Automatic data processing method and device in complex scene of online car-hailing risk control strategy

Abstract

The invention relates to the related technical field of online car-hailing risk control strategies, in particular to an automatic data processing method and device in an online car-hailing risk control strategy complex scene. The method comprises the following steps: establishing communication with a preset database based on a preset program; wherein the preset program is constructed through a Python programming language, and the preset database is an integration based on the Aliyun TableStore service; defining a data structure and a primary key in the database on the basis of data required to be used during online car-hailing risk control strategy testing so as to construct a TableStore table; based on the online car-hailing risk control strategy, constructing a group of labels, the group of labels including concerned information points in the online car-hailing risk control strategy and selectable ranges of values of the information points; and based on the tag, using the data structure and the primary key to generate data for the TableStore table.

Description

Technical Field

[0001] The present application relates to the technical field related to risk control strategies for online ride-hailing services, and specifically to a method and device for automated data processing in complex scenarios of risk control strategies for online ride-hailing services. Background Art

[0002] When executing the risk control system to test the risk control strategy, there is a strong reliance on the business data of the order (such as driver information, order information, billing information, coordinate information, etc.). Compared with traditional industries, the online car-hailing industry has more complex business processes and more diverse data. It is extremely difficult to get the expected data directly after completing the order according to the scenario.

[0003] During testing, after the business data of the order is stored in the database, it often fails to meet the expected requirements and needs to be manually modified, which consumes a lot of time and energy, but it is still difficult to ensure that the final test results can reach the ideal state.

[0004] The accuracy of order business data directly affects the accuracy and effectiveness of risk control strategies. However, the process of constructing data according to risk control strategies is extremely complicated, and scenario construction is cumbersome. Summary of the invention

[0005] In view of this, the embodiments of the present application are committed to providing a method and device for automatic data processing in complex scenarios of online car-hailing risk control strategies.

[0006] This application provides a method for automatic data processing in complex scenarios of online car-hailing risk control strategies, including: Establishing communication with a preset database based on a preset procedure; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; Based on the data required for online car-hailing risk control strategy testing, define the data structure and primary key in the database to build a TableStore table; Based on the risk control strategy of online ride-hailing, a set of tags is constructed, which includes the information points concerned in the risk control strategy of online ride-hailing and the optional range of values ​​of the information points. The optional range is obtained based on historical data; or obtained by manual input; Based on the tag, data is generated to the TableStore table using the data structure and the primary key.

[0007] In some embodiments, it also includes: Based on another online ride-hailing risk control strategy, another set of tags is constructed, and the other set of tags includes information points of concern in the other online ride-hailing risk control strategy, and an optional range of values ​​for the information points.

[0008] Based on the other set of tags, data is updated or generated to the TableStore table using the data structure and primary key.

[0009] In some embodiments, it also includes: Adjust the label.

[0010] In some embodiments, the preset program imports all components of Alibaba Cloud Table Store SDK; The preset program is provided with necessary parameters for communicating with the preset database.

[0011] In some embodiments, the necessary parameters include a service address of the preset database.

[0012] In some embodiments, establishing communication with a preset database based on a preset program includes: Entering account information into the preset program; The preset program establishes communication with a preset database based on the account information.

[0013] The present application provides a data automation processing device for complex scenarios of online car-hailing risk control strategies, including: A communication module, used to establish communication with a preset database based on a preset program; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; Define a module for the data needed for online car-hailing risk control strategy testing. In the database, define the data structure and primary key to build a TableStore table. A construction module is used to construct a set of labels based on the risk control strategy of the online car-hailing service. This set of labels includes information points of concern in the risk control strategy of the online car-hailing service and the optional range of values ​​of the information points.

[0014] A generation module is used to generate data for the TableStore table based on the label using the data structure and the primary key.

[0015] The present application provides an electronic device, including: A processor, and a memory for storing a program executable by the processor; The processor is used to implement the data automation processing method in complex scenarios of online car-hailing risk control strategies as described above by running the program in the memory.

[0016] The present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the method for automatic data processing in a complex scenario of the online car-hailing risk control strategy as described above.

[0017] The present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for automatic data processing in complex scenarios of online car-hailing risk control strategies.

[0018] The present application provides a method for automatic data processing in complex scenarios of online car-hailing risk control strategies. First, based on a preset program, communication with a preset database is established; wherein the preset program is constructed using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; based on the data required for testing the online car-hailing risk control strategy, in the database, data structures and primary keys are defined to construct a TableStore table; based on the online car-hailing risk control strategy, a set of tags is constructed, and this set of tags includes information points of concern in the online car-hailing risk control strategy, and the optional range of values ​​for the information points. Based on the tags, data is generated to the TableStore table using the data structure and primary key. With such a configuration, the present invention can generate high-quality data tags in a short time through automated technical means. It can automatically identify and generate the required data tags according to preset rules and conditions, greatly reducing the need for manual intervention and improving the quality and consistency of data tags. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 It is a flow chart of a method for automated data processing in a complex scenario of online car-hailing risk control strategy provided by an embodiment of the present application.

[0021] Figure 2 It is a partial flow chart of a method provided in one embodiment of the present application.

[0022] Figure 3 It is a structural schematic diagram of a method and device for automated data processing in a complex scenario of online car-hailing risk control strategy provided by an embodiment of the present application.

[0023] Figure 4It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Application Overview In the traditional testing process, generating high-quality data labels often requires a lot of manual intervention. This is not only time-consuming and labor-intensive, but also prone to introducing errors, affecting the accuracy of the test results. Specifically, in the risk control test of online ride-hailing, different risk control strategies require different test data. Manually generating this data is not only inefficient, but also difficult to cover all possible test scenarios.

[0026] In order to solve the above problems, the present invention provides a flexible data generation mechanism that can quickly generate expected order data, driver data, route data and billing data according to the specific requirements of the risk control strategy. These data can be customized according to different test scenarios to ensure the comprehensiveness and accuracy of the test.

[0027] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0028] Exemplary Methods Figure 1 This is a flow chart of a method for automatic data processing in a complex scenario of online car-hailing risk control strategy provided by an embodiment of the present application. Figure 1 and Figure 2 As shown, the method includes the following contents.

[0029] Step S110, establishing communication with a preset database based on a preset program; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; The purpose of this step is to ensure that the preset program can effectively exchange data with the database integrated with the Alibaba Cloud TableStore service. The preset program built in the Python programming language initializes the connection with the TableStore service by importing the Alibaba Cloud TableStore SDK. This involves configuring parameters such as database access rights, security keys, and service addresses (Endpoints). Specifically, an OTS (TableStore) client object is created in the program, which is responsible for managing all interactions with the TableStore service. Through this client, the program can perform subsequent data definition, data writing, and query operations.

[0030] Step S120: Based on the data required for the online car-hailing risk control strategy test, define the data structure and primary key in the database to build a TableStore table; The purpose of defining the data structure and primary key is to organize and store the data required for the online car-hailing risk control strategy test in the TableStore table. Create a new table in the TableStore service and define the primary key of the table (which can be a single column or a composite primary key). The primary key is used to uniquely identify each record in the table to ensure fast retrieval and accurate update of data. After determining the primary key of the table, you also need to define other properties of the table, such as the data type of the column, column name, etc. These definitions will determine the structure of the table and the types of data that can be stored.

[0031] Step S130, based on the risk control strategy of the online car-hailing service, construct a set of tags, which includes the information points of concern in the risk control strategy of the online car-hailing service and the optional range of values ​​of the information points.

[0032] The purpose of building labels is to identify and classify the key information points of concern in the risk control strategy of online ride-hailing, as well as the possible value ranges of these information points. According to the specific needs of the risk control strategy of online ride-hailing, determine the data points that need to be monitored, such as driver behavior, order status, transaction amount, etc., and define one or more labels for each data point. Each label corresponds to one or more possible value ranges, which are used for subsequent data generation and risk control strategy evaluation. The construction of labels may involve complex business logic and risk control rules, and requires a deep understanding of the business process and risk control requirements of online ride-hailing. The design of labels should be flexible enough to adapt to different test scenarios and strategy adjustments. Among them, the optional range is obtained based on historical data; or, it is obtained by manual input; this can ensure that the optional range is as comprehensive and reasonable as possible.

[0033] Step S140: Based on the label, use the data structure and primary key to generate data for the TableStore table.

[0034] Based on the constructed tags and defined data structures, data is generated into the TableStore table to support the testing and evaluation of risk control strategies. Specifically, a Python program is used to generate data according to preset rules and conditions, and the data is written into the TableStore table. This process may involve batch insertion or update of data to simulate real business scenarios. The data generation process needs to ensure the consistency and accuracy of the data, and may require the use of complex algorithms and logical operations to simulate different risk control scenarios. The generated data will be used for subsequent risk control strategy testing, so it needs to be able to truly reflect the complexity of the online car-hailing business.

[0035] The above steps together constitute a complete data automation processing flow. From establishing database communication to the final generation of data, each step aims to improve the efficiency and accuracy of online car-hailing risk control strategy testing.

[0036] Furthermore, the method for automatic data processing in complex scenarios of online ride-hailing risk control strategies is characterized in that it also includes: constructing another set of labels based on another online ride-hailing risk control strategy, the other set of labels including information points of concern in the other online ride-hailing risk control strategy, and an optional range of values ​​for the information points; based on the other set of labels, using the data structure and primary key, updating or generating data to the TableStore table.

[0037] Specifically, when there is another risk control strategy for online ride-hailing, we first need to deeply analyze the unique information points that the strategy focuses on. For example, the new strategy may focus on passenger behavior analysis, and the information points of concern may include the distribution of passengers' boarding and alighting locations, travel time preferences, travel route patterns, etc. These information points are different from previous risk control strategies (such as focusing on driver behavior or order transaction characteristics) and are determined from a new risk assessment perspective.

[0038] Determine the range of values: For each new information point, clarify its reasonable range of values. Taking the distribution of passenger boarding and alighting locations as an example, the range of values ​​may be the various area codes or geographic coordinates within the city; the range of values ​​for travel time preferences may be different time periods of the day (such as peak hours, night time, etc.); the range of values ​​for travel route patterns may involve common route types (such as short distances within the city, long distances across regions, etc.) and the passing points of specific routes. This step ensures that the generated data is meaningful and effective under the new strategy.

[0039] Update or generate data based on new labels: With the previously defined data structure and primary key, the system can accurately integrate the newly generated data into the existing TableStore table. The data structure specifies the storage format and field type of the new data, ensuring data consistency and compatibility. The primary key ensures the unique identification of the new data in the table, facilitating subsequent data query, update, and association operations. Update data: If some data related to the new policy already exists in the table, but needs to be adjusted according to the new label, then update the data. For example, according to the new passenger behavior analysis strategy, it is found that some passengers have a higher risk of orders in specific time periods and areas. Then, the data related to these orders can be updated, and their risk levels can be marked or relevant comments can be added so that the risk control system can identify and process them in time. If there is no data in the table that meets the requirements of the new policy, a new data record is generated according to the new label. For example, in order to test the new passenger behavior-related risk control strategy, the system can generate a series of simulated order data, including passenger information, order details, itinerary routes, etc., according to the distribution of passenger boarding and alighting locations, travel time preferences, and other information specified in the new label, so as to enrich the data sample and fully verify the effectiveness of the new strategy.

[0040] With this setup, the entire data automation processing system is highly adaptable by being able to build corresponding labels and update or generate data based on different online ride-hailing risk control strategies. Whether facing a changing market environment, emerging risk types, or adjustments to the company's internal risk control strategies, the system can respond quickly without large-scale redevelopment or data reconstruction. Different risk control strategies can be tested and verified in a variety of ways based on the same data. This helps to comprehensively evaluate the performance of various strategies in complex online ride-hailing business scenarios, discover potential strategy loopholes or deficiencies, and provide a rich practical basis for further optimizing and improving risk control strategies. At the same time, parallel testing and data interaction of multiple strategies can also help discover possible synergies or conflicts between different strategies, thereby achieving optimal configuration of the overall risk control system.

[0041] In some embodiments, the step further includes adjusting the label.

[0042] During the implementation process, according to new business needs or a deeper understanding of the risk control strategy, the information points contained in the label can be added or deleted. For example, the initial risk control strategy may only focus on the basic information of the driver (such as driving experience, age) and the transaction amount of the order to build the label. As the business develops, it is found that the model of the vehicle, annual inspection status and other information also have an important impact on risk assessment. At this time, these new information points can be added to the label. Conversely, if some information points are proven to have little impact on the risk control effect in actual analysis, or the acquisition cost is too high and the value is not great, they can be deleted from the label. Value range adjustment: The adjustment of the value range of information points is also critical. For example, for the information point of driver age, the original value range was set to 18-65 years old. However, as industry research has found that drivers between the ages of 55-65 have a higher probability of certain specific risks (such as fatigue driving related risks), the value range of this age group may be further subdivided to more accurately assess risks. Or, as the business expands to new regions, local traffic regulations or market environments are different, resulting in changes in the reasonable value range of certain information points. For example, the mileage of orders may be generally longer in new regions, and its upper limit needs to be adjusted accordingly. Modification of logical relationships: The logical relationship between information points in the label may also need to be adjusted. For example, the original label logic is "driver's driving experience <3 years and order amount >500 yuan" as a judgment condition for high-risk orders. With the accumulation and analysis of data, it is found that this simple logical relationship cannot accurately reflect the actual risk, and it may be modified to "(driver's driving experience <3 years and order amount >500 yuan) or (driver's number of violation records >5 times and order mileage >100 kilometers)", and define labels through more complex and reasonable logical combinations to improve the accuracy of risk assessment.

[0043] The significance of adjusting labels includes: Adapting to business changes: The online car-hailing industry is a dynamically developing industry, with market environment, user needs, policies and regulations and other factors constantly changing. Adjusting labels can enable data processing and risk control strategies to closely follow these changes, ensuring that the system can always effectively identify and respond to emerging risks. For example, as competition in the shared travel market intensifies, new unfair competition behaviors (such as malicious order brushing, false orders, etc.) may emerge. By adjusting labels, information related to these behaviors can be promptly incorporated into the risk assessment system to ensure a fair competition environment for the platform. Optimizing risk control strategies: Based on more accurate and comprehensive labels, risk control strategies can be continuously optimized. More accurate labels can help the system more accurately screen out high-risk orders and drivers, and improve the pertinence and effectiveness of risk control. For example, by adjusting labels, the risk assessment model can more finely distinguish different degrees of risk, so that more appropriate risk control measures can be taken, such as early warning prompts for low-risk situations, direct interception or in-depth investigation of high-risk situations, and improve the utilization efficiency of risk control resources. Improving the value of data utilization: Reasonable adjustment of labels can better tap the potential value of data. Different tag combinations and value ranges can analyze data from different angles and discover risk patterns and business rules hidden in the data. For example, by adjusting the tags, it is found that orders in certain time periods and specific areas and specific attribute combinations of drivers (such as novice drivers taking orders in remote areas at night) have higher risks. This not only helps optimize the current risk control strategy, but also provides decision support for business operations, such as adjusting the dispatch strategy and optimizing driver training content.

[0044] In some embodiments, the preset program imports all components of the Alibaba Cloud Table Store SDK; the preset program is set with necessary parameters for communicating with the preset database. The necessary parameters include the service address of the preset database. The communication with the preset database based on the preset program includes: inputting account information to the preset program; the preset program establishes communication with the preset database based on the account information.

[0045] Importing all components of the Alibaba Cloud Table Store SDK provides the program with a rich functional interface, enabling the program to interact with the Table Store database. Setting the necessary parameters, including the service address, is the key configuration to achieve this interaction. The SDK components rely on these parameters to determine the specific location and method of communicating with the database. For example, the service address is the basis for the SDK component to find the database service endpoint. Only when you know the correct address can you initiate an effective communication request.

[0046] Ensure communication security and accuracy: At the same time, parameters such as security keys (AccessKey ID and AccessKey Secret) play an identity authentication role in the communication process. Combined with the correct service address, it ensures that only authorized programs can establish a connection with the specified database instance, ensuring data security. This collaborative working mechanism ensures the accuracy, security, and functional integrity of communication between programs and databases, laying a solid foundation for subsequent data operations.

[0047] The process and significance of entering account information to establish communication: Entering account information into the preset program is an important step to establish communication with the preset database. Account information is usually associated with an Alibaba Cloud account and may include a user name, password, or other identity authentication credentials. The program performs an identity authentication process internally based on this account information. This process is similar to presenting an identity certificate when entering a protected area in real life. Only legitimate account holders can obtain access rights. Through the verification of account information, the database can identify the user or organization to which the program requesting communication belongs, and assign corresponding resource access rights according to pre-set permissions. For example, different accounts may have different database operation permissions, such as read-only access, read-write access, or access rights to specific tables. This ensures that each user can only interact with the database within the scope of their authorization, preventing unauthorized operations and data leakage risks, while also providing personalized database service experience for different users. Once the account information is verified, the program can establish a secure communication channel with the preset database based on this. This communication channel will follow Alibaba Cloud's security protocol to encrypt and protect the transmitted data to prevent the data from being stolen or tampered with during transmission. In the entire process of online car-hailing risk control data processing, a secure and reliable communication channel is the key link to ensure data integrity and confidentiality, ensuring that sensitive risk control data is safely transmitted and interacted between the program and the database, thereby ensuring the stable operation of the entire system and data security.

[0048] The following is a comprehensive description of the preferred embodiments of the present invention: In the risk control test of online ride-hailing, different risk control strategies require different test data. Manually generating this data is not only inefficient, but also difficult to cover all possible test scenarios. Based on this, the present invention provides a flexible data generation mechanism that can quickly generate expected order data, driver data, route data, and billing data according to the specific requirements of the risk control strategy. These data can be customized according to different test scenarios to ensure the comprehensiveness and accuracy of the test.

[0049] Furthermore, the traditional data manufacturing process is complicated and cumbersome, requiring multiple steps and the collaboration of multiple people. This not only prolongs the time for data preparation, but also increases the risk of errors. The present invention simplifies the data manufacturing process by integrating the Python programming language and the Alibaba Cloud Table Store service. The system can automatically complete the generation, storage and update of data, greatly improving the efficiency and flexibility of data preparation. This not only shortens the test cycle, but also improves the accuracy of the test results.

[0050] Furthermore, during the test process, a large number of data tags often need to be added or modified. Manual operation is not only inefficient, but also prone to errors. The present invention supports batch addition and batch modification of tag types and data values. Users can complete the update of a large number of data tags at one time through simple operations, greatly improving the efficiency of data management. This not only saves time and manpower, but also ensures the consistency and accuracy of the data.

[0051] Specifically, this solution aims to establish an efficient and secure method through the integration of Python programming language and Alibaba Cloud Table Store service. The goal is to achieve fast storage, query and update of key data in risk control scenarios to support the real-time decision-making needs of risk control systems.

[0052] Specific design techniques include: Programming language: Python, due to its rich library support and easy readability, is suitable for rapid development and maintenance.

[0053] Data storage service: Alibaba Cloud Table Store, a distributed NoSQL database service, is suitable for large-scale structured data storage, especially for high-concurrency and low-latency scenarios.

[0054] SDK: The Python SDK officially provided by Alibaba Cloud simplifies the interaction process with Table Store.

[0055] Furthermore, the risk control system is composed of strategies, which are composed of rules, which are composed of data tags according to the calculation method; During the test process, in order to verify the accuracy of the risk control system strategy, it is necessary to use the technology used in this patent to simulate the production or modification of a batch of data used in the rules under the risk control strategy based on the labels used in the rules under the strategy. The purpose of this application is to produce label data that meets the rules under the strategy and improve the efficiency of test execution. When testing the risk control strategy, it is necessary to manually modify the data. However, the data scenario for creating data labels according to the risk control strategy is relatively complex and the process is relatively cumbersome. This patent uses technical interaction with Table Store to add or modify data in batches.

[0056] Specifically, first write a Python program to communicate with Alibaba Cloud's Table Store service. To do this, first import all components of the Alibaba Cloud Table Store SDK into the program so that you can use the preset classes and functions in the SDK to perform operations. Next, set the necessary connection parameters in the program: including Endpoint (service address), AccessKey ID, AccessKey Secret (security key), and instance name. These parameters are used to confirm the user's identity and point to the specific Table Store instance to be operated. Based on these parameters, you need to create or initialize an OTS (Table Store) client object. Determine the name of the table to be operated and prepare a set of primary key values. This step is to accurately locate specific records in the table. In order to simulate the data requirements in the risk control scenario, design a set of tags, each of which must specify its name, data type, and specific value. These tag data will reflect the information points of concern in the risk control system. Finally, using the previously prepared data structure, write code logic to batch add new data to the Table Store table or update existing data to ensure that the risk control system can successfully obtain and apply this data.

[0057] In summary, the solution provided by this application has the following effects: The application of automation technology greatly reduces the need for manual operation, making the insertion and update of data labels fast and efficient. This not only saves labor costs, but also speeds up the entire data processing process and improves work efficiency. Manual operation is prone to introduce errors, while the automation process can significantly reduce this risk. Through preset logical operation rules, the automation system can accurately and correctly perform the assignment of labels, avoiding data inconsistencies or errors caused by human negligence. The ability to support multiple sets of label systems on the same order data means that labels can be flexibly adjusted and applied according to different business needs and scenarios. This provides greater freedom and adaptability for policy formulation. Strategy verification acceleration: With the help of automated data labels, test engineers can verify policy logic more quickly without waiting for a long data preparation process. This helps to find problems and make adjustments in a timely manner, ensuring that the policy achieves the expected accuracy before implementation. Improved policy execution accuracy: Through sophisticated logical operation rules, automated data labels ensure high-precision execution of policies. This means that companies can rely more confidently on data-driven decisions and improve the accuracy and effectiveness of overall operations.

[0058] Specifically, if a specific risk control strategy needs to be tested, the solution provided by this application can automatically generate a batch of expected order data according to the requirements of the strategy and automatically store them in the warehouse. During the test process, if some data is found to be not in line with expectations, the solution provided by this application can automatically adjust the data generation rules and regenerate the data without manual intervention.

[0059] Exemplary Devices The device embodiments of the present application can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0060] Figure 3 The figure is a block diagram of a method and device for automatically processing data in a complex scenario of online car-hailing risk control strategy provided by an embodiment of the present application. Figure 3 As shown, the device comprises: A communication module 31, used to establish communication with a preset database based on a preset program; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; A definition module 32 is used to define the data structure and primary key in the database based on the data required for the online car-hailing risk control strategy test to build a TableStore table; Construction module 33 is used to construct a set of tags based on the risk control strategy of the online car-hailing service. This set of tags includes information points of concern in the risk control strategy of the online car-hailing service and the optional range of values ​​of the information points.

[0061] The generating module 34 is used to generate data to the TableStore table based on the label using the data structure and the primary key.

[0062] Exemplary Electronic Devices Below, reference Figure 4 To describe an electronic device according to an embodiment of the present application. Figure 4 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0063] like Figure 4 As shown, electronic device 400 includes one or more processors 410 and memory 420 .

[0064] The processor 410 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0065] The memory 420 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 410 may run the program instructions to implement the data automation processing method and / or other desired functions in the complex scenarios of the online car-hailing risk control strategy of each embodiment of the present application described above. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0066] In one example, the electronic device 400 may further include: an input device 430 and an output device 440 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0067] In addition, the input device 430 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 440 may output various information to the outside, including analysis results, etc. The output device 440 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0068] Of course, to simplify, Figure 4 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0069] Exemplary computer program products and computer-readable storage media In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for automatic data processing in complex scenarios of online car-hailing risk control strategies according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0070] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0071] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the method for automated data processing in complex scenarios of online car-hailing risk control strategies according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0072] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0073] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for automatic data processing in complex scenarios of online car-hailing risk control strategies, characterized in that: include: Establishing communication with a preset database based on a preset procedure; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; Based on the data required for online car-hailing risk control strategy testing, define the data structure and primary key in the database to build a TableStore table; Based on the risk control strategy of online ride-hailing, a set of labels is constructed, and the set of labels includes information points of interest in the risk control strategy of online ride-hailing, and an optional range of values ​​of the information points; wherein the optional range is obtained based on historical data; or obtained by manual input; Based on the tag, data is generated to the TableStore table using the data structure and the primary key.

2. The method for automatic data processing in complex scenarios of online car-hailing risk control strategies according to claim 1 is characterized in that: Also includes: Based on another online car-hailing risk control strategy, construct another set of tags, the other set of tags including information points of interest in the other online car-hailing risk control strategy and an optional range of values ​​for the information points; Based on the other set of tags, data is updated or generated to the TableStore table using the data structure and primary key.

3. The method for automatic data processing in complex scenarios of online car-hailing risk control strategies according to claim 1 is characterized in that: Also includes: Adjust the label.

4. The method for automatic data processing in complex scenarios of online car-hailing risk control strategies according to claim 1 is characterized in that: The preset program imports all components of Alibaba Cloud Table Store SDK; The preset program is provided with necessary parameters for communicating with the preset database.

5. The method for automatic data processing in complex scenarios of online car-hailing risk control strategy according to claim 4 is characterized in that: The necessary parameters include the service address of the preset database.

6. The method for automatic data processing in complex scenarios of online car-hailing risk control strategies according to claim 1 is characterized in that: The establishing of communication with a preset database based on a preset program includes: Entering account information into the preset program; The preset program establishes communication with a preset database based on the account information.

7. A data automation processing device for complex scenarios of online car-hailing risk control strategies, characterized in that: include: A communication module, used to establish communication with a preset database based on a preset program; The preset program is built using the Python programming language, and the preset database is an integration based on the Alibaba Cloud TableStore service; Define a module for the data needed for risk control strategy testing based on online ride-hailing services. In the database, define the data structure and primary key to build a TableStore table. A construction module is used to construct a set of labels based on the risk control strategy of the online car-hailing service, and the set of labels includes information points of interest in the risk control strategy of the online car-hailing service and the optional range of values ​​of the information points; A generation module is used to generate data for the TableStore table based on the label using the data structure and the primary key.

8. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is used to implement the method for automatic data processing in a complex scenario of the online car-hailing risk control strategy as described in any one of claims 1 to 6 by running the program in the memory.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the method for automatic data processing in a complex scenario of an online car-hailing risk control strategy as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method for automatic data processing in a complex scenario of an online car-hailing risk control strategy as described in any one of claims 1 to 6.

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