Data processing method and device, electronic equipment, computer readable storage medium and computer program product

Abstract

The invention provides a data processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps that in response to a triggering condition meeting data statistics, first task information corresponding to the triggering condition is determined, the triggering condition comprises at least one of a timing triggering condition, a subscription message triggering condition and an interface calling triggering condition, and the first task information at least comprises a statistical dimension and a statistical mode; acquiring to-be-counted data corresponding to the statistical dimension from a database; processing the to-be-counted data based on the statistical mode to obtain a data statistical result; and storing the data statistical result, and outputting the data statistical result. According to the invention, the accuracy and flexibility of data statistics can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] With the rapid development of the esports industry, esports events have attracted a large audience and participants, becoming an important cultural and sporting activity. Esports event statistics not only provide viewers with real-time match data results, such as win / loss ratios and scores, enhancing the viewing experience, but also help event organizers understand audience preferences and market trends, providing decision-making support for event planning and operation. Therefore, esports event statistics are of significant research value for improving event quality, optimizing the audience experience, evaluating player performance, and promoting industry research and development.

[0003] In related technologies, data statistics are performed manually and through pre-defined tasks. However, this method cannot guarantee the accuracy of the statistical results, nor can it guarantee the complete availability and update speed of the data, resulting in low accuracy of the data statistics. Summary of the Invention

[0004] This application provides a data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product that can improve the accuracy and flexibility of data statistics.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] This application provides a data processing method, the method comprising:

[0007] In response to the triggering condition of data statistics being met, the first task information corresponding to the triggering condition is determined. The triggering condition includes at least one of the following: timed triggering condition, message subscription triggering condition, and interface call triggering condition. The first task information includes at least the statistical dimension and the statistical method.

[0008] Retrieve the statistical data corresponding to the statistical dimension from the database;

[0009] The statistical data to be analyzed is processed using the aforementioned statistical method to obtain statistical results.

[0010] Store the statistical results of the data, and output the statistical results of the data.

[0011] This application provides a data processing apparatus, including:

[0012] The task determination module is used to determine the first task information corresponding to the triggering condition in response to the triggering condition that meets the data statistics. The triggering condition includes at least one of the following: timed triggering condition, message subscription triggering condition, and interface call triggering condition. The first task information includes at least the statistical dimension and the statistical method.

[0013] The data acquisition module is used to retrieve the statistical data corresponding to the statistical dimension from the database;

[0014] The data statistics module is used to process the data to be analyzed based on the statistical method to obtain the data statistics results;

[0015] The data output module is used to store the data statistical results and output the data statistical results.

[0016] This application provides an electronic device, the electronic device comprising:

[0017] Memory is used to store executable instructions or computer programs.

[0018] The processor, when executing computer-executable instructions or computer programs stored in the memory, implements the data processing method provided in the embodiments of this application.

[0019] This application provides a computer-readable storage medium storing computer-executable instructions or computer programs, which are executed by a processor to implement the data processing method provided in this application.

[0020] This application provides a computer program product, including computer-executable instructions or a computer program, which, when executed by a processor, implements the data processing method provided in this application.

[0021] The embodiments of this application have the following beneficial effects:

[0022] By applying the embodiments of this application, in response to the fulfillment of a data statistics triggering condition, first task information corresponding to the triggering condition is determined. The triggering condition includes at least one of the following: a timed triggering condition, a message subscription triggering condition, or an interface call triggering condition. The first task information includes at least a statistical dimension and a statistical method. Combining multiple triggering conditions for data statistics diversifies the triggering conditions for data statistics, and each triggering condition corresponds to a different data statistics task, improving the flexibility of data statistics. Then, the statistical data to be analyzed corresponding to the statistical dimension is retrieved from the database, and the statistical data to be analyzed is processed based on the statistical method to obtain the data statistics result. The data statistics result is then stored and output. Thus, based on the task information corresponding to the fulfilled triggering condition, the statistical data to be analyzed is processed using the statistical dimension and statistical method in the task information, improving the accuracy of data statistics. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the application mode of the data processing method provided in the embodiments of this application;

[0024] Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0025] Figure 3A This is a first flowchart illustrating the data processing method provided in an embodiment of this application;

[0026] Figure 3B This is a second flowchart illustrating the data processing method provided in the embodiments of this application;

[0027] Figure 3C This is a schematic diagram of the third process of the data processing method provided in the embodiments of this application;

[0028] Figure 3D This is a schematic diagram of the fourth process of the data processing method provided in the embodiments of this application;

[0029] Figure 4 This is a schematic diagram of the interface for displaying online data statistical results provided in an embodiment of this application;

[0030] Figure 5 This is a schematic diagram of the data statistical results table provided in the embodiments of this application;

[0031] Figure 6 This is a schematic diagram of the push notification message interface provided in an embodiment of this application;

[0032] Figure 7 This is a flowchart illustrating the timed polling-triggered data statistics provided in an embodiment of this application;

[0033] Figure 8This is a schematic diagram of the process of triggering data statistics by subscription server messages provided in an embodiment of this application;

[0034] Figure 9 This is a schematic diagram of the process of triggering data statistics by interface call according to an embodiment of this application;

[0035] Figure 10 This is a flowchart illustrating the statistical results of push data provided in an embodiment of this application.

[0036] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0039] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0040] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0041] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0042] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0043] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0044] 1) Live esports broadcast: refers to the activity of transmitting and displaying the real-time video, audio and commentary of esports competitions via the Internet. Viewers can watch the live video of the competition through various platforms (such as TV, computer, mobile phone, etc.) and interact with other viewers through functions such as bullet comments and chat rooms.

[0045] 2) Event data: refers to various data generated in e-sports competitions, including but not limited to data on match progress, player performance, and match results.

[0046] This application provides a data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product that can improve the accuracy and flexibility of data statistics.

[0047] The following describes exemplary applications of the electronic devices provided in the embodiments of this application. These electronic devices can be implemented as various types of terminals such as laptops, tablets, desktop computers, set-top boxes, smartphones, smart speakers, smartwatches, smart TVs, and in-vehicle terminals, or as servers. The following will describe exemplary applications when the device is implemented as a server.

[0048] See Figure 1 , Figure 1 This is a schematic diagram illustrating the application mode of the data processing method provided in the embodiments of this application, for example. Figure 1 The system involves server 200, network 300, and terminal 400. Terminal 400 is connected to server 200 through network 300, which can be a wide area network, a local area network, or a combination of both.

[0049] During the data statistics process, terminal 400 sends data statistics setting information to server 200. Based on the data statistics setting information, server 200 sets various trigger conditions and the first task information corresponding to each trigger condition. When a trigger condition that meets the data statistics is detected, server 200 determines the first task information corresponding to the trigger condition. The first task information includes at least the statistical dimension and the statistical method. Server 200 retrieves the statistical data to be analyzed from the database corresponding to the statistical dimension. Server 200 processes the statistical data to be analyzed based on the statistical method to obtain the data statistics result. Server 200 stores the data statistics result and outputs the data statistics result. Outputting the data statistics result can be sending the data statistics result to a target device, which can be terminal 400 or other devices, such as a sports commentary device.

[0050] In some embodiments, the server (e.g., server 200) can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal 400 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, in-vehicle terminal, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment.

[0051] See Figure 2 , Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may be a terminal or a server. Figure 2 The illustrated electronic device 500 includes at least one processor 410, a memory 450, and at least one network interface 420. The various components in the electronic device 500 are coupled together via a bus system 440. It is understood that the bus system 440 is used to implement communication between these components. In addition to a data bus, the bus system 440 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 2 The general labeled all buses as Bus System 440.

[0052] The processor 410 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0053] The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state storage, hard disk drives, optical disk drives, etc. The memory 450 may optionally include one or more storage devices physically located away from the processor 410.

[0054] The memory 450 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory. ， The volatile memory can be random access memory (RAM). The memory 450 described in the embodiments of this application is intended to include any suitable type of memory.

[0055] In some embodiments, memory 450 is capable of storing data to support various operations, examples of which include programs, modules, and data structures or subsets or supersets thereof, as illustrated below.

[0056] Operating system 451 includes system programs for handling various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, and driver layer, for implementing various basic business functions and handling hardware-based tasks.

[0057] The network communication module 452 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 including Bluetooth, WiFi, and Universal Serial Bus (USB).

[0058] In some embodiments, the apparatus provided in this application can be implemented in software. Figure 2 A data processing device 455 stored in memory 450 is shown. It may be software in the form of programs and plug-ins, including the following software modules: task determination module 4551, data acquisition module 4552, data statistics module 4553, and data output module 4554. These modules are logical and can therefore be arbitrarily combined or further divided according to the functions they implement. The functions of each module will be described below.

[0059] In some embodiments, the terminal or server can implement the data processing method provided in this application by running various computer-executable instructions or computer programs. For example, computer-executable instructions can be microprogram-level commands, machine instructions, or software instructions. Computer programs can be native programs or software modules in an operating system; they can be native applications (APPs), i.e., programs that need to be installed in the operating system to run, such as cloud computing APPs or instant messaging APPs; or they can be applets that can be embedded in any APP, i.e., programs that only need to be downloaded to a browser environment to run. In summary, the aforementioned computer-executable instructions can be any form of instruction, and the aforementioned computer programs can be any form of application, module, or plugin.

[0060] The data processing method provided in this application will be described in conjunction with exemplary applications and implementations of the server devices provided in the embodiments of this application.

[0061] The following describes the data processing method provided in the embodiments of this application. For example, in order to facilitate understanding of the data processing method provided in the embodiments of this application, this application is used in the context of e-sports competitions, with the data to be collected being competition data.

[0062] As mentioned above, the electronic device implementing the data processing method of the embodiments of this application can be a terminal, a server, or a combination of both. The following description uses an electronic device as a server as an example to illustrate the data processing method provided in the embodiments of this application. See also... Figure 3A , Figure 3A This is a first flowchart illustrating the data processing method provided in the embodiments of this application, which will be combined with... Figure 3A The steps shown are explained.

[0063] In step 301, in response to the fulfillment of the triggering condition for data statistics, the first task information corresponding to the triggering condition is determined. The first task information includes at least the statistical dimension and the statistical method.

[0064] Here, the triggering conditions include at least one of the following: timed triggering conditions, message subscription triggering conditions, and API call triggering conditions. The statistical data can be tournament data, which refers to various data generated in esports competitions, including but not limited to match progress, player performance, and match results. The first task information is the data statistics task information. The statistical dimensions in the first task information refer to data dimensions such as cumulative score, win / loss ratio, and match duration. The statistical methods in the first task information are the methods for obtaining the data statistics results, corresponding to statistical algorithms such as weighted summation, average score calculation, cumulative summation, and sorting.

[0065] In some embodiments, the types of timed trigger conditions include a first type and a second type, see [link / reference]. Figure 3B , Figure 3B This is a second flowchart illustrating the data processing method provided in the embodiments of this application, which can be used... Figure 3B Steps 3001A to 3003A determine whether the timed triggering conditions are met, as explained in detail below.

[0066] In step 3001A, the current time and the historical time of the last data statistics are obtained.

[0067] Before performing data statistics, determine the current time and the historical time when data statistics were last started.

[0068] In step 3002A, when the current time is a preset time, it is determined that the timing trigger condition of the first type is met.

[0069] Here, the first type of timed trigger condition refers to the condition that triggers data statistics when the time reaches a preset time point. If the current time is the preset time point, then the first type of timed trigger condition for data statistics is met.

[0070] For example, when performing statistics on event data, 12:00 AM is used as the preset time. If the current time is 12:00 AM, the first type of timed trigger condition for event data statistics is met.

[0071] In step 3003A, when the time interval between the current moment and the historical moment reaches a preset duration, it is determined that the second type of timing trigger condition is met.

[0072] Here, the second type of timed trigger condition refers to the condition that triggers data statistics when the time interval between the current moment and the last historical moment for which data statistics were performed reaches a preset time interval. If the time interval between the current moment and the historical moment reaches a preset duration, that is, the second type of timed trigger condition for data statistics is met.

[0073] For example, statistics are collected on live event data. The statistics are set to be collected every 10 minutes, with a preset duration of 10 minutes. If the time interval between the current moment and the historical moment reaches 10 minutes, the second type of timed trigger condition for live event data statistics is determined to be met.

[0074] Continue to refer to Figure 3B , Figure 3A In step 301 shown, "in response to the triggering condition of data statistics being met, determine the first task information corresponding to the triggering condition," can be achieved through... Figure 3B Step 3011A is implemented, and the details are explained below.

[0075] In step 3011A, in response to the fulfillment of the timed trigger condition, the first task information corresponding to the timed trigger condition is determined based on the type of the timed trigger condition.

[0076] In response to the fulfillment of a timed trigger condition, when the fulfilled timed trigger condition is a first type of timed trigger condition, the first task information corresponding to the first type of timed trigger condition is determined. The first task information corresponding to the first type of timed trigger condition includes at least: the statistical dimension corresponding to the first type of timed trigger condition and the statistical method corresponding to the first type of timed trigger condition.

[0077] When the timed trigger condition is of the second type, the first task information corresponding to the second type of timed trigger condition is determined. The first task information corresponding to the second type of timed trigger condition includes at least: the statistical dimension corresponding to the second type of timed trigger condition and the statistical method corresponding to the second type of timed trigger condition.

[0078] For example, if the event data statistics meet a timed trigger condition, and the timed trigger condition is of type one, then the first task information corresponding to the type one timed trigger condition is determined. This first task information includes at least the statistical dimension corresponding to the type one timed trigger condition, such as: calculating the cumulative score of driver 1 in each day's racing events. This first task information also includes at least the statistical method corresponding to the type one timed trigger condition, such as: calculating driver 1's cumulative score in each day's racing events using a cumulative summation method. If the timed trigger condition is of type two, then the first task information corresponding to the type two timed trigger condition is determined. This first task information includes at least the statistical dimension corresponding to the type two timed trigger condition, such as: calculating the number of successful races driver 1 in today's racing events. This first task information also includes at least the statistical method corresponding to the type two timed trigger condition, such as: calculating the number of successful races driver 1 in today's racing events using a cumulative summation method.

[0079] In this embodiment of the application, the types of timed triggering conditions include a first type and a second type. In response to the satisfaction of the timed triggering conditions, different data statistics task information corresponding to the timed triggering conditions can be determined based on the different types of timed triggering conditions, thereby realizing the diversification of timed triggering conditions. According to the requirements, the appropriate type of timed triggering condition can be selected to trigger data statistics, which improves the efficiency of data statistics.

[0080] In some embodiments, see Figure 3C , Figure 3C This is a schematic diagram of the third process of the data processing method provided in the embodiments of this application, which can be used... Figure 3C Step 3001B determines whether the subscription message triggering conditions are met, which will be explained in detail below.

[0081] In step 3001B, when a preset subscription message is detected in the server message, it is determined that the subscription message triggering condition is met.

[0082] Here, the preset subscription message is a Kafka message. Kafka messages are middleware messages generated by the server. By subscribing to server messages, the data to be statistically analyzed stream is obtained in real time. When the preset subscription message is detected in the data to be statistically analyzed stream, it is determined that the subscription message triggering condition for data statistics is met.

[0083] For example, when performing event data statistics, the preset subscription message is the end of a round of matches. When the message indicating the end of a round of matches is detected in the data stream to be collected, it is determined that the subscription message triggering condition for event data statistics is met.

[0084] Continue to refer to Figure 3C , Figure 3A In step 301 shown, "in response to the triggering condition of data statistics being met, determine the first task information corresponding to the triggering condition," can be achieved through... Figure 3C Steps 3011B to 3012B are implemented, and will be explained in detail below.

[0085] In step 3011B, in response to the fulfillment of the subscription message triggering condition, the message type of the subscription message is determined.

[0086] When the subscription message triggering conditions for data statistics are met, the message type of the subscription message is determined. The message type of the subscription message can be a control message type such as topic creation and deletion, a logic message type for business processing, an event message type such as user registration and click events, or a status update message type such as system status and task progress.

[0087] For example, when performing event data statistics, the subscription message is a round of matches ending message, and the message type for the subscription message is a status update message type.

[0088] In step 3012B, the first task information corresponding to the subscription message triggering condition is determined based on the message type.

[0089] When the message type of the subscribed message is a status update message, determine the first task information corresponding to the trigger condition for subscribing to the status update message. The first task information corresponding to the trigger condition for subscribing to the status update message includes at least: the statistical dimension and the statistical method corresponding to the trigger condition. The process for determining the first task information corresponding to the trigger conditions for other types of subscribed messages is the same as described above and will not be repeated here.

[0090] For example, if the race data statistics meet the subscription message trigger condition, and the subscription message is a race end message with a status update message type, then determine the first task information corresponding to the subscription status update message trigger condition. This first task information should at least include the statistical dimension corresponding to the subscription status update message trigger condition, such as: whether the shortest race time for a driver in the current race has broken the race time record. This first task information should at least include the statistical method corresponding to the subscription status update message trigger condition, such as: determining whether the shortest race time has broken the race time record by comparing the shortest race time with the race time record.

[0091] In this embodiment of the application, when a preset subscription message is detected in the server message, it is determined that the subscription message triggering condition is met. Based on the message type of the subscription message, the data statistics task information corresponding to the subscription message triggering condition is determined. The subscription message can be obtained from the real-time data stream generated by the server, ensuring the timely update of the statistical data to be collected and improving the accuracy of the data statistics.

[0092] In some embodiments, see Figure 3D , Figure 3D This is a schematic diagram of the fourth process of the data processing method provided in the embodiments of this application, which can be used to... Figure 3D Step 3001C determines that the interface call triggering conditions are met, which will be explained in detail below.

[0093] In step 3001C, when a call request for a preset interface is received, it is determined that the interface call triggering condition is met.

[0094] Here, the preset interface refers to the pre-specified data interface. When a call request for the specified data interface is received, the interface call triggering conditions that meet the data statistics are determined.

[0095] For example, when performing race data statistics, the default interface is the racing race data interface. When a call request to the racing race data interface is received, the interface call triggering conditions for racing race data statistics are determined to be met.

[0096] Continue to refer to Figure 3D , Figure 3A In step 301 shown, "in response to the triggering condition of data statistics being met, determine the first task information corresponding to the triggering condition," can be achieved through... Figure 3C Steps 3011C to 3012C are implemented, and will be explained in detail below.

[0097] In step 3011C, in response to the satisfaction of the interface call triggering condition, the interface call parameters are obtained from the call request.

[0098] When the conditions for triggering an interface call for data statistics are met, the interface call parameters are obtained from the call request for the preset interface. The interface call parameters include data identifier, data query conditions, interface configuration parameters, etc.

[0099] For example, when performing race data statistics, the default interface is the racing race data interface. In response to the interface call triggering conditions that meet the race data statistics requirements, the season number parameter of the racing race data interface call is obtained from the call request.

[0100] In step 3012C, the first task information corresponding to the interface call triggering condition is determined based on the interface call parameters.

[0101] Based on the preset interface call parameters, the first task information corresponding to the interface call trigger condition shall include at least: the statistical dimension corresponding to the interface call trigger condition and the statistical method corresponding to the interface call trigger condition.

[0102] For example, the event data statistics meet the interface call trigger conditions. The preset interface is the racing event data interface. Based on the season number parameter of the racing event data interface call, the first task information corresponding to the racing event data interface call trigger conditions is determined. This first task information includes at least the statistical dimension corresponding to the racing event data interface call trigger conditions, such as: calculating the average score of all drivers in the current season's races. This first task information also includes at least the statistical method corresponding to the racing event data interface call trigger conditions, such as: calculating the average score of all drivers in the current season's races using an average score calculation method.

[0103] In this embodiment, upon receiving a call request for a preset interface, it is determined that the interface call triggering condition is met. Based on the interface call parameters in the call request, the data statistics task information corresponding to the interface call triggering condition is determined, which can directly trigger data statistics through the preset interface, thereby improving the efficiency of data statistics.

[0104] Continue to refer to Figure 3A In step 302, the statistical data corresponding to the statistical dimension is obtained from the database.

[0105] Here, the database includes statistical data corresponding to different statistical dimensions.

[0106] In some embodiments, the statistical data corresponding to the statistical dimension can be obtained from the database by performing the following steps: determining the data table containing the statistical data corresponding to the statistical dimension, and the data interface corresponding to the statistical dimension; calling the data interface to obtain the statistical data corresponding to the statistical dimension from the data table.

[0107] Here, because different data tables exist in the database, the statistical data to be collected for each statistical dimension may be in the same data table or in different data tables. The data interface corresponding to the statistical dimension is used to retrieve the statistical data to be collected for that statistical dimension. The data table containing the statistical data to be collected for the statistical dimension is identified as the target data table, and the data interface corresponding to the statistical dimension is identified as the target data interface. By calling the target data interface, the statistical data to be collected for the statistical dimension is retrieved from the target data table in the database.

[0108] In this embodiment of the application, the data interface corresponding to the statistical dimension is called to obtain the statistical data to be obtained from the database, thereby achieving accurate acquisition of the statistical data and improving the accuracy and processing efficiency of data statistics.

[0109] Continue to refer to Figure 3A In step 303, the statistical data to be processed is based on statistical methods to obtain the statistical results.

[0110] In some embodiments, the statistical data to be processed based on a statistical method can be performed by performing the following steps to obtain the statistical results: preprocessing the statistical data to be processed to obtain the processed statistical data; calling the statistical algorithm corresponding to the statistical method to process the processed statistical data to obtain the statistical results.

[0111] Here, the data to be statistically analyzed undergoes preprocessing such as data filtering and cleaning. Statistical algorithms, such as weighted summation, average score calculation, and cumulative summation, are then applied to calculate the average score and cumulative summation. The preprocessed data is then processed to obtain the final statistical results. For example, taking the subscription message trigger condition for data statistics as an example, when performing race data statistics, the subscription message is the end message of a race round. The statistical dimension corresponding to the subscription message trigger condition is whether the shortest race time for a driver in a race round has broken the race time record. The data to be statistically analyzed is the race time data for each driver in a race round. The data to be statistically analyzed is preprocessed, and the statistical method corresponding to the subscription message trigger condition determines whether the shortest race time for a driver in a race round has broken the race time record by comparing the shortest race time with the race time record.

[0112] In this embodiment of the application, statistical data is processed based on statistical methods corresponding to different triggering conditions to obtain statistical results. It is possible to process statistical data based on different statistical algorithms corresponding to statistical methods, thereby realizing the diversification of statistical methods.

[0113] Continue to refer to Figure 3A In step 304, the data statistics results are stored and output.

[0114] In some embodiments, the data statistics results can be stored and output by performing the following steps: adding the data statistics results to the data table corresponding to the data to be counted, or updating the historical statistics results in the data table to the data statistics results; calling the data interface corresponding to the statistical dimension to output the data statistics results.

[0115] Here, the data table corresponding to the data to be statistically analyzed in the database is determined. If the data table does not contain historical statistical results corresponding to the data statistics, the data statistics are added to the data table. If the data table contains historical statistical results corresponding to the data statistics, the data table is updated based on the historical statistical results, resulting in an updated data table. By calling the data interface corresponding to the statistical dimension, the statistical results for the corresponding statistical dimension are output.

[0116] In this embodiment of the application, the statistical results are added to the data table corresponding to the data to be statistically analyzed, or the historical statistical results in the data table are updated to the statistical results, which ensures the timely updating of the data table corresponding to the data to be statistically analyzed, and facilitates further analysis of the data to be statistically analyzed.

[0117] In some embodiments, the following steps may also be performed: in response to receiving a task setting request, the task setting request is parsed to obtain a target triggering condition and task update information corresponding to the target triggering condition, wherein the task update information includes at least one of statistical dimension update information and statistical method update information; first task information corresponding to the target triggering condition is obtained; and the first task information is updated based on the task update information to obtain second task information corresponding to the target triggering condition.

[0118] Here, upon receiving a task setting request, the request is parsed. The parsing result includes the target trigger condition and the corresponding task update information. The target trigger condition includes at least one of a timed trigger condition, a subscription message trigger condition, or an API call trigger condition. The task update information and the first task information corresponding to the target trigger condition are determined. Based on the task update information, at least one of the statistical dimensions and statistical methods in the first task information is updated to obtain the updated second task information. For example, the target trigger condition is an API call trigger condition, and the task update information includes statistical dimension update information, which may include information such as statistical dimensions to be added or deleted, etc. For example, the statistical dimension update information could be the addition of a match win rate. The target statistical dimension in the statistical dimension update information is determined, and the statistical dimension corresponding to the API call trigger condition is replaced with the target statistical dimension to obtain the second task information corresponding to the API call trigger condition.

[0119] In this embodiment of the application, when a task setting request is received, the task setting request is parsed to obtain the target triggering condition and the task update information corresponding to the target triggering condition. Based on the task update information, the first task information corresponding to the target triggering condition is updated to obtain the second task information. This allows for timely updates of the data statistics task information corresponding to the target triggering condition based on the task setting request, thereby improving the flexibility of data statistics.

[0120] In some embodiments, the following steps may also be performed: in response to receiving a data query request, the data query request is parsed to obtain the query dimension; the data to be queried corresponding to the query dimension is obtained from the database; a download address corresponding to the data to be queried is generated, and the download address is sent to the terminal corresponding to the query request.

[0121] Here, the data query request includes parameters such as query dimensions and query data format. Upon receiving a data query request for statistical results, the request is parsed to obtain the query dimensions corresponding to the statistical results. Based on the parsed query dimensions, the data to be retrieved from the database is determined. The data to be queried is saved as a data file. Based on the storage location and path of the data file, a download address for the data to be queried is generated and sent to the terminal corresponding to the query request.

[0122] In this embodiment of the application, when a data query request is received, the data to be queried corresponding to the query dimension is obtained from the database, a download address corresponding to the data to be queried is generated, and the download address is sent to the terminal corresponding to the query request, thereby realizing the push of the download address of the data statistics results and improving the efficiency of obtaining the data statistics results.

[0123] In some embodiments, when the database stores match data, the following steps can also be performed: in response to meeting the data report push conditions, determining target data from the database based on preset match system and match schedule information; generating a match data report based on the target data, and pushing the match data report.

[0124] Here, match data refers to data collected during esports competitions. It records participants' behaviors, decisions, and results during matches, and can be used to analyze player strategies, trends, and patterns. Data report push conditions can be based on fixed time intervals, such as daily, weekly, monthly, or quarterly, or specific events such as match progress updates or match endings. Match rules are a series of rules and procedures established for the competition, and match schedule information refers to the specific time of each round of the competition. Under the condition of meeting the data report push conditions, key information such as match type, match stage, participating teams or players, match date and time can be obtained through preset match rules and match schedule information. Based on the key information, SQL query statements are constructed and executed in the database to obtain the target data. Using data visualization tools, match data reports are generated based on the target data. Match data reports can be text or spreadsheet files and pushed to the data user terminal. Alternatively, according to the actual needs of the match, match data such as match scores can be used as key data. When key data is detected in the database approaching preset data nodes, match data reports are automatically pushed.

[0125] In this embodiment of the application, in response to the fulfillment of the data report push conditions, a game data report is generated based on the target data in the database and pushed, thereby realizing the push of data statistical results and improving the efficiency of obtaining data statistical results.

[0126] In some embodiments, the data processing method provided in this application can be applied to the field of cloud technology. On the cloud platform, in response to a trigger condition for data statistics being met, first task information corresponding to the trigger condition is determined. The trigger condition includes at least one of the following: a timed trigger condition, a message subscription trigger condition, or an interface call trigger condition. The first task information includes at least a statistical dimension and a statistical method. By combining multiple trigger conditions for data statistics, the trigger conditions for data statistics are diversified, and each trigger condition corresponds to a different data statistics task, improving the flexibility of data statistics. Then, the cloud server retrieves the statistical data to be analyzed corresponding to the statistical dimension from the database, processes the statistical data based on the statistical method, obtains the data statistics result, stores the data statistics result, and outputs the data statistics result. Thus, by using the statistical dimensions and statistical methods in the task information corresponding to the met trigger conditions to process the statistical data to be analyzed, the accuracy of data statistics performed by the cloud server is improved.

[0127] The following will describe an exemplary application of the data processing method provided in the embodiments of this application in a sports event data statistics scenario.

[0128] In related technologies, event data statistics rely on manual statistics and pre-defined initial tasks to process the event data. Manual statistics cannot guarantee the accuracy of the data statistics results, while pre-defined initial tasks cannot guarantee the complete availability and update speed of the data, resulting in low real-time performance and accuracy of data statistics.

[0129] This application proposes a data processing method to address the problems existing in related technologies, which includes the following improvements compared to related technologies:

[0130] 1) Three triggering methods are used to perform different data statistics to meet the data needs of the live event broadcast and the data backend: timed polling triggering statistics is used to meet periodic statistical needs, and data statistics are triggered by two time conditions: preset time interval or preset time point; subscription server message triggering statistics is triggered based on different subscribed Kafka messages to ensure the timeliness of data statistics; interface call triggering statistics is achieved by calling different interfaces and different interface call parameters to process the data that needs to be statistically analyzed in a timely manner, ensuring the accuracy and timeliness of data statistics.

[0131] 2) By storing the reported event data in a database, the system parses and statistically analyzes the data, stores the statistical results back into the database, and provides a data interface for the live streaming and event data backend to access these results. Data reports are generated based on the statistical results, which can be automatically pushed to the system, and reminders for key data items are provided.

[0132] The data processing method proposed in this application can provide data support for online display of event data, offline download of event data, and real-time updated statistical data during live event broadcasts, thereby meeting the data needs of live broadcasts. To meet users' needs for online display of event data, a data interface providing statistical results is called to the webpage for display. Users can filter data based on conditions such as event type and statistical dimensions to quickly find the required statistical results. For example, see [reference]. Figure 4 , Figure 4 This is a schematic diagram of the interface for displaying online data statistics results provided in this application embodiment. Area 401 includes filtering conditions for event types such as "2024 Racing Mobile Game Spring Season," "2023 Racing Mobile Game Annual Finals," "2023 Racing Mobile Game Autumn Season," and "2023 Racing Mobile Game Spring Season." Area 402 includes filtering conditions for event statistics dimensions such as "Data Item," "Select All," "Reset," "Minimum Number of Matches to Calculate Track Win Rate," "Highlight Color," "Configure Today's Matches," and "Download Table."

[0133] To facilitate offline data analysis and archiving, users can download event data tables via a link. This download link allows filtering of event data statistics based on different parameters provided, catering to personalized user needs. See the example below. Figure 5 , Figure 5 This is a schematic diagram of the data statistics table provided in the embodiment of this application. In area 501, there are statistical results of event data such as "Name", "Number of Games Appeared", "Win Rate", "Average Match Hours", "Average Winning Hours", and "Average Losing Hours".

[0134] For statistical data requiring real-time updates during live event broadcasts, corresponding event trigger mechanisms should be set up. For example, in racing mobile game competitions, track record statistics should be calculated at the end of each match, updating the data statistics to the database and retrieving the current track's finish time record via a data interface. Event data statistics results should be pushed out based on different data needs. By retrieving daily race schedule information from the event database, the statistical results should be filtered and organized, generating a PDF file and pushing it to relevant data users. For example, key data from both teams in the day's races can be filtered and compared to create an easy-to-view PDF file, which should then be pushed to the event director and commentators. Depending on the specific needs of the event, some key data with topical relevance can be highlighted. For example, in racing mobile game competitions, it's possible to monitor whether a player's track record count or score is approaching a significant data milestone, and send alerts when that milestone is about to be reached. (See example for reference.) Figure 6 , Figure 6 This is a schematic diagram of the push notification message interface provided in an embodiment of this application. In area 601, there are notification message contents such as "{"Player Name": "Dongfang", "Total Number of Appearances": 197, "Difference from Data Node": 3}" and "{"Player Name": "Xiaoyu", "Total Score": 14928, "Difference from Data Node": 72}".

[0135] The data processing method proposed in this application performs different data statistics based on three different statistical methods: timed polling-triggered statistics, subscription server message-triggered statistics, and interface call-triggered statistics. Timed polling-triggered statistics executes data statistics tasks periodically according to preset time intervals or preset time points, which is suitable for scenarios that require periodic data statistics, such as: performing season data statistics every 30 minutes; triggering updates and statistics of live streaming usage data every 10 minutes to ensure the real-time and accuracy of live streaming data, while non-live streaming usage data is updated daily to ensure timely updates and accuracy of non-live streaming usage data.

[0136] For example, subscription server message-triggered statistics will trigger a data statistics task when a specific message is received. This is suitable for situations where data statistics need to be performed based on changes in external data. For instance, the statistics of racing track record data can be triggered by subscribing to the end message of each game. When a new game end information is updated, the statistics program will immediately receive the corresponding Kafka message and trigger the relevant data statistics task to ensure timely data statistics.

[0137] For example, when an interface call triggers statistics, a data statistics task is triggered upon receiving an interface call request. This is suitable for data statistics scenarios that require real-time response to external call requests. For instance, the current match schedule ID and the current number of matches can be passed in through a preset data interface, and real-time data statistics can be performed based on the information passed in, ensuring that the data statistics results are updated synchronously with the match progress.

[0138] Example, reference Figure 7 , Figure 7 This is a schematic diagram of the process of triggering data statistics through timed polling according to an embodiment of this application. The electronic device implementing the data processing method of this embodiment can be a server, combined with... Figure 7 The steps outlined in this application will be explained to illustrate the timed polling-triggered data statistics process provided in the embodiments of this application.

[0139] In step 701, it is determined whether the fixed duration has been reached.

[0140] The set time interval is used as the time condition for triggering the data statistics task. If the fixed duration is reached, i.e., the current time meets the set time interval condition, then proceed to step 703, where the corresponding statistics program is activated according to the corresponding time condition, thereby triggering the data statistics task for the fixed duration, such as executing a program for season statistics. If the fixed duration is not reached, the data statistics process ends.

[0141] In step 702, it is determined whether a certain time has been reached.

[0142] A specific time point is used as the time condition for triggering a data statistics task. If a certain time is reached, meaning the current time meets the time condition of the specific time point, then proceed to step 703, where the corresponding statistical program is activated based on the time condition, thereby triggering a data statistics task for that specific time, such as performing statistical processing on all data after each match day. If the specific time is not reached, the data statistics process ends.

[0143] In step 704, the basic data in the database is obtained.

[0144] Call the statistics program to retrieve basic data from the database.

[0145] In step 705, a statistical program is invoked to process the basic data.

[0146] Call statistical programs and process basic data according to different statistical dimensions and items. The processing includes, but is not limited to, data filtering, calculation, and aggregation.

[0147] In step 706, the statistical results are written into the database.

[0148] Finally, the processed statistical results are written into the database to ensure that different types of statistical results can be effectively stored and managed.

[0149] In step 707, the data interface is called to retrieve the data statistics results from the database.

[0150] Based on the data tables containing different statistical results in the database, data interfaces with different filtering conditions are provided. By calling these interfaces, users can obtain statistical results and meet their data statistics needs in various scenarios. For example, different data interfaces may be available for different statistical dimensions, such as season statistics. Users can choose the appropriate interface to obtain the required statistical results for further analysis and application.

[0151] Example, reference Figure 8 , Figure 8 This is a schematic diagram illustrating the process of data statistics triggered by a subscription server message, as provided in an embodiment of this application. The electronic device implementing the data processing method of this embodiment can be a server, combined with... Figure 8 The steps shown illustrate the process of triggering data statistics via subscription server messages provided in this application embodiment.

[0152] In step 801, the statistics program is invoked to subscribe to and parse server messages.

[0153] The system invokes a statistics program to subscribe to Kafka messages generated by the server and retrieves and parses the event message stream in real time. Based on the content and characteristics of the event messages, it can accurately identify different specific message types.

[0154] In step 802, it is determined whether a specific message needs to be obtained.

[0155] If a specific message is parsed from the event message stream, proceed to step 803. Based on this specific message, activate the corresponding statistical program. For example, if the specific message is "race game over," upon receiving this message, start the track record statistics program, check the current game data, and calculate whether a new track record has been created to ensure timely updates to the event record data. If no specific message is parsed from the event message stream, end the data statistics process.

[0156] In step 804, the basic data in the database is obtained.

[0157] Retrieve basic event data from the database, which includes match results, player information, etc.

[0158] In step 805, a statistical program is invoked to process the basic data.

[0159] Call statistical programs and process basic data according to different statistical dimensions and items. The processing includes, but is not limited to, data filtering, calculation, and aggregation.

[0160] In step 806, the statistical results are written into the database.

[0161] Finally, the processed statistical results are stored in the corresponding data tables in the database for subsequent management and querying.

[0162] In step 807, the data interface is called to retrieve the data statistics results from the database.

[0163] It provides multiple data interfaces to meet users' data statistics needs in different scenarios. Users can call the corresponding data interface based on their filtering criteria to query data statistics results. For example, if a user wants to obtain the performance of a certain player in a specific series, they can pass the relevant event information to the data interface to obtain the required data statistics.

[0164] Example, reference Figure 9 , Figure 9 This is a schematic diagram illustrating the process of triggering data statistics via an interface call provided in an embodiment of this application. The electronic device implementing the data processing method of this embodiment can be a server, combined with... Figure 9 The steps shown illustrate the process of triggering data statistics through interface calls provided in the embodiments of this application.

[0165] In step 901, the specified interface is called and parameters are passed in.

[0166] By calling the specified interface, necessary parameters or configuration information can be passed, which will serve as the basis for triggering data statistics tasks.

[0167] In step 902, the response interface call triggers the statistics program.

[0168] When a request to call a specified API is received, different data statistics tasks will be triggered depending on the API called and the parameters passed in. For example, API A, after receiving the match number of the current game, will quickly calculate the data for that match; API B, after receiving the season number, will return the speedrun scores of all players in the current season and download the corresponding data statistics table. The API design is based on the characteristics and needs of the event data, ensuring that users can obtain the data and information they need through simple API calls.

[0169] In step 903, basic data is retrieved from the database.

[0170] Based on the different data statistics tasks triggered by the API call, the required basic data is obtained from the database.

[0171] In step 904, a statistical program is invoked to process the data.

[0172] Data is processed according to different statistical dimensions and items. The processing includes querying data from the database, cleaning data, and calculating statistical indicators to ensure that the final output data statistics are accurate and meet the user's needs.

[0173] In step 905, the statistical results are written into the database.

[0174] After data processing is completed, some statistical results will be stored in the database according to different types of data statistics results. Then, step 906 will be taken to return the statistical results through the data interface. The statistical results stored in the database will be provided to the corresponding type of data interface to avoid repeated data processing operations, improve system performance and efficiency, and also make it convenient for users to directly obtain the required statistical results for subsequent query and analysis.

[0175] In step 907, the data statistics results are returned through the data interface.

[0176] After the data processing is complete, the statistical results are returned directly through the data interface.

[0177] Example, reference Figure 10 , Figure 10 This is a flowchart illustrating the statistical results of push data provided in an embodiment of this application. The electronic device implementing the data processing method of this embodiment can be a server, combined with... Figure 10 The steps outlined in this application will be explained to illustrate the process of generating statistical results for push data.

[0178] In step 1001, data statistics results are retrieved from the database as required.

[0179] Based on different push notification needs, the statistical results in the database are filtered and organized to extract relevant statistical results, ensuring that the required statistical results are obtained.

[0180] In step 1002, the data statistics results are processed according to the requirements.

[0181] Based on different push notification needs, the obtained statistical data results are further processed.

[0182] In step 1003, the data statistics results are generated into a file.

[0183] Based on different needs, the statistical results can be converted into corresponding PDF files for later use.

[0184] In step 1004, the data statistics results are converted into text.

[0185] Based on different needs, the statistical results can be converted into corresponding text tables for later use.

[0186] In step 1005, the data is pushed to the data user.

[0187] The completed statistical results are promptly pushed to the end users of the data to ensure that they can obtain the necessary data information efficiently and accurately.

[0188] The statistical results obtained through the embodiments of this application can provide a data foundation for topics and discussions during live sports broadcasts. It not only provides data used in live broadcasts to help viewers understand the game's progress and increase audience participation, but also provides statistical results to commentators. Commentators can analyze and compare these results to explain various situations and decisions during the game, increasing the professionalism and objectivity of their commentary. This is of great significance for improving the quality of live sports broadcasts and providing viewers with a richer viewing experience.

[0189] In the aforementioned application scenarios for event data statistics, employing three data collection methods—timed polling to trigger statistics, subscription to server messages to trigger statistics, and API calls to trigger statistics—not only improves the timeliness and accuracy of data statistics but also ensures data integrity and reliability. Furthermore, it provides diverse display options for data statistics results, including online display, offline download, and real-time updates during live streams. This caters to users' data needs in different scenarios, allowing them to choose the desired data display method and quickly obtain the required statistical results, thus enhancing the user experience.

[0190] The following description continues to illustrate the exemplary structure of the data processing apparatus 455 provided in the embodiments of this application as a software module. In some embodiments, such as Figure 2 As shown, the software modules stored in the data processing device 455 in the memory 450 may include: a task determination module 4551, used to determine first task information corresponding to the trigger condition in response to the trigger condition of data statistics being met, the trigger condition including at least one of the following: timed trigger condition, subscription message trigger condition, interface call trigger condition, the first task information including at least statistical dimension and statistical method; a data acquisition module 4552, used to acquire the statistical data to be collected corresponding to the statistical dimension from the database; a data statistics module 4553, used to process the statistical data to be collected based on the statistical method to obtain the data statistics result; and a data output module 4554, used to store the data statistics result and output the data statistics result.

[0191] In some embodiments, the types of timed triggering conditions include a first type and a second type. The task determination module 4551 is further configured to obtain the current time and the historical time of the last data statistics; when the current time is a preset time, it is determined that the timed triggering condition of the first type is met; when the time interval between the current time and the historical time reaches a preset duration, it is determined that the timed triggering condition of the second type is met; in response to the timed triggering condition being met, the first task information corresponding to the timed triggering condition is determined based on the type of the timed triggering condition.

[0192] In some embodiments, the task determination module 4551 is further configured to determine that the subscription message triggering condition is met when a preset subscription message is detected in the server message; in response to the subscription message triggering condition being met, determine the message type of the subscription message; and determine the first task information corresponding to the subscription message triggering condition based on the message type.

[0193] In some embodiments, the task determination module 4551 is further configured to determine that the interface call triggering condition is met when a call request for a preset interface is received; in response to the interface call triggering condition being met, obtain interface call parameters from the call request; and determine the first task information corresponding to the interface call triggering condition based on the interface call parameters.

[0194] In some embodiments, the data acquisition module 4552 is further configured to determine the data table containing the statistical data to be collected for the statistical dimension, and the data interface corresponding to the statistical dimension; and to call the data interface to obtain the statistical data to be collected for the statistical dimension from the data table.

[0195] In some embodiments, the data statistics module 4553 is further configured to preprocess the data to be analyzed to obtain processed data; and to call the statistical algorithm corresponding to the statistical method to process the processed data to obtain the data statistics result.

[0196] In some embodiments, the data output module 4554 is further configured to add the data statistical results to the data table corresponding to the data to be statistically analyzed, or update the historical statistical results in the data table to the data statistical results; and call the data interface corresponding to the statistical dimension to output the data statistical results.

[0197] In some embodiments, the task determination module 4551 is further configured to, in response to receiving a task setting request, parse the task setting request to obtain a target triggering condition and task update information corresponding to the target triggering condition, wherein the task update information includes at least one of statistical dimension update information and statistical method update information; obtain first task information corresponding to the target triggering condition; and update the first task information based on the task update information to obtain second task information corresponding to the target triggering condition.

[0198] In some embodiments, the data output module 4554 is further configured to, in response to receiving a data query request, parse the data query request to obtain the query dimension; retrieve the data to be queried corresponding to the query dimension from the database; generate a download address corresponding to the data to be queried; and send the download address to the terminal corresponding to the query request.

[0199] In some embodiments, when the database stores game data, the data output module 4554 is further configured to, in response to meeting the data report push conditions, determine target data from the database based on preset game competition rules and game schedule information; generate a game data report based on the target data; and push the game data report.

[0200] This application provides a computer program product, which includes computer-executable instructions or a computer program stored in a computer-readable storage medium. The processor of an electronic device reads the computer-executable instructions or computer program from the computer-readable storage medium and executes the computer-executable instructions or computer program, causing the electronic device to perform the data processing method described in this application.

[0201] This application provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will execute the data processing method provided in this application, for example... Figure 3A The data processing method is shown.

[0202] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a variety of devices including one or any combination of the above-mentioned memories.

[0203] In some embodiments, computer-executable instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0204] As an example, computer-executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files that store one or more modules, subroutines, or code sections).

[0205] As an example, computer-executable instructions can be deployed to execute on a single electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0206] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A data processing method, characterized in that, The method includes: In response to the triggering condition of data statistics being met, the first task information corresponding to the triggering condition is determined. The triggering condition includes at least one of the following: timed triggering condition, message subscription triggering condition, and interface call triggering condition. The first task information includes at least the statistical dimension and the statistical method. Retrieve the statistical data corresponding to the statistical dimension from the database; The statistical data to be analyzed is processed using the aforementioned statistical method to obtain statistical results. Store the statistical results of the data, and output the statistical results of the data.

2. The method according to claim 1, characterized in that, The types of the timed triggering conditions include a first type and a second type, and the method further includes: Get the current time and the last historical time when data statistics were performed; When the current time is a preset time, it is determined that the timing trigger condition of the first type is met; When the time interval between the current moment and the historical moment reaches a preset duration, it is determined that the second type of timed triggering condition is met. Correspondingly, the step of determining the first task information corresponding to the trigger condition in response to the fulfillment of the data statistics trigger condition includes: In response to the fulfillment of a timed trigger condition, the first task information corresponding to the timed trigger condition is determined based on the type of the timed trigger condition.

3. The method according to claim 1, characterized in that, The method further includes: When a preset subscription message is detected in the server messages, it is determined that the subscription message triggering condition is met; Correspondingly, the step of determining the first task information corresponding to the trigger condition in response to the fulfillment of the data statistics trigger condition includes: In response to the fulfillment of the subscription message triggering condition, the message type of the subscription message is determined; Based on the message type, determine the first task information corresponding to the subscription message triggering condition.

4. The method according to claim 1, characterized in that, The method further includes: When a call request for a preset interface is received, it is determined that the interface call triggering conditions are met; Correspondingly, the step of determining the first task information corresponding to the trigger condition in response to the fulfillment of the data statistics trigger condition includes: In response to the fulfillment of the interface call triggering condition, the interface call parameters are obtained from the call request; Based on the interface call parameters, determine the first task information corresponding to the interface call triggering condition.

5. The method according to any one of claims 1 to 4, characterized in that, The step of retrieving the statistical data corresponding to the statistical dimension from the database includes: Determine the data table containing the statistical data to be analyzed for the statistical dimension, and the data interface corresponding to the statistical dimension; Call the data interface to retrieve the statistical data corresponding to the statistical dimension from the data table.

6. The method according to any one of claims 1 to 4, characterized in that, The process of processing the statistical data to obtain the statistical results based on the statistical method includes: The statistical data to be analyzed is preprocessed to obtain the processed statistical data. The statistical algorithm corresponding to the statistical method is invoked to process the processed statistical data and obtain the statistical results.

7. The method according to claim 5, characterized in that, The process of storing and outputting the data statistics results includes: The statistical results are added to the data table corresponding to the data to be statistically analyzed, or the historical statistical results in the data table are updated to the statistical results. Call the data interface corresponding to the statistical dimension and output the statistical results.

8. The method according to any one of claims 1 to 4, characterized in that, The method further includes: In response to receiving a task setting request, the task setting request is parsed to obtain the target triggering condition and the task update information corresponding to the target triggering condition. The task update information includes at least one of statistical dimension update information and statistical method update information. Obtain the first task information corresponding to the target triggering condition; Based on the task update information, the first task information is updated to obtain the second task information corresponding to the target triggering condition.

9. The method according to any one of claims 1 to 4, characterized in that, The method further includes: In response to receiving a data query request, the data query request is parsed to obtain the query dimensions; Retrieve the query data corresponding to the query dimension from the database; Generate a download address corresponding to the data to be queried, and send the download address to the terminal corresponding to the query request.

10. The method according to any one of claims 1 to 4, characterized in that, When the database stores game data, the method further includes: In response to meeting the data report push conditions, the target data is determined from the database based on the preset match system and match schedule information; A game data report is generated based on the target data, and the game data report is pushed out.

11. A data processing apparatus, characterized in that, The device includes: The task determination module is used to determine the first task information corresponding to the triggering condition in response to the triggering condition that meets the data statistics. The triggering condition includes at least one of the following: timed triggering condition, message subscription triggering condition, and interface call triggering condition. The first task information includes at least the statistical dimension and the statistical method. The data acquisition module is used to retrieve the statistical data corresponding to the statistical dimension from the database; The data statistics module is used to process the data to be analyzed based on the statistical method to obtain the data statistics results; The data output module is used to store the data statistical results and output the data statistical results.

12. An electronic device, characterized in that, The electronic device includes: Memory is used to store executable instructions or computer programs. A processor, when executing computer-executable instructions or computer programs stored in the memory, implements the data processing method according to any one of claims 1 to 10.

13. A computer-readable storage medium storing computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the data processing method according to any one of claims 1 to 10.

14. A computer program product comprising computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the data processing method according to any one of claims 1 to 10.

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