Batch task processing method and user behavior point query method

Abstract

The embodiments of the present specification provide a batch task processing method and a user behavior point query method, comprising distributing tasks to corresponding computing clusters and disaster recovery clusters according to the identification interval of the identification information of each task, partition scheduling the batch tasks through multiple computing clusters, and synchronously scheduling and processing the tasks by using the computing clusters and the disaster recovery clusters, thereby improving the parallel capability and processing efficiency of the tasks. Moreover, through odd and even task fragmentation processing of each identification interval, the computing clusters and the disaster recovery clusters can schedule and process different task fragments respectively, thereby avoiding resource waste and data conflicts caused by scheduling and processing the same task by both, ensuring load balancing of the computing clusters and the disaster recovery clusters, and additionally, without the need to reconfigure the task fragments of the computing clusters and the disaster recovery clusters in the case of identification interval configuration changes, thereby improving the stability and efficiency of batch task processing.

Description

TECHNICAL FIELD

[0001] One or more embodiments of the present specification relate to the technical field of big data processing, and in particular to a batch task processing method and a user behavior point query method. BACKGROUND

[0002] Batch task processing refers to a processing manner of processing a certain amount of data or tasks in batches in a data processing process. The batch task has the characteristics of a large amount of data and can be automatically executed in a certain order without human intervention until the batch task processing is completed.

[0003] The processing efficiency of the batch task depends on the task scheduling capability of the computing cluster. Since the batch task generally has no high time efficiency requirement, the centralized scheduling manner is used in the related technical solution to sequentially execute the task by using one or more computing clusters, and the data processing period is long, which is difficult to meet the needs of some high time efficiency scenarios. SUMMARY

[0004] To improve the batch task processing efficiency, the embodiments of the present specification provide a batch task processing method and device, a user behavior point query method and device, an electronic device, a storage medium, and a computer program product.

[0005] In a first aspect, the embodiments of the present specification provide a batch task processing method applied to a data processing system, wherein the data processing system includes a plurality of computing clusters and a disaster recovery cluster corresponding to each computing cluster. The method comprises:

[0006] According to the identification interval to which the identification information of each task in the batch task belongs, the batch task is distributed to the computing cluster and the disaster recovery cluster corresponding to the identification interval, wherein each computing cluster corresponds to an identification interval, and the identification information of the identification interval corresponding to the plurality of computing clusters is a continuous value;

[0007] For each computing cluster, the computing cluster reads and executes a task with a first identification from the distributed batch task, and the disaster recovery cluster corresponding to the computing cluster reads and executes a task with a second identification from the distributed batch task, wherein one of the first identification and the second identification is identification information of an odd position in the identification interval, and the other is identification information of an even position in the identification interval.

[0008] The task processing result of each task is stored in the computing cluster and the disaster recovery cluster thereof.

[0009] In some embodiments, the task includes a first task and a second task different from the first task.

[0010] The process of reading and executing the tasks identified by the first identifier by the computing cluster comprises: in successive time periods, the computing cluster alternately reads and executes the first task identified by the first identifier and the second task identified by the first identifier in turn.

[0011] The process of reading and executing the tasks identified by the second identifier by the disaster recovery cluster comprises: in successive time periods, the disaster recovery cluster alternately reads and executes the first task identified by the second identifier and the second task identified by the second identifier in turn.

[0012] In some embodiments, the process of reading and executing the first task identified by the first identifier and the second task identified by the first identifier by the computing cluster in turn in successive time periods comprises: in odd time periods, the computing cluster reads and executes the first task identified by the first identifier; and in even time periods, the computing cluster reads and executes the second task identified by the first identifier.

[0013] In some embodiments, the process of reading and executing the first task identified by the second identifier and the second task identified by the second identifier by the disaster recovery cluster in turn in successive time periods comprises: in odd time periods, the disaster recovery cluster reads and executes the first task identified by the second identifier; and in even time periods, the disaster recovery cluster reads and executes the second task identified by the second identifier.

[0014] In some embodiments, the tasks comprise a user behavior score calculation task and / or a user behavior parameter calculation task, wherein the user behavior score calculation task depends on the user behavior parameter.

[0015] In some embodiments, when the tasks comprise a user behavior score calculation task, the process of executing the user behavior score calculation task comprises:

[0016] obtaining user behavior data, the user behavior data comprising historical behavior data and current behavior data, the current behavior data comprising at least part of the user behavior parameter obtained in advance;

[0017] generating a plurality of behavior factor parameters according to the historical behavior data and the current behavior data, and generating the user behavior score according to the plurality of behavior factor parameters, the task processing result comprising the user behavior score.

[0018] In some embodiments, the obtaining of the user behavior data comprises: obtaining the user behavior data from a database corresponding to the user identifier.

[0019] In some embodiments, the storing of the task processing result of each task in the computing cluster and its disaster recovery cluster comprises: storing each user behavior score in a database corresponding to the user identifier.

[0020] In a second aspect, the embodiments of the present specification provide a method for querying user behavior points, comprising:

[0021] obtaining a user query instruction, wherein the user query instruction comprises a user identifier;

[0022] querying, according to the user identifier, user behavior points corresponding to a current calculation period from a database, wherein the user behavior points of each calculation period in the database are calculated by the batch task processing method in any of the foregoing embodiments, and the task processing result comprises the user behavior points;

[0023] in response to the user behavior points corresponding to the user identifier in the current calculation period not being calculated, performing a user behavior point calculation task to obtain the user behavior points in the current calculation period; and in response to the user behavior points corresponding to the user identifier in the current calculation period being calculated, reading the user behavior points;

[0024] displaying the user behavior points in the current calculation period.

[0025] In a third aspect, the embodiments of the present specification provide a batch task processing apparatus applied to a data processing system, wherein the data processing system comprises a plurality of calculation clusters and a disaster recovery cluster corresponding to each calculation cluster, and the apparatus comprises:

[0026] a task scheduling module configured to distribute, according to an identification interval to which identification information of each task in a batch task belongs, the batch task to a calculation cluster and a disaster recovery cluster corresponding to the identification interval, wherein each calculation cluster corresponds to an identification interval, and identification information of the identification intervals corresponding to the plurality of calculation clusters is continuous values;

[0027] a task execution module configured to, for each calculation cluster, read and execute a task of a first identification from the distributed batch task, and a disaster recovery cluster corresponding to the calculation cluster reads and executes a task of a second identification from the distributed batch task, wherein one of the first identification and the second identification is identification information of odd bits in the identification interval, and the other is identification information of even bits in the identification interval;

[0028] a result storage module configured to store a task processing result of each task in the calculation cluster and the disaster recovery cluster.

[0029] In some embodiments, the task comprises a first task and a second task different from the first task; and the task execution module is configured to:

[0030] in consecutive time periods, the calculation cluster alternately reads and executes the first task of the first identification and the second task of the first identification in turn;

[0031] In the continuous time periods, the disaster recovery cluster alternately reads and executes the first task of the second identity and the second task of the second identity in turn.

[0032] In some embodiments, the task execution module is configured to:

[0033] In odd time periods, the computing cluster reads and executes the first task of the first identity; in even time periods, the computing cluster reads and executes the second task of the first identity.

[0034] In odd time periods, the disaster recovery cluster reads and executes the first task of the second identity; in even time periods, the disaster recovery cluster reads and executes the second task of the second identity.

[0035] In some embodiments, the task includes a user behavior score calculation task and / or a user behavior parameter calculation task, wherein the user behavior score calculation task depends on the user behavior parameter.

[0036] In some embodiments, the task execution module is configured to:

[0037] Obtain user behavior data, the user behavior data including historical behavior data and current behavior data, the current behavior data including at least part of the user behavior parameter obtained in advance;

[0038] Generate a plurality of behavior factor parameters according to the historical behavior data and the current behavior data, and generate the user behavior score according to the plurality of behavior factor parameters, the task processing result including the user behavior score.

[0039] In some embodiments, the task execution module is configured to:

[0040] Read the user behavior data from a database corresponding to the user representation according to the user identity;

[0041] Store each user behavior score in a database corresponding to the user identity.

[0042] In a fourth aspect, the embodiments of the present specification provide a user behavior score query device, including:

[0043] An instruction obtaining module configured to obtain a user query instruction, the user query instruction including a user identity;

[0044] The integral query module is configured to query the user behavior integral corresponding to the current calculation period from the database according to the user identifier, the user behavior integral of each calculation period in the database is calculated by the batch task processing method in any of the above embodiments, the task processing result includes the user behavior integral; in response to the user behavior integral corresponding to the user identifier in the current calculation period not being calculated, a user behavior integral calculation task is performed to obtain the user behavior integral in the current calculation period; and in response to the user behavior integral corresponding to the user identifier in the current calculation period being calculated, the user behavior integral is read.

[0045] The integral display module is configured to display the user behavior integral in the current calculation period.

[0046] In a fifth aspect, the embodiments of the present specification provide an electronic device, comprising:

[0047] a processor;

[0048] a memory storing computer instructions, the computer instructions being executed by the processor to implement the method in any of the above embodiments.

[0049] In a sixth aspect, the embodiments of the present specification provide a computer program product, which is executed to implement the method in any of the above embodiments.

[0050] The method of the embodiments of the present specification comprises distributing tasks to corresponding computing clusters and disaster recovery clusters according to the identification interval of each task, partitioning and scheduling batch tasks by multiple computing clusters, and synchronously scheduling and processing tasks by computing clusters and disaster recovery clusters, thereby improving task parallelism and processing efficiency. Moreover, by performing odd and even task fragmentation processing on each identification interval, the computing cluster and the disaster recovery cluster can schedule and process different task fragments, avoiding resource waste and data conflicts, while ensuring load balancing of the computing cluster and the disaster recovery cluster. In addition, in the case of changes in the identification interval configuration, it is not necessary to reconfigure the task fragments of the computing cluster and the disaster recovery cluster, thereby improving the stability and efficiency of batch task processing. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is an architecture diagram of a data processing system in an exemplary embodiment of the present specification.

[0052] Figure 2 is a task scheduling logic diagram of a data processing system in an exemplary embodiment of the present specification.

[0053] Figure 3is a flow chart of a batch task processing method in an exemplary embodiment of the present specification.

[0054] Figure 4 is a schematic diagram of a batch task processing method in an exemplary embodiment of the present specification.

[0055] Figure 5 is a flow chart of a batch task processing method in an exemplary embodiment of the present specification.

[0056] Figure 6 is a system architecture diagram relied on by a financial service platform in an exemplary embodiment of the present specification.

[0057] Figure 7 is a flow chart of a user behavior point query method in an exemplary embodiment of the present specification.

[0058] Figure 8 is a structural block diagram of a batch task processing device in an exemplary embodiment of the present specification.

[0059] Figure 9 is a structural block diagram of a user behavior point query device in an exemplary embodiment of the present specification.

[0060] Figure 10 is a structural block diagram of an electronic device in an exemplary embodiment of the present specification. DETAILED DESCRIPTION

[0061] The user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present specification are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0062] User behavior points refer to quantitative scores for evaluating a user's behavior ability based on the user's historical behavior or data. For example, taking a financial service platform as an example, the financial service platform often generates credit points corresponding to each user, which can reflect the user's credit ability. For another example, the financial service platform can also generate financial points corresponding to the user, which can reflect the user's financial ability.

[0063] In the financial service platform, the user behavior points need to be dynamically updated with the continuously generated behavior data of the user, so that the user behavior points can accurately reflect the current behavior ability of the user. For a large number of users, the task of calculating the user behavior points of each user is a batch task, also called a run batch task, which means that the financial service platform needs to calculate the user behavior points of each user according to the behavior data of the user and store them, and read the current latest user behavior points from the database and show them to the user when the user calls for query.

[0064] In the related technology, the batch task of calculating the user behavior points has no high requirement for timeliness, for example, the user behavior points are generally updated weekly, that is, a period of 7 days, so that the financial service platform can calculate the user behavior points of all users within 7 days. At present, the batch task processing of the user behavior points adopts a centralized scheduling scheme, which uses one or more computing clusters to sequentially schedule and process tasks until the batch task processing is completed.

[0065] With the increasing requirement for data timeliness, the batch task processing method in the related technology cannot meet the business needs of modern finance pursuing agile response, for example, in the user behavior points daily update scenario, it is required to complete the calculation and storage of the user behavior points of all users every day, and the batch task processing of millions of users cannot meet the requirement of daily update timeliness.

[0066] Therefore, the embodiments of the present specification provide a batch task processing method and device, a user behavior point query method and device, a data processing system, a storage medium and a computer program product, which are aimed at using system disaster recovery mechanism and combining distributed scheduling to improve the scheduling and processing speed of batch tasks and meet the requirements of high timeliness task scenarios.

[0067] System disaster recovery refers to measures and calculations taken to ensure that a data system can quickly recover or continue to provide services when an unexpected service interruption occurs. Among them, system backup is a key component of system disaster recovery. For example, for a data system, in order to avoid service interruption caused by natural disasters, technical failures, human errors or other unexpected events, it can still ensure normal service operation. A disaster recovery system can be set up for the data system, which has the same configuration as the data system and can provide the same capabilities, so that the disaster recovery system can ensure normal service operation when a disaster occurs.

[0068] In the related art, when batch task processing is performed, a data system is taken as a master system, and batch task processing is concentrated in the master system, the master system schedules and sequentially executes tasks, and a disaster recovery system is only used as a data backup, resulting in waste of computing resources. In order to improve the efficiency of batch task processing, the data system and the disaster recovery system can be used to jointly schedule and execute batch tasks, but this approach faces two main problems: one is the data conflict problem, in order to avoid data conflicts between the two systems, an efficient and stable task scheduling scheme is needed to reasonably schedule tasks; the other is the load balancing problem, and the load of the data system and the disaster recovery system needs to be reasonably allocated.

[0069] Based on this, the batch task processing method and device provided by the embodiments of the present specification can be applied to a data processing system, Figure 1 The architecture diagram of the data processing system of an example embodiment of the present specification is shown, and the following will be described in combination with Figure 1

[0070] In the embodiments of the present specification, the data processing system includes a plurality of computing clusters, and each computing cluster is provided with a corresponding disaster recovery cluster. For example Figure 1 In the example, the data processing system includes Rz41A, Rz42A,..., Rz4nA, a total of n computing clusters, and Rz41B, Rz42B,..., Rz4nB, a total of n disaster recovery clusters. Taking Rz41A and Rz41B as an example, the computing cluster Rz41A and the disaster recovery cluster Rz41B are configured and have exactly the same data, and the capabilities they can provide are also exactly the same.

[0071] Each computing cluster or disaster recovery cluster can be understood as a physical machine room, which can include one or more servers for providing computing power, and can also include one or more databases for providing storage power. A server or a database can refer to a physical device, or can refer to a logical device configured by virtualization technology. In the embodiments of the present specification, the number of servers and databases included in different computing clusters can be the same or different, and the specific number of servers or databases included in the computing clusters is not limited.

[0072] In the embodiments of the present specification, the plurality of computing clusters and disaster recovery clusters need to be managed according to the task identification information of the batch task. The task identification information refers to the task identification used to classify batch tasks. For example, in some embodiments, taking the user behavior points calculation task as an example, the task identification information can be the UID (User Identification, user identification) of the user or a part of the digital number in the UID.

[0073] For example, refer to Figure 1 ​As shown, in one example, assuming the system includes 4 computing clusters Rz41A~Rz44A and 4 disaster recovery clusters Rz41B~Rz44B, the task identification information can adopt certain two digits in the UID of the user, so that the identification information interval corresponding to all tasks is [00, 99], a total of 100 identification information.

[0074] In this example, the identification information interval [00, 99] can be evenly divided into 4 identification intervals according to the number of computing clusters, and allocated to each computing cluster and its disaster recovery cluster respectively. For example Figure 1 In the example, the identification interval corresponding to the computing cluster Rz41A is [00, 01, 02, …, 24], and similarly, the identification interval corresponding to the disaster recovery cluster Rz41B of the computing cluster Rz41A is also [00, 01, 02, …, 24]. The identification interval corresponding to the computing cluster Rz42A is [25, 26, 27, …, 49], and similarly, the identification interval corresponding to the disaster recovery cluster Rz42B of the computing cluster Rz42A is also [25, 26, 27, …, 49]. The identification interval corresponding to the computing cluster Rz43A is [50, 51, 52, …, 74], and similarly, the identification interval corresponding to the disaster recovery cluster Rz43B of the computing cluster Rz43A is also [50, 51, 52, …, 74]. The identification interval corresponding to the computing cluster Rz44A is [75, 76, 77, …, 99], and similarly, the identification interval corresponding to the disaster recovery cluster Rz44B of the computing cluster Rz44A is also [75, 76, 77, …, 99], which can be seen from Table 1 shown below:

[0075] Table 1 Identification interval

[0076] Computing cluster Disaster recovery cluster Identification interval Rz41A Rz41B [00,01,02,…,24] Rz42A Rz42B [25,26,27,…,49] Rz43A Rz43B [50,51,52,…,74] Rz44A Rz44B [75,76,77,…,99]

[0077] Through the interval division of the task identification information, when processing batch tasks, the tasks can be distributed according to the identification interval, realizing distributed scheduling and processing of batch tasks. Figure 2 The task scheduling and distribution logic based on the system architecture is shown in Figure 1 The following will be described in conjunction with Figure 2 .

[0078] As Figure 2As shown, the data processing system of the embodiments of the present disclosure adopts a three-layer task distribution mechanism. In the present example, in the first layer (Layer 1), the system task scheduler distributes batch tasks to Rz41A-Rz44A and Rz41B-Rz44B according to the aforementioned identification intervals, laying the foundation for subsequent refined task processing. In the second layer (Layer 2), taking Rz41A and Rz41B as an example, the identification intervals corresponding to Rz41A and Rz41B are split, and a task processing process is created for each identification information (UID), so that each system process can focus on processing the tasks of one identification information. In the third layer (Layer 3), the system can grab specific tasks from each identification information (UID) for processing. For example, taking UID:00 as an example, tasks “2088…00x” and “2088…00y” are tasks subordinate to the identification information “00”.

[0079] In one example, each system process can grab multiple tasks for processing at a time, for example, each system process can grab 10 tasks for processing, so that for one computing cluster Rz41A, parallel processing of 250 tasks can be realized at the same time, and for the computing clusters Rz41A-Rz44A, parallel processing of 1000 tasks can be realized at the same time.

[0080] In the above task scheduling and processing process, for the same group of computing clusters and disaster recovery clusters, since the identification intervals corresponding to the two are the same, data conflict problems may occur. For example, taking computing cluster Rz41A and disaster recovery cluster Rz41B as an example, Rz41A schedules and processes tasks with identification information “00”, at the same time, if Rz41B also schedules and processes tasks with identification information “00”, it will cause repeated calculation, resulting in waste of computing resources, and when writing task processing results, repeated writing can easily cause database errors.

[0081] Therefore, in the embodiments of the present disclosure, on the basis of the above system, the interval fragmentation scheduling method can be further combined to ensure the task parallel stability and load balancing of the computing clusters and the disaster recovery clusters. The following will be described in combination with Figure 3 .

[0082] As Figure 3 shown, in some embodiments, the batch task processing method of the embodiments of the present disclosure comprises:

[0083] S310, according to the identification interval to which the identification information of each task in the batch task belongs, distribute the batch task to the computing cluster and its disaster recovery cluster corresponding to the identification interval.

[0084] In the embodiments of the present specification, when scheduling and processing batch tasks, the system can first determine the identification interval to which each task belongs according to the identification information of each task.

[0085] For example, in the example described above, the batch task is a user behavior point calculation task, each task represents the calculation of the user behavior points of a user, and the identification information of each task can be represented by two digits in the UID. The system task scheduler can obtain the identification information of each task, and then distribute the tasks to the computing clusters and their disaster recovery clusters corresponding to the identification intervals based on the division of the identification intervals described above.

[0086] For example, in an example, the identification information of a certain task is "03", so that through the identification intervals shown in Table 1 described above, it can be determined that the identification information of the task belongs to the identification interval [00, 01, 02, …, 24], so that the system task scheduler can distribute the task to the computing cluster Rz41A and the disaster recovery cluster Rz41B.

[0087] Through the above method process, the scheduling and distribution of batch tasks in the first level (level 1) can be realized, that is, the batch tasks are distributed to each computing cluster and its disaster recovery cluster according to different identification intervals.

[0088] S320, for each computing cluster, the computing cluster reads and executes the first identified task from the distributed batch tasks, and the disaster recovery cluster of the computing cluster reads and executes the second identified task from the distributed batch tasks.

[0089] As can be understood from the foregoing, the identification interval corresponding to each computing cluster and its disaster recovery cluster is the same, so the distributed batch tasks are also the same. For example, taking the computing cluster Rz41A and the disaster recovery cluster Rz41B as an example, in order to avoid data conflicts when scheduling and processing tasks, in the embodiments of the present specification, the tasks in the identification interval can be processed by fragmentation, so that the computing cluster Rz41A and the disaster recovery cluster Rz41B process different task fragments, respectively.

[0090] For example, in some embodiments, the identification interval corresponding to the computing cluster and the disaster recovery cluster can be divided into a first identification and a second identification, the first identification can be the identification information of the odd number in the identification interval, and the second identification can be the identification information of the even number in the identification interval. Alternatively, the first identification can be the identification information of the even number in the identification interval, and the second identification can be the identification information of the odd number in the identification interval.

[0091] For example, in one example, still taking the computing cluster Rz41A and the disaster recovery cluster Rz41B as an example, the corresponding identification interval is [00, 01, 02, …, 24], which can be divided into a first identification [00, 02, 04, 06, 08, 10, 12, 14, 16, 18, 20, 22, 24] and a second identification [01, 03, 05, 07, 09, 11, 13, 15, 17, 19, 21, 23].

[0092] Therefore, in the task scheduling of the second level (level 2), the computing cluster Rz41A can only call the tasks with the first identification from all the batch tasks distributed, and the disaster recovery cluster Rz41B can only call the tasks with the second identification from all the batch tasks distributed. Then in the third level (level 3), the computing cluster Rz41A and the disaster recovery cluster Rz41B can execute specific tasks from the scheduled task shards and obtain the corresponding task processing results.

[0093] It can be understood that by dividing the identification interval into the first identification and the second identification, the computing cluster and the disaster recovery cluster can schedule and process different task shards, respectively, so as to avoid scheduling and processing the same tasks, and to avoid resource waste and data conflict.

[0094] In addition, it is worth noting that the scheduling method in the embodiments of the present specification is different from simply processing the batch tasks of the computing cluster and the disaster recovery cluster in batches, but divides the batch tasks by identification information, which can ensure the load balancing of the computing cluster and the disaster recovery cluster.

[0095] For example, in the foregoing example, it can be seen that the identification interval of the computing cluster Rz41A and the disaster recovery cluster Rz41B is a continuous value, and by processing the task shards of the odd bits and the even bits, the number of task identification information allocated to the computing cluster Rz41A and the disaster recovery cluster Rz41B can be kept basically consistent, so that the number of specific tasks scheduled and processed by the two can also be kept basically consistent, so that the resources of the computing cluster and the disaster recovery cluster can be fully scheduled and utilized, and the load balancing can be ensured.

[0096] Moreover, in the embodiments of the present specification, the identification interval is processed by the task shards of the odd bits and the even bits, which is different from the scheme of fixedly configuring the identification interval for the computing cluster and the disaster recovery cluster, and can avoid the redundant operation of repeatedly configuring the task shards when the identification interval configuration changes.

[0097] For example, in one related technical example, if the computing cluster Rz41A is pre-configured to schedule tasks in the interval [00, 01, …, 11] and the disaster recovery cluster Rz41B is pre-configured to schedule tasks in the interval [12, 13, 14, …, 24], once the identification intervals of the computing cluster Rz41A and the disaster recovery cluster Rz41B change, the computing cluster and the disaster recovery cluster need to be re-configured to schedule the task shards.

[0098] For example, the system is expanded to include 10 computing clusters and 10 disaster recovery clusters as shown in the architecture of FIG. 8, and the identification intervals of the computing cluster Rz41A and the disaster recovery cluster Rz41B change to [00, 01, 02, …, 09]. In the above scheme, the computing cluster Rz41A needs to be re-configured to schedule the task shards in the interval [00, 01, 02, 03, 04], and the disaster recovery cluster Rz41B needs to be configured to schedule the task shards in the interval [05, 06, 07, 08, 09], which is complicated. Figure 1 In the embodiment of the present disclosure, however, no matter how the identification intervals of the computing cluster Rz41A and the disaster recovery cluster Rz41B change, the computing cluster Rz41A and the disaster recovery cluster Rz41B will always schedule the shards according to the odd and even identification information, so that the task scheduling logic of the computing cluster Rz41A and the disaster recovery cluster Rz41B does not need to be re-configured, greatly simplifying the complexity of the system configuration operation.

[0099] S330, storing the task processing result of each task in the computing cluster and its disaster recovery cluster.

[0100] After the above task scheduling and processing process, the task processing result of each task can be calculated in sequence, and the task processing result can be stored next.

[0101] For example, taking a user behavior point calculation task as an example, in combination with FIG. 8, after the computing cluster Rz41A invokes and executes the task “2088…00x”, the user behavior points corresponding to the task can be obtained, and then the user behavior points calculated are stored in the computing cluster Rz41A and the disaster recovery cluster Rz41B.

[0102] It can be understood that, in order to keep the data of Rz41A and Rz41B synchronized, no matter whether the task processing result calculated by Rz41A or the task processing result calculated by Rz41B, the task processing result is written into Rz41A and Rz41B for storage. In addition, it can be understood that, since the computing cluster Rz41A and the disaster recovery cluster Rz41B are scheduled by the task shards as described above, when the task processing result is written, the problem of data conflict does not occur, and the data storage efficiency is accelerated. Figure 2

[0103] ​In some embodiments, when storing the task processing result, a corresponding database can be configured in advance for each identification information, so that the task processing result of the task of the identification information is stored in the corresponding database, that is, a database is configured and maintained for each identification information of the user, and the database is only used to store the user data of the corresponding identification information, thereby optimizing the data access path and improving the data reading and writing efficiency.

[0104] The above only takes a set of computing clusters and disaster recovery clusters (i.e., Rz41A and Rz41B) as an example to describe the process of the batch task processing method. For other computing clusters and disaster recovery clusters, the processing can be performed according to the above method process in turn, and details are not described herein. Through the above batch task processing method, the batch tasks are processed in turn until all the batch tasks are processed.

[0105] As known from the above, in the embodiments of the present disclosure, the batch tasks are scheduled by multiple computing clusters, and the tasks are scheduled and processed by the computing clusters and the disaster recovery clusters synchronously, thereby improving the parallel processing capability and the processing efficiency. In addition, by performing the odd and even task sharding processing on each identification interval, the computing clusters and the disaster recovery clusters can schedule and process different task shards, so that the resource waste and data conflict caused by scheduling and processing the same task by the computing clusters and the disaster recovery clusters are avoided, and the load balancing of the computing clusters and the disaster recovery clusters is ensured. In addition, the redundant operation of repeatedly configuring the task shards when the identification interval configuration changes is avoided.

[0106] It is worth noting that in some scenarios, for complex batch task processing jobs, the job process can include multiple types of tasks. For example, in the user behavior point calculation scenario, in order to comprehensively evaluate the user behavior points, more user behavior parameters can be used, and the calculation of these user behavior parameters is also a batch task processing. For example, taking financial points as an example, in order to calculate the financial points of a user, the holding ratio and yield of the user in the historical period can be obtained, which are user behavior parameters. The process of calculating the user behavior parameters of a large number of users is also a batch task processing process. Therefore, it can be understood that for complex batch task processing scenarios, multiple types of tasks can be included.

[0107] In some embodiments of the present disclosure, in order to improve the batch task processing efficiency of such complex multi-task scenarios, a time-alternating peak-shifting processing method is proposed, and the computing clusters or the disaster recovery clusters can alternately process the two types of tasks on the time axis, thereby fully utilizing the cluster resources and greatly improving the batch task processing efficiency. The following will be described in combination with Figure 4 .

[0108] In some embodiments, the task type of the batch task includes a first task and a second task. For example, the first task is a user behavior score calculation task, and specifically, the user behavior score calculation task can be a financial score calculation task, a credit score calculation task, etc. The second task is a user behavior parameter calculation task, and specifically, the user behavior parameter calculation task can be an AUM (Assets Under Management) calculation task, a holding ratio calculation task, etc.

[0109] In the embodiments of the present specification, when the cluster schedules and executes tasks, the time length of task processing can be divided into multiple continuous time periods on a time axis. For example, in one example, the time length of the computing cluster to grab and execute a task once is about 200 ms to 400 ms. Considering factors such as data fluctuations, a time period of 1 s can be set, that is, every 1 s on the time axis is a time period.

[0110] In the present example, in combination with Figure 2 and Figure 4 shown, the computing cluster Rz41A and the disaster recovery cluster Rz41B are taken as examples. The scheduling of the first task or the second task by Rz41A and Rz41B can refer to the foregoing Figure 3 embodiments, and will not be described in detail. In summary, the computing cluster Rz41A can schedule the first task or the second task of the first identifier, and the disaster recovery cluster Rz41B can schedule the first task or the second task of the second identifier.

[0111] For the computing cluster Rz41A, in the embodiments of the present specification, in the continuous time periods, the computing cluster Rz41A can alternately read and execute the first task and the second task of the first identifier in sequence. For example, in the time period T, Rz41A can grab and execute the first task of the first identifier, and in the time period T+1, Rz41A can grab and execute the second task of the first identifier, and in the time period T+2, Rz41A can continue to grab and execute the first task of the first identifier, and so on.

[0112] Similarly, for the disaster recovery cluster Rz41B, in the embodiments of the present specification, in the continuous time periods, the disaster recovery cluster Rz41B can alternately read and execute the first task and the second task of the second identifier in sequence. For example, in the time period T, Rz41B can grab and execute the first task of the second identifier, and in the time period T+1, Rz41B can grab and execute the second task of the second identifier, and in the time period T+2, Rz41B can continue to grab and execute the first task of the second identifier, and so on.

[0113] In the above examples, when the cluster executes the task to obtain a task processing result, the task processing result can be stored in the corresponding database. Those skilled in the art can refer to the foregoing, and details are not described herein.

[0114] In combination Figure 4 As shown in the above examples, for each computing cluster or disaster recovery cluster, by processing tasks in an alternating staggered manner on the time axis, different types of batch task processing operations can be finely scheduled in time, ensuring that the computing power cluster is in an operation state every time period. Not only can the computing power cluster resources be fully utilized to improve the processing efficiency of complex multi-task scenarios, but also the database write pressure can be dispersed to improve the overall processing efficiency and response speed of the database.

[0115] In the following embodiments of the present specification, the user behavior points calculation task will be taken as an example to describe the specific task processing process. In the following embodiments, the user behavior points will be taken as an example to describe the “financial points”. However, those skilled in the art can understand that the user behavior points are not limited to “financial points”, but can also be, for example, “credit points”, and the present specification will not be described herein.

[0116] Financial points are indicators introduced by a financial service platform to objectively quantify a user's financial ability. For users, understanding their financial points can help them obtain more accurate financial strategy guidance, further optimize asset allocation, and promote the steady growth of assets. The unique value of the financial points system lies in its ability to convert complex financial behavior patterns of users into intuitive quantitative scores, greatly enhancing the transparency and operability of personal financial management, and providing users with a new perspective of scientific and personalized asset management.

[0117] In related technologies, the financial points update mechanism of the financial service platform is generally weekly, that is, the financial points of the user are updated every 7 days. In the embodiments of the present specification, through the above batch task processing method, the batch task processing period for calculating the user's financial points can be shortened to several hours or even several hours, so that daily updates of the financial points can be realized, that is, the financial points of the user are updated every day, which greatly meets the high timeliness requirements of the financial service platform for data processing.

[0118] In the above embodiments of the present specification, the scheduling and processing of the batch task for calculating the financial points by the data processing system have been described. In the following, the process of calculating the user's financial points will be described in combination with Figure 5 the process of calculating the user's financial points.

[0119] As Figure 5 shown, in some embodiments, the batch task processing method of the present specification includes the following steps in the process of executing the user behavior points calculation task:

[0120] S510, obtain user behavior data.

[0121] S520, generate a plurality of behavior factor parameters according to the historical behavior data and the current behavior data, and generate a user behavior score according to the plurality of behavior factor parameters.

[0122] It is worth noting that in the related art scheme for calculating the user financial management score, the historical period data of the user is generally quantitatively evaluated to generate the score of the financial management score.

[0123] In the embodiments of the present specification, the behavior factors affecting the financial management score are divided into two parts: "offline factors" and "online factors". The offline factor refers to an impact factor abstracted from the offline behavior data of the user, and the online factor refers to an impact factor abstracted from the current behavior data of the user. Therefore, in the embodiments of the present specification, the obtained user behavior data also includes two parts: one is historical behavior data for representing historical behavior of the user, and the other is current behavior data for representing current behavior of the user.

[0124] For example, the historical behavior data can include: fund average holding time, fund investment amount, and transaction frequency in the historical period, etc. These behavior data are calculated from the historical data and are used to reflect the historical financial management habits of the user. The current behavior data can include: user holdings, holding good ratio, AUM, etc. These behavior data are related to the current behavior of the user and can reflect the current fund situation of the user.

[0125] In some embodiments, the specific parameters of the historical behavior data and the current behavior data can also be calculated by the aforementioned batch task processing method, which will not be repeated in the present specification. Taking a user as an example, when obtaining the user behavior data of the user, the user behavior data of the user can be read from the aforementioned corresponding database according to the user identifier (such as UID) of the user. Those skilled in the art can understand this, and the present specification will not be repeated.

[0126] In some embodiments, after obtaining the user behavior data, a plurality of behavior factor parameters can be generated according to the historical behavior data and the current behavior data included in the user behavior data. Each behavior factor parameter can be understood as a parameter obtained by quantifying a certain item of user behavior data, which is used to represent a sub-item score of the financial management score. After obtaining these behavior factor parameters, the final user behavior score can be calculated according to the behavior factor parameters by, for example, weighted fusion. The user behavior score is the financial management score. Those skilled in the art can refer to the related art for the quantification and weighted fusion calculation process of the behavior factor parameters, and the specific details will not be repeated.

[0127] After the financial score of the user is calculated, the financial score can be stored in the database corresponding to the user, waiting for the user to query and call. Through the process of the above embodiment, the financial score corresponding to each user can be calculated. In some examples of the embodiment, the above batch task processing method process can be executed every 1 day in combination with the foregoing batch task processing method process, and the latest financial score can be stored in each database, waiting for the user to query and call.

[0128] As known from the above, in the embodiment of the present specification, the user behavior points are calculated by combining the historical behavior data and the current behavior data, which can more comprehensively evaluate the user behavior ability, thereby improving the accuracy of the user behavior points.

[0129] In some embodiments, the present specification provides a query method of user behavior points, which can be applied to a financial service platform, Figure 6 The system architecture diagram of the financial service platform in some embodiments of the present specification is shown, and the following will be described in combination with Figure 6 .

[0130] As Figure 6 shown, the system architecture of the financial service platform can include a server 11, a network 12, and several electronic devices such as a PC (Personal Computer) 13, a mobile phone 14, etc.

[0131] The server 11 can be a physical server containing a standalone host, or the server 11 can be a virtual server carried by a host cluster. In the running process, the server 11 can run the server-side program of the financial service platform to realize the service end of the financial service platform. In some embodiments, the server 11 can also serve as the data processing system shown in the foregoing Figure 1 .

[0132] The PC 13 and the mobile phone 14 are only part of the types of electronic devices that can be used by the user. In fact, the user can obviously also use electronic devices of the following types: tablet devices, notebook computers, PDAs (Personal Digital Assistants), wearable devices (such as smart glasses, smart watches, etc.), etc., and one or more embodiments of the present specification do not limit this. In the running process, the electronic device can run the client-side program of the financial service platform to realize the client of the financial service platform.

[0133] The application program of the client of the financial service platform can be started and run on the electronic device. The client-side program can be a native application installed on the electronic device, or the client-side program can be an applet, a fast application, or other similar forms. Of course, when using web page technologies such as HTML5 or the like, the related functions can be implemented through a page displayed by a browser, which can be a standalone browser application or a browser module embedded in some application.

[0134] The network 12 between the electronic devices such as the PC 13 and the mobile phone 14 and the server 11 can be based on the communication mode supported by the corresponding electronic device, and can specifically select a wired or wireless network to realize communication, and the present specification does not limit this. For example, the PC 13 can support wired and wireless communication at the same time, so that wired or wireless network can be used to realize communication according to the needs, and the mobile phone 14 usually only supports wireless communication, so that wireless network can be used to realize communication.

[0135] In one example, the user can query the user behavior points of the user through the client of the financial service platform running on the mobile phone 14, and then the server 11 reads and returns the user behavior points of the user by executing the implementation method of the present specification described below, which will be described below in combination with Figure 7 .

[0136] As shown in Figure 7 , in some embodiments, the query method of the user behavior points of the present specification includes:

[0137] S710, obtaining a user query instruction.

[0138] As shown in Figure 6 , the user can click the control of the query of the user behavior points through the client interface of the financial service platform, so that the client can detect the user query instruction and send the user query instruction to the server. In the embodiments of the present specification, the user query instruction includes a user identifier, which can be a UID, for example.

[0139] S720, querying the user behavior points corresponding to the current calculation period from the database according to the user identifier, and in response to the user behavior points not being calculated, performing a user behavior points calculation task to obtain the user behavior points of the current calculation period; and in response to the user behavior points being calculated, reading the user behavior points.

[0140] In the embodiments of the present specification, after obtaining the user query instruction, the server can query the user behavior points of the current calculation period in the corresponding database. The calculation period refers to the time period for periodic calculation of user behavior points. For example, in an example, the financial service platform calculates the user behavior points once a day, and the calculation period is 1 day.

[0141] When the user queries the user behavior points of the current day, the user behavior points of the current day of the user may have been calculated through the batch task processing method described above and stored in the database. In this case, the user behavior points of the current calculation period can be directly read from the database, and the user behavior points of the current day are the latest user behavior points.

[0142] However, at the time of user query, the user behavior points of the current day of the user may not have been calculated, because batch tasks need to be processed in sequence. If the batch task has not processed the user's task at the time of user query, the database does not have the user behavior points of the current day of the user. In this case, in order to immediately respond to the user's query operation, the user's calculation task does not need to be waited for batch task processing, but the user's calculation task is called and executed separately to generate the latest user behavior points. At the same time, the user's calculation task can be deleted in the batch task.

[0143] S730, display the user behavior points of the current calculation period.

[0144] In any of the above cases, after obtaining the user behavior points of the current calculation period of the user, the server can return the user behavior points to the client and render and display on the client interface, so that the user can view the latest user behavior points of the current day.

[0145] In some embodiments, the financial service platform can further display one or more user behavior parameters described above, that is, in the above embodiments, the user behavior parameters corresponding to each user are also stored in the database. When the user triggers a query instruction to query a user behavior parameter on the client, the financial service platform can display the queried user behavior parameter to the user according to the method described above, and details are not repeated.

[0146] As described above, in the embodiments of the present specification, the user behavior points are calculated by combining historical behavior data and current behavior data, which improves the accuracy of the user behavior points. Moreover, by combining batch task and real-time calculation, the task of the queried user can be processed preferentially when the user queries, so as to meet the real-time query of the user. Furthermore, by the batch task scheduling method described above, the batch task processing efficiency is improved, the system response speed is improved, and the task demand with high timeliness requirement is met.

[0147] In some embodiments, the present specification provides a batch task processing apparatus, which can be applied to a data processing system comprising a plurality of computing clusters and a disaster recovery cluster corresponding to each computing cluster. As shown in the figure, the apparatus comprises: Figure 8

[0148] a task scheduling module 10 configured to distribute the batch tasks to the computing clusters and the disaster recovery clusters corresponding to the identification intervals according to the identification information of each task in the batch tasks, wherein each computing cluster corresponds to an identification interval, and the identification information of the identification intervals corresponding to the plurality of computing clusters are continuous values;

[0149] a task execution module 20 configured to, for each computing cluster, read and execute a first-identified task from the distributed batch tasks, and the disaster recovery cluster corresponding to the computing cluster reads and executes a second-identified task from the distributed batch tasks, wherein one of the first-identified and the second-identified is the identification information of the odd bits in the identification interval, and the other is the identification information of the even bits in the identification interval;

[0150] a result storage module 30 configured to store the task processing results of each task in the computing cluster and the disaster recovery cluster.

[0151] In some embodiments, the tasks comprise a first task and a second task different from the first task; the task execution module 20 is configured to:

[0152] in a continuous time period, the computing cluster alternately reads and executes the first task of the first-identified and the second task of the first-identified in turn;

[0153] in a continuous time period, the disaster recovery cluster alternately reads and executes the first task of the second-identified and the second task of the second-identified in turn.

[0154] In some embodiments, the task execution module 20 is configured to:

[0155] in an odd time period, the computing cluster reads and executes the first task of the first-identified; in an even time period, the computing cluster reads and executes the second task of the first-identified;

[0156] in an odd time period, the disaster recovery cluster reads and executes the first task of the second-identified; in an even time period, the disaster recovery cluster reads and executes the second task of the second-identified.

[0157] In some embodiments, the tasks comprise a user behavior score calculation task and / or a user behavior parameter calculation task, wherein the user behavior score calculation task depends on the user behavior parameter.​

[0158] In some embodiments, the task execution module 20 is configured to:

[0159] obtain user behavior data, the user behavior data comprising historical behavior data and current behavior data, the current behavior data comprising at least part of user behavior parameters obtained in advance;

[0160] generate a plurality of behavior factor parameters according to the historical behavior data and the current behavior data, and generate the user behavior score according to the plurality of behavior factor parameters, the task processing result comprising the user behavior score.

[0161] In some embodiments, the task execution module 20 is configured to:

[0162] read the user behavior data from a database corresponding to the user representation according to the user identifier;

[0163] store each user behavior score in a database corresponding to the user identifier.

[0164] In some embodiments, the present specification provides a user behavior score query device, as shown in Figure 9 The device comprises:

[0165] an instruction obtaining module 40 configured to obtain a user query instruction, the user query instruction comprising a user identifier;

[0166] a score query module 50 configured to query a user behavior score corresponding to a current calculation period from a database according to the user identifier, the user behavior score of each calculation period in the database being calculated by the batch task processing method of any of the foregoing embodiments, the task processing result comprising the user behavior score; in response to the user behavior score corresponding to the user identifier in the current calculation period not being calculated, performing a user behavior score calculation task to obtain the user behavior score in the current calculation period; in response to the user behavior score corresponding to the user representation in the current calculation period having been calculated, reading the user behavior score;

[0167] a score display module 60 configured to display the user behavior score in the current calculation period.

[0168] In some embodiments, the present specification provides an electronic device, comprising:

[0169] a processor;

[0170] a memory storing computer instructions, the computer instructions being executed by the processor to implement the method of any of the foregoing embodiments.

[0171] In some embodiments, the present specification provides a computer program product, which, when executed, implements the method according to any of the preceding embodiments.

[0172] Figure 10 is a schematic structural diagram of an electronic device provided by an exemplary embodiment, through which the processor or the storage medium function described above can be implemented.

[0173] Please refer to Figure 10 At the hardware level, the electronic device includes a processor 702, an internal bus 704, a network interface 706, a memory 708, and a non-volatile memory 710, and of course, other hardware required for functions. One or more embodiments of the present specification can be implemented in a software manner, such as reading a corresponding computer program from the non-volatile memory 710 into the memory 708 by the processor 702 and then running. Of course, in addition to the software implementation, one or more embodiments of the present specification do not exclude other implementation manners, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or a logic device.

Claims

1. A batch task processing method, characterized in that, Applied to a data processing system, the data processing system comprising multiple computing clusters and a corresponding disaster recovery cluster for each computing cluster, the method includes: Based on the identifier range to which the identifier information of each task in the batch task belongs, the batch task is distributed to the computing cluster and its disaster recovery cluster corresponding to the identifier range. Each computing cluster corresponds to an identifier range, and the identifier information of the identifier ranges corresponding to the multiple computing clusters are continuous values. For each computing cluster, the computing cluster reads and executes a task with a first identifier from the distributed batch tasks, and the corresponding disaster recovery cluster reads and executes a task with a second identifier from the distributed batch tasks. The first identifier and the second identifier are, respectively, identifier information with an odd number of bits in an identifier interval and identifier information with an even number of bits in an identifier interval. The task includes a first task and a second task, where the first task is different from the second task and depends on the second task. The process of the computing cluster reading and executing the task with the first identifier includes: in a continuous time period, the computing cluster sequentially and alternately reads and executes the first task with the first identifier and the second task with the first identifier. The process of the disaster recovery cluster reading and executing the task with the second identifier includes: in a continuous time period, the disaster recovery cluster sequentially and alternately reads and executes the first task with the second identifier and the second task with the second identifier. The task processing results of each task are stored in the computing cluster and its disaster recovery cluster.

2. The method according to claim 1, characterized in that, The provision that the computing cluster sequentially and alternately reads and executes the first task and the second task of the first identifier in a continuous time period includes: in odd-numbered time periods, the computing cluster reads and executes the first task of the first identifier; in even-numbered time periods, the computing cluster reads and executes the second task of the first identifier. The provision that the disaster recovery cluster sequentially and alternately reads and executes the first task of the second identifier and the second task of the second identifier in a continuous time period includes: in odd-numbered time periods, the disaster recovery cluster reads and executes the first task of the second identifier; in even-numbered time periods, the disaster recovery cluster reads and executes the second task of the second identifier.

3. The method according to claim 1 or 2, characterized in that, The first task is to calculate user behavior scores, and the second task is to calculate user behavior parameters.

4. The method according to claim 1, characterized in that, When the task includes a user behavior score calculation task, the process of performing the user behavior score calculation task includes: Acquire user behavior data, which includes historical behavior data and current behavior data, wherein the current behavior data includes at least some user behavior parameters obtained in advance; Multiple behavioral factor parameters are generated based on the historical behavioral data and the current behavioral data, and the user behavior score is generated based on the multiple behavioral factor parameters. The task processing result includes the user behavior score.

5. The method according to claim 4, characterized in that, The process of obtaining user behavior data includes: reading the user behavior data from a database corresponding to the user representation based on the user identifier; The step of storing the task processing results of each task in the computing cluster and its disaster recovery cluster includes: storing each user behavior score in a database corresponding to the user identifier.

6. A method for querying user behavior points, characterized in that, include: Obtain a user query instruction, wherein the user query instruction includes a user identifier; The user behavior score corresponding to the current calculation period is retrieved from the database based on the user identifier. The user behavior score for each calculation period in the database is calculated by the batch task processing method according to any one of claims 1 to 5. The task processing result includes the user behavior score. In response to the fact that the user behavior score corresponding to the user identifier has not been calculated in the current calculation cycle, a user behavior score calculation task is executed to obtain the user behavior score for the current calculation cycle. In response to the fact that the user behavior score corresponding to the user representation in the current calculation cycle has been calculated, the user behavior score is read. Display the user behavior score for the current calculation period.

7. A batch task processing device, characterized in that, The device is applied to a data processing system, which includes multiple computing clusters and a corresponding disaster recovery cluster for each computing cluster. The task scheduling module is configured to distribute the batch tasks to the computing clusters and their disaster recovery clusters corresponding to the identification intervals, based on the identification interval to which the identification information of each task in the batch tasks belongs. Each computing cluster corresponds to an identification interval, and the identification information of the multiple computing clusters corresponding to the identification intervals are continuous values. The task execution module is configured such that, for each computing cluster, the computing cluster reads and executes a task with a first identifier from a distributed batch of tasks, and the corresponding disaster recovery cluster reads and executes a task with a second identifier from the distributed batch of tasks. The first identifier and the second identifier are, respectively, identifier information with an odd number of bits in an identifier interval, and identifier information with an even number of bits in an identifier interval. The task includes a first task and a second task, where the first task is different from the second task and depends on the second task. The process of the computing cluster reading and executing the task with the first identifier includes: in a continuous time period, the computing cluster sequentially and alternately reads and executes the first task with the first identifier and the second task with the first identifier. The process of the disaster recovery cluster reading and executing the task with the second identifier includes: in a continuous time period, the disaster recovery cluster sequentially and alternately reads and executes the first task with the second identifier and the second task with the second identifier. The results storage module is configured to store the task processing results of each task in the computing cluster and its disaster recovery cluster.

8. A device for querying user behavior points, characterized in that, include: The instruction acquisition module is configured to acquire user query instructions, wherein the user query instructions include a user identifier; The points query module is configured to query the user behavior points corresponding to the current calculation period from the database based on the user identifier. The user behavior points for each calculation period in the database are calculated by the batch task processing method according to any one of claims 1 to 5. The task processing result includes the user behavior points. In response to the fact that the user behavior score corresponding to the user identifier has not been calculated in the current calculation cycle, a user behavior score calculation task is executed to obtain the user behavior score for the current calculation cycle. In response to the fact that the user behavior score corresponding to the user representation in the current calculation cycle has been calculated, the user behavior score is read. The points display module is configured to display the user's behavior points for the current calculation period.

9. A computer program product, characterized in that, When the computer program product is executed, it implements the method as described in any one of claims 1 to 6.

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