Configuration method and device for order receiving setting item of online car-hailing driver, computer equipment and storage medium

Abstract

The invention relates to a configuration method and device for order receiving setting items of online car-hailing drivers, computer equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional state data of an online car-hailing driver; determining one or more to-be-optimized setting items based on the multi-dimensional state data; generating optimization suggestion information of the order receiving setting item based on the one or more to-be-optimized setting items and the real-time position information of the online car-hailing driver, and sending the optimization suggestion information to terminal equipment of the online car-hailing driver; and in response to an optimization confirmation instruction sent by the terminal equipment of the online car-hailing driver, generating a batch operation instruction for starting or adjusting one or more to-be-optimized setting items, and sending the batch operation instruction to the terminal equipment of the online car-hailing driver, so that the terminal equipment configures the state of each to-be-optimized setting item based on the batch operation instruction. By adopting the method, the order receiving efficiency of the online car-hailing driver can be improved, and the overall operation efficiency and service quality of the online car-hailing platform can be improved.

Description

Technical Field

[0001] This application relates to the technical field of driver-side systems for ride-hailing platforms, and in particular to a method, apparatus, computer equipment, and storage medium for configuring order-accepting settings for ride-hailing drivers. Background Technology

[0002] With the rapid development of mobile internet technology, ride-hailing services have become an important supplement to urban public transportation systems due to their convenient and efficient travel services. In the context of increasingly fierce industry competition, drivers' order-accepting efficiency not only directly determines their income levels but also has a crucial impact on the stability of ride-hailing platform capacity and the user's travel experience.

[0003] To improve driver order-taking efficiency, existing technologies generally adopt a passive detection and manual configuration operation management model. For example, the system uses a periodic detection mechanism to identify whether a driver is in an abnormal empty-driving state, that is, whether they are online but have no orders to accept. If so, the system checks the driver's qualifications, such as whether they are in restricted driving hours, and basic operational settings, such as whether the carpooling mode is enabled, and pushes a reminder message to the driver for abnormal status.

[0004] However, when the system detects that a driver's settings (such as carpooling mode) are abnormally disabled, the driver must manually check the settings one by one and re-enable the corresponding function. This operation requires an average of 5 clicks and takes about 10 seconds. During this time, the driver cannot accept new orders in a timely manner, which can easily lead to lost orders. This not only reduces the driver's income but also affects the overall operational efficiency and service quality of the ride-hailing platform. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for configuring order-accepting settings for ride-hailing drivers to address the aforementioned technical issues. This would improve the order-accepting efficiency of ride-hailing drivers, prevent them from missing a large number of orders due to time-consuming operations and response delays, and simultaneously improve the overall operational efficiency and service quality of ride-hailing platforms.

[0006] According to a first aspect of certain exemplary embodiments of the present disclosure, a method for configuring order-accepting settings for a ride-hailing driver is provided, comprising: acquiring multi-dimensional status data of the ride-hailing driver, the multi-dimensional status data including qualification data, permission data, setting data, and historical behavior data; determining one or more settings to be optimized based on the multi-dimensional status data; generating optimization suggestion information for the order-accepting settings based on the one or more settings to be optimized and the real-time location information of the ride-hailing driver, and sending the optimization suggestion information to the ride-hailing driver's terminal device; and, in response to a confirmation optimization instruction sent by the ride-hailing driver's terminal device, generating a batch operation instruction for enabling or adjusting one or more settings to be optimized, and sending the batch operation instruction to the ride-hailing driver's terminal device, so that the terminal device configures the status of each setting to be optimized based on the batch operation instruction.

[0007] According to certain exemplary embodiments of this disclosure, qualification data includes the account status and order-accepting qualification information of the ride-hailing driver; permission data includes the status of each permission setting of the ride-hailing driver; setting data includes the on / off status of each order-accepting setting item of the ride-hailing driver; and historical behavior data includes the number of missed orders corresponding to each unactivated setting item within a historical time interval.

[0008] According to certain exemplary embodiments of this disclosure, before acquiring the multidimensional status data of a ride-hailing driver, a method for configuring the order-accepting settings of a ride-hailing driver further includes: triggering the step of acquiring multidimensional status data when it is detected that the ride-hailing driver is in an empty driving state without orders and online for more than a preset time, or when a one-click optimization instruction is received from the ride-hailing driver's terminal device.

[0009] According to certain exemplary embodiments of this disclosure, determining one or more settings to be optimized based on multidimensional state data includes: determining the ride-hailing driver's eligibility to accept orders based on the ride-hailing driver's account status and order-accepting qualification information in the qualification data; querying whether there are one or more target settings that have the corresponding qualifications but are not activated in the ride-hailing driver's order-accepting settings based on the ride-hailing driver's eligibility to accept orders and the on / off status of each order-accepting setting of the ride-hailing driver in the setting data; if so, determining one or more target settings as one or more settings to be optimized; if not, filtering out one or more unactivated settings with missed orders exceeding a preset threshold based on the missed order volume corresponding to each unactivated setting in the historical time interval in the historical behavior data, and determining one or more unactivated settings as one or more settings to be optimized.

[0010] According to certain exemplary embodiments of this disclosure, the optimization suggestion information for order-taking settings includes a target combination of settings to be optimized and the expected increase in order volume corresponding to the optimized combination of settings; generating optimization suggestion information for order-taking settings based on one or more settings to be optimized and the real-time location information of the ride-hailing driver includes: obtaining the real-time location information of the ride-hailing driver when the terminal device obtains location permission based on the location permission setting status in the permission data; obtaining ride-hailing order demand information within a preset range around the ride-hailing driver based on the real-time location information, the order demand information including order type, order quantity, and order distribution; and associating one or more settings to be optimized. The analysis process involves excluding logically conflicting combinations of settings to be optimized, and identifying one or more logically compatible combinations. Based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information from the driver qualification data, the expected increase in orders after optimization is calculated for each of the one or more combinations of settings to be optimized. Based on the expected increase in orders for each combination of settings to be optimized, target combinations of settings to be optimized that meet preset optimization conditions are selected. Based on the target combinations of settings to be optimized and the expected increase in orders after optimization, optimization suggestions for order-accepting settings are generated.

[0011] According to certain exemplary embodiments of this disclosure, based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information in the qualification data of ride-hailing drivers, the expected increase in order volume corresponding to each combination of one or more optimization settings is calculated after optimization. This includes: analyzing the correlation between the activation status of each optimization setting and the corresponding order volume based on the historical behavior data of ride-hailing drivers; determining the order-accepting type of ride-hailing drivers based on the order-accepting qualification information in the qualification data of ride-hailing drivers; analyzing the number and distribution of target orders matching the order-accepting type of ride-hailing drivers based on the order quantity and distribution of each type of order in the ride-hailing order demand information and the order-accepting type of ride-hailing drivers; and calculating the expected increase in order volume after each combination of one or more optimization settings is activated based on the correlation between the activation status of each optimization setting and the corresponding order volume, the number and distribution of target orders.

[0012] According to certain exemplary embodiments of this disclosure, a method for configuring order-accepting settings for ride-hailing drivers further includes: after generating optimization suggestion information for order-accepting settings, determining the display priority of each setting to be optimized based on the expected increase in order-accepting volume corresponding to each setting to be optimized in the target setting to be optimized; determining the missed order-accepting volume corresponding to each setting to be optimized in the target setting to be optimized based on the missed order-accepting volume in historical behavior data; generating text content for prompting optimization based on the missed order-accepting volume corresponding to each setting to be optimized; sorting the text content according to display priority, pushing it to the ride-hailing driver's terminal device, and prompting the ride-hailing driver to click the confirm optimization button to trigger the confirmation optimization instruction.

[0013] According to a second aspect of certain exemplary embodiments of the present disclosure, a configuration device for ride-hailing driver order-accepting settings is provided, characterized in that the device includes: an acquisition module, configured to acquire multi-dimensional status data of the ride-hailing driver, the multi-dimensional status data including qualification data, permission data, setting data, and historical behavior data; a first determination module, configured to determine one or more settings to be optimized based on the multi-dimensional status data; a first generation module, configured to generate optimization suggestion information for order-accepting settings based on one or more settings to be optimized and the real-time location information of the ride-hailing driver, and send the optimization suggestion information to the ride-hailing driver's terminal device; and a second generation module, configured to generate batch operation instructions for enabling or adjusting one or more settings to be optimized in response to a confirmation optimization instruction sent by the ride-hailing driver's terminal device, and send the batch operation instructions to the ride-hailing driver's terminal device so that the terminal device configures the status of each setting to be optimized based on the batch operation instructions.

[0014] According to a third aspect of certain exemplary embodiments of the present disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0015] According to a fourth aspect of certain exemplary embodiments of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0016] The aforementioned method, apparatus, computer device, and storage medium for configuring order-accepting settings for ride-hailing drivers involve: acquiring multi-dimensional status data of the ride-hailing driver, including qualification data, permission data, setting data, and historical behavior data; determining one or more settings to be optimized based on the multi-dimensional status data; generating optimization suggestions for the order-accepting settings based on the one or more settings to be optimized and the real-time location information of the ride-hailing driver, and sending the optimization suggestions to the ride-hailing driver's terminal device; and generating batch operation instructions for enabling or adjusting one or more settings to be optimized in response to a confirmation optimization command sent by the ride-hailing driver's terminal device, and sending the batch operation instructions to the ride-hailing driver's terminal device so that the terminal device configures the status of each setting to be optimized based on the batch operation instructions.

[0017] Therefore, by acquiring multi-dimensional status data including ride-hailing drivers' qualification data, permission data, setting data, and historical behavior data, it is possible to accurately and comprehensively grasp key data related to ride-hailing drivers' order acceptance. Based on this multi-dimensional status data, one or more settings that affect order acceptance efficiency can be accurately identified for optimization. Then, based on these identified settings and the driver's real-time location information, optimization suggestions for order acceptance settings more suited to the driver's current operating scenario are generated and simultaneously sent to the driver's terminal device. This allows the driver to quickly understand the one or more settings affecting order acceptance efficiency and their expected benefits, and prompts them to confirm whether to perform batch optimization. Furthermore, in response to the confirmation optimization command sent by the driver's terminal device, a batch operation command is generated to enable or adjust one or more settings. This batch operation command is sent to the driver's terminal device, enabling the terminal device to configure the status of each setting based on the batch operation command, thus achieving one-click batch enabling or adjustment of the settings. Compared to existing technologies where drivers need to manually check and enable each order-accepting setting item, this batch automated configuration method significantly reduces the time spent on setting adjustments, thereby avoiding missing a large number of orders due to operation time and response delays. This not only improves the order-accepting efficiency of ride-hailing drivers, but also effectively enhances the overall operational efficiency and service quality of ride-hailing platforms. Attached Figure Description

[0018] Figure 1 A schematic diagram illustrating an example of applying the existing method for configuring ride-hailing driver order-accepting settings to a driver's terminal device; Figure 2 This is a flowchart illustrating a method for configuring order-accepting settings for a ride-hailing driver, as shown in some exemplary embodiments of this disclosure. Figure 3This is a flowchart illustrating a method for determining one or more settings to be optimized based on multidimensional state data in some exemplary embodiments of this disclosure; Figure 4 This is a flowchart illustrating a method for generating optimization suggestion information for order acceptance settings in some exemplary embodiments of this disclosure; Figure 5 This is a flowchart illustrating a method for calculating the expected increase in order volume corresponding to the optimized combination of each set of settings in some exemplary embodiments of this disclosure. Figure 6 This is a flowchart illustrating a method for visually displaying various settings to be optimized in some exemplary embodiments of this disclosure; Figure 7 This is a schematic diagram illustrating an example of a method for configuring order-accepting settings for ride-hailing drivers, as disclosed herein, applied to a driver's terminal device. Figure 8 This is a structural block diagram of a configuration device for order-accepting settings of a ride-hailing driver in some other exemplary embodiments of the present disclosure; Figure 9 This is a diagram illustrating the internal structure of a computer device in some other exemplary embodiments of this disclosure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] The following detailed descriptions are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, electronic devices, storage media, and / or computer program products described herein. However, upon understanding the disclosure of this disclosure, various changes, modifications, and equivalents of the methods, apparatus, storage media, and / or computer program products described herein will become apparent. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding the disclosure of this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0021] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, electronic devices, and / or storage media described herein, many of which will become clear upon understanding this disclosure.

[0022] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof. Unless otherwise stated, “ / ” means “or,” for example, A / B can mean A or B; “and / or” in the text is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can mean: A alone, A and B simultaneously, and B alone. Furthermore, in the description of embodiments of the invention, “multiple” means two or more.

[0023] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0024] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in some of the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0025] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0026] In the following description, embodiments will be described in detail with reference to the accompanying drawings. However, embodiments may be implemented in various forms and are not limited to the examples described herein.

[0027] Figure 1 This diagram illustrates an example of how a prior art method for configuring ride-hailing driver order-accepting settings is applied to a driver's terminal device. (Refer to...) Figure 1In existing technologies, to help ride-hailing drivers manage order-accepting settings, driver-side applications provide order-accepting setting configuration functions, offering a combination of passive detection and manual configuration to adjust various setting parameters related to ride-hailing order acceptance. For example, such as... Figure 1 As shown, firstly, when a ride-hailing driver is in a waiting state on the driver's app and has not received any orders for a preset time threshold (e.g., 5 minutes), the driver's electronic device displays interface 10. This interface 10 includes a pop-up window 101 to inform the driver that they are currently in a state of not receiving orders for an extended period. It also prompts the driver that the system has automatically performed a waiting state detection and found an anomaly in the detection results. Furthermore, the driver clicks the "View Details" button 1011 in the pop-up window 101. In response to the driver's click of the "View Details" button 1011, the driver's electronic device will jump to interface 20. This interface 20 displays the specific anomalies identified by the system, such as detected qualification anomalies (e.g., hitting traffic restrictions in device account issues), order acceptance setting configuration issues (e.g., reservation order settings issues), and other issues affecting the driver's order acceptance volume (e.g., location not updating in a timely manner), along with corresponding order acceptance suggestions (e.g., "Currently, the waiting state mode is only enabled for [reservation orders], which may result in missing 5 orders. It is recommended to enable the real-time order acceptance switch") to prompt the ride-hailing driver to manually check and adjust the specific abnormal order acceptance settings.

[0028] Furthermore, when a driver needs to adjust the reservation settings in the abnormal order acceptance settings, they must click the "Go to Enable" button 2011 in the "Reservation Orders" section of interface 20. In response to clicking the "Go to Enable" button 2011, the driver's electronic device will redirect to interface 30, which displays several order acceptance settings switches, such as "Pick-up Orders," "Place Orders," and "Reservation Orders." At this point, the driver must manually check the enabling status of each setting and manually enable any abnormal items indicated by interface 20 (such as reservation order switches being off). After enabling, the driver must click the "Save Settings" button 3011 at the bottom of interface 30 to complete the configuration process for this order acceptance setting. This adjustment process requires an average of 5 clicks and takes approximately 10 seconds. During this time, the driver cannot accept new orders in a timely manner, easily leading to order loss, which not only reduces the driver's income but also affects the overall operational efficiency and service quality of the ride-hailing platform.

[0029] It is understood that the preset duration threshold, anomaly detection type, and number of settings in the above interface can be adjusted according to the functional rules of the ride-hailing platform. The settings can include scenario-based options such as "airport pickup orders" and "carpooling orders," as well as switches for the basic order range. This application embodiment does not limit this. Furthermore, the default state and display order of the settings can also change dynamically with the platform's operational strategy. This application embodiment does not limit this.

[0030] In summary, the existing technology, which combines passive detection with manual matching of ride-hailing driver order-taking settings, has the following technical problems: 1. Lack of correlation analysis in setting optimization: Only isolated suggestions are provided for single anomalies (such as only prompting to enable real-time orders), without analyzing the linkage between various settings and their comprehensive impact on the overall order volume. Drivers find it difficult to determine the optimal solution for setting combinations, cannot fully tap the order potential, and affect the overall capacity allocation of the platform.

[0031] 2. The manual operation process is cumbersome and time-consuming: Drivers need to manually complete multiple steps, which can easily cause drivers to miss a large number of potential orders during peak periods, reducing drivers' income and affecting the operational efficiency and service quality of ride-hailing platforms.

[0032] The abbreviations and key terms in this disclosure are explained as follows: 1. Order Detection: This refers to the technical function of ride-hailing drivers that uses automated programs to periodically scan drivers' order-accepting qualifications (such as traffic restriction rules and account status) and order-accepting settings (such as real-time order switch).

[0033] 2. One-click optimization: This refers to a function module where the driver can trigger the system to automatically detect, generate suggestions, and apply them in batches for multiple optimizable settings with a single click.

[0034] 3. Combination of settings to be optimized: refers to the set of adjustable parameters that affect the success rate of drivers accepting orders, including but not limited to: real-time single switch, carpooling single switch, and route-sharing mode.

[0035] 4. Multidimensional status scanning: refers to the system's joint detection mechanism for driver qualifications, equipment status, historical behavior, and the status of settings on / off.

[0036] In some exemplary embodiments of this disclosure, such as Figure 2 As shown, a method for configuring the order-accepting settings for ride-hailing drivers is provided. Taking the application of this method to a ride-hailing driver order-accepting settings configuration system as an example, the method includes the following steps: Step 201: Obtain multi-dimensional status data of ride-hailing drivers.

[0037] Specifically, the multidimensional status data includes qualification data, permission data, setting data, and historical behavior data. The ride-hailing driver's order-accepting settings configuration system synchronously collects multidimensional status data from multiple sources, including the ride-hailing platform's account management system, permission management module, setting storage module, and order log database. This ensures that the collected multidimensional status data covers the key influencing factors of the entire ride-hailing driver's order-accepting process, providing comprehensive support for subsequent optimization decisions.

[0038] Step 202: Determine one or more settings to be optimized based on multidimensional state data.

[0039] Specifically, by performing correlation analysis and rule filtering on qualification data, permission data, setting data, and historical behavior data in multidimensional status data, one or more settings that affect the efficiency of order processing can be identified and optimized.

[0040] Step 203: Generate optimization suggestions for the order-accepting settings based on one or more settings to be optimized and the real-time location information of the ride-hailing driver, and send the optimization suggestions to the ride-hailing driver's terminal device.

[0041] Specifically, based on one or more identified settings to be optimized and the real-time location information of ride-hailing drivers, the system analyzes the comprehensive impact of each setting on order volume, combining the order demand characteristics around the driver's real-time location with historical order data to generate optimization suggestions for the order-taking settings. These suggestions include the optimal combination of settings to be optimized and the expected increase in order volume after optimization. The generated suggestions are pushed to the ride-hailing drivers' devices via pop-ups, message notifications, etc., allowing drivers to clearly and intuitively understand the optimization direction and potential benefits of the optimized settings. This provides clear guidance for drivers to quickly confirm optimization operations and accurately adjust order-taking settings.

[0042] Step 204: In response to the confirmation optimization command sent by the ride-hailing driver's terminal device, generate a batch operation command for enabling or adjusting one or more settings to be optimized, and send the batch operation command to the ride-hailing driver's terminal device so that the terminal device can configure the status of each setting to be optimized based on the batch operation command.

[0043] Specifically, after receiving the confirmation optimization instruction sent by the ride-hailing driver through their terminal device, the system automatically generates batch operation instructions for enabling or adjusting one or more optimization settings based on the optimal combination of settings to be optimized in the optimization suggestion information and the target state of each optimized setting. These batch operation instructions are then sent to the driver's terminal device. Upon receiving the batch operation instructions, the terminal device automatically executes the enabling or adjusting actions for the corresponding settings, requiring no manual intervention. After completion, the system simultaneously feeds back the configuration results to the ride-hailing platform's backend system, enabling rapid activation of the order-acceptance settings configuration, significantly reducing adjustment time, and preventing order loss during peak periods due to operational delays.

[0044] In one example, qualification data includes the ride-hailing driver's account status and order-accepting qualification information; permission data includes the status of the ride-hailing driver's various permission settings; setting data includes the on / off status of the ride-hailing driver's various order-accepting settings; and historical behavior data includes the number of missed orders corresponding to each unactivated setting within a historical time interval.

[0045] Specifically, qualification data includes the account status of ride-hailing drivers (e.g., normal, banned) and order-accepting qualification information (e.g., types of orders that can be accepted, traffic restrictions, etc.); permission data covers the permission settings for each order-accepting function, including location permissions, notification permissions, and other permissions that directly affect order reception and response; setting data includes the on / off status of each order-accepting setting, such as the on / off status of real-time orders, carpooling orders, and route-sharing modes; historical behavior data includes the number of missed orders within a historical preset time range (e.g., the most recent month) due to a certain setting not being enabled, which can intuitively reflect the correlation between settings and order-accepting performance, providing data support for optimization decisions.

[0046] The aforementioned method for configuring order-taking settings for ride-hailing drivers acquires multi-dimensional status data, including the driver's qualification data, permission data, setting data, and historical behavior data. This allows for an accurate and comprehensive understanding of key data related to order taking, enabling the accurate identification of one or more settings that affect order-taking efficiency based on the multi-dimensional status data. Then, based on these identified settings and the driver's real-time location information, optimization suggestions are generated to better suit the driver's current operational scenario. These suggestions are simultaneously sent to the driver's terminal device, allowing the driver to quickly understand the one or more settings affecting order-taking efficiency and their expected benefits, and prompting the driver to confirm whether to perform batch optimization. Furthermore, in response to the confirmation optimization command sent by the ride-hailing driver's terminal device, a batch operation command is generated to enable or adjust one or more settings to be optimized. This batch operation command is then sent to the driver's terminal device, enabling the terminal device to configure the status of each setting to be optimized based on the batch operation command, thereby achieving one-click batch enabling or adjustment of the settings to be optimized. Compared to the existing technology where drivers need to manually check and enable each order-accepting setting item, this batch automated configuration method significantly reduces the time spent on setting adjustments, thus avoiding missing a large number of orders due to operation time and response delays. This not only improves the order-accepting efficiency of ride-hailing drivers but also effectively enhances the overall operational efficiency and service quality of the ride-hailing platform.

[0047] In some exemplary embodiments of this disclosure, based on the above embodiments, it is further explained that before step 201, that is, before obtaining the multi-dimensional status data of the ride-hailing driver, a method for configuring the order-accepting settings of a ride-hailing driver further includes the following steps: when it is detected that the ride-hailing driver is in an empty driving state without orders and online for more than a preset time, or when a one-click optimization instruction is received from the ride-hailing driver's terminal device, the step of obtaining multi-dimensional status data is triggered.

[0048] Specifically, the ride-hailing driver's order-accepting settings configuration system identifies whether a driver is currently in an empty-driving state (online but without orders) by monitoring the driver's online status and order acceptance. When the system detects that a driver has been in an empty-driving state for more than a preset time (e.g., 5 minutes, which can be adjusted according to actual operating scenarios), it determines that the driver's order-accepting efficiency may be limited by the settings, thus triggering the multi-dimensional status data acquisition step. This involves comprehensively collecting multi-dimensional status data, including qualification data, permission data, setting data, and historical behavior data. This provides a foundation for accurately identifying settings that affect order-taking efficiency based on multi-dimensional status data. Alternatively, the ride-hailing driver's order-taking settings configuration system may have a "one-click optimization" function button on the driver's electronic device. When a ride-hailing driver actively clicks this "one-click optimization" function button based on their own order-taking needs, the driver's terminal device will send a one-click optimization command carrying the driver's identifier to the ride-hailing driver's order-taking settings configuration system. After receiving the one-click optimization command sent by the driver's terminal device, the ride-hailing driver's order-taking settings configuration system will also directly trigger the multi-dimensional status data acquisition step, quickly responding to the driver's proactive optimization request.

[0049] In some exemplary embodiments of this disclosure, based on the above embodiments, a specific implementation method for determining one or more settings to be optimized based on multidimensional state data is further described. For example... Figure 3 As shown, step 202 above, which is the step of determining one or more settings to be optimized based on multidimensional state data, may specifically include the following steps: Step 301: Determine the ride-hailing driver's eligibility to accept orders based on the account status and order-accepting qualification information of the ride-hailing driver in the qualification data.

[0050] Step 302: Based on the ride-hailing driver's order-accepting qualifications and the on / off status of each order-accepting setting item of the ride-hailing driver in the setting data, query whether there are one or more target settings items in the ride-hailing driver's order-accepting settings that have the corresponding qualifications but are not turned on.

[0051] Step 303: If so, identify one or more target settings as one or more settings to be optimized.

[0052] Step 304: If not, then based on the number of missed orders corresponding to each unactivated setting within the historical time interval in the historical behavior data, filter out one or more unactivated settings whose number of missed orders exceeds a preset threshold, and determine one or more unactivated settings as one or more settings to be optimized.

[0053] Specifically, the system for configuring ride-hailing drivers' order-accepting settings conducts a comprehensive analysis of multi-dimensional status data by first screening based on qualifications and then filtering based on historical behavior data, thereby accurately identifying one or more settings that affect the order-accepting efficiency of ride-hailing drivers and requiring optimization. First, based on the account status (e.g., normal, banned) and order-accepting qualification information (e.g., traffic restriction restrictions) in the qualification data of the multi-dimensional status data, the order-accepting qualifications of ride-hailing drivers are clarified. Then, combined with the on / off status of each order-accepting setting item of the ride-hailing driver in the setting data of the multi-dimensional status data, it is determined whether there are any target settings in the current order-accepting settings of the ride-hailing driver who have the corresponding order-accepting qualifications but whose settings are not enabled. If so, the target settings that have the corresponding order-accepting qualifications but whose settings are not enabled are directly identified as one or more unenabled settings. If not, the missed order volume corresponding to each unenabled setting item in the historical behavior data of the multi-dimensional status data is further retrieved, and one or more unenabled settings that have missed order volume exceeding a preset threshold (e.g., 1 order, which can be adjusted according to platform operation strategy, regional order popularity, or driver order target, etc.) are selected and identified as settings to be optimized. This method of first screening based on qualifications and then using historical behavior data as a backup screening can maximize the selection of optimization settings that have actual value in improving order-taking efficiency and meet the driver's compliant order-taking capabilities. This satisfies the platform's needs for refined capacity allocation and personalized order-taking optimization for drivers, thereby improving driver order-taking efficiency and the overall operational quality of the platform.

[0054] Based on the above embodiments, the optimization suggestion information for order-taking settings includes a target combination of settings to be optimized and the expected increase in order-taking volume after optimization of the target combination of settings to be optimized. This further explains the specific implementation method of generating optimization suggestion information for order-taking settings based on one or more settings to be optimized and the real-time location information of ride-hailing drivers. For example... Figure 4 As shown, step 203 above, which is the step of generating optimization suggestions for order-accepting settings based on one or more settings to be optimized and the real-time location information of ride-hailing drivers, may specifically include the following steps: Step 401: Based on the location permission setting status recognition in the permission data, when the terminal device obtains location permission, obtain the real-time location information of the ride-hailing driver.

[0055] Step 402: Obtain ride-hailing order demand information within a preset range around the ride-hailing driver based on real-time location information.

[0056] The order demand information includes order type, order quantity, and order distribution.

[0057] Step 403: Perform correlation analysis on one or more settings to be optimized, exclude combinations of settings to be optimized that have logical conflicts, and determine one or more combinations of settings to be optimized that are logically compatible.

[0058] Step 404: Based on ride-hailing order demand information, ride-hailing driver historical behavior data, and ride-hailing driver qualification data, calculate the expected increase in orders corresponding to each combination of one or more optimization settings after optimization.

[0059] Step 405: Based on the expected increase in order volume corresponding to each combination of settings to be optimized, select the target combination of settings to be optimized whose expected increase in order volume meets the preset optimization conditions.

[0060] Step 406: Generate optimization suggestion information for order receiving settings based on the target combination of settings to be optimized and the expected increase in order volume corresponding to the optimized target combination of settings.

[0061] Specifically, after identifying one or more settings to be optimized, the ride-hailing driver's order-taking settings configuration system analyzes these settings in conjunction with the driver's real-time location information to generate optimization suggestions that better suit the driver's current operating scenario. First, the system verifies the location permission settings in the permission data. Only when the system confirms that the driver's terminal device has successfully acquired and enabled location permissions can it obtain the driver's real-time location information, preventing invalid location data due to missing permissions. Then, the system delineates a preset radius around the driver, centered on the acquired real-time location and extending a preset distance (e.g., 3 kilometers, dynamically adjustable based on city order density). It then extracts ride-hailing order demand information within this preset radius from the platform's order scheduling database. This information includes order types (e.g., real-time orders, carpooling orders, pre-booked orders), the number of orders for each type, and the order distribution, thus clarifying the order demand situation at the driver's current location.

[0062] Subsequently, the ride-hailing driver's order-taking settings configuration system conducts correlation analysis on one or more identified settings to be optimized. Through a preset conflict rule base, it identifies and excludes combinations of settings that have logical conflicts, such as combinations that cannot be effective simultaneously, like "premium order mode" and "premium vehicle order mode". It then filters out one or more logically compatible combinations of settings to be optimized. Furthermore, based on the obtained ride-hailing order demand information, the ride-hailing driver's historical behavior data (such as the number of missed orders in the past month due to a certain setting not being enabled), and the ride-hailing driver's qualification data (such as the types of orders the ride-hailing driver can accept), it performs multi-dimensional quantitative calculations to calculate the expected increase in orders corresponding to each combination of settings to be optimized. For example, the system for configuring ride-hailing drivers' order-accepting settings first determines the types of orders a driver can accept based on their order-accepting qualification information, then filters out the number and distribution of target orders that match the order type by combining order demand information; at the same time, it analyzes the correlation between the activation status of each setting to be optimized and the number of orders accepted by analyzing historical behavioral data, and finally calculates the expected increase in the number of orders accepted after optimizing each set of settings by combining the correlation with the number and distribution of target orders.

[0063] Furthermore, the ride-hailing driver's order-taking settings configuration system filters out target settings combinations that meet preset optimization conditions based on the expected increase in orders for each combination of settings to be optimized. For example, it filters out the combination of settings with the highest expected increase in orders, or the combination with the highest proportion of high-premium orders, as the target combination of settings to be optimized. It should be noted that these preset optimization conditions can be configured uniformly by the platform according to regional operation strategies, or they can be selected by ride-hailing drivers based on their own order-taking preferences, ensuring that the selected target combinations of settings to be optimized better match the drivers' actual operational needs. Finally, the ride-hailing driver's order-taking settings configuration system integrates and encapsulates the specific content of the target combinations of settings to be optimized and their corresponding expected increase in orders, generating standardized order-taking settings optimization suggestions. Therefore, through automated intelligent scene matching, combined conflict verification, and revenue quantification calculation, the system achieves precise matching between order-taking setting optimization suggestions and driver qualifications, real-time order demand, and historical operating habits. This significantly reduces the operational costs for drivers to manually adjust settings while ensuring the compliance and effectiveness of the optimization plan. At the same time, the optimization condition screening mechanism, which supports two-way customization of platform strategies and driver preferences, further enhances the personalization level of the optimization plan. This helps improve the order-taking efficiency of ride-hailing drivers and provides technical support for ride-hailing platforms to refine capacity scheduling and optimize overall service quality.

[0064] Based on the above embodiments, this paper further explains the specific implementation method for calculating the expected increase in order volume corresponding to each combination of optimization settings in one or more combinations of optimization settings, based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information in the qualification data of ride-hailing drivers. For example... Figure 5 As shown, step 404 above, which is the step of calculating the expected increase in orders after optimizing each combination of one or more combinations of settings, based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information in the qualification data of ride-hailing drivers, can specifically include the following steps: Step 501: Analyze the relationship between the activation status of each setting to be optimized and the corresponding number of orders based on the historical behavior data of ride-hailing drivers.

[0065] Step 502: Determine the order acceptance type of the ride-hailing driver based on the order acceptance qualification information in the ride-hailing driver's qualification data.

[0066] Step 503: Based on the number and distribution of each type of order in the ride-hailing order demand information and the order acceptance type of the ride-hailing driver, analyze the number and distribution of target orders that match the order acceptance type of the ride-hailing driver.

[0067] Step 504: Based on the relationship between the activation status of each optimization setting item and the corresponding order volume, the target order quantity and distribution, calculate the expected increase in order volume after each combination of optimization settings items is activated in one or more combinations of optimization settings items.

[0068] Specifically, regarding the calculation steps for the expected increase in order volume after activating each combination of optimization settings, the system first performs a correlation analysis on the historical behavior data of ride-hailing drivers to determine the relationship between the activation status of each optimization setting and the corresponding order volume. Second, based on the driver's qualification data, the system clarifies the types of ride-hailing orders the driver can accept. Then, it matches the types of ride-hailing orders the driver can accept with the ride-hailing order demand information within a preset surrounding area, filters out target orders of the same type, and counts the number and distribution of these target orders. Finally, based on the analyzed correlation between the activation status of each optimization setting and the order volume, and combined with the number and distribution characteristics of the target orders, the expected increase in order volume corresponding to each optimization setting in each logically compatible combination is calculated by summing the results, thus obtaining the expected increase in order volume corresponding to each combination of optimization settings after optimization. Therefore, the projected increase in orders for each combination of settings to be optimized, calculated using the above method, can deeply couple the historical operating patterns and qualification constraints of ride-hailing drivers with real-time order scenarios. This avoids blind calculations that are divorced from actual qualifications and do not refer to historical data, making the projected increase in orders for each combination of settings to be optimized more scientific and accurate. This provides data support for the subsequent selection of target combinations and the generation of optimization suggestions.

[0069] In some exemplary embodiments of this disclosure, based on the above embodiments, such as Figure 6 As shown, a method for configuring the order-accepting settings for ride-hailing drivers may further include the following steps: Step 601: After generating optimization suggestion information for order receiving settings, determine the display priority of each setting to be optimized based on the expected increase in order receiving volume corresponding to each setting to be optimized in the target settings to be optimized.

[0070] Step 602: Based on the missed order volume in the historical behavior data, determine the missed order volume corresponding to each target optimization setting item, and generate copy content for prompting optimization based on the missed order volume corresponding to each optimization setting item.

[0071] Step 603: After sorting the text content according to display priority, push it to the ride-hailing driver's terminal device and prompt the ride-hailing driver to click the confirm optimization button to trigger the confirmation optimization instruction.

[0072] Specifically, after generating optimization suggestions for order-accepting settings, the ride-hailing driver's order-accepting settings configuration system first determines the display priority of each setting to be optimized from high to low based on the expected increase in order volume corresponding to each setting in the target set to be optimized. Settings with higher expected increase in order volume are displayed higher on the terminal device's display interface, making it easier for drivers to quickly focus on core optimization points. Then, the system retrieves the missed order volume corresponding to each setting to be optimized from historical behavior data and combines this data with the missed order volume to generate text prompts for optimization, such as "If the downward order listening mode is not enabled, you may miss 5 orders; if enabled, the expected increase in order volume is 8." The generated prompts are then sorted and integrated according to the determined display priority, and pushed to the ride-hailing driver's terminal device in the form of pop-ups or message notifications. This allows the driver to intuitively understand the order-taking settings that need optimization and their value. The driver is then prompted to click the "Confirm Optimization" button to trigger the confirmation optimization command. This enables the ride-hailing driver's order-taking settings configuration system to respond to the confirmation optimization command by generating batch operation commands to enable or adjust one or more optimization settings. These batch operation commands are then sent to the driver's terminal device, allowing the terminal device to configure the status of each optimization setting based on the batch operation commands. This achieves one-click batch enabling or adjustment of optimization settings. Therefore, through priority sorting and precise push of prompts, ride-hailing drivers can intuitively perceive the optimization value and quickly focus on core optimization points. Furthermore, one-click confirmation triggers batch configuration, significantly reducing the driver's operating costs, improving the efficiency of order-taking settings adjustment, effectively preventing missed orders due to operation delays, and thus improving driver order-taking efficiency and the service quality of the ride-hailing platform.

[0073] For example, regarding the configuration method of the ride-hailing driver's order-accepting settings disclosed herein, this paper further illustrates a specific implementation method of applying the configuration method of the ride-hailing driver's order-accepting settings to the driver's terminal device. For instance... Figure 7As shown, after the driver's electronic device completes the analysis of the settings to be optimized based on multi-dimensional status data, interface 70 will be displayed. This interface 70 includes a pop-up window 701, which directly displays the optimization suggestions information analyzed by the ride-hailing driver's order-taking settings configuration system. This optimization suggestion information not only clarifies the number of missed orders corresponding to each setting to be optimized (such as "listen for orders below" and "reception special offer"), but also simultaneously provides the expected number of new orders after the optimization of each setting. The ride-hailing driver only needs to click the "One-click optimization, increase order volume" button 7011 in the pop-up window 701 to trigger the confirmation optimization command. This allows the ride-hailing driver's order-taking settings configuration system to respond to the confirmation optimization command by generating batch operation commands for enabling or adjusting one or more settings to be optimized, and sending these batch operation commands to the ride-hailing driver's terminal device. Ultimately, the terminal device can automatically configure the setting status of each setting to be optimized based on these batch operation commands, thereby realizing one-click batch enabling or adjustment of the settings to be optimized without manual operation item by item.

[0074] In summary, this disclosure provides a method for configuring order-accepting settings for ride-hailing drivers: 1. Implement correlation analysis and combination optimization of settings: By analyzing the correlation of each setting to be optimized, select logically compatible combinations of settings to be optimized and quantify the comprehensive benefits, helping drivers directly obtain the optimal setting plan, fully tap the potential for order taking, and assist the platform in refining the allocation of transportation capacity.

[0075] 2. Simplify the operation process to achieve one-click batch configuration: Replace multi-step manual operation with the batch operation method of "one-click optimization", compress the time spent on setting adjustment to the second level, avoid the loss of orders due to operation delays during peak order periods, improve drivers' order acceptance efficiency and income, and optimize the overall operational efficiency and service quality of the platform.

[0076] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0077] In some exemplary embodiments of this disclosure, such as Figure 8As shown, a configuration device for ride-hailing driver order-accepting settings is provided, including an acquisition module 801, a first determination module 802, a first generation module 803, and a second generation module 804. The acquisition module 801 is used to acquire multi-dimensional status data of the ride-hailing driver, including qualification data, permission data, setting data, and historical behavior data. The first determination module 802 is used to determine one or more settings to be optimized based on the multi-dimensional status data. The first generation module 803 is used to generate optimization suggestion information for the order-accepting settings based on one or more settings to be optimized and the real-time location information of the ride-hailing driver, and send the optimization suggestion information to the ride-hailing driver's terminal device. The second generation module 804 is used to respond to a confirmation optimization command sent by the ride-hailing driver's terminal device, generate batch operation commands for enabling or adjusting one or more settings to be optimized, and send the batch operation commands to the ride-hailing driver's terminal device, so that the terminal device configures the status of each setting to be optimized based on the batch operation commands.

[0078] In one embodiment of this disclosure, the qualification data includes the ride-hailing driver's account status and order-accepting qualification information; the permission data includes the status of each permission setting of the ride-hailing driver; the setting data includes the on / off status of each order-accepting setting item of the ride-hailing driver; and the historical behavior data includes the number of missed orders corresponding to each unactivated setting item within a historical time interval.

[0079] In one embodiment of this disclosure, a configuration device for ride-hailing driver order-accepting settings further includes: a trigger module, used to trigger the multi-dimensional status data acquisition step when it is detected that the ride-hailing driver is in an empty driving state without orders and online for more than a preset time, or when a one-click optimization instruction is received from the ride-hailing driver's terminal device.

[0080] In one embodiment of this disclosure, the first determining module 802 is specifically used for: determining the ride-hailing driver's eligibility to accept orders based on the ride-hailing driver's account status and order-accepting qualification information in the qualification data; querying whether there are one or more target settings in the ride-hailing driver's order-accepting settings that have the corresponding qualifications but are not enabled, based on the ride-hailing driver's eligibility to accept orders and the on / off status of each order-accepting setting item in the setting data; if yes, determining one or more target settings as one or more settings to be optimized; if no, filtering out one or more unenabled settings with missed orders exceeding a preset threshold based on the missed order volume corresponding to each unenabled setting item in the historical time interval in the historical behavior data, and determining one or more unenabled settings as one or more settings to be optimized.

[0081] In one embodiment of this disclosure, the optimization suggestion information for order-taking settings includes a target combination of settings to be optimized and the expected increase in order volume corresponding to the optimized combination of settings; the first generation module 803 is specifically used for: when the terminal device obtains location permission based on the location permission setting status in the permission data, obtaining the real-time location information of the ride-hailing driver; obtaining ride-hailing order demand information within a preset range around the ride-hailing driver based on the real-time location information, the order demand information including order type, order quantity and order distribution; performing correlation analysis on one or more settings to be optimized, and excluding settings with logical conflicts. The system identifies one or more logically compatible combinations of settings to be optimized. Based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information from the driver qualification data, it calculates the expected increase in orders after optimization for each of the one or more combinations of settings to be optimized. Based on the expected increase in orders for each combination of settings to be optimized, it selects target combinations of settings to be optimized that meet preset optimization conditions. Based on the target combinations of settings to be optimized and the expected increase in orders after optimization, it generates optimization suggestions for order-accepting settings.

[0082] In one embodiment of this disclosure, based on ride-hailing order demand information, historical behavior data of ride-hailing drivers, and order-accepting qualification information in the qualification data of ride-hailing drivers, the expected increase in order volume corresponding to each combination of one or more optimization settings is calculated. This includes: analyzing the correlation between the activation status of each optimization setting and the corresponding order volume based on the historical behavior data of ride-hailing drivers; determining the order-accepting type of ride-hailing drivers based on the order-accepting qualification information in the qualification data of ride-hailing drivers; analyzing the number and distribution of target orders matching the order-accepting type of ride-hailing drivers based on the order quantity and distribution of each type of order in the ride-hailing order demand information and the order-accepting type of ride-hailing drivers; and calculating the expected increase in order volume after each combination of one or more optimization settings is activated based on the correlation between the activation status of each optimization setting and the corresponding order volume, the number and distribution of target orders.

[0083] In one embodiment of this disclosure, a configuration device for ride-hailing driver order-accepting settings further includes: a second determining module, configured to determine the display priority of each optimization setting based on the expected increase in order-accepting volume corresponding to each optimization setting in the target optimization setting after generating optimization suggestion information for the order-accepting settings; a third generating module, configured to determine the missed order-accepting volume corresponding to each optimization setting in the target optimization setting based on the missed order-accepting volume in historical behavior data, and generate text content for prompting optimization based on the missed order-accepting volume corresponding to each optimization setting; and a push module, configured to sort the text content according to display priority, push it to the ride-hailing driver's terminal device, and prompt the ride-hailing driver to click the confirm optimization button to trigger the confirm optimization instruction.

[0084] Specific limitations regarding the configuration device for ride-hailing driver order-accepting settings can be found in the above-described method for configuring ride-hailing driver order-accepting settings, and will not be repeated here. Each module in the aforementioned configuration device for ride-hailing driver order-accepting settings can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0085] In some exemplary embodiments of this disclosure, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data related to order-taking settings. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for configuring order-taking settings for ride-hailing drivers.

[0086] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0087] In some exemplary embodiments of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for configuring order-accepting settings for a ride-hailing driver as described in any of the exemplary embodiments above.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for configuring order-accepting settings for ride-hailing drivers, characterized in that, The method includes: Obtain multi-dimensional status data of ride-hailing drivers, including qualification data, permission data, setting data, and historical behavior data; Based on the multidimensional state data, one or more settings to be optimized are determined; Based on the one or more settings to be optimized and the real-time location information of the ride-hailing driver, optimization suggestions for the order-accepting settings are generated, and the optimization suggestions are sent to the ride-hailing driver's terminal device. In response to a confirmation optimization command sent by the ride-hailing driver's terminal device, a batch operation command is generated for enabling or adjusting one or more settings to be optimized. The batch operation command is then sent to the ride-hailing driver's terminal device, so that the terminal device configures the status of each setting to be optimized based on the batch operation command.

2. The method according to claim 1, characterized in that, The qualification data includes the ride-hailing driver's account status and order-accepting qualification information; the permission data includes the status of the ride-hailing driver's various permission settings; the setting data includes the on / off status of the ride-hailing driver's various order-accepting settings; and the historical behavior data includes the number of missed orders corresponding to each unactivated setting within a historical time interval.

3. The method according to claim 1 or 2, characterized in that, Before obtaining the multidimensional status data of ride-hailing drivers, the method further includes: When it is detected that the ride-hailing driver has been in an empty driving state without orders and online for more than a preset time, or when a one-click optimization command is received from the ride-hailing driver's terminal device, the step of obtaining the multi-dimensional status data is triggered.

4. The method according to claim 2, characterized in that, The step of determining one or more settings to be optimized based on the multidimensional state data includes: The ride-hailing driver's eligibility to accept orders is determined based on the account status and order-accepting qualification information of the ride-hailing driver in the qualification data. Based on the ride-hailing driver's order-accepting qualifications and the on / off status of each order-accepting setting item of the ride-hailing driver in the setting data, query whether there are one or more target setting items in the ride-hailing driver's order-accepting settings that have the corresponding qualifications but are not turned on; If so, then the one or more target settings are identified as the one or more settings to be optimized; If not, based on the number of missed orders corresponding to each unactivated setting within the historical time interval in the historical behavior data, one or more unactivated settings with a missed order volume exceeding a preset threshold are selected, and the one or more unactivated settings are identified as the one or more settings to be optimized.

5. The method according to claim 4, characterized in that, The optimization suggestion information for the order receiving settings includes the target combination of settings to be optimized and the expected increase in order receiving volume after the target combination of settings to be optimized is optimized. The process of generating optimization suggestions for order-accepting settings based on the one or more settings to be optimized and the real-time location information of the ride-hailing driver includes: When the location permission setting status in the permission data is recognized and the terminal device obtains location permission, the real-time location information of the ride-hailing driver is obtained. Based on the real-time location information, obtain ride-hailing order demand information within a preset range around the ride-hailing driver. The order demand information includes order type, order quantity, and order distribution. A correlation analysis is performed on the one or more settings to be optimized to eliminate combinations of settings to be optimized that have logical conflicts, and to determine one or more combinations of settings to be optimized that are logically compatible. Based on the ride-hailing order demand information, the ride-hailing driver's historical behavior data, and the ride-hailing driver's qualification data, calculate the expected increase in orders corresponding to each combination of the one or more combinations of optimization settings after optimization. Based on the expected increase in order volume corresponding to each combination of settings to be optimized, target combinations of settings to be optimized that meet the preset optimization conditions are selected. Based on the target combination of settings to be optimized and the expected increase in order volume corresponding to the optimized target combination of settings, optimization suggestion information for the order receiving settings is generated.

6. The method according to claim 5, characterized in that, Based on the ride-hailing order demand information, the ride-hailing driver's historical behavior data, and the ride-hailing driver's order-accepting qualification information in the driver's qualification data, calculate the expected increase in orders corresponding to each combination of the one or more optimization settings after optimization, including: Based on the historical behavior data of the ride-hailing drivers, the correlation between the activation status of each setting to be optimized and the corresponding number of orders is analyzed. The order-taking qualification information in the ride-hailing driver's qualification data is used to determine the ride-hailing driver's order-taking type; Based on the order quantity and distribution of each type of order in the ride-hailing order demand information and the order acceptance type of the ride-hailing driver, analyze the number and distribution of target orders that match the order acceptance type of the ride-hailing driver; Based on the correlation between the activation status of each optimization setting item and the corresponding order volume, as well as the target order quantity and distribution, the expected increase in order volume after each combination of optimization settings items is activated is calculated.

7. The method according to claim 1, characterized in that, The method further includes: After generating the optimization suggestion information for the order receiving settings, the display priority of each setting to be optimized is determined based on the expected increase in order receiving volume corresponding to each setting to be optimized in the target settings to be optimized. Based on the missed order volume in the historical behavior data, determine the missed order volume corresponding to each of the target settings to be optimized, and generate text content for prompting optimization based on the missed order volume corresponding to each setting to be optimized. After sorting the text content according to the display priority, it is pushed to the ride-hailing driver's terminal device, and the ride-hailing driver is prompted to click the confirm optimization button to trigger the confirm optimization instruction.

8. A device for configuring order-accepting settings for ride-hailing drivers, characterized in that, The method includes: The acquisition module is used to acquire multi-dimensional status data of ride-hailing drivers, including qualification data, permission data, setting data, and historical behavior data. The first determining module is used to determine one or more settings to be optimized based on the multidimensional state data; The first generation module is used to generate optimization suggestion information for the order acceptance settings based on the one or more settings to be optimized and the real-time location information of the ride-hailing driver, and send the optimization suggestion information to the terminal device of the ride-hailing driver. The second generation module is used to respond to the confirmation optimization instruction sent by the ride-hailing driver's terminal device, generate a batch operation instruction for enabling or adjusting one or more settings to be optimized, and send the batch operation instruction to the ride-hailing driver's terminal device so that the terminal device configures the status of each setting to be optimized based on the batch operation instruction.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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