Optimal dispatch path matching method, device, electronic equipment, and storage medium

Abstract

This application discloses an optimal order dispatch path matching method, apparatus, electronic device, and storage medium. The method includes: classifying objects within a specified group according to designated acceptance habit indicators to obtain target groups corresponding to different order dispatch channels, wherein each order dispatch channel corresponds one-to-one with a target group; determining the target channel influence factor for each order dispatch channel; obtaining the order dispatch order allocated to the target group under different order dispatch channels; and adjusting the initial execution path of the target group based on the target channel influence factor and the order dispatch order to obtain the optimal order dispatch path for the target group. This application solves the technical problem of mechanically simplistic order dispatch patterns and low application efficiency caused by manually setting order dispatch channels, business-channel matching relationships, and order dispatch priorities in related technologies.

Description

Technical Field

[0001] This application relates to the field of big data applications, and more specifically, to an optimal dispatch path matching method, device, electronic equipment, and storage medium. Background Technology

[0002] Telecommunications electronic channels receive millions of visitors daily, involving nearly 50 types of services and dozens of marketing channels. Given such a large number of service types and downstream marketing channels, efficient service processing is crucial. We need to select the optimal dispatch path from all order dispatch routes to maximize resource efficiency.

[0003] The current system's order scheduling method involves binding business rules to the program code. Order dispatch channels, matching relationships between business processes and channels, and order dispatch priorities are manually set; this is accomplished through stored procedures that manually update intermediate data tables. When business rules change, the corresponding code must also be modified synchronously.

[0004] Because telecommunications business rules are often very complex and are constantly being updated and changed, even small rule changes require development, testing, verification, and deployment processes, resulting in a rigid order dispatching model and low application efficiency.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This application provides an optimal order dispatch path matching method, apparatus, electronic device, and storage medium to at least solve the technical problem of mechanical and inefficient order dispatch mode caused by manually setting order dispatch channels, matching relationships between business and channels, order dispatch priorities, etc. in related technologies.

[0007] According to one aspect of the embodiments of this application, an optimal order dispatch path matching method is provided, comprising: classifying each object in a specified group according to a specified acceptance habit index to obtain target groups corresponding to different order dispatch channels, wherein the order dispatch channel corresponds one-to-one with the target group; determining the target channel influence factor corresponding to different order dispatch channels; obtaining the order dispatch order allocated to the target group under different order dispatch channels; adjusting the initial execution path of the target group according to the target channel influence factor and the order dispatch order to obtain the optimal order dispatch path for the target group.

[0008] Optionally, the initial execution path of the target group is adjusted according to the target channel influence factor and the order of dispatch to obtain the optimal order dispatch path for the target group, including: determining the matching degree function according to the target channel influence factor and the order of execution; determining the weight of each object according to the matching degree function; and determining the optimal order dispatch path for the target group according to the weight.

[0009] Optionally, the target channel influencing factors for different order dispatch channels are determined, including: determining the order obstruction influencing factor function for the target group based on the number of unsuccessful pushes due to timeout, the verification results of the opportunity order interface, and whether the order has reached the customer; determining the order triggering speed influencing factor function for the target group based on the order generation time, the opportunity order channel processing time, and the opportunity order contact time with the customer; determining the order factor function based on the number of successfully converted orders and the number of opportunity orders; and determining the target channel influencing factor based on the order obstruction influencing factor function, the order triggering speed influencing factor function, and the order factor function.

[0010] Optionally, before obtaining the order allocation sequence for the target group under different order allocation channels, the process includes: obtaining the initial values ​​of each constant corresponding to the initialization execution path; determining the target channel type corresponding to the target group, obtaining the target position corresponding to the target channel type on the initialization path; and updating the initial values ​​of the constants at the target position according to the target channel type.

[0011] Optionally, before classifying each object in the specified group according to the specified acceptance habit indicators to obtain the target group corresponding to different order dispatch channels, the method further includes: determining the mean and standard deviation of the acceptance habit indicators; at least standardizing the acceptance habit indicators according to the mean and standard deviation to obtain the target acceptance habit indicators; and determining the specified acceptance habit indicators according to the target acceptance habit indicators.

[0012] Optionally, the designated acceptance habit indicators are determined based on the target acceptance habit indicators, including: determining the correlation coefficient between the target acceptance habit indicators and other acceptance habit indicators; constructing a matrix corresponding to the correlation coefficients, and determining the feature values ​​of the target acceptance habit indicators based on the matrix; and selecting the designated acceptance habit indicators from the target acceptance habit indicators based on the magnitude of the feature values.

[0013] Optionally, at least the acceptance habit index is standardized based on the mean and standard deviation to obtain the target acceptance habit index, including: determining the index value corresponding to the acceptance habit index; obtaining the difference between the index value and the mean, and obtaining the target acceptance habit index based on the ratio of the difference to the standard deviation.

[0014] According to another aspect of the embodiments of this application, an optimal order dispatch path matching device is also provided, comprising: a classification module, used to classify each object in a specified group according to a specified acceptance habit index to obtain target groups corresponding to different order dispatch channels, wherein the order dispatch channel corresponds one-to-one with the target group; a determination module, used to determine the target channel influence factor corresponding to different order dispatch channels; an acquisition module, used to acquire the order dispatch order allocated to the target group under different order dispatch channels; and an adjustment module, used to adjust the initial execution path of the target group according to the target channel influence factor and the order dispatch order to obtain the optimal order dispatch path for the target group.

[0015] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any optimal dispatch path matching method.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement any optimal dispatch path matching method.

[0017] In this embodiment, by classifying each object in a specified group according to a specified acceptance habit index, the target groups corresponding to different order dispatch channels are obtained, wherein there is a one-to-one correspondence between the order dispatch channel and the target group; the target channel influence factor corresponding to different order dispatch channels is determined; the order dispatch order allocated to the target group under different order dispatch channels is obtained; the initial execution path of the target group is adjusted according to the target channel influence factor and the order dispatch order to obtain the optimal order dispatch path for the target group, thereby achieving the purpose of automatically planning the order dispatch path, thus realizing the technical effects of saving order dispatch time, optimizing the order dispatch path, and improving order dispatch efficiency. In addition, it solves the technical problem of mechanical and single order dispatch mode and low application efficiency caused by manually setting order dispatch channels, business and channel matching relationship, order dispatch priority, etc. in related technologies. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a flowchart illustrating an optional optimal dispatch path matching method according to an embodiment of this application.

[0020] Figure 2 This is a flowchart of the intelligent scheduling algorithm for business opportunity orders in this embodiment;

[0021] Figure 3This is a diagram showing a list of indicators related to acceptance habits across various channels;

[0022] Figure 4 This is a schematic diagram of the order dispatch path adjustment process;

[0023] Figure 5 This is a schematic diagram of the model optimization path;

[0024] Figure 6 This is a schematic diagram of an optional optimal dispatch path matching device according to an embodiment of this application;

[0025] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of this application is shown. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of this application, an embodiment of an optimal dispatch path matching method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is the optimal dispatch path matching method according to the embodiments of this application, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Classify each object in the specified group according to the specified acceptance habit index to obtain the target group corresponding to different order dispatch channels, wherein the order dispatch channel corresponds one-to-one with the target group.

[0031] Step S104: Determine the target channel influencing factors corresponding to different order dispatch channels;

[0032] Step S106: Obtain the order of order allocation for the target group under different order allocation channels;

[0033] Step S108: Adjust the initial execution path of the target group according to the target channel influence factor and order dispatching order to obtain the optimal order dispatching path for the target group.

[0034] This optimal dispatch path matching method classifies objects within a specified group based on designated acceptance habit indicators to obtain target groups corresponding to different dispatch channels, where each dispatch channel corresponds one-to-one with a target group. It determines the target channel influence factor for each dispatch channel, obtains the dispatch order allocated to the target group under different dispatch channels, and adjusts the initial execution path of the target group based on the target channel influence factor and dispatch order to obtain the optimal dispatch path for the target group. This achieves the goal of automatically planning dispatch paths, thereby saving dispatch time, optimizing dispatch paths, and improving dispatch efficiency. Furthermore, it solves the technical problem of mechanically and inefficiently using dispatch modes caused by manually setting dispatch channels, business and channel matching relationships, and dispatch priorities in related technologies.

[0035] In some optional embodiments of this application, the initial execution path of the target group is adjusted according to the target channel influence factor and the order of dispatch to obtain the optimal order dispatch path of the target group. This can be achieved in the following way: Specifically, a matching degree function is determined according to the target channel influence factor and the order of execution; the weight of each object is determined according to the matching degree function; and the optimal order dispatch path of the target group is determined according to the weight.

[0036] Specifically, determining the target channel influencing factors for different order dispatch channels can be achieved through the following steps: First, determine the order obstruction influencing factor function for the target group based on the number of unsuccessful pushes due to timeouts, the results of the opportunity order interface verification, and whether the order reached the customer. Second, determine the order triggering speed influencing factor function for the target group based on the order generation time, the opportunity order channel processing time, and the opportunity order contact time with the customer. Third, determine the order factor function based on the number of successfully converted orders and the number of opportunity orders. Finally, determine the target channel influencing factor based on the order obstruction influencing factor function, the order triggering speed influencing factor function, and the order factor function.

[0037] In some optional embodiments of this application, before obtaining the order allocation order for the target group under different order allocation channels, the initial values ​​of each constant corresponding to the initialization execution path can be obtained; the target channel type corresponding to the target group can be determined, and the target position corresponding to the target channel type on the initialization path can be obtained; the initial values ​​of the constants at the target position can be updated according to the target channel type.

[0038] In some embodiments of this application, before classifying each object in a specified group according to a specified acceptance habit index to obtain the target group corresponding to different order dispatch channels, the average value and standard deviation of the acceptance habit index can be determined; at least the acceptance habit index is standardized according to the average value and standard deviation to obtain the target acceptance habit index; and the specified acceptance habit index is determined according to the target acceptance habit index.

[0039] In some embodiments of this application, determining a designated acceptance habit indicator based on a target acceptance habit indicator can involve determining the correlation coefficient between the target acceptance habit indicator and other acceptance habit indicators; constructing a matrix corresponding to the correlation coefficient; determining the feature value of the target acceptance habit indicator based on the matrix; and selecting the designated acceptance habit indicator from the target acceptance habit indicator based on the magnitude of the feature value.

[0040] As an optional implementation method, the acceptance habit index is standardized based on the mean and standard deviation to obtain the target acceptance habit index, which can determine the index value corresponding to the acceptance habit index; the difference between the index value and the mean is obtained, and the target acceptance habit index is obtained based on the ratio of the difference to the standard deviation.

[0041] The above technical solutions of the embodiments of this application will now be described with reference to a specific example, as follows:

[0042] Figure 2 This is a flowchart of the intelligent scheduling algorithm for business opportunity orders in this embodiment, as follows: Figure 2 As shown, the algorithm mainly includes: user channel acceptance preference grouping, setting initialization path, and then adjusting the order dispatch path to optimize the model.

[0043] Specifically, the user segmentation setting initialization path can be: (1) Through business scenario analysis, find out the user's acceptance habit indicators in the existing downstream order dispatch system and segment the users.

[0044] ① Identify relevant indicators of user acceptance habits

[0045] Let A, B, and C be channels, and there be a total of p metrics. The metrics for each channel are represented as follows:

[0046] A_X={AI_X I AI_X2, AI_X3, ..., AI_X P},

[0047] B_X={ZW_X I ,ZW_X2,ZW_X3,……,ZW_X P},

[0048] C_X={WX_X I ,WX_X2,WX_X3,……,WX_X P},

[0049] X = {A_X,B_X,C_X} Figure 3 This is a list of indicators related to acceptance habits across various channels, such as... Figure 3 As shown.

[0050] ② Principal Component Analysis for User Segmentation

[0051] Based on the obtained user acceptance habit indicators X, the principal components of each user's acceptance habits are calculated. First, the data is standardized and dimensionality reduced to generate a component list. The component with the highest ranking is then selected for user segmentation.

[0052] a. Standardization processing

[0053] Each indicator is standardized individually so that the mean of each indicator is 0 and the variance is 1, thereby minimizing differences in the dimensions and orders of magnitude of the indicator values. The calculation formula is as follows:

[0054]

[0055] j = 1, 2, 3, ..., p-1,

[0056] M = A, B, C

[0057] X ij(M) Let be the value of the j-th indicator derived from M in the i-th sample; Let be the average value of the j-th indicator derived from M across all samples; Let be the standard deviation of the j-th indicator derived from M across all samples.

[0058] b. Calculate the correlation coefficient

[0059] VARIABLE X i(M) With variable X j(M) Correlation coefficient r ij(M) Calculation formula:

[0060]

[0061] In the formula Let be the average value of the i-th indicator derived from M.

[0062] c. Calculate the eigenvectors

[0063] Let the feature value derived from the i-th index of M be λi(M), and let |R-λ_(1(M))|=0, |R-λ_(2(M))|=0, …, |R-λ_(p(M))|=0, that is:

[0064]

[0065]

[0066] Find the eigenvalues ​​λi and corresponding eigenvectors μi (i = 1, 2, ..., p) for each indicator, and arrange the eigenvalues ​​in descending order.

[0067] d. Principal component grouping

[0068] From (λ_(j(M))ER)yi=0, we can obtain the principal component yi, and the variance contribution rate of the principal component yi is:

[0069]

[0070] In the formula, the representativeness of yi for information x1, x2, ..., xp is proportional to the value of εi.

[0071] Using cluster analysis, users with similar principal components are grouped into groups. The principal component A_y is for customers who are more mobile phone-oriented, the principal component B_y is for customers who are more WeChat-oriented, and the principal component C_y is for customers who are more outbound call-oriented.

[0072] (2) Set the initial dispatch path

[0073] The initialization execution path is set to seq0(a,b,c,d,f). The seq0 of each category of people is set to a constant initial value that meets the requirements. Customers who are biased towards channel A are given an absolute advantage in channel A, customers who are biased towards channel B are given an absolute advantage in channel B, and customers who are biased towards channel C are given an absolute advantage in channel C. The A group, B group, etc. obtained by the clustering method can be assigned different initialization execution paths based on the distance from the cluster center of the customer group with the clear channel advantage.

[0074] 2. Adjust the order dispatch path

[0075] The most direct factor in evaluating order dispatch results is conversion rate. However, in practice, different business units have different requirements for the timeliness of order dispatch, and environmental factors can also affect marketing results. Here, nine environmental factors are defined, including the number of times orders timed out and failed to be pushed, the time when opportunity orders are generated, the channels to which orders are dispatched, whether orders are converted, the verification results of opportunity orders, the time it takes for opportunity orders to reach customers, the order dispatch quota for each channel, whether orders reach customers, and the channel processing time for opportunity orders. Three factors affecting the environment are calculated: order obstruction impact factor, order reach speed impact factor, and order conversion factor.

[0076] (1) Calculate the impact of channel environment on order dispatch.

[0077] Let X i It is one of all the orders in the Ω order team;

[0078] N: Number of business opportunity orders;

[0079] seq ij The execution order of order i in channel j.

[0080] i = 1, 2, ..., n,

[0081] j = 1, 2, ..., 5

[0082] If order i is assigned to channel j and fails to push successfully within timeout, the number of times o ij(t＝0) Business opportunity order interface verification result p ij(t＝0) Has the order reached the customer? ij(t＝0) ,

[0083] The function of the impact factor on order disruption for each user group:

[0084]

[0085] If order i is assigned to channel j, the order generation time r ij(t＝1) Business opportunity order channel processing time v ij(t＝1) Business opportunity order contact time d ij(t＝1) ,

[0086] Functional factor influencing order delivery speed for each user group:

[0087]

[0088] Order conversion factor function for each user group

[0089]

[0090] Comprehensive Channel Influence Factor

[0091]

[0092] (2) Adjust the path

[0093] The matching degree function (or influence function) G can be calculated from the most recently dispatched order rk and the original channel execution order seq. λ is assigned a weight manually based on the business scenario, as shown in the following formula:

[0094]

[0095] Figure 4 This is a diagram illustrating the order dispatch path adjustment process, as shown below. Figure 4 As shown, different paths can be assigned to different batches of users.

[0096] Figure 5 This is a schematic diagram of the model optimization path, such as... Figure 5 As shown, the algorithm model is optimized by continuously updating user behavior preference data, downstream system environment data, and order conversion data.

[0097] Figure 6 This is an optimal dispatch path matching device according to an embodiment of this application, such as... Figure 6 As shown, the device includes:

[0098] The classification module 40 is used to classify each object in the specified group according to the specified acceptance habit indicators, so as to obtain the target group corresponding to different order dispatch channels, wherein the order dispatch channel corresponds one-to-one with the target group;

[0099] Module 42 is used to determine the target channel influence factor corresponding to different order dispatch channels;

[0100] The acquisition module 44 is used to acquire the order of order allocation for the target group under different order allocation channels;

[0101] The adjustment module 46 is used to adjust the initialization execution path of the target group according to the target channel influence factor and the order dispatching order, so as to obtain the optimal order dispatching path for the target group.

[0102] In this optimal dispatch path matching device, the classification module 40 is used to classify each object in a specified group according to a specified acceptance habit index to obtain the target group corresponding to different dispatch channels, wherein the dispatch channel and the target group are in one-to-one correspondence; the determination module 42 is used to determine the target channel influence factor corresponding to different dispatch channels; the acquisition module 44 is used to acquire the dispatch order allocated to the target group under different dispatch channels; and the adjustment module 46 is used to adjust the initial execution path of the target group according to the target channel influence factor and the dispatch order to obtain the optimal dispatch path of the target group, thereby achieving the purpose of automatically planning the dispatch path, thus realizing the technical effects of saving dispatch time, optimizing dispatch path, and improving dispatch efficiency. In addition, it solves the technical problem of mechanical and single dispatch mode and low application efficiency caused by manually setting dispatch channels, business and channel matching relationship, dispatch priority, etc. in related technologies.

[0103] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any optimal dispatch path matching method.

[0104] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0105] Based on the specified acceptance habit indicators, the objects in the specified group are classified to obtain the target groups corresponding to different order dispatch channels. The order dispatch channels correspond one-to-one with the target groups. The target channel influence factors corresponding to different order dispatch channels are determined. The order dispatch order allocated to the target groups under different order dispatch channels is obtained. The initial execution path of the target groups is adjusted according to the target channel influence factors and the order dispatch order to obtain the optimal order dispatch path for the target groups.

[0106] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0107] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the optimal dispatch path matching method described above.

[0108] Optionally, when executed by a processor, the computer program may perform the following steps:

[0109] Based on the specified acceptance habit indicators, the objects in the specified group are classified to obtain the target groups corresponding to different order dispatch channels. The order dispatch channels correspond one-to-one with the target groups. The target channel influence factors corresponding to different order dispatch channels are determined. The order dispatch order allocated to the target groups under different order dispatch channels is obtained. The initial execution path of the target groups is adjusted according to the target channel influence factors and the order dispatch order to obtain the optimal order dispatch path for the target groups.

[0110] An electronic device is provided according to an embodiment of the present application, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described optimal dispatch path matching methods.

[0111] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0112] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0113] like Figure 7 As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 702 or a computer program loaded from storage unit 708 into random access memory (RAM) 703. RAM 703 may also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0114] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0115] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the optimal dispatch path matching method. For example, in some embodiments, the optimal dispatch path matching method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the optimal dispatch path matching method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the optimal dispatch path matching method by any other suitable means (e.g., by means of firmware).

[0116] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0120] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0121] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0122] In the relevant embodiments of this application, by classifying each object in a specified group according to a specified acceptance habit index, the target groups corresponding to different order dispatch channels are obtained, wherein there is a one-to-one correspondence between the order dispatch channel and the target group; the target channel influence factor corresponding to different order dispatch channels is determined; the order dispatch order allocated to the target group under different order dispatch channels is obtained; the initial execution path of the target group is adjusted according to the target channel influence factor and the order dispatch order to obtain the optimal order dispatch path for the target group, thereby achieving the purpose of automatically planning the order dispatch path, thus realizing the technical effects of saving order dispatch time, optimizing the order dispatch path, and improving order dispatch efficiency. In addition, it solves the technical problem of mechanical and single order dispatch mode and low application efficiency caused by manually setting order dispatch channels, business and channel matching relationship, order dispatch priority, etc. in related technologies.

[0123] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0124] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0126] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0128] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0129] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An optimal order routing path matching method, characterized in that, The method comprises the following steps: determining the average value and the standard deviation of the acceptance habit index; and performing standardization processing on the acceptance habit index according to at least the average value and the standard deviation to obtain a target acceptance habit index; determining the correlation coefficient of the target acceptance habit index and other acceptance habit indexes; constructing a matrix corresponding to the correlation coefficient, determining the eigenvalue of the target acceptance habit index according to the matrix; and screening a specified acceptance habit index from the target acceptance habit index according to the size of the eigenvalue. classifying each object in a specified group according to the specified acceptance habit index to obtain a corresponding target group under different order distribution channels, wherein the order distribution channel and the target group are in one-to-one correspondence; determining a target channel influence factor corresponding to the different order distribution channels, wherein an order blocking influence factor function of a target group is determined according to the number of times of timeout and unsuccessful pushing, the opportunity order interface verification result and whether the order reaches the customer; an order triggering speed influence factor function of a target group is determined according to the order generation time, the opportunity order channel processing time and the opportunity order customer contact time; an order factor function is determined according to the number of successfully converted orders and the number of opportunity orders; and the target channel influence factor is determined according to the order blocking influence factor function, the order triggering speed influence factor function and the order factor function; obtaining the distribution order sequence of the target group under different order distribution channels; adjusting the initial execution path of the target group according to the target channel influence factor and the order sequence to obtain the optimal order distribution path of the target group, wherein a matching degree function is determined according to the target channel influence factor and the execution sequence; the weight of each object is determined according to the matching degree function, and the optimal order distribution path of the target group is determined according to the weight.

2. The method of claim 1, wherein, Before obtaining the distribution order sequence of the target group under different order distribution channels, the method further comprises: obtaining each constant initial value corresponding to the initial execution path; determining the target channel type corresponding to the target group, obtaining the target position corresponding to the target channel type on the initial execution path; updating the constant initial value at the target position according to the target channel type.

3. The method of claim 1, wherein, The method for performing standardization processing on the acceptance habit index according to at least the average value and the standard deviation to obtain a target acceptance habit index comprises the following steps: determining the index value corresponding to the acceptance habit index; obtaining the difference value between the index value and the average value, and obtaining the target acceptance habit index according to the ratio of the difference value to the standard deviation.

4. An optimal path matching apparatus, characterized by comprising: The method comprises the following steps: The classification module is configured to determine an average value and a standard deviation of the accepted habit index; perform standardization processing on the accepted habit index to obtain a target accepted habit index according to at least the average value and the standard deviation; determine a correlation coefficient of the target accepted habit index and other accepted habit indexes; construct a matrix corresponding to the correlation coefficient, determine an eigenvalue of the target accepted habit index according to the matrix; filter out a specified accepted habit index from the target accepted habit index according to a size of the eigenvalue; and classify each object in a specified group according to the specified accepted habit index, to obtain a corresponding target group under different order distribution channels, wherein the order distribution channel and the target group are in one-to-one correspondence. The determination module is configured to determine a target channel influence factor corresponding to the different order distribution channels, wherein an order blocking influence factor function of a target group is determined according to a number of times of timeout and unsuccessful pushing, a business order interface verification result, and whether an order reaches a customer; an order triggering speed influence factor function of a target group is determined according to an order generation time, a business order channel processing time, and a business order customer contact time; and an order factor function is determined according to a number of successfully converted orders and a number of business orders; and the target channel influence factor is determined according to the order blocking influence factor function, the order triggering speed influence factor function, and the order factor function. The acquisition module is configured to acquire an order distribution sequence of the target group under different order distribution channels. The adjustment module is configured to adjust an initial execution path of the target group according to the target channel influence factor and the order distribution sequence, to obtain an optimal order distribution path of the target group, wherein a matching degree function is determined according to the target channel influence factor and an executed sequence; a weight of each object is determined according to the matching degree function; and the optimal order distribution path of the target group is determined according to the weight.

5. A non-volatile storage medium, characterized by, The storage medium includes a stored program, wherein the program controls a device in which the storage medium is located to perform the optimal order distribution path matching method in any one of claims 1 to 3 when the program is running.

6. An electronic device, comprising: The device includes: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the optimal order distribution path matching method in any one of claims 1 to 3.

Images