Method and device for determining operation configuration

Abstract

The invention relates to the technical field of computers, and discloses an operation configuration determination method and device. The method comprises the following steps: acquiring behavior data information and a registration area of a target user; determining historical data information corresponding to the registration area according to the registration area; wherein the historical data information comprises at least one of order dispatching radius, order category, order timeliness type, order dispatching round number and driver qualification; and determining operation configuration of the target user in the registration area based on the behavior data information and the historical data information of the target user.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for determining an operation configuration. Background Art

[0002] With the rapid development of Internet technology and the popularity of smart phones, the online car-hailing industry has risen rapidly around the world. This emerging mode of transportation not only provides people with a more convenient and economical travel option, but also injects new vitality into the job market. Online car-hailing platforms efficiently connect passengers and drivers through mobile Internet technology, achieving optimal allocation of resources. However, how to provide personalized operation configuration for online car-hailing users (including drivers and passengers) to improve service quality and user satisfaction has become an important challenge facing online car-hailing platforms.

[0003] In the online car-hailing industry, user registration is the basic link of platform operation. When users register, online car-hailing platforms usually rely on professionals to select operation configurations for users based on their experience. These professionals will combine user information and other information to develop personalized service plans for users.

[0004] However, when choosing operational configurations, professionals often rely on personal experience and intuition, which results in highly subjective configuration results and is unable to provide users with more appropriate operational configurations. Summary of the invention

[0005] In view of this, the present invention provides a method and device for determining an operation configuration.

[0006] In a first aspect, the present invention provides a method for determining an operational configuration, the method comprising: obtaining behavioral data information and a registration area of ​​a target user; determining historical data information corresponding to the registration area based on the registration area; wherein the historical data information comprises at least one of a dispatch radius, an order category, an order timeliness type, a number of dispatch rounds, and driver qualifications; determining the operational configuration of the target user in the registration area based on the behavioral data information and the historical data information of the target user.

[0007] The method for determining the operational configuration provided in this embodiment can more accurately understand the driver's driving habits, preferences, and market characteristics of the region by obtaining the behavioral data information and registration area of ​​the target driver. Combined with historical data information, such as dispatch radius, order category, order time type, number of dispatch rounds, and driver qualifications, the understanding of the driver's operational needs can be further refined. Based on this information, a personalized operational configuration is formulated for the target driver in the registration area, which helps to improve operational efficiency and service quality. At the same time, there is no need for professionals to select operational configurations for users based on experience, which improves the accuracy of the operational configuration.

[0008] In one possible implementation, historical data information corresponding to the registration area is determined based on the registration area, including: inputting the registration area into an information matching model to determine the historical data information corresponding to the registration area; or, based on the registration area, matching the historical data information corresponding to the registration area from a historical database.

[0009] The method for determining the operation configuration provided in this embodiment is that the information matching model is trained based on advanced algorithms and a large amount of historical data, and can quickly and accurately match relevant historical data information according to the registration area. Compared with manual query and screening, the information matching model greatly improves work efficiency and reduces the possibility of human error.

[0010] In addition, historical databases usually contain a wealth of historical data information, which has been strictly screened and verified and has high integrity and reliability. Through historical database matching, it can be ensured that the historical data information obtained is accurate and reliable, providing strong support for subsequent decision-making and analysis.

[0011] In one possible implementation, the steps of constructing an information matching model include: obtaining initial information data during the operation of the business system; transforming the initial information data to obtain historical data information; inputting the historical data information into a neural network model for training to obtain an information matching model.

[0012] The method for determining the operational configuration provided in this embodiment can automatically learn and identify features in historical data information through a neural network model, and can complete the training and optimization of the model without human intervention. This automation and intelligent feature greatly improves the efficiency of data processing. In addition, a fully trained neural network model has high prediction accuracy and generalization ability. This means that the model can not only accurately match known data, but also make reasonable predictions and judgments on unknown or newly emerging data.

[0013] In one possible implementation, when there are multiple registration areas, the method also includes: determining historical data information corresponding to the target registration area based on the target registration area; wherein the target registration area is any one of the multiple registration areas; determining the operation configuration of the target user in the target registration area based on the target user's behavior data information and the historical data information corresponding to the target registration area; displaying the operation configuration of the target registration area to the target user; and executing the target operation configuration in response to a trigger operation of the target user on the target operation configuration; wherein the target operation configuration is any one of the multiple operation configurations.

[0014] The method for determining the operation configuration provided in this embodiment can accurately match the personalized operation configuration according to the characteristics of different registration areas and the needs of target users, which helps to meet the differentiated needs of different areas and users and improve the pertinence and effectiveness of services.

[0015] In addition, the separate formulation and execution of operational configurations for different registration areas can allocate resources more reasonably and avoid waste and duplication of resources. This helps enterprises achieve optimal allocation and efficient use of resources.

[0016] In addition, the operation configuration of the target registration area is displayed to the target user, and the user is allowed to make selections and trigger operations, which increases user participation and interactivity. Users can make choices based on their needs and preferences, which improves the initiative and personalization of the service.

[0017] In a second aspect, the present invention provides a device for determining an operation configuration. The device includes: a first acquisition module for acquiring the behavior data information and registration area of ​​a target driver; a first determination module for determining the historical data information corresponding to the registration area according to the registration area; wherein the historical data information includes: at least one of the dispatch radius, order category, order time type, dispatch round number, and driver qualification; a second determination module for determining the operation configuration of the target user in the registration area based on the behavior data information and historical data information of the target user.

[0018] In one possible implementation, the first determination module includes: a first determination unit, used to input the registration area into the information matching model to determine the historical data information corresponding to the registration area; or, a second determination unit, used to match the historical data information corresponding to the registration area from the historical database based on the registration area.

[0019] In one possible implementation, the device also includes: a second acquisition module, used to acquire initial information data during the operation of the business system; a conversion module, used to convert the initial information data to obtain historical data information; and a training module, used to input the historical data information into a neural network model for training to obtain an information matching model.

[0020] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the method for determining the operational configuration of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining the operational configuration of the first aspect or any corresponding embodiment thereof.

[0022] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for determining the operational configuration of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 is a flow chart of a method for determining an operation configuration according to an embodiment of the present invention;

[0025] Figure 2 is a schematic diagram of a system for determining an operation configuration according to an embodiment of the present invention;

[0026] Figure 3 is a structural block diagram of an apparatus for determining an operation configuration according to an embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] Based on relevant technologies, with the rapid development of Internet technology and the popularity of smart phones, the online car-hailing industry has risen rapidly around the world. This emerging mode of transportation not only provides people with more convenient and economical travel options, but also injects new vitality into the job market. Online car-hailing platforms efficiently connect passengers and drivers through mobile Internet technology, achieving optimal allocation of resources. However, how to provide personalized operation configuration for online car-hailing users (including drivers and passengers) to improve service quality and user satisfaction has become an important challenge facing online car-hailing platforms.

[0030] In the online car-hailing industry, user registration is the basic link of platform operation. When users register, online car-hailing platforms usually rely on professionals to select operation configurations for users based on their experience. These professionals will combine user information and other information to develop personalized service plans for users.

[0031] However, when choosing operational configurations, professionals often rely on personal experience and intuition, which results in highly subjective configuration results and is unable to provide users with more appropriate operational configurations.

[0032] Based on this, the present invention provides a method for determining operational configuration. By obtaining the behavioral data information and registration area of ​​the target driver, this method can more accurately understand the driver's driving habits, preferences, and market characteristics of the area where he is located. Combined with historical data information, such as dispatch radius, order category, order time type, number of dispatch rounds, and driver qualifications, the understanding of the driver's operational needs can be further refined. Based on this information, a personalized operational configuration is formulated for the target driver in the registration area, which helps to improve operational efficiency and service quality. At the same time, there is no need for professionals to select operational configurations for users based on experience, which improves the accuracy of the operational configuration.

[0033] According to an embodiment of the present invention, an embodiment of a method for determining an operational configuration is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] In this embodiment, a method for determining an operation configuration is provided, which can be used for computer equipment, such as a computer, a server, etc. Figure 1 is a flow chart of a method for determining an operation configuration according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0035] Step S101, obtaining the behavior data information and registration area of ​​the target user.

[0036] The target user can represent the driver who has registered on the online ride-hailing platform. The behavioral data information can represent the data of the driver's driving and operating habits, such as average speed, average daily mileage, type of orders accepted, etc. The registration area can represent the geographical location area selected by the driver when registering or automatically assigned by the system. For example: M city, N city, etc., are not specifically limited here. Specifically, the driver's driving data can be collected through vehicle-mounted equipment or mobile phone APP. The registration area can be obtained using the information filled in by the driver when registering. For example: Target driver A is registered in M ​​city, and his behavioral data information shows that he prefers to take short-distance orders during peak hours in the morning and evening.

[0037] Step S102, according to the registration area, determine the historical data information corresponding to the registration area; wherein the historical data information includes: at least one of the dispatch radius, order category, order time type, number of dispatch rounds, and driver qualification.

[0038] According to the driver's registration area, the relevant operation data of the area is retrieved from the historical database, such as dispatch radius, order category, order time type, number of dispatch rounds and driver qualifications. Among them, historical data information: summary of the operation data of drivers in a certain area in the past period of time. The dispatch radius can be the maximum distance limit when the system assigns orders to drivers. The order category can be different types of orders such as express, private car, and carpooling. The order time type can be an instant order, a scheduled order, etc. The number of dispatch rounds can be the number of times an order is assigned to a driver by the system. The driver's qualifications can be the driver's driving experience, service evaluation, etc.

[0039] As an example, historical data can be obtained through database query. Historical data can also be obtained through neural network models, etc., which are not specifically limited here. For example: Driver A registered in City M, his historical data information shows that the dispatch radius of the area is 5 kilometers, the order category is mainly express, the order time type is mostly instant order, the average number of dispatch rounds is about 2, and the driver qualifications are generally high.

[0040] Step S103: determining the operation configuration of the target user in the registration area based on the behavior data information and historical data information of the target user.

[0041] Combine the behavior data of the target driver and the historical data of the registration area to formulate personalized operation configuration for them, such as optimizing the dispatching strategy, adjusting the order category preference, setting a reasonable online time, etc. The operation configuration can represent the operation strategy and service parameters formulated for a specific driver in a specific area.

[0042] As an example, a machine learning algorithm can be used to train data and generate personalized operation configurations. A suitable operation plan can be formulated for the driver based on the statistical laws of historical data. The operation configuration can be manually adjusted in combination with the driver's personal preferences and regional characteristics, etc., which are not specifically limited here and can be implemented by those skilled in the art.

[0043] The method for determining the operational configuration provided in this embodiment can more accurately understand the driver's driving habits, preferences, and market characteristics of the region by obtaining the behavioral data information and registration area of ​​the target driver. Combined with historical data information, such as dispatch radius, order category, order time type, number of dispatch rounds, and driver qualifications, the understanding of the driver's operational needs can be further refined. Based on this information, a personalized operational configuration is formulated for the target driver in the registration area, which helps to improve operational efficiency and service quality. At the same time, there is no need for professionals to select operational configurations for users based on experience, which improves the accuracy of the operational configuration.

[0044] For example, for driver A, who is registered in city M and prefers to take short-distance orders during peak hours in the morning and evening, the following operational configuration can be developed for him: increase the dispatch frequency of short-distance orders during peak hours in the morning and evening, and reduce the allocation of long-distance or scheduled orders; adjust the dispatch radius based on historical data to ensure that orders can be delivered efficiently and accurately; at the same time, assign more high-quality orders to the driver based on his service evaluation and driving experience.

[0045] In a possible implementation, the above step S102 includes:

[0046] Step a1, inputting the registration area into the information matching model to determine the historical data information corresponding to the registration area;

[0047] Using a pre-trained information matching model, the driver's registration area is used as input. The model automatically outputs historical data information related to the registration area through internal algorithms and logic. These information data may include but are not limited to dispatch radius, order category, order time type, number of dispatch rounds, driver qualifications, etc. Specifically, the information matching model can be a model based on machine learning algorithms such as neural networks, decision trees, and support vector machines. The model has learned the association between the registration area and historical data information during the training phase. When a new registration area is entered, the model can quickly and accurately output the corresponding historical data information. For example: an information matching model, when the registration area "M City" is entered, the model outputs the corresponding historical data information, such as a dispatch radius of 5 kilometers, order categories are mainly express, and order time types are mostly instant orders.

[0048] Step a2: According to the registration area, historical data information corresponding to the registration area is matched from the history database.

[0049] By directly querying the historical database, historical data information related to the registration area is retrieved from the database based on the registration area as the query condition. These data may also include dispatch radius, order category, order time type, etc. Specifically, the historical database can be a relational database, NoSQL database, or other type of data storage system. The query statement (such as an SQL statement) extracts relevant data from the database based on the registration area as a screening condition. The extracted data may need to be further processed and analyzed to obtain the final historical data information.

[0050] The method for determining the operation configuration provided in this embodiment is that the information matching model is trained based on advanced algorithms and a large amount of historical data, and can quickly and accurately match relevant historical data information according to the registration area. Compared with manual query and screening, the information matching model greatly improves work efficiency and reduces the possibility of human error. In addition, the historical database usually contains a wealth of historical data information, which has been strictly screened and verified and has high integrity and reliability. Through historical database matching, it can be ensured that the historical data information obtained is accurate and reliable, providing strong support for subsequent decision-making and analysis.

[0051] In a possible implementation, the step of building an information matching model includes:

[0052] Step b1, obtaining initial information data during the operation of the business system.

[0053] Initial information data can represent the data collected by the business system during operation. For example, business systems such as order systems, scheduling systems, and asset systems collect data through asynchronous log output during operation. For example, dispatch radius, order category, order timeliness type, number of dispatch rounds, driver willingness, driver qualifications, etc. The data collection service logs are pulled regularly through scheduled tasks, converted into metadata, and uniformly stored in the database.

[0054] Step b2, converting the initial information data to obtain historical data information.

[0055] The initial information data is cleaned, sorted, converted and aggregated to obtain structured and standardized historical data information. This data will be used to train the neural network model to establish a mapping relationship between input (such as registration area) and output (such as historical data information).

[0056] Step b3, inputting historical data information into the neural network model for training to obtain an information matching model.

[0057] Use historical data information as training data and input it into a pre-designed neural network model for training. By adjusting the parameters and structure of the model, the model can learn the mapping relationship between input and output, thereby obtaining an information matching model. Specifically, select a suitable neural network architecture, such as a fully connected neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), etc. Use the training data set to train the model, and adjust the model parameters through the back propagation algorithm. Use the validation data set to evaluate the performance of the model, and perform model selection and tuning. Finally, a trained information matching model is obtained, which can be used to predict and match new input data.

[0058] The method for determining the operational configuration provided in this embodiment can automatically learn and identify features in historical data information through a neural network model, and can complete the training and optimization of the model without human intervention. This automated and intelligent feature greatly improves the efficiency of data processing. In addition, a fully trained neural network model has high prediction accuracy and generalization ability. This means that the model can not only accurately match known data, but also make reasonable predictions and judgments on unknown or newly emerging data. For example: using a fully connected neural network model, taking historical data information (such as registration area, dispatch radius, order category, etc.) as input, the model is trained to predict the historical data information corresponding to the new registration area. By adjusting the parameters of the neural network such as the number of layers, the number of nodes, and the activation function, an information matching model that can accurately match input and output is finally obtained.

[0059] In a possible implementation, when there are multiple registration areas, the method further includes:

[0060] Step c1, according to the target registration area, determining the historical data information corresponding to the target registration area; wherein the target registration area is any one of the multiple registration areas.

[0061] From the existing historical data set, according to the specific target registration area (which may be selected by the user or recommended by the system), retrieve and extract the historical data information related to the area. This information may include the dispatch mode, order distribution, user preferences, driver behavior characteristics, etc. of the area. Specifically, you can use database query statements (such as SQL) to extract relevant data from the historical database based on the target registration area as a screening condition. Use data indexing or caching technology to speed up the data retrieval process. Preprocess the extracted data, such as deduplication, normalization, encoding, etc., for subsequent use.

[0062] Step c2: determining the operation configuration of the target user in the target registration area based on the behavior data information of the target user and the historical data information corresponding to the target registration area.

[0063] Combine the target user's behavioral data (such as the user's order history, preference settings, activity, etc.) and the historical data information of the target registration area, and generate personalized operational configurations for the user in a specific area through algorithms or rules. These configurations may involve dispatch strategies, pricing strategies, promotional activities, etc. Specifically, machine learning algorithms (such as classification, regression, clustering, etc.) can be used to analyze the target user's behavioral data to predict their possible behavior in the target registration area. Combined with historical data information, the output of the algorithm is calibrated and optimized to generate a more accurate operational configuration. Operational configurations can also be manually or semi-automatically formulated based on business rules and expert experience.

[0064] Step c3: Display the operation configuration of the target registration area to the target user.

[0065] The generated operational configurations are presented to target users in a user-friendly manner so that they can understand and decide whether to accept these configurations. The presentation may include in-app notifications, SMS, emails, web pages, etc.

[0066] Step c4, in response to a trigger operation of the target user on the target operation configuration, executing the target operation configuration; wherein the target operation configuration is any one of a plurality of operation configurations.

[0067] When the user accepts or selects a certain operation configuration, the system needs to respond to the user's triggering operation (such as clicking to confirm, submitting an order, etc.) and perform corresponding operations according to the selected configuration. This may involve order processing, price adjustment, resource allocation, etc.

[0068] The method for determining the operation configuration provided in this embodiment can accurately match personalized operation configurations according to the characteristics of different registration areas and the needs of target users. This helps to meet the differentiated needs of different areas and users and improve the pertinence and effectiveness of services. In addition, the operation configurations of different registration areas are formulated and executed separately, which can allocate resources more reasonably and avoid waste of resources and repeated investment. This helps enterprises to achieve optimal allocation and efficient use of resources. In addition, the operation configuration of the target registration area is displayed to the target user, and it is allowed to select and trigger operations, which increases user participation and interactivity. Users can make choices based on their needs and preferences, which improves the initiative and personalization of services.

[0069] refer to Figure 2 , Figure 2 is a schematic diagram of a system for determining an operation configuration according to an embodiment of the present invention.

[0070] In one possible implementation, the system includes multiple modules such as order system, scheduling system, big data service, intelligent analysis system, asset system and SAAS platform, which work together to realize the complete business process from order receipt to operation configuration recommendation.

[0071] Order system: Customers submit orders through the order system, and the system receives and stores order information. Order information includes key information such as order category and order time type, which is used for subsequent scheduling and resource allocation.

[0072] Dispatching system: The dispatching system receives order information from the order system and allocates resources based on conditions such as dispatch radius, driver willingness, and driver qualifications.

[0073] The system generates a task list and assigns the tasks to the appropriate drivers or resources.

[0074] Big data service: The data collection module collects real-time data from the order system and dispatch system by asynchronously outputting logs, including real-time data of buried points in different cities. These data are converted into metadata and stored in the database for subsequent analysis. The AI ​​computing operation configuration module uses big data and machine learning algorithms to calculate and analyze production data within a certain period of time. Based on the analysis results, the module outputs operational configuration recommendations for the corresponding city, such as optimizing dispatch strategies and adjusting prices.

[0075] Intelligent analysis system: The intelligent analysis system reads the metadata in the big data service and applies the big data model algorithm for calculation and analysis. Based on the analysis results, the system gives operational configuration recommendations for each city. The results of each model training need to be manually proofread to ensure that the output configuration is more accurate and reliable.

[0076] Asset System: The asset system serves as a supporting infrastructure to ensure the smooth operation of the entire business process. It is responsible for managing and maintaining various resources in the system, such as vehicles, drivers, equipment, etc.

[0077] SAAS platform: The SAAS platform provides the recommended operation configuration for each city by querying the configuration data provided by the intelligent analysis system. The platform also provides a one-click configuration and easy operation function, allowing tenants to easily access the system and start operations. Tenants do not need to study the operation rules and configuration content in depth, but only need to configure according to the platform's recommendations.

[0078] In this embodiment, a device for determining an operation configuration 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.

[0079] This embodiment provides a device for determining an operation configuration, such as Figure 3 As shown, it includes: a first acquisition module 301, used to obtain the behavior data information and registration area of ​​the target driver; a first determination module 302, used to determine the historical data information corresponding to the registration area according to the registration area; wherein the historical data information includes: at least one of the dispatch radius, order category, order time type, number of dispatch rounds, and driver qualification; a second determination module 303, used to determine the operation configuration of the target user in the registration area based on the behavior data information and historical data information of the target user.

[0080] In one possible implementation, the first determination module 302 includes: a first determination unit, used to input the registration area into the information matching model to determine the historical data information corresponding to the registration area; or, a second determination unit, used to match the historical data information corresponding to the registration area from the historical database based on the registration area.

[0081] In one possible implementation, the device also includes: a second acquisition module, used to acquire initial information data during the operation of the business system; a conversion module, used to convert the initial information data to obtain historical data information; and a training module, used to input the historical data information into a neural network model for training to obtain an information matching model.

[0082] In one possible implementation, when there are multiple registration areas, the above-mentioned device also includes: a third determination module, used to determine the historical data information corresponding to the target registration area according to the target registration area; wherein the target registration area is any one of the multiple registration areas; a fourth determination module, used to determine the operation configuration of the target user in the target registration area based on the target user's behavior data information and the historical data information corresponding to the target registration area; a display module, used to display the operation configuration of the target registration area to the target user; an execution module, used to execute the target operation configuration in response to the target user's trigger operation on the target operation configuration; wherein the target operation configuration is any one of the multiple operation configurations.

[0083] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0084] The device for determining the operational configuration in this embodiment is presented in the form of a functional unit, where the functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0085] The embodiment of the present invention also provides a computer device having the above Figure 3 The operating configuration shown is determined by the device.

[0086] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0087] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0088] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0089] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device 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.

[0090] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0091] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0092] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0093] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0094] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining an operation configuration, characterized in that: The method comprises: Obtain the target user's behavior data information and registration area; According to the registered area, determine the historical data information corresponding to the registered area; wherein the historical data information includes: at least one of the dispatch radius, order category, order timeliness type, number of dispatch rounds, and driver qualifications; Based on the target user's behavioral data and historical data information, determine the target user's operational configuration in the registration area.

2. The method for determining the operation configuration according to claim 1, characterized in that: According to the registration area, determining historical data information corresponding to the registration area includes: Inputting the registered area into an information matching model to determine historical data information corresponding to the registered area; or, According to the registered area, historical data information corresponding to the registered area is matched from a historical database.

3. The method for determining the operation configuration according to claim 1, characterized in that: The steps of building an information matching model include: Obtain initial information data during the operation of the business system; Converting the initial information data to obtain historical data information; The historical data information is input into a neural network model for training to obtain an information matching model.

4. The method for determining the operation configuration according to claim 1, characterized in that: When there are multiple registration areas, the method further includes: According to the target registration area, determine the historical data information corresponding to the target registration area; wherein the target registration area is any one of the multiple registration areas; Determine the operation configuration of the target user in the target registration area based on the behavior data information of the target user and the historical data information corresponding to the target registration area; Displaying the operation configuration of the target registration area to the target user; In response to a trigger operation of a target user on a target operation configuration, the target operation configuration is executed; wherein the target operation configuration is any one of a plurality of operation configurations.

5. A device for determining an operation configuration, characterized in that: The device comprises: The first acquisition module is used to obtain the behavior data information and registration area of ​​the target driver; A first determination module is used to determine historical data information corresponding to the registration area according to the registration area; wherein the historical data information includes: at least one of the dispatch radius, order category, order time type, number of dispatch rounds, and driver qualification; The second determination module is used to determine the operation configuration of the target user in the registration area based on the behavior data information and historical data information of the target user.

6. The device for determining the operation configuration according to claim 5, characterized in that: The first determining module includes: A first determining unit is configured to input the registration area into an information matching model to determine historical data information corresponding to the registration area; or The second determining unit is used to match the historical data information corresponding to the registration area from the history database according to the registration area.

7. The device for determining the operation configuration according to claim 6, characterized in that: The device also includes: The second acquisition module is used to acquire initial information data during the operation of the business system; A conversion module, used to convert the initial information data to obtain historical data information; The training module is used to input the historical data information into the neural network model for training to obtain an information matching model.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for determining the operation configuration according to any one of claims 1 to 4 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for determining the operation configuration according to any one of claims 1 to 4.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for determining the operation configuration according to any one of claims 1 to 4.

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