Region division method and system based on online car-hailing driver activity logic

Abstract

The invention discloses a region division method and system based on online car-hailing driver activity logic. The invention relates to the technical field of online car hailing. A data offline synchronization task is created by using a DataWorks platform, a data source is configured as a MySQL table of a business party, and a target source is an ODPS table. Updating the data on the basis of a period for setting synchronous task scheduling; when the synchronization task is executed, the administrative division data are contained in the ODPS table; based on the MySQL data source type, connection information, including a database address, a port number, a user name, a password and a table name to be synchronized, of a MySQL table provided by a business party is input; according to the invention, through accurate region division and an intelligent scheduling strategy, the online car-hailing platform can more efficiently distribute orders to the most appropriate drivers, and empty driving and waiting time is reduced, so that the overall operation efficiency is remarkably improved. According to the invention, the problem of low efficiency caused by repeated order receiving or cross-region order receiving is reduced, and the work of a driver is smoother.

Description

Technical Field

[0001] The present invention relates to the technical field of online car-hailing, and in particular to a regional planning technology for online car-hailing, and in particular to a regional division method and system based on the activity logic of online car-hailing drivers. Background Art

[0002] In the online car-hailing industry, drivers are the core elements of the service chain, and their operational efficiency and behavior pattern analysis play a vital role in improving the overall service quality and passenger satisfaction. In order to achieve this goal, refined operational strategies and in-depth data analysis for drivers are particularly important. Specifically, based on the performance data of drivers in different geographical activity areas, corresponding activation measures and reward mechanisms are formulated and implemented to effectively improve the activity of drivers, thereby better responding to passengers' travel needs and further promoting the growth of drivers' income on this basis.

[0003] During the implementation process, considering the hierarchical structure of my country's administrative divisions, including provinces, cities, and counties (districts), accurate management and application of drivers' geolocation data has become the key. For example, the administrative division code of Shandong Province is 370000, Jinan City is 370100, and Pingyin County is 370124. The activity location of drivers is usually accurate to the district and county level code. However, when formulating activation and marketing strategies for drivers, it is often necessary to consider data aggregation at the district, county or city level, which requires the original district and county codes to be converted accordingly.

[0004] The solution of the existing technology is to build a MaxCompute database table based on national administrative divisions, which records in detail key information such as administrative division codes, corresponding Chinese names, administrative division levels, and parent administrative division codes. In actual applications, the downstream system can obtain the required parent code by associating this administrative division table, thereby supporting data analysis and policy formulation at different levels. However, its technical defect is that whenever it is necessary to perform analysis or policies based on administrative division data at different levels, it is necessary to repeatedly write logic to determine whether the current coding level meets the requirements and decide whether to perform coding conversion to obtain the parent code. This repetitive work not only increases development costs, but also seriously affects processing efficiency, especially when facing multi-scenario and high-frequency data application needs, the problem is particularly prominent.

[0005] To this end, the present invention proposes a method and system for regional division based on the activity logic of online car-hailing drivers. Summary of the invention

[0006] In view of this, the present invention hopes to provide a method and system for regional division based on the activity logic of online car-hailing drivers to solve or alleviate the technical problems existing in the prior art, that is, how to further optimize the regional division of the activity logic of online car-hailing drivers, reduce duplication of work, and at least provide a beneficial option for this; the technical solution of the present invention is implemented as follows:

[0007] First, the regional division method based on the activity logic of online car-hailing drivers:

[0008] 1. Overview:

[0009] The present invention aims to achieve refined division of online car-hailing drivers' activity areas by efficiently integrating and utilizing the standard administrative division data of the Ministry of Civil Affairs of the People's Republic of China, combined with the powerful data processing capabilities of the Alibaba Cloud DataWorks platform. The solution first uses the data synchronization function of DataWorks to synchronize the data in the business party's MySQL table to the ODPS table in real time to ensure the timeliness and accuracy of the data. Then, through the data cleaning and integration steps, the MaxCompute administrative division table is generated to provide a basis for subsequent data analysis. On this basis, the UDF function is developed to realize the flexible conversion of administrative division codes to meet the diverse needs of downstream systems for regional division data. Finally, the downstream system can quickly obtain the division data of the driver's activity area by simply calling the UDF function, thereby supporting more accurate driver activity operations and data analysis.

[0010] (II) Technical solution:

[0011] In order to achieve the above technical objectives, the present invention selects to execute the following steps S1 to S4.

[0012] 2.1 Step S1, data synchronization:

[0013] Use the DataWorks platform to create an offline data synchronization task, configure the data source as the business side's MySQL table, and the target source as the ODPS table.

[0014] Update the data based on the period of synchronization task scheduling. When executing the synchronization task, store the administrative division data in the ODPS table.

[0015] 2.1.1 Step S100, create a data offline synchronization task:

[0016] Name the task and describe its basic information.

[0017] 2.1.2 Step S101, data engineering:

[0018] Based on the MySQL data source type, enter the connection information of the MySQL table provided by the business party, including the database address, port number, user name, password, and the name of the table to be synchronized.

[0019] 2.1.3 Step S102, configuration:

[0020] Configure the target source type as ODPS table, select or create a target ODPS project, and specify the table name where the synchronized data should be stored; set the scheduling period for the synchronization task, such as daily, weekly, or monthly, and determine the specific execution time of the task.

[0021] 2.1.4 Step S103, start:

[0022] Save and submit the synchronization task configuration, preview the task, and start the synchronization task.

[0023] 2.2 Step S2, data integration:

[0024] Clear the data by administrative division code, record the update time in reverse order, and take the latest data; use SQL table to aggregate the data of province table, city table and district table to generate MaxCompute administrative division table.

[0025] 2.2.1 Step S200, perform data cleaning:

[0026] Create SQL script tasks to perform data cleansing and integration operations.

[0027] 2.2.2 Step S201, perform data partitioning:

[0028] In the SQL script, write a query statement to group the data by administrative division code. For each administrative division code, use the SQL window function or grouping aggregation function to select the data record with the latest update time.

[0029] 2.2.3 Step S202, summary:

[0030] Based on the preset SQL statement, use the JOIN operation or UNION operation to summarize the cleaned province table, city table, and district and county table data.

[0031] 2.2.4 Step S203, insert:

[0032] In the SQL script, create a MaxCompute administrative division table and insert the integrated data into the table; define the table column name and data type.

[0033] 2.2.5 Step S204, execute SQL script task:

[0034] Execute SQL script tasks to clean and integrate data.

[0035] 2.3 Step S3, execute the UDF function:

[0036] Based on the Python resource node created in the DataWorks resource module, execute the calculation logic of the administrative division code corresponding to the parent administrative division code; based on the UDF function node, reference the Python resource node created by S4 and fill in the corresponding class name.

[0037] 2.3.1 Step S300, create a UDF function node:

[0038] Create a UDF function node in the data development module; in the UDF function node configuration, introduce the preset Python resource node.

[0039] 2.3.3 Step S301, determine the class name:

[0040] Fill in the corresponding class name in the Python resource node, including the method to implement the calculation logic of the administrative division code corresponding to the parent administrative division code; then save and submit the UDF function node configuration.

[0041] 2.3.4 Step S302, create a script task:

[0042] Create an SQL script task to call the UDF function and perform calculations on the administrative division code corresponding to the parent administrative division code. In the SQL script, write a query statement, call the UDF function, and pass in the administrative division code to be calculated as a parameter.

[0043] 2.4 Step S4, downstream applications:

[0044] The downstream system creates a Python resource node and obtains the regional division data of the driver's activity area by calling the space name and UDF function name; and applies the obtained regional division data to the operation and data analysis of the driver's activities.

[0045] 2.4.1 Step S400, create an interface:

[0046] Create a new API interface in the data service or API management module to call the UDF function created upstream. The parameters for configuring the API interface include the space name, UDF function name, and the parameters that need to be passed in (administrative division code).

[0047] 2.4.2 Step S401, call:

[0048] The downstream system calls the UDF function created upstream through the API interface and passes in the corresponding parameters to obtain the regional division data of the driver's activity area.

[0049] The downstream system receives and parses the regional division data returned by the UDF function to ensure the integrity and accuracy of the data.

[0050] 2.4.3 Step S402, application:

[0051] Apply the acquired regional division data to the operation and data analysis of driver activities, such as optimizing driver dispatch strategies or analyzing driver activity patterns.

[0052] (III) Mechanism for solving technical problems:

[0053] 3.1 Data synchronization and update mechanism:

[0054] The present invention can ensure that administrative division data can be synchronized from the business party's MySQL table to the ODPS table in real time or regularly through the offline data synchronization task of the DataWorks platform, so as to maintain the timeliness and latest status of the data.

[0055] 3.2 Data cleaning and integration mechanism:

[0056] This invention ensures that each piece of data is up-to-date and unique. It effectively avoids duplication of work in subsequent analysis. The cleaned province, city, district and county table data are aggregated into the MaxCompute administrative division table to form a complete administrative division system, which is convenient for regional division and data analysis.

[0057] 3.3Application of UDF function:

[0058] The present invention realizes flexible conversion from administrative division codes to specific area names based on UDF functions. This function enables downstream systems to easily obtain division information of driver activity areas as needed without repeatedly writing conversion logic. Moreover, the encapsulation and reuse of UDF functions greatly simplifies the development complexity of downstream systems and improves the efficiency of data processing and area division.

[0059] 3.4 Intelligent area recommendation and scheduling:

[0060] Through regional division, online ride-hailing platforms can dispatch drivers more intelligently and assign orders to the most suitable drivers based on real-time order demand and driver location, thereby reducing idle driving and waiting time and improving operational efficiency.

[0061] Second, the regional division system based on the activity logic of online car-hailing drivers:

[0062] The method comprises a memory and a processor connected to the memory, wherein the memory stores program instructions, and when the program instructions are executed by the processor, the above-mentioned area division method is implemented based on the following modules connected to the processor:

[0063] (1) Data processing module for the collection, synchronization, cleaning, integration and storage of administrative division data;

[0064] (2) Intelligent dispatch module for zone division and driver activity data;

[0065] (3) a user interaction module for interaction between the online ride-hailing platform and users (including drivers and passengers);

[0066] (4) A monitoring and optimization module used to monitor the operation of the online ride-hailing platform in real time and make optimization adjustments based on the monitoring results.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. Improve operational efficiency: Through precise regional division and intelligent dispatching strategies, the present invention enables the online car-hailing platform to more efficiently allocate orders to the most suitable drivers, reduce idle driving and waiting time, and thus significantly improve overall operational efficiency.

[0069] 2. Optimize driver experience: The present invention reduces the inefficiency caused by repeated orders or cross-region orders, making the driver's work smoother.

[0070] 3. Reduce operating costs: The present invention helps reduce the operating costs of online ride-hailing platforms by reducing duplication of work and optimizing scheduling. For example, reducing idle driving and waiting time means lower fuel consumption and vehicle maintenance costs.

[0071] 4. Improve service quality: The precise regional division and personalized recommendation system of the present invention can better meet the travel needs of passengers, improve passenger satisfaction and loyalty, and thus enhance the market competitiveness of the online car-hailing platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0073] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0074] Figure 2 It is a schematic diagram of the system composition of the present invention. DETAILED DESCRIPTION

[0075] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below;

[0076] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0077] Explanation of relevant terms:

[0078] (1) DataWorks platform: a one-stop big data development and management platform provided by Alibaba.

[0079] (2) Configure data source (MySQL table): Specify the source of data storage, which is the table in the MySQL database.

[0080] (3) Target source (ODPS table): The storage destination of data after synchronization or processing, which is the table in ODPS (Open Data Processing Service).

[0081] (4) SQL: Structured Query Language, used for data query, update and management.

[0082] (5) MaxCompute administrative division table: a table created on the MaxCompute platform to store administrative division data.

[0083] (6) UDF function: User-defined function, used to extend the functionality of SQL and implement specific data processing logic.

[0084] (7) DataWorks resource module: A module on the DataWorks platform that manages and stores various resources, such as scripts and functions.

[0085] (8) Python resource node: A node created in the DataWorks resource module for writing and executing Python code.

[0086] (9) Class name: In object-oriented programming, the name used to identify and reference a class.

[0087] (10) Downstream system: In the data processing flow, the system or application located after the current processing link.

[0088] (11) Space name: On DataWorks or other similar platforms, it is used to distinguish namespaces of different projects or environments.

[0089] (12) Operation and data analysis of driver activities: The process of operational management and data analysis of online ride-hailing driver activities.

[0090] Embodiment 1: Figure 1 As shown, this embodiment discloses a method for dividing regions based on the activity logic of online car-hailing drivers, including the following implementation steps:

[0091] In this embodiment, regarding step 1: creating a DataWorks workspace and applying for resources: first, create a new workspace on Alibaba Cloud's DataWorks platform and apply for necessary computing resources and storage resources based on data processing requirements. This step is the basis for subsequent data processing tasks.

[0092] Specifically, fill in the necessary information to create a workspace through the Web interface of the DataWorks platform. Apply for the corresponding ECS ​​computing instance and OSS storage resources in the Alibaba Cloud console based on the estimated data processing volume and concurrency requirements. Bind the applied resources to the workspace to ensure that the data processing task can be executed smoothly.

[0093] In this example, regarding step 2: creating MaxCompute tables: in the DataWorks workspace, use SQL syntax to create three MaxCompute tables to store data on provinces, cities, and counties. These tables will serve as the basis for subsequent data cleaning and administrative division table production.

[0094] Specifically, use the data development module of DataWorks to write and execute the CREATE TABLE SQL statement to create the province table, city table, and county table. Ensure that each table contains key fields such as administrative division code and administrative division name, and set reasonable data types and storage formats.

[0095] Furthermore, the execution program (SQL) of this step is as follows:

[0096]

[0097] In the above program, the CREATE TABLE SQL syntax is used to create a table in MaxCompute, including defining the table's fields, data types, and storage formats.

[0098] In this embodiment, regarding step 3: configuring the data synchronization node: under the data integration module of DataWorks, a new offline synchronization node is created to synchronize the data in the business party's MySQL table to the MaxCompute table created in step 2. This step ensures the real-time and consistency of the data.

[0099] Specifically, select DataX as the data synchronization tool, configure its data source as the business side's MySQL table, and the target data source as the MaxCompute table. Set the field mapping relationship to ensure that the data in the MySQL table can be accurately synchronized to the MaxCompute table. Configure the scheduling dependency and execution cycle to ensure that the data synchronization task can be automatically executed at the scheduled time interval.

[0100] Furthermore, the execution program (json) of this step is as follows:

[0101]

[0102]

[0103]

[0104] In the above program, the DataX JSON file is configured to define the data source (MySQL) and target data source (MaxCompute), including setting field mapping, synchronization strategy, error handling, and performance optimization parameters.

[0105] In this embodiment, regarding step 4: Data cleaning node configuration: in the data development module of DataWorks, create data cleaning nodes for the province table, city table, and district and county table. The cleaning solution is to partition by administrative division code, record the update time in reverse order, and take the latest record to ensure the uniqueness of the administrative division code.

[0106] Specifically, use SQL statements to write data cleaning logic, sort the records of each administrative division code through the ROW_NUMBER() window function, and select the records with the latest update time.

[0107] Furthermore, the execution program (SQL) of this step is as follows:

[0108]

[0109] In the above program, SQL statements are used to clean the data, remove duplicate records, select the latest records, etc. The ROW_NUMBER() window function is used to partition and sort the data.

[0110] In this embodiment, regarding step 5: Administrative division table production node configuration: in the data development module of DataWorks, a new administrative division table production node is created. The code logic of this node is divided into two parts: first, the administrative division table is created, then the data of the province table, city table, and district table are associated together, and grouped according to the administrative division code, the latest data is taken, and finally written into the administrative division table.

[0111] Specifically, use the CREATE TABLE SQL statement to create an administrative division table and set reasonable fields and data types. Use the UNION ALL operator to merge the data in the province table, city table, and district and county table, and use the ROW_NUMBER() window function to sort the records of each administrative division code, and select the records with positive administrative level and the latest update time. Use the INSERTOVERWRITE TABLE SQL statement to write the processed data into the administrative division table, and configure the scheduling dependency and execution cycle of the task.

[0112] Furthermore, the execution program (SQL) of this step is as follows:

[0113]

[0114]

[0115] ...;--Subsequent code includes ROW_NUMBER() and WHERE clause to select the latest record

[0116] In the above program, CREATE TABLE is used to create the administrative division table. UNION ALL is used to merge the data of the province table, city table, and district and county table. ROW_NUMBER() and INSERTOVERWRITE TABLE are used to select and insert the latest record.

[0117] In this embodiment, regarding step 6: creating a Python resource node: under the resource module of DataWorks, create a Python resource node to implement the processing logic of region division. This node will serve as the basis for subsequent UDF functions.

[0118] Specifically, load the administrative division table generated in step 4 into the resource list. Perform null value and length checks on the input parameter to ensure the validity of the input. Return the corresponding parent administrative division code or the input administrative division code according to the level of the administrative division code.

[0119] In this embodiment, regarding step 7: creating a UDF function node: under the function module of DataWorks, create a UDF function node and reference the Python resource and corresponding class name uploaded in step 6. This step encapsulates the Python code into a UDF function so that it can be called in subsequent data processing tasks.

[0120] Specifically, create a new UDF function node under the function module of DataWorks. Select the Python resource uploaded in step 6 as the function implementation code and specify the class name. Configure the function's input parameters and return type to ensure that the function can be called and executed correctly.

[0121] In this embodiment, regarding step 8: calling the UDF function to obtain the regional division: when obtaining the driver activity regional division, you can directly use [space name.UDF function name (administrative division code)] to call the function. This step applies the UDF function to the actual business scenario to obtain the regional division data of the driver's activity.

[0122] Specifically, in the data development module of DataWorks or other business systems, call the UDF function through SQL statements. Pass in the administrative division code of the driver's activity as a parameter to obtain the corresponding regional division data. Apply the obtained regional division data to the operation and data analysis of driver activities to support business decision-making and optimization.

[0123] Furthermore, the execution program (SQL) of this step is as follows:

[0124] SELECT your_udf_function(division_code)FROM your_table;

[0125] That is, call the UDF function in the SQL statement, pass in the administrative division code as a parameter, and obtain and apply the regional division data.

[0126] Embodiment 2: Figure 2 As shown, based on the first embodiment, this embodiment further discloses a regional division system based on the activity logic of online car-hailing drivers: comprising a memory, a processor connected to the memory, wherein the memory stores program instructions, and when the program instructions are executed by the processor, the above-mentioned regional division method is implemented based on the following modules connected to the processor:

[0127] (1) Data processing module for the collection, synchronization, cleaning, integration and storage of administrative division data, including:

[0128] Access administrative division data from the business side's MySQL table to ensure the authority and accuracy of the data;

[0129] Use the offline data synchronization task on the DataWorks platform to synchronize administrative division data to the ODPS table in real time or periodically to maintain the timeliness of the data.

[0130] Clean the synchronized data to remove duplicate, outdated or invalid records, and aggregate the cleaned province, city, district and county table data into a MaxCompute administrative division table to facilitate subsequent data analysis and application;

[0131] (2) Intelligent dispatch module for zone division and driver activity data, including:

[0132] According to the MaxCompute administrative division table and the actual needs of the online car-hailing platform, the driver activity area is accurately divided;

[0133] According to the real-time order demand and driver location, combined with the regional division results, the order is assigned to the most suitable driver to reduce the idle driving and waiting time;

[0134] Combine the driver's historical order records, preferred areas and other information to achieve personalized order recommendations, and improve the driver's willingness to accept orders and satisfaction;

[0135] (3) A user interaction module for interaction between the online ride-hailing platform and users (including drivers and passengers), including:

[0136] It provides functions such as order acceptance, navigation, and evaluation for drivers, while displaying personalized order recommendations and regional division information to help drivers work more efficiently.

[0137] It provides functions such as calling, payment, and evaluation to ensure that passengers can conveniently enjoy online car-hailing services. At the same time, the passenger terminal can also display information such as the driver's real-time location and estimated arrival time to improve passenger satisfaction.

[0138] (4) A monitoring and optimization module for real-time monitoring of the operation of the online ride-hailing platform and for optimizing and adjusting the system based on the monitoring results, including:

[0139] The real-time monitoring function of the DataWorks platform can be used to monitor key indicators such as the division of driver activity areas, order allocation efficiency, driver willingness to accept orders, and passenger satisfaction in real time.

[0140] Use big data analysis tools to conduct in-depth analysis of monitoring data, tap operational potential, and identify potential problems.

[0141] Based on the results of data analysis, the regional division algorithm, scheduling strategy, user interaction interface, etc. are optimized and adjusted to ensure that the online ride-hailing platform can continue to operate efficiently.

[0142] All the above embodiments only express the implementation methods of the relevant practical applications of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

[0143] For those skilled in the art, it can be further appreciated that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0144] At the same time, those skilled in the art can understand that all or part of the processes in all the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Claims

1. A regional division method based on the activity logic of online car-hailing drivers, characterized in that: The following steps are included: S1, create a data offline synchronization task, configure the data source as the business side's MySQL table, and the target source as the ODPS table; S2: Group by administrative division code, record the update time in reverse order, and take the latest data. Use SQL table to aggregate the data in province table, city table, and district and county table to generate MaxCompute administrative division table. S3, the created Python resource node, executes the calculation logic of the administrative division code corresponding to the parent administrative division code; based on the UDF function node, references the Python resource node created by S4 and fills in the corresponding class name; S4, the downstream system creates a Python resource node and obtains the regional division data of the driver's activity area by calling the space name and UDF function name.

2. The area division method according to claim 1, characterized in that: In S1, the DataWorks platform is used to create a data offline synchronization task, and the data is updated based on the period of setting the synchronization task scheduling; when the synchronization task is executed, the administrative division data is stored in the ODPS table.

3. The area division method according to claim 2, characterized in that: The implementation method of S1 includes: S100, creating a data offline synchronization task; S101, based on the MySQL data source type, input the connection information of the MySQL table provided by the business party, including the database address, port number, user name, password, and table name to be synchronized; S102, configure the target source type to be an ODPS table, select or create a target ODPS project, and specify the table name where the synchronized data should be stored.

4. The area division method according to claim 1, characterized in that: The implementation method of S2 includes: S200, create SQL script tasks to perform data cleaning and integration operations; S201, in the SQL script, write a query statement to group the data according to the administrative division code; S202, based on a preset SQL statement, using a JOIN operation or a UNION operation, aggregates the cleaned data of the province table, the city table, and the district and county table; S203: In the SQL script, create a MaxCompute administrative division table and insert the integrated data into the table.

5. The area division method according to claim 4, characterized in that: In S201, for each administrative division code, the window function or grouping aggregation function of SQL is used to select the data record with the latest update time.

6. The area division method according to claim 1, characterized in that: The implementation method of S3 includes: S300, creating a UDF function node; in the UDF function node configuration, introducing a preset Python resource node; S301, fill in the corresponding class name in the Python resource node, including a method for implementing the calculation logic of the administrative division code corresponding to the parent administrative division code; S302, creating a SQL script task for calling a UDF function and executing the calculation of an administrative division code corresponding to a parent administrative division code.

7. The area division method according to claim 6, characterized in that: In the S302, the administrative division code to be calculated is also input as a parameter in the SQL script.

8. The area division method according to claim 1, characterized in that: The implementation method of S4 includes: S400, creating a new API interface in the data service or API management module for calling the UDF function created upstream; configuring the parameters of the API interface including the space name, the UDF function name, and the parameters to be passed in; S401, the downstream system calls the UDF function created upstream through the API interface and passes in corresponding parameters to obtain the regional division data of the driver's activity area; the downstream system receives and parses the regional division data returned by the UDF function.

9. A regional division system based on the activity logic of online car-hailing drivers, characterized by: The method comprises a memory and a processor connected to the memory, wherein the memory stores program instructions, and when the program instructions are executed by the processor, the area division method according to any one of claims 1 to 8 is implemented.

10. The area division system according to claim 9, characterized in that: The processor is also connected to Data processing module for the collection, synchronization, cleaning, integration and storage of administrative division data; Intelligent dispatch module for zoning and driver activity data; A user interaction module for interaction between the online ride-hailing platform and users; A monitoring and optimization module used to monitor the operation of the online ride-hailing platform in real time and make optimization adjustments based on the monitoring results.

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