Password application transaction data management method and system, electronic equipment and storage medium

Abstract

The invention discloses a password application transaction data management method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining current moment data, and determining a target timing task from preset timing tasks according to the current moment data; the preset timing task comprises data statistics tasks of different time dimensions; executing a corresponding data statistics task according to the target timed task, and generating a transaction data table; the transaction data table corresponds to the data statistics task; acquiring a data query request, wherein the data query request comprises a data query time period; splitting the data query time period through a preset splitting rule to obtain target time period data; the preset splitting rule is constructed according to data statistics tasks of different time dimensions; and querying from the corresponding transaction data table according to the target time period data to obtain target transaction data. According to the embodiment of the invention, password application transaction data management can be realized, and the transaction data query efficiency is improved. The method can be widely applied to the technical field of data management.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular to a method, system, electronic device and storage medium for managing cryptographic application transaction data. Background Art

[0002] Cryptographic applications usually exist in the form of service interfaces, such as data encryption and data decryption service interfaces. A collection of multiple interfaces is called a cryptographic application. Among them, a request and call process of each service interface can be called a transaction, and the data generated in this process is called transaction data. Since these transaction data are usually accompanied by various sensitive data, it is necessary to monitor, manage and analyze these transaction data to understand the operating status of related cryptographic applications in real time. However, in the related art, the amount of cryptographic application transaction data is relatively large, and the efficiency of data query management is low. For example, when performing queries with a large time span, the query request is relatively slow, and even query failure may occur.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a cryptographic application transaction data management method, system, electronic device and storage medium, which can realize cryptographic application transaction data management and effectively improve the efficiency of transaction data query.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application provides a method for managing cryptographic application transaction data, the method comprising the following steps:

[0006] Acquire current time data to determine a target scheduled task from preset scheduled tasks according to the current time data; wherein the preset scheduled tasks include data statistics tasks of different time dimensions;

[0007] Execute corresponding data statistics tasks according to the target timing tasks to generate a transaction data table; wherein the transaction data table corresponds to the data statistics tasks;

[0008] Obtaining a data query request; wherein the data query request includes a data query time period;

[0009] The data query time period is split according to a preset splitting rule to obtain target time period data; wherein the preset splitting rule is constructed according to the data statistical tasks of different time dimensions;

[0010] The target transaction data is obtained by querying from the corresponding transaction data table according to the target time period data.

[0011] In some embodiments, the obtaining of current time data to determine a target scheduled task from preset scheduled tasks according to the current time data includes:

[0012] When it is determined that the time interval between the current time data and the historical execution time data is greater than the first interval, the first scheduled task in the preset scheduled tasks is determined as the target scheduled task; wherein the first scheduled task includes a second statistics task;

[0013] Alternatively, when it is determined that the time interval between the current moment data and the historical execution time data is greater than a second interval, the second scheduled task in the preset scheduled task is determined as the target scheduled task; wherein, the second interval is greater than the first interval, and the second scheduled task includes a sub-statistical task and an hour-statistical task.

[0014] In some embodiments, the step of executing a corresponding data statistics task according to the target scheduled task to generate a transaction data table includes:

[0015] When it is determined that the target scheduled task is the first scheduled task, the transaction data of the first time period is queried, and data statistics are performed every second to update the second data table.

[0016] In some embodiments, the performing of corresponding data statistics tasks according to the target scheduled tasks to generate a transaction data table further includes:

[0017] When it is determined that the target scheduled task is the second scheduled task, it is determined whether the time interval between the current time data and the deadline of the first statistical task is greater than a third interval; wherein the deadline of the first statistical task is determined by the previous execution of the sub-statistical task;

[0018] When it is determined that the time interval between the current moment data and the first statistical task deadline is greater than the third interval, query the first statistical data from the first statistical task deadline to the first target deadline in the second data table to update the sub-data table by using the first statistical data; wherein the first target deadline is determined by adding the first statistical task deadline to the first statistical duration;

[0019] Updating the first statistical task deadline according to the first target deadline;

[0020] When it is determined that the time interval between the updated first statistical task deadline and the second statistical task deadline is greater than a fourth interval, query the second statistical data from the second statistical task deadline to the second target deadline in the sub-data table to update the data table with the second statistical data; wherein the second target deadline is determined by adding the second statistical task deadline to the second statistical duration;

[0021] The second statistical task deadline is updated according to the second target deadline.

[0022] In some embodiments, the data query time period is split according to a preset splitting rule to obtain target time period data, including:

[0023] When it is determined that the data query time period is less than the second statistical time length, a first splitting task is determined according to the query deadline, query start time and the first statistical task deadline of the data query time period, so as to split the data query time period through the first splitting task to obtain the target time period data; wherein the first splitting task includes a minute time splitting task;

[0024] When it is determined that the data query time period is greater than the second statistical time length, a second splitting task is determined according to the query deadline, the query start time, the first statistical task deadline and the second statistical task deadline, so as to split the data query time period through the second splitting task to obtain the target time period data; wherein the second splitting task includes the minute time splitting task and the hour time splitting task.

[0025] In some embodiments, the method further comprises:

[0026] Get system restart time;

[0027] When it is determined that the time interval between the system restart time and the first statistical task deadline is greater than or equal to a fourth interval, a preset restart statistical task is executed, and the first statistical task deadline is updated; wherein the preset restart statistical task includes the sub-statistical task and the hourly statistical task;

[0028] When it is determined that the time interval between the system restart time and the updated first statistical task deadline is less than a fifth interval, updating the first statistical task deadline according to the system restart time;

[0029] The hourly statistical task is executed according to the updated deadline of the first statistical task.

[0030] In some embodiments, the method further comprises:

[0031] Determine the preset supplementary time information according to the transaction data time information in the historical database; wherein the preset supplementary time information includes the starting time of the minute supplementary statistical task and the starting time of the hour supplementary statistical task;

[0032] Obtaining preset supplementary deadline information; wherein the preset supplementary deadline information includes a minute supplementary statistical task deadline and an hour supplementary statistical task deadline;

[0033] Constructing a supplement time list according to the preset supplement time information and the preset supplement deadline time information;

[0034] The preset supplementary statistical task is executed according to the supplementary time list; wherein the preset supplementary statistical task includes at least one of the second statistical task, the minute statistical task and the hour statistical task.

[0035] To achieve the above purpose, another aspect of the embodiment of the present application provides a cryptographic application transaction data management system, the system comprising:

[0036] The first module is used to obtain current time data to determine a target scheduled task from preset scheduled tasks according to the current time data; wherein the preset scheduled tasks include data statistics tasks of different time dimensions;

[0037] The second module is used to execute the corresponding data statistics task according to the target scheduled task to generate a transaction data table; wherein the transaction data table corresponds to the data statistics task;

[0038] The third module is used to obtain a data query request; wherein the data query request includes a data query time period;

[0039] The fourth module is used to split the data query time period according to a preset splitting rule to obtain target time period data; wherein the preset splitting rule is constructed according to the data statistical tasks of different time dimensions;

[0040] The fifth module is used to query and obtain target transaction data from the corresponding transaction data table according to the target time period data.

[0041] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising:

[0042] at least one processor;

[0043] at least one memory for storing at least one program;

[0044] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0045] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0046] The embodiments of the present application at least include the following beneficial effects: The present application provides a method, system, electronic device and storage medium for managing cryptographic application transaction data. The scheme obtains the current time data to determine the target timed task from the preset timed task according to the current time data, and then executes the corresponding data statistics task according to the target timed task to generate a transaction data table. Among them, the preset timed task in the embodiment of the present invention includes data statistics task transaction data tables of different time dimensions corresponding to the data statistics task. Further, the embodiment of the present invention obtains a data query request, including a data query time period. Then, the embodiment of the present invention splits the data query time period by the preset splitting rules constructed according to the data statistics task of different time dimensions to obtain the target time period data, and then queries the target transaction data from the corresponding transaction data table according to the target time period data, thereby realizing the management of cryptographic application transaction data. It is easy to understand that the embodiment of the present invention generates the corresponding transaction data table by executing the data statistics tasks of different time dimensions corresponding to the target timed task, and then splits the data query time period by the different time dimensions corresponding to the data statistics task when performing data query, so as to perform data query according to the split target time period, thereby effectively improving the efficiency of transaction data query. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a cryptographic application transaction data management method provided by an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of a process for executing a first scheduled task provided by an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of a flow chart of executing a second scheduled task provided by an embodiment of the present invention;

[0050] Figure 4 It is a flowchart of executing a divided statistical task and a time statistical task provided by an embodiment of the present invention;

[0051] Figure 5 It is a flow chart of a shutdown and restart supplementary statistics mechanism provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural diagram of a cryptographic application transaction data management system provided by an embodiment of the present invention;

[0053] Figure 7 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0055] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0056] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0059] Cryptographic application transaction data: refers to a series of data generated by a cryptography-related application when users call the transaction service interface it provides, such as the total number of transactions, the number of failed transactions, the number of transactions per second (TPS), the round-trip time (RTT) of a single transaction, and the "quantity ranking chart for a single attribute", transaction trend chart, and round-trip time trend chart for a single transaction that can be produced after statistical analysis of the request data.

[0060] Downsampling: It is a technology widely used in fields such as data processing and analysis. It is generally used to process large-scale data sets. By selecting a part of the samples from the original data set, a smaller data set can be obtained. This data set is used instead of the overall data for processing and analysis. Downsampling can help reduce computing time and storage space.

[0061] Scheduled task: refers to a task that performs a specified operation after a certain period of time.

[0062] Intense minute and zero second: refers to a time where the second digit is fixed at 00 and the minute digit is one of 00, 10, 20, 30, 40, and 50.

[0063] Zero minutes and zero seconds: refers to a time where both the minute and description are fixed at 00.

[0064] Query time: refers to a query time period. Generally, the strategy of "including the first and excluding the last" is adopted, such as 2024-08-28T10:05:35Z to 2024-08-28T10:10:00Z. The queried data should include the data corresponding to the query start time, that is, the data corresponding to 2024-08-28T10:05:35Z, but does not include the data corresponding to the query end time, that is, it does not include the data corresponding to 2024-08-28T10:10:00Z.

[0065] Time comparison: refers to the comparison between two time points. For example, if 2024-08-28T10:05:35Z is later than 2024-08-28T10:10:00Z, the comparison between the two time points is expressed as 2024-08-28T10:05:35Z<2024-08-28T10:10:00Z. Accordingly, by expanding this writing method, the time difference can be expressed by a mathematical expression. For example, if 2024-08-28T10:00:00Z is 10 minutes later than 2024-08-28T10:10:00Z, it can be written as 2024-08-28T10:10:00Z-2024-08-28T10:00:00Z=10 minutes, and the rest is similar.

[0066] Cryptographic applications usually exist in the form of service interfaces, such as data encryption and data decryption service interfaces. A collection of multiple interfaces is called a cryptographic application. Among them, a request and call process of each service interface can be called a transaction, and the data generated in this process is called transaction data. Since these transaction data are usually accompanied by various sensitive data, it is necessary to monitor, manage and analyze these transaction data to understand the operating status of related cryptographic applications in real time. However, in the related art, the amount of cryptographic application transaction data is relatively large, and the efficiency of data query management is low. For example, when performing queries with a large time span, the query request is relatively slow, and even query failure may occur.

[0067] In view of this, a cryptographic application transaction data management method, system, electronic device and storage medium are provided in the embodiments of the present application. The scheme obtains the current time data to determine the target timed task from the preset timed tasks according to the current time data, and then executes the corresponding data statistics task according to the target timed task to generate a transaction data table. Among them, the preset timed tasks in the embodiments of the present invention include several data statistics tasks of different time dimensions, and the transaction data tables correspond to the data statistics tasks. Furthermore, the embodiments of the present invention obtain a data query request, including a data query time period. Then, the embodiments of the present invention split the data query time period through the preset splitting rules determined by different time dimensions to obtain the target time period data, and then query the target transaction data from the corresponding transaction data table according to the target time period data, thereby realizing the management of cryptographic application transaction data and effectively improving the efficiency of transaction data query.

[0068] The cryptographic application transaction data management method provided in the embodiment of the present application relates to the field of data management technology. The cryptographic application transaction data management method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements the cryptographic application transaction data management method, etc., but is not limited to the above forms.

[0069] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0070] Figure 1 is an optional flowchart of the cryptographic application transaction data management method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S150.

[0071] Step S110: obtaining current time data, so as to determine a target scheduled task from preset scheduled tasks according to the current time data, wherein the preset scheduled tasks include data statistics tasks of different time dimensions.

[0072] Step S120: Execute the corresponding data statistics task according to the target scheduled task to generate a transaction data table, wherein the transaction data table corresponds to the data statistics task.

[0073] Step S130: Obtain a data query request, wherein the data query request includes a data query time period.

[0074] Step S140: Split the data query time period according to the preset splitting rules to obtain the target time period data, wherein the preset splitting rules are constructed according to the data statistics tasks of different time dimensions.

[0075] Step S150: Query and obtain target transaction data from the corresponding transaction data table according to the target time period data.

[0076] During the working process of this specific embodiment, the embodiment of the present invention first obtains the current time data to determine the target timed task from the preset timed tasks according to the current time data. Specifically, the preset timed tasks in the embodiment of the present invention include several data statistics tasks of different time dimensions. Among them, the data statistics task in the embodiment of the present invention refers to the task of performing statistics on the cryptographic application transaction data. Correspondingly, the preset timed tasks in the embodiment of the present invention include data statistics tasks of different time dimensions, such as data statistics tasks corresponding to time dimensions such as seconds, minutes, hours, days, weeks or months. Among them, the embodiment of the present invention determines the target timed task from the preset timed tasks by judging whether the corresponding execution cycle or timing condition is reached through the current time data. Then, the embodiment of the present invention executes the corresponding data statistics task according to the target timed task to generate a transaction data table. Specifically, the transaction data table in the embodiment of the present invention corresponds to the data statistics task one by one. For example, if the data estimation task in the embodiment of the present invention is the data statistics of the second time dimension, the generated transaction data table is the second data table. Accordingly, after determining the target timed task, the embodiment of the present invention executes the data statistics task corresponding to the target timed task to perform data statistics on the transaction data according to the corresponding time dimension and generate a transaction data table.

[0077] Further, the embodiment of the present invention obtains a data query request to split the data query time period through a preset splitting rule to obtain the target time period data. Specifically, the data query request in the embodiment of the present invention includes the data query time period, that is, the data query request carries the time period information required to be queried. At the same time, the preset splitting rule in the embodiment of the present invention is constructed according to the data statistical tasks of different time dimensions. It is easy to understand that the present example stores the transaction data according to different time dimensions by executing the data statistical task corresponding to the target timing task during the process of collecting and storing transaction data. Therefore, in the process of performing data query, the embodiment of the present invention first splits the data query time period through the preset splitting rule, thereby dividing the data query time period into time periods of different dimensions to obtain the target time period. Among them, the preset splitting rule corresponds to the time dimension division and data extraction process in the executed data statistical task, so that the divided time period can correspond to each transaction data table. Finally, the embodiment of the present invention obtains the target transaction data by querying from the corresponding transaction data table according to the target time period data. Specifically, in the embodiment of the present invention, after the target time period data is obtained by division, the corresponding transaction data table is determined according to each time period, and then the data of the corresponding time period is queried from these transaction data tables to obtain the target transaction data. For example, the target time period data is divided into second time periods, minute time periods, hour time periods, and day time periods, etc. The embodiment of the present invention queries the transaction data of these time periods from corresponding transaction data tables, such as the second data table, minute data table, hour data table, and day data table, so as to obtain the target transaction data.

[0078] In some embodiments of the present invention, obtaining the current time data to determine the target scheduled task from the preset scheduled tasks according to the current time data includes but is not limited to the following steps:

[0079] When it is determined that the time interval between the current time data and the historical execution time data is greater than the first interval, the first scheduled task in the preset scheduled tasks is determined as the target scheduled task, wherein the first scheduled task includes a second statistics task.

[0080] Alternatively, when it is determined that the time interval between the current time data and the historical execution time data is greater than a second interval, the second scheduled task in the preset scheduled task is determined as the target scheduled task. The second interval is greater than the first interval, and the second scheduled task includes a sub-statistic task and an hour-statistic task.

[0081] In this specific embodiment, the embodiment of the present invention first determines the target scheduled task based on the relationship between the current time data and the historical execution time. Specifically, when it is determined that the time interval between the current time data and the historical execution time data is greater than the first interval, the embodiment of the present invention determines the first scheduled task in the preset scheduled tasks as the target scheduled task. Among them, the historical execution time in the embodiment of the present invention refers to the time when the target scheduled task was last executed. For example, Figure 2 As shown, when it is determined that the current time has exceeded the first interval, such as 10 seconds, from the last execution of the target scheduled task, the embodiment of the present invention uses the first scheduled task in the preset scheduled task as the target scheduled task and executes the corresponding scheduled task content. Wherein, the first scheduled task in the embodiment of the present invention includes a second statistical task, that is, after determining that the time interval between the current time data and the historical execution time data is greater than the first interval, the embodiment of the present invention executes the corresponding second statistical task. Or, when it is determined that the time interval between the current time data and the historical execution time data is greater than the second interval, the embodiment of the present invention determines the second scheduled task in the preset scheduled task as the target scheduled task. Wherein, the second scheduled task in the embodiment of the present invention includes a minute statistical task and an hour statistical task. Exemplarily, when it is determined that the current time data has exceeded the second interval, such as 1 minute, from the last execution, the embodiment of the present invention executes the second scheduled task, including a minute statistical task and an hour statistical task. It is easy to understand that the embodiment of the present invention, by setting the first scheduled task and the second scheduled task, combines the corresponding second statistical task, minute statistical task and hour statistical task, so as to perform three-layer down sampling of the huge data, effectively improving the efficiency of data statistics and management.

[0082] In some embodiments of the present invention, executing corresponding data statistics tasks according to the target timing tasks to generate a transaction data table includes but is not limited to the following steps:

[0083] When the target scheduled task is determined to be the first scheduled task, the transaction data of the first time period is queried, and data statistics are performed every second to update the second data table.

[0084] In this specific embodiment, when it is determined that the target scheduled task is the first scheduled task, the embodiment of the present invention first queries the transaction data of the first time period, and performs data statistics every second, and updates the second data table. Specifically, the second data table in the example of the present invention refers to a data table for storing the transaction data obtained by the second statistics task. Correspondingly, the first time period refers to a pre-set time period for data collection each time the first scheduled task is executed, such as one minute before the current moment. Exemplarily, the embodiment of the present invention executes the first scheduled task every 10 seconds. When it is determined that more than 10 seconds have passed since the last execution of the first scheduled task, the transaction data of the previous 1 minute is queried, and statistics are performed according to 1 second to obtain the corresponding transaction data, such as "Transactions per second (TPS)" and "Average round-trip delay of a single transaction within 1 second". Then, the embodiment of the present invention takes the time of occurrence of the transaction as the time, and writes it into a table specified by the database, such as the service_seconds table, that is, updates the second data table.

[0085] In some embodiments of the present invention, the embodiments of the present invention execute corresponding data statistics tasks according to the target timing tasks to generate a transaction data table, and also include but are not limited to the following steps:

[0086] When the target scheduled task is determined to be the second scheduled task, it is determined whether the time interval between the current time data and the deadline of the first statistical task is greater than a third interval. The deadline of the first statistical task is determined by the previous execution of the sub-statistical task.

[0087] When it is determined that the time interval between the current moment data and the first statistical task deadline is greater than the third interval, the first statistical data from the first statistical task deadline to the first target deadline in the second data table is queried to update the sub-data table with the first statistical data, wherein the first target deadline is determined by adding the first statistical task deadline to the first statistical duration.

[0088] The first statistical task deadline is updated according to the first target deadline.

[0089] When it is determined that the time interval between the updated first statistical task deadline and the second statistical task deadline is greater than the fourth interval, the second statistical data from the second statistical task deadline to the second target deadline in the sub-data table is queried to update the time data table with the second statistical data, wherein the second target deadline is determined by adding the second statistical task deadline to the second statistical duration.

[0090] The second statistical task deadline is updated according to the second target deadline.

[0091] During the working process of this specific embodiment, when it is determined that the target timed task is the second timed task, the embodiment of the present invention executes the corresponding sub-statistical task and hourly statistical task. Specifically, the embodiment of the present invention first determines whether the time interval between the current moment data and the deadline of the first statistical task is greater than the third interval. Accordingly, the deadline of the first statistical task in the embodiment of the present invention refers to the minute statistical task deadline, that is, the deadline of the last sub-statistical task, which is determined by the time of the last execution of the sub-statistical task. Among them, the third interval refers to the execution cycle interval of the sub-statistical task, which is preset. Then, when it is determined that the time interval between the current moment data and the deadline of the first statistical task is greater than the third interval, the embodiment of the present invention queries the first statistical data from the deadline of the first statistical task to the first target deadline in the second data table, thereby updating the sub-data table through the first statistical data. Accordingly, the first target deadline in the embodiment of the present invention is obtained by adding the current first statistical task deadline to the first statistical duration, such as the first target deadline = the first statistical task deadline + 10 minutes. For example, if the time difference between the current moment data and the deadline of the first statistical task is greater than 20 minutes, the sub-statistical task is executed. Among them, the embodiment of the present invention queries the transaction data from the first statistical task deadline to the first statistical task deadline plus 10 minutes (the first target deadline) in the second data table, that is, the first statistical data, and writes these data into the sub-data table. At the same time, the embodiment of the present invention updates the first statistical task deadline according to the first target deadline. For example, the embodiment of the present invention uses the first target deadline as the new first statistical task deadline.

[0092] Further, the embodiment of the present invention determines whether the time interval between the updated first statistical task deadline and the second statistical task deadline is greater than the fourth interval. Wherein, the second statistical task deadline in the embodiment of the present invention refers to the hourly statistical task deadline, that is, the deadline of the last hourly statistical task, which is determined by the time of the last execution of the hourly statistical task. Accordingly, the fourth interval refers to the execution cycle or interval of the hourly statistical task, which is associated with the execution time of the sub-statistical task. Wherein, when it is determined that the time interval between the updated first statistical task deadline and the second statistical task deadline is greater than the fourth interval, the embodiment of the present invention queries the second statistical data from the second statistical task deadline to the second target deadline in the sub-data table, and updates the hourly data table through the second statistical data. Accordingly, in the embodiment of the present invention, the second target deadline is determined by adding the second statistical duration to the second statistical task deadline, such as the second target deadline = the second statistical task deadline + 60 minutes. For example, if the time difference between the latest minute statistical task deadline (the updated first statistical task deadline) and the second statistical task deadline is greater than 60 minutes, the hourly statistical task is executed. The embodiment of the present invention queries the transaction data within the time range from the second statistical task deadline to the second statistical task deadline + 60 minutes from the sub-data table to obtain the second statistical data, and stores the second statistical data in the time data table. At the same time, the embodiment of the present invention updates the second statistical task deadline according to the second target deadline, such as using the second target deadline as the new second statistical task deadline.

[0093] For example, Figure 3 and Figure 4As shown, before executing the second scheduled task, the embodiment of the present invention first performs data initialization. Specifically, the embodiment of the present invention first obtains the deadline of the previous minute statistical task from the specified file mins-task.end to initialize the deadline of the minute statistical task, that is, the deadline of the first statistical task. If the file does not exist, take the previous full minute and zero second time of the current time. For example, if the current time is 2024-08-28T00:00:05Z, then take the value 2024-08-28T00:00:00Z. If the current time is the full minute and zero second time, directly take the current time as the deadline of the first statistical task. At the same time, the embodiment of the present invention obtains the deadline of the previous hourly statistical task from the specified file hrs-task.end to initialize the deadline of the hourly statistical task, that is, the deadline of the second statistical task. If the file does not exist, then take the last zero minute and zero second time of the current time, for example, if the current time is 2024-08-28T00:05:05Z, then take the value 2024-08-28T00:00:00Z, if the current time is zero minute and zero second time, then directly take the current time as the second statistical task deadline. In addition, in the embodiment of the present invention, the minute and hour statistical task deadlines after initialization need to write their respective values ​​into the specified file (mins-task.end and hrs-task.end, respectively) for storage, so as to obtain and use them next time. Then, every 1 minute, that is, when the time from the current moment to the last execution is more than 1 minute, execute the second scheduled task. Accordingly, the embodiment of the present invention determines whether the time difference between the current time and the minute statistical task deadline is greater than or equal to 20 minutes. If the execution condition is judged to be true, that is, it is determined that the time difference is greater than 20 minutes, the corresponding minute statistical task (divided statistical task) and hourly statistical task (hourly statistical task) are executed.

[0094] Among them, in the process of executing the minute statistics task, the embodiment of the present invention queries the data in the data table (service_seconds table) of the "second statistics task" in the database, and the query time is "minute statistics task deadline" to "minute statistics task deadline + 10 minutes", and statistics are performed according to 10 minutes to obtain the first statistical data, such as "total number of transactions in 10 minutes" and "average round-trip delay of single transaction processing in 10 minutes", and the last second of these 10 minutes is used as the time, and written into the table specified by the database, such as the service_mins table. For example, in the embodiment of the present invention, the query time is 2024-08-28T00:00:00Z to 2024-08-28T00:10:00Z, then 2024-08-28T00:09:59Z is selected as the time. At the same time, the embodiment of the present invention determines whether it is necessary to execute the hour statistics task according to the latest minute statistics task deadline. For example, the embodiment of the present invention determines whether the latest minute statistics task deadline minus the hour statistics task deadline is greater than or equal to 60 minutes. Correspondingly, when the execution condition is judged to be true, the embodiment of the present invention queries the data in the data table (service_min table) of the "minute statistics task" in the database, and the query time is from "hour statistics task deadline" to "hour statistics task deadline + 60 minutes", and statistics are performed according to 60 minutes to obtain second statistical data, such as "total number of transactions in 60 minutes" and "average round-trip delay of single transaction processing in 60 minutes", and the last second of these 60 minutes is used as the time, and written into the table specified by the database, such as the service_hrs table. For example, if the query time is from 2024-08-28T00:00:00Z to 2024-08-28T01:00:00Z, the embodiment of the present invention selects 2024-08-28T00:59:59Z as the time. At the same time, the embodiment of the present invention uses "hour statistics task deadline + 60 minutes" as the new time of "hour statistics task deadline", and writes the time value into the designated file hrs-task.end for storage.

[0095] It is easy to understand that in the embodiment of the present invention, the "minute statistics task deadline" will be updated when the minute statistics task ends, and this time is also one of the factors of the start condition of the hour statistics task. Therefore, in the embodiment of the present invention, the layered collection tasks are linked, rather than independent. After the minute statistics task is executed, if the hour statistics task execution condition is met, the execution of the hour statistics task will be triggered. Accordingly, the embodiment of the present invention performs layered data statistics through "second statistics task", "minute statistics task" and "hour statistics task", which can collect and count huge data, that is, a three-layer downsampling method, which effectively improves the efficiency of data statistics management. At the same time, the embodiment of the present invention is also scalable, and can be expanded according to the level of transaction data generated by the cryptographic application that the monitoring product needs to monitor, such as adding a "day statistics task" for 1 day of statistics, and a "monthly statistics task" for 1 month of statistics.

[0096] In some embodiments of the present invention, the data query time period is split according to a preset splitting rule to obtain target time period data, including but not limited to the following steps:

[0097] When it is determined that the data query time period is less than the second statistical time length, a first splitting task is determined according to the query deadline, query start time and first statistical task deadline of the data query time period, so as to split the data query time period through the first splitting task to obtain target time period data. The first splitting task includes a minute time splitting task.

[0098] When it is determined that the data query time period is greater than the second statistical time length, a second splitting task is determined according to the query deadline, the query start time, the first statistical task deadline, and the second statistical task deadline, so as to split the data query time period through the second splitting task to obtain the target time period data. The second splitting task includes a minute time splitting task and an hour time splitting task.

[0099] In this specific embodiment, the embodiment of the present invention first determines the relationship between the data query time period and the second statistical duration. When it is determined that the data query time period is less than the second statistical duration, the embodiment of the present invention determines the first splitting task according to the query deadline, query start time and first statistical task deadline of the data query time period, and then splits the data query time period through the first splitting task to obtain the target time period data. Specifically, the first splitting task in the embodiment of the present invention includes a minute time splitting task. Among them, when the data query time period is less than the first statistical duration, such as 10 minutes, there is no need to split the data query time period, and directly query the data from the second data table. Then, when the data query time period is greater than the first statistical duration, the embodiment of the present invention analyzes whether the data query time period is less than the second statistical duration, such as 60 minutes. When it is determined that the data query time period is less than the second statistical duration, the embodiment of the present invention combines the query deadline, query start time and the first statistical task deadline for analysis, so as to determine the splitting means to be taken, that is, the first splitting task, and then splits the data query time period to obtain the target time period data. Exemplarily, when it is determined that the data query time period is less than the second statistical duration, the embodiment of the present invention first determines whether the query deadline is less than the first statistical task deadline, that is, whether the query deadline is before the first statistical task deadline. When it is determined that the query deadline is less than the first statistical task deadline, the embodiment of the present invention performs the minute time splitting task on the data query time period. On the contrary, when it is determined that the query deadline is greater than the first statistical task deadline, the embodiment of the present invention determines whether the query start time is greater than or equal to the first statistical task deadline, or whether the time interval between the first statistical task deadline and the query start time is less than 10 minutes, that is, whether (query start time ≥ minute task deadline) or (minute task deadline-query start time <10 minutes) is satisfied. Accordingly, when it is determined that the conditions are met, it means that there is no need to split the time period, directly query the second data table, and jump out of the splitting process. On the contrary, when it is determined that the corresponding conditions are not met, the embodiment of the present invention uses the query start time to the minute task deadline as the time period, executes the minute time splitting task, and obtains the execution result. At the same time, the embodiment of the present invention adds a time period to the end of the split time period list of the execution result, and the time period is from the task deadline to the query deadline in minutes, and the corresponding query data table is service_seconds. Accordingly, the example of the present invention returns the result and ends the split process.

[0100] In addition, when it is determined that the data query time period is greater than the second statistical duration, the embodiment of the present invention determines the second splitting task according to the query deadline, the query start time, the first statistical task deadline and the second statistical task deadline, and then splits the data query time period through the second splitting task to obtain the target time period data. Specifically, the second splitting task in the embodiment of the present invention includes a minute time splitting task and an hour time splitting task. When the data query time period is greater than the second statistical duration, such as the query time period ≥ 60 minutes, the embodiment of the present invention analyzes the query deadline, the query start time, the first statistical task deadline and the second statistical task deadline to determine the second splitting task to be executed, and then splits the data query time period to obtain the target time period data. Exemplarily, the embodiment of the present invention first determines whether the query deadline is less than the first statistical task deadline, that is, whether the query deadline < minute task deadline is satisfied. Correspondingly, when it is determined that the judgment result is true, the embodiment of the present invention executes the hour time splitting task for the data query time period, and returns the execution result to complete the splitting. On the contrary, when the judgment result is determined to be false, the embodiment of the present invention analyzes whether the query start time is greater than or equal to the first statistical task deadline, or whether the time interval between the first statistical task deadline and the query start time is less than 10 minutes, that is, whether (query start time ≥ minute task deadline) or (minute task deadline - query start time <10 minutes) is satisfied. Accordingly, when the judgment result is determined to be true, it means that no splitting is required, and the embodiment of the present invention queries from the service_seconds table and ends the splitting. On the contrary, when the judgment result is determined to be false, the embodiment of the present invention determines whether the query start time is greater than or equal to the second statistical task deadline, or whether the time interval between the first statistical task deadline and the query start time is less than 60 minutes, that is, whether (query start time ≥ hour task deadline) or (minute task deadline - query start time <60 minutes) is satisfied. Accordingly, when the judgment result is determined to be true, the embodiment of the present invention uses the query start time to the minute task deadline as the time period, splits the task by the minute time splitting task, and obtains the execution result. Then, a time period is added to the end of the split time period list of the execution result. The time period is from the minute task deadline to the query deadline, and the corresponding query data table is service_seconds. On the contrary, when it is determined that the judgment result is false, the embodiment of the present invention uses the query start time to the hour task deadline as the time period, splits it through the hour time splitting task, and obtains the first execution result. At the same time, the embodiment of the present invention uses the hour task deadline to the minute task deadline as the time period, splits it through the minute time splitting task, and obtains the second execution result.Next, the embodiment of the present invention integrates the first execution result and the second execution result, and adds a time period at the end of the obtained list of split time periods. This time period is from the minute task deadline to the query deadline, and the corresponding query data table is service_seconds. Return the result and end the splitting.

[0101] It should be noted that during the execution of the minute time splitting task in the embodiment of the present invention, first, it is judged whether the start time is a time of the whole minute and zero seconds. If so, the start time is recorded as T1; otherwise, according to the start time, the next time of the whole minute and zero seconds is taken and recorded as T1. At the same time, the embodiment of the present invention judges whether the end time is a time of the whole minute and zero seconds. If so, the end time is recorded as T2; otherwise, according to the end time, the previous time of the whole minute and zero seconds is taken and recorded as T2. Herein, the start time and the end time in the embodiment of the present invention respectively refer to the start time and the end time of the time period sent for splitting in the minute time splitting task. Correspondingly, the embodiment of the present invention creates a time list for returning the result. Then, when it is determined that the start time < T1, a time period is added to the time list for returning the result: from the start time to T1, and the required query data table is judged according to the time difference; otherwise, it is not added. At the same time, if T1 < T2, a time period is added to the time list for returning the result: from the start time to T1, and the required query data table is judged according to the time difference; otherwise, it is not added. In addition, if T2 < the end time, a time period is added to the time list for returning the result: from T2 to the end time, and the required query data table is judged according to the time difference; otherwise, it is not added. Finally, the embodiment of the present invention returns the time list and the query data tables corresponding to each time period.

[0102] In addition, in the process of splitting the hourly time task in the embodiment of the present invention, it is first determined whether the start time is the time of zero minutes and zero seconds. If so, the start time is recorded as T3. Otherwise, according to the start time, the next time of zero minutes and zero seconds is taken and recorded as T3. At the same time, the embodiment of the present invention creates a time list for returning results. Then, the embodiment of the present invention determines whether it satisfies T3 - start time ≥ 10 minutes. When it is determined that the judgment result is true, the embodiment of the present invention uses the period from the start time to T3 as the time period and splits it through the minute time splitting task, and adds the obtained result to the return list. Otherwise, at this time, if the start time < T3, a time period is added to the time list for returning the result: from the start time to T3, and the corresponding query data table is service_seconds. Further, the embodiment of the present invention determines whether the end time is the time of zero minutes and zero seconds. If so, the end time is recorded as T4. Otherwise, according to the end time, the previous time of zero minutes and zero seconds is taken and recorded as T4. Then, if T3 < T4, a time period is added to the time list for returning the result: from T3 to T4, and the required query data table is determined according to the time difference, otherwise it is not added. Then, the embodiment of the present invention determines whether it satisfies end time - T4 ≥ 10 minutes. If the judgment is true, the period from T4 to the end time is used as the time period and split through the minute time splitting task, and the obtained result is added to the return list. If the judgment result is false, at this time, if T4 < end time, a time period is added to the time list for returning the result: from T4 to the end time, and the corresponding query data table is service_seconds. Finally, the embodiment of the present invention returns the time list and the query data tables corresponding to each time period.

[0103] It should be noted that the embodiment of the present invention determines the transaction data table to be queried according to the time difference of the target time period data. For example, if the time difference < 10 minutes, the data table of the "second statistical task" (service_seconds table, second data table) is queried. If 10 minutes ≤ time difference < 60 minutes, the data table of the "minute statistical task" (service_min table, minute data table) is queried. If the time difference ≥ 60 minutes, the data table of the "hourly statistical task" (service_hrs table, hour data table) is used.

[0104] In some embodiments of the present invention, the password application transaction data management method provided by the embodiments of the present invention further includes but is not limited to the following steps:

[0105] Obtain the system restart time.

[0106] When it is determined that the time interval between the system restart time and the end time of the first statistical task is greater than or equal to the fourth interval, execute the preset restart statistical task and update the end time of the first statistical task. The preset restart statistical task includes the minute statistical task and the hourly statistical task.

[0107] When it is determined that the time interval between the system restart time and the updated first statistical task deadline is less than the fifth interval, the first statistical task deadline is updated according to the system restart time.

[0108] The statistical task is executed according to the updated first statistical task deadline.

[0109] In this specific embodiment, the monitoring and analysis product of cryptographic application transaction data needs to supplement the missing statistical data after it is shut down and restarted. Therefore, the embodiment of the present invention is provided with a corresponding shutdown and restart supplementary statistical mechanism. Specifically, the embodiment of the present invention first obtains the system restart time, and when it is determined that the time interval between the system restart time and the deadline of the first statistical task is greater than or equal to the fourth interval, the preset restart statistical task including the sub-statistical task and the hourly statistical task is executed, and the deadline of the first statistical task is updated. For example, Figure 5 As shown, after the monitoring and analysis product is restarted, the current time value is obtained, and this value is used as the current time value in the subsequent steps, that is, the system restart time. Then, it is determined whether the condition of the current time-minute statistical task deadline ≥ 20 minutes is met. If the judgment is true, the sub-statistical task and the hourly statistical task are executed. On the contrary, if the judgment is false, no additional statistics are required, and the mechanism process ends. Among them, since the sub-statistical task and the hourly statistical task are linked, after executing the sub-statistical task, modifying its deadline or directly re-taking the minute statistical task deadline, that is, updating the first statistical task deadline, may trigger the hourly statistical task.

[0110] Further, the embodiment of the present invention determines whether the time interval between the system restart time and the updated first statistical task deadline is less than the fifth interval. When it is determined that the time interval is less than the fifth interval, the embodiment of the present invention updates the first statistical task deadline according to the system restart time. Specifically, since the first statistical task deadline will be modified in the previous execution of the sub-statistical task, the embodiment of the present invention needs to combine the updated first statistical task deadline to determine whether the current time-updated minute statistical task deadline ≥ 10 minutes is satisfied. If the judgment is true, the sub-statistical task and the hourly statistical task are continued to be executed. On the contrary, if the judgment is false, that is, the current time-updated minute statistical task deadline <10 minutes, then the "minute statistical task deadline" is set to the last full minute and zero second time of the current time (system restart time) to update the first statistical task deadline. Then, the embodiment of the present invention executes the hourly statistical task through the updated first statistical task deadline, thereby completing the shutdown and restart supplementary statistics of transaction data.

[0111] It is easy to understand that the execution condition of the minute statistics task in the embodiment of the present invention is "current time - minute task deadline ≥ 20 minutes", so when the monitoring and analysis product suddenly stops, the most serious situation in the minute statistics task data table (service_min table) is the lack of data for the last 20 minutes before the shutdown, and the minute statistics task counts data within 10 minutes each time, so for different lengths of downtime, in this mechanism, the minute statistics task needs to be executed at most twice. Among them, for different lengths of downtime, the embodiment of the present invention adopts different operation methods. For example, when the system restart time - the first statistical task deadline < 20 minutes: no additional statistics are required, and the statistical task is automatically corrected. When the system restart time - the first statistical task deadline ≥ 20 minutes, the embodiment of the present invention executes the minute statistics task and counts the time period data from the first statistical task deadline to the first statistical task deadline + 10 minutes. Then, the embodiment of the present invention obtains the time value of the last full minute and zero second according to the restart time and saves it to the mins-task.end file to update the first statistical task deadline and restore the normal operation of the statistical task. In addition, when the system restart time - the first statistical task deadline ≥ 30 minutes, the embodiment of the present invention executes the sub-statistical task to count the time period data from the first statistical task deadline to the first statistical task deadline + 10 minutes. At the same time, the embodiment of the present invention executes the sub-statistical task to count the time period data from the first statistical task deadline + 10 minutes to the first statistical task deadline + 20 minutes. Finally, the embodiment of the present invention obtains the time value of the last full minute and zero second according to the restart time and saves it to the mins-task.end file to update the first statistical task deadline and then restore the normal operation of the statistical task.

[0112] In some embodiments of the present invention, the cryptographic application transaction data management method provided by the embodiments of the present invention further includes but is not limited to the following steps:

[0113] The preset supplementary time information is determined according to the transaction data time information in the historical database, wherein the preset supplementary time information includes the starting time of the minute supplementary statistical task and the starting time of the hour supplementary statistical task.

[0114] The preset supplementary deadline information is obtained, wherein the preset supplementary deadline information includes the minute supplementary statistical task deadline and the hour supplementary statistical task deadline.

[0115] A supplement time list is constructed according to the preset supplement time information and the preset supplement deadline time information.

[0116] The preset supplementary statistical task is executed according to the supplementary time list, wherein the preset supplementary statistical task includes at least one of a second statistical task, a minute statistical task and an hour statistical task.

[0117] In this specific embodiment, in order to be compatible with the existing old data, the embodiment of the present invention performs supplementary statistics on the old data through the old data supplementary statistics mechanism. In the embodiment of the present invention, the old data supplementary statistics mechanism is performed before the shutdown and restart supplementary statistics mechanism is started. Accordingly, when the statistical task initializes the "minute statistical task deadline" and the "hour statistical task deadline", the problem of being unable to find the specified file occurs, indicating that the current operation is the first time, and the embodiment of the present invention performs supplementary statistics on the old data. In the embodiment of the present invention, the data statistical task is a forward time statistics, and the old data supplementary statistics mechanism is equivalent to a reverse time statistics. Accordingly, the embodiment of the present invention first determines the preset supplementary time information according to the transaction data time information in the historical database. Specifically, the historical database in the embodiment of the present invention refers to the database of the password application program, which stores the corresponding transaction data. In the embodiment of the present invention, the preset supplementary time information includes the starting time of the minute supplementary statistics task and the starting time of the hour supplementary statistics task. For example, according to the time of the first transaction data in the historical database, the embodiment of the present invention takes the next full minute and zero second as the starting time of the minute supplementary statistics task, that is, the starting time of the minute supplementary statistics task, and takes the next zero minute and zero second as the starting time of the hour supplementary statistics task, that is, the immediate supplementary statistics task deadline.

[0118] At the same time, the embodiment of the present invention obtains preset supplementary deadline information. Specifically, the preset supplementary deadline information in the embodiment of the present invention includes the deadline of the supplementary statistical task by minute and the deadline of the supplementary statistical task by hour. For example, the embodiment of the present invention reads the deadline of each supplementary statistical task ("sub-supplementary statistical task" and "hourly supplementary statistical task") from the supplementary statistical time file (mins-task.recover and hrs-task.recover). If the corresponding file is not read or the corresponding data does not exist in the database, the "minute statistical task deadline" and "hourly statistical task deadline" are taken, and each supplementary statistical task time is written to the supplementary statistical time file (mins-task.recover and hrs-task.recover) for storage. Further, the embodiment of the present invention constructs a supplementary time list according to the preset supplementary time information and the preset supplementary deadline information, and then executes the preset supplementary statistical task according to the supplementary time list. Specifically, the embodiment of the present invention gradually divides the time forward by 1 day from the deadline of the supplementary statistical task to the start time, that is, the preset supplementary time information and the preset supplementary deadline information, to obtain a time list, that is, the supplementary time list. Among them, the backward supplementary statistics in the embodiment of the present invention are more conducive to the monitoring and analysis product to provide recent query results in a shorter time. Accordingly, the preset supplementary statistical tasks in the embodiment of the present invention include at least one of the second statistical task, the minute statistical task and the hour statistical task. The embodiment of the present invention executes the corresponding "second statistical task", "minute statistical task" or "small statistical task" according to the constructed supplementary time list, thereby completing the supplementary statistics of old data.

[0119] It should be noted that in the embodiment of the present invention, the supplementary statistical time files (mins-task.recover and hrs-task.recover) record the supplementary statistical time executed. If the supplementary statistical task fails or the monitoring product is shut down and restarted midway, it will not affect the supplementary statistical mechanism from being started again. By deleting the statistical task time files (mins-task.end and hrs-task.end), the supplementary statistical mechanism will be automatically continued from the breakpoint.

[0120] It is easy to understand that the embodiments of the present invention can perform lossless collection according to the required data indicators, greatly reducing the time required for querying / counting massive data, returning query results more effectively, enabling monitoring and analysis products to support time queries with a larger span, and improving the practicality and comparability of data analysis. At the same time, the embodiments of the present invention also include a compensation mechanism when monitoring and analysis products are shut down for maintenance or when the collection mechanism of this technology is used midway, so that compensation is performed automatically and reliably.

[0121] See also Figure 6The embodiment of the present application also provides a cryptographic application transaction data management system, which can implement the above-mentioned cryptographic application transaction data management method, and the system includes:

[0122] The first module 210 is used to obtain current time data to determine a target scheduled task from preset scheduled tasks according to the current time data. The preset scheduled tasks include data statistics tasks of different time dimensions.

[0123] The second module 220 is used to execute the corresponding data statistics task according to the target scheduled task and generate a transaction data table, wherein the transaction data table corresponds to the data statistics task.

[0124] The third module 230 is used to obtain a data query request, wherein the data query request includes a data query time period.

[0125] The fourth module 240 is used to split the data query time period according to the preset splitting rules to obtain the target time period data. The preset splitting rules are constructed according to the data statistics tasks of different time dimensions.

[0126] The fifth module 250 is used to query and obtain target transaction data from the corresponding transaction data table according to the target time period data.

[0127] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned password application transaction data management method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0129] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] See also Figure 7 , Figure 7 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0131] The processor 310 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0132] The memory 320 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 320 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 320, and the processor 310 calls and executes the cryptographic application transaction data management method of the embodiment of this application;

[0133] Input / output interface 330, used to implement information input and output;

[0134] Communication interface 340, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0135] bus 350 , which transmits information between the various components of the device (e.g., processor 310 , memory 320 , input / output interface 330 , and communication interface 340 );

[0136] The processor 310 , the memory 320 , the input / output interface 330 and the communication interface 340 are connected to each other in communication within the device via the bus 350 .

[0137] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned cryptographic application transaction data management method is implemented.

[0138] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0139] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor 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.

[0140] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0141] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0142] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0144] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0145] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0146] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for managing cryptographic application transaction data, characterized in that: The method comprises the following steps: Acquire current time data to determine a target scheduled task from preset scheduled tasks according to the current time data; wherein the preset scheduled tasks include data statistics tasks of different time dimensions; Execute corresponding data statistics tasks according to the target timing tasks to generate a transaction data table; wherein the transaction data table corresponds to the data statistics tasks; Obtaining a data query request; wherein the data query request includes a data query time period; The data query time period is split according to a preset splitting rule to obtain target time period data; wherein the preset splitting rule is constructed according to the data statistical tasks of different time dimensions; The target transaction data is obtained by querying from the corresponding transaction data table according to the target time period data.

2. The method according to claim 1, characterized in that The obtaining of the current time data to determine the target scheduled task from the preset scheduled tasks according to the current time data includes: When it is determined that the time interval between the current time data and the historical execution time data is greater than the first interval, the first scheduled task in the preset scheduled tasks is determined as the target scheduled task; wherein the first scheduled task includes a second statistics task; Alternatively, when it is determined that the time interval between the current moment data and the historical execution time data is greater than a second interval, the second scheduled task in the preset scheduled task is determined as the target scheduled task; wherein, the second interval is greater than the first interval, and the second scheduled task includes a sub-statistical task and an hour-statistical task.

3. The method according to claim 2, characterized in that The step of executing corresponding data statistics tasks according to the target timing tasks to generate a transaction data table includes: When it is determined that the target scheduled task is the first scheduled task, the transaction data of the first time period is queried, and data statistics are performed every second to update the second data table.

4. The method according to claim 3, characterized in that The executing of corresponding data statistics tasks according to the target timing tasks to generate a transaction data table also includes: When it is determined that the target scheduled task is the second scheduled task, it is determined whether the time interval between the current time data and the deadline of the first statistical task is greater than a third interval; wherein the deadline of the first statistical task is determined by the previous execution of the sub-statistical task; When it is determined that the time interval between the current moment data and the first statistical task deadline is greater than the third interval, query the first statistical data from the first statistical task deadline to the first target deadline in the second data table to update the sub-data table by using the first statistical data; wherein the first target deadline is determined by adding the first statistical task deadline to the first statistical duration; Updating the first statistical task deadline according to the first target deadline; When it is determined that the time interval between the updated first statistical task deadline and the second statistical task deadline is greater than a fourth interval, query the second statistical data from the second statistical task deadline to the second target deadline in the sub-data table to update the data table with the second statistical data; wherein the second target deadline is determined by adding the second statistical task deadline to the second statistical duration; The second statistical task deadline is updated according to the second target deadline.

5. The method according to claim 4, characterized in that The data query time period is split according to a preset splitting rule to obtain target time period data, including: When it is determined that the data query time period is less than the second statistical time length, a first splitting task is determined according to the query deadline, query start time and the first statistical task deadline of the data query time period, so as to split the data query time period through the first splitting task to obtain the target time period data; wherein the first splitting task includes a minute time splitting task; When it is determined that the data query time period is greater than the second statistical time length, a second splitting task is determined according to the query deadline, the query start time, the first statistical task deadline and the second statistical task deadline, so as to split the data query time period through the second splitting task to obtain the target time period data; wherein the second splitting task includes the minute time splitting task and the hour time splitting task.

6. The method according to claim 4, characterized in that The method further comprises: Get system restart time; When it is determined that the time interval between the system restart time and the first statistical task deadline is greater than or equal to a fourth interval, a preset restart statistical task is executed, and the first statistical task deadline is updated; wherein the preset restart statistical task includes the sub-statistical task and the hourly statistical task; When it is determined that the time interval between the system restart time and the updated first statistical task deadline is less than a fifth interval, updating the first statistical task deadline according to the system restart time; The hourly statistics task is executed according to the updated deadline of the first statistics task.

7. The method according to claim 2, characterized in that The method further comprises: Determine the preset supplementary time information according to the transaction data time information in the historical database; wherein the preset supplementary time information includes the starting time of the minute supplementary statistical task and the starting time of the hour supplementary statistical task; Obtaining preset supplementary deadline information; wherein the preset supplementary deadline information includes a minute supplementary statistical task deadline and an hour supplementary statistical task deadline; Constructing a supplementary time list according to the preset supplementary time information and the preset supplementary deadline time information; The preset supplementary statistical task is executed according to the supplementary time list; wherein the preset supplementary statistical task includes at least one of the second statistical task, the minute statistical task and the hour statistical task.

8. A cryptographic application transaction data management system, characterized in that: The system comprises: The first module is used to obtain current time data to determine a target scheduled task from preset scheduled tasks according to the current time data; wherein the preset scheduled tasks include data statistics tasks of different time dimensions; The second module is used to execute the corresponding data statistics task according to the target scheduled task to generate a transaction data table; wherein the transaction data table corresponds to the data statistics task; The third module is used to obtain a data query request; wherein the data query request includes a data query time period; The fourth module is used to split the data query time period according to a preset splitting rule to obtain target time period data; wherein the preset splitting rule is constructed according to the data statistical tasks of different time dimensions; The fifth module is used to query and obtain target transaction data from the corresponding transaction data table according to the target time period data.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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