Method and apparatus for allocating pre-order orders

Abstract

The present disclosure provides a pre-order allocation method and device, and relates to the technical field of Internet application. A specific embodiment of the method comprises the following steps: determining a journey starting point and a journey starting time corresponding to a pre-order to be allocated; determining a target time for allocating the pre-order according to the journey starting point and the journey starting time; and allocating the pre-order in response to the arrival of the target time. This embodiment can allocate the pre-order at a more reasonable time, avoiding the problem that the service provider waits for a long time after receiving the order or does not successfully match the service provider until the order journey starting time, thereby improving the service efficiency and the utilization rate of service resources.

Description

Technical Field

[0001] This disclosure relates to the field of Internet application technology, and in particular to a method and apparatus for allocating reservation orders. Background Technology

[0002] In recent years, with the continuous development of internet technology, the integration of offline business opportunities with the internet has given rise to a new O2O (Online to Offline) business model, making the internet a platform for offline transactions. Currently, O2O has entered a stage of rapid development, with transportation O2O services being one of the more successful examples. Taking vehicle O2O services as an example, there are various types, such as ride-hailing, premium ride-hailing, carpooling, test drive, and car rental services. Some types of vehicle services support advance booking, allowing service requesters to place orders in advance. The server allocates orders at a fixed time before the order's start time (e.g., half an hour or 20 minutes before the start time). However, this order allocation method often results in long waiting times for service providers after accepting orders, or failure to find a service provider by the order's start time, leading to low service efficiency and low resource utilization. Summary of the Invention

[0003] To address the aforementioned technical problems, this disclosure provides a method and apparatus for allocating reservation orders.

[0004] According to a first aspect of the present disclosure, a method for allocating reservation orders is provided, comprising:

[0005] Determine the origin and start time of the trips corresponding to the reservation orders to be assigned;

[0006] The target time for allocating the reservation order is determined based on the trip origin and trip start time;

[0007] In response to the arrival of the target time, the reservation order is assigned.

[0008] According to a second aspect of the present disclosure, a reservation order allocation device is provided, comprising:

[0009] The first determining unit is configured to determine the trip origin and trip start time corresponding to the reservation order to be assigned;

[0010] The second determining unit is configured to determine the target time for allocating the reservation order based on the trip origin and the trip start time;

[0011] The allocation unit is configured to allocate the reservation order in response to the arrival of the target time.

[0012] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, the storage medium storing a computer program that, when executed by a processor, implements the method for requesting service resources as described in any one of the first aspects.

[0013] According to a fourth aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for requesting service resources as described in any of the first aspects above.

[0014] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0015] The reservation order allocation method and apparatus provided in the embodiments of this disclosure determine the trip origin and trip start time corresponding to the reservation order to be allocated, determine the target time for allocating the reservation order based on the trip origin and trip start time, and allocate the reservation order in response to the arrival of the target time. This enables the allocation of reservation orders at a more reasonable time, avoiding problems such as excessively long waiting times for service providers after accepting orders, or failure to successfully match a service provider until the trip start time, thus improving service efficiency and the utilization rate of service resources.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] Figure 1 This is a schematic diagram of an exemplary system architecture for applying embodiments of this disclosure;

[0019] Figure 2 This is a flowchart illustrating a method for allocating reservation orders according to an exemplary embodiment of the present disclosure;

[0020] Figure 3 This is a flowchart illustrating another method for allocating reservation orders according to an exemplary embodiment of the present disclosure;

[0021] Figure 4 This is a flowchart illustrating another method for allocating reservation orders according to an exemplary embodiment of the present disclosure;

[0022] Figure 5 This disclosure is a block diagram of a reservation order allocation device according to an exemplary embodiment;

[0023] Figure 6 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0024] 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 numerals 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 disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0025] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] See Figure 1 Here is a schematic diagram of an exemplary system architecture for applying embodiments of this disclosure:

[0028] like Figure 1 As shown, system architecture 100 may include terminal devices, such as terminal devices 101 and 102, network 103, and server 104 illustrated. It should be understood that... Figure 1 The number or type of terminal devices, networks, and servers shown are merely illustrative. Depending on implementation needs, there can be any number or type of terminal devices, networks, and servers.

[0029] Network 103 is a medium used to provide a communication link between terminal devices and servers. Network 103 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0030] Terminal devices 101 and 102 can interact with the server via network 103 to receive or send requests or information. Terminal devices 101 and 102 can be various electronic devices, including but not limited to smartphones, tablets, smart wearable devices, and personal digital assistants.

[0031] Server 104 can be a server that provides various services. A server can store, analyze, and process received data, and can also send control commands or requests to terminal devices or other servers. A server can respond to user service requests and provide services. It is understood that a server can provide one or more services, and the same service can be provided by multiple servers.

[0032] based on Figure 1 The system architecture shown in this disclosure, in one embodiment, allows a terminal device to send a reservation order to be assigned to a server via a network. The server can determine the target time for assigning the reservation order based on the trip origin and trip start time corresponding to the reservation order, and assign the reservation order when the target time arrives.

[0033] The present disclosure will now be described in detail with reference to specific embodiments.

[0034] like Figure 2 As shown, Figure 2 This is a flowchart illustrating a method for allocating reservation orders according to an exemplary embodiment. This method can be applied to a server. The method may include the following steps:

[0035] In step 201, the origin and start time of the trip corresponding to the reservation order to be assigned are determined.

[0036] In this embodiment, the service involved can be an O2O service for transportation, taking vehicle O2O services as an example, including various types such as express car services, premium car services, ride-sharing services, test drive services, and car rental services. Some types of vehicle services support advance booking, allowing service requesters to place orders in advance. Therefore, the reservation orders to be allocated can be reservation orders placed in advance by service requesters for transportation O2O services.

[0037] In this embodiment, the trip origin and trip start time corresponding to the reservation order can be obtained based on the order content of the reservation order to be assigned.

[0038] In step 202, the target time for allocating the reservation order is determined based on the trip origin and trip start time.

[0039] In this embodiment, the target time for allocating the reservation order can be determined based on the trip origin and trip start time corresponding to the reservation order to be allocated. This target time can be a more reasonable time to allocate the reservation order.

[0040] In one implementation of this embodiment, historical data can be statistically analyzed to obtain the distribution pattern of transportation capacity in time and space. Based on the trip origin and trip start time, the optimal time for allocating the reservation order can be estimated as the target time according to the distribution pattern.

[0041] In another implementation of this embodiment, machine learning methods can be used to determine the target time for allocating reservation orders. Specifically, a pre-trained target model can be obtained, and the target model can be used to predict the best time to allocate the reservation order as the target time based on the trip origin and trip start time corresponding to the reservation order.

[0042] It is understandable that the target time for allocating the reservation order can also be determined in other ways based on the origin and start time of the trip, and this disclosure does not limit this aspect.

[0043] In step 203, the reservation order is assigned in response to the arrival of the target time.

[0044] In this embodiment, the aforementioned reservation order is allocated when the target time arrives. For example, the reservation order can be played at the target time, or a suitable service provider can be matched for the reservation order at the target time. It is understood that this disclosure does not limit the specific method of allocating the reservation order.

[0045] The reservation order allocation method provided in the above embodiments of this disclosure determines the trip origin and trip start time corresponding to the reservation order to be allocated, determines the target time for allocating the reservation order based on the trip origin and trip start time, and allocates the reservation order in response to the arrival of the target time. This allows for the allocation of reservation orders at a more reasonable time, avoiding problems such as excessively long waiting times for service providers after accepting orders, or failure to successfully match a service provider until the trip start time, thus improving service efficiency and the utilization rate of service resources.

[0046] Figure 3 This is a flowchart illustrating another method for allocating reservation orders according to an exemplary embodiment. This embodiment details the process of determining a target time for allocating reservation orders, and the method can be applied to a server. The method may include the following steps:

[0047] In step 301, the origin and start time of the trip corresponding to the reservation order to be assigned are determined.

[0048] In step 302, the pre-trained target model is obtained.

[0049] In step 303, the target time is determined using the target model based on the above-mentioned trip start point and trip start time.

[0050] In this embodiment, the target model may include any one of the following: logistic regression (LR) model; decision tree model; neural network model.

[0051] In this embodiment, the target time can be determined using a target model based on the aforementioned trip start point and trip start time in the following manner: First, target information can be obtained based on the aforementioned trip start point and trip start time. Then, target feature information can be extracted from the target information and input into the target model. The target time can be obtained from the output of the target model.

[0052] In step 304, the reservation order is assigned in response to the arrival of the target time.

[0053] It should be noted that, regarding the relationship with Figure 2 The same steps in the embodiments are described above. Figure 3 The details in the embodiments will not be repeated here; relevant content can be found in [link to relevant documentation]. Figure 2 Example.

[0054] The reservation order allocation method provided in the above embodiments of this disclosure determines the trip origin and trip start time corresponding to the reservation order to be allocated, obtains a pre-trained target model, determines the target time based on the trip origin and trip start time using the target model, and allocates the reservation order in response to the arrival of the target time. Because machine learning methods are used to determine the target time for allocating reservation orders, the target time is more reasonable, thereby improving the rationality of service resource allocation and further improving service efficiency and the utilization rate of service resources.

[0055] Figure 4 This is a flowchart illustrating another method for allocating reservation orders according to an exemplary embodiment. This embodiment details the process of determining a target time using a target model, and this method can be applied to a server. The method may include the following steps:

[0056] In step 401, the origin and start time of the trip corresponding to the reservation order to be assigned are determined.

[0057] In step 402, the pre-trained target model is obtained.

[0058] In this embodiment, the target model can be trained as follows: First, obtain the sample information of the order sample dataset. The sample information may include the historical year-on-year reference information, historical month-on-month reference information, and real-time reference information corresponding to the trip origin of the order sample.

[0059] Next, sample feature information is obtained based on the sample information, and the allocation time information of the sample orders is obtained based on the sample information. This sample feature information may include historical year-on-year feature information, historical month-on-month feature information, and real-time feature information. The historical year-on-year feature information may include, but is not limited to, the total order volume, capacity, order response rate, order response time, and dynamic pricing features of the service request region corresponding to the origin of the sample order within a preset historical year-on-year period.

[0060] The historical month-on-month characteristic information of the sample may include, but is not limited to, the total order volume characteristic information, transportation capacity characteristic information, order response rate characteristic information, order response time characteristic information, dynamic price adjustment characteristic information, etc., within the preset period of the month-on-month comparison of the sample orders, corresponding to the service request area of ​​the trip origin.

[0061] Real-time feature information of the sample may include, but is not limited to, features of the start time of the sample trip, and features of the total number of orders, capacity, order response rate, order response time, dynamic pricing, etc., within the preset real-time time period of the sample order, in the service request area corresponding to the trip start point of the sample order.

[0062] Finally, the model is trained using the sample feature information and the allocation time information of the sample orders to obtain the target model.

[0063] In step 403, target information is obtained based on the above-mentioned trip start point and trip start time.

[0064] In this embodiment, specifically, firstly, the service request area corresponding to the aforementioned trip start point can be determined. This service request area corresponding to the trip start point can be a region within a preset range surrounding the trip start point, for example, a circular region with the trip start point as the center and a preset distance as the radius. It is understood that this disclosure is not limited in the specific division of the service request area corresponding to the trip start point.

[0065] Next, the historical year-on-year preset time period, historical month-on-month preset time period, and real-time preset time period corresponding to the start time of the aforementioned trip are determined. Reference information for the service request region is then obtained within each of these preset time periods to derive the target information. This reference information for the service request region may include, but is not limited to, total order volume information, capacity information, order response rate information, order response time information, dynamic pricing information, etc., for that service request region.

[0066] In step 404, target feature information is extracted from the target information.

[0067] In this embodiment, the target feature information may include: historical year-on-year feature information, historical month-on-month feature information, and real-time feature information. The historical year-on-year feature information may include one or more of the following: total order volume feature information for the service request region within a preset historical year-on-year period; transportation capacity feature information for the service request region within a preset historical year-on-year period; order response rate feature information for the service request region within a preset historical year-on-year period; order response time feature information for the service request region within a preset historical year-on-year period; and dynamic pricing feature information for the service request region within a preset historical year-on-year period, etc.

[0068] Historical month-on-month characteristic information may include one or more of the following: the total order volume characteristic information of the service request area within the historical month-on-month preset period; the transportation capacity characteristic information of the service request area within the historical month-on-month preset period; the order response rate characteristic information of the service request area within the historical month-on-month preset period; the order response time characteristic information of the service request area within the historical month-on-month preset period; and the dynamic price adjustment characteristic information of the service request area within the historical month-on-month preset period.

[0069] Real-time feature information may include one or more of the following: total order volume feature information of the service request area within a real-time preset time period; transportation capacity feature information of the service request area within a real-time preset time period; order response rate feature information of the service request area within a real-time preset time period; order response time feature information of the service request area within a real-time preset time period; dynamic price adjustment feature information of the service request area within a real-time preset time period, etc.

[0070] In step 405, the target feature information is input into the target model to obtain the target time from the output of the target model.

[0071] In step 406, the reservation order is assigned in response to the arrival of the target time.

[0072] It should be noted that, regarding the relationship with Figure 2 and Figure 3 The same steps in the embodiments are described above. Figure 4The details in the embodiments will not be repeated here; relevant content can be found in [link to relevant documentation]. Figure 2 and Figure 3 Example.

[0073] The reservation order allocation method provided in the above embodiments of this disclosure determines the trip origin and trip start time corresponding to the reservation order to be allocated, obtains a pre-trained target model, acquires target information based on the trip origin and trip start time, extracts target feature information from the target information, inputs the target feature information into the target model to obtain the target time output by the target model, and allocates the reservation order in response to the arrival of the target time. Because a machine learning method is used to determine the target time for allocating reservation orders, the target time is more reasonable, thus enabling the allocation of reservation orders at a more reasonable time. This more effectively avoids the problem of service providers waiting too long after accepting an order, or failing to successfully match a service provider until the trip start time, further improving the rationality of service resource allocation and increasing service efficiency and service resource utilization.

[0074] It should be noted that although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0075] Corresponding to the aforementioned embodiments of the reservation order allocation method, this disclosure also provides embodiments of the reservation order allocation device.

[0076] like Figure 5 As shown, Figure 5 This disclosure is a block diagram of a reservation order allocation device according to an exemplary embodiment, the device including: a first determining unit 501, a second determining unit 502 and an allocation unit 503.

[0077] The first determining unit 501 is configured to determine the trip origin and trip start time corresponding to the reservation order to be assigned.

[0078] In this embodiment, the service involved can be an O2O service for transportation, taking vehicle O2O services as an example, including various types such as express car services, premium car services, ride-sharing services, test drive services, and car rental services. Some types of vehicle services support advance booking, allowing service requesters to place orders in advance. Therefore, the reservation orders to be allocated can be reservation orders placed in advance by service requesters for transportation O2O services.

[0079] In this embodiment, the trip origin and trip start time corresponding to the reservation order can be obtained based on the order content of the reservation order to be assigned.

[0080] The second determining unit 502 is configured to determine the target time for allocating the reservation order based on the aforementioned trip origin and trip start time.

[0081] In this embodiment, the target time for allocating the reservation order can be determined based on the trip origin and trip start time corresponding to the reservation order to be allocated. This target time can be a more reasonable time to allocate the reservation order.

[0082] In one implementation of this embodiment, historical data can be statistically analyzed to obtain the distribution pattern of transportation capacity in time and space. Based on the trip origin and trip start time, the optimal time for allocating the reservation order can be estimated as the target time according to the distribution pattern.

[0083] In another implementation of this embodiment, machine learning methods can be used to determine the target time for allocating reservation orders. Specifically, a pre-trained target model can be obtained, and the target model can be used to predict the best time to allocate the reservation order as the target time based on the trip origin and trip start time corresponding to the reservation order.

[0084] It is understandable that the target time for allocating the reservation order can also be determined in other ways based on the origin and start time of the trip, and this disclosure does not limit this aspect.

[0085] The allocation unit 503 is configured to allocate the reservation order in response to the arrival of the target time.

[0086] In this embodiment, the aforementioned reservation order is allocated when the target time arrives. For example, the reservation order can be played at the target time, or a suitable service provider can be matched for the reservation order at the target time. It is understood that this disclosure does not limit the specific method of allocating the reservation order.

[0087] The reservation order allocation device provided in the above embodiments of this disclosure determines the trip origin and trip start time corresponding to the reservation order to be allocated, determines the target time for allocating the reservation order based on the trip origin and trip start time, and allocates the reservation order in response to the arrival of the target time. This allows for the allocation of reservation orders at a more reasonable time, avoiding problems such as excessively long waiting times for service providers after accepting orders, or failure to successfully match a service provider until the trip start time, thus improving service efficiency and the utilization rate of service resources.

[0088] In some alternative implementations, the second determining unit 502 may include: an acquisition subunit and a determining subunit (not shown).

[0089] The acquisition sub-unit is configured to acquire a pre-trained target model.

[0090] The sub-unit is configured to determine the target time using the target model based on the aforementioned travel start point and travel start time.

[0091] In this embodiment, the target model can be trained as follows: First, obtain the sample information of the order sample dataset. The sample information may include the historical year-on-year reference information, historical month-on-month reference information, and real-time reference information corresponding to the trip origin of the order sample.

[0092] Next, sample feature information is obtained based on the sample information, and the allocation time information of the sample orders is obtained based on the sample information. This sample feature information may include historical year-on-year feature information, historical month-on-month feature information, and real-time feature information. The historical year-on-year feature information may include, but is not limited to, the total order volume, capacity, order response rate, order response time, and dynamic pricing features of the service request region corresponding to the origin of the sample order within a preset historical year-on-year period.

[0093] The historical month-on-month characteristic information of the sample may include, but is not limited to, the total order volume characteristic information, transportation capacity characteristic information, order response rate characteristic information, order response time characteristic information, dynamic price adjustment characteristic information, etc., within the preset period of the month-on-month comparison of the sample orders, corresponding to the service request area of ​​the trip origin.

[0094] Real-time feature information of the sample may include, but is not limited to, features of the start time of the sample trip, and features of the total number of orders, capacity, order response rate, order response time, dynamic pricing, etc., within the preset real-time time period of the sample order, in the service request area corresponding to the trip start point of the sample order.

[0095] Finally, the model is trained using the sample feature information and the allocation time information of the sample orders to obtain the target model.

[0096] In this embodiment, the target model may include any one of the following: logistic regression (LR) model; decision tree model; neural network model.

[0097] In this embodiment, the target time can be determined using a target model based on the aforementioned trip start point and trip start time in the following manner: First, target information can be obtained based on the aforementioned trip start point and trip start time. Then, target feature information can be extracted from the target information and input into the target model. The target time can be obtained from the output of the target model.

[0098] In some alternative implementations, the determining subunit may include an information acquisition subunit and an extraction subunit (not shown).

[0099] The information acquisition subunit is configured to acquire target information based on the aforementioned trip start point and trip start time.

[0100] In this embodiment, specifically, firstly, the service request area corresponding to the aforementioned trip start point can be determined. This service request area corresponding to the trip start point can be a region within a preset range surrounding the trip start point, for example, a circular region with the trip start point as the center and a preset distance as the radius. It is understood that this disclosure is not limited in the specific division of the service request area corresponding to the trip start point.

[0101] Next, the historical year-on-year preset time period, historical month-on-month preset time period, and real-time preset time period corresponding to the start time of the aforementioned trip are determined. Reference information for the service request region is then obtained within each of these preset time periods to derive the target information. This reference information for the service request region may include, but is not limited to, total order volume information, capacity information, order response rate information, order response time information, dynamic pricing information, etc., for that service request region.

[0102] The extraction subunit is configured to extract target feature information from the target information.

[0103] The input sub-unit is configured to input target feature information into the target model in order to obtain the target time from the output of the target model.

[0104] In some alternative implementations, the information acquisition subunit is configured to: determine the service request area corresponding to the starting point of the trip, determine the historical year-on-year preset time period, the historical month-on-month preset time period, and the real-time preset time period corresponding to the starting time of the trip, and obtain the reference information corresponding to the service request area within the historical year-on-year preset time period, the historical month-on-month preset time period, and the real-time preset time period, respectively, so as to obtain the target information.

[0105] In some alternative implementations, the target feature information may include: historical year-on-year feature information, historical month-on-month feature information, and real-time feature information.

[0106] In some alternative implementations, the historical year-on-year characteristic information may include one or more of the following: the total order volume characteristic information of the service request region within a preset historical year-on-year period; the transportation capacity characteristic information of the service request region within a preset historical year-on-year period; the order response rate characteristic information of the service request region within a preset historical year-on-year period; the order response time characteristic information of the service request region within a preset historical year-on-year period; and the dynamic price adjustment characteristic information of the service request region within a preset historical year-on-year period.

[0107] Historical month-on-month characteristic information may include one or more of the following: the total order volume characteristic information of the service request area within a historical month-on-month preset period; the transportation capacity characteristic information of the service request area within a historical month-on-month preset period; the order response rate characteristic information of the service request area within a historical month-on-month preset period; the order response time characteristic information of the service request area within a historical month-on-month preset period; and the dynamic price adjustment characteristic information of the service request area within a historical month-on-month preset period.

[0108] Real-time feature information may include one or more of the following: total order volume feature information of the service request area within a real-time preset time period; transportation capacity feature information of the service request area within a real-time preset time period; order response rate feature information of the service request area within a real-time preset time period; order response time feature information of the service request area within a real-time preset time period; and dynamic price adjustment feature information of the service request area within a real-time preset time period.

[0109] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0110] It should be understood that the aforementioned device can be pre-installed on the server or loaded onto the server via download or other means. The corresponding units in the aforementioned device can cooperate with the units on the server to implement the reservation order allocation scheme.

[0111] Embodiments of this disclosure may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code.

[0112] Accordingly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figures 2 to 4 The method for allocating reservation orders provided in any embodiment.

[0113] The aforementioned computer-readable storage medium may be the computer-readable storage medium included in the apparatus described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a terminal or server. The computer-readable storage medium stores one or more programs that are used by one or more processors to execute the reservation order allocation method described in this disclosure.

[0114] Computer storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0115] Corresponding to the above-described method for allocating reservation orders, this disclosure also proposes... Figure 6 The diagram shown is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present disclosure. Please refer to... Figure 6 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it, forming a reservation order allocation device at the logical level. Of course, in addition to software implementation, this disclosure does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0116] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0117] It should be understood that this disclosure is not limited to the precise structures 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 disclosure is limited only by the appended claims.

Claims

1. A method for allocating pre-booked orders, characterized in that, The method includes: Determine the origin and start time of the trip corresponding to the reservation order to be assigned, wherein the start time of the trip is the time when the service provider provides the reservation service; The target time for allocating the reservation order is determined based on the trip origin and trip start time, and the target time is the time when the reservation order allocation operation is initiated. In response to the arrival of the target time, the reservation order is assigned; The step of determining the target time for allocating the reservation order based on the trip origin and trip start time includes: Obtain the pre-trained target model; Target information is obtained based on the trip origin and trip start time. The target information includes reference information of the service request area corresponding to the trip origin within the historical year-on-year preset time period, historical month-on-month preset time period, and real-time preset time period corresponding to the trip start time. The reference information includes total order information, capacity information, order response rate information, order response time information, and / or dynamic price adjustment information. Extract target feature information from the target information; The target feature information is input into the target model to obtain the target time from the output of the target model.

2. The method according to claim 1, characterized in that, The target feature information includes: historical year-on-year feature information, historical month-on-month feature information, and real-time feature information.

3. The method according to claim 2, characterized in that, The historical year-on-year characteristic information includes one or more of the following: The total order volume characteristics of the service request region within the historical year-on-year preset time period; Within the historical year-on-year preset time period, the capacity characteristic information of the service request area; The order response rate characteristic information of the service request area within the historical year-on-year preset time period; Within the historical year-on-year preset time period, the order response time characteristic information of the service request area; The dynamic price adjustment characteristic information of the service request area within the historical year-on-year preset time period; The historical month-on-month characteristic information includes one or more of the following: The total order volume characteristics of the service request region within the historical month-on-month preset time period; Within the historical month-on-month preset period, the capacity characteristic information of the service request area; The order response rate characteristic information of the service request area within the historical month-on-month preset time period; Within the historical month-on-month preset time period, the order response time characteristic information of the service request area; The dynamic pricing characteristic information of the service request area within the historical month-on-month preset time period; The real-time feature information includes one or more of the following: The total order volume characteristics of the service request area within the real-time preset time period; Within the real-time preset time period, the capacity characteristic information of the service request area; Within the real-time preset time period, the order response rate characteristic information of the service request area; Within the real-time preset time period, the order response duration characteristic information of the service request area; The dynamic pricing characteristic information of the service request area within the real-time preset time period.

4. A device for distributing pre-orders, characterized in that, The device includes: The first determining unit is configured to determine the trip origin and trip start time corresponding to the reservation order to be assigned; The second determining unit is configured to determine a target time for allocating the reservation order based on the trip origin and the trip start time, wherein the target time is the time when the reservation order allocation operation is initiated. An allocation unit is configured to allocate the reservation order in response to the arrival of the target time, wherein the trip start time is the time when the service provider offers the reservation service; The second determining unit includes: The sub-unit is configured to acquire a pre-trained target model. The information acquisition subunit is configured to acquire target information based on the trip origin and trip start time. The target information includes reference information of the service request area corresponding to the trip origin within the historical year-on-year preset time period, historical month-on-month preset time period, and real-time preset time period corresponding to the trip start time. The reference information includes total order information, capacity information, order response rate information, order response time information, and / or dynamic price adjustment information. The extraction subunit is configured to extract target feature information from the target information; The input subunit is configured to input the target feature information into the target model to obtain the target time from the output of the target model.

5. The apparatus according to claim 4, characterized in that, The target feature information includes: historical year-on-year feature information, historical month-on-month feature information, and real-time feature information.

6. The apparatus according to claim 5, characterized in that, The historical year-on-year characteristic information includes one or more of the following: The total order volume characteristics of the service request region within the historical year-on-year preset time period; Within the historical year-on-year preset time period, the capacity characteristic information of the service request area; The order response rate characteristic information of the service request area within the historical year-on-year preset time period; Within the historical year-on-year preset time period, the order response time characteristic information of the service request area; The dynamic price adjustment characteristic information of the service request area within the historical year-on-year preset time period; The historical month-on-month characteristic information includes one or more of the following: The total order volume characteristics of the service request region within the historical month-on-month preset time period; Within the historical month-on-month preset period, the capacity characteristic information of the service request area; The order response rate characteristic information of the service request area within the historical month-on-month preset time period; Within the historical month-on-month preset time period, the order response time characteristic information of the service request area; The dynamic pricing characteristic information of the service request area within the historical month-on-month preset time period; The real-time feature information includes one or more of the following: The total order volume characteristics of the service request area within the real-time preset time period; Within the real-time preset time period, the capacity characteristic information of the service request area; Within the real-time preset time period, the order response rate characteristic information of the service request area; Within the real-time preset time period, the order response duration characteristic information of the service request area; The dynamic pricing characteristic information of the service request area within the real-time preset time period.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the reservation order allocation method according to any one of claims 1-3.

8. An electronic 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 program, it implements the method of any one of claims 1-3.

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