Online car-hailing scheduling method, device and equipment and storage medium

Abstract

The invention relates to an online car-hailing scheduling method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a reservation order request of an online car-hailing order of a driver, analyzing preference information set by the driver from the reservation order request, and enabling the preference information to comprise starting point position information and terminal point position information; determining a first area according to the starting point position information and a preset order receiving distance; determining a second area according to the final position information and a preset order receiving distance; acquiring a plurality of target online car-hailing orders of which the starting points are located in the first area and the ending points are located in the second area within a set time range; analyzing the plurality of online car-hailing orders in the first area by adopting a gridding processing mode, and generating a heat area of the first area based on an analysis result; and feeding back the popularity area to the driver to prompt the driver to go to the popularity area to receive the online car-hailing order. According to the method, the accuracy and efficiency of online car-hailing scheduling can be improved.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a method, apparatus, device and storage medium for online ride-hailing dispatch. Background Technology

[0002] With the popularization of mobile internet and the rise of the sharing economy, ride-hailing services have developed rapidly, solving various problems in the traditional taxi industry and meeting people's needs for convenient, economical and high-quality travel.

[0003] In existing ride-hailing dispatch methods, when a driver accepts an order on a ride-hailing platform, if the driver has a desired destination, they typically set the start and end points on the platform and wait for the system to match orders that meet their criteria. However, if no suitable orders are available in the vicinity for an extended period, the driver can only wait passively or drive empty to the desired area or location. Furthermore, the route lacks intelligent planning, and drivers often choose routes blindly, hoping to find suitable orders along the way. This dispatch method makes the success rate of order acceptance highly dependent on luck, severely impacting driver efficiency and consequently reducing driver experience and trust in the platform. Simultaneously, driving empty not only increases drivers' operating costs but also leads to a decrease in the overall capacity dispatch efficiency of the ride-hailing platform, resulting in increased waste of empty vehicle resources. Summary of the Invention

[0004] Therefore, it is necessary to provide a ride-hailing dispatch method, device, computer equipment, and storage medium to address the aforementioned technical problems. This would not only improve the success rate of ride-hailing drivers accepting orders, thereby enhancing their experience and trust in the platform, but also reduce drivers' empty mileage and waiting time, lowering their operating costs. This would improve the accuracy and efficiency of ride-hailing dispatch and reduce the waste of empty vehicle resources.

[0005] A ride-hailing dispatch method includes: receiving a reservation order request from a driver; parsing the driver's preference information from the reservation order request, the preference information including origin location information and destination location information; determining a first region based on the origin location information and a preset pickup distance; determining a second region based on the destination location information and a preset pickup distance; acquiring multiple target ride-hailing orders within a set time range whose origin is located in the first region and whose destination is located in the second region; analyzing the multiple target ride-hailing orders in the first region using a grid-based processing method, and generating a heat map of the first region based on the analysis results; and providing feedback on the heat map to the driver to prompt the driver to go to the heat map to pick up ride-hailing orders.

[0006] In one embodiment, the initial value of the preset order-accepting distance is set by the system default and is dynamically adjusted by the number of orders of multiple target ride-hailing orders with the starting point in the first area and the ending point in the second area within a set time range. When the number of orders of multiple target ride-hailing orders is less than the preset number of orders, the value of the preset order-accepting distance is increased.

[0007] In one embodiment, the target ride-hailing orders include historical ride-hailing orders and current ride-hailing orders. A grid-based processing method is used to analyze multiple target ride-hailing orders in a first region, and a heat map of the first region is generated based on the analysis results. This includes: dividing the first region into grids according to latitude and longitude; obtaining the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region; obtaining the weights of historical ride-hailing orders and current ride-hailing orders; performing a weighted calculation on the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region based on the weights of historical and current ride-hailing orders to obtain the predicted number of ride-hailing orders for each grid in the first region; and analyzing the heat map of the first region based on the predicted number of ride-hailing orders for each grid in the first region.

[0008] In one embodiment, analyzing the number of predicted ride-hailing orders in each grid within a first region to determine a hot zone includes: merging multiple grids within the first region that are adjacent in location and whose difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold to obtain one or more merged regions; calculating the total number of predicted ride-hailing orders in each merged region; calculating the average number of predicted ride-hailing orders in each merged region based on the total number of predicted ride-hailing orders in each merged region and the total number of grids in each merged region; determining the hot zone value for each merged region based on the average number of predicted ride-hailing orders in each merged region; and determining the hot zone based on the hot zone value for each merged region.

[0009] In one embodiment, providing feedback on the heat region to the driver includes: displaying the heat region in a color-rendered manner on the driver's terminal device; after the step of providing feedback on the heat region to the driver, a ride-hailing dispatch method further includes: monitoring the distance of the driver's ride-hailing vehicle's heat region; controlling the color depth of the heat region according to the distance; wherein, the smaller the distance between the driver's ride-hailing vehicle and the heat region, the darker the color of the heat region, and when it is detected that the driver's ride-hailing vehicle has left the heat region, the greater the distance between the driver's ride-hailing vehicle and the heat region, the lighter the color of the heat region.

[0010] In one embodiment, a ride-hailing dispatch method further includes: when it is detected that a driver's ride-hailing vehicle is driving towards a hot area, monitoring whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot area that meet the driver's dispatch conditions; if so, the driver is assigned a ride-hailing order that meets the driver's dispatch conditions; if not, continuing to monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot area that meet the driver's dispatch conditions, and reducing the hot value of the hot area if it is detected that the driver has driven into the hot area and stayed there for more than a preset time without receiving any ride-hailing orders.

[0011] In one embodiment, the heat zone includes a first heat zone and a second heat zone, where the heat value of the first heat zone is greater than that of the second heat zone. A ride-hailing dispatch method further includes: if it is detected that a driver's ride-hailing vehicle has entered the first heat zone and has remained there for more than a preset time without receiving any ride-hailing orders, prompting the driver to proceed to the second heat zone; when it is detected that the driver's ride-hailing vehicle is moving towards the second heat zone, monitoring whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second heat zone that meet the driver's dispatch conditions; if so, assigning a ride-hailing order that meets the driver's dispatch conditions to the driver; if not, continuing to monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second heat zone that meet the driver's dispatch conditions, and if it is detected that the driver has entered the second heat zone and has remained there for more than a preset time without receiving any ride-hailing orders, prompting the driver to leave the second heat zone.

[0012] A ride-hailing dispatch device includes: a receiving module for receiving a driver's ride-hailing order reservation request and parsing the driver's preference information from the reservation request, the preference information including origin location information and destination location information; a first determining module for determining a first area based on the origin location information and a preset order-taking distance; a second determining module for determining a second area based on the destination location information and a preset order-taking distance; an acquiring module for acquiring multiple target ride-hailing orders within a set time range whose origin is in the first area and whose destination is in the second area; a generating module for dividing the multiple target ride-hailing orders in the first area using a gridded processing method and generating a heat map of the first area based on the analysis results; and a feedback module for drivers to provide feedback on the heat map to prompt drivers to go to the heat map to receive ride-hailing orders.

[0013] A computer device includes 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 methods described above.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0015] The aforementioned ride-hailing dispatch method, apparatus, computer equipment, and storage medium receive ride-hailing order reservation requests from drivers, parse driver-set preference information from the reservation requests (including origin and destination location information), determine a first region based on the origin location information and a preset pickup distance, determine a second region based on the destination location information and the preset pickup distance, acquire multiple target ride-hailing orders within a set time range whose origins are located in the first region and whose destinations are located in the second region, analyze the multiple target ride-hailing orders in the first region using a grid-based processing method, and generate a hotspot region for the first region based on the analysis results, and provide feedback on the hotspot region to the driver to prompt the driver to go to the hotspot region to accept ride-hailing orders. Therefore, by satisfying driver order preferences and predicting available hotspot regions based on the number of ride-hailing orders, drivers are guided to the hotspot regions to accept orders, improving the accuracy and efficiency of ride-hailing dispatch. In addition, it can not only increase the success rate of ride-hailing drivers in accepting orders, thereby improving their experience and trust in the platform, but also reduce drivers' empty driving rate and waiting time, thereby reducing drivers' operating costs, improving the overall operational efficiency of ride-hailing platforms, and reducing the waste of empty vehicle resources. Attached Figure Description

[0016] Figure 1 This is an application environment diagram of a ride-hailing dispatching method in one embodiment;

[0017] Figure 2 This is a flowchart illustrating a ride-hailing dispatch method in one embodiment;

[0018] Figure 3 This is a schematic diagram of a process for generating a heat region in a first region in one embodiment;

[0019] Figure 4 This is a schematic diagram of a process for determining a hot zone in a first region based on the number of predicted ride-hailing orders for each grid within the first region, as described in one embodiment.

[0020] Figure 5 This is a schematic diagram of a process for controlling the color depth of a heat zone in one embodiment;

[0021] Figure 6 This is a schematic diagram of a process for allocating ride-hailing orders in one embodiment;

[0022] Figure 7 This is a schematic diagram of a process for allocating ride-hailing orders within a second popularity zone, as shown in one embodiment.

[0023] Figure 8 This is a block diagram of the internal structure of a ride-hailing dispatch device in one embodiment;

[0024] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0025] 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.

[0026] This application provides a ride-hailing dispatch method, applicable to, for example, ride-hailing vehicle dispatching methods. Figure 1 The application environment shown. For example... Figure 1 As shown, the ride-hailing dispatch system 100 is used to execute a ride-hailing dispatch method according to this application. Exemplarily, the ride-hailing dispatch system 100 can be used to handle the business processing, data storage, and dispatch logic of the ride-hailing platform, and interacts with the driver's terminal device 102 via a network. The driver's terminal device 102 can be a mobile terminal, such as a mobile phone, tablet computer, or in-vehicle terminal. Specifically, the system receives a reservation request for a ride-hailing order from the driver's terminal device 102 from the ride-hailing platform in the ride-hailing dispatch system 100. The system parses the driver's preference information from the reservation request, which may include origin and destination location information. It then determines a first region based on the origin location information and a preset pickup distance; a second region based on the destination location information and a preset pickup distance; acquires multiple target ride-hailing orders within a set time range whose origin is in the first region and whose destination is in the second region; analyzes these multiple target ride-hailing orders in the first region using a grid-based processing method; and generates a heat map for the first region based on the analysis results. Finally, it sends the heat map information back to the driver's terminal device 102 to prompt the driver to go to the heat map to accept ride-hailing orders.

[0027] In one embodiment, such as Figure 2 As shown, a ride-hailing dispatch method is provided, which can be applied to... Figure 1 Taking the ride-hailing dispatch system 100 in China as an example, the following steps are included:

[0028] S201: Receive the driver's ride-hailing order reservation request and parse the driver's preference information from the reservation request.

[0029] In this embodiment, the ride-hailing platform receives a ride-hailing booking request from a driver sent by terminal device 102, and parses the driver's preference information from the request. This preference information may include origin and destination location information, which can be specified by the driver when accepting the order. For example, the origin location can be the starting point set by the driver before accepting the order, or it can be the real-time location of the ride-hailing vehicle during the order process. The destination location can be the destination set by the driver in advance. By extracting the driver's preference information, the ride-hailing platform can intelligently match orders according to the driver's actual needs, filter orders that match the driver's preference information, and then intelligently recommend orders to the driver to improve order matching accuracy and driver satisfaction.

[0030] S202, determine the first area based on the starting point location information and the preset order receiving distance.

[0031] In this embodiment, a first region is determined based on the driver's set starting location information and a preset order-taking distance. For example, if the default preset order-taking distance of the ride-hailing platform is 3 kilometers, then a circular area within 3 kilometers of the starting location is determined as the first region to define the driver's starting range for accepting orders. By setting a reasonable preset order-taking distance to limit the driver's starting range for accepting orders, drivers are prevented from increasing fuel consumption and time costs due to accepting far-distance passenger orders, thus improving operational efficiency. At the same time, it defines clear geographical boundaries for the selection of subsequent orders, reducing data processing volume.

[0032] S203, determine the second area based on the destination location information and the preset order acceptance distance.

[0033] In this embodiment of the application, similar to step S202, a second area is determined based on the destination location information set by the driver and in combination with the preset order-accepting distance. For example, a circular area within 3 kilometers of the destination location is determined as the second area, centered on the destination location, so as to facilitate the filtering of orders that are consistent with the driver's destination (such as orders that are on the way home for the driver). A high degree of destination matching can improve the driver's satisfaction with accepting orders.

[0034] In one embodiment, the initial value of the preset order-accepting distance is set by the system default and is dynamically adjusted by the number of orders of multiple target ride-hailing orders within a set time range, where the starting point is located in the first region and the ending point is located in the second region. When the number of orders of multiple target ride-hailing orders is less than the preset number of orders, the value of the preset order-accepting distance is increased.

[0035] Specifically, the initial value of the preset order-accepting distance (such as 3 kilometers) is usually set by the system default and serves as the initial matching range for the driver's starting and ending points for accepting orders. After the system determines the first and second regions based on the initial value of the preset order-taking distance (e.g., 3 kilometers), the starting point location information, and the ending point location information, if the number of multiple target ride-hailing orders with the starting point in the first region and the ending point in the second region within the set time range (e.g., within the most recent month and the same time period as the driver's current location) is less than the preset threshold (e.g., 20 orders), the order-taking distance is automatically increased (e.g., from 3 kilometers to 5 kilometers) to expand the starting point and ending point range of the driver's order-taking (i.e., the geographical range of the first and second regions). This increases the number of target ride-hailing orders that meet the conditions (starting point in the first region and ending point in the second region) within the set time range (e.g., within the most recent month and the same time period as the driver's current location), until the number of target ride-hailing orders reaches the preset threshold (e.g., 20 orders) or the maximum order-taking distance limit (e.g., 10 kilometers), ensuring that the number of orders used for analysis reaches statistical significance (≥20 orders), making the generated hotspot regions more valuable for reference.

[0036] S204: Obtain multiple target ride-hailing orders within a set time range whose starting point is located in the first region and whose destination is located in the second region.

[0037] In this embodiment, the ride-hailing platform can obtain multiple target ride-hailing orders from historical and / or real-time order databases within a set time range (e.g., within the most recent month and at the same time as the driver's current location), with the starting point located in a first region and the destination located in a second region. This allows the platform to filter out orders that highly match the driver's order-taking needs, effectively improving the accuracy of subsequent analysis based on target ride-hailing orders. This, in turn, provides drivers with more accurate order recommendations, increases the success rate of drivers accepting orders, and enhances the driver's order-taking experience and trust in the ride-hailing platform.

[0038] The time range is determined based on the driver's current order-taking time period and historical time periods. If the driver's current order-taking time period is during the morning rush hour, the time range is set to all or part of the morning rush hour periods within the historical time period. Specifically, the time range not only considers traditional time ranges, such as the most recent week or the most recent month, but also dynamically adjusts the selected time range based on the driver's current order-taking time period. For example, if the ride-hailing platform detects that the driver's current time period is during the weekday morning rush hour of 7:00-9:00, the platform can retrieve multiple target ride-hailing orders from the historical and / or real-time order database for the most recent month's weekday morning rush hour (7:00-9:00) with origins in the first region and destinations in the second region. This accurately filters out target ride-hailing orders that match the characteristics of that time period, avoiding inaccurate predictions due to large differences in order demand across different time periods. This improves the accuracy of subsequent analysis based on target ride-hailing orders, providing drivers with more accurate order recommendations, increasing their order-taking success rate, and ultimately enhancing their order-taking experience and trust in the ride-hailing platform.

[0039] S205 uses a grid-based processing method to analyze multiple target ride-hailing orders in the first region and generates a heat zone for the first region based on the analysis results.

[0040] In this embodiment, the ride-hailing platform uses a grid-based processing method to analyze multiple target ride-hailing orders within a first region. That is, the first region is divided into several geographical grids (e.g., a 100M×100M grid) according to latitude and longitude. The target ride-hailing orders within each geographical grid are analyzed, and then areas with high order demand within the first region are identified based on the analysis results (e.g., the number of orders). This generates "hotspot areas" in the first region, which can guide drivers to these high-demand hotspot areas, increasing the success rate of order acceptance. This improves the driver's order acceptance experience and trust in the platform, while reducing the time drivers spend driving empty and waiting, lowering drivers' operating costs (e.g., fuel consumption), improving the accuracy and efficiency of the ride-hailing platform's dispatching, and reducing the waste of empty vehicle resources.

[0041] It should be noted that the meshing process in this embodiment can dynamically select the optimal mesh size based on the specific needs of different areas, rather than using the same meshing method. For example, fine-grained meshing (such as 50m×50m) can be used for areas with high order density, such as commercial districts and office buildings, while coarse-grained meshing (such as 500m×500m) can be used for areas with low order density, such as suburbs and residential areas. Dynamically adjusting the mesh density can effectively reduce unnecessary computation and improve processing efficiency.

[0042] S206 provides drivers with information on popular areas to guide them to pick up ride-hailing orders in those areas.

[0043] In this embodiment, the ride-hailing platform generates trending areas and feeds them back to the drivers to prompt them to accept ride-hailing orders in those areas. Typically, the trending areas are displayed to the drivers via their terminal device 102 (such as a mobile phone), helping them quickly make informed order-accepting decisions. Drivers can dynamically adjust their routes based on these trending areas, proactively heading to areas with more order opportunities, thus increasing their order-acceptance success rate. This enhances their order-accepting experience and trust in the platform, while simultaneously reducing empty driving and waiting time, thereby improving the operational efficiency of the ride-hailing platform.

[0044] The aforementioned ride-hailing dispatch method receives ride-hailing order requests from drivers, parses driver-set preference information from these requests (including origin and destination locations), determines a first region based on the origin location and a preset pickup distance, and determines a second region based on the destination location and the same preset pickup distance. It then acquires multiple target ride-hailing orders within a set time range whose origins are in the first region and whose destinations are in the second region. A grid-based approach is used to analyze these multiple target ride-hailing orders in the first region, generating a "hotspot" region based on the analysis results. Finally, the hotspot region is fed back to the driver to guide them to accept ride-hailing orders in that region. Therefore, by satisfying driver preferences and predicting available hotspot regions based on the number of ride-hailing orders, the method guides drivers to these regions, improving the accuracy and efficiency of ride-hailing dispatch. Furthermore, it not only increases the success rate of ride-hailing drivers accepting orders, enhancing their experience and trust in the platform, but also reduces empty mileage and waiting time, lowering operating costs and improving the overall operational efficiency of the ride-hailing platform while minimizing the waste of empty vehicle resources.

[0045] In one embodiment, the target ride-hailing order may include historical ride-hailing orders and current ride-hailing orders, such as... Figure 3 As shown, step S205 above, which is to analyze multiple target ride-hailing orders in the first region using a gridded processing method and generate a heat zone for the first region based on the analysis results, may include the following steps:

[0046] S301, the first region is divided into grids according to latitude and longitude, and the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region are obtained.

[0047] In this embodiment, the first region is divided into several grids (e.g., 100m×100m) according to latitude and longitude. The number of historical ride-hailing orders and the number of current ride-hailing orders with the starting point in the first region and the ending point in the second region are obtained within a set time range (e.g., within the most recent month and the same time period as the driver's current time) in each grid. This provides a basis for the subsequent weighted fusion of historical and current ride-hailing orders. At the same time, the grid division converts continuous geographic space into discrete grid units, which helps to eliminate the ambiguity of geographic boundaries and is beneficial to the quantitative calculation of spatial data.

[0048] S302, obtain the weight of historical ride-hailing orders and the weight of current ride-hailing orders.

[0049] Specifically, to make the analysis more accurate, ride-hailing platforms assign different weights to historical and current ride-hailing orders based on their impact on the prediction results. For example, historical ride-hailing orders might have a weight of 0.5, and current ride-hailing orders might have a weight of 0.5. The weighting can be adjusted based on several factors, such as the time period of the order, the distance of the order, and the urgency of the demand. The weight of historical ride-hailing orders reflects long-term demand trends, while the weight of current ride-hailing orders reflects the urgency of real-time demand.

[0050] S303, based on the weight of historical ride-hailing orders and the weight of current ride-hailing orders, the number of historical ride-hailing orders and the number of current ride-hailing orders in each grid of the first region are weighted and calculated to obtain the predicted number of ride-hailing orders for each grid in the first region.

[0051] Furthermore, based on the weights of historical ride-hailing orders (e.g., 0.5) and current ride-hailing orders (e.g., 0.5), a weighted calculation is performed on the number of historical and current ride-hailing orders in each grid within the first region to calculate the predicted number of ride-hailing orders for each grid within the first region. Through this weighted calculation, the ride-hailing platform can combine historical trends and real-time demand to obtain more accurate order prediction results, improving the accuracy of the predictions and avoiding errors caused by relying solely on one type of data.

[0052] S304: Analyze the number of predicted ride-hailing orders for each grid within the first region to determine the hottest areas of the first region.

[0053] Furthermore, by analyzing the predicted order volume of each grid, the hot zones within the first region are identified—areas with the most concentrated or active demand. These areas are likely to see a large number of ride-hailing orders, becoming key areas for drivers to accept orders. Drivers can use this information to plan their routes accordingly to these hot zones. Therefore, this not only increases the success rate of ride-hailing drivers accepting orders, improving their experience and trust in the platform, but also reduces empty mileage and waiting time, lowering their operating costs and improving the overall operational efficiency of the ride-hailing platform, thus reducing the waste of empty vehicle resources.

[0054] In one embodiment, a ride-hailing dispatching method may further include: adjusting the predicted number of ride-hailing orders for each grid within a first region based on external influencing factors.

[0055] Specifically, external influencing factors include at least one of the following: weather conditions, holiday information, passenger behavior information, traffic control information, and large-scale event information. By adjusting the predicted number of ride-hailing orders based on external influencing factors, such as appropriately increasing the predicted number of ride-hailing orders during holidays or periods of high demand due to severe weather, the order demand of each grid can be predicted more accurately. This allows for the rational allocation of ride-hailing resources and improves the overall scheduling efficiency and operational effectiveness of the ride-hailing platform.

[0056] In one embodiment, such as Figure 4 As shown, step S304 above, which is to analyze the popularity zone of the first region based on the number of predicted ride-hailing orders for each grid in the first region, may include the following steps:

[0057] S401, merge multiple grids in the first region that are adjacent in location and whose difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold to obtain one or more merged regions.

[0058] In this embodiment, multiple grids that are adjacent in location within the first region and have similar predicted order numbers (i.e., the difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold, such as 5 orders) are merged into one or more merged regions. By merging grids with small differences in the number of predicted orders, the predicted ride-hailing orders within the same merged region are considered to have the same popularity, which reduces the computational cost of analyzing each grid individually, improves the efficiency of determining popular regions within the first region, and helps to quickly determine popular regions to improve the system's response speed.

[0059] S402, calculate the total number of predicted ride-hailing orders for each merged region.

[0060] Specifically, the predicted number of ride-hailing orders for all grids within each merged region is summed to obtain the total predicted number of ride-hailing orders for each merged region.

[0061] S403, calculate the average number of predicted ride-hailing orders for each merged region based on the total number of predicted ride-hailing orders for each merged region and the total number of grids within each merged region.

[0062] Furthermore, based on the total predicted number of ride-hailing orders in each merged region and the total number of grids within each merged region, the average predicted number of ride-hailing orders in each merged region is calculated. This average predicted number of ride-hailing orders is then used to determine the popularity value of each merged region. For example, if a merged region contains 5 grids, and the predicted number of orders for each grid is 10, 12, 11, 9, and 8 respectively, then the total predicted number of ride-hailing orders in that merged region is 50, and the average predicted number of ride-hailing orders in that merged region is 10.

[0063] S404 determines the popularity value of each merged region based on the average predicted number of ride-hailing orders in each merged region.

[0064] Specifically, by converting the average predicted order quantity of each merged region into a heat value, the demand intensity of ride-hailing orders in that merged region can be quantitatively represented. For example, the average predicted order quantity of each merged region can be converted into a heat value through mapping, mapping the average order quantity within a region to a specified heat value range. For instance, if the minimum predicted order quantity of each grid within the first region is 0 and the maximum is 100, the average predicted order quantity of each merged region can be mapped to a standardized heat value range (e.g., [0, 100]) using a linear formula. The standardization calculation can be performed using the following formula:

[0065]

[0066] The above formula can map the average predicted number of orders in each merged region to a unified range of popularity values, so as to identify the popular areas with high demand for ride-hailing orders based on the popularity values ​​of each merged region.

[0067] S405, determine the heat zone based on the heat value of each merged zone.

[0068] Specifically, the final hottest regions are selected based on the calculated popularity values ​​of each merged region. For example, the top N (where N is a positive integer greater than 0) merged regions can be chosen as the final hottest regions, and the parameter N can be dynamically adjusted according to the region type (e.g., N=5 for city centers, N=2 for suburbs). By providing drivers with information on hottest regions with high popularity values ​​(e.g., the top three), drivers are guided to these regions to accept ride-hailing orders. Therefore, this not only increases the success rate of ride-hailing drivers accepting orders, improving their experience and trust in the platform, but also reduces drivers' empty mileage and waiting time, lowering their operating costs and improving the overall operational efficiency of the ride-hailing platform, thus reducing the waste of empty vehicle resources.

[0069] In one embodiment, step S206, namely, providing feedback on the heat area to the driver, includes: displaying the heat area in a color-rendered manner on the driver's terminal device; such as... Figure 5 As shown, after step S206 above, a ride-hailing dispatch method may further include the following steps:

[0070] S501 monitors the distance between the driver's ride-hailing vehicle and the hot zone.

[0071] S502 controls the color depth of the heat zone based on distance.

[0072] In this embodiment, the smaller the distance between the driver's ride-hailing vehicle and the heat zone, the darker the color of the heat zone. Conversely, when the driver's ride-hailing vehicle is detected leaving the heat zone, the greater the distance between the driver's ride-hailing vehicle and the heat zone, the lighter the color of the heat zone.

[0073] Specifically, by displaying "hot zones" using color rendering on the driver's terminal device (such as a mobile phone or in-vehicle terminal), for example, using red, orange, and yellow to represent different "hot zones" according to their heat value from high to low, drivers can quickly and intuitively identify high-heat areas by color and proactively go to those areas to accept orders, reducing the time drivers spend blindly cruising and improving their order-accepting efficiency. Furthermore, the ride-hailing platform monitors the distance between the ride-hailing vehicle's current location and the hot zones in real time and adjusts the color depth of the hot zones accordingly. This dynamic adjustment mechanism allows drivers to clearly understand the relationship between their current location and the hot zones, avoiding missed order opportunities due to unclear location information. As drivers approach a hot zone (e.g., a red hot zone), the color of that zone gradually deepens to indicate that the system predicts high demand for ride-hailing orders in that area, suggesting that drivers can head there to pick up orders. Conversely, as drivers leave the hot zone and move further away, the color lightens to indicate a decrease in demand, preventing drivers from misjudging that high demand still exists and turning back. This visual feedback helps drivers quickly abandon areas that have cooled down and move to other high-demand areas to pick up orders, thus improving their order-taking efficiency.

[0074] In one embodiment, such as Figure 6 As shown, a ride-hailing dispatch method may further include the following steps:

[0075] S601: When it is detected that a driver's ride-hailing vehicle is driving towards a hot area, monitor whether there are any ride-hailing orders that meet the driver's dispatch conditions among the ride-hailing orders generated in real time in the hot area.

[0076] If S602A is the case, then ride-hailing orders that meet the driver's dispatch conditions will be assigned to the driver.

[0077] S602B, if not, continue to monitor whether there are any ride-hailing orders that meet the driver's dispatch conditions among the ride-hailing orders generated in real time in the hot zone, and reduce the hot zone's heat value if it is detected that the driver has driven into the hot zone and stayed for more than the preset time without receiving any ride-hailing orders.

[0078] In this embodiment, when the ride-hailing platform detects that a driver's vehicle is traveling towards a high-traffic area (e.g., a red high-traffic area), it further monitors whether there are any ride-hailing orders in the real-time generated orders within that high-traffic area that meet the driver's dispatch conditions. That is, it checks whether there are any ride-hailing orders whose origin is in the high-traffic area and whose destination is in the second area. If so, the platform prioritizes assigning such ride-hailing orders to the driver, ensuring that the driver can receive orders promptly after entering the high-traffic area, thereby reducing the driver's empty driving and waiting time and improving the driver's order-receiving efficiency. If not, the ride-hailing platform will continue monitoring until... When the system detects that a driver has entered a high-traffic area and stayed there for more than a preset time (e.g., 10 minutes) without receiving any ride-hailing orders, the system will automatically reduce the high-traffic value of that area and change its color (e.g., from red to white) to prompt the driver to leave. This prevents drivers from staying in the high-traffic area for extended periods without receiving orders, improves the accuracy of ride-hailing platform dispatching, reduces drivers' ineffective waiting time, lowers unnecessary fuel consumption and time costs, and allows drivers to promptly go to other more suitable areas to find order opportunities, thus improving the dispatching efficiency of the ride-hailing platform.

[0079] In one embodiment, the heat region includes a first heat region and a second heat region, wherein the heat value of the first heat region is greater than the heat value of the second heat region, such as... Figure 7 As shown, a ride-hailing dispatch method may further include the following steps:

[0080] S701, if it detects that the driver's ride-hailing vehicle has driven into the first hot zone and has stayed there for more than the preset time without receiving any ride-hailing orders, it will prompt the driver to go to the second hot zone.

[0081] S702, when it detects that the driver's ride-hailing vehicle is driving towards the second hot zone, monitors whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second hot zone that meet the driver's dispatch conditions.

[0082] If S703A is the case, then ride-hailing orders that meet the driver's dispatch conditions will be assigned to the driver.

[0083] S703B, if not, continue to monitor whether there are any ride-hailing orders that meet the driver's dispatch conditions among the ride-hailing orders generated in real time in the second hot zone, and if it is detected that the driver has driven into the second hot zone and stayed for more than the preset time without receiving any ride-hailing orders, prompt the driver to leave the second hot zone.

[0084] In this embodiment of the application, when the ride-hailing platform detects that the driver has stayed in the first hot zone for a certain period of time without accepting any orders, the ride-hailing platform will prompt the driver to go to the second hot zone. When a driver is detected heading towards a secondary hot zone, the ride-hailing platform monitors the real-time generated ride-hailing orders within that zone to see if any orders meet the driver's dispatch criteria (i.e., orders originating and ending within the secondary hot zone). If such orders are found, the platform assigns them to the driver, ensuring they receive orders promptly upon entering the hot zone. This reduces empty driving and waiting time, improving the driver's order-accepting efficiency. If no such orders are found, the platform continues monitoring the real-time generated ride-hailing orders within the secondary hot zone until a driver is detected driving into the zone and staying for more than a preset time (e.g., 10 minutes) without receiving any orders. At this point, the platform prompts the driver to leave the secondary hot zone, preventing prolonged stays without orders. This reduces ineffective waiting time, unnecessary fuel consumption and time costs, and allows drivers to find more suitable areas to accept orders, improving the overall dispatch efficiency of the ride-hailing platform.

[0085] In some embodiments, when a ride-hailing platform detects that a driver has remained in a second popularity zone for a certain period of time without accepting any orders, the platform will prompt the driver to move to a third popularity zone, and then find ride-hailing orders that meet the dispatching criteria for the driver in the third popularity zone. The process is the same as described above. Figure 7 The method described in the previous section is similar and will not be repeated here. When the ride-hailing platform detects that a driver has stayed in the previous high-demand area for a certain period of time without accepting any orders, the platform will sequentially prompt the driver to go to the next high-demand area according to the high-demand value and search for ride-hailing orders that meet the dispatching conditions until a ride-hailing order that meets the driver's dispatching conditions is found and dispatched, or the driver has already reached the destination and the trip ends. This ride-hailing dispatching method optimizes the efficiency and intelligence of the dispatching system by guiding drivers to different high-demand areas layer by layer and dynamically monitoring the driver's order acceptance status. It reduces the time cost of drivers waiting in vain and the cost of empty driving, thereby improving the overall operational efficiency of the ride-hailing platform. By guiding drivers to areas with high predicted order demand, it increases the success rate of drivers accepting orders, thereby improving the driver's experience and trust in the platform.

[0086] It should be understood that although the steps in the flowchart are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings 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.

[0087] This application also provides a ride-hailing dispatch device. For example... Figure 8 As shown, a ride-hailing dispatch device includes a receiving module 801, a first determining module 802, a second determining module 803, an acquiring module 804, a generating module 805, and a feedback module 806. The receiving module 801 receives ride-hailing order reservation requests from drivers and parses the driver's preference information from the reservation request, including origin and destination location information. The first determining module 802 determines a first region based on the origin location information and a preset pickup distance. The second determining module 803 determines a second region based on the destination location information and a preset pickup distance. The acquiring module 804 acquires multiple target ride-hailing orders within a set time range whose origin is in the first region and whose destination is in the second region. The generating module 805 uses a grid-based processing method to divide the multiple target ride-hailing orders in the first region and generates a heat map of the first region based on the analysis results. The feedback module 806 allows drivers to provide feedback on the heat map, prompting them to go to the heat map to receive ride-hailing orders.

[0088] In one embodiment, the initial value of the preset order-accepting distance is set by the system default and is dynamically adjusted by the number of orders of multiple target ride-hailing orders with the starting point in the first area and the ending point in the second area within a set time range. When the number of orders of multiple target ride-hailing orders is less than the preset number of orders, the value of the preset order-accepting distance is increased.

[0089] In one embodiment, the target ride-hailing orders include historical ride-hailing orders and current ride-hailing orders. The generation module 805 is specifically used to: divide a first region into grids based on latitude and longitude; obtain the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region; obtain the weights of historical ride-hailing orders and the weights of current ride-hailing orders; perform a weighted calculation on the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region based on the weights of historical ride-hailing orders and the weights of current ride-hailing orders to obtain the predicted number of ride-hailing orders for each grid in the first region; and analyze the heat zone of the first region based on the predicted number of ride-hailing orders for each grid in the first region.

[0090] In one embodiment, analyzing the number of predicted ride-hailing orders in each grid within a first region to determine a hot zone includes: merging multiple grids within the first region that are adjacent in location and whose difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold to obtain one or more merged regions; calculating the total number of predicted ride-hailing orders in each merged region; calculating the average number of predicted ride-hailing orders in each merged region based on the total number of predicted ride-hailing orders in each merged region and the total number of grids in each merged region; determining the hot zone value for each merged region based on the average number of predicted ride-hailing orders in each merged region; and determining the hot zone based on the hot zone value for each merged region.

[0091] In one embodiment, the feedback module 806 is specifically used to: display the heat area in a color rendering manner on the driver's terminal device; a ride-hailing dispatch device further includes: a first monitoring module, used to monitor the distance of the driver's ride-hailing vehicle's heat area; and a control module, used to control the color depth of the heat area according to the distance; wherein, the smaller the distance between the driver's ride-hailing vehicle and the heat area, the darker the color of the heat area, and when the driver's ride-hailing vehicle is detected to have left the heat area, the greater the distance between the driver's ride-hailing vehicle and the heat area, the lighter the color of the heat area.

[0092] In one embodiment, a ride-hailing dispatch device further includes: a second monitoring module, configured to monitor whether there are ride-hailing orders in the real-time generated ride-hailing orders in the hot zone that meet the driver's dispatch conditions when the driver's ride-hailing vehicle is detected to be driving towards the hot zone; a first allocation module, configured to allocate ride-hailing orders that meet the driver's dispatch conditions to the driver if the order is yes; and a third monitoring module, configured to continue monitoring whether there are ride-hailing orders in the real-time generated ride-hailing orders in the hot zone that meet the driver's dispatch conditions if the order is no, and to reduce the hot zone's heat value if the driver is detected to have driven into the hot zone and stayed there for more than a preset time without receiving any ride-hailing orders.

[0093] In one embodiment, the heat zone includes a first heat zone and a second heat zone, where the heat value of the first heat zone is greater than that of the second heat zone. A ride-hailing dispatch device further includes: a prompting module, used to prompt the driver to proceed to the second heat zone if the driver's ride-hailing vehicle has traveled into the first heat zone and has remained there for more than a preset time without receiving any ride-hailing orders; a fourth monitoring module, used to monitor whether there are ride-hailing orders in the second heat zone that meet the driver's dispatch conditions if the driver's ride-hailing vehicle is traveling towards the second heat zone; a second allocation module, used to allocate ride-hailing orders that meet the driver's dispatch conditions to the driver if the driver's ride-hailing vehicle is traveling towards the second heat zone; and a fifth monitoring module, used to continue monitoring whether there are ride-hailing orders in the second heat zone that meet the driver's dispatch conditions if the driver's ride-hailing vehicle is not traveling towards the second heat zone, and to prompt the driver to leave the second heat zone if the driver has traveled into the second heat zone and has remained there for more than a preset time without receiving any ride-hailing orders.

[0094] For specific limitations regarding a ride-hailing dispatch device, please refer to the limitations of a ride-hailing dispatch method described above, which will not be repeated here. Each module in the aforementioned ride-hailing dispatch device 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 a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0095] In one embodiment, 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 computational 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 stored in the non-volatile storage media. The database stores sensor data and facial images. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a liveness detection method based on user actions.

[0096] 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 computer device on which the present application is intended to be applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0097] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: receiving a ride-hailing order reservation request from a driver; parsing the driver's preference information from the reservation request, the preference information including origin location information and destination location information; determining a first region based on the origin location information and a preset order-taking distance; determining a second region based on the destination location information and the preset order-taking distance; acquiring multiple target ride-hailing orders within a set time range whose origin is located in the first region and whose destination is located in the second region; analyzing the multiple target ride-hailing orders in the first region using a grid-based processing method, and generating a heat map region for the first region based on the analysis results; and providing feedback on the heat map region to the driver to prompt the driver to go to the heat map region to accept ride-hailing orders.

[0098] In one embodiment, the initial value of the preset order-accepting distance is set by the system default and is dynamically adjusted by the number of orders of multiple target ride-hailing orders with the starting point in the first area and the ending point in the second area within a set time range. When the number of orders of multiple target ride-hailing orders is less than the preset number of orders, the value of the preset order-accepting distance is increased.

[0099] In one embodiment, the target ride-hailing orders include historical ride-hailing orders and current ride-hailing orders. When the processor executes the computer program to implement the above-mentioned step of analyzing multiple target ride-hailing orders in the first region using a gridded processing method and generating a heat zone of the first region based on the analysis results, the specific steps are as follows: the first region is divided into grids according to latitude and longitude; the number of historical ride-hailing orders and the number of current ride-hailing orders in each grid of the first region are obtained; the weights of historical ride-hailing orders and current ride-hailing orders are obtained; the number of historical ride-hailing orders and the number of current ride-hailing orders in each grid of the first region are weighted according to the weights of historical ride-hailing orders and current ride-hailing orders to obtain the predicted number of ride-hailing orders in each grid of the first region; and the heat zone of the first region is analyzed based on the predicted number of ride-hailing orders in each grid of the first region.

[0100] In one embodiment, when the processor executes a computer program to implement the above-described step of analyzing the number of predicted ride-hailing orders in each grid within the first region to determine the heat zone, the specific steps are as follows: Multiple grids within the first region that are adjacent in location and whose difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold are merged to obtain one or more merged regions; the total number of predicted ride-hailing orders in each merged region is calculated; the average number of predicted ride-hailing orders in each merged region is calculated based on the total number of predicted ride-hailing orders in each merged region and the total number of grids in each merged region; the heat value of each merged region is determined based on the average number of predicted ride-hailing orders in each merged region; and the heat zone is determined based on the heat value of each merged region.

[0101] In one embodiment, when the processor executes the computer program to implement the above-mentioned step of providing feedback on the heat area to the driver, it specifically implements the following steps: displaying the heat area in a color-rendered manner on the driver's terminal device; after the step of providing feedback on the heat area to the driver, the processor also implements the following steps when executing the computer program: monitoring the distance of the driver's ride-hailing vehicle's heat area; controlling the color depth of the heat area according to the distance; wherein, the smaller the distance between the driver's ride-hailing vehicle and the heat area, the darker the color of the heat area, and when the driver's ride-hailing vehicle is detected to have left the heat area, the greater the distance between the driver's ride-hailing vehicle and the heat area, the lighter the color of the heat area.

[0102] In one embodiment, when the processor executes the computer program, it further implements the following steps: when it detects that a driver's ride-hailing vehicle is driving towards a hot area, it monitors whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot area that meet the driver's dispatch conditions; if so, the driver is assigned a ride-hailing order that meets the driver's dispatch conditions; if not, it continues to monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot area that meet the driver's dispatch conditions, and if it detects that the driver has driven into the hot area and stayed there for more than a preset time without receiving any ride-hailing orders, it reduces the heat value of the hot area.

[0103] In one embodiment, the heat zone includes a first heat zone and a second heat zone, where the heat value of the first heat zone is greater than that of the second heat zone. When the processor executes the computer program, it further implements the following steps: if it detects that a driver's ride-hailing vehicle has entered the first heat zone and has stayed there for more than a preset time without receiving any ride-hailing orders, it prompts the driver to go to the second heat zone; when it detects that the driver's ride-hailing vehicle is moving to the second heat zone, it monitors whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second heat zone that meet the driver's dispatch conditions; if so, it assigns a ride-hailing order that meets the driver's dispatch conditions to the driver; if not, it continues to monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second heat zone that meet the driver's dispatch conditions, and if it detects that the driver has entered the second heat zone and has stayed there for more than a preset time without receiving any ride-hailing orders, it prompts the driver to leave the second heat zone.

[0104] 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, and when executed, it 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 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various 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 RAMbus dynamic RAM (RDRAM), etc.

[0105] 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.

[0106] 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 dispatching ride-hailing vehicles, characterized in that, The method includes: The system receives a ride-hailing order reservation request from a driver and parses the driver's preference information from the reservation request. The preference information includes the origin location information and the destination location information. The first area is determined based on the starting point location information and the preset order-taking distance; The second area is determined based on the destination location information and the preset order-taking distance; Obtain multiple target ride-hailing orders within a set time range whose starting point is located in the first region and whose ending point is located in the second region; A grid-based processing method is used to analyze multiple target ride-hailing orders in the first region, and a heat map of the first region is generated based on the analysis results; The system provides feedback on the hotspot areas to the drivers, prompting them to go to those areas to accept ride-hailing orders.

2. The method according to claim 1, characterized in that, The initial value of the preset order-accepting distance is set by the system default and is dynamically adjusted based on the number of orders of multiple target ride-hailing orders within the set time range, where the starting point is located in the first area and the ending point is located in the second area. When the number of orders of the multiple target ride-hailing orders is less than the preset number of orders, the value of the preset order-accepting distance increases.

3. The method according to claim 1, characterized in that, The target ride-hailing orders include historical ride-hailing orders and current ride-hailing orders. The process of analyzing multiple target ride-hailing orders in the first region using a grid-based processing method, and generating a heat map of the first region based on the analysis results, includes: The first region is divided into grids based on latitude and longitude, and the number of historical ride-hailing orders and the number of current ride-hailing orders for each grid in the first region are obtained. Obtain the weights of historical ride-hailing orders and the weights of current ride-hailing orders; The number of historical ride-hailing orders and the number of current ride-hailing orders in each grid within the first region are weighted and calculated to obtain the predicted number of ride-hailing orders for each grid within the first region. The popularity zones of the first region are determined by analyzing the number of predicted ride-hailing orders for each grid within the first region.

4. The method according to claim 3, characterized in that, The step of analyzing the predicted number of ride-hailing orders in each grid within the first region to determine the hot zones includes: Multiple grids that are adjacent in the first region and whose difference in the number of predicted ride-hailing orders is less than or equal to a preset threshold are merged to obtain one or more merged regions; Calculate the total number of predicted ride-hailing orders for each merged region; The average number of predicted ride-hailing orders for each merged region is calculated based on the total number of predicted ride-hailing orders for each merged region and the total number of grids within each merged region. The popularity value of each merged region is determined based on the average predicted number of ride-hailing orders in each merged region. The heat regions are determined based on the heat values ​​of each merged region.

5. The method according to claim 1, characterized in that, The step of providing feedback to the driver regarding the heat region includes: The heat zone is displayed in a color-rendered manner on the driver's terminal device; After the step of providing feedback to the driver regarding the temperature zone, the method further includes: Monitor the distance between the driver's ride-hailing vehicle and the heat zone; The color depth of the heat zone is controlled according to the distance; The closer the driver's ride-hailing vehicle is to the heat zone, the darker the color of the heat zone. Conversely, when the driver's ride-hailing vehicle is detected leaving the heat zone, the greater the distance between the driver's ride-hailing vehicle and the heat zone, the lighter the color of the heat zone.

6. The method according to claim 4, characterized in that, The method further includes: When it is detected that the driver's ride-hailing vehicle is driving towards the hot area, monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot area that meet the driver's dispatch conditions; If so, then assign a ride-hailing order that meets the driver's dispatch conditions to the driver; If not, continue to monitor whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the hot zone that meet the driver's dispatch conditions, and if it is detected that the driver has driven into the hot zone and stayed for more than a preset time without receiving any ride-hailing orders, reduce the hot zone's heat value.

7. The method according to claim 6, characterized in that, The heat region includes a first heat region and a second heat region, wherein the heat value of the first heat region is greater than the heat value of the second heat region, and the method further includes: If the system detects that the driver's ride-hailing vehicle has entered the first hot zone and has stayed there for more than the preset time without receiving any ride-hailing orders, the driver will be prompted to go to the second hot zone. When the driver's ride-hailing vehicle is detected to be traveling towards the second hot zone, the system monitors whether there are any ride-hailing orders in the real-time generated ride-hailing orders in the second hot zone that meet the driver's dispatch conditions. If so, then assign a ride-hailing order that meets the driver's dispatch conditions to the driver; If not, continue to monitor whether there are any ride-hailing orders that meet the driver's dispatch conditions among the ride-hailing orders generated in real time in the second hot zone. If it is detected that the driver has driven into the second hot zone and stayed for more than the preset time without receiving any ride-hailing orders, prompt the driver to leave the second hot zone.

8. A ride-hailing dispatching device, characterized in that, The device includes: The receiving module is used to receive the reservation order request of the driver's ride-hailing order, and parse the preference information set by the driver from the reservation order request. The preference information includes the origin location information and the destination location information. The first determining module is used to determine the first area based on the starting point location information and the preset order-accepting distance; The second determining module is used to determine the second area based on the destination location information and the preset order-accepting distance; The acquisition module is used to acquire multiple target ride-hailing orders within a set time range whose starting point is located in the first area and whose ending point is located in the second area; The generation module is used to analyze multiple ride-hailing orders in the first region using a gridded processing method, and generate a heat zone of the first region based on the analysis results; The feedback module is used to provide feedback on the hot areas to the driver, so as to prompt the driver to go to the hot areas to receive ride-hailing orders.

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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