Data processing method, device, electronic device and storage medium

Abstract

Disclosed is a data processing method, device, electronic device and storage medium, comprising: obtaining data to be processed; the data to be processed includes account attribute data and the number of accounts corresponding to the account attribute data; sorting the account attribute data to obtain an account attribute sequence of the account attribute data; determining the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data and a preset ranking strategy; using the ranking result of the account attribute data as the ranking result of the account corresponding to the account attribute data. The method can improve the processing efficiency of the ranking result of the account.

Description

Technical Field

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

[0002] As the need for sorting in big data scenarios becomes more and more common, more and more services need to provide ranking information for accounts.

[0003] There are many methods for ranking accounts, such as skip list algorithm, bucket counting method or hive (a data warehouse tool based on Hadoop (a distributed system framework)) divide-and-conquer sorting method, etc. When these methods are used for ranking, accounts need to be ranked one by one to obtain the ranking order of each account.

[0004] However, as the number of accounts increases, the above method of ranking accounts one by one will increase the amount of computation and computational complexity of the server, resulting in low ranking efficiency. Summary of the invention

[0005] The present disclosure provides a data processing method, device, electronic device and storage medium to at least solve the problem of low efficiency of account ranking in related technologies. The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a data processing method is provided, comprising:

[0007] Acquire data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data;

[0008] Sorting each of the account attribute data to obtain the account attribute sequence;

[0009] For each of the account attribute data, determining a ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy;

[0010] The ranking result of the account attribute data is used as the ranking result of at least one account corresponding to the account attribute data.

[0011] In one embodiment, determining the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy includes:

[0012] For the first account attribute data in the account attribute sequence, determining the number of accounts corresponding to the first account attribute data as the ranking result corresponding to the first account attribute data;

[0013] For each other account attribute data except the first account attribute data in the account attribute sequence, the ranking result of the current other account attribute data is determined according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence.

[0014] In one embodiment, after determining the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy, the method further includes:

[0015] Obtaining a ranking query table according to each of the account attribute data and the corresponding ranking results;

[0016] In response to a ranking query request for a target account, a ranking result of the target account is determined in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account.

[0017] In one embodiment, in response to the ranking query request for the target account, determining the ranking result of the target account in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account includes:

[0018] In response to a ranking query request for a target account, querying whether there is current account attribute data of the target account in the ranking query table;

[0019] If the account attribute data does not exist, fill in the data interval of any adjacent account attribute data in the ranking query table with new account attribute data; the new account attribute data is obtained by taking values ​​in the data interval according to the preset numerical interval with two adjacent account attribute data as the limit;

[0020] Determine the ranking result corresponding to the smaller account attribute data of the two account attribute data as the ranking result corresponding to the new account attribute data, and obtain an updated ranking query table;

[0021] In the updated ranking query table, the ranking result of the target account is determined according to the current account attribute data corresponding to the target account.

[0022] In one embodiment, after using the ranking result of the account attribute data as the ranking result of the account corresponding to the account attribute data, the method further includes:

[0023] Obtaining a preset target ranking threshold, and determining an attribute data threshold according to the target ranking threshold and the ranking result of the account attribute data;

[0024] In response to a level query request for a target account, a level corresponding to the target account is determined according to current account attribute data of the target account and the attribute data threshold.

[0025] In one embodiment, determining the attribute data threshold according to the target ranking threshold and the ranking result of the account attribute data includes:

[0026] According to the target ranking threshold, querying whether there is a target ranking result that is the same as the target ranking threshold in the ranking results of the account attribute data;

[0027] If there is a target ranking result that is the same as the target ranking threshold, the account attribute data corresponding to the target ranking result is used as the attribute data threshold corresponding to the target ranking threshold;

[0028] If there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, the minimum ranking result is determined as the target ranking result, and the account attribute data corresponding to the target ranking result is added with a fixed threshold as the attribute data threshold corresponding to the target ranking threshold.

[0029] In one embodiment, obtaining the data to be processed includes:

[0030] Acquire account attribute data of all accounts, and aggregate the account attribute data of all accounts to obtain account groups containing accounts with the same account attribute data;

[0031] The number of accounts included in each of the account groups is counted, and the account attribute data of each of the account groups and the number of accounts corresponding to the account attribute data are associated to obtain the data to be processed.

[0032] According to a second aspect of an embodiment of the present disclosure, there is provided a data processing device, including:

[0033] An acquisition unit is configured to acquire data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data;

[0034] a sorting unit, configured to sort each of the account attribute data to obtain an account attribute sequence of the account attribute data;

[0035] a determining unit configured to determine, for each of the account attribute data, a ranking result of the account attribute data according to an arrangement order of the account attribute data in the account attribute sequence, a number of accounts corresponding to the account attribute data, and a preset ranking strategy;

[0036] The matching unit is configured to execute the ranking result of the account attribute data as the ranking result of at least one account corresponding to the account attribute data.

[0037] In one embodiment, the determining unit further includes:

[0038] A first determining subunit is configured to execute, for a first account attribute data in the account attribute sequence, determining the number of accounts corresponding to the first account attribute data as a ranking result corresponding to the first account attribute data;

[0039] The second determination subunit is configured to determine the ranking result of the current other account attribute data for each other account attribute data except the first account attribute data in the account attribute sequence according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence.

[0040] In one embodiment, the data processing device further includes:

[0041] A construction unit is configured to obtain a ranking query table according to each of the account attribute data and the corresponding ranking result;

[0042] The query unit is configured to execute a ranking query request for a target account and determine a ranking result of the target account in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account.

[0043] In one embodiment, the query unit further includes:

[0044] A first query subunit is configured to execute, in response to a ranking query request for a target account, query whether there is current account attribute data of the target account in the ranking query table;

[0045] A filling subunit is configured to fill in new account attribute data in a data interval of any adjacent account attribute data in the ranking query table if such data does not exist; the new account attribute data is obtained by taking values ​​in the data interval according to the preset numerical interval with two adjacent account attribute data as the limit;

[0046] A third determining subunit is configured to determine the ranking result corresponding to the smaller account attribute data of the two account attribute data as the ranking result corresponding to the new account attribute data, to obtain an updated ranking query table;

[0047] The fourth determining subunit is configured to execute in the updated ranking query table, and determine the ranking result of the target account according to the current account attribute data corresponding to the target account.

[0048] In one embodiment, the data processing device further includes:

[0049] an acquisition and determination unit, configured to execute acquisition of a preset target ranking threshold, and determine an attribute data threshold according to the target ranking threshold and a ranking result of the account attribute data;

[0050] The level determination unit is configured to execute, in response to a level query request for a target account, determine the level corresponding to the target account according to current account attribute data of the target account and the attribute data threshold.

[0051] In one embodiment, the obtaining and determining unit further includes:

[0052] A query determination subunit is configured to execute, according to the target ranking threshold, querying whether there is a target ranking result that is the same as the target ranking threshold in the ranking results of the account attribute data;

[0053] A fifth determining subunit is configured to execute, if there is a target ranking result that is the same as the target ranking threshold, using the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold;

[0054] The sixth determination subunit is configured to execute, if there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, determine the minimum ranking result as the target ranking result, and add a fixed threshold to the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold.

[0055] In one embodiment, the acquisition unit includes:

[0056] an acquisition subunit, configured to acquire account attribute data of all accounts, and aggregate the account attribute data of all accounts to obtain account groups containing accounts with the same account attribute data;

[0057] The statistical subunit is configured to perform statistical analysis on the number of accounts included in each of the account groups, and associate the account attribute data of each of the account groups with the number of accounts corresponding to the account attribute data to obtain the data to be processed.

[0058] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0059] processor;

[0060] a memory for storing instructions executable by the processor;

[0061] The processor is configured to execute the instructions to implement the data processing method as described in any embodiment of the first aspect above.

[0062] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, including:

[0063] When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method described in any embodiment of the first aspect above.

[0064] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including:

[0065] When the instruction is executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method described in any embodiment of the first aspect.

[0066] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0067] Acquire data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each of the account attribute data; sort each of the account attribute data to obtain the account attribute sequence; for each of the account attribute data, determine the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy; use the ranking result of the account attribute data as the ranking result of at least one account corresponding to the account attribute data. By adopting this method, the ranking result of the account attribute data is determined by the number of accounts, and then the ranking result of the account attribute data is used as the ranking result of the corresponding account, thereby reducing the amount of data processing during the account ranking process and improving the efficiency of the account ranking.

[0068] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0070] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment;

[0071] Figure 2 is a flowchart showing a step of determining a ranking result of account attribute data according to an exemplary embodiment;

[0072] Figure 3 is a flowchart showing a step of determining a target account ranking result according to an exemplary embodiment;

[0073] Figure 4 is a flowchart showing a step of querying ranking results of a target account according to an exemplary embodiment;

[0074] Figure 5 is a flow chart showing a step of querying the level of a target account according to an exemplary embodiment;

[0075] Figure 6 is a flowchart showing a step of determining a corresponding level of a target account according to an exemplary embodiment;

[0076] Figure 7 is a flowchart showing a step of obtaining data to be processed according to an exemplary embodiment;

[0077] Figure 8is a block diagram of a data processing device according to an exemplary embodiment;

[0078] Fig. 9 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0079] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0080] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0081] It should also be noted that the account information (including but not limited to account device information, account personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the account or fully authorized by all parties.

[0082] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment. Figure 1 As shown, the data processing method can be applied to a server or a terminal, and the embodiments of the present disclosure do not limit this. In the embodiments of the present disclosure, the server or the terminal is collectively referred to as a computer device for general description, and the data processing method includes the following steps.

[0083] In step S110, data to be processed is obtained.

[0084] The data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data.

[0085] In implementation, when it is necessary to rank all accounts in the system, the computer device obtains the data to be processed corresponding to all accounts. The data to be processed includes the attribute values ​​corresponding to the account attributes of all accounts (i.e., account attribute data) and the number of accounts corresponding to each account attribute data. Among them, the account attributes in the data to be processed can be evaluation indicators for evaluating accounts, such as account scores, etc. The specific content of the account attributes is not limited in the embodiments of the present disclosure. These data to be processed can be stored in the local cache of the computer device so that the computer device can directly obtain them, or they can be stored in a database. The embodiments of the present disclosure do not limit the storage location of the data to be processed.

[0086] For example, as shown in Table 1, taking the account attribute as the account score as an example, in the data to be processed, the account score value (ie, the attribute value) is 1000, and the corresponding number of accounts is 90 (people), the account score value is 900, and the corresponding number of accounts is 10 (people), the account score value is 800, and the corresponding number of accounts is 40 (people), and the account score value is 650, and the corresponding number of accounts is 20 (people).

[0087] Table 1

[0088] Account attribute data (account rating value) Number of accounts (persons) 1000 90 900 10 800 40 650 20

[0089] Optionally, the data processing process in the embodiment of the present disclosure (i.e., including the step of obtaining the data to be processed) can be a cyclic process, that is, the computer device regularly obtains the data to be processed at preset time intervals, and processes the data to be processed within each time interval, thereby obtaining a real-time ranking result of the account.

[0090] In step S120, each account attribute data is sorted to obtain an account attribute sequence.

[0091] In implementation, since multiple accounts have the same account attribute data, that is, there is a many-to-one correspondence between accounts and account attribute data, the account ranking process can be transformed into the account attribute data ranking process, thereby reducing the amount of data processing in the ranking process and improving the ranking efficiency. After the computer device obtains the data to be processed, the different account attribute data in the data to be processed can be sorted from large to small to obtain an account attribute sequence containing different account attribute data.

[0092] Specifically, in the process of sorting different account attribute data, the computer device can use a group sorting method, for example, using the group by() method to sort the account attribute data in descending order to obtain an account attribute sequence of the sorted account attribute data. Taking Table 1 as an example, the account attribute sequence of the sorted account attribute data shown in Table 1 is {account attribute data 1000, account attribute data 900, account attribute number, 800, account attribute number 650}.

[0093] In step S130, for each account attribute data, a ranking result of the account attribute data is determined according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy.

[0094] In implementation, each account attribute data is processed in turn according to the order in which the account attribute data is arranged from large to small in the account attribute sequence, that is, the ranking result of the current account attribute data is determined according to the number of accounts corresponding to the account attribute data and the preset ranking strategy. Then, the computer device stores the ranking result of the account attribute data in the TreeMap.

[0095] Among them, for the same account attribute data corresponding to multiple accounts, the ranking results corresponding to the accounts are the same, and the ranking result account attribute sequence composed of the ranking results corresponding to the account attribute data is not a continuous data account attribute sequence. For example, the ranking result account attribute sequence can be (unit: name): 90, 100, 120, 160, 175..., that is, the ranking result is not a continuous sequential account attribute sequence.

[0096] In step S140, the ranking result of the account attribute data is used as the ranking result of at least one account corresponding to the account attribute data.

[0097] In implementation, the computer device can map the ranking results of the account attribute data to the ranking results of each account based on the corresponding relationship between the account attribute data and each account. Then, the computer device can push the ranking results of each account to the corresponding account display page, so that each account can clearly understand its ranking among all accounts.

[0098] Specifically, for each account attribute data, the ranking of each account corresponding to the account attribute data is determined as the ranking of the account attribute data. For example, after the computer device completes the processing of the above step S130, the computer device determines the account attribute data and the ranking result of the account attribute data as shown in Table 2 below:

[0099] Table 2

[0100] Account attribute data Number of accounts (persons) Ranking result(name) 1000 90 90 900 10 100 800 40 140 650 60 160

[0101] As can be seen from Table 2, since the ranking results of account attribute data are determined based on the number of accounts, the ranking results of accounts with the same account attribute data are the same, and the ranking results of accounts are not continuous account attribute sequences. For example, if the account attribute data corresponding to a certain account is 900, then the ranking result corresponding to the account is 100. If the account attribute data corresponding to a certain account is 650, then the ranking result corresponding to the account is 160. It can be seen that the ranking results of all accounts can be determined based on the account attribute data corresponding to the account.

[0102] Optionally, after each data processing to obtain the account ranking results, the ranking results of all accounts can be added with a version identifier (eg, version number) and archived, so as to facilitate querying the ranking results of previous ranking processes based on the version identifier.

[0103] In the above data processing method, a computer device obtains data to be processed. The data to be processed includes account attribute data and the number of accounts corresponding to the account attribute data. Then, the computer device sorts the account attribute data in the data to be processed to obtain an account attribute sequence of the account attribute data. Then, the computer device determines the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and the preset ranking strategy. Finally, the computer device uses the ranking result of the account attribute data as the ranking result of each account. By adopting this method, the ranking result of the account attribute data is determined based on the number of accounts, and then the ranking result of the account attribute data is used as the ranking result of the corresponding account, which reduces the amount of data processing in the data processing process and improves the efficiency of account ranking.

[0104] In an exemplary embodiment, if Figure 2 As shown, in step S130, according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and the preset ranking strategy, determining the ranking result of the account attribute data can be specifically achieved by the following steps:

[0105] In step S131, for the first account attribute data in the account attribute sequence, the number of accounts corresponding to the first account attribute data is determined as the ranking result corresponding to the first account attribute data.

[0106] In implementation, the data values ​​of the account attribute data in the account attribute sequence of the account attribute data are arranged in descending order. The computer device can determine the ranking result corresponding to each account attribute data in sequence. Among them, different ranking strategies can be adopted for account attribute data with different sorting orders. Therefore, the computer device can determine the ranking strategy corresponding to the account attribute data according to the sorting order of the account attribute data, and thus determine the ranking result of the account attribute data based on the ranking strategy.

[0107] Specifically, for the first account attribute data in the account attribute sequence (i.e., the largest account attribute data in the account attribute sequence), the computer device may determine the number of accounts corresponding to the first account attribute data as the ranking result corresponding to the first account attribute data. For example, in the account attribute sequence of account attribute data, 1000 is the first account attribute data in the account attribute sequence (i.e., the largest account attribute data), and the number of accounts corresponding to the account attribute data 1000 is 90, then the ranking result of the account attribute data 1000 is 90. For another example, 1010 is the first account attribute data in the account attribute sequence (i.e., the largest account attribute data), and the number of accounts corresponding to the account attribute data 1010 is 60, then the ranking result of the account attribute data 1010 is 60.

[0108] In step S132, for each other account attribute data except the first account attribute data in the account attribute sequence, the ranking result of the current other account attribute data is determined according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence.

[0109] In implementation, for other account attribute data except the first account attribute data in the account attribute sequence, the computer device may sequentially calculate the sum of the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data, and determine the sum as the ranking result of the other account attribute data. The previous account attribute data is the account attribute data that ranks before the other account attribute data in the account attribute sequence.

[0110] For example, if the account attribute data 900 is other account attribute data except the first account attribute data, the number of accounts corresponding to the account attribute data 900 is 10, and the ranking result corresponding to the previous account attribute data 1000 is 90, then the computer device calculates the sum of the number of accounts corresponding to the current account attribute data 900 and the ranking result of the previous account attribute data as 10+90=100. Then, the computer device determines the calculated sum of 100 as the ranking result of the account attribute data 900, that is, 100.

[0111] In this embodiment, when the computer device sorts the account attribute data, it determines the ranking result of the account attribute data based on the number of accounts corresponding to the account attribute data and the arrangement order of the account attribute data in the account attribute sequence, simplifies the processing logic of ranking the account attribute data one by one, reduces the data processing volume, and improves data processing efficiency.

[0112] In an exemplary embodiment, if Figure 3 As shown, in addition to relying on the ranking results of accounts regularly pushed by computer devices, each account can also actively query the current ranking results during the business execution process. Therefore, the data processing method also includes:

[0113] In step S310, a ranking query table is obtained according to the attribute data of each account and the corresponding ranking results.

[0114] In implementation, the computer device determines the ranking result corresponding to each account attribute data, and can establish a corresponding relationship between the account attribute data and the ranking result to obtain a ranking query table (also called userRankMap). Specifically, the ranking query table is shown in Table 3 below:

[0115] Table 3

[0116] Account attribute data Ranking result(name) 1000 90 900 100 … …

[0117] In the ranking query table (ie, Table 3), for each acquired account attribute data of any target account, there is a corresponding ranking result, and the data values ​​of the ranking results are not continuous, for example, there is no 99th place.

[0118] In step S320, in response to the ranking query request for the target account, the ranking result of the target account is determined in the ranking query table according to a preset ranking table query strategy and the current account attribute data corresponding to the target account.

[0119] In implementation, the computer device receives a ranking query request from a target account, and obtains the current account attribute data of the target account according to the ranking query request. In response to the ranking query request, the computer device determines the ranking result of the target account in the ranking query table according to a preset ranking table query strategy and the current account attribute data of the target account with a time complexity of O(1).

[0120] In this embodiment, the target account actively queries the ranking results by initiating a ranking query request, and then the computer device constructs a ranking query table based on the ranking results of the determined account attribute data, so that based on the ranking query table, the target account can more quickly query the ranking results of the current account attribute data, thereby reducing the complexity of the ranking query and enhancing the timeliness of the ranking result query.

[0121] Since the target account is in a data processing cycle when querying (i.e., the time interval between the last ranking result push and the next ranking result push), during this data processing cycle, the current account attribute data of the target account may have changed compared to the account attribute data when the ranking query table was constructed. Therefore, in the current ranking query table, when querying the ranking results based on the changed account attribute data, it may happen that the current account attribute data cannot be queried in the current ranking query table (i.e., the current ranking query table does not contain the changed account attribute data). Therefore, the preset ranking table query strategy includes a method for filling the ranking query table so that the updated ranking query table can contain the updated account attribute data of the account. Accordingly, if Figure 4 As shown, in step S320, according to the preset ranking table query strategy and the current account attribute data corresponding to the target account, determining the ranking result of the target account in the ranking query table can be specifically achieved by the following steps:

[0122] In step S321 , in response to a ranking query request for a target account, a query is made as to whether current account attribute data of the target account exists in the ranking query table.

[0123] In implementation, the computer device queries whether there is current account attribute data of the target account in the ranking query table in response to a ranking query request for the target account.

[0124] Step S322: If the new account attribute data does not exist, fill in the data interval of any adjacent account attribute data in the ranking query table with the new account attribute data.

[0125] The new account attribute data is obtained by taking two adjacent account attribute data as the limit and taking the value within the data interval according to the preset numerical interval.

[0126] In implementation, if it does not exist, the computer device fills new account attribute data in the data interval composed of any two adjacent account attribute data in the ranking query table according to the preset ranking table query strategy and the preset data value interval. For example, the preset data value interval is 1, and two adjacent account attribute data in the ranking query table are 1000 and 900 respectively. Then, after the computer device fills the data interval composed of these two account attribute data according to the data value interval of 1, the data interval becomes [1000, 999, 998, 997, 996, 995, 994, ... 901, 900]. The new account attribute data filled in this data interval is 999, 998, 997 ... 901.

[0127] The setting of the preset data value interval may be pre-set based on the distribution characteristics of the current account attribute data, which is not limited in the embodiments of the present disclosure.

[0128] In step S323, the ranking result corresponding to the smaller account attribute data of the two account attribute data is determined as the ranking result corresponding to the new account attribute data, and an updated ranking query table is obtained.

[0129] In implementation, the computer device determines the ranking result corresponding to the smaller account attribute data in the boundary value of the data interval corresponding to each pair of account attribute data as the ranking result corresponding to the new account attribute data, thereby obtaining an updated ranking query table. Specifically, for example, in the filled data interval [1000, 999, 998, 997, 996, 995, 994, ... 901, 900], 999, 998, 997 ... 901, etc. are all new account attribute data, and the ranking result of the new account attribute data is determined as the ranking result corresponding to the account attribute data 900, that is, the ranking results corresponding to the account attribute data 999 to 900 are all 100, thereby obtaining an updated ranking query table. As shown in Table 4 below:

[0130] Table 4

[0131]

[0132]

[0133] Among them, Table 4 shows only a portion of the ranking query table filled with numerical intervals in the data interval [1000,900]. For other data intervals of account attribute data that are spaced between two of each other in the ranking query table, they are also filled according to the preset numerical intervals, and the ranking results of the new account attribute data are determined. The disclosed embodiment will not be repeated. When the data intervals of the original account attribute data in all ranking query tables are filled, the computer device obtains an updated complete ranking query table.

[0134] In step S324, in the updated ranking query table, the ranking result of the target account is determined according to the current account attribute data corresponding to the target account.

[0135] In implementation, the computer device determines the ranking result of the target account in the updated ranking query table according to the current account attribute data corresponding to the target account. For example, if the current account attribute data of the target account corresponds to 950, then based on the current account attribute data 950, the computer device finds that the ranking result of the target account is 100 in the updated ranking query table.

[0136] Among them, the ranking result obtained by the target account initiating a ranking query request can also be considered as the current temporary ranking result of the target account. The temporary ranking result queried during the periodic data processing process can be further refreshed and replaced by the ranking result of the account pushed next time.

[0137] In this embodiment, in view of the situation where the current account attribute data of the target account does not exist in the ranking query table, a ranking result query method for filling the ranking query table is provided. Through this method, the ranking query table is filled, and the ranking query table data query function is provided for the target account, thereby improving the success rate of the ranking query.

[0138] In an exemplary embodiment, if Figure 5 As shown, when the accounts are ranked, in addition to obtaining the ranking result of each account, the level of each account can also be determined based on the ranking of all accounts. Furthermore, the data processing method further includes:

[0139] In step S510, a preset target ranking threshold is obtained, and an attribute data threshold is determined according to the target ranking threshold and the ranking result of the account attribute data.

[0140] In implementation, the computer device obtains a preset target ranking threshold. The target ranking threshold can be set according to actual needs and is used to divide the level corresponding to each account. Specifically, when the account attribute data of each account keeps changing dynamically, the target ranking threshold can be fixed. Therefore, even if the account attribute data corresponding to the target ranking threshold keeps changing, the level determined by the target ranking threshold will not change relative to the ranking distribution of all accounts, which complies with the principle that the account level ratio remains unchanged. Therefore, the computer device pre-sets the target ranking threshold, determines the attribute data threshold corresponding to the target ranking threshold, and then divides the level of all accounts under the current account attribute data based on the attribute data threshold. For example, the target ranking threshold is 100, as shown in Table 1, and the computer device determines the account attribute data with a ranking result equal to 100 in the corresponding relationship between the account attribute data and the ranking result contained in Table 1, that is, account attribute data 900, and determines the account attribute data 900 as the attribute data threshold.

[0141] Optionally, the number of target ranking thresholds may be one or more, and the specific number may be set according to actual grading requirements. The embodiment of the present disclosure does not limit the number of target ranking thresholds. In the case where there are multiple target ranking thresholds, the computer device may determine the attribute data threshold corresponding to each target ranking threshold through step S510, so as to subsequently grade the account.

[0142] In step S520, in response to the level query request for the target account, the level corresponding to the target account is determined according to the current account attribute data and the attribute data threshold of the target account.

[0143] In implementation, in response to a level query request for a target account, the computer device can obtain the current account attribute data of the target account, and then compare the current account attribute data with the attribute data threshold to determine the level of the target account. For example, the current account attribute data of the target account is 950, and the computer device divides all the account attribute data into three levels according to the attribute data thresholds corresponding to each level that have been determined (900 and 1001, respectively), and less than 900 is divided into the "LAST" level; greater than or equal to 900 and less than 1000 is divided into the "SECOND" level; and greater than or equal to 1001 is divided into the "TOP" level. Furthermore, the computer device compares the current account attribute data 950 with the attribute data thresholds corresponding to each level, and determines that the current level of the target account is the "SECOND" level.

[0144] In this embodiment, a ranking level query method is provided, through which a ranking level query service is provided for a target account.

[0145] In an exemplary embodiment, if Figure 6 As shown, in step S520, according to the current account attribute data and the attribute data threshold of the target account, determining the level corresponding to the target account can be specifically achieved by the following steps:

[0146] In step S521, based on the target ranking threshold, it is queried in the ranking results of the account attribute data whether there is a target ranking result that is the same as the target ranking threshold.

[0147] In practice, since there is discontinuity between the ranking results of each account attribute data in the embodiment of the present disclosure, it may be impossible to directly query the target ranking threshold in each ranking result. Therefore, when the computer device obtains the preset target ranking threshold, it will first query whether there is a target ranking result that is the same as the target ranking threshold in the ranking results of the account attribute data according to the target ranking threshold.

[0148] For example, if a preset target ranking threshold is 100, then there is a target ranking result that is the same as the target ranking threshold in the ranking results of each account attribute data shown in Table 1. If a preset target ranking threshold is 10, then there is no target ranking result that is the same as the target ranking threshold in the ranking results of each account attribute data shown in Table 1. Therefore, corresponding to these two different situations, the computer device can respectively execute two different ranking determination strategies.

[0149] In step S522, if there is a target ranking result that is the same as the target ranking threshold, the account attribute data corresponding to the target ranking result is used as the attribute data threshold corresponding to the target ranking threshold.

[0150] In implementation, if there is a target ranking result that is the same as the target ranking threshold, the computer device directly determines the account attribute data corresponding to the target ranking result. Furthermore, the computer device uses the account attribute data corresponding to the target ranking result as the attribute data threshold of the level that is less than or equal to the target ranking threshold. For example, for a preset target ranking threshold of 100 in the above step S610, there is a target ranking result that is the same as the target ranking threshold in the ranking results of each account attribute data shown in Table 1. The account attribute data corresponding to the target ranking result is 900, and the computer device uses 900 as the attribute data threshold of the level that is less than or equal to 100 (target ranking threshold).

[0151] In step S523, if there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, the minimum ranking result is determined as the target ranking result, and the account attribute data corresponding to the target ranking result is added with a fixed threshold as the attribute data threshold corresponding to the target ranking threshold.

[0152] In implementation, if there is no target ranking result that is the same as the target ranking threshold, the computer device may first determine the ranking result that is greater than the target ranking threshold among the ranking results corresponding to all account attribute data, and then determine the minimum ranking result among these ranking results as the target ranking result. For example, when a preset target ranking threshold is 10, there is no target ranking result that is the same as the target ranking threshold among the ranking results of each account attribute data shown in Table 3, and each ranking result shown in Table 3 is greater than the preset target ranking threshold, then the computer device determines the smallest ranking result among all ranking results greater than 10, that is, 90th, as the target ranking result. Then, the computer device adds a fixed threshold to the account attribute data corresponding to the target ranking result (90th) as the attribute data threshold of the level that is less than or equal to the target ranking threshold. For example, after determining that 90 is the target ranking result, the account attribute data 1000 corresponding to 90 (target ranking result) is added to a fixed threshold (for example, the fixed threshold is 1), and the sum of the two is equal to 1001. The computer device then uses 1001 as the attribute data threshold for the level that is less than or equal to the target ranking threshold (10).

[0153] In an exemplary embodiment, if Figure 7 As shown, in step S130, the specific process of obtaining the data to be processed can be implemented by the following steps:

[0154] In step S710, the account attribute data of all accounts are obtained, and the account attribute data of all accounts are aggregated to obtain account groups containing accounts with the same account attribute data.

[0155] In implementation, the computer device uses a multi-threaded scanning method to obtain the account attribute data of all accounts, and aggregates the account attribute data of all accounts to divide the accounts with the same account attribute data into the same account group. For example, if the account attribute data of multiple accounts are all 1000, then the multiple accounts are determined to be one account group. Similarly, if the account attribute data of multiple accounts is 900, then the multiple accounts are determined to be another account group.

[0156] Optionally, the computer device pre-sets a data adoption cycle, and then the computer device can periodically obtain account attribute data of all accounts based on the adoption cycle.

[0157] In step S720, the number of accounts included in each account group is counted, and the account attribute data of each account group and the number of accounts corresponding to the account attribute data are used as data to be processed.

[0158] In implementation, the computer device counts the number of accounts contained in each account group. For example, in the account group with the same account attribute data of 1000, the number of accounts is 90, and in the account group with the same account attribute data of 900, the number of accounts is 10. Then, the computer device uses the account attribute data of each account group and the number of accounts corresponding to the account attribute data as the data to be processed to rank the accounts in this round and obtain the ranking results of each account.

[0159] It should be understood that although Figure 1-Figure 7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 7 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0160] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0161] Figure 8 is a device block diagram according to an exemplary embodiment. Figure 8 The device includes an acquisition unit 801, a sorting unit 802, a determination unit 803 and a matching unit 804.

[0162] The acquisition unit 801 is configured to acquire the data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data.

[0163] The sorting unit 802 is configured to sort each account attribute data to obtain an account attribute sequence of the account attribute data.

[0164] The determining unit 803 is configured to determine the ranking result of the account attribute data for each account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy.

[0165] The matching unit 804 is configured to execute the ranking result of the account attribute data as the ranking result of at least one account corresponding to the account attribute data.

[0166] In an exemplary embodiment, the determining unit 803 further includes:

[0167] The first determining subunit is configured to execute, for the first account attribute data in the account attribute sequence, determining the number of accounts corresponding to the first account attribute data as a ranking result corresponding to the first account attribute data;

[0168] The second determination subunit is configured to determine the ranking result of the current other account attribute data according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence for each other account attribute data except the first account attribute data in the account attribute sequence.

[0169] In an exemplary embodiment, the data processing device 800 further includes:

[0170] A construction unit is configured to execute, according to each account attribute data and the corresponding ranking result, to obtain a ranking query table;

[0171] The query unit is configured to execute, in response to a ranking query request for a target account, and determine a ranking result of the target account in a ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account.

[0172] In an exemplary embodiment, the query unit further includes:

[0173] A first query subunit is configured to execute, in response to a ranking query request for a target account, query whether there is current account attribute data of the target account in the ranking query table;

[0174] The filling subunit is configured to fill in new account attribute data in a data interval of any adjacent account attribute data in the ranking query table according to a preset numerical interval if the new account attribute data does not exist; the new account attribute data is obtained by taking values ​​in the data interval according to the preset numerical interval with two adjacent account attribute data as the limit;

[0175] A third determining subunit is configured to determine the ranking result corresponding to the smaller account attribute data of the two account attribute data as the ranking result corresponding to the new account attribute data, and obtain an updated ranking query table;

[0176] The fourth determining subunit is configured to execute in the updated ranking query table, and determine the ranking result of the target account according to the current account attribute data corresponding to the target account.

[0177] In an exemplary embodiment, the data processing apparatus further includes:

[0178] An acquisition and determination unit is configured to execute acquisition of a preset target ranking threshold, and determine an attribute data threshold according to the target ranking threshold and a ranking result of the account attribute data;

[0179] The level determination unit is configured to execute, in response to a level query request for a target account, and determine the level corresponding to the target account according to current account attribute data and an attribute data threshold of the target account.

[0180] In an exemplary embodiment, the obtaining and determining unit further includes:

[0181] A query determination subunit is configured to execute, according to the target ranking threshold, a query in the ranking results of the account attribute data to determine whether there is a target ranking result that is the same as the target ranking threshold;

[0182] A fifth determining subunit is configured to execute, if there is a target ranking result that is the same as the target ranking threshold, using the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold;

[0183] The sixth determination subunit is configured to execute, if there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, determine the minimum ranking result as the target ranking result, and add a fixed threshold to the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold.

[0184] In an exemplary embodiment, the acquiring unit 801 includes:

[0185] An acquisition subunit is configured to acquire account attribute data of all accounts, and aggregate the account attribute data of all accounts to obtain account groups of accounts containing the same account attribute data;

[0186] The statistical subunit is configured to perform statistics on the number of accounts included in each account group, and associate the account attribute data of each account group with the number of accounts corresponding to the account attribute data to obtain data to be processed.

[0187] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0188] Fig. 9 1 is a block diagram of an electronic device 900 according to an exemplary embodiment. For example, the electronic device 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0189] Reference Fig. 9 , the electronic device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0190] The processing component 902 generally controls the overall operation of the electronic device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 902 may include one or more modules to facilitate the interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate the interaction between the multimedia component 908 and the processing component 902.

[0191] The memory 904 is configured to store various types of data to support operations on the electronic device 900. Examples of such data include instructions for any application or method operating on the electronic device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, an optical disk, or a graphene memory.

[0192] The power supply component 906 provides power to the various components of the electronic device 900. The power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 900.

[0193] The multimedia component 908 includes a screen that provides an output interface between the electronic device 900 and the account. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the account. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the electronic device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0194] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the electronic device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting an audio signal.

[0195] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0196] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the electronic device 900. For example, the sensor assembly 914 can detect the open / closed state of the electronic device 900, the relative positioning of the components, such as the display and keypad of the electronic device 900, and the sensor assembly 914 can also detect the position change of the electronic device 900 or the components of the electronic device 900, the presence or absence of contact with the electronic device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the electronic device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0197] The communication component 916 is configured to facilitate wired or wireless communication between the electronic device 900 and other devices. The electronic device 900 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0198] In an exemplary embodiment, the electronic device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0199] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the above instructions can be executed by the processor 920 of the electronic device 900 to perform the above method. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0200] In an exemplary embodiment, a computer program product is further provided. The computer program product includes instructions. The instructions can be executed by the processor 920 of the electronic device 900 to complete the above method.

[0201] In an exemplary embodiment, a computer program product is further provided. The computer program product includes instructions. The instructions can be executed by a processor of the electronic device S00 to complete the above method.

[0202] It should be noted that the above-mentioned devices, electronic devices, computer-readable storage media, computer program products, etc. may also include other implementation methods according to the description of the method embodiments. The specific implementation methods can refer to the description of the relevant method embodiments, which will not be described one by one here.

[0203] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0204] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data; Sorting each of the account attribute data to obtain an account attribute sequence; For each of the account attribute data, determining a ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy; Using the ranking result of the account attribute data as the ranking result of at least one account corresponding to the account attribute data; The determining the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy includes: For the first account attribute data in the account attribute sequence, determining the number of accounts corresponding to the first account attribute data as a ranking result corresponding to the first account attribute data; For each other account attribute data except the first account attribute data in the account attribute sequence, the ranking result of the current other account attribute data is determined according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence.

2. The data processing method according to claim 1, characterized in that: After determining the ranking result of the account attribute data according to the arrangement order of the account attribute data in the account attribute sequence, the number of accounts corresponding to the account attribute data, and a preset ranking strategy, the method further includes: Obtaining a ranking query table according to each of the account attribute data and the corresponding ranking results; In response to a ranking query request for a target account, a ranking result of the target account is determined in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account.

3. The data processing method according to claim 2, characterized in that: The step of responding to the ranking query request for the target account and determining the ranking result of the target account in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account includes: In response to a ranking query request for a target account, querying whether there is current account attribute data of the target account in the ranking query table; If the account attribute data does not exist, fill in the data interval of any adjacent account attribute data in the ranking query table with new account attribute data; the new account attribute data is obtained by taking values ​​in the data interval according to the preset numerical interval with two adjacent account attribute data as the limit; Determine the ranking result corresponding to the smaller account attribute data of the two account attribute data as the ranking result corresponding to the new account attribute data, and obtain an updated ranking query table; In the updated ranking query table, the ranking result of the target account is determined according to the current account attribute data corresponding to the target account.

4. The data processing method according to claim 1, characterized in that: After using the ranking result of the account attribute data as the ranking result of the account corresponding to the account attribute data, the method further includes: Obtaining a preset target ranking threshold, and determining an attribute data threshold according to the target ranking threshold and the ranking result of the account attribute data; In response to a level query request for a target account, a level corresponding to the target account is determined according to current account attribute data of the target account and the attribute data threshold.

5. The data processing method according to claim 4, characterized in that: The determining of the attribute data threshold according to the target ranking threshold and the ranking result of the account attribute data includes: According to the target ranking threshold, querying whether there is a target ranking result that is the same as the target ranking threshold in the ranking results of the account attribute data; If there is a target ranking result that is the same as the target ranking threshold, the account attribute data corresponding to the target ranking result is used as the attribute data threshold corresponding to the target ranking threshold; If there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, the minimum ranking result is determined as the target ranking result, and the account attribute data corresponding to the target ranking result is added with a fixed threshold as the attribute data threshold corresponding to the target ranking threshold.

6. The data processing method according to claim 1, characterized in that: The obtaining of the data to be processed comprises: Acquire account attribute data of all accounts, and aggregate the account attribute data of all accounts to obtain account groups containing accounts with the same account attribute data; The number of accounts included in each of the account groups is counted, and the account attribute data of each of the account groups and the number of accounts corresponding to the account attribute data are associated to obtain the data to be processed.

7. A data processing device, characterized in that: The device comprises: An acquisition unit is configured to acquire data to be processed; the data to be processed includes at least one account attribute data and the number of accounts corresponding to each account attribute data; a sorting unit, configured to sort each of the account attribute data to obtain an account attribute sequence of the account attribute data; a determining unit configured to determine, for each of the account attribute data, a ranking result of the account attribute data according to an arrangement order of the account attribute data in the account attribute sequence, a number of accounts corresponding to the account attribute data, and a preset ranking strategy; a matching unit configured to use the ranking result of the account attribute data as the ranking result of at least one account corresponding to the account attribute data; The determining unit further includes: A first determining subunit is configured to execute, for a first account attribute data in the account attribute sequence, determining the number of accounts corresponding to the first account attribute data as a ranking result corresponding to the first account attribute data; The second determination subunit is configured to determine the ranking result of the current other account attribute data for each other account attribute data except the first account attribute data in the account attribute sequence according to the number of accounts corresponding to each other account attribute data and the ranking result corresponding to the previous account attribute data of the current other account attribute data in the account attribute sequence.

8. The data processing device according to claim 7, characterized in that: The data processing device further comprises: A construction unit is configured to obtain a ranking query table according to each of the account attribute data and the corresponding ranking result; The query unit is configured to execute a ranking query request for a target account and determine a ranking result of the target account in the ranking query table according to a preset ranking table query strategy and current account attribute data corresponding to the target account.

9. The data processing device according to claim 8, characterized in that: The query unit also includes: A first query subunit is configured to execute, in response to a ranking query request for a target account, query whether there is current account attribute data of the target account in the ranking query table; A filling subunit is configured to fill in new account attribute data in a data interval of any adjacent account attribute data in the ranking query table if such data does not exist; the new account attribute data is obtained by taking values ​​in the data interval according to the preset numerical interval with two adjacent account attribute data as the limit; A third determining subunit is configured to determine the ranking result corresponding to the smaller account attribute data of the two account attribute data as the ranking result corresponding to the new account attribute data, to obtain an updated ranking query table; The fourth determining subunit is configured to execute in the updated ranking query table, and determine the ranking result of the target account according to the current account attribute data corresponding to the target account.

10. The data processing device according to claim 7, characterized in that: The data processing device further comprises: an acquisition and determination unit, configured to execute acquisition of a preset target ranking threshold, and determine an attribute data threshold according to the target ranking threshold and the ranking result of the account attribute data; The level determination unit is configured to execute, in response to a level query request for a target account, determine the level corresponding to the target account according to current account attribute data of the target account and the attribute data threshold.

11. The data processing device according to claim 10, characterized in that: The acquisition and determination unit further includes: A query determination subunit is configured to execute, according to the target ranking threshold, querying whether there is a target ranking result that is the same as the target ranking threshold in the ranking results of the account attribute data; A fifth determining subunit is configured to execute, if there is a target ranking result that is the same as the target ranking threshold, using the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold; The sixth determination subunit is configured to execute, if there is no target ranking result that is the same as the target ranking threshold, then among all ranking results that are greater than the target ranking threshold, determine the minimum ranking result as the target ranking result, and add a fixed threshold to the account attribute data corresponding to the target ranking result as the attribute data threshold corresponding to the target ranking threshold.

12. The data processing device according to claim 7, characterized in that: The acquisition unit comprises: an acquisition subunit, configured to acquire account attribute data of all accounts, and aggregate the account attribute data of all accounts to obtain account groups containing accounts with the same account attribute data; The statistical subunit is configured to perform statistical analysis on the number of accounts included in each of the account groups, and associate the account attribute data of each of the account groups with the number of accounts corresponding to the account attribute data to obtain the data to be processed.

13. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the data processing method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method according to any one of claims 1 to 6.

15. A computer program product, comprising instructions, characterized in that: When the instruction is executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method as described in any one of claims 1 to 6.

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