Business analysis method and device, storage medium, electronic device and program product

Abstract

The embodiment of the invention provides a business analysis method and device, a storage medium, an electronic device and a program product, and the method comprises the steps: determining a target task for analyzing a marked business; collecting target data required for executing the target task from a plurality of platforms associated with the target service according to a preset rule; under the condition that the target task comprises a plurality of associated first tasks, determining an association relationship of the plurality of first tasks; the execution sequence of a plurality of second tasks included in the target task is adjusted according to the incidence relation, and the second tasks comprise the first task; and executing the target task according to the execution sequence based on the target data so as to analyze the target business.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of communications, and in particular, to a service analysis method, device, storage medium, electronic device, and program product. Background Art

[0002] By analyzing business data, multiple effects can be achieved, such as evaluating business conditions and predicting business development trends. The business data analysis method in the related technology executes data analysis tasks one by one in the order in which the tasks are initiated. Therefore, when there are related tasks between multiple tasks, the order of task execution cannot be adjusted, resulting in poor business analysis results and slow business analysis speed. Summary of the invention

[0003] The embodiments of the present invention provide a business analysis method, device, storage medium, electronic device and program product to at least solve the problem in the related art that the business analysis effect is poor and the business analysis speed is slow due to executing data analysis tasks one by one in the order in which the tasks are initiated when performing data analysis.

[0004] According to one embodiment of the present invention, a business analysis method is provided, comprising: determining a target task for analyzing a target business; collecting target data required for executing the target task from multiple platforms associated with the target business according to predetermined rules; determining the association relationship among the multiple associated first tasks when the target task includes multiple associated first tasks; adjusting the execution order of multiple second tasks included in the target task according to the association relationship, wherein the second tasks include the first tasks; and executing the target tasks in the execution order based on the target data to analyze the target business.

[0005] In an exemplary embodiment, the multiple platforms include: a platform for executing the target business, a platform that records the asset information and identity information of the target object executing the target business, and a platform for executing an associated business of the target business, wherein collecting the target data required to execute the target task from the multiple platforms associated with the target business according to predetermined rules includes: determining a target time period for collecting the target data according to the execution rules of the target business; and collecting the target data from the multiple platforms within the target time period.

[0006] In an exemplary embodiment, collecting the target data from the multiple platforms within the target time period includes: collecting first data of the target business from the multiple platforms; deduplicating the first data to obtain intermediate data; and formatting and integrating the intermediate data to obtain the target data.

[0007] In an exemplary embodiment, executing the target task in the execution order based on the target data to analyze the target business includes: determining the first node for executing each second task respectively according to the number of the plurality of second tasks included in the target task and the association relationship between the plurality of first tasks, and the node attributes of the plurality of first nodes for executing the plurality of second tasks; and executing the corresponding second tasks in the execution order through the plurality of first nodes to analyze the target business.

[0008] In an exemplary embodiment, the node attributes include: the number of the first nodes, the load condition of each of the first nodes, and historical task processing information of each of the first nodes.

[0009] In an exemplary embodiment, after executing the corresponding second task in the execution order through multiple first nodes to analyze the target business, the method also includes: storing the second data generated when analyzing the target business under the target path; encrypting the second data to obtain third data, and outputting the third data.

[0010] According to another embodiment of the present invention, a business analysis device is provided, comprising: a first determination module, used to determine a target task for analyzing a target business; a collection module, used to collect target data required for executing the target task from multiple platforms associated with the target business according to predetermined rules; a second determination module, used to determine the association relationship among multiple associated first tasks when the target task includes multiple associated first tasks; an adjustment module, used to adjust the execution order of multiple second tasks included in the target task according to the association relationship, wherein the second task includes the first task; and an analysis module, used to execute the target task in the execution order based on the target data to analyze the target business.

[0011] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0012] According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0013] According to yet another embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0014] Through the present invention, since the execution order of tasks is adjusted according to the association relationship between tasks when there are multiple tasks to be processed, it is possible to solve the problem in the related technology that data analysis tasks are executed one by one according to the order in which the tasks are initiated during data analysis, resulting in poor business analysis results and slow business analysis speed, thereby improving the task processing effect and processing efficiency and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a hardware structure block diagram of a mobile terminal according to a service analysis method according to an embodiment of the present invention;

[0016] Figure 2 The process of the business analysis method according to the embodiment of the present invention is Figure 1 ;

[0017] Figure 3 is a schematic diagram of a data analysis method according to an embodiment of the present invention;

[0018] Figure 4 is a schematic diagram of a data collection method according to an embodiment of the present invention;

[0019] Figure 5 is a schematic diagram of a business analysis method according to an embodiment of the present invention;

[0020] Figure 6 The process of business analysis according to an embodiment of the present invention is Figure 2 ;

[0021] Figure 7 is a schematic diagram of the structure of a service analysis device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 is a hardware structure block diagram of a mobile terminal according to a service analysis method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0025] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the business analysis method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal 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.

[0026] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] In this embodiment, a service analysis method running on the above mobile terminal or is provided. Figure 2The process of the business analysis method according to the embodiment of the present invention is Figure 1 ,like Figure 2 As shown, the process includes the following steps:

[0028] Step S202, determining a target task for analyzing the target business;

[0029] Step S204, collecting target data required for executing the target task from multiple platforms associated with the target business according to a predetermined rule;

[0030] Step S206, when the target task includes multiple associated first tasks, determining the association relationship between the multiple first tasks;

[0031] Step S208, adjusting the execution order of a plurality of second tasks included in the target task according to the association relationship, wherein the second tasks include the first task;

[0032] Step S210: executing the target task in the execution order based on the target data to analyze the target business.

[0033] In step S202, illustratively, Figure 3 is a schematic diagram of a data analysis method according to an embodiment of the present invention, such as Figure 3 As shown, determining the target task for analyzing the target business includes but is not limited to: parsing request information submitted by a user for analyzing the target business, and determining the target task according to task parameters (e.g., configuration parameters, data sources, query conditions, task types, etc.) included in the request information, wherein determining the target task according to the task parameters included in the request information includes but is not limited to: determining a Spark task application for executing the target task and a Spark application submission script according to the task parameters, so as to execute the target task in a Spark computing environment (e.g., an Apache Spark cluster).

[0034] In the above steps, illustratively, when the target task is a data collection task, step 202 and step S204 are executed, and after step S204, the target data is stored. Figure 4 is a schematic diagram of a data collection method according to an embodiment of the present invention, such as Figure 4As shown, collecting the target data required to execute the target task from multiple platforms associated with the target business according to predetermined rules includes but is not limited to: selecting a task script template based on the data parameters (data source type, data format, frequency, incremental full amount) included in the request submitted by the user to determine the task script for collecting data (the script file includes data conditional query, data filtering method and format conversion method); collecting the target data from multiple platforms associated with the target business through the task script, wherein, optionally, when collecting the target data, data collection can be performed through Kafka, DataX, and Sqoop. The storage of the target data includes but is not limited to: storing structured data (for example, processed market monitoring data) through a data warehouse, and storing unstructured data (for example, log files and CSV files, etc.) through Hadoop.

[0035] In the above steps, when there are multiple tasks to be processed, the execution order of the tasks is adjusted according to the association between the tasks (for example, when the tasks to be processed include optimization operations and data analysis operations on the same data, the execution order is adjusted to first execute the data optimization operation, and then execute the data analysis operation immediately after the data optimization operation). This solves the problem in the related technology that data analysis tasks are executed one by one in the order in which the tasks are initiated during data analysis, resulting in poor business analysis results and slow business analysis speed. This improves the task processing effect and efficiency, and improves user experience.

[0036] The execution subject of the above steps may be a terminal device that can carry a data analysis program, etc., but is not limited thereto.

[0037] In an optional embodiment, the multiple platforms include: a platform for executing the target business, a platform that records the asset information and identity information of the target object executing the target business, and a platform for executing an associated business of the target business, wherein collecting the target data required to execute the target task from the multiple platforms associated with the target business according to predetermined rules includes: determining a target time period for collecting the target data according to the execution rules of the target business; and collecting the target data from the multiple platforms within the target time period.

[0038] In the above steps, exemplarily, the target time periods corresponding to the multiple platforms in the multiple target time periods are determined according to the inactive time periods of the target business in the corresponding platforms. For example, in the real estate transaction platform, 12 to 3 am is an inactive time period for the real estate transaction business. Therefore, the target time period of the inactive time is determined within the time interval from 12 to 3 am.

[0039] In the above steps, by determining the target time period for collecting the target data according to the execution rules of the target business, data collection can be performed during the time period when the target business is not active, thereby reducing the interference of data collection on business operations. At the same time, data collection is performed when the system load is low, which can improve the efficiency and speed of data collection.

[0040] In an optional embodiment, collecting the target data from the multiple platforms within the target time period includes: collecting first data of the target business from the multiple platforms; deduplicating the first data to obtain intermediate data; and formatting and integrating the intermediate data to obtain the target data.

[0041] In the above steps, deduplication processing can eliminate duplicate records in the data set, reduce data redundancy, ensure data uniqueness, thereby improving data accuracy and reliability, and deduplication can reduce the storage space occupied by data.

[0042] In an optional embodiment, executing the target task in the execution order based on the target data to analyze the target business includes: determining the first node used to execute each second task according to the number of multiple second tasks included in the target task and the relationship between the multiple first tasks, and the node attributes of multiple first nodes used to execute the multiple second tasks; and executing the corresponding second tasks in the execution order through multiple first nodes to analyze the target business.

[0043] In the above steps, the node attributes include but are not limited to: the task category that the node can process, the node's ability to process tasks of the corresponding task category, etc., for example, the computing performance of the computing node that performs the data analysis operation. Exemplarily, when the second task includes: the storage task of real estate transaction data, the analysis task of real estate transaction data, and the first node includes: the second node with computing power (computing power score 92), the third node with storage power (storage power score 95), and the fourth node with both computing power and storage power (computing power score 90, storage power score 90), the second node is selected to perform the analysis task of real estate transaction data, and the third node is selected to perform the storage task of real estate transaction data, and after the second node completes the analysis task, the third node performs the storage task based on the execution result of the second node; when the second task includes: the storage task of real estate transaction data, the analysis task of real estate transaction data, and the first node includes: the second node with computing power (computing power score 60), the third node with storage power (storage power score 70), and the fourth node with both computing power and storage power (computing power score 90, storage power score 90), the fourth node is selected to perform the analysis task first and then perform the storage task based on the execution result of the analysis task.

[0044] In the above steps, by allocating tasks considering node attributes and task characteristics, it can be ensured that each task is executed on the node that best suits its needs, thereby improving resource utilization and processing efficiency. At the same time, arranging the execution order according to the dependencies between tasks and node capabilities can reduce waiting time and context switching, and speed up task completion.

[0045] In an optional embodiment, the node attributes include: the number of the first nodes, the load condition of each of the first nodes, and the historical task processing information of each of the first nodes.

[0046] In the above steps, the load condition of the first node includes but is not limited to: CPU usage, memory usage, network bandwidth, etc., and the historical task processing information includes but is not limited to: task success rate, average task completion time, fault records, etc.

[0047] In an optional embodiment, after executing the corresponding second task in the execution order through multiple first nodes to analyze the target business, the method also includes: storing the second data generated when analyzing the target business under the target path; encrypting the second data to obtain third data, and outputting the third data.

[0048] In the above steps, the encryption methods include but are not limited to: AES encryption, RSA asymmetric encryption, etc. By encrypting the data before outputting it, the security of the data during transmission is ensured, thereby effectively preventing data leakage.

[0049] The business analysis method in this application is described below in conjunction with specific application scenarios:

[0050] Figure 5 is a schematic diagram of a business analysis method according to an embodiment of the present invention. Figure 5As shown, exemplary, the data sources of data collection include: real estate production business system data (MySQL), business log data, and bank and institution reported data. Among them, the real estate production business system data is a structured data source, specifically, including: real estate transactions, customer information and other data; business log data is unstructured data, including: log files generated by the system or application; the formats of bank and institution reported data (CSV / JSON) include CSV, JSON, etc., and the bank and institution reported data include: financial reports, transaction data, etc. The data acquisition module uses tools such as DataX / Sqoop / Kafka to collect data from the above multiple data sources. The data acquisition module stores the collected data in the data storage module. The data processing and analysis is based on the data input by the data acquisition module and the data obtained from the data storage module. Data processing and analysis are performed, and the data generated by the execution of the processing and analysis operation is stored in the data storage module. In the case of receiving a user's query request, the data query service module provides query services based on the data in the data storage module, and feeds back the queried data to the user through the data display module. The user performs task management and status alarms on the tasks included in the data module and the data processing and analysis module based on the task management module. Exemplarily, the data query service module is built based on the Spring Boot framework and provides data query services. The data query services include: querying a variety of real estate market monitoring data, such as house sales, pre-sale funds, maintenance funds, practitioners and home buyers, etc. The data query service module supports a variety of query interfaces, such as RESTful API, Web services, etc., which are easy to integrate and expand. The data display module is used to present the results provided by the data query service to the user in a visual way. The data display module includes: a front-end large screen interface for real-time display of market monitoring conditions, including charts, maps, data summaries, etc., so as to help users intuitively understand the data; a data report generation function for generating reports in various formats, such as PDF, Excel, etc. At the same time, the data report generation function can provide users with customized report templates to meet different data display requirements. Data reports can be generated regularly or instantly according to user requests. The task management module is used to comprehensively manage and schedule tasks, support timed and event-driven automatic task scheduling, provide real-time monitoring and detailed logging functions, and can track task status and perform troubleshooting. The module's management interface allows administrators to add, modify, delete, query, and debug tasks, send SMS or email alerts when anomalies occur, and provide detailed information viewing and history query functions to facilitate problem troubleshooting and task optimization. The task management module supports the following task types: Data synchronization tasks, which are used to read data from a data source and synchronize it to a target location. Users can configure parameters such as source address, target address, data type, table name or file name, and synchronization frequency.The task management module is responsible for scheduling tasks to ensure regular synchronization and integrity of data; data reading tasks are used to extract data from specified data sources and create temporary views for subsequent processing. This task configuration includes data source information, reading conditions, and view definitions; SQL query tasks are used to execute SQL query tasks and write and execute Spark SQL statements. The task management module ensures the correctness of SQL query tasks and monitors their execution; executes prediction tasks, which are used to use pre-trained models and call the model building and prediction API provided by Spark ML based on input feature indicators to predict indicators such as regional housing prices, sales units and areas, and sales cycles; executes processing script tasks through remote service interfaces, which can process some operations that Spark SQL does not support, such as UPDATE statements; executes data processing scripts through remote API interfaces, and the task management module can supplement the functional limitations of Spark SQL and implement more complex data operations.

[0051] Figure 6 The process of business analysis according to an embodiment of the present invention is Figure 2 ,like Figure 6 As shown, the process includes the following steps:

[0052] Step S602, creating a data collection task;

[0053] Step S604, creating a data processing and analysis task;

[0054] Step S606, executing the collection task and the data processing and analysis task;

[0055] Step S608, determining whether the scheduled execution task is successfully executed, if it is successfully executed, executing, jumping to step S612, if it fails, executing step S610;

[0056] Step S610, status alarm;

[0057] Step S612, writing the result data;

[0058] Step S614, determining whether there is an associated task, if there is an associated task, jumping to step S606, if there is no associated task, executing step S616.

[0059] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0060] In this embodiment, a business analysis device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0061] Figure 7 is a structural block diagram of a service analysis device according to an embodiment of the present invention. Figure 7 As shown, the device includes: a first determination module 72, used to determine a target task for analyzing a target business; a collection module 704, used to collect target data required for executing the target task from multiple platforms associated with the target business according to a predetermined rule; a second determination module 706, used to determine the association relationship among multiple associated first tasks when the target task includes multiple associated first tasks; an adjustment module 708, used to adjust the execution order of multiple second tasks included in the target task according to the association relationship, wherein the second task includes the first task; and an analysis module 710, used to execute the target task in the execution order based on the target data to analyze the target business.

[0062] In an optional embodiment, the multiple platforms include: a platform for executing the target business, a platform that records the asset information and identity information of the target object executing the target business, and a platform for executing related businesses of the target business, wherein the collection module 704 includes: a first determination unit, used to determine the target time period for collecting the target data according to the execution rules of the target business; and a collection unit, used to collect the target data from the multiple platforms within the target time period.

[0063] In an optional embodiment, the collection unit includes: a collection subunit, used to collect first data of the target business from multiple platforms; a deduplication subunit, used to deduplicate the first data to obtain intermediate data; and an integration subunit, used to adjust the format of the intermediate data and integrate the data to obtain the target data.

[0064] In an optional embodiment, the analysis module 710 includes: a first determination unit, used to determine the first node used to execute each second task according to the number of multiple second tasks included in the target task, the association relationship between the multiple first tasks, and the node attributes of multiple first nodes used to execute the multiple second tasks; an analysis unit, used to analyze the target business by executing the corresponding second tasks in the execution order through multiple first nodes.

[0065] In an optional embodiment, the node attributes include: the number of the first nodes, the load condition of each of the first nodes, and the historical task processing information of each of the first nodes.

[0066] In an optional embodiment, the device also includes: a storage module, which is used to store second data generated when analyzing the target business under the target path after executing the corresponding second task in the execution order through multiple first nodes to analyze the target business; an encryption module, which is used to encrypt the second data to obtain third data, and output the third data.

[0067] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0068] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0069] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0070] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0071] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0072] An embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0073] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0074] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A business analysis method, characterized in that: include: Determine the target tasks for analyzing the target business; Collecting target data required for executing the target task from multiple platforms associated with the target business according to a predetermined rule; In a case where the target task includes a plurality of associated first tasks, determining an associated relationship among the plurality of first tasks; adjusting the execution order of a plurality of second tasks included in the target task according to the association relationship, wherein the second tasks include the first task; The target tasks are executed according to the execution order based on the target data to analyze the target business.

2. The method according to claim 1, characterized in that: The multiple platforms include: a platform for executing the target business, a platform recording the asset information and identity information of the target object for executing the target business, and a platform for executing the associated business of the target business, wherein: Collecting target data required for executing the target task from multiple platforms associated with the target business according to predetermined rules includes: determining a target time period for collecting the target data according to the execution rules of the target business; and collecting the target data from multiple platforms within the target time period.

3. The method according to claim 2, characterized in that Collecting the target data from the plurality of platforms within the target period includes: first data of the target business collected from the plurality of platforms; Performing deduplication processing on the first data to obtain intermediate data; The intermediate data is formatted and integrated to obtain the target data.

4. The method according to claim 1, characterized in that: Executing the target task in the execution order based on the target data to analyze the target business includes: Determine, according to the number of the plurality of second tasks included in the target task, the association relationship between the plurality of first tasks, and the node attributes of the plurality of first nodes for executing the plurality of second tasks, the first node for executing each of the second tasks; The target business is analyzed by executing the corresponding second tasks in the execution order through multiple first nodes.

5. The method according to claim 4, characterized in that The node attributes include: the number of the first nodes, the load condition of each of the first nodes, and the historical task processing information of each of the first nodes.

6. The method according to claim 4, characterized in that After the corresponding second tasks are executed by the plurality of first nodes in the execution order to analyze the target business, the method further includes: storing the second data generated when analyzing the target business under the target path; The second data is encrypted to obtain third data, and the third data is output.

7. A business analysis device, characterized in that: include: A first determination module is used to determine a target task for analyzing a target business; A collection module, used for collecting target data required for executing the target task from multiple platforms associated with the target business according to a predetermined rule; A second determining module, configured to determine the association relationship between the plurality of associated first tasks when the target task includes a plurality of associated first tasks; an adjusting module, configured to adjust the execution order of a plurality of second tasks included in the target task according to the association relationship, wherein the second tasks include the first task; An analysis module is used to execute the target task in the execution order based on the target data to analyze the target business.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 6 when executed by a processor.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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