Data processing method and device, equipment, medium and product

Abstract

The invention provides a data processing method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence, in particular to the technical fields of big data, cloud computing and the like. The data processing method comprises the steps that in response to triggering time for reaching a preset target operation, predetermined statistical data of a target object are obtained, and the statistical data are determined based on detailed data of the target object; obtaining target data corresponding to the target operation based on the statistical data; and based on the target data, executing the target operation on the target object.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the technical fields of big data, cloud computing, etc., and in particular to a data processing method, device, equipment, medium and product. Background Art

[0002] In the big data scenario, how to process data efficiently and accurately is a problem that needs to be solved. Summary of the invention

[0003] The present disclosure provides a data processing method, apparatus, device, medium and product.

[0004] According to one aspect of the present disclosure, a data processing method is provided, comprising: in response to reaching a trigger time of a preset target operation, obtaining statistical data of a predetermined target object, wherein the statistical data is determined based on detailed data of the target object; based on the statistical data, obtaining target data corresponding to the target operation; and based on the target data, performing the target operation on the target object.

[0005] According to another aspect of the present disclosure, a settlement method is provided, comprising: in response to reaching a preset settlement time of a target merchant, obtaining a preset time period balance of the target merchant, wherein the preset time period balance is obtained by statistically analyzing transaction detail data within the preset time period; based on the preset time period balance, obtaining a withdrawal amount; and based on the withdrawal amount, performing a settlement operation on the target merchant.

[0006] According to another aspect of the present disclosure, a data processing device is provided, including: a first acquisition module, used to acquire statistical data of a predetermined target object in response to reaching a trigger time of a preset target operation, wherein the statistical data is determined based on detailed data of the target object; a second acquisition module, used to acquire target data corresponding to the target operation based on the statistical data; and a processing module, used to perform the target operation on the target object based on the target data.

[0007] According to another aspect of the present disclosure, a settlement device is provided, including: a first acquisition module, used to obtain a preset time period balance of a preset target merchant in response to reaching the preset settlement time of the target merchant, wherein the preset time period balance is obtained after statistics of transaction detail data within the preset time period; a second acquisition module, used to obtain a withdrawal amount based on the preset time period balance; and a settlement module, used to perform a settlement operation on the target merchant based on the withdrawal amount.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method as described in any of the above aspects.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods according to any one of the above aspects.

[0010] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the methods described in any one of the above aspects.

[0011] According to the embodiments of the present disclosure, data processing can be performed efficiently and accurately.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0014] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0015] Figure 2 is a schematic diagram of an implementation system for implementing an embodiment of the present disclosure;

[0016] Figure 3 is a schematic diagram of the overall architecture for implementing the embodiments of the present disclosure;

[0017] Figure 4 is a schematic diagram according to a second embodiment of the present disclosure;

[0018] Figure 5 is a schematic diagram according to a third embodiment of the present disclosure;

[0019] Figure 6 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0020] Figure 7 is a schematic diagram according to a fifth embodiment of the present disclosure;

[0021] Figure 8It is a schematic diagram of an electronic device used to implement the data processing method or settlement method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] Taking the merchant settlement scenario as an example, the merchant usually initiates a settlement request, the platform conducts real-time statistics on the merchant's transaction data, obtains the withdrawal amount, and then settles the withdrawal amount.

[0024] In this way, on the one hand, automatic settlement cannot be achieved because it needs to be initiated manually by the merchant. On the other hand, the merchant's transaction data volume may be very large, and real-time statistics usually take a long time, and may even fail, resulting in failure of the settlement task.

[0025] In order to perform data processing efficiently and accurately, the present disclosure provides the following embodiments.

[0026] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure. This embodiment provides a data processing method, such as Figure 1 As shown, the method includes:

[0027] 101. In response to reaching a trigger time of a preset target operation, obtain statistical data of a predetermined target object, where the statistical data is determined based on detailed data of the target object.

[0028] 102. Based on the statistical data, obtain target data corresponding to the target operation.

[0029] 103. Based on the target data, perform the target operation on the target object.

[0030] The trigger time refers to the execution time of the target operation. Taking the case where the target operation is executed by the platform as an example, the trigger time can be set by the platform; or, it can also be set by the target object. For example, the platform can provide a setting interface to the target object, and the target object sets the trigger time through the setting interface.

[0031] The trigger time may be a periodic time point, such as a time point every X days; or, it may be one or more set time points.

[0032] The target operation refers to the operation to be performed.

[0033] The target object refers to the object corresponding to the target operation.

[0034] Target data refers to the data corresponding to the target operation.

[0035] Taking the merchant settlement scenario as an example, the target operation can be the settlement operation, the target object can be the target merchant, and the target data can be the withdrawal amount.

[0036] The target object usually generates a large amount of detailed data. For example, merchants usually generate a large amount of transaction flow data every day, such as tens of thousands of transaction records. If these transaction flow data are counted in real time, the efficiency will be poor due to the large amount of data, and it may even cause the statistical task to fail.

[0037] To this end, the detailed data can be counted in advance to obtain statistical data, and then the target data can be obtained based on the statistical data, without the need to count all the detailed data in real time to obtain the target data.

[0038] In this way, when the target operation needs to be performed, statistical data can be directly obtained, such as the end-of-day balance, and the withdrawal amount can be calculated based on the end-of-day balance, without the need to perform real-time statistics on the detailed data to obtain the withdrawal amount.

[0039] After obtaining the target data, you can perform target operations based on the target data. For example, based on the withdrawal amount, perform settlement operations on the target merchant.

[0040] In this embodiment, data processing is performed after the trigger time of the target operation is reached. There is no need to manually initiate a processing request, so automated data processing can be achieved. The target data is obtained based on statistical data, and the statistical data is predetermined, which can avoid the problem of heavy processing burden caused by real-time statistical detailed data, thereby performing data processing efficiently and accurately.

[0041] In order to better understand the present disclosure, the application scenarios involved in the present disclosure are described as follows:

[0042] Figure 2 It is a schematic diagram of an implementation system for implementing the embodiments of the present disclosure.

[0043] Take the merchant settlement scenario as an example. Figure 2 As shown, this scenario involves: a platform party 201 and at least one merchant party 202a~202n.

[0044] Each merchant (referred to as merchant) can record its detailed data (such as transaction flow data) in the database 203. The database can be a MySQL database, which is a commonly used relational database.

[0045] In the related technology, a merchant can send a settlement request to the platform. Based on the settlement request, the platform reads the merchant's transaction flow data from the database in real time, and performs statistics in real time to obtain the maximum withdrawal amount. For example, the real-time calculated balance is used as the maximum withdrawal amount, and then the part that does not exceed the maximum withdrawal amount is settled.

[0046] For a certain merchant, a large amount of detailed data may be generated every day. If these detailed data are counted in real time, the task may fail.

[0047] To this end, the embodiment of the present disclosure can perform detailed data statistics in advance before settlement, obtain statistical data, and perform subsequent processing based on the statistical data.

[0048] Figure 3 It is a schematic diagram of the overall architecture for implementing the embodiments of the present disclosure.

[0049] like Figure 3 As shown, the detailed data generated by each merchant can be recorded in a database (such as a MySQL database), and the detailed data is usually recorded in the database in real time, that is, each time a detailed data is generated, the detailed data is recorded in the database in real time. For some hot merchants, a large amount of detailed data is generated every day.

[0050] The platform can aggregate a large amount of detailed data into a single summary data at a preset summary time. The summary time is, for example, the early morning of each day. In this way, the platform can aggregate the detailed data of each merchant in the preset time period (such as the previous day) at the early morning of each day to obtain the summary data of the merchant + time dimension.

[0051] Specifically, the platform can use offline synchronization tools to read the detailed data (transaction flow data) of the target merchant from the database to the big data platform (such as Hadoop), and then use big data analysis tools (such as Spark) to generate a single record (aggregate data) of the merchant and date dimension, which records the merchant's income, refunds, commissions, adjustments and other detailed data on that date, so that a large number of records can be aggregated into a single record. Hadoop is an open source distributed computing platform, and Spark is a fast and general big data processing engine.

[0052] After obtaining the summary data, you can count the summary data at a preset statistical time to obtain statistical data.

[0053] Taking the statistical data of the end-of-day balance as an example, the formula is: Balance (T+1 day) = Balance (T day) + Balance on the day (T+1 day); among which, T day refers to a certain day, and T+1 day refers to the day after T day.

[0054] The calculation of the end-of-day balance can be performed through a scheduled online service, that is, when the scheduled time is reached, the summary data is calculated through online calculation to obtain the end-of-day balance. In this way, the summary data can be obtained through offline calculation, the data level can be reduced, and the summary data can be calculated online through online scheduled tasks to obtain the end-of-day balance, thereby improving processing efficiency.

[0055] After obtaining summary data and statistical data (such as end-of-day balance), they can be recorded in a database, so that the database can record the detailed data, summary data and statistical data of the target object.

[0056] The data output externally may include summary data and detailed data, which are specifically output to other systems through a preset interface, such as the merchant side, the financial side, a third party, etc. The preset interface may specifically be a Hypertext Transfer Protocol (HTTP) interface.

[0057] Taking merchant query summary data as an example, the merchant can send summary data query instructions to the platform through the HTTP interface. The summary data query instructions can include a query period (such as a certain day, a certain month, etc.). The platform reads the summary data of the query period from the database and sends it to the merchant.

[0058] Merchants can also query detailed data. At this time, the merchant can send detailed data query instructions to the platform through the HTTP interface. The detailed data query instructions can include a query period (such as a certain day, a certain month, etc.). The platform reads the detailed data of the query period from the search analysis engine and sends it to the merchant.

[0059] The search and analysis engine obtains and stores detailed data from the database, and can specifically obtain detailed data from the database through a message queue. The search and analysis engine is, for example, Elasticsearch, which is a distributed data search and analysis engine.

[0060] In addition, the platform can also obtain detailed data from the database and perform real-time statistics on it similar to related technologies. The balance obtained based on real-time statistics can be called the real-time balance, which can also be recorded in the database.

[0061] In this way, by calculating the end-of-day balance and the real-time balance, backup operations can be implemented to improve redundancy and enhance data security.

[0062] Based on this, the settlement process can include:

[0063] When the settlement trigger time is reached, the end-of-day balance and real-time balance are obtained from the database;

[0064] Calculate the base amount, base amount = min (end-of-day balance, real-time balance), which means taking the minimum value of the end-of-day balance and the real-time balance;

[0065] According to the preset rules, the base amount is deducted to obtain the withdrawal amount;

[0066] Pay the withdrawal amount to the merchant.

[0067] In this way, an automatic and efficient merchant settlement process can be achieved.

[0068] In combination with the above application scenarios, the present disclosure also provides the following embodiments.

[0069] Figure 4 is a schematic diagram according to the second embodiment of the present disclosure. This embodiment provides a data processing method, such as Figure 4 As shown, the method includes:

[0070] 401. In response to reaching a preset aggregation time, aggregating multiple pieces of detailed data of a target object into a single piece of aggregated data.

[0071] For example, an offline task can be preset. When the preset summary time (such as the early morning of each day) is reached, the detailed data in the current summary period is summarized. At the early morning of (T+1), the detailed data generated on day T is summarized, and multiple records are summarized into a single record, which is used as the summary data. The summary data is based on the dimensions of merchant + time. For example, for a certain merchant, a single record of the merchant on day T is obtained, and the single record records multiple flow data such as the income and expenditure generated by the merchant on day T.

[0072] 402. In response to reaching a preset statistical time, statistics are performed on the summary data to obtain statistical data of the target object.

[0073] 403. Store the statistical data in a database.

[0074] For example, a preset online task is used to perform statistics on the summary data to obtain statistical data of the target object. After the statistical data is obtained, it can be recorded in a database.

[0075] In this embodiment, by aggregating multiple pieces of detailed data into a single piece of summary data and performing statistics on the summary data, the number of data records that need to be read during statistics can be reduced, processing efficiency can be improved, and the success rate of executing statistical tasks can be increased.

[0076] 404. In response to reaching a preset target operation trigger time, obtain the statistical data from the database.

[0077] 405. Based on the statistical data, obtain target data corresponding to the target operation.

[0078] Among them, the statistical data can be processed based on preset rules to obtain target data.

[0079] For example, the benchmark data can be obtained based on statistical data, and then the deduction data can be calculated according to preset rules, and the deduction data can be subtracted from the benchmark data to obtain the target data.

[0080] 406. Perform the target operation on the target object based on the target data.

[0081] In some embodiments, at least one of the following items may also be performed:

[0082] In response to a query instruction for the summary data, the summary data is output.

[0083] In response to a query instruction for the detailed data, the detailed data is output.

[0084] For detailed data:

[0085] In response to a query instruction for the detailed data, the detailed data can be obtained from a search analysis engine and output, wherein the search analysis engine obtains the detailed data from a database.

[0086] Furthermore, the search analysis engine may obtain the detailed data from the database via a message queue.

[0087] In this embodiment, by outputting summary data and / or detailed data, relevant data can be queried to meet diverse query requirements.

[0088] In this embodiment, by obtaining detailed data from the search analysis engine, the interaction with the database can be reduced, the communication performance can be improved, and the query efficiency can be improved.

[0089] In this embodiment, the detailed data is stored from the database into the search analysis engine through the message queue, which can improve the timeliness of data transmission and enhance the timeliness and accuracy of the detailed data in the search analysis engine.

[0090] When the above data processing method is applied to a merchant settlement scenario, the corresponding settlement method can refer to the following embodiment.

[0091] Figure 5 is a schematic diagram according to the third embodiment of the present disclosure. This embodiment provides a settlement method, such as Figure 5 As shown, the method includes:

[0092] 501. In response to reaching a preset settlement time of a target merchant, a preset time period balance of the target merchant is obtained, where the preset time period balance is obtained by counting transaction detail data within the preset time period.

[0093] 502. Based on the preset time period balance, obtain a withdrawal amount.

[0094] 503. Perform a settlement operation on the target merchant based on the withdrawal amount.

[0095] Among them, the preset time period can be counted in days, and the corresponding preset time period balance can be called the end-of-day balance. Then, the transaction details data of each day can be counted in advance to obtain the end-of-day balance, and the end-of-day balance can be recorded in the database. In this way, the end-of-day balance can be obtained directly from the database without the need for real-time statistics, thereby improving processing efficiency.

[0096] After obtaining the end-of-day balance, the withdrawal amount can be calculated based on the end-of-day balance. For example, the deduction amount can be calculated based on the preset rules, and the withdrawal amount can be obtained by subtracting the deduction amount from the end-of-day balance. Alternatively, the real-time balance can be calculated, and the minimum value of the end-of-day balance and the real-time balance can be used as the base amount, and the deduction amount can be subtracted from the base amount to obtain the withdrawal amount.

[0097] After obtaining the withdrawal amount, pay the withdrawal amount to the target merchant.

[0098] In this embodiment, the settlement operation is performed when the settlement time is reached, so that automated settlement processing can be achieved; the withdrawal amount is obtained based on the balance of the preset time period, and the balance of the preset time period is predetermined, which can avoid the problem of heavy processing burden caused by real-time statistical detailed data, thereby performing settlement operations efficiently and accurately.

[0099] Figure 6 It is a schematic diagram according to the fourth embodiment of the present disclosure. This embodiment provides a data processing device. The device 600 includes: a first acquisition module 601, a second acquisition module 602 and a processing module 603.

[0100] The first acquisition module 601 is used to obtain statistical data of a predetermined target object in response to reaching the trigger time of a preset target operation, and the statistical data is determined based on the detailed data of the target object; the second acquisition module 602 is used to obtain target data corresponding to the target operation based on the statistical data; the processing module 603 is used to perform the target operation on the target object based on the target data.

[0101] In this embodiment, data processing is performed after the trigger time of the target operation is reached. There is no need to manually initiate a processing request, so automated data processing can be achieved. The target data is obtained based on statistical data, and the statistical data is predetermined, which can avoid the problem of heavy processing burden caused by real-time statistical detailed data, thereby performing data processing efficiently and accurately.

[0102] In some embodiments, the apparatus 600 further includes:

[0103] A summarizing module, configured to summarize the plurality of detailed data into a single piece of summary data in response to reaching a preset summarizing time;

[0104] A statistical module, configured to perform statistics on the summary data in response to reaching a preset statistical time, so as to obtain the statistical data;

[0105] The recording module is used to record the statistical data in a database so as to obtain the statistical data from the database at the trigger time.

[0106] In this embodiment, by aggregating multiple pieces of detailed data into a single piece of summary data and performing statistics on the summary data, the number of data records that need to be read during statistics can be reduced, processing efficiency can be improved, and the success rate of executing statistical tasks can be increased.

[0107] In some embodiments, the apparatus 600 further includes:

[0108] The first output module is used to output the summary data in response to a query instruction for the summary data.

[0109] In some embodiments, the apparatus 600 further includes:

[0110] The second output module is used to output the detailed data in response to a query instruction for the detailed data.

[0111] In this embodiment, by outputting summary data and / or detailed data, relevant data can be queried to meet diverse query requirements.

[0112] In some embodiments, the second output module is further used to:

[0113] In response to a query instruction for the detail data, the detail data is acquired and output from a search analysis engine, and the search analysis engine acquires the detail data from a database.

[0114] In this embodiment, by obtaining detailed data from the search analysis engine, the interaction with the database can be reduced, the communication performance can be improved, and the query efficiency can be improved.

[0115] In some embodiments, the search analysis engine obtains the detailed data from the database via a message queue.

[0116] In this embodiment, the detailed data is stored from the database into the search analysis engine through the message queue, which can improve the timeliness of data transmission and enhance the timeliness and accuracy of the detailed data in the search analysis engine.

[0117] Figure 7 It is a schematic diagram according to the fifth embodiment of the present disclosure. This embodiment provides a settlement device, and the device 700 includes: a first acquisition module 701, a second acquisition module 702 and a settlement module 603.

[0118] The first acquisition module 701 is used to obtain a preset time period balance of the target merchant in response to reaching the preset settlement time of the target merchant, and the preset time period balance is obtained after statistics of transaction details data within the preset time period; the second acquisition module 702 is used to obtain the withdrawal amount based on the preset time period balance; the settlement module 703 is used to perform settlement operations on the target merchant based on the withdrawal amount.

[0119] In this embodiment, the settlement operation is performed when the settlement time is reached, so that automated settlement processing can be achieved; the withdrawal amount is obtained based on the balance of the preset time period, and the balance of the preset time period is predetermined, which can avoid the problem of heavy processing burden caused by real-time statistical detailed data, thereby performing settlement operations efficiently and accurately.

[0120] It can be understood that in the embodiments of the present disclosure, the same or similar contents in different embodiments can be referenced to each other.

[0121] It can be understood that the “first”, “second”, etc. in the embodiments of the present disclosure are only used for distinction and do not indicate the degree of importance, time sequence, etc.

[0122] It is understandable that unless there is any special limitation on the sequence of steps in the process, it means that the timing relationship between these steps is not limited.

[0123] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0124] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0125] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0126] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0127] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0128] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as data processing methods or settlement methods. For example, in some embodiments, the data processing method or settlement method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the data processing method or settlement method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the data processing method or settlement method in any other appropriate manner (e.g., by means of firmware).

[0129] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable task processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0131] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0134] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0135] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0136] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: In response to reaching a trigger time of a preset target operation, obtaining statistical data of a predetermined target object, wherein the statistical data is determined based on detailed data of the target object; Based on the statistical data, obtaining target data corresponding to the target operation; Based on the target data, the target operation is performed on the target object.

2. The method according to claim 1, further comprising: In response to reaching a preset aggregation time, aggregating the plurality of detailed data into a single piece of aggregated data; In response to reaching a preset statistical time, performing statistics on the summary data to obtain the statistical data; The statistical data are recorded in a database, so as to obtain the statistical data from the database at the trigger time.

3. The method according to claim 2, further comprising: In response to a query instruction for the summary data, the summary data is output.

4. The method according to claim 1, further comprising: In response to a query instruction for the detailed data, the detailed data is output.

5. The method according to claim 4, wherein: The step of outputting the detailed data in response to a query instruction for the detailed data comprises: In response to a query instruction for the detail data, the detail data is acquired and output from a search analysis engine, and the search analysis engine acquires the detail data from a database.

6. The method according to claim 5, wherein: The search analysis engine obtains the detailed data from the database through a message queue.

7. A settlement method, comprising: In response to reaching a preset settlement time of a target merchant, obtaining a preset time period balance of the target merchant, wherein the preset time period balance is obtained by statistically analyzing transaction details data within the preset time period; Based on the balance of the preset period, obtaining a withdrawal amount; Based on the withdrawal amount, a settlement operation is performed on the target merchant.

8. A data processing device, comprising: A first acquisition module, configured to acquire statistical data of a predetermined target object in response to reaching a trigger time of a preset target operation, wherein the statistical data is determined based on detailed data of the target object; A second acquisition module, used for acquiring target data corresponding to the target operation based on the statistical data; A processing module is used to perform the target operation on the target object based on the target data.

9. The apparatus according to claim 8, further comprising: A summarizing module, configured to summarize the plurality of detailed data into a single piece of summary data in response to reaching a preset summarizing time; A statistical module, configured to perform statistics on the summary data in response to reaching a preset statistical time, so as to obtain the statistical data; The recording module is used to record the statistical data in a database so as to obtain the statistical data from the database at the trigger time.

10. The apparatus according to claim 9, further comprising: The first output module is used to output the summary data in response to a query instruction for the summary data.

11. The apparatus according to claim 8, further comprising: The second output module is used to output the detailed data in response to a query instruction for the detailed data.

12. The device according to claim 11, wherein: The second output module is further used for: In response to a query instruction for the detail data, the detail data is acquired and output from a search analysis engine, and the search analysis engine acquires the detail data from a database.

13. The device according to claim 12, wherein: The search analysis engine obtains the detailed data from the database through a message queue.

14. A settlement device, comprising: A first acquisition module, configured to acquire a preset time period balance of a preset target merchant in response to reaching a preset settlement time of the target merchant, wherein the preset time period balance is obtained by statistically analyzing transaction details data within the preset time period; A second acquisition module, used to acquire a withdrawal amount based on the preset time period balance; A settlement module is used to perform a settlement operation on the target merchant based on the withdrawal amount.

15. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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