Data processing method and device, equipment and medium

Abstract

The embodiment of the invention relates to a data processing method and device, equipment and a medium. The method comprises the steps that a data aggregation protocol is acquired; wherein the data aggregation protocol comprises an identifier of a target transaction, a to-be-aggregated parameter corresponding to a target event in the target transaction, and data structure information of the to-be-aggregated parameter corresponding to the target event; screening out target burying point data corresponding to a to-be-aggregated parameter from the operation data based on the to-be-aggregated parameter corresponding to the target event under the condition of obtaining the operation data; and performing aggregation processing on the target burying point data based on the data structure information to obtain a burying point data set corresponding to the target transaction. According to the embodiment of the invention, the efficiency of obtaining the required burying point data is better improved, and the timeliness of performing subsequent processing on the obtained burying point data set can be better guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the field of information technology, and in particular, to a data processing method, apparatus, device, and medium. Background Art

[0002] Data logging is a common data collection method. By adopting the logging method to collect the required running data (i.e., logged data), the running situation of a software application or the application usage situation of a user can be analyzed. However, the efficiency of obtaining the required logged data in the prior art is relatively low. Summary of the Invention

[0003] To solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a data processing method, apparatus, device, and medium.

[0004] An embodiment of the present disclosure provides a data processing method, the method including: obtaining a data aggregation protocol; wherein, the data aggregation protocol includes an identifier of a target transaction, an aggregation parameter to be aggregated corresponding to a target event in the target transaction, and data structure information of the aggregation parameter to be aggregated corresponding to the target event; in the case of obtaining running data, based on the aggregation parameter to be aggregated corresponding to the target event, screening out target logged data corresponding to the aggregation parameter to be aggregated from the running data; and performing an aggregation process on the target logged data based on the data structure information to obtain a logged data set corresponding to the target transaction.

[0005] Optionally, the target event includes at least a start event and an intermediate event, and the intermediate event is an event that occurs between the start event and the end event.

[0006] Optionally, the method further includes: determining a target data source corresponding to the target transaction from existing data sources; wherein different data sources are used to store different target transactions; and storing the logged data set corresponding to the target transaction into the target data source; wherein the logged data sets corresponding to target transactions occurring at different times are independent of each other.

[0007] Optionally, the method further includes: in the case of receiving a data screening condition, extracting a target set from the logged data sets stored in the target data source based on the data screening condition, so as to perform a preset operation based on the target set.

[0008] Optionally, the data screening condition includes a time screening condition; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; the extracting of the target set from the set of buried point data stored in the target data source based on the data screening condition includes: determining a target time period based on the time screening condition; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extracting the set of buried point data whose occurrence time corresponding to the start event is within the target time period from the set of buried point data stored in the target data source, and using the extracted set of buried point data as the target set.

[0009] Optionally, the data screening condition includes a quantity screening condition; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; the extracting of the target set from the set of buried point data stored in the target data source based on the data screening condition includes: determining a target quantity based on the quantity screening condition; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extracting the most recently obtained target quantity of sets of buried point data from the set of buried point data stored in the target data source, and using the extracted set of buried point data as the target set.

[0010] Optionally, the number of the target sets is multiple, and the preset operation includes: respectively extracting the target buried point data corresponding to the target parameter from the multiple target sets; based on the extracted target buried point data, obtaining the optimized data corresponding to the target parameter, so as to execute the target event to which the target parameter belongs based on the optimized data corresponding to the target parameter.

[0011] Optionally, the method is applied to the client; the data aggregation protocol is a protocol sent by the server to the client.

[0012] An embodiment of the present disclosure provides a data processing device, including: a protocol acquisition module, configured to acquire a data aggregation protocol; wherein, the data aggregation protocol includes an identifier of a target transaction, a parameter to be aggregated corresponding to a target event in the target transaction, and data structure information of the parameter to be aggregated corresponding to the target event; a data screening module, configured to, when the running data is acquired, screen out the target buried point data corresponding to the parameter to be aggregated from the running data based on the parameter to be aggregated corresponding to the target event; a data aggregation module, configured to perform an aggregation process on the target buried point data based on the data structure information to obtain a set of buried point data corresponding to the target transaction.

[0013] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the data processing method provided by the embodiment of the present disclosure.

[0014] An embodiment of the present disclosure also provides a computer-readable storage medium storing a computer program for executing the data processing method provided by the embodiment of the present disclosure.

[0015] The above technical solution provided by the embodiment of the present disclosure can first obtain a data aggregation protocol, which can clearly indicate the identifier of the target transaction, the aggregation parameters to be aggregated corresponding to the target event in the target transaction, and the data structure information of the aggregation parameters corresponding to the target event. Thus, when the running data is obtained, the target buried point data corresponding to the aggregation parameters can be directly filtered out from the running data based on the aggregation parameters corresponding to the target event, and the target buried point data is aggregated based on the data structure information to obtain a buried point data set corresponding to the target transaction. The above method can efficiently and reliably obtain the required buried point data set corresponding to the target transaction based on the data aggregation protocol for subsequent analysis and processing, which preferably improves the efficiency of obtaining the required buried point data and also helps to preferably ensure the timeliness of subsequent processing of the obtained buried point data set.

[0016] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used 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

[0017] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure.

[0018] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of a data processing method provided by an embodiment of the present disclosure;

[0020] Figure 2 It is a schematic data structure diagram provided by an embodiment of the present disclosure;

[0021] Figure 3A schematic diagram of data processing interaction provided by an embodiment of the present disclosure;

[0022] Figure 4 A schematic diagram of a data processing flow provided by an embodiment of the present disclosure;

[0023] Figure 5 A schematic structural diagram of a data processing device provided by an embodiment of the present disclosure;

[0024] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0025] In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0026] Many specific details are set forth in the following description in order to provide a thorough understanding of the present disclosure, but the present disclosure may be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the present disclosure, rather than all embodiments.

[0027] Figure 1 A schematic flow diagram of a data processing method provided by an embodiment of the present disclosure. This method can be executed by a data processing device, where the device can be implemented by software and / or hardware and is generally integrated in an electronic device, such as a user terminal such as a mobile phone, a tablet computer, a computer, etc. As Figure 1 shown, the method mainly includes the following steps S102 to step S106:

[0028] Step S102, obtain a data aggregation protocol, where the data aggregation protocol includes an identifier of a target transaction, aggregation parameters to be aggregated corresponding to a target event in the target transaction, and data structure information of the aggregation parameters to be aggregated corresponding to the target event.

[0029] The identifier of the target transaction can be information such as the name or code of the target transaction. The target transaction can be a shooting transaction, a video editing transaction, etc. In practical applications, transactions can also be divided according to program execution units, and the transaction type can be flexibly set according to requirements. A transaction usually contains multiple events, specifically including a start event, an end event, and one or more intermediate events between the start event and the end event. For example, a video editing transaction includes a start event (entering the video editing page), an end event (publishing the video), and intermediate events (such as entering an entrance, enhancing video quality, obtaining the preview video frame rate, decreasing the frame rate, etc.). In practical applications, the buried point parameters (i.e., the parameters to be aggregated) corresponding to the events to be buried (i.e., the target events) can be specified. In some specific embodiments, the target events at least include the start event and the intermediate event. The intermediate event is an event that occurs between the start event and the end event. For example, for a shooting transaction, the shooting transaction is pre-configured as the target transaction in the data aggregation protocol, and the start event (triggering the shooting control) and the intermediate event (obtaining the shooting frame rate) of the shooting transaction are used as the target events, and the occurrence time corresponding to the start event and the shooting frame rate corresponding to the intermediate event are used as the parameters to be aggregated. Further, the target events also include the end event. Among them, the parameters to be aggregated corresponding to the start event include, but are not limited to, the occurrence time of the start event. This parameter helps to directly filter the target transaction based on the time dimension in the subsequent process; the parameters to be aggregated corresponding to the intermediate event can be the parameters involved in the intermediate event, which can be flexibly set according to requirements and are not limited here.

[0030] In addition, the data structure information of the parameters to be aggregated can be indicated in the data aggregation protocol. This data structure information can be used to represent the arrangement order corresponding to different target events, the arrangement order of multiple parameters to be aggregated corresponding to the same target event (which can be represented in the form of a parameter list), the parameter names, parameter types, and data formats corresponding to each parameter to be aggregated, etc. Further, the data aggregation protocol can also contain aggregation conditions, such as the number of transactions to be aggregated, etc. The data aggregation protocol can be flexibly set according to the aggregation requirements and is not limited here.

[0031] Step S104, when the running data is obtained, based on the parameters to be aggregated corresponding to the target events, filter out the target buried point data corresponding to the parameters to be aggregated from the running data.

[0032] The running data is the data generated by the user terminal when running a specified application or program. When the user terminal obtains the running data, it can directly and quickly find the target buried-point data corresponding to the parameter to be aggregated based on information such as the name of the parameter to be aggregated corresponding to the target event. The specific value corresponding to the parameter to be aggregated is the target buried-point data. For example, if the name of the parameter to be aggregated is the shooting frame rate and the shooting frame rate recorded in the running data is 60fps, then the target buried-point data corresponding to the shooting frame rate is 60fps. Since the data aggregation protocol uses transactions, target events in the transactions, and parameters to be aggregated corresponding to the target events for hierarchical indexing, the required target buried-point data can be obtained efficiently and directly.

[0033] Step S106: Aggregate the target buried-point data based on the data structure information to obtain a set of buried-point data corresponding to the target transaction.

[0034] In some embodiments, the target data structure corresponding to the target buried-point data can be determined based on the data structure information, and the target buried-point data can be aggregated according to the target data structure. Specifically, the user terminal can parse the data structure information and map the data structure information to a data structure available to the user terminal. It can also be understood as converting the data structure information in the data aggregation protocol into a data structure template available to the user terminal, so as to aggregate the target buried-point data based on the available data structure template to obtain a set of buried-point data corresponding to the target transaction. This set of buried-point data can be presented in the form of an array as a whole.

[0035] The above method can efficiently and reliably obtain the set of buried-point data corresponding to the target transaction required, so as to facilitate subsequent analysis and processing, which better improves the efficiency of obtaining the required buried-point data and also helps to better ensure the timeliness of subsequent processing of the obtained set of buried-point data.

[0036] On the basis of the above, in some embodiments, the data processing method provided by the embodiments of the present disclosure further includes the following steps (1) and (2):

[0037] Step (1): Determine the target data source corresponding to the target transaction from the existing data sources; where different data sources are used to store different target transactions. In practical applications, multiple data sources for storing different target transactions can be set in advance. The key of each data source is the transaction identifier (such as the transaction name). Based on the transaction identifier corresponding to the data source, the data source corresponding to the target transaction (i.e., the target data source) can be directly indexed and found. By storing the data corresponding to different transactions in different data sources respectively, it is more convenient for searching and processing.

[0038] Step (2), storing the set of buried point data corresponding to the target transaction into the target data source; wherein, the sets of buried point data corresponding to target transactions occurring at different times are independent of each other. For example, if a user performs N video editing processes, that is, executes N video editing transactions, the start times of different video editing transactions are different, and each video editing transaction corresponds to a corresponding set of buried point data. The N sets of buried point data are stored independently in the data source corresponding to the video editing transaction, that is, there are N arrays stored in this data source. For ease of understanding, reference can be made to Figure 2 A schematic diagram of a data structure as shown to simply illustrate the data structure framework in the data source, that is, to illustrate that the target data source corresponding to the target transaction includes target transaction 1 to target transaction N. The data structure corresponding to each target transaction includes a start event and intermediate events 1,..., intermediate event n, and each event specifically records the corresponding buried point data. The sets of buried point data (that is, arrays) corresponding to different target transactions are independent of each other, which is more convenient for subsequent extraction (also called cutting) of the required arrays according to needs.

[0039] Furthermore, the above method provided by the embodiments of the present disclosure further includes: when a data screening condition is obtained, extracting a target set from the set of buried point data stored in the target data source based on the data screening condition, so as to perform a preset operation based on the target set. In practical applications, the data screening condition can be set in the data aggregation protocol, or directly sent by the server to the user terminal, or directly set at the user terminal. The embodiments of the present disclosure do not limit the acquisition method of the data screening condition. The data screening condition can include a time screening condition and / or a quantity screening condition. Based on the data screening condition, a target set that meets the requirements can be extracted, so as to perform a preset operation based on the target set. The embodiments of the present disclosure do not limit the specific content of the preset operation, and any operation that requires a set of buried point data can be used as the preset operation.

[0040] In some specific implementation examples, the data screening condition includes a time screening condition; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; extracting a target set from the set of buried point data stored in the target data source based on the data screening condition includes: determining a target time period based on the time screening condition; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extracting the set of buried point data whose occurrence time corresponding to the start event is within the target time period from the set of buried point data stored in the target data source, and using the extracted set of buried point data as the target set. For example, if the target time period is within 24 hours, then based on the occurrence time corresponding to the start event in the set of buried point data, extract the set of buried point data whose start event occurrence time is within 24 hours from the target data source as the target set.

[0041] In some specific implementation examples, the data screening conditions include quantity screening conditions; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; extracting the target set from the set of buried point data stored in the target data source based on the data screening conditions includes: determining the target quantity based on the quantity screening conditions; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extracting the most recently obtained target quantity of sets of buried point data from the set of buried point data stored in the target data source, and using the extracted sets of buried point data as the target set. For example, if the target quantity is 5, then based on the occurrence time corresponding to the start event in the set of buried point data, 5 sets of buried point data that were most recently obtained are extracted from the target data source as the target set.

[0042] Through the above method, it is possible to directly and quickly determine whether a set of buried point data meets the data screening conditions based on the occurrence time corresponding to the start event in each set of buried point data, without the need to analyze and search through a large amount of buried point data, greatly improving the extraction efficiency of the target set and making it more convenient to quickly and timely perform subsequent analysis and data optimization on the extracted target set.

[0043] In practical applications, the number of target sets is multiple, and the preset operations include: respectively extracting the target buried point data corresponding to the target parameter from multiple target sets; based on the extracted target buried point data, obtaining the optimized data corresponding to the target parameter, so as to execute the target event to which the target parameter belongs based on the optimized data corresponding to the target parameter. The method of obtaining the optimized data corresponding to the target parameter based on the extracted target buried point data includes, but is not limited to, averaging the extracted target buried point data and using the average result as the optimized data corresponding to the target parameter. For example, the average value of the shooting frame rates corresponding to the most recent 5 shooting transactions is calculated and used as the shooting frame rate to be used when performing subsequent shooting transactions. Applying the above method to the shooting scenario can dynamically optimize the parameters of the shooting transaction according to the user's previous shooting habits, thereby providing a better shooting experience for the user.

[0044] The inventor has found through research that in the related art, in most cases, the user side needs to upload a large amount of buried point data to the server side. The server side analyzes and filters the buried point data and then distributes the useful buried point data to the user side. However, this method is relatively cumbersome and inefficient, and the data interaction process will consume a certain amount of time, making the data no longer real-time. The buried point data required by the user side obtained through the server side is not real-time data and is difficult to be applied to scenarios with high real-time requirements. In addition, there are also some existing technologies that require the business side to write a code program to obtain the required buried point data and inject it into the user side separately through methods such as releasing a version, hoping that the user side can directly obtain the required buried point data. However, this method is also relatively cumbersome and inefficient. Therefore, the data processing method provided by the embodiments of the present disclosure is applied to the user side, and the data aggregation protocol is a protocol sent by the server side to the user side. In actual applications, the business side can configure the data aggregation protocol according to requirements through a specified platform. The data aggregation protocol is used to indicate the specific method of aggregating running data. The user side can receive and save the latest data aggregation protocol sent by the server side and perform filtering and aggregation processing on the running data according to the data aggregation protocol to obtain the required aggregated data, which is more convenient and fast, greatly improves the data processing efficiency, and better guarantees the data real-time. For ease of understanding, reference can be made to Figure 3 A schematic diagram of a data processing interaction as shown, mainly showing the interaction between the user side, the server side and the platform, mainly including the following steps S302 to step S310:

[0045] Step S302, the platform uploads the data aggregation protocol to the server side. Specifically, the platform can be a platform facing the business side, which can provide a configuration interface for the business side to configure the data aggregation protocol. The business side can configure the data aggregation protocol through this interface, such as setting the target transaction to be aggregated in the data aggregation protocol, the target event corresponding to the target transaction, and the parameters to be aggregated corresponding to the target event.

[0046] Step S304, the server side distributes the data aggregation protocol to the user side.

[0047] Step S306, the user side saves the data aggregation protocol.

[0048] Step S308, the user side parses the data aggregation protocol saved locally during cold start.

[0049] Step S310, in the case of obtaining running data, process the running data based on the data aggregation protocol to obtain a set of buried point data. The specific method can refer to the foregoing related content and will not be elaborated here.

[0050] In the above manner, the business side can flexibly configure the data aggregation protocol according to requirements and send it to the user side through the server side. The user side can efficiently and reliably extract the required buried point data set from the running data without uploading the running data to the server side for processing, effectively ensuring the real-time nature of the data and improving the data processing efficiency.

[0051] Further, the embodiments of the present disclosure provide a schematic diagram of a data processing flow as shown in Figure 4 and can be executed by the user side, including the following steps S402 to step S414:

[0052] Step S402, start the target application.

[0053] Step S404, determine whether there is a data aggregation protocol locally. If so, execute step S406; if not, end.

[0054] Step S406, parse the data aggregation protocol to obtain a locally available data structure template.

[0055] Step S408, obtain the running data. Specifically, the running data includes the original buried point data. In practical applications, the running data can be obtained based on the running situation of the target application, and the running situation of the target application mainly depends on user operations.

[0056] Step S410, determine whether the running data is the buried point data that needs to be aggregated. If so, execute step S412; if not, end.

[0057] Step S412, determine whether the running data contains the start buried point data. Among them, the start buried point data can be the buried point data corresponding to the start event. If so, execute step S414; if not, end.

[0058] Step S414, process the running data based on the data aggregation protocol to obtain a buried point data set. For example, the running data can be trimmed, filtered, etc. based on the data aggregation protocol to obtain the required target buried point data, and the target buried point data is formed into a buried point data set according to the data structure indicated by the data aggregation protocol.

[0059] In summary, for the data processing method provided by the embodiments of the present disclosure, the user side can directly and efficiently obtain the buried point data set corresponding to the required target transaction based on the data aggregation protocol for subsequent analysis and processing, which better improves the efficiency of obtaining the required buried point data and also helps to better ensure the timeliness of subsequent processing of the obtained buried point data set.

[0060] Corresponding to the foregoing data processing method, the embodiments of the present disclosure also provide a data processing device. Figure 5Schematic diagram of a data processing device provided by an embodiment of the present disclosure. The device can be implemented by software and / or hardware and is generally integrated in an electronic device, such as Figure 5 As shown, the data processing device includes:

[0061] A protocol acquisition module 502, configured to acquire a data aggregation protocol; wherein, the data aggregation protocol includes the name of a target transaction, the to-be-aggregated parameters corresponding to a target event in the target transaction, and the data structure information of the to-be-aggregated parameters corresponding to the target event;

[0062] A data screening module 504, configured to, when running data is acquired, screen out target buried point data corresponding to the to-be-aggregated parameters from the running data based on the to-be-aggregated parameters corresponding to the target event;

[0063] A data aggregation module 506, configured to perform an aggregation process on the target buried point data based on the data structure information to obtain a buried point data set corresponding to the target transaction.

[0064] The above device can efficiently and reliably obtain the buried point data set corresponding to the required target transaction based on the data aggregation protocol for subsequent analysis and processing, which preferably improves the efficiency of obtaining the required buried point data and also helps to preferably ensure the timeliness of subsequent processing of the obtained buried point data set.

[0065] In some embodiments, the target event includes at least a start event and an intermediate event, and the intermediate event is an event that occurs between the start event and the end event.

[0066] In some embodiments, the device further includes a set storage module, configured to determine a target data source corresponding to the target transaction from existing data sources; wherein, different data sources are used to store different target transactions; and store the buried point data set corresponding to the target transaction into the target data source; wherein, the buried point data sets corresponding to target transactions occurring at different times are independent of each other.

[0067] In some embodiments, the device further includes a set extraction module, configured to, when a data screening condition is received, extract a target set from the buried point data set stored in the target data source based on the data screening condition to perform a preset operation based on the target set.

[0068] In some embodiments, the data screening condition includes a time screening condition; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; the set extraction module is specifically configured to: determine a target time period based on the time screening condition; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extract from the set of buried point data stored in the target data source the set of buried point data whose occurrence time corresponding to the start event is within the target time period, and use the extracted set of buried point data as the target set.

[0069] In some embodiments, the data screening condition includes a quantity screening condition; the parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; the set extraction module is specifically configured to: determine a target quantity based on the quantity screening condition; based on the occurrence time corresponding to the start event in the set of buried point data stored in the target data source, extract from the set of buried point data stored in the target data source the target number of the most recently obtained sets of buried point data, and use the extracted set of buried point data as the target set.

[0070] In some embodiments, the number of the target sets is multiple, and the preset operation includes: respectively extracting the target buried point data corresponding to the target parameter from the multiple target sets; based on the extracted target buried point data, obtaining the optimized data corresponding to the target parameter, so as to execute the target event to which the target parameter belongs based on the optimized data corresponding to the target parameter.

[0071] In some embodiments, the device is applied to the user side; the data aggregation protocol is a protocol sent by the server side to the user side.

[0072] The data processing device provided by the embodiments of the present disclosure can execute the data processing method provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects for executing the method.

[0073] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the device embodiment described above can refer to the corresponding process in the method embodiment, which will not be elaborated here.

[0074] The embodiments of the present disclosure provide an electronic device, which includes: a storage device, on which a computer program is stored; a processing device, configured to execute the computer program in the storage device to implement the steps of any method in the present disclosure.

[0075] Next, refer to Figure 6, which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The shown electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0076] As Figure 6 shown, the electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 602 or the programs loaded from the storage device 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0077] Generally, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 6 the shown electronic device 600 has various devices, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be alternatively implemented or had.

[0078] Particularly, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.

[0079] In addition to the above methods and devices, embodiments of the present disclosure may also be computer program products, which include computer program instructions that, when run on a processor, cause the processor to execute the image processing methods provided by the embodiments of the present disclosure. The computer program products may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0080] In addition, embodiments of the present disclosure may also be computer-readable storage media, on which computer program instructions are stored, and when the computer program instructions are run on a processor, the processor is caused to execute the data processing methods provided by the embodiments of the present disclosure.

[0081] The computer-readable storage media may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but not be limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 above.

[0082] Embodiments of the present disclosure also provide a computer program product, including a computer program / instructions, which when executed by a processor implement the data processing methods in the embodiments of the present disclosure.

[0083] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0084] For example, when responding to an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operations of the present disclosure technical solution based on the prompt message.

[0085] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0086] It can be understood that the above process of notifying and obtaining user authorization is only illustrative and does not limit the implementation manner of the present disclosure. Other manners that meet relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0087] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0088] The above are only specific implementation manners of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that Including: Obtain a data aggregation protocol; wherein, the data aggregation protocol includes an identifier of a target transaction, an aggregation parameter to be aggregated corresponding to a target event in the target transaction, and data structure information of the aggregation parameter to be aggregated corresponding to the target event; When the running data is obtained, based on the aggregation parameter to be aggregated corresponding to the target event, filter out the target buried point data corresponding to the aggregation parameter to be aggregated from the running data; Perform an aggregation process on the target buried point data based on the data structure information to obtain a buried point data set corresponding to the target transaction.

2. The method according to claim 1, wherein The target event includes at least a start event and an intermediate event, and the intermediate event is an event that occurs between the start event and the end event.

3. The method according to claim 1, wherein The method further includes: Determine a target data source corresponding to the target transaction from existing data sources; wherein, different data sources are used to store different target transactions; Store the buried point data set corresponding to the target transaction into the target data source; wherein, the buried point data sets corresponding to target transactions occurring at different times are independent of each other.

4. The method according to claim 3, wherein The method further includes: When a data filtering condition is obtained, extract a target set from the buried point data sets stored in the target data source based on the data filtering condition, so as to perform a preset operation based on the target set.

5. The method according to claim 4, wherein The data filtering condition includes a time filtering condition; the aggregation parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; The extracting the target set from the buried point data sets stored in the target data source based on the data filtering condition includes: Determine a target time period based on the time filtering condition; Based on the occurrence time corresponding to the start event in the buried point data sets stored in the target data source, extract the buried point data sets whose occurrence time corresponding to the start event is within the target time period from the buried point data sets stored in the target data source, and use the extracted buried point data sets as the target set.

6. The method according to claim 4, wherein The data filtering condition includes a quantity filtering condition; the aggregation parameter to be aggregated corresponding to the target event includes the occurrence time corresponding to the start event; The extracting the target set from the buried point data sets stored in the target data source based on the data filtering condition includes: Determine a target quantity based on the quantity filtering condition; Based on the occurrence time corresponding to the start event in the buried point data sets stored in the target data source, extract the most recently obtained target quantity of buried point data sets from the buried point data sets stored in the target data source, and use the extracted buried point data sets as the target set.

7. The method according to claim 4, characterized in that, The number of the target sets is multiple, and the preset operation includes: Extract the target buried point data corresponding to the target parameter from each of the multiple target sets; Based on the extracted target buried point data, obtain the optimization data corresponding to the target parameter, so as to execute the target event to which the target parameter belongs based on the optimization data corresponding to the target parameter.

8. The method according to any one of claims 1 to 7, characterized in that, The method is applied to a user terminal; the data aggregation protocol is a protocol sent by a server to the user terminal.

9. A data processing device, characterized in that, Including: A protocol acquisition module, configured to acquire a data aggregation protocol; wherein, the data aggregation protocol includes an identifier of a target transaction, aggregation parameters to be aggregated corresponding to a target event in the target transaction, and data structure information of the aggregation parameters to be aggregated corresponding to the target event; A data screening module, configured to, when the running data is acquired, screen out target buried point data corresponding to the aggregation parameters to be aggregated from the running data based on the aggregation parameters to be aggregated corresponding to the target event; A data aggregation module, configured to perform an aggregation process on the target buried point data based on the data structure information to obtain a buried point data set corresponding to the target transaction.

10. An electronic device, characterized in that, The electronic device includes: A storage device, on which a computer program is stored; A processing device, configured to execute the computer program in the storage device to implement the steps of the data processing method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is configured to execute the data processing method according to any one of claims 1-8 above.

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