Time slice data processing method and device, computer device and storage medium

Abstract

Embodiments of the present disclosure relate to a time slice data processing method and device, computer equipment and a storage medium. The main steps of the foregoing method include: obtaining a plurality of time slice data of a target account; and performing at least one round of update operation when the achievement condition of the service activity is not met. By implementing the foregoing method, the result of the service activity participated by the online car-hailing driver can be stably and timely updated by obtaining a plurality of time slice data of a target account and determining the time slice data to be counted in each round of update operation according to a preset statistical quantity. Meanwhile, since the time length required for updating is related to the time length of the time slice and the preset statistical quantity, and the time length of the time slice and the statistical quantity can be set according to actual needs, it is beneficial to improve the experience of the online car-hailing driver while reasonably allocating computing resources.

Description

Technical Field

[0001] This disclosure relates to the field of computer data processing technology, and in particular to a time-slice data processing method, apparatus, computer equipment, and storage medium. Background Technology

[0002] In the ride-hailing service industry, there are various service programs offered to ride-hailing drivers. For example, some programs encourage drivers to actively drive, with the corresponding condition being that the accumulated driving time during the program's validity period exceeds a certain set time. When a ride-hailing driver meets the conditions within the program's validity period, they will receive a reward provided by the ride-hailing platform.

[0003] Typically, ride-hailing drivers can log in to a target account using a ride-hailing app to perform actions such as listening for orders, accepting orders, initiating passenger pick-up and drop-off, and reporting order completion. The organizers of the service activity, such as ride-hailing service operation platforms, can obtain data on these actions associated with the target account and, after the driver finishes driving, use this data to calculate the driver's driving hours for the day, thereby determining whether the conditions have been met.

[0004] However, this data processing method makes it impossible for drivers to determine the outcome of the service activities they participate in during their shifts. Sometimes, this leads to drivers continuing to participate in a service activity even after the conditions have been met, limiting their participation in other service activities. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for processing time-slice data that can update the results of participating in ride-hailing services in a timely manner, in response to the above-mentioned technical problems.

[0006] In a first aspect, embodiments of this disclosure provide a time-slice data processing method, the method comprising:

[0007] Obtain multiple time-slice data for the target account;

[0008] If the conditions for fulfilling the service activity are not met, at least one round of update operations shall be performed.

[0009] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0010] In some embodiments, statistical processing is performed on the duration data in the time slice data to be statistically analyzed in this round, including:

[0011] Retrieve the target field corresponding to the achieved condition;

[0012] Determine the duration type identifier in the time slice data to be analyzed in this round that matches the target field;

[0013] Statistical processing is performed on the duration data corresponding to the duration type identifier that matches the target field in the time slice data to be statistically analyzed in this round.

[0014] In some embodiments, the duration type identifier includes a dispatch duration identifier, a call duration identifier, a pick-up and drop-off duration identifier, or a specified business service duration identifier.

[0015] In some embodiments, obtaining multiple time-slice data of a target account includes:

[0016] Retrieve multiple raw time-slice data sent by the message queue;

[0017] The acquired raw time slice data is compared with the time slice data stored in the database;

[0018] If the feature information of the acquired original time slice data is the same as the feature information of at least one time slice data in the database, the original time slice data shall be discarded.

[0019] All original time slice data that were not discarded after comparison will be used as the time slice data for the target account.

[0020] In some embodiments, the conditions for achieving a service activity include the cumulative valid duration within the activity's validity period reaching a preset cumulative duration threshold; correspondingly, the time slice data processing method further includes: when the duration data corresponding to the discarded original time slice data is greater than the duration data corresponding to the time slice data with the same feature information in the database, determining the difference between the two duration data;

[0021] The duration data in the time slice data to be statistically analyzed in this round is statistically processed. Based on the statistical processing results, it is determined whether the conditions for achieving the target have been met. This includes: updating the cumulative effective duration based on the sum of the differences between each duration data in the time slice data to be processed in this round and the two duration data; and determining whether the conditions for achieving the target have been met based on the comparison between the updated cumulative effective duration and the cumulative duration threshold.

[0022] In some embodiments, each time slice data also includes spatiotemporal data associated with the target account, the spatiotemporal data being used to represent the location and / or service time associated with the target account. Accordingly, statistical processing is performed on the duration data in the time slice data to be statistically analyzed in this round, including: taking the time slice data corresponding to the spatiotemporal data located in a specified data interval as the target time slice data, obtaining the cumulative value of the duration data of the same type in the target time slice data, and using the cumulative value as the statistical processing result.

[0023] In some embodiments, there are multiple service activities, and different service activities correspond to different achievement conditions. Accordingly, when the achievement conditions of a service activity are not met, at least one update operation is performed, including: when the achievement conditions of any service activity are not met, performing the corresponding update operation. Accordingly, the time slice data processing method further includes: obtaining a query request from the login device of the target account; determining the target activity from multiple service activities based on the query request; determining the achievement progress of the target activity based on the latest update operation of the target activity; and feeding back the achievement progress to the login device.

[0024] In a second aspect, embodiments of this disclosure provide a time-slice data processing apparatus, the apparatus comprising:

[0025] The data acquisition module is used to acquire multiple time-slice data of the target account. Each time-slice data includes the duration type identifier and duration data corresponding to the time slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration.

[0026] The update module is used to perform at least one round of update operations when the conditions for achieving a service activity are not met. The update operations include: determining the time slice data to be statistically analyzed in this round from the acquired time slice data according to the preset statistical quantity, performing statistical processing on the duration data in the time slice data to be statistically analyzed in this round, and determining whether the conditions for achieving the activity are met based on the statistical processing results.

[0027] In a third aspect, embodiments of this disclosure provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0028] Obtain multiple time-slice data for the target account;

[0029] If the conditions for fulfilling the service activity are not met, at least one round of update operations shall be performed.

[0030] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0031] In a fourth aspect, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0032] Obtain multiple time-slice data for the target account;

[0033] If the conditions for fulfilling the service activity are not met, at least one round of update operations shall be performed.

[0034] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0035] The aforementioned time-slice data processing method, apparatus, computer equipment, and storage medium acquire multiple time-slice data points from the target account and determine the time-slice data to be statistically analyzed in each round of update operations according to a preset statistical quantity. This ensures that the results of ride-hailing drivers' participation in service activities can be updated stably and promptly. Furthermore, since the update duration is related to the corresponding time slice length and the preset statistical quantity, and the time slice length and statistical quantity can be set according to actual needs, this facilitates both improving the ride-hailing driver experience and rationally allocating computing resources. Attached Figure Description

[0036] Figure 1 This is a diagram illustrating the application environment of the time-slice data processing method in some embodiments;

[0037] Figure 2 This is a flowchart illustrating the time-slice data processing method in some embodiments;

[0038] Figure 3 This is a flowchart illustrating the steps involved in the update operation in some embodiments;

[0039] Figure 4This is a flowchart illustrating the steps involved in providing feedback on progress in some embodiments;

[0040] Figure 5 This is a structural block diagram of the time-slice data processing apparatus in some embodiments;

[0041] Figure 6 This is a diagram showing the internal structure of a computer device in some embodiments. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this disclosure 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 disclosure.

[0043] The time-slice data processing method provided in this disclosure can be applied to, for example... Figure 1 In the application environment shown, server 101 can communicate with terminal 102 via a network to obtain multiple time-slice data. Server 101 can be implemented using a standalone server or a server cluster consisting of multiple servers. Terminal 102 can be, but is not limited to, various in-vehicle terminals, personal computers, laptops, smartphones, and tablets. In other application environments, terminal 102 can also be replaced by a server other than server 101. In short, the time-slice data processing method can be applied to a variety of environments.

[0044] Applying time-slice data processing methods Figure 1 Taking server 101 as an example, in some embodiments, such as Figure 2 As shown, the time slice data processing method includes steps S201 and S202 that can be executed by the server 101. Each step will be described in detail below.

[0045] Step S201: Obtain multiple time slice data for the target account.

[0046] Each time slice data includes a duration type identifier and duration data corresponding to the time slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration.

[0047] The target account mentioned in this article refers to an identification feature for ride-hailing drivers. Typically, a target account is an account created by a ride-hailing service platform for the driver to record information during the ride-hailing service process. Ride-hailing service platforms include, but are not limited to, major platforms such as Didi Chuxing, Gaode Taxi, Ruqi Chuxing, Shouqi Limousine & Chauffeur, Cao Cao Chuxing, and Meituan Taxi.

[0048] Multiple time-slice data for a target account refers to multiple time-slice data associated with the target account. In some optional implementations, this time-slice data can be obtained by calling the data interface of a ride-hailing application. The ride-hailing application can be an application developed or provided by the aforementioned ride-hailing service operation platform, used by ride-hailing drivers to perform one or more operations such as listening for orders, accepting orders, reporting the start of passenger pick-up and drop-off, reporting order completion, querying relevant information about the target account, and querying relevant information about the ride-hailing service. The server or terminal running the ride-hailing application can determine the service duration corresponding to the duration type identifier based on the ride-hailing vehicle's location information, the ride-hailing driver's operation information, and other associated information of the target account. Of course, in some other optional implementations, the time-slice data provided by the server can be received by sending a request to the server of the ride-hailing service operation platform to obtain multiple time-slice data for the target account.

[0049] Furthermore, the multiple time-slice data for the target account can refer to either unprocessed or preprocessed time-slice data. In some optional implementations, unprocessed time-slice data, i.e., raw time-slice data, can be obtained by calling the aforementioned data interface. Preprocessing the raw time-slice data yields the aforementioned multiple time-slice data for the target account. Preprocessing methods include discarding more than one piece of raw time-slice data, adjusting the format of the raw time-slice data, or extracting specific types of data from the raw time-slice data. Of course, other preprocessing methods are also possible and are not specifically limited here. Therefore, the raw time-slice data should also include the duration type identifier and duration data corresponding to the respective time-slice. In some optional implementations, the raw time-slice data may contain more or less information than the preprocessed time-slice data.

[0050] In some alternative implementations, the time slice data may also include information about the time slice corresponding to the time slice data.

[0051] The term "time slice" as used in this document refers to a segment of time, the length of which can be selected according to actual needs. In some optional embodiments, the length of the time slice can be 30 seconds, 1 minute, or 2 minutes. In some optional embodiments, the length of the time slice is within the range of 30 seconds to 2 minutes.

[0052] Typically, a time period can be divided into multiple consecutive time slices. For example, if the duration of a time slice is 1 minute, the time segments of the 1st minute, 2nd minute, 3rd minute, and 4th minute within a given time period can be extracted as time slices. Alternatively, time slices can be extracted from time segments that are not sequentially connected within a given time period. For instance, if the duration of a time slice is 1 minute, the time segments of the 1st minute, 3rd minute, 5th minute, and 7th minute within a given time period can be extracted as time slices. When a ride-hailing driver provides ride-hailing services within the aforementioned time periods, the time slice data can record the various types of service durations corresponding to each time slice. This time period can be the time from when the driver starts driving to when they finish driving, or it can be certain time segments during the driver's use of the ride-hailing application.

[0053] Obtaining multiple time slice data for a target account can include obtaining individual time slice data one by one, or obtaining two or more time slice data at once; there are no particular restrictions here.

[0054] The duration type identifier and duration data included in the time slice data are used to determine whether the conditions for achieving the service activity are met.

[0055] The service activities mentioned in this article can include various activities organized, co-organized, released, or designed by ride-hailing service operation platforms, including but not limited to various activities launched to incentivize ride-hailing drivers, various activities launched to assess ride-hailing drivers, and various activities launched to regulate ride-hailing services.

[0056] Different service activities may have different requirements for service duration. Duration type identifiers can be used to distinguish different service duration types, and different duration type identifiers represent different service duration types.

[0057] In some optional implementations, the duration type identifier includes a dispatch duration identifier, a call duration identifier, a pick-up and drop-off duration identifier, or a specified service duration identifier.

[0058] The "Driving Duration" identifier indicates that the recorded item is the driving duration, and the recorded duration data corresponding to that item represents the duration of the driving operation within the corresponding time slice. In this article, "driving out" generally refers to providing ride-hailing services; accepting orders, picking up and dropping off passengers, and other operations or behaviors can all be considered driving out. Correspondingly, "ending out" generally refers to ceasing to provide ride-hailing services, such as ceasing to accept orders and stop picking up and dropping off passengers. Typically, the start and end times of driving out can be determined by obtaining the actions performed by the ride-hailing driver through the ride-hailing application. For example, the start time of the driver accepting an order through the application can be considered the start time of driving out, and the end time can be considered the time the driver reports order completion through the application. Of course, other methods can be used to determine the start and end times of driving out, depending on actual needs, which will not be elaborated upon here.

[0059] The listener's duration identifier indicates that the recorded item is a listener's duration. The recorded duration data corresponding to this item represents the time spent listening to one or more types of orders within the corresponding time slice. In some cases, the listener's operation can be further subdivided into listening to real-time orders or listening to pre-booked orders, and may also include listening to other types of orders, without particular limitation. Therefore, there may be multiple different listener's duration identifiers to achieve the purpose of recording different types of listener's durations. For example, time slice data may include a first listener's duration identifier, a second listener's duration identifier, and a third listener's duration identifier. The duration data corresponding to the first listener's duration identifier represents the time spent listening to all types of orders within the corresponding time slice, the duration data corresponding to the second listener's duration identifier represents the time spent listening to real-time orders within the corresponding time slice, and the duration data corresponding to the third listener's duration identifier represents the time spent listening to pre-booked orders within the corresponding time slice.

[0060] The pick-up / drop-off duration identifier indicates that the recorded item is the pick-up / drop-off duration. The recorded duration data corresponding to this item represents the time spent traveling to pick up passengers, waiting for passengers, and / or transporting passengers to their destination within the corresponding time slice. Therefore, there may be multiple different pick-up / drop-off duration identifiers to achieve the purpose of recording the duration of different stages in the entire pick-up / drop-off process.

[0061] The designated service duration identifier indicates that the recorded item is a designated service duration. The recorded duration data corresponding to this item represents the duration occupied by providing the designated service within the corresponding time slice. Designated services can be designed according to actual needs; for example, a designated service could be a ride-hailing service, a carpooling service, or a premium ride service. Therefore, there may be multiple different designated service identifiers to achieve the purpose of recording the duration of providing different designated services.

[0062] In addition to the various identifiers listed above, the duration type identifier can also be used to indicate that the recorded item is of other duration types, without any special restrictions.

[0063] In some optional implementations, step S201 can be started when the operation of the ride-hailing driver starting the vehicle is detected; and step S201 can be ended when the operation of the ride-hailing driver returning the vehicle is detected.

[0064] Step S202: If the conditions for fulfilling the service activity are not met, perform at least one round of update operations.

[0065] In step S202, there may be a situation where, when there are multiple service activities, at least one round of update operation can be performed for a certain service activity if the conditions for achieving a certain service activity are not met, so as to determine in a timely manner whether the conditions for achieving a certain service activity are met.

[0066] The conditions for achieving a service activity include at least the following: within the activity's validity period, the cumulative valid duration corresponding to at least one duration type identifier reaches a preset cumulative duration threshold. The cumulative valid duration can be the sum of the duration data corresponding to the respective duration type identifiers in the time-slice data. For example, when the achievement condition for a service activity is that the cumulative valid duration corresponding to a vehicle trip duration identifier reaches a preset cumulative duration threshold within the activity's validity period, the total vehicle trip duration within the activity's validity period can be calculated based on multiple time-slice data points. This total duration can be considered as the cumulative valid duration. When this total duration reaches the cumulative duration threshold, the achievement condition is considered met, and accordingly, the ride-hailing service operation platform can issue rewards to ride-hailing drivers based on the target account.

[0067] It should be noted that the threshold mentioned in this article can include cases that are equal to or greater than this threshold.

[0068] To allow for a more timely determination of whether the conditions have been met, an update operation can be performed. For example... Figure 3 As shown, the update operation mentioned in step S202 may include the following steps:

[0069] Step S301: Based on the preset statistical quantity, determine the time slice data to be statistically analyzed in this round from the acquired time slice data, and perform statistical processing on the duration data in the time slice data to be statistically analyzed in this round.

[0070] Step S302: Based on the statistical processing results, determine whether the conditions for achieving the goal have been met.

[0071] The preset statistical quantity represents the number of time slices to be statistically analyzed in this round, and can be set according to actual needs. In step S301, determining the time slices to be statistically analyzed in this round from the acquired time slice data based on the preset statistical quantity includes: determining the time slices with a statistical quantity from the acquired time slice data. For example, when the statistical quantity is 10, it means selecting 10 time slices from the acquired time slice data as the time slices to be statistically analyzed in this round. Of course, determining the time slices to be statistically analyzed in this round from the acquired time slice data based on the preset statistical quantity can also include: determining the time slices with a statistical quantity from the acquired time slice data according to a preset time order. In this case, the time order of multiple time slices can be determined based on the time of output of the time slice data or the time corresponding to the time slice recorded in the time slice data. When selecting the time slices to be statistically analyzed in this round, the earliest time slice data that has not been statistically analyzed can be selected as the time slices to be statistically analyzed in this round, based on the time order.

[0072] Typically, the number of statistics corresponding to each round of update operations can be the same, but in some alternative implementations, the number of statistics corresponding to one round of update operations can be different from the number of statistics corresponding to another round of update operations.

[0073] The time slice data acquired in step S301 refers to the time slice data of the target account obtained by executing step S201. Considering that the process of acquiring multiple time slice data in step S201 may be lengthy, it is possible that step S202 or step S301 may be executed concurrently during the execution of step S201. Therefore, step S201 may include the following situation: during the execution of step S202 or step S301, the time slice data of the target account is still being continuously acquired. As long as the number of time slice data acquired during the execution of step S201 reaches the statistical limit, and the condition for achieving the target account is not met, step S301 can be started. As step S201 continues to be executed, more and more time slice data of the target account is acquired, and step S301 can be executed multiple times during this period. That is, multiple rounds of update operations can be performed during the execution of step S201. Of course, in some optional embodiments, step S201 can be completed first, and then steps S202 and S301 can be started. In any case, step S201 can be performed once or multiple times, and the update operation in step S202 can also be performed in one or more rounds.

[0074] In some optional implementations, all acquired time slice data that has not undergone statistical processing can be used as the time slice data to be statistically analyzed in the current round. In some possible cases, the number of acquired, unprocessed time slice data can be updated, and when this number reaches a preset statistical quantity, all acquired, unprocessed time slice data can be determined as the time slice data to be statistically analyzed in the current round.

[0075] The statistical processing in step S301 includes accumulating one or more types of duration data from each time slice data to be statistically analyzed in this round. For example, if there are 10 time slice data to be statistically analyzed in this round, each time slice data includes a vehicle departure duration identifier and corresponding duration data, as well as a call order duration identifier and corresponding duration data. In this case, each time slice data includes two types of duration data, and one or both types of duration data can be selected for statistical processing. When selecting two types of duration data for statistical processing, the duration data corresponding to the vehicle departure duration identifier in the 10 time slice data can be accumulated. In addition, the duration data corresponding to the call order duration identifier in the 10 time slice data can be accumulated. At this time, the statistical processing result includes the result of accumulating the duration data corresponding to the vehicle departure duration identifier and the result of accumulating the duration data corresponding to the call order duration identifier.

[0076] Of course, in other optional implementations, statistical processing may include accumulating certain duration data according to preset rules. For example, based on the output time corresponding to the time slice data, duration data within time slice data whose output time falls within a specific time period can be selected and accumulated, with the accumulated result used as one of the results of statistical processing. The aforementioned specific time period may be a preset peak travel period of a day or another time period of a day. For example, based on the location corresponding to the time slice data, duration data within time slice data whose location falls within a specific location range can be selected and accumulated, with the accumulated result used as one of the results of statistical processing. The aforementioned location may be the location of the ride-hailing vehicle during the generation of the time slice data. The specific location range may be a designated city or a specific area within a city.

[0077] In step S302, based on the statistical processing results, it is determined whether the achievement condition has been met. Specific methods may include: generating information to indicate that the achievement condition has been met or generating information to indicate that the achievement condition has not been met.

[0078] Of course, the aforementioned specific methods may also include generating information to represent the progress of ride-hailing drivers' participation in service activities. The progress can represent the difference between the current cumulative effective time and a preset cumulative time threshold. In some optional implementations, the aforementioned progress can be represented in the form of a visual progress bar.

[0079] In some alternative implementations, there are multiple service activities, each corresponding to different fulfillment conditions. Accordingly, such as... Figure 4 As shown, step S202 may include the following steps: Step S2021, when the achievement conditions of any service activity are not met, perform the corresponding update operation. Accordingly, the time slice data processing method may also include the following steps: Step S401, obtain a query request from the login device of the target account; Step S402, determine the target activity from multiple service activities according to the query request; Step S403, determine the achievement progress of the target activity according to the latest update operation of the target activity; Step S404, provide feedback on the achievement progress to the login device.

[0080] Typically, ride-hailing drivers or related personnel can log into the target account on electronic devices such as mobile phones, computers, and in-vehicle terminals, and send query requests to learn about the progress of one or more service activities. The login device refers to the aforementioned electronic device logged into the target account. The aforementioned target activity is the service activity corresponding to the query request. When a ride-hailing driver wants to know the progress of certain service activities, they can send query requests to server 101 for these service activities through the logged-in device. Server 101 can then identify these service activities as target activities based on the query requests and provide feedback on their progress.

[0081] In some optional implementations, there are multiple service activities, each corresponding to different achievement conditions. Step S202 may include the following steps: when the achievement condition of any service activity is not met, perform a corresponding update operation. After each round of update operations, report the achievement progress to the logged-in device. At this time, it is not necessary to obtain a query request; the achievement progress can be reported proactively.

[0082] In some optional implementations, a notification message is sent to the logged-in device upon completion of each round of update operations. The notification message includes the result of the ride-hailing driver's participation in the service activity, including the progress of the service activity or whether the conditions for achieving the goal have been met.

[0083] In some alternative implementations, determining the current cumulative valid duration is equivalent to determining whether the condition has been met. Therefore, in practical applications, the current cumulative valid duration can be displayed in the user interface as a prompt.

[0084] The above is a description of some of the main contents of steps S201, S202, S301 and S302.

[0085] The aforementioned time-slice data processing method acquires multiple time-slice data points from the target account and determines the time-slice data to be statistically analyzed in each round of updates according to a preset statistical quantity. This ensures that the results of ride-hailing drivers' participation in service activities can be updated stably and promptly. Furthermore, since the update duration is related to the corresponding time slice length and the preset statistical quantity, and the time slice length and statistical quantity can be set according to actual needs, this method facilitates the rational allocation of computing resources while improving the ride-hailing driver experience.

[0086] In some embodiments, statistical processing of duration data in the current round of time slice data to be statistically analyzed may include the following steps: obtaining the target field corresponding to the achievement condition; determining the duration type identifier in the current round of time slice data that matches the target field; and performing statistical processing on the duration data corresponding to the duration type identifier in the current round of time slice data that matches the target field. There may be one or more target fields corresponding to the achievement condition. In some optional embodiments, a one-to-one correspondence between the target field and the duration type identifier can be established in advance, and the target field and its corresponding duration type identifier can be stored in a database. When an update operation needs to be performed for a certain service activity, the corresponding target field can be determined based on the achievement condition of the service activity, and the duration type identifier corresponding to the corresponding target field can be retrieved from the database. The duration type identifier matching the target field refers to the duration type identifier that has a corresponding relationship with the target field. In some cases, the target field corresponding to the achievement condition may be consistent with the duration type identifier in the time slice data.

[0087] Different service activities correspond to different achievement conditions. By determining the duration data to be statistically processed through the target field of the achievement conditions, it is possible to flexibly perform corresponding update operations on multiple service activities.

[0088] Therefore, when the time slice data contains multiple duration type identifiers and corresponding duration data, different update operations can be performed according to different service activities, so that ride-hailing drivers participating in multiple service activities at the same time can understand the progress of each service activity in a timely manner.

[0089] In some embodiments, step S201 may include the following steps: acquiring multiple raw time slice data sent by the message queue; comparing the acquired raw time slice data with the time slice data stored in the database; discarding the raw time slice data when the feature information of the acquired raw time slice data is the same as the feature information of at least one time slice data in the database; and using all the raw time slice data that are not discarded after comparison as the time slice data of the target account.

[0090] Typically, terminal 102 runs a ride-hailing application, generating raw time-slice data. This raw time-slice data is then sent to a message queue via the ride-hailing application's data interface. Server 101 then retrieves the raw time-slice data from the message queue. Considering the possibility of data transmission errors during the generation and transmission of raw time-slice data, server 101 might retrieve the same raw time-slice data from the message queue at two different times. This would affect the accuracy of the statistical processing of duration data during update operations. For example, server 101 might retrieve raw time-slice data in the morning of the same day, with a start time of 9:00:00 and a duration of 20 seconds. If server 101 then retrieves another raw time-slice data in the afternoon of the same day, also with a start time of 9:00:00 and a duration of 20 seconds, the afternoon-retrieved raw time-slice data can be discarded to avoid duplicate statistical processing of the same duration data in subsequent operations.

[0091] Therefore, by storing the statistically processed time-slice data in a database, whenever new raw time-slice data is obtained, it can be compared with the time-slice data stored in the database to determine whether the same time-slice data has been obtained before. Specifically, the feature information in the new raw time-slice data can be compared with the feature information of the time-slice data in the database. If the feature information of the obtained raw time-slice data (i.e., the aforementioned new raw time-slice data) is the same as the feature information of at least one time-slice data in the database, it can be determined that the raw time-slice data has been obtained before, and the raw time-slice data can be discarded.

[0092] The aforementioned feature information is used to distinguish different time-slice data. In some optional implementations, the start or end time of the time slice in the time-slice data can be used as feature information. Alternatively, the output time of the time-slice data or the location data in the time-slice data can be used as feature information. Of course, other data or characters can also be used as feature information to distinguish different time-slice data. The aforementioned location data can be the location information of a ride-hailing vehicle, such as latitude and longitude data. Time-slice data can include location data. Typically, the location information corresponding to any time from the start to the end of the time slice can be taken as location data.

[0093] In some alternative implementations, the feature information includes both time data and location data, thereby improving the accuracy of comparing the acquired raw time slice data with the time slice data stored in the database. The time data can be at least one of the start time, end time, or output time of the time slice.

[0094] In some embodiments, the achievement conditions for a service activity include the cumulative valid duration within the activity's validity period reaching a preset cumulative duration threshold. Accordingly, the time-slice data processing method may include the following steps: when the duration data corresponding to the discarded original time-slice data is greater than the duration data corresponding to the time-slice data with the same feature information in the database, determine the difference between the two duration data. Accordingly, statistical processing is performed on the duration data in the time-slice data to be statistically analyzed in this round. Based on the statistical processing results, it is determined whether the achievement conditions are met. Specifically, this may include the following steps: updating the cumulative valid duration based on the sum of the differences between each duration data in the time-slice data to be processed in this round and the two duration data; and determining whether the achievement conditions are met based on the comparison between the updated cumulative valid duration and the cumulative duration threshold.

[0095] In some cases, the duration data of the original time slice data and the time slice data in the database, which have the same characteristics, may not be consistent. For example, the duration data of the former may be 50 seconds, while the duration data of the latter may be 20 seconds. In this case, it cannot be determined which duration data in the time slice data is true. Since the time slice data in the database has been statistically processed beforehand, if the duration data of the original time slice data is not considered and it is assumed that the duration data in the original time slice data is true, the actual cumulative effective time of ride-hailing drivers may be reduced. Therefore, the difference between the two duration data can be determined and added to the cumulative effective time. That is, by calculating the difference between 50 seconds and 20 seconds to get 30 seconds, the 30 seconds can be added to the cumulative effective time, thus increasing the cumulative effective time by 30 seconds. This can better ensure the accuracy of statistical processing while taking into account the interests of ride-hailing drivers.

[0096] The cumulative valid duration is updated based on the sum of the differences between each duration data in the current round of time slice data and the two duration data. This includes adding the previous cumulative valid duration, each duration data in the current round of time slice data, and the difference between the two duration data to obtain the updated cumulative valid duration. The cumulative valid duration is used to represent the total service duration corresponding to the corresponding duration type identifier.

[0097] In some optional implementations, determining whether the achievement condition has been met based on the comparison between the updated cumulative effective duration and the cumulative duration threshold may include: determining that the achievement condition has not been met when the updated cumulative effective duration is less than the cumulative duration threshold; and determining that the achievement condition has been met when the updated cumulative effective duration is not less than the cumulative duration threshold.

[0098] In some optional implementations, determining whether the achievement condition has been met based on the comparison result of the updated cumulative effective duration and the cumulative duration threshold may include: displaying the achievement progress of the service activity in the form of a visual progress bar based on the comparison result of the updated cumulative effective duration and the cumulative duration threshold.

[0099] In some embodiments, each time slice data also includes spatiotemporal data associated with the target account. Accordingly, the statistical processing of the duration data in the time slice data to be statistically analyzed in this round may include the following steps: taking the time slice data corresponding to the spatiotemporal data located in the specified data interval as the target time slice data, obtaining the cumulative value of the same type of duration data in the target time slice data, and taking the cumulative value as the statistical processing result.

[0100] Spatiotemporal data is used to represent the location and / or service time associated with the target account. Spatiotemporal data can include the time data and / or location data mentioned earlier in the feature information. Location data represents the location of the login device or ride-hailing vehicle of the target account, and is therefore associated with the target account. Location data can be represented using one-dimensional, two-dimensional, or three-dimensional location coordinates. Time data represents the moment when the ride-hailing driver provides ride-hailing services, and is therefore also associated with the target account. This can meet the assessment requirements of some service activities. For example, some service activities may require ride-hailing drivers to provide ride-hailing services for a certain duration in a specific area during a specific time period. In this case, spatiotemporal data can be used to determine whether the duration data in certain time slices is the duration data generated by the ride-hailing driver when starting work in the specific time period and specific area. If not, this time slice data is not the target time slice data and is not suitable for performing update operations for the corresponding service activity.

[0101] The aforementioned specified data intervals include specified time data intervals and / or specified location data intervals. A time-slice of data can be designated as the target time-slice if the time data corresponding to that time-slice falls within a specified time data interval; similarly, it can be designated as the target time-slice if the location data corresponding to that time-slice falls within a specified location data interval; or it can be designated as the target time-slice if both the time data and location data corresponding to that time-slice fall within a specified time data interval. Time data intervals are typically represented in the form of time periods, for example, from 8:00:00 to 20:00:00. Location data intervals are typically represented in the form of electronic fences. Of course, specified data intervals can also be represented in other forms.

[0102] By using spatiotemporal data to select target time slices, it is possible to more accurately determine whether the conditions for achieving the activity service have been met.

[0103] In some embodiments, the time-slice data may further include data representing the type of ride-hailing driver or data representing the registered city associated with the target account. The aforementioned data on the type of ride-hailing driver includes categories such as self-employed, driverless franchise, and vehicle-affiliated franchise, which can reflect a ride-hailing driver's identity. This identity includes, but is not limited to: the driver being a full-time driver for the ride-hailing platform (corresponding to self-employed), the driver being a driver employed by a fleet (corresponding to driverless franchise), or the driver being a temporary part-time individual driver (corresponding to vehicle-affiliated franchise). The data on the registered city associated with the target account is used to display the registered location of the ride-hailing driver.

[0104] In some alternative implementations, when the ride-hailing driver's vehicle termination operation is detected, the data duration of the acquired time slice data that has not reached the preset statistical quantity and has not been statistically processed can be statistically processed to update the cumulative valid duration.

[0105] It should be understood that, although Figure 2-4 The steps in the flowchart are shown sequentially according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Figure 2-4 Unless otherwise expressly stated herein, the steps illustrated and other steps involved in the embodiments are not subject to strict order restrictions and may be performed in other orders. Furthermore, at least some steps in the foregoing embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0106] This disclosure also provides a time-slice data processing apparatus, such as... Figure 5 As shown, the time slice data processing apparatus 500 includes:

[0107] The data acquisition module 501 is used to acquire multiple time-slice data of the target account;

[0108] Update module 502 is used to perform at least one round of update operations when the conditions for fulfilling a service activity are not met.

[0109] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0110] In some embodiments, the update module 502 may include:

[0111] The target field acquisition unit (not shown) is used to acquire the target field corresponding to the achievement condition;

[0112] The matching unit (not shown) is used to determine the duration type identifier in the time slice data to be counted in this round that matches the target field;

[0113] The statistical processing unit (not shown) is used to perform statistical processing on the duration data corresponding to the duration type identifier that matches the target field in the time slice data to be statistically analyzed in this round.

[0114] In some embodiments, the data acquisition module 501 may include:

[0115] The raw time slice data acquisition unit (not shown) is used to acquire multiple raw time slice data sent by the message queue;

[0116] The comparison unit (not shown) is used to compare the acquired raw time slice data with the time slice data stored in the database.

[0117] The data processing unit (not shown) is used to discard the original time slice data when the feature information of the acquired original time slice data is the same as the feature information of at least one time slice data in the database, and use all the original time slice data that are not discarded after comparison as the time slice data of the target account.

[0118] In some embodiments, the achievement conditions for a service activity include the cumulative valid duration within the activity's validity period reaching a preset cumulative duration threshold. Accordingly, the time-slice data processing apparatus 500 may further include: a difference determination module (not shown), used to determine the difference between two duration data when the duration data corresponding to the discarded original time-slice data is greater than the duration data corresponding to time-slice data with the same feature information in the database. Correspondingly, the update module 502 may include: a duration update unit (not shown), used to update the cumulative valid duration based on the sum of the differences between each duration data in the current round of time-slice data and the two duration data; and a result update unit (not shown), used to determine whether the achievement conditions have been met based on the comparison result between the updated cumulative valid duration and the cumulative duration threshold.

[0119] In some embodiments, each time slice data further includes spatiotemporal data associated with the target account, the spatiotemporal data being used to represent the location and / or service time associated with the target account. Accordingly, the update module 502 may include: a target data determination unit (not shown), used to take the time slice data corresponding to the spatiotemporal data located in a specified data interval as the target time slice data, obtain the cumulative value of the same type of duration data in the target time slice data, and use the cumulative value as the statistical processing result.

[0120] In some embodiments, there are multiple service activities, and different service activities correspond to different achievement conditions. Accordingly, the update module 502 may include: an update operation unit (not shown), used to perform a corresponding update operation when the achievement condition of any service activity is not met. Accordingly, the time slice data processing apparatus 500 may also include: a request acquisition module (not shown), used to acquire a query request from the login device of the target account; a target activity determination module (not shown), used to determine the target activity from multiple service activities according to the query request; and a feedback module (not shown), used to determine the achievement progress of the target activity according to the latest update operation of the target activity; and to provide feedback on the achievement progress to the login device.

[0121] Specific limitations regarding the time-slice data processing device 500 can be found in the limitations of the time-slice data processing method described above, and will not be repeated here. Each module in the aforementioned time-slice data processing device 500 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 operations corresponding to each module.

[0122] This disclosure also provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores statistically processed time-slice data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements the time-slice data processing method described in any embodiment of this document.

[0123] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the embodiments of this disclosure and do not constitute a limitation on the computer devices on which the embodiments of this disclosure are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0124] The computer device provided in this disclosure may include 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:

[0125] Obtain multiple time-slice data for the target account;

[0126] If the conditions for fulfilling the service activity are not met, at least one round of update operations shall be performed.

[0127] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0128] In some other embodiments, the processor may also implement other steps of the time slice data processing method in any of the foregoing embodiments when executing the computer program.

[0129] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0130] Obtain multiple time-slice data for the target account;

[0131] If the conditions for fulfilling the service activity are not met, at least one round of update operations shall be performed.

[0132] Each time-slice data includes a duration type identifier and duration data corresponding to that time-slice. The duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. The update operation includes: determining the time-slice data to be statistically analyzed in this round from the acquired time-slice data according to the preset statistical quantity; performing statistical processing on the duration data in the time-slice data to be statistically analyzed in this round; and determining whether the conditions for achieving the target have been met based on the statistical processing results.

[0133] In some other embodiments, when the computer program is executed by the processor, it may also implement other steps of the time slice data processing method in any of the foregoing embodiments.

[0134] 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 aforementioned computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this disclosure can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0135] 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.

[0136] The above embodiments merely illustrate several implementation methods of this disclosure, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of protection of this disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these all fall within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the appended claims.

Claims

1. A time-slice data processing method, characterized in that, The method includes: The process involves: acquiring multiple raw time-slice data sent from the message queue; comparing the acquired raw time-slice data with the time-slice data stored in the database; discarding the raw time-slice data if its feature information matches that of at least one time-slice data in the database; using all raw time-slice data that are not discarded after the comparison as the time-slice data for the target account; determining the difference between the two time-slice data if the duration corresponding to the discarded raw time-slice data is greater than the duration corresponding to the time-slice data with the same feature information in the database; wherein each time-slice data includes a duration type identifier and duration data corresponding to the corresponding time-slice, the duration type identifier indicating the service duration type of the ride-hailing service associated with the target account, and the duration data used to record the corresponding service duration; If the conditions for achieving the service activity are not met, at least one round of update operation is performed; wherein, the conditions for achieving the service activity include the cumulative effective duration within the activity validity period reaching a preset cumulative duration threshold; the update operation includes: determining the time slice data to be statistically analyzed in this round from the acquired time slice data according to a preset statistical quantity, performing statistical processing on the duration data in the time slice data to be statistically analyzed in this round, and determining whether the conditions for achieving the activity are met based on the statistical processing result, including: updating the cumulative effective duration based on the sum of the differences between each duration data in the time slice data to be statistically analyzed in this round and the two duration data; and determining whether the conditions for achieving the activity are met based on the comparison result between the updated cumulative effective duration and the cumulative duration threshold. The update operation further includes: when a vehicle collection operation associated with the target account is detected, performing statistical processing on the acquired time slice data that has not reached the preset statistical quantity and has not been statistically processed, and updating the cumulative valid duration.

2. The method according to claim 1, characterized in that, The statistical processing of the duration data in the time slice data to be statistically analyzed in this round includes: Obtain the target field corresponding to the condition being met; Determine the duration type identifier in the time slice data to be statistically analyzed in this round that matches the target field; Statistical processing is performed on the duration data corresponding to the duration type identifier that matches the target field in the time slice data to be statistically analyzed in this round.

3. The method according to claim 1, characterized in that, The duration type identifier includes vehicle dispatch duration identifier, order listening duration identifier, pick-up and drop-off duration identifier, or designated business service duration identifier.

4. The method according to claim 1, characterized in that, Each of the time slice data also includes spatiotemporal data associated with the target account, the spatiotemporal data being used to represent the location and / or service time associated with the target account; The statistical processing of duration data in the time slice data to be statistically analyzed in this round includes: taking the time slice data corresponding to the spatiotemporal data located in the specified data interval as the target time slice data, obtaining the cumulative value of the same type of duration data in the target time slice data, and taking the cumulative value as the statistical processing result.

5. The method according to claim 1, characterized in that, There are multiple service activities, and different service activities correspond to different achievement conditions; The step of performing at least one update operation when the conditions for achieving a service activity are not met includes: performing a corresponding update operation when any of the conditions for achieving a service activity are not met; The method further includes: obtaining a query request from the login device of the target account; determining a target activity from multiple service activities based on the query request; determining the achievement progress of the target activity based on the latest update operation of the target activity; and feeding back the achievement progress to the login device.

6. A time-slice data processing apparatus, characterized in that, The device includes: The data acquisition module is used to acquire multiple raw time-slice data sent by the message queue; compare the acquired raw time-slice data with the time-slice data stored in the database; discard the raw time-slice data when the feature information of the acquired raw time-slice data is the same as the feature information of at least one time-slice data in the database; take all raw time-slice data that are not discarded after the comparison as the time-slice data of the target account; determine the difference between the two time-slice data when the duration data corresponding to the discarded raw time-slice data is greater than the duration data corresponding to the time-slice data with the same feature information in the database; wherein, each time-slice data includes a duration type identifier and duration data corresponding to the corresponding time-slice, the duration type identifier indicates the service duration type of the ride-hailing service associated with the target account, and the duration data is used to record the corresponding service duration. An update module is used to perform at least one round of update operations when the achievement conditions of a service activity are not met. The achievement conditions of the service activity include the cumulative effective duration within the activity's validity period reaching a preset cumulative duration threshold. The update operation includes: determining the time slice data to be statistically analyzed in the current round from the acquired time slice data according to a preset statistical quantity; performing statistical processing on the duration data in the time slice data to be statistically analyzed in the current round; and determining whether the achievement conditions are met based on the statistical processing result. This includes: updating the cumulative effective duration based on the sum of the differences between each duration data in the time slice data to be statistically analyzed in the current round and the two duration data; and determining whether the achievement conditions are met based on the comparison result between the updated cumulative effective duration and the cumulative duration threshold. The update operation further includes: when a vehicle collection operation associated with the target account is detected, performing statistical processing on the acquired time slice data that has not reached the preset statistical quantity and has not been statistically processed, and updating the cumulative effective duration.

7. 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 5.

8. 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 5.

Images