Data processing method and device and electronic equipment

Abstract

The invention discloses a data processing method and device and electronic equipment, and relates to the technical field of Internet. The method comprises the following steps: matching the sex, character type and personalized item of a passenger with the character type and personalized item of the passenger expected by a driver, and matching the sex, character type and vehicle type of the driver with the sex, character type and vehicle type of the driver expected by the passenger to obtain a first matching degree; bidirectional personalized selection of a driver and passengers is realized; the target order of the passenger is determined according to the first matching degree, and after the target order is completed, evaluation of the target order by both the passenger and the driver is fed back to the platform, so that the matching algorithm is continuously optimized, and the subsequent matching accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and more specifically, to a data processing method, apparatus, and electronic device. Background Technology

[0002] In ride-hailing service platforms, the matching mechanisms are mostly quite simplistic. Traditional matching methods primarily rely on basic information such as the passenger's location, travel time, and destination, pairing the passenger with a nearby driver who has available capacity. While this method can meet passengers' travel needs to some extent, it has significant limitations, neglecting the personalized experience of passengers during the ride. Different passengers have different personalities and preferences; some prefer talkative drivers for conversation during the trip, while others prefer quiet and attentive drivers, or some may prefer female drivers or specific vehicle types.

[0003] Typically, matching mechanisms cannot achieve personalized matching between passengers and drivers based on personality, gender, vehicle type, etc., which may lead to a poor passenger experience. Furthermore, drivers are often in a passive position when accepting orders, lacking autonomy in choosing which orders to take. Drivers may also have different attitudes towards certain types of passengers due to various reasons, such as personal preferences and service experience. Therefore, existing matching mechanisms cannot meet drivers' needs to select passengers according to their own preferences.

[0004] Therefore, how to satisfy the personalized matching of passengers and drivers while realizing the two-way selection of passengers and drivers in the platform that distributes trip orders has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of this application propose a data processing method, apparatus, and electronic device that can achieve two-way personalized matching between passengers and drivers, and improve matching accuracy based on a feedback mechanism, thereby achieving the goal of simultaneously improving the passenger's riding experience and the driver's order acceptance satisfaction.

[0006] The following technical solution is adopted in this application.

[0007] In a first aspect, embodiments of this application provide a data processing method, which includes: The system acquires a passenger's first characteristic and a first preset characteristic, and a driver's second characteristic and a second preset characteristic. The first characteristic includes the passenger's gender, personality type, and personal preferences; the first preset characteristic indicates the passenger's desired driver's gender, personality type, and vehicle type. The second characteristic includes the driver's gender, personality type, and vehicle type; the second preset characteristic indicates the driver's desired passenger's personality type and personal preferences. The system matches the first characteristic, the first preset characteristic, the second characteristic, and the second preset characteristic to obtain a first matching degree between the passenger and the driver. Based on the passenger's itinerary, multiple trip orders and a corresponding first matching degree for each trip order are obtained. The itinerary includes the passenger's departure point, destination, and departure time. The trip orders are obtained based on the passenger's itinerary. The system determines the passenger's target order from the trip orders where the first matching degree reaches a first threshold.

[0008] In some embodiments, matching the first feature, the first preset feature, the second feature, and the second preset feature to obtain a first matching degree between the passenger and the driver includes: Based on the first feature and its first weight, a first initial matching degree is obtained by matching with the second preset feature; based on the second feature and its second weight, a second initial matching degree is obtained by matching with the first preset feature; based on the first initial matching degree and the second initial matching degree, a first matching degree is obtained.

[0009] In some embodiments, determining a passenger's target order from trip orders with a first matching degree reaching a first threshold includes: From trip orders that have reached a first matching degree threshold, identify multiple candidate trip orders; based on the selection methods set by passengers and drivers on the platform, identify the target trip order from among the multiple candidate trip orders.

[0010] In some embodiments, determining a target order from multiple candidate trip orders based on the selection methods set by the passenger and driver in the platform includes: Based on the fact that both parties set their selection method on the platform as platform recommendation, the trip order with the highest matching degree among multiple candidate trip orders is determined as the target order; based on the fact that at least one party set their selection method on the platform as self-selection, the trip order selected by the passenger or driver from multiple candidate trip orders is determined as the target order.

[0011] In some embodiments, after determining the passenger's target order from trip orders with a first matching degree reaching a first threshold, the method further includes: Obtain the passenger's first rating of the target order; adjust the second weight of the driver's second feature based on the first rating.

[0012] In some embodiments, adjusting the second weight of a driver's second characteristic based on a first score includes: If the first score is greater than the second threshold, increase the value of the second weight; if the first score is less than the second threshold, decrease the value of the second weight; if the first score is equal to the second threshold, keep the value of the second weight unchanged.

[0013] In some embodiments, after determining the passenger's target order from trip orders with a first matching degree reaching a first threshold, the method further includes: Obtain the driver's second rating for the target order; adjust the first weight of the passenger's first feature based on the second rating.

[0014] In some embodiments, adjusting the first weight of a passenger's first characteristic based on a second score, the method further includes: If the second score is greater than the second threshold, increase the value of the first weight; if the second score is less than the second threshold, decrease the value of the first weight; if the second score is equal to the second threshold, keep the value of the first weight unchanged.

[0015] According to a second aspect of the embodiments of this application, a data processing apparatus is provided, the apparatus comprising: an acquisition module, configured to acquire a first feature and a first preset feature of a passenger, and a second feature and a second preset feature of a driver; wherein the first feature includes the passenger's gender, personality type, and personalized items, and the first preset feature indicates the passenger's desired driver's gender, personality type, and vehicle type; the second feature includes the driver's gender, personality type, and vehicle type, and the second preset feature indicates the driver's desired passenger's personality type and personalized items; a matching module, configured to match the first feature, the first preset feature, the second feature, and the second preset feature to obtain a first matching degree between the passenger and the driver; a first processing module, configured to obtain multiple trip orders and a first matching degree corresponding to each trip order based on the passenger's itinerary; the itinerary includes the passenger's departure point, destination, and departure time; the trip orders are obtained based on the passenger's itinerary; and a second processing module, configured to determine the passenger's target order from the trip orders whose first matching degree reaches a first threshold.

[0016] According to a third aspect of the embodiments of this application, an electronic device is provided, the electronic device comprising: a processor; and a memory storing electronic device readable instructions, wherein when the electronic device readable instructions are executed by the processor, the above-mentioned data processing method is implemented.

[0017] In this application's solution, firstly, the system matches the passenger's first characteristic with a second preset characteristic and the driver's second characteristic with the first preset characteristic based on the similarities and differences in the passenger's and driver's features. A first matching degree is obtained by adding points for similarities and subtracting points for differences, thus increasing the dimensions of matching options for both parties and meeting their individual needs, thereby improving the passenger's riding experience and the driver's order acceptance satisfaction. Secondly, the system sets the method for obtaining target orders based on the needs of either the passenger or the driver, including obtaining target orders based on platform recommendations or by self-selection, further increasing the flexibility of two-way selection. Finally, the system combines the passenger's first rating of the target order and the driver's second rating of the target order to adjust the weights of the first and second features respectively, thereby continuously optimizing the matching algorithm and enabling the platform to continuously improve matching accuracy and service quality.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This is a schematic diagram illustrating a data processing method provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart illustrating a data processing method provided in an embodiment of this application.

[0022] Figure 3 This is a flowchart illustrating a method for obtaining a first matching degree, provided in an embodiment of this application.

[0023] Figure 4 This is a flowchart illustrating a method for obtaining a target order, as provided in an embodiment of this application.

[0024] Figure 5 This is a flowchart illustrating a processing method for adjusting a second weight based on a first score, as provided in an embodiment of this application.

[0025] Figure 6 This is a flowchart illustrating a processing method for adjusting a first weight based on a second score, provided in an embodiment of this application.

[0026] Figure 7This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application.

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through specific embodiments. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] In conventional technologies, platforms that distribute passenger trip orders to vehicles mostly collect passengers' personalized needs, such as vehicle type requirements and route selections, to dispatch vehicles of the appropriate type and complete the trip order based on the passenger's chosen route. While this optimizes the passenger's riding environment and experience to some extent, it only satisfies the passenger's needs and does not involve a two-way selection process between passengers and drivers. Drivers lose their autonomy in selecting orders, making it impossible for them to filter orders based on their own circumstances, which reduces their work enthusiasm. Therefore, the lack of dimensions for personalized order filtering leads to a lack of accuracy in matching, hindering the optimization of the platform's matching algorithm and the continuous improvement of the platform's service quality.

[0031] The data processing method provided in this application is intended to solve the above-mentioned technical problems of the prior art.

[0032] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0033] Figure 1 This is a schematic diagram illustrating a scenario of the data processing method provided in an embodiment of this application. For example... Figure 1 As shown, the data processing method provided in this application embodiment includes a first module 101 and a second module 102.

[0034] In one optional implementation, the first module 101 and the second module 102 refer to software units or modules. Specifically, the first module 101 stores the information acquired by the second module 102, obtains the matching degree of the second module 102, and optimizes the matching degree based on feedback from the second module 102; the second module 102 acquires information about passengers and drivers.

[0035] In another alternative implementation, the first module 101 and the second module 102 refer to hardware devices.

[0036] For example, the first module 101 may include, but is not limited to, electronic devices with data processing capabilities such as computers, host computers, servers, or data centers.

[0037] As an example, the second module 102 may include, but is not limited to, vehicles and mobile devices.

[0038] Optionally, the first module 101 and the second module 102 can communicate via wired or wireless connections. Wired connections may include, but are not limited to, buses, fiber optic cables, or network cables. Wireless connections may include, for example, transmission control protocol / internet protocol (TCP / IP), wireless local area network (WLAN) protocols, and remote direct memory access (RDMA) over converged ethernet (RoCE) protocols.

[0039] The following is combined Figure 1 The first module 101 and the second module 102 shown illustrate the data processing method provided in this application embodiment: First, the first module 101 can interact with the second module 102 via wireless communication. Second, passengers and drivers input a first feature and a first preset feature, a second feature, and a second preset feature through mobile devices in the second module 102. The first module 101 obtains information from the second module 102, performs information matching using an algorithm that filters for identical and different items, and obtains a first matching degree. Furthermore, the first module 101 determines vehicles for multiple candidate trip orders in the second module 102 based on the passenger's itinerary and the first matching degree, and then determines the target order based on the selection method set by the passenger or driver. Finally, passengers and drivers input feedback on the target order through mobile devices in the second module 102, and the first module 101 adjusts the weights of the first feature and the second feature based on the feedback.

[0040] Below Figure 1 Based on the first module 101 and the second module 102 shown, the data processing method provided in the embodiments of this application will be further described, such as... Figure 2 The diagram illustrates a data processing method. In a specific embodiment, this data processing method can be applied to, for example... Figure 7 The data processing device 700 and the electronic device 800 equipped with the data processing device 700 are shown. Figure 8 The specific process of the embodiments of this application will be described below. Of course, it is understood that this method can be executed by a cloud server with computing power. The following will focus on... Figure 2 The process shown is described in detail, and the data processing method may specifically include the following steps 201 to 204.

[0041] Step 201: Obtain the passenger's first characteristics and first preset characteristics, and the driver's second characteristics and second preset characteristics; wherein, the first characteristics include the passenger's gender, personality type, and personalized items, and the first preset characteristics are used to indicate: the passenger's desired driver's gender, personality type, and vehicle type; the second characteristics include the driver's gender, personality type, and vehicle type, and the second preset characteristics are used to indicate: the driver's desired passenger's personality type and personalized items.

[0042] In this application embodiment, the first feature is the information about oneself entered by the passenger in the platform according to the travel needs, such as the passenger's gender as male or female, personality type as quiet and reserved or lively and talkative, etc. Personalized matters are other needs of the passenger besides gender and personality type, including whether they are traveling with children, whether they are traveling with pets, whether they are traveling with elderly people, etc.

[0043] In this application embodiment, the first preset feature is the information about the desired driver that the passenger inputs in the platform according to the ride needs. For example, the passenger expects the driver to be male or female, have a quiet and reserved personality or be lively and talkative, and expect the driver's vehicle type to be SUV (sport utility vehicle), sedan, or commercial vehicle.

[0044] In this embodiment of the application, the second feature is the information about oneself entered by the driver in the platform, such as the driver's gender as male or female, personality type as quiet and reserved, lively and talkative, and the vehicle type as SUV, sedan, or commercial vehicle.

[0045] In this embodiment of the application, the second preset feature is the information about the desired passenger input by the driver in the platform. For example, the driver expects the passenger to be quiet and reserved or lively and talkative. The driver also expects the passenger to have personal preferences such as not bringing large pets, not smoking in the vehicle, and not accepting intoxicated passengers.

[0046] For example, a passenger can use a ride-hailing app on their mobile device to input their primary characteristics as male, quiet and reserved personality type, and personal preference as having a large pet. They can also input their desired driver's primary preset characteristics as male, quiet and reserved personality type, and SUV vehicle type.

[0047] For example, the driver can also use a dispatching application on a mobile device to input their second characteristic as male, lively and talkative personality type, and SUV vehicle type, and input the second preset characteristics of their desired passenger as lively and talkative, and no smoking in the vehicle, etc.

[0048] As another example, based on the information exchange between the platform and the mobile device via wireless communication, after the characteristics input by the passenger and driver, the platform that distributes trip orders to the vehicle can obtain relevant information from the mobile devices of the passenger and driver, and store the obtained information in the platform's memory or storage, or in a database or other storage device that the platform supports access, etc., which is not limited in this application.

[0049] Step 202: Match the first feature, the first preset feature, the second feature, and the second preset feature to obtain the first matching degree between the passenger and the driver.

[0050] In the embodiments of this application, the first matching degree is an indicator that quantifies the degree of similarity or correlation between two or more objects.

[0051] In this application embodiment, the degree of fit between the first feature and the second preset feature, and between the second feature and the first preset feature, in a specific dimension is measured by a certain algorithm or standard. For example, different scores are assigned to the first feature, the first preset feature, the second feature, and the second preset feature, and the calculated score is used to represent the first matching degree between the passenger and the driver based on an algorithm that adds points to features with the same items and subtracts points from features with different items.

[0052] For example, when inputting features, passengers and drivers assign different scores to each feature. For instance, if the driver cares whether passengers smoke, the driver will assign a higher score to the second preset feature that smoking is prohibited in the vehicle. Conversely, if the passenger does not care much about the driver's gender, the passenger will assign a lower score to the first preset feature that the driver's gender is.

[0053] For example, after a passenger submits a trip order on a mobile device, the platform automatically calculates the scores of the passenger and driver based on their characteristics and corresponding values, and finally provides several candidate trip orders with higher total scores for the passenger and driver to choose from.

[0054] Step 203: Based on the passenger's itinerary, obtain multiple itinerary orders and the first matching degree corresponding to each itinerary order; the itinerary includes the passenger's departure point, destination, and departure time; the itinerary orders are obtained based on the passenger's itinerary.

[0055] In this embodiment of the application, the trip order is an order initiated by the passenger through a ride-hailing application on their mobile device when traveling.

[0056] For example, the platform can generate multiple trip orders based on the passenger's departure point, destination, and departure time, and based on the unaccepted vehicles near the passenger's departure point. By matching the second feature and second preset feature of the driver of each trip order with the first feature and first preset feature of the passenger who placed the order, the first matching degree corresponding to each trip order can be calculated.

[0057] Step 204: Determine the passenger's target order from the trip orders where the first matching degree reaches the first threshold.

[0058] In the embodiments of this application, the first threshold is a value set for filtering the first matching degree.

[0059] In this embodiment of the application, when the first matching degree reaches the first threshold, it can be regarded as the first matching degree is high; when the first matching degree does not reach the first threshold, it is regarded as the first matching degree is low. The first threshold can be obtained by using a big data algorithm based on the platform's historical travel orders to obtain a reasonable value.

[0060] For example, among the multiple trip orders obtained in step 203, trip orders with a first matching degree reaching a first threshold are filtered out to obtain multiple candidate trip orders with a higher first matching degree ranking; the target order for this trip is determined according to the selection method set by the driver and passenger, either through platform recommendation or self-selection.

[0061] For example, when the driver or passenger chooses the option to select by themselves, the driver can select a target order from the trip orders with the highest first-match ranking based on their own vehicle condition or service experience, and the passenger can also select a target order from the trip orders with the highest first-match ranking based on whether they are traveling with a pet.

[0062] In this embodiment, firstly, a target order is determined through a two-way selection process between passengers and drivers based on the matching degree of the trip orders. This matching process includes not only matching the basic information of the passengers and drivers but also matching their personalized preferences. This achieves personalized two-way matching while enriching the dimensions of the matching preferences, thus improving the passenger's riding experience and the driver's order acceptance satisfaction. Secondly, passengers and drivers can set their own methods for selecting the target order. The platform can directly assign the order with the highest matching degree as the target order, or the passenger or driver can select a target order from the trip orders with higher matching degree rankings to complete the trip. These two order generation methods cater to the different usage habits of passengers and drivers, further enhancing the user experience for both parties.

[0063] Regarding how to match the first feature, the first preset feature, the second feature, and the second preset feature to obtain the first matching degree between the passenger and the driver, embodiments of this application provide an optional implementation method, such as... Figure 3 The flowchart shown is a processing method for obtaining the first matching degree, which may specifically include the following steps 301 to 303.

[0064] Step 301: Match the first feature with the second preset feature based on the first feature and the first weight of the first feature to obtain the first initial matching degree.

[0065] In this embodiment of the application, the first weight is the proportion of the passenger's first feature when calculating the first matching degree, which is set according to the driver's evaluation of the passenger in the trip order.

[0066] For example, the range of values ​​assigned to the characteristics of passengers and drivers is [0, 10], and the first weight of the first characteristic is set to 0.8. Passengers assign a value of 7 to gender, 8 to personality type, and 5 to bringing a large pet in the first characteristic; drivers assign a value of 6 to passenger personality type in the second preset characteristic. When the driver is very concerned about whether the passenger brings a large pet, a higher score is given to the second preset characteristic of bringing a large pet, that is, a value of 9 is assigned to not bringing a large pet in the second preset characteristic of passenger personalization.

[0067] For example, when performing matching, keywords under the same item are extracted from the first feature and the second preset feature. When the keywords are the same, the two values ​​are added together. When the keywords are different, the two values ​​are subtracted. Finally, the values ​​obtained under each item are summed to obtain the first initial matching degree.

[0068] In the first optional example, the gender keywords are extracted from the first feature and the second preset feature. When all keywords are female or all keywords are male, the gender value in the first feature is multiplied by the first weight of 0.8. Since there is no gender value in the second preset feature, the first value is obtained directly.

[0069] In the second optional example, keywords of personality type are extracted from the first feature and the second preset feature. Similarly, when the keyword is "always lively and talkative" or other similar keywords, the value of personality type in the first feature is multiplied by the first weight 0.8, and then added to the value of personality type in the second preset feature 6 to obtain the second value.

[0070] In the third optional example, keywords of personalized items are extracted from the first feature and the second preset feature. When the passenger's keyword is "bringing a large pet" and the driver's keyword is "not bringing a large pet", the value of the personalized item in the first feature (5) is multiplied by the first weight (0.8) and then subtracted from the value of the personalized item in the second preset feature (9) to obtain the third value.

[0071] For example, the first initial matching degree can be obtained by summing the above three values.

[0072] Step 302: Match the second feature with the first preset feature according to the second feature and the second weight of the second feature to obtain the second initial matching degree.

[0073] In this embodiment of the application, the second weight is the proportion of the driver's second feature when calculating the first matching degree, which is set according to the passenger's evaluation of the driver in the trip order.

[0074] For example, the second weight of the second feature is set to 0.7. The driver assigns a value of 5 to gender, 6 to personality type, and 7 to vehicle type in the second feature. When the passenger does not care much about the driver's gender, the passenger assigns a relatively low score to the first preset feature of driver's gender. That is, the passenger assigns a value of 4 to the driver's gender in the first preset feature, and assigns a value of 7 to other personality types and 6 to vehicle type.

[0075] For example, when performing matching, keywords under the same item are extracted from the second feature and the first preset feature. Similarly, when the keywords are the same, the two values ​​are added together, and when the keywords are different, the two values ​​are subtracted. Finally, the values ​​obtained under each item are summed to obtain the second initial matching degree.

[0076] In the first optional example, the gender keywords are extracted from the second feature and the first preset feature. When the keywords are all female or all male, the gender value of the second feature is multiplied by the second weight of 0.7 and then added to the gender value of the second preset feature of 4 to obtain the first value.

[0077] In the second optional example, keywords of personality type are extracted from the second feature and the first preset feature. When the keyword is "driver is lively and talkative, passenger expects driver to be quiet and taciturn", the value of personality type in the first feature is 6, multiplied by the first weight 0.7, and then subtracted from the value of personality type in the second preset feature 7 to obtain the second value.

[0078] In the third optional example, the keywords of vehicle type are extracted from the second feature and the first preset feature. When the keywords are both SUV, the value of vehicle type in the second feature is 7, multiplied by the second weight 0.7, and then added to the value of vehicle type in the first preset feature is 6 to obtain the third value.

[0079] For example, the second initial matching degree can be obtained by summing the above three values.

[0080] Step 303: Obtain the first matching degree based on the first initial matching degree and the second initial matching degree.

[0081] In this embodiment of the application, the first initial matching degree is obtained by adding the first initial matching degree and the second initial matching degree.

[0082] In this embodiment of the application, when the basic information of passengers and drivers is matched, the matching degree between passengers and drivers is considered from multiple dimensions of personalized matters, which can meet the personalized needs of both parties, improve the passenger's riding experience and the driver's order acceptance satisfaction, and also achieve the goal of improving the matching accuracy.

[0083] Based on the above, this application provides an optional implementation method for determining a passenger's target order from trip orders that have reached a first matching degree of a first threshold, such as... Figure 4 The flowchart shown is a processing method for obtaining a target order, which may specifically include the following steps 401 to 402.

[0084] Step 401: Determine multiple candidate trip orders from the trip orders whose first matching degree reaches the first threshold.

[0085] In this embodiment of the application, the candidate trip order is a trip order that can be selected by either the passenger or the driver.

[0086] Optionally, based on the origin and destination of the passenger's trip, a vehicle at the origin is dispatched, and a first matching degree is calculated based on the first feature, first preset feature, second feature, and second preset feature of the driver and passenger within the dispatch range. Trip orders with a first matching degree reaching a first threshold are determined as candidate trip orders.

[0087] Step 402: Determine the target order from multiple candidate trip orders based on the selection methods set by the passenger and driver in the platform.

[0088] In this embodiment of the application, the target order is the trip order for the passenger's current journey, which is determined based on the successful matching of the passenger and the driver.

[0089] For example, when a passenger submits a trip order, if the passenger's primary characteristic is a quiet and reserved personality, and their desired driver is female, has a calm and reserved personality, and is an SUV, and the passenger selects "self-selection" for the target order generation method; and if one of the drivers within the dispatch range has a secondary characteristic set to female, calm and reserved, and is a mid-size SUV, and their passenger preference is set to a quiet and non-communicative passenger, then the platform compares the information of both parties and arrives at a higher primary matching score. This driver's trip order will then be added to the passenger's candidate list, and the passenger's submitted trip order will also be added to the driver's candidate list. Through this mutual selection process, the candidate trip order is determined as the target order.

[0090] Regarding how to determine the target order from multiple candidate trip orders based on the selection method, this application embodiment provides an optional implementation method, which includes the following steps 412 to 422.

[0091] Step 412: Based on the platform recommendation set by both parties, the trip order with the highest matching degree is determined as the target order from among multiple candidate trip orders.

[0092] In this embodiment of the application, when the passenger and driver receive multiple candidate trip orders, and both parties set their selection method on the platform to platform recommendation, the candidate trip order with the highest first matching degree among the multiple candidate trip orders is determined as the target order for this trip.

[0093] Step 422: Based on the selection method set by at least one party in the platform as self-selection, the trip order selected by the passenger or driver from multiple candidate trip orders is determined as the target order.

[0094] In this embodiment of the application, when both the passenger and the driver receive multiple candidate trip orders, and both parties have set their selection method on the platform to self-selection, the passenger selects one of the candidate trip orders from the multiple candidate trip orders and sends a matching request to the driver. At the same time, the driver also selects one of the candidate trip orders from the multiple candidate trip orders and sends a matching request to the passenger.

[0095] In this embodiment of the application, when a passenger and a driver receive multiple candidate trip orders, if the passenger's selection method is set to self-selection and the driver's selection method is set to platform recommendation, or if the passenger's selection method is set to platform recommendation and the driver's selection method is set to self-selection, then the party making the self-selection selects a candidate trip order and sends a matching request to the other party, while the party recommending through the platform determines the candidate trip order with the highest matching degree and sends a matching request to the other party.

[0096] Optionally, if both the passenger and the driver send a matching request for the same candidate trip order, the match will be successful automatically, and the candidate trip order will be identified as the target order.

[0097] Optionally, if the passenger and the driver send matching requests for two different candidate trip orders, and either party accepts the matching request, the match is considered successful, and the candidate trip order that accepts the matching request is determined as the target order.

[0098] Optionally, if the passenger and driver send matching requests for two different candidate trip orders, and neither party accepts the matching request, the matching is considered unsuccessful. The passenger or driver can be prompted to select a new trip order from the candidate trip orders and send a matching request to the other party.

[0099] Based on the above, after determining the passenger's target order from the trip orders with a first matching degree reaching a first threshold, this application embodiment provides an optional implementation method, such as... Figure 5 The flowchart shown is a processing method for adjusting the second weight based on the first score, which may specifically include the following steps 501 to 502.

[0100] Step 501: Obtain the passenger's first rating for the target order.

[0101] In this embodiment of the application, the first score is a score given by the passenger based on the satisfaction with the ride experience of this trip order and the accuracy of the matching degree.

[0102] For example, after the trip, passengers can rate and leave comments on the trip through an application on their mobile devices, such as rating the driver's satisfaction and the accuracy of the match.

[0103] Step 502: Adjust the second weight of the driver's second characteristic based on the first score.

[0104] For example, after the trip, passengers can provide feedback on the driver and the matching service for that trip. The feedback information will be stored in the data storage module to provide a basis for subsequent matching optimization calculations involving that driver.

[0105] Specifically, the method for adjusting the second weight of the driver's second feature based on the first score in step 502 includes: If the first score is greater than the second threshold, increase the value of the second weight; if the first score is less than the second threshold, decrease the value of the second weight; if the first score is equal to the second threshold, keep the value of the second weight unchanged.

[0106] In this embodiment of the application, the second threshold is a value set for filtering the first score.

[0107] In this embodiment of the application, when the first score reaches the second threshold, it can be regarded as the first score being high; when the first score does not reach the second threshold, it is regarded as the first score being low. The second threshold can also be obtained by using big data algorithms based on the platform's historical travel orders to obtain a reasonable value.

[0108] For example, if multiple passengers give a driver a low first rating and the feedback is that the driver's actual personality type does not match the personality type that the driver set as a second characteristic on the platform, such as the driver setting the personality type as calm and quiet, but passengers actually think the driver is too talkative, the platform will reduce the second weight of the driver's personality type and prompt the driver to improve the second characteristic.

[0109] For example, if multiple passengers give a driver a high first rating, and a certain matching combination, such as quiet passengers and quiet drivers, generally has a high rating, the platform will increase the second weight of the driver's personality type to further improve the accuracy of the matching.

[0110] In this embodiment of the application, passengers can provide a first rating for the current order trip. The weight value assigned to the second feature by the driver can be optimized based on the first rating to improve the matching accuracy.

[0111] Building upon the above, after determining the passenger's target order from trip orders that have reached a first matching degree of a first threshold, this application embodiment also provides an optional implementation method, such as... Figure 6 The diagram illustrates a method for adjusting the first weight based on the second score. A detailed explanation follows, which may include steps 601 to 602.

[0112] Step 601: Obtain the driver's second rating for the target order.

[0113] In this embodiment of the application, the second score is given by the driver based on the satisfaction with the acceptance of the trip order and the accuracy of the matching.

[0114] For example, after the trip, the driver can rate and leave comments on the trip through an application on their mobile device, such as rating passenger satisfaction and the accuracy of the match.

[0115] Step 602: Adjust the first weight of the passenger's first feature based on the second score.

[0116] For example, after the trip is completed, the driver will also provide feedback on the passengers and the matching service of the trip. The feedback information is also stored in the data storage module to provide a basis for subsequent matching optimization calculations involving the passenger.

[0117] Specifically, the method for adjusting the first weight of the passenger's first feature based on the second score in step 602 includes: If the second score is greater than the second threshold, increase the value of the first weight; if the second score is less than the second threshold, decrease the value of the first weight; if the second score is equal to the second threshold, keep the value of the first weight unchanged.

[0118] In this embodiment of the application, when the second score reaches the second threshold, it can be regarded as the second score being high; when the second score does not reach the second threshold, it is regarded as the second score being low.

[0119] For example, if multiple drivers give a passenger a low second rating, and the feedback indicates that the passenger's actual personalized behavior does not match the personalized behavior the passenger set on the platform, such as the passenger setting a non-smoking behavior but actually smoking multiple times during the trip, the platform will reduce the first weight of the passenger's personalized behavior and prompt the passenger to improve the first characteristic.

[0120] For example, if multiple drivers give a passenger a high second rating, and a certain matching combination generally receives a high rating, the platform will increase the first weight of that passenger's status.

[0121] In this embodiment, the driver can finally provide a second score for the current order trip. This second score can be used to optimize the weight value assigned to the first feature by the passenger, thereby improving the matching accuracy.

[0122] To achieve the functions of the above embodiments, the data processing method includes hardware structures and / or software modules corresponding to each function. Those skilled in the art should readily recognize that, based on the units and method steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed through hardware or computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0123] exist Figures 2 to 6 Based on the data processing methods described above, this application also provides a data processing apparatus in its embodiments, such as... Figure 7 The diagram shows the structure of a data processing device 700, which includes: an acquisition module 710, a matching module 720, a first processing module 730, and a second processing module 740.

[0124] The acquisition module 710 is used to acquire a first characteristic and a first preset characteristic of the passenger, and a second characteristic and a second preset characteristic of the driver; wherein, the first characteristic includes the passenger's gender, personality type, and personalized requests; the first preset characteristic indicates the passenger's desired driver's gender, personality type, and vehicle type; the second characteristic includes the driver's gender, personality type, and vehicle type; the second preset characteristic indicates the driver's desired passenger's personality type and personalized requests; wherein, the acquisition module 710 may include, for example, Figure 1 The mobile devices in the first module 101 and the second module 102 shown.

[0125] Matching module 720 is used to match the first feature, the first preset feature, the second feature, and the second preset feature to obtain a first matching degree between the passenger and the driver; wherein, matching module 720 may include, for example, Figure 1 The first module 101 shown.

[0126] The first processing module 730 is used to obtain multiple trip orders and a first matching degree corresponding to each trip order based on the passenger's itinerary; the itinerary includes the passenger's departure point, destination, and departure time; the trip orders are obtained based on the passenger's itinerary; wherein, the first processing module 730 may include, for example, Figure 1 The first module 101 and the second module 102 are shown.

[0127] The second processing module 740 is configured to determine a passenger's target order from trip orders where the first matching degree reaches a first threshold; wherein, the second processing module 740 may include, for example, Figure 1 The first module 101 and the second module 102 are shown.

[0128] In some embodiments, the matching module 720 includes: matching with a second preset feature according to a first feature and a first weight of the first feature to obtain a first initial matching degree; matching with the first preset feature according to a second feature and a second weight of the second feature to obtain a second initial matching degree; and obtaining a first matching degree according to the first initial matching degree and the second initial matching degree.

[0129] In some embodiments, the second processing module 740 includes: determining a plurality of candidate trip orders from trip orders with a first matching degree reaching a first threshold; and determining a target order from the plurality of candidate trip orders according to the selection method set by the passenger and the driver in the platform.

[0130] In some embodiments, the second processing module 740 further includes: determining the trip order with the highest first matching degree as the target order from multiple candidate trip orders based on the fact that both parties set their selection method in the platform as platform recommendation; and determining the trip order selected by the passenger or driver from multiple candidate trip orders as the target order based on the fact that at least one party set its selection method in the platform as self-selection.

[0131] In other embodiments, the second processing module 740 includes: obtaining a first rating from a passenger for a target order; and adjusting a second weight of a second feature of the driver based on the first rating.

[0132] In other embodiments, the second processing module 740 further includes: increasing the value of the second weight if the first score is greater than the second threshold; decreasing the value of the second weight if the first score is less than the second threshold; and keeping the value of the second weight unchanged if the first score is equal to the second threshold.

[0133] In some embodiments, the second processing module 740 includes: obtaining a second rating of the driver for the target order; and adjusting a first weight of a first characteristic of the passenger based on the second rating.

[0134] In some embodiments, the second processing module 740 further includes: increasing the value of the first weight if the second score is greater than the second threshold; decreasing the value of the first weight if the second score is less than the second threshold; and keeping the value of the first weight unchanged if the second score is equal to the second threshold.

[0135] According to one aspect of the embodiments of this application, Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8 As shown, the electronic device 800 includes a processor 810 and one or more memories 820. The one or more memories 820 are used to store program instructions executed by the processor 810. When the processor 810 executes the program instructions, it implements the above-described data processing method.

[0136] Furthermore, the processor 810 may include one or more processing cores. The processor 810 runs or executes instructions, programs, code sets, or instruction sets stored in the memory 820, and retrieves data stored in the memory 820. Optionally, the processor 810 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 810 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor and may be implemented using a separate communication chip.

[0137] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0138] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0140] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0141] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method of processing data, characterized by, The method is applied to a platform for distributing travel orders of vehicles, and the method comprises: obtaining first characteristics and first preset characteristics of a passenger and second characteristics and second preset characteristics of a driver; wherein the first characteristics comprise gender, personality type and individualized matters of the passenger, and the first preset characteristics are used to indicate gender, personality type and vehicle type of the driver expected by the passenger; the second characteristics comprise gender, personality type and vehicle type of the driver, and the second preset characteristics are used to indicate personality type and individualized matters of the passenger expected by the driver; matching the first characteristics, the first preset characteristics, the second characteristics and the second preset characteristics to obtain a first matching degree of the passenger and the driver; obtaining a plurality of travel orders and a first matching degree corresponding to each travel order according to a travel of the passenger; the travel comprises a departure place, a destination and a departure time of the passenger; and the travel order is obtained according to the travel of the passenger; determining a target order of the passenger from the travel orders whose first matching degrees reach a first threshold.

2. The method of claim 1, wherein, The matching of the first characteristics, the first preset characteristics, the second characteristics and the second preset characteristics to obtain the first matching degree of the passenger and the driver comprises: matching the first characteristics and a first weight of the first characteristics with the second preset characteristics to obtain a first initial matching degree; matching the second characteristics and a second weight of the second characteristics with the first preset characteristics to obtain a second initial matching degree; obtaining the first matching degree according to the first initial matching degree and the second initial matching degree.

3. The method of claim 1, wherein, The determination of the target order of the passenger from the travel orders whose first matching degrees reach the first threshold comprises: determining a plurality of candidate travel orders from the travel orders whose first matching degrees reach the first threshold; determining the target order in the plurality of candidate travel orders according to a selection mode set by the passenger and the driver in the platform.

4. The method of claim 3, wherein, The determination of the target order in the plurality of candidate travel orders according to the selection mode set by the passenger and the driver in the platform comprises: determining, according to the selection mode set by both parties in the platform being platform recommendation, a travel order with the highest first matching degree in the plurality of candidate travel orders as the target order; determining, according to the selection mode set by at least one party in the platform being self-selection, a travel order selected by the passenger or the driver in the plurality of candidate travel orders as the target order.

5. The method of claim 1, wherein, After the determination of the target order of the passenger from the travel orders whose first matching degrees reach the first threshold, the method further comprises: obtaining a first score of the target order given by the passenger; adjusting a second weight of the second characteristics of the driver according to the first score.

6. The method of claim 5, wherein, The adjustment of the second weight of the second characteristics of the driver according to the first score comprises: increasing the value of the second weight if the first score is greater than a second threshold; decreasing the value of the second weight if the first score is less than the second threshold. If the first score is equal to a second threshold value, a value of the first weight is kept unchanged.

7. The method of claim 1, wherein, After determining the target order of the passenger from the trip orders whose first matching degrees reach a first threshold value, the method further comprises: obtaining a second score of the driver for the target order; adjusting a first weight of the first feature of the passenger according to the second score.

8. The method of claim 7, wherein, The method further comprises: if the second score is greater than a second threshold value, increasing the value of the first weight; if the second score is less than the second threshold value, decreasing the value of the first weight; if the second score is equal to the second threshold value, keeping the value of the first weight unchanged.

9. A data processing device, characterized by comprising: The device is applied to a platform for distributing trip orders to vehicles, and the device comprises: an obtaining module, configured to obtain a first feature and a first preset feature of a passenger and a second feature and a second preset feature of a driver; the first feature comprises a gender, a personality type and a personalized matter of the passenger, and the first preset feature is used to indicate a gender, a personality type and a vehicle type of a driver expected by the passenger; the second feature comprises a gender, a personality type and a vehicle type of the driver, and the second preset feature is used to indicate a personality type and a personalized matter of a passenger expected by the driver; a matching module, configured to match the first feature, the first preset feature, the second feature and the second preset feature to obtain a first matching degree of the passenger and the driver; a first processing module, configured to obtain a plurality of trip orders and a first matching degree corresponding to each of the trip orders according to a trip of the passenger; the trip comprises a departure place, a destination and a departure time of the passenger; and the trip order is obtained according to the trip of the passenger; a second processing module, configured to determine a target order of the passenger from the trip orders whose first matching degrees reach a first threshold value.

10. An electronic device, comprising: The electronic device comprises: a processor; a memory, the memory having electronic device readable instructions stored thereon, the electronic device readable instructions being executed by the processor to implement the processing method according to any one of claims 1 to 8.

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