Data processing method, device, electronic device and storage medium

Abstract

The present invention discloses a data processing method, device, electronic device and storage medium, the method comprising: obtaining user behavior data within a target time period in response to a behavior query instruction, then preprocessing the user behavior data to obtain first date data corresponding to the user's effective behavior, then mapping the first date data so that the mapped first date data meets a preset rule, and associating the user behavior data with the first date data that meets the preset rule to obtain target data after the association process, and finally displaying the target data on the current browsing interface. By adopting the embodiment of the present invention, the original query based on the user behavior data can be replaced with a query of the target data associated with the user's effective behavior, thereby eliminating the user's invalid behavior data, ensuring the continuity and authenticity of the user's effective behavior data, and effectively improving the accuracy of the data statistics and analysis of the user's behavior.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data processing method, device, electronic equipment and storage medium. Background Art

[0002] User behavior refers to the behavior of users on the product, which is actually manifested as relevant user behavior data. In general, by analyzing different data, we can provide direction for product iteration and development. In the process of user behavior analysis, the continuity of data plays a vital role in the analysis of user behavior.

[0003] However, in the existing user behavior analysis process, there are user behavior data that are not related to the real user behavior. These data will interfere with the continuity and authenticity of the user behavior data to a certain extent, affecting the data statistics and analysis of user behavior. Therefore, in the existing user behavior data processing process, a method that can improve the accuracy of user behavior analysis is urgently needed. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a data processing method, device, electronic device and storage medium to solve the technical problem of low accuracy in the existing user behavior analysis process.

[0005] In a first aspect, to achieve the above-mentioned purpose, an embodiment of the present invention provides a data processing method, including:

[0006] In response to the behavior query instruction, obtaining user behavior data within a target time period, wherein the user behavior data includes user data, behavior data, and date data;

[0007] Preprocessing the user behavior data to obtain first date data corresponding to the user's effective behavior;

[0008] Performing mapping processing on the first date data so that the mapped first date data satisfies a preset rule;

[0009] Associating the user behavior data with the first date data that meets a preset rule to obtain target data after association processing;

[0010] The target data is displayed on the current browsing interface.

[0011] Furthermore, the preprocessing of the user behavior data to obtain the first date data corresponding to the user's effective behavior includes:

[0012] Determining second date data corresponding to the invalid behavior of the user;

[0013] The second date data is deleted from the date data of the user behavior data to obtain the first date data corresponding to the user's effective behavior.

[0014] Furthermore, the mapping process is performed on the first date data so that the mapped first date data satisfies a preset rule, including:

[0015] The first date data are arranged in ascending order, and data in the first date data are mapped in the target time period in sequence starting from the first data, so that the first date data are arranged in ascending order in a continuous value manner according to a preset time unit within the target time period.

[0016] Furthermore, the user behavior data is associated with the first date data that meets a preset rule to obtain the associated target data, including:

[0017] Associating the total data table corresponding to the user behavior data with the valid data table corresponding to the first date data to obtain an associated data table;

[0018] All data related to the second date data are filtered out from the associated data table to obtain a target data table corresponding to the target data.

[0019] Furthermore, the target data table includes a kudu table. After the step of obtaining the target data after the association processing, the data processing method further includes:

[0020] The kudu table is cached.

[0021] Furthermore, the data processing method further includes:

[0022] In response to the date conversion instruction, the date data in the target data is converted into date data corresponding to a natural day.

[0023] Furthermore, the association processing includes a leftjoin operation.

[0024] In a second aspect, in order to solve the same technical problem, an embodiment of the present invention provides a data processing device, including:

[0025] An acquisition module, configured to acquire user behavior data within a target time period in response to a behavior query instruction, wherein the user behavior data includes user data, behavior data, and date data;

[0026] A processing module, used for preprocessing the user behavior data to obtain first date data corresponding to the user's effective behavior;

[0027] A mapping module, used for performing mapping processing on the first date data so that the mapped first date data satisfies a preset rule;

[0028] An association module, used to associate the user behavior data with the first date data that meets a preset rule to obtain target data after association processing;

[0029] The display module is used to display the target data on the current browsing interface.

[0030] In a third aspect, in order to solve the same technical problem, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, the steps in any one of the above-mentioned data processing methods are implemented.

[0031] In a fourth aspect, in order to solve the same technical problem, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to perform the steps in any one of the above-mentioned data processing methods.

[0032] The embodiment of the present invention provides a data processing method, device, electronic device and storage medium. The method maps the first date data corresponding to the user's effective behavior, and associates the user behavior data with the mapped first date data to obtain the associated target data, thereby replacing the original query based on the user behavior data with the query of the target data associated with the user's effective behavior. In addition, by eliminating the display of the user's invalid behavior data, the continuity and authenticity of the user's effective behavior data can be guaranteed, effectively improving the accuracy of the data statistics and analysis of the user's behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of a data processing method provided by an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of an application scenario in which the data processing method provided by the embodiment of the present invention is not applied;

[0035] Figure 3 This is a schematic diagram of an application scenario in which the data processing method provided by an embodiment of the present invention is applied;

[0036] Figure 4 is a schematic diagram of another application scenario in which the data processing method provided by the embodiment of the present invention is not applied;

[0037] Figure 5 is a schematic diagram of another application scenario to which the data processing method provided by an embodiment of the present invention is applied;

[0038] Figure 6 is a structural schematic diagram of a data processing device provided by an embodiment of the present invention;

[0039] Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present invention;

[0040] Figure 8 This is another structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0043] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0044] In related technologies, by analyzing the user behavior data of users in the target product, it is possible to provide direction for the iteration and development of the target product, thereby completing the optimization of the target product step by step. Among them, in the process of analyzing the user behavior data, the authenticity and continuity of the user behavior data play a vital role. Therefore, if in the process of user behavior analysis, there is user behavior data that is not related to the real user behavior, this data will interfere with the continuity and authenticity of the user behavior data to a certain extent, thereby affecting the data statistics and analysis of user behavior, and ultimately affecting the iteration and development of the target product, reducing the user experience.

[0045] In order to solve the technical problems existing in the related art, the embodiment of the present invention provides a data processing method, see Figure 1, Figure 1 is a flow chart of a data processing method provided by an embodiment of the present invention, such as Figure 1 As shown, the data processing method provided by the embodiment of the present invention includes steps 101 to 105.

[0046] Step 101, in response to a behavior query instruction, obtaining user behavior data within a target time period, wherein the user behavior data includes user data, behavior data, and date data.

[0047] In this embodiment, in one case, the behavior query instruction can be a behavior query text instruction input by the user, or a behavior query instruction generated by a behavior query operation performed by the user on the browsing interface of the product. In other cases, the behavior query instruction can also be a voice instruction input by the user, and the product is controlled by the voice instruction to generate a corresponding behavior query instruction. Specifically, the behavior query instruction can query the behavior data of the user in the product, such as registration, browsing, payment, logout, and cancellation behavior data.

[0048] The target time period is the time period that the user wants to query, for example, the target time period is from February 1, 2022 to February 28, 2022. Then the product will query all user behavior data in February 2022. Specifically, user behavior data includes user data such as user ID, behavior data such as registration and deregistration, and date data such as February 5, 2022.

[0049] Step 102: pre-process the user behavior data to obtain first date data corresponding to the user's effective behavior.

[0050] Among them, valid user behavior mainly refers to user behavior during the normal use period of the product. For example, when the product is a securities trading product, since trading behavior will not occur during holidays, user behavior performed during non-holiday periods is considered valid user behavior, and user behavior performed during holidays is considered invalid user behavior. Therefore, the first date data corresponding to valid user behavior is the date data of user behavior performed during non-holiday periods.

[0051] For example, in February 2022, users of this securities trading product performed different user behaviors such as logging in and browsing every day. However, February 1 to February 6 of that year was the Spring Festival, and February 12 to February 13, February 19 to February 20, and February 26 to February 27 were weekends, that is, these time periods were holidays, so user behaviors during these holidays were invalid user behaviors, and user behaviors during non-holidays in February were valid user behaviors.

[0052] Therefore, the step of preprocessing the user behavior data to obtain the first date data corresponding to the user's valid behavior specifically includes: determining the second date data corresponding to the user's invalid behavior; deleting the second date data from the date data of the user behavior data to obtain the first date data corresponding to the user's valid behavior.

[0053] For example, if the user behavior data is data for the entire month of February 2022, then the second date data corresponding to the user's invalid behavior from February 1 to February 6, February 12 to February 13, February 19 to February 20, and February 26 to February 27 are deleted from the date data for the entire month of February 2022. In this way, the first date data corresponding to the user's valid behavior can be obtained.

[0054] Step 103: Mapping the first date data so that the mapped first date data meets a preset rule.

[0055] The preset rule may be a pre-defined date sorting rule, for example, a sequential arrangement rule such as ascending or descending order of date data.

[0056] In this embodiment, the step of mapping the first date data so that the mapped first date data satisfies a preset rule specifically includes: arranging the first date data in ascending order, and mapping the data in the first date data in sequence, starting from the first data, within the target time period, so that the first date data is arranged in ascending order in a continuous value manner according to preset time units within the target time period.

[0057] By mapping the discontinuous first date data into a continuous date range, the continuity of the user's valid data is ensured, and the accuracy of the user behavior data analysis process is improved.

[0058] Step 104 , the user behavior data is associated with the first date data that meets a preset rule to obtain associated target data.

[0059] In this embodiment, the association process includes a leftjoin operation. Since the user behavior data is stored in a data table in a database, the step of associating the user behavior data with the first date data that meets the preset rule to obtain the target data after the association process specifically includes: associating the total data table corresponding to the user behavior data with the valid data table corresponding to the first date data to obtain an associated data table; filtering out all data related to the second date data from the associated data table to obtain a target data table corresponding to the target data.

[0060] In one implementation, the user behavior data in this embodiment is raw data, and the data format of the total data table where the raw data is located is shown in Table 1 below, which shows the user behavior data for each day of February 2022. Table 1 only shows part of the data for February 2022, and other date data not shown does not mean that they are not recorded in Table 1.

[0061]

[0062]

[0063] Table 1

[0064] The data format of the valid data table corresponding to the first date data is shown in Table 2 below. Table 2 shows the data format of the first date data after mapping, that is, the data format of the valid data table. Table 2 only shows a part of the first date data after mapping, and other date data not shown does not mean that it is not recorded in Table 2.

[0065] Original time Valid time (day) Validity period (week) …… 2022-2-7 2022-2-1 1 …… …… …… …… …… 2022-2-11 2022-2-5 1 …… 2022-2-14 2022-2-6 1 …… …… …… …… …… 2022-2-18 2022-2-10 2 …… 2022-2-21 2022-2-11 2 …… …… …… …… …… 2022-2-25 2022-2-15 3 …… 2022-2-28 2022-2-16 3 ……

[0066] Table 2

[0067] Among them, Table 2 includes original time, effective time (day) and effective time (week). The original time is the original time (non-holiday date) of the first date data corresponding to the user's effective data in the original data; the effective time (day) is the time data that satisfies the descending order after the first date data is mapped. Specifically, this column of data is mainly in days, and each first date data is continuously represented by continuous values; the effective time (week) is the time data that satisfies the descending order after the first date data is mapped. Specifically, this column of data is mainly in weeks (7 days). In addition, Table 2 can also include other time data, such as effective time (month), effective time (quarter), effective time (year), etc., as long as it is the time data representing the first date data after mapping processing, and they are not listed here one by one.

[0068] After obtaining the total data table 1 and the effective data table 2 of the original data, the table 1 and the table 2 can be associated to obtain the associated data table after the association process. Specifically, this embodiment uses the leftjoin operation to associate table 1 and table 2. In this embodiment, the associated data table is shown in Table 3 below. Among them, Table 3 only shows a part of the data after the association process, and other data not shown does not mean that it is not recorded in Table 3.

[0069] Original time User ID Behavioral events Valid time (day) …… 2022-2-1 111 Log in Null …… 2022-2-2 111 Browse Null …… …… …… …… Null …… 2022-2-6 222 Sign out Null …… 2022-2-7 111 Sign out 2022-2-1 …… …… …… …… …… …… 2022-2-11 222 Browse 2022-2-5 …… 2022-2-12 111 Log in Null …… 2022-2-13 111 Browse Null …… …… …… …… …… …… 2022-2-27 111 Browse Null …… 2022-2-28 222 Log in 2022-2-16 ……

[0070] Table 3

[0071] Among them, in the column data where the effective time (day) is located in Table 3, since the transaction behavior will not occur during holidays, the effective time corresponding to the holiday time in the original time is Null, indicating invalid time.

[0072] After obtaining the associated data table 3, all data related to the second date data are filtered out from the associated data table 3, that is, invalid data is deleted from the table 3, that is, the data in the Null part is deleted from the table 3, so that the target data table corresponding to the target data can be obtained. Specifically, the target data table is shown in Table 4 below.

[0073] Original time User ID Behavioral events Valid time (day) …… 2022-2-7 111 Sign out 2022-2-1 …… 2022-2-8 …… …… 2022-2-2 …… 2022-2-9 222 Browse 2022-2-3 …… 2022-2-10 111 Log in 2022-2-4 …… 2022-2-11 111 Browse 2022-2-5 …… 2022-2-14 …… …… 2022-2-6 …… 2022-2-15 222 Log in 2022-2-7 …… 2022-2-16 …… …… 2022-2-8 …… 2022-2-17 …… …… 2022-2-9 …… 2022-2-18 …… …… 2022-2-10 …… 2022-2-21 …… …… 2022-2-11 …… 2022-2-22 …… …… 2022-2-12 …… 2022-2-23 …… …… 2022-2-13 …… 2022-2-24 …… …… 2022-2-14 …… 2022-2-25 …… …… 2022-2-15 …… 2022-2-28 222 Log in 2022-2-16 ……

[0074] Table 4

[0075] In some embodiments, the target data table includes a kudu table, and after the step of obtaining the target data after association processing, the data processing method further includes: caching the kudu table.

[0076] Among them, Kudu can provide both faster batch processing speed and stronger reading and writing capabilities. Therefore, using Kudu tables can provide faster query speeds for products, thereby achieving faster data analysis.

[0077] Step 105: display the target data on the current browsing interface.

[0078] After obtaining the target data table, the data corresponding to the valid data (day) from 2022-2-1 to 2022-2-16 is displayed on the current browsing interface. By displaying continuous valid data on the product's browsing interface, the original query based on user behavior data can be replaced with a query of target data associated with the user's valid behavior, thereby eliminating the display of invalid user behavior data, thereby ensuring the continuity and authenticity of the user's valid behavior data, and effectively improving the accuracy of data statistics and analysis of user behavior.

[0079] As an optional embodiment, the data processing method provided in this embodiment further includes: in response to a date conversion instruction, converting the date data in the target data into date data corresponding to a natural day.

[0080] When you need to query the actual natural day time corresponding to continuous valid data, you only need to enter the date conversion instruction, or trigger the date conversion control on the browsing interface of the current product, so that the product can automatically convert the date data in the target data into the date data corresponding to the natural day. Specifically, according to the mapping relationship corresponding to the above Table 4, the effective time of each row of data in Table 4 can be converted into the corresponding original time.

[0081] In one embodiment, see Figure 2 and Figure 3 , Figure 2 is a schematic diagram of an application scenario in which the data processing method provided by the embodiment of the present invention is not applied. Figure 3 Schematic diagram of an application scenario in which the data processing method provided by an embodiment of the present invention is applied. Figure 2 and Figure 3 As shown, the data processing method provided in this embodiment is mainly used to analyze daily active user data indicators in securities trading apps.

[0082] Specifically, Figure 2 As shown, when the data processing method provided by the embodiment of the present invention is not applied, that is, when the active users of the securities trading APP in the past 30 days (2022-1-29 to 2022-2-27) are queried, the securities trading APP will eventually display the following Figure 2 The results shown are based on user behavior data for all dates. Figure 2 It can be seen that there are obvious fluctuations in user behavior data. Among them, 1-29 to 2-6 are the Spring Festival holidays, 2-11 to 2-12, 2-19 to 2-20 are weekends, all of which are non-trading dates. The existence of these data interferes with normal data statistics and has a certain impact on the accuracy of the data, resulting in a reduction in the accuracy of the analysis of the active user data indicators of the APP, affecting the iteration and development of the target product.

[0083] Please see Figure 3 In the process of analyzing the active user data indicators of the securities trading APP, after applying the data processing method provided by the embodiment of the present invention, that is, by mapping the first date data corresponding to the user's effective behavior, and associating the user behavior data with the mapped first date data, the target data after the association processing can be obtained, so that the original query based on the user behavior data of all dates can be replaced by the query of the target data associated with the user's effective behavior, that is, the user behavior data of non-holidays, thereby eliminating the display of invalid user behavior data, ensuring the continuity and authenticity of the user's effective behavior data, and effectively improving the accuracy of data statistics and analysis of user behavior.

[0084] In another embodiment, see Figure 4 and Figure 5 , Figure 4 is a schematic diagram of another application scenario in which the data processing method provided by the embodiment of the present invention is not applied. Figure 5 is another application scenario schematic diagram of the data processing method provided by the embodiment of the present invention, such as Figure 4 and Figure 5 As shown, the data processing method provided in this embodiment is mainly used to analyze the conversion of the core process of the product of securities trading APP. Among them, the conversion of the core process of the product is mainly analyzed through three steps: APP activation, APP browsing page and APP crash.

[0085] Specifically, Figure 4 As shown, when the data processing method provided by the embodiment of the present invention is not applied, that is, when the conversion status of the core process of the product of the securities trading APP in the past 14 days (2022-2-14 to 2022-2-27) is queried, the securities trading APP will eventually display the following Figure 4 The results shown are based on user behavior data for all dates. Figure 4 It can be seen that the final total conversion rate displayed by the APP is only 1.46%. However, 2-11 to 2-12, 2-19 to 2-20, and 2-26 to 2-27 are weekends, which are non-trading dates. If these dates are included, the statistical funnel conversion situation will be lower, because on these actual dates, the probability of users using the App to trade securities is very low, which leads to deviations in the calculation of the entire conversion funnel, resulting in a reduction in the accuracy of the analysis of the conversion of the core product process of the APP, affecting the iteration and development of the target product.

[0086] Please see Figure 5 In the process of analyzing the conversion of the core process of the product of the securities trading APP, after applying the data processing method provided by the embodiment of the present invention, all non-trading day data can be shielded, so that the total conversion rate displayed by the APP is prompted, which is increased from the original 1.46% to 1.9%.

[0087] In summary, the data processing method provided by the embodiment of the present invention includes obtaining user behavior data within a target time period in response to a behavior query instruction, then preprocessing the user behavior data to obtain first date data corresponding to the user's effective behavior, then mapping the first date data so that the mapped first date data meets a preset rule, and associating the user behavior data with the first date data that meets the preset rule to obtain the target data after the association process, and finally displaying the target data on the current browsing interface. By adopting the embodiment of the present invention, the original query based on user behavior data can be replaced with a query of the target data associated with the user's effective behavior, thereby eliminating the user's invalid behavior data, ensuring the continuity and authenticity of the user's effective behavior data, and effectively improving the accuracy of the data statistics and analysis of the user's behavior.

[0088] According to the method described in the above embodiments, this embodiment will be further described from the perspective of a data processing device. The data processing device can be implemented as an independent entity or integrated into an electronic device, such as a terminal. The terminal can include a mobile phone, a tablet computer, etc.

[0089] See also Figure 6 , Figure 6 is a structural diagram of a data processing device provided by an embodiment of the present invention, such as Figure 6 As shown, the data processing device 600 provided by the embodiment of the present invention includes: an acquisition module 601 , a processing module 602 , a mapping module 603 , an association module 604 and a display module 605 .

[0090] The acquisition module 601 is used to respond to the behavior query instruction and acquire the user behavior data within the target time period, wherein the user behavior data includes user data, behavior data and date data.

[0091] The processing module 602 is used to pre-process the user behavior data to obtain the first date data corresponding to the user's effective behavior.

[0092] In this embodiment, the processing module 602 is specifically used to determine the second date data corresponding to the invalid behavior of the user; delete the second date data from the date data of the user behavior data to obtain the first date data corresponding to the valid behavior of the user.

[0093] The mapping module 603 is used to perform mapping processing on the first date data so that the mapped first date data meets a preset rule.

[0094] In this embodiment, the mapping module 603 is specifically used to arrange the first date data in ascending order, and map the data in the first date data in the target time period starting from the first data, so that the first date data is arranged in ascending order in a continuous value manner according to a preset time unit within the target time period.

[0095] The association module 604 is used to associate the user behavior data with the first date data that meets a preset rule to obtain target data after association processing.

[0096] In this embodiment, the association processing includes a leftjoin operation. Specifically, the association module 604 is specifically used to perform association processing on the total data table corresponding to the user behavior data and the valid data table corresponding to the first date data to obtain an associated data table; filter out all data related to the second date data from the associated data table to obtain a target data table corresponding to the target data.

[0097] As an optional embodiment, the target data table includes a kudu table, and the data processing device provided in the embodiment of the present invention further includes a cache module, which is mainly used to cache the kudu table.

[0098] The display module 605 is used to display the target data on the current browsing interface.

[0099] In some embodiments, the data processing device provided by the embodiments of the present invention further includes a conversion module, which is mainly used to convert the date data in the target data into date data corresponding to a natural day in response to a date conversion instruction.

[0100] During specific implementation, the above modules and / or units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above modules and / or units can refer to the previous method embodiments. The specific beneficial effects that can be achieved can also refer to the beneficial effects in the previous method embodiments, which will not be repeated here.

[0101] Also, see Figure 7 , Figure 7 1 is a schematic diagram of a structure of an electronic device provided by an embodiment of the present invention, and the electronic device may be a mobile terminal such as a smart phone, a tablet computer, or the like. Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702. The processor 701 is electrically connected to the memory 702.

[0102] The processor 701 is the control center of the electronic device 700. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device 700 and processes data by running or loading applications stored in the memory 702 and calling data stored in the memory 702, thereby monitoring the electronic device 700 as a whole.

[0103] In this embodiment, the processor 701 in the electronic device 700 loads instructions corresponding to the processes of one or more application programs into the memory 702 according to the following steps, and the processor 701 runs the application programs stored in the memory 702 to implement various functions:

[0104] Determine a target mask image corresponding to the image to be processed according to the enhanced region determination instruction;

[0105] The image to be processed and the target mask image are input into the trained image enhancement model to obtain the target enhanced image output by the image enhancement model.

[0106] The electronic device 700 can implement the steps in any embodiment of the data processing method provided in the embodiments of the present invention, and therefore can achieve the beneficial effects that can be achieved by any data processing method provided in the embodiments of the present invention. Please refer to the previous embodiments for details and will not be repeated here.

[0107] See also Figure 8 , Figure 8 is another structural schematic diagram of an electronic device provided by an embodiment of the present invention, such as Figure 8 As shown, Figure 8 The electronic device 800 is a block diagram of a specific structure of an electronic device provided by an embodiment of the present invention, and the electronic device can be used to implement the data processing method provided in the above embodiment. The electronic device 800 can be a mobile terminal such as a smart phone or a laptop computer.

[0108] The RF circuit 810 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices. The RF circuit 810 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity module (SIM) cards, memories, etc. The RF circuit 810 can communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The above-mentioned wireless networks can use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE802.11g and / or IEEE802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, even those that have not yet been developed.

[0109] The memory 820 may be used to store software programs and modules, such as program instructions / modules corresponding to the data processing method in the above-mentioned embodiment. The processor 880 executes various functional applications and data processing by running the software programs and modules stored in the memory 820, that is, realizing the following functions:

[0110] Determine a target mask image corresponding to the image to be processed according to the enhanced region determination instruction;

[0111] The image to be processed and the target mask image are input into the trained image enhancement model to obtain the target enhanced image output by the image enhancement model.

[0112] The memory 820 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 820 may further include a memory remotely arranged relative to the processor 880, and these remote memories may be connected to the electronic device 800 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0113] The input unit 830 can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit 830 may include a touch-sensitive surface 831 and other input devices 832. The touch-sensitive surface 831, also known as a touch display screen or touchpad, can collect user touch operations on or near it (such as operations performed by users using fingers, styluses, or any other suitable objects or accessories on or near the touch-sensitive surface 831), and drive corresponding connection devices according to a pre-set program. Optionally, the touch-sensitive surface 831 may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 880, and can receive and execute commands sent by the processor 880. In addition, the touch-sensitive surface 831 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch-sensitive surface 831, the input unit 830 may further include other input devices 832. Specifically, the other input devices 832 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

[0114] The display unit 840 can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device 800, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit 840 may include a display panel 841, and optionally, the display panel 841 may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface 831 may cover the display panel 841, and when the touch-sensitive surface 831 detects a touch operation on or near it, it is transmitted to the processor 880 to determine the type of the touch event, and then the processor 880 provides corresponding visual output on the display panel 841 according to the type of the touch event. Although in the figure, the touch-sensitive surface 831 and the display panel 841 are implemented as two independent components to implement input and output functions, in some embodiments, the touch-sensitive surface 831 can be integrated with the display panel 841 to implement input and output functions.

[0115] The electronic device 800 may also include at least one sensor 850, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 841 according to the brightness of the ambient light, and the proximity sensor may generate an interrupt when the flip cover is closed or closed. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that can also be configured in the electronic device 800, they will not be repeated here.

[0116] The audio circuit 860, the speaker 861, and the microphone 862 can provide an audio interface between the user and the electronic device 800. The audio circuit 860 can transmit the electrical signal converted from the received audio data to the speaker 861, which is converted into a sound signal for output; on the other hand, the microphone 862 converts the collected sound signal into an electrical signal, which is received by the audio circuit 860 and converted into audio data, and then the audio data is output to the processor 880 for processing, and then sent to another terminal through the RF circuit 810, or the audio data is output to the memory 820 for further processing. The audio circuit 860 may also include an earplug jack to provide communication between an external headset and the electronic device 800.

[0117] The electronic device 800 can help the user receive requests, send information, etc. through the transmission module 870 (such as a Wi-Fi module), which provides the user with wireless broadband Internet access. Although the transmission module 870 is shown in the figure, it is understandable that it is not a necessary component of the electronic device 800 and can be omitted as needed without changing the essence of the invention.

[0118] The processor 880 is the control center of the electronic device 800. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 820, and calling data stored in the memory 820, it executes various functions of the electronic device 800 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 880 may include one or more processing cores; in some embodiments, the processor 880 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 880.

[0119] The electronic device 800 also includes a power supply 890 (such as a battery) for supplying power to various components. In some embodiments, the power supply can be logically connected to the processor 880 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 890 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0120] Although not shown, the electronic device 800 also includes a camera (such as a front camera, a rear camera), a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and are configured to be executed by one or more processors. One or more programs include instructions for performing the following operations:

[0121] Determine a target mask image corresponding to the image to be processed according to the enhanced region determination instruction;

[0122] The image to be processed and the target mask image are input into the trained image enhancement model to obtain the target enhanced image output by the image enhancement model.

[0123] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above modules can be found in the previous method embodiments, which will not be repeated here.

[0124] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium, which stores a plurality of instructions, and the instructions can be loaded by a processor to execute the steps of any embodiment of the data processing method provided in the embodiment of the present invention.

[0125] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0126] Since the instructions stored in the storage medium can execute the steps in any embodiment of the data processing method provided in the embodiments of the present invention, the beneficial effects that can be achieved by any data processing method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0127] The above is a detailed introduction to a data processing method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application. Moreover, for those of ordinary skill in the art, without departing from the principles of the present invention, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A data processing method, characterized in that: include: In response to the behavior query instruction, obtaining user behavior data within a target time period, wherein the user behavior data includes user data, behavior data, and date data; Preprocessing the user behavior data to obtain first date data corresponding to the user's effective behavior; wherein the user's effective behavior includes the user's behavior within the normal use period of the product; Determining second date data corresponding to the invalid behavior of the user; Arrange the first date data in ascending order, and map the data in the first date data in the target time period in sequence starting from the first data, so that the first date data is arranged in ascending order in a continuous value manner according to a preset time unit within the target time period; Associating the user behavior data with the first date data that meets a preset rule to obtain target data after association processing; Displaying the target data on the current browsing interface; The step of associating the user behavior data with the first date data satisfying a preset rule to obtain target data after the associative processing includes: Associating the total data table corresponding to the user behavior data with the valid data table corresponding to the first date data to obtain an associated data table; All data related to the second date data are filtered out from the associated data table to obtain a target data table corresponding to the target data.

2. The data processing method according to claim 1, characterized in that: The preprocessing of the user behavior data to obtain the first date data corresponding to the user's effective behavior includes: The second date data is deleted from the date data of the user behavior data to obtain the first date data corresponding to the user's effective behavior.

3. The data processing method according to claim 1, characterized in that: The target data table includes a kudu table. After the step of obtaining the target data after the association processing, the data processing method further includes: The kudu table is cached.

4. The data processing method according to claim 1, characterized in that: Also includes: In response to the date conversion instruction, the date data in the target data is converted into date data corresponding to a natural day.

5. The data processing method according to claim 1, characterized in that: The association process includes a left join operation.

6. A data processing device, characterized in that: include: An acquisition module, configured to acquire user behavior data within a target time period in response to a behavior query instruction, wherein the user behavior data includes user data, behavior data, and date data; A processing module, configured to pre-process the user behavior data to obtain first date data corresponding to valid user behavior; wherein the valid user behavior includes user behavior during a normal use period of the product; and determine second date data corresponding to invalid user behavior; A mapping module, used for arranging the first date data in ascending order, and mapping the data in the first date data in sequence from the first data in the target time period, so that the first date data is arranged in ascending order in a continuous value manner according to a preset time unit in the target time period; An association module, used to associate the user behavior data with the first date data that meets a preset rule to obtain target data after association processing; A display module, used for displaying the target data on the current browsing interface; The association module is further used for: Associating the total data table corresponding to the user behavior data with the valid data table corresponding to the first date data to obtain an associated data table; All data related to the second date data are filtered out from the associated data table to obtain a target data table corresponding to the target data.

7. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, the steps in the data processing method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the steps in the data processing method according to any one of claims 1 to 5.

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