Order generation method and device, equipment and medium

Abstract

The invention discloses an order generation method and device, equipment and a medium, and the method comprises the steps: determining a current scene of a current order when a pricing request of the current order is received; order features of the current order and scene features corresponding to the current scene are obtained, and a preset order resource consumption value and a preset order resource consumption coefficient corresponding to the current scene are determined; determining a recommended order resource consumption value according to an association relationship among the first order resource consumption value, the second order resource consumption value, a preset order resource consumption value, the mileage and a preset order resource consumption coefficient; generating a new order based on the recommended order resource consumption value; according to the method, the recommended order resource consumption values and the recommended order resource consumption values corresponding to different scenes are generated according to the scene features and the order features, so that dynamic calculation of the order resource consumption is realized, the recommended order resource consumption is more reasonable, and the order pairing success rate is finally improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to an order generation method, apparatus, device, and medium. Background Art

[0002] In the logistics industry, in order to improve the response rate of drivers to freight orders, there is currently a mechanism where users can actively pay additional fees (tips) to drivers to increase the attractiveness of orders. Existing price recommendations often aim to maximize revenue, thus recommending overly high price increases to users, resulting in a low order matching rate. Summary of the Invention

[0003] In view of the above-mentioned drawbacks of the prior art, this application provides an order generation method, apparatus, device, and medium to solve the defects in the prior art.

[0004] To achieve the above and other objectives, this application provides an order generation method, which includes:

[0005] When receiving a price increase request for the current order, determine the current scenario of the current order;

[0006] Obtain the order characteristics of the current order and the scenario characteristics corresponding to the current scenario, where the order characteristics include the first order resource consumption value and the second order resource consumption value, and the scenario characteristics include mileage;

[0007] Determine the preset order resource consumption value and the preset order resource consumption coefficient corresponding to the current scenario; wherein, the preset order resource consumption value is generated based on the order characteristics of historical orders and the scenario characteristics of the historical scenarios corresponding to the historical orders, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within a historical time period;

[0008] Determine the recommended order resource consumption value according to the association relationship among the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient;

[0009] Generate a new order based on the recommended order resource consumption value.

[0010] In an embodiment of the present invention, determining the preset order resource consumption value includes:

[0011] Using the first order resource consumption value of historical orders, the scenario characteristics of historical scenarios, and the matching rate corresponding to historical orders as training samples, establish a matching rate model;

[0012] Predict the matching rates corresponding to different scenarios based on the matching rate model;

[0013] Based on a pre - established first association relationship, with the goal of maximizing the matching rate, determine the preset order resource consumption value through integer programming. The first association relationship represents the association between the matching rate and the preset order resource consumption value.

[0014] In an embodiment of the present invention, establishing the first association relationship includes:

[0015] Determine the objective function according to the total number of orders, the monthly growth rate of the matching order volume in the city, the monthly growth rate of the matching order volume in the city line, the first weight value of the monthly growth rate of the matching order volume in the corresponding city, the second weight value of the monthly growth rate of the matching order volume in the corresponding city line, and the matching rate model;

[0016] Determine the constraint conditions according to the first minimum order resource consumption value and the first maximum order resource consumption value determined based on the mileage in the scenario features, and the second minimum order resource consumption value and the second maximum order resource consumption value determined based on the vehicle type features in the scenario features;

[0017] Establish the first association relationship based on the objective function and the constraint conditions.

[0018] In an embodiment of the present invention, determining the preset order resource consumption coefficient includes:

[0019] Obtain the orders with successful price increases within a preset historical time period;

[0020] Estimate the probability density distribution of the orders with successful price increases, and determine the preset order resource consumption coefficient according to the maximum value of the probability density distribution.

[0021] In an embodiment of the present invention, the method further includes:

[0022] Push a recommended list to the user side. The recommended list at least includes the recommended order resource consumption value. When the recommended list further includes other order resource consumption values, the recommended order resource consumption value is used as the first recommended object in the recommended list. The recommended order resource consumption value and other order resource consumption values are arranged in an arithmetic - increasing manner.

[0023] In an embodiment of the present invention, obtaining the scenario corresponding to the current order includes:

[0024] Determine the scenario corresponding to the current order according to the scenario features;

[0025] The scenario features include at least one of the following: vehicle type features, city line features, mileage, and the real - time response rate feature of the origin flexible grid O.

[0026] In one embodiment of the present invention, determining the recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient includes:

[0027] When the first order resource consumption value is less than the preset order resource consumption value, using the difference between the preset order resource consumption value and the first order resource consumption value multiplied by the mileage as the recommended order resource consumption value;

[0028] When the first order resource consumption value is greater than or equal to the preset order resource consumption value, using the second order resource consumption value multiplied by the preset order resource consumption coefficient as the recommended order resource consumption value.

[0029] To achieve the above object and other objects, the present application provides an order generation device, and the order generation device includes:

[0030] A scenario determination module, configured to determine the current scenario of the current order when receiving a price increase request for the current order;

[0031] A feature determination module, configured to obtain the order features of the current order and the scenario features corresponding to the current scenario, where the order features include the first order resource consumption value and the second order resource consumption value, and the scenario features include the mileage;

[0032] A first order resource consumption value determination module, configured to determine the preset order resource consumption value and the preset order resource consumption coefficient corresponding to the current scenario; wherein, the preset order resource consumption value is generated based on the scenario features of the historical scenario, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within the historical time period;

[0033] A second order resource consumption value determination module, configured to determine the recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient;

[0034] An order generation module, configured to generate a new order based on the recommended order resource consumption value.

[0035] To achieve the above object and other objects, the present application provides an order generation device, including:

[0036] One or more processors; and

[0037] A memory, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enabling the memory to implement the order generation method.

[0038] To achieve the above and other objectives, the present application provides a machine-readable medium storing instructions that, when executed by one or more processors, cause the processors to execute the order generation method described above.

[0039] Advantages of the present application:

[0040] An order generation method of the present application includes: when a price increase request for a current order is received, determining the current scenario of the current order; obtaining the order characteristics of the current order and the scenario characteristics corresponding to the current scenario, where the order characteristics include a first order resource consumption value and a second order resource consumption value, and the scenario characteristics include mileage; determining the preset order resource consumption value and the preset order resource consumption coefficient corresponding to the current scenario; wherein, the preset order resource consumption value is generated based on the order characteristics of historical orders and the scenario characteristics of the historical scenarios corresponding to the historical orders, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within a historical time period; determining a recommended order resource consumption value according to the correlation between the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient; generating a new order based on the recommended order resource consumption value; the present application generates a recommended order resource consumption value based on scenario characteristics and order characteristics, and different scenarios correspond to different recommended order resource consumption values, realizing the dynamic calculation of order resource consumption, making the recommended order resource consumption more reasonable, and ultimately improving the order matching success rate.

[0041] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. In the drawings:

[0043] Figure 1 Schematic diagram of the implementation environment of the order generation method shown in an exemplary embodiment of the present application;

[0044] Figure 2 Principle block diagram of the order generation method in an embodiment of the present application;

[0045] Figure 3 Flowchart of determining the preset order resource consumption value in an embodiment of the present application;

[0046] Figure 4Flowchart of establishing the first association relationship according to an embodiment of the present application;

[0047] Figure 5 Block diagram of an order generation device shown in an embodiment of the present application.

[0048] Figure 6 Schematic structural diagram of a computer system of a memory suitable for implementing embodiments of the present application. Detailed implementation manners

[0049] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0050] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0051] Although terms such as "first", "second", "A", and "B" can be used herein to describe various elements, these elements should not be limited by these terms and are only used to distinguish one element from another. For example, without departing from the scope of the following technology, the first element can be called the second element, and similarly, the second element can be called the first element. The term "and / or" includes combinations of multiple related items or any item among multiple related items.

[0052] As used herein, unless the context indicates otherwise, the singular form is also intended to include the plural form. It will be understood that the term "comprising" means the presence of the described features, quantities, steps, operations, elements, or combinations thereof, but does not exclude the presence or addition of one or more other features, quantities, steps, operations, elements, components, or combinations thereof.

[0053] Before the detailed description, it is intended to clarify that the division of components in this specification is only based on the main functions of each component. That is, two or more of the components described below may be combined into one component, or may be divided into two or more components according to more detailed functions. In addition to the main functions of the components, each of the components described below may also perform some or all of the functions of other components, and some of the main functions of each component may be specifically performed by other components.

[0054] Figure 1 is a schematic diagram of an order generation implementation environment according to an embodiment of the present application. Please refer to Figure 1 In this implementation environment, there are a client 110 and a server 120. The client 110 and the server 120 communicate with each other through a wireless network. The client sends a price increase request corresponding to the current order to the server based on the current order. When the server receives the price increase request for the current order, it determines the current scenario of the current order and obtains the scenario features corresponding to the current scenario. The scenario features include the first order resource consumption value, the second order resource consumption value, and the mileage; it determines the preset order resource consumption value and the preset order resource consumption coefficient corresponding to the current scenario; wherein, the preset order resource consumption value is generated based on the scenario features of the historical scenario, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within the historical time period; it determines the recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient; and generates a new order based on the recommended order resource consumption value. When the server determines the recommended order resource consumption, it compares the size of the first order resource consumption value with the preset order resource consumption value. When the first order resource consumption value is less than the preset order resource consumption value, the difference between the preset order resource consumption value and the first order resource consumption value is multiplied by the mileage as the recommended order resource consumption value; when the first order resource consumption value is greater than or equal to the preset order resource consumption value, the second order resource consumption value is multiplied by the preset order resource consumption coefficient as the recommended order resource consumption value. The present application combines the scenario features and the order features to generate the recommended order resource consumption value and the preset order resource consumption coefficient. When the first order resource consumption value is less than the preset order resource consumption value, it guides the user to increase the first order resource consumption value to the preset order resource consumption value; when the first order resource consumption value is greater than or equal to the preset order resource consumption value, it calculates the price increase amount through the preset order resource consumption coefficient; by adopting the above calculation method of the price increase amount, the dynamic calculation of the price increase amount is realized, making the recommended price increase amount more reasonable, and ultimately improving the matching rate.

[0055] It should be understood that Figure 1The numbers of the client 110 and the server 120 in it are only illustrative. According to actual needs, there can be any number of clients 110 and servers 120.

[0056] Among them, the client 110 can be any electronic device with a user input interface, including but not limited to smartphones, tablets, laptop computers, computers, in-vehicle computers, etc. Among them, the user input interface includes but not limited to touch screens, keyboards, physical buttons, audio pickup devices, etc.

[0057] Among them, the server 120 can be a server that provides various services. It can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. This is not limited here.

[0058] The client 110 can communicate with the server 120 through wireless networks such as 3G (third-generation mobile information technology), 4G (fourth-generation mobile information technology), 5G (fifth-generation mobile information technology), etc. This is not limited here either.

[0059] Embodiments of the present application respectively propose an order generation method, an order generation device, an order generation device, and a computer-readable storage medium. These embodiments will be described in detail below.

[0060] Please refer to Figure 2 , Figure 2 which is a flowchart of an order generation method according to an embodiment of the present application. Specifically, as shown in Figure 2 The order generation method at least includes steps S210 - step S250:

[0061] Step S210, when receiving a price increase request for the current order, determine the current scenario of the current order;

[0062] In the logistics industry, in order to improve the driver's response rate to freight orders, there is a mechanism where users can actively pay additional fees (tips) to drivers to increase the attractiveness of orders. In this case, the user sends a price increase request to the server through the client, the server receives the price increase request, and then determines the current scenario corresponding to the current order.

[0063] Step S220, obtain the order characteristics of the current order and the scenario characteristics corresponding to the current scenario, where the order characteristics include a first order resource consumption value and a second order resource consumption value, and the scenario characteristics include mileage;

[0064] After determining the current scenario of the current order, feature extraction is performed on the order features and scenario features to obtain scenario features and order features. The first order resource consumption value refers to the transportation cost that the user needs to pay the driver for each kilometer the vehicle travels when transporting the user's goods; the second order resource consumption value refers to the transportation cost that the user needs to pay the driver for the driver to complete the current order; the mileage refers to the distance that the vehicle needs to travel to complete the current order. When the server receives the user's price increase request, it determines the scenario corresponding to the user's current order, and performs subsequent calculation of the recommended order resource consumption value under the scenario corresponding to the current order.

[0065] Step S230, determining the preset order resource consumption value and the preset order resource consumption coefficient corresponding to the current scenario; wherein, the preset order resource consumption value is generated based on the order features of historical orders and the scenario features of the historical scenarios corresponding to the historical orders, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within a historical time period;

[0066] It should be noted that the preset order resource consumption values and the preset order resource consumption coefficients corresponding to different scenarios can be pre-stored in the database, that is, the corresponding relationship between the scenario and the preset order resource consumption value and the corresponding relationship between the scenario and the preset order resource consumption coefficient are stored in the database. When calculating the recommended order resource consumption value, first determine the current scenario corresponding to the current order, and then call the preset order resource consumption coefficient corresponding to the current scenario in the database based on the current scenario and the corresponding relationship between the scenario and the preset order resource consumption value, and call the preset order resource consumption coefficient corresponding to the current scenario in the database based on the current scenario and the corresponding relationship between the scenario and the preset order resource consumption coefficient.

[0067] It should be noted that the preset order resource consumption value is determined and stored in the database in advance. Please refer to Figure 3 , Figure 3 is a flowchart for determining the preset order resource consumption value in an embodiment of the present application. In Figure 3 it, determining the preset order resource consumption value corresponding to the current scenario includes:

[0068] Step S310, using the first order resource consumption value of the historical order, the scenario features of the historical scenario, and the matching rate corresponding to the historical order as training samples to establish a matching rate model;

[0069] In an embodiment, the matching rate model can adopt a model based on the S-Learner framework, which is a causal inference model (based on causal forest). For a given scenario and order information (price), it can predict the matching rate of the order, and determine whether to match according to the matching rate (return 1 if the match is successful, return 0 if the match fails).

[0070] The matching rate model is trained based on training samples, where the training samples include the first order resource consumption value of historical orders, the scenario features of historical scenarios, and the matching rate corresponding to historical orders; the first order resource consumption value of historical orders and the scenario features of historical scenarios are used as inputs, and the matching rate corresponding to historical orders is used as the output.

[0071] It can be understood that the specific implementation process of training the matching rate model based on training samples is as follows: Obtain training samples, including the first order resource consumption value, scenario features, and matching rate. The first order resource consumption value and scenario features are used as model input parameters, and the matching rate is used as the model output parameter. Among them, the scenario features include at least one of the following: vehicle type features, city line features, mileage, and the real-time response rate feature of the origin flexible grid O; input the model input parameters into the matching rate model to obtain the matching rate; calculate the cross-entropy between the matching rate and the actual matching probability to obtain the loss function of the order matching rate model; if the loss function of the matching rate model converges, it is determined that the training of the matching rate model is completed; if the loss function of the matching rate model does not converge, adjust the parameters of the matching rate model, and return to execute the step of inputting the model input parameters into the matching rate model to obtain the matching probability until the loss function of the matching rate model converges.

[0072] Step S320, predicting the matching rates corresponding to different scenarios based on the matching rate model;

[0073] Step S330, based on the pre-established first association relationship, with the goal of maximizing the matching rate, determine the preset order resource consumption value through integer programming, where the first association relationship represents the association relationship between the matching rate and the preset order resource consumption value.

[0074] Please refer to Figure 4 , Figure 4 which is the flowchart of establishing the first association relationship in an embodiment of this application. In Figure 4 it, pre-establishing the first association relationship includes:

[0075] Step S410, determining the objective function according to the total order volume, the monthly growth rate of the matching order volume in the city, the monthly growth rate of the matching order volume in the city line, the first weight value of the monthly growth rate of the matching order volume in the corresponding city, the second weight value of the monthly growth rate of the matching order volume in the corresponding city line, and the matching rate model;

[0076] When determining the objective function, obtain the total order volume N, the monthly growth rate of the matching order volume in the city g city (x i ), the monthly growth rate of the matching order volume in the city line g cityline (x i ), the first weight value w of the monthly growth rate of the matching order volume in the corresponding city high, the second weight value w of the monthly growth rate of the paired order volume corresponding to the city line low , a pairing rate model, and then establish an objective function. Specifically, the objective function is expressed as:

[0077]

[0078] where N represents the total order volume, and x i represents the scenario feature, represents the first order resource consumption value corresponding to the scenario feature x i ; represents the pairing rate model; g city (x i ) represents the monthly growth rate of the paired order volume of the corresponding city for the scenario feature x i ; g cityline (x i ) represents the monthly growth rate of the paired order volume of the corresponding city line for the scenario feature x i ; w high and w low represent preset weight values.

[0079] Step S420, determine the constraint conditions according to the first minimum order resource consumption value and the first maximum order resource consumption value determined based on the mileage in the scenario features, and the second minimum order resource consumption value and the second maximum order resource consumption value determined based on the vehicle type features in the scenario features;

[0080] When determining the constraint conditions, obtain the first minimum order resource consumption value determined based on the mileage the first maximum order resource consumption value determined based on the mileage the first minimum order resource consumption value determined based on the vehicle type features and the first maximum order resource consumption value determined based on the vehicle type features Then, based on the first minimum order resource consumption value the first maximum order resource consumption value the second minimum order resource consumption value and the second maximum order resource consumption value to determine the constraint conditions. Specifically, the constraint conditions are expressed as:

[0081]

[0082] When performing scenario division, the mileage will be segmented, and a first minimum order resource consumption value and a first maximum order resource consumption value will be set within this mileage segment; at the same time, a first minimum order resource consumption value and a first maximum order resource consumption value will also be set according to the vehicle type, represents the first minimum order resource consumption value within a certain mileage segment, represents the first minimum order resource consumption value corresponding to a certain model or certain models, represents the first maximum order resource consumption value within a certain mileage segment, represents the first maximum order resource consumption value corresponding to a certain model or certain models.

[0083] Step S430, determine the first association relationship based on the objective function and the constraint conditions.

[0084]

[0085] subject

[0086] In one embodiment, determining the preset order resource consumption coefficient includes: obtaining the successful markup orders within the preset historical time period; estimating the probability density distribution of the successful markup orders, and determining the preset order resource consumption coefficient according to the maximum value of the probability density distribution.

[0087] Specifically, in each scenario, in order to remove the limitation of the prior assumption of the parameter method, the Gaussian kernel density estimation method with higher accuracy in the non - parameter method is used to fit the probability density distribution of the successful markup orders within the preset historical time period. For the probability density distribution, the efficient Newton method is used to obtain the maximum value point of the distribution as the preset order resource consumption coefficient.

[0088] Step S240, determine the recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient;

[0089] In one embodiment, when the first order resource consumption value is less than the preset order resource consumption value, determining the recommended markup amount according to the reference first order resource consumption value, the first order resource consumption value and the mileage includes: using the difference between the reference first order resource consumption value and the first order resource consumption value of the current order multiplied by the mileage as the recommended markup amount; when the first order resource consumption value is greater than or equal to the reference first order resource consumption value, determining the recommended markup amount according to the second order resource consumption value of the current order and the preset recommended markup coefficient includes: using the second order resource consumption value of the current order multiplied by the preset recommended markup coefficient as the recommended markup amount.

[0090] Step S250, generate a new order based on the recommended order resource consumption value.

[0091] After generating the recommended order resource consumption value, a new order can be generated by replacing the order resource consumption value of the original order with the recommended order resource consumption value.

[0092] In one embodiment, obtaining the scenario corresponding to the current order includes:

[0093] Determining the scenario corresponding to the current order according to the scenario features; the scenario features include at least one of the following: vehicle type, city line, mileage, and real-time response rate of the starting flexible grid O.

[0094] When dividing the scenarios, divide them according to the scenario features in the historical time period, that is, divide the vehicle type, city line, mileage, and real-time response rate of the orders in the past 14 days into buckets to divide fine-grained scenarios. For example, classify the vehicle type and city line respectively, segment the mileage and real-time response rate of the starting flexible grid O, and then combine the classified or segmented scenario features to obtain multiple combinations, and each combination can be divided into a scenario.

[0095] In one embodiment, the method further includes: pushing a recommendation list to the user side, where the recommendation list includes at least the recommended order resource consumption value; when the recommendation list further includes other order resource consumption values, the recommended order resource consumption value is used as the first recommended object in the recommendation list; the recommended order resource consumption value and other order resource consumption values are arranged in an arithmetic progression in an increasing manner.

[0096] It should be noted that if there are multiple recommended order resource consumption values, for example, if there are 4, the recommended order resource consumption value calculated through steps S210 - S240 is used as the first value in the recommendation list, and the remaining 3 recommended order resource consumption values are arranged in an arithmetic progression in the recommendation list, and the difference between adjacent two recommended order resource consumption values is the same as the difference in the manual recommendation price increase list.

[0097] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0098] Figure 5 is a block diagram of an order generation device shown in an embodiment of the present application. This device can be applied to Figure 1 the shown implementation environment and is specifically configured in the server. This device can also be applicable to other exemplary implementation environments and is specifically configured in other devices. The embodiments of the present application do not limit the implementation environment applicable to this device.

[0099] As Figure 5 shown, an order generation device includes:

[0100] A scenario determination module 510, configured to determine the current scenario of the current order when receiving a price increase request for the current order;

[0101] A feature determination module 520, configured to obtain an order feature of a current order and a scene feature corresponding to the current scene, where the order feature includes a first order resource consumption value and a second order resource consumption value, and the scene feature includes mileage;

[0102] A first order resource consumption value determination module 530, configured to determine a preset order resource consumption value and a preset order resource consumption coefficient corresponding to the current scene; wherein, the preset order resource consumption value is generated based on the scene features of historical scenes, and the preset order resource consumption coefficient is generated based on orders with successful price increases within a historical time period;

[0103] A second order resource consumption value determination module 540, configured to determine a recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient;

[0104] An order generation module 550, configured to generate a new order based on the recommended order resource consumption value.

[0105] It should be noted that the order generation device provided in the above embodiment and the order generation method provided in the above embodiment belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiment, and will not be elaborated here. In practical applications, the order generation device provided in the above embodiment may, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0106] An embodiment of the present application further provides an order generation device, including: one or more processors; and a memory, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the memory implements the order generation method in the above embodiment.

[0107] An embodiment of the present application further provides one or more machine-readable media, on which instructions are stored, and when executed by one or more processors, the instructions cause the processors to execute the order generation method in the above embodiment.

[0108] Figure 6 The structural schematic diagram of a computer system of a memory suitable for implementing the embodiment of the present application is shown. It should be noted that Figure 6 The computer system of the memory shown is only an example, and should not bring any limitations to the functions and usage scopes of the embodiments of the present application.

[0109] Such as Figure 6As shown, the computer system includes a Central Processing Unit (CPU), which can perform various appropriate actions and processes according to programs stored in a Read-Only Memory (ROM) or programs loaded from a storage section into a Random Access Memory (RAM), such as executing the methods in the above embodiments. In the RAM, various programs and data required for system operations are also stored. The CPU, ROM, and RAM are connected to each other via a bus. An Input / Output (I / O) interface is also connected to the bus.

[0110] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including, for example, a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc. and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. A removable medium, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive as needed so that a computer program read from it can be installed into the storage section as needed.

[0111] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the order generation method described in the foregoing embodiments. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section, and / or installed from a removable medium. When the computer program is executed by a Central Processing Unit (CPU), various functions defined in the system of the present application are executed.

[0112] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0114] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves.

[0115] Another aspect of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of the computer, the computer is caused to execute the order generation method as described above. The computer-readable storage medium may be included in the memory described in the above embodiments, or may exist separately without being assembled into the memory.

[0116] Another aspect of this application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the order generation method provided in each of the above embodiments.

[0117] The above embodiments are only used to exemplarily illustrate the principles and effects of this application, rather than to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed in this application should still be covered by the claims of this application.

Claims

1. An order generation method, characterized in that: The order generation method comprises: When receiving a price increase request for the current order, determining the current scenario of the current order; Obtaining order features of the current order and scenario features corresponding to the current scenario, wherein the order features include a first order resource consumption value and a second order resource consumption value, and the scenario features include mileage; Determine a preset order resource consumption value and a preset order resource consumption coefficient corresponding to the current scenario; wherein the preset order resource consumption value is generated based on order characteristics of historical orders and scene characteristics of historical scenes corresponding to the historical orders, and the preset order resource consumption coefficient is generated based on orders with successful price increases within a historical time period; Determine a recommended order resource consumption value according to a correlation relationship among the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient; A new order is generated based on the recommended order resource consumption value.

2. The order generation method according to claim 1, characterized in that: Determining the preset order resource consumption value includes: The matching rate model is established by taking the first order resource consumption value of historical orders, the scene characteristics of historical scenes and the matching rates corresponding to historical orders as training samples; The matching rates corresponding to different scenarios are predicted based on the matching rate model; Based on a pre-established first association relationship, the preset order resource consumption value is determined by an integer programming method with the goal of maximizing the matching rate, wherein the first association relationship represents an association relationship between the matching rate and the preset order resource consumption value.

3. The order generation method according to claim 2, characterized in that: Establishing the first association relationship includes: Determine the objective function based on the total number of orders, the monthly growth rate of paired orders in the city, the monthly growth rate of paired orders in the city line, the first weight value of the monthly growth rate of paired orders in the corresponding city, the second weight value of the monthly growth rate of paired orders in the corresponding city line, and the matching rate model; Determine the constraint condition according to a first minimum order resource consumption value and a first maximum order resource consumption value determined based on the mileage in the scenario characteristics, and a second minimum order resource consumption value and a second maximum order resource consumption value determined based on the vehicle type characteristics in the scenario characteristics; The first association relationship is established based on the objective function and the constraint condition.

4. The order generation method according to claim 1, characterized in that: Determining the preset order resource consumption coefficient includes: Get the successful price increase orders within the preset historical time period; The probability density distribution of the successfully priced orders is estimated, and a preset order resource consumption coefficient is determined according to a maximum value of the probability density distribution.

5. The order generation method according to claim 1, characterized in that: The method further comprises: A recommendation list is pushed to a user terminal, wherein the recommendation list at least includes a recommended order resource consumption value; when the recommendation list also includes other order resource consumption values, the recommended order resource consumption value serves as the first recommendation object of the recommendation list; and the recommended order resource consumption value and other order resource consumption values ​​are arranged in an arithmetically increasing manner.

6. The order generation method according to claim 1, characterized in that: The scenario corresponding to the current order is obtained, including: Determine the scenario corresponding to the current order based on the scenario characteristics; The scene characteristics include at least one of the following: vehicle type characteristics, city line characteristics, mileage, starting point flexible grid O real-time response rate characteristics.

7. The order generation method according to claim 1, characterized in that: The determining of the recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and the preset order resource consumption coefficient includes: When the first order resource consumption value is less than the preset order resource consumption value, the difference between the preset order resource consumption value and the first order resource consumption value multiplied by the mileage is used as the recommended order resource consumption value; When the first order resource consumption value is greater than or equal to the preset order resource consumption value, the second order resource consumption value is multiplied by the preset order resource consumption coefficient as the recommended order resource consumption value.

8. An order generating device, characterized in that: The order generating device comprises: A scenario determination module, for determining a current scenario of a current order upon receiving a price increase request for the current order; A feature determination module, used to obtain order features of a current order and scene features corresponding to a current scene, wherein the order features include a first order resource consumption value and a second order resource consumption value, and the scene features include mileage; A first order resource consumption value determination module is used to determine a preset order resource consumption value and a preset order resource consumption coefficient corresponding to the current scenario; wherein the preset order resource consumption value is generated based on the scenario characteristics of the historical scenario, and the preset order resource consumption coefficient is generated based on the orders with successful price increases within the historical time period; A second order resource consumption value determination module, configured to determine a recommended order resource consumption value according to the first order resource consumption value, the second order resource consumption value, the preset order resource consumption value, the mileage, and a preset order resource consumption coefficient; An order generation module is used to generate a new order based on the recommended order resource consumption value.

9. An order generating device, characterized in that: include: one or more processors; and A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the memory implements the order generation method as described in any one of claims 1 to 7.

10. A machine-readable medium, characterized in that Instructions are stored thereon, which, when executed by one or more processors, enable the processors to execute the order generation method as described in any one of claims 1-7.

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