks to detect whether the task to be executed is completed on time.

[0111] As can be seen from the above embodiments, the delayed task processing method provided by the embodiment of the present application adopts a sparse storage method for a large number of detection tasks within each second, and a detection task record will be generated even when there is no detection task, so that the starting position and the end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics that the amount of data supported by the non-relational database is not limited by memory, and the whole block data reading characteristics, it can quickly read tens of millions of detection tasks within a second, that is, the storage space of the detection task is exchanged for the reading time of the detection task, and the detection task batch processing in seconds is optimized, and tens of millions of detection tasks can be triggered in seconds; therefore, in the case of a large number of concurrent tasks to be executed with completion time requirements, the detection performance of detecting whether the detection of the tasks to be executed is completed on time can be effectively improved, and the detection task data volume index can be effectively improved. In addition, this processing method fully considers the capacity and persistent storage problems of the detection task, and therefore supports the triggering of detection tasks far away from the current time (such as several months), and there is no upper limit to the support for future time.

[0112] Fourth embodiment

[0113] In the above-mentioned embodiment, a delayed task processing method is provided, and correspondingly, the present application also provides a delayed task processing device. The device corresponds to the embodiment of the above-mentioned method. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described below is only illustrative.

[0114] The present application provides a delayed task processing device, comprising:

[0115] A detection task storage unit, used to store detection tasks corresponding to a trigger time in seconds and having a number equal to a second-level processing quantity threshold in a non-relational database, wherein the detection task includes a detection task sequence number;

[0116] A task sequence number allocation unit, used to allocate an unoccupied detection task sequence number corresponding to the target completion time of the task to be executed, and store the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number;

[0117] A detection task reading unit, used to read detection tasks corresponding to the target second-level trigger time and having a number equal to a second-level processing number threshold from a database according to a start position and an end position of the detection task corresponding to the target second-level trigger time;

[0118] The detection task execution unit is used to execute the detection tasks corresponding to the tasks to be executed among the read detection tasks, so as to detect whether the tasks to be executed are completed on time.

[0119] Fifth embodiment

[0120] In the above-mentioned embodiment, a delayed task processing method is provided, and correspondingly, the present application also provides an electronic device. The device corresponds to the embodiment of the above-mentioned method. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described below is only illustrative.

[0121] An electronic device of this embodiment includes: a processor and a memory; the memory is used to store a program for implementing any of the above methods, and the device is powered on and runs the program of the method through the processor.

[0122] Sixth embodiment

[0123] In the above-mentioned embodiment, a delayed task processing method is provided, and correspondingly, the present application also provides a delayed queue system. The system corresponds to the embodiment of the above-mentioned method. Since the system embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described below is only illustrative.

[0124] A delay queue system of this embodiment includes: a task generation device and a delay task processing device.

[0125] The task generation device is used to generate a to-be-executed task with a completion time requirement, and send the to-be-executed task to the delayed task processing device. The delayed task processing device is as described in the fifth embodiment above, and will not be described in detail here.

[0126] The task production device can be deployed on the task generation server, which can generate tasks with completion time requirements of 10,000 or more per second. The task production device can be deployed on the delayed task processing server, which can support 10,000 task completion detection requests per second, support the centralized triggering of 10 million detection tasks per second, detect whether the tasks to be executed have been completed before the expected time, and promptly know the tasks that have not been completed on time.

[0127] like Figure 2As shown, by deploying the task production device on the physical management platform, logistics orders at the level of 10,000 or more per second can be generated; accordingly, by delaying the task processing service end, logistics orders that are not fulfilled on time can be detected in time so as to intervene and solve the problem in time.

[0128] As can be seen from the above embodiments, the delay queue system provided by the embodiment of the present application adopts a sparse storage method for a large number of detection tasks within each second, and even if there is no detection task, a detection task record will be generated, so that the starting position and the end position of the detection task per second can be quickly determined without multiple database queries. At the same time, combined with the characteristics that the amount of data supported by the non-relational database is not limited by memory, and the whole block data reading characteristics, it can quickly read tens of millions of detection tasks within a second, that is, the storage space of the detection task is exchanged for the reading time of the detection task, and the detection task batch processing in seconds is optimized, and tens of millions of detection tasks can be triggered in seconds; therefore, in the case of a large number of concurrent tasks to be executed with completion time requirements, the detection performance of detecting whether the detection of the tasks to be executed is completed on time can be effectively improved, and the detection task data volume index can be effectively improved. In addition, this processing method fully considers the capacity and persistent storage problems of the detection task, and therefore supports the triggering of detection tasks far away from the current time (such as several months), and there is no upper limit to the support for future time.

[0129] Seventh embodiment

[0130] In the above-mentioned embodiment, a logistics order fulfillment detection method is provided. Correspondingly, the present application also provides an order timeout payment detection method. This method corresponds to the embodiment of the above-mentioned method. Since this embodiment is basically similar to the method embodiment 1, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiment 1. The method embodiment described below is only illustrative.

[0131] In this embodiment, the order payment overtime detection method may include the following steps:

[0132] Step 1: storing in a non-relational database order timeout payment detection tasks corresponding to a second-level trigger time and having a number equal to a second-level processing quantity threshold, wherein the detection task includes a detection task sequence number;

[0133] Step 2: Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number;

[0134] Step 3: According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and corresponding to the target second-level trigger time are read from the database;

[0135] Step 4: Execute the inspection task corresponding to the order among the read inspection tasks to detect whether the order is paid on time.

[0136] The order may be an order generated by an e-commerce platform. In order to effectively sell goods, it is usually required that the order be paid within a certain time limit, such as within 1 hour. If the payment is overdue, the buyer can be contacted to remind him to pay as soon as possible. If the payment is delayed, the commodity inventory occupied by the order can be released. The method provided in the embodiment of the present application can quickly detect whether tens of millions of orders per second are overdue for payment.

[0137] Eighth embodiment

[0138] In the above-mentioned embodiment, a logistics order fulfillment detection method is provided. Correspondingly, the present application also provides an order timeout payment detection method. This method corresponds to the embodiment of the above-mentioned method. Since this embodiment is basically similar to the method embodiment 1, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiment 1. The method embodiment described below is only illustrative.

[0139] In this embodiment, the order payment overtime detection method may include the following steps:

[0140] Step 1: storing in a non-relational database an order timeout acceptance detection task corresponding to a trigger time in seconds and having a number equal to a processing quantity threshold in seconds, wherein the detection task includes a detection task sequence number;

[0141] Step 2: Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number;

[0142] Step 3: According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the detection tasks whose number is the second-level processing number threshold and corresponding to the target second-level trigger time are read from the database;

[0143] Step 4: Execute the inspection task corresponding to the order among the read inspection tasks to check whether the order is received on time.

[0144] The order may be a meal order generated by a food delivery platform, and the offline store merchant needs to confirm the order generated by the food delivery platform in a timely manner. In order to deliver the food to the buyer on time, the platform usually requires the merchant to confirm receipt of the order within a certain time limit, such as accepting the order within 15 minutes. If the merchant accepts the order after the time limit, the offline store can be contacted to remind it to accept the order as soon as possible to avoid the failure to deliver the food to the buyer on time. The method provided in the embodiment of the present application can quickly detect whether tens of millions of orders per second have been accepted after the time limit.

[0145] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0146] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0147] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0148] 1. Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0149] 2. Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

Claims

1. A logistics order fulfillment detection method, characterized in that: include: A fixed number of logistics order fulfillment detection tasks corresponding to a trigger time in seconds, the number of which is a threshold of the number of processing in seconds, are stored in a non-relational database, wherein the detection task includes a detection task sequence number, and the detection task sequence number includes the detection task trigger time in seconds and the generation sequence number of the detection task in seconds; Assign an unoccupied inspection task sequence number corresponding to its target fulfillment time to the logistics order, and store the logistics order information in the inspection task corresponding to the unoccupied inspection task sequence number, so that the inspection task corresponding to the logistics order includes the inspection task sequence number and the logistics order information, and the inspection task not corresponding to the logistics order only includes the inspection task sequence number; According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the fixed number of logistics order fulfillment detection tasks corresponding to the target second-level trigger time and the number of which is the second-level processing quantity threshold are read in bulk from the non-relational database; Execute the inspection tasks corresponding to the logistics order among the read inspection tasks to check whether the logistics order is fulfilled on time.

2. The method according to claim 1, characterized in that Also includes: According to the logistics order detection rules, the target fulfillment time of multiple logistics orders received at different times is set to the same time.

3. The method according to claim 1, characterized in that Receive logistics order detection requests through streaming computing services; The step of allocating an unoccupied inspection task sequence number corresponding to the target fulfillment time to the logistics order includes: Store the occupied detection task sequence numbers of the second-level trigger time into the cache; Determine the unoccupied detection task sequence number according to the occupied detection task sequence number in the cache; The executing of the inspection task corresponding to the logistics order among the inspection tasks read includes: Through the streaming computing service, the detection task corresponding to the logistics order is executed within the second trigger time.

4. The method according to claim 1, characterized in that: The step of allocating an unoccupied inspection task sequence number corresponding to the target fulfillment time to the logistics order includes: If the generation sequence number of the detection task in the current second-level time is greater than the second-level processing quantity threshold, the unoccupied detection task sequence number is determined according to the next second time and the re-counted sequence number.

5. The method according to claim 2, characterized in that: The method of reading, in whole blocks, the fixed number of logistics order fulfillment detection task records corresponding to the target second-level trigger time and the number of which is the second-level processing quantity threshold and corresponds to the target second-level trigger time from the non-relational database according to the detection task start position and end position corresponding to the target second-level trigger time, includes: Through the second-level timing scheduler, according to the starting and ending positions of the detection tasks corresponding to the target second-level trigger time, the detection task records corresponding to the target second-level trigger time and the number of detection task records corresponding to the second-level processing quantity threshold are read from the database.

6. The method according to claim 1, characterized in that The duration of the target fulfillment time relative to the generation time of the detection task includes: month-level duration and year-level duration.

7. A method for detecting overtime payment of an order, characterized in that: include: A fixed number of order timeout payment detection tasks corresponding to a second-level trigger time and having a processing quantity threshold in a second-level are stored in a non-relational database, wherein the detection task includes a detection task sequence number, and the detection task sequence number includes a detection task trigger time in a second level and a generation sequence number of the detection task in a second-level time; Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number, so that the detection task corresponding to the order includes the detection task sequence number and the order information, and the detection task not corresponding to the order only includes the detection task sequence number; According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the fixed number of order timeout payment detection tasks corresponding to the target second-level trigger time and having a number equal to the second-level processing quantity threshold value are read in one block from the non-relational database; Execute the inspection task corresponding to the order among the read inspection tasks to check whether the order is paid on time.

8. A method for detecting an order overtime, characterized in that: include: A fixed number of order timeout acceptance detection tasks corresponding to a second-level trigger time and a second-level processing quantity threshold are stored in a non-relational database, wherein the detection task includes a detection task sequence number, and the detection task sequence number includes the second-level trigger time of the detection task and the generation sequence number of the detection task within the second-level time; Allocate an unoccupied detection task sequence number corresponding to the target payment time for the order, and store the order information in the detection task corresponding to the unoccupied detection task sequence number, so that the detection task corresponding to the order includes the detection task sequence number and the order information, and the detection task not corresponding to the order only includes the detection task sequence number; According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the fixed number of detection tasks corresponding to the target second-level trigger time and the number of which is the second-level processing number threshold are read in one block from the non-relational database; Execute the inspection task corresponding to the order among the read inspection tasks to check whether the order is accepted on time.

9. A delayed task processing method, characterized in that: include: A fixed number of detection tasks corresponding to the second-level trigger time and having a processing quantity threshold in seconds is stored in a non-relational database, wherein the detection task includes a detection task sequence number, and the detection task sequence number includes the second-level trigger time of the detection task and the generation sequence number of the detection task in the second-level time; Assign an unoccupied detection task sequence number corresponding to the target completion time of the task to be executed, and store the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number, so that the detection task corresponding to the task to be executed includes the detection task sequence number and the task information to be executed, and the detection task not corresponding to the task to be executed only includes the detection task sequence number; According to the starting position and the ending position of the detection task corresponding to the target second-level trigger time, the fixed number of detection tasks corresponding to the target second-level trigger time and the number of which is the second-level processing number threshold are read in one block from the non-relational database; Execute the detection tasks corresponding to the tasks to be executed among the read detection tasks to detect whether the tasks to be executed are completed on time.

10. A delayed task processing device, characterized in that: include: A detection task storage unit, used to store in a non-relational database a fixed number of detection tasks corresponding to a trigger time in seconds, the number of which is a threshold of the number of processing in seconds, wherein the detection task includes a detection task sequence number, which includes the detection task trigger time in seconds and the generation sequence number of the detection task in seconds; A task sequence number allocation unit is used to allocate an unoccupied detection task sequence number corresponding to the target completion time of the task to be executed, and store the task information to be executed in the detection task corresponding to the unoccupied detection task sequence number, so that the detection task corresponding to the task to be executed includes the detection task sequence number and the task information to be executed, and the detection task not corresponding to the task to be executed only includes the detection task sequence number; A detection task reading unit, configured to read, in whole blocks from the non-relational database, the fixed number of detection tasks corresponding to the target second-level trigger time and having a number equal to the second-level processing number threshold, according to the detection task start position and end position corresponding to the target second-level trigger time; The detection task execution unit is used to execute the detection tasks corresponding to the tasks to be executed among the read detection tasks, so as to detect whether the tasks to be executed are completed on time.

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