CN111274106B

CN111274106B - Order data analysis method and device and electronic equipment

Info

Publication number: CN111274106B
Application number: CN201811475041.0A
Authority: CN
Inventors: 窦奇伟; 路劲; 郄小虎; 卓呈祥
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2018-12-04
Filing date: 2018-12-04
Publication date: 2023-09-08
Anticipated expiration: 2038-12-04
Also published as: CN111274106A

Abstract

The application provides an order data analysis method, an order data analysis device and electronic equipment, wherein the order data analysis method comprises the following steps: acquiring order data of a target user from an order server, wherein the order data comprises order resources; calculating a resource floating area of the target user according to the order resource; and calculating to obtain the upper limit of the resource matched with the target user according to the resource floating area or/and the order data. By analyzing the order data of the user, the demand condition of the user on the resources can be known, and therefore the demand of the user can be better known.

Description

Order data analysis method and device and electronic equipment

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for analyzing order data, and an electronic device.

Background

Various types of applications are running, and users have also become the main service objects for each application. The order data of the user can be used to represent some habits of the user, but the background server in the prior art only records the order data of the user, and no other processing exists, so that the application situation of the resource cannot be well known.

Disclosure of Invention

Accordingly, an object of the embodiments of the present application is to provide a method, an apparatus, and an electronic device for analyzing order data, which can analyze custom of a user better by analyzing order data of the user.

According to one aspect of the application, an electronic device is provided that may include one or more storage media and one or more processors in communication with the storage media. One or more storage media store machine-readable instructions executable by a processor. When the electronic device is in operation, the processor and the storage medium communicate over the bus, and the processor executes the machine-readable instructions to perform one or more of the following:

acquiring order data of a target user from an order server, wherein the order data comprises order resources;

calculating a resource floating area of the target user according to the order resource;

and calculating to obtain the upper limit of the resource matched with the target user according to the resource floating area or/and the order data.

According to the method provided by the embodiment of the application, the resource floating area of the user can be obtained by analyzing the order data of the user, and the upper limit of the resource which can be matched with the target user can be further calculated according to the resource floating area, so that the order data of the user can be better known, and the degree of understanding of the habit of the user is improved.

In some embodiments, the step of calculating the resource floating area of the target user according to the order resource includes:

calculating an average value and variance of order resources in the order data;

and calculating the resource floating area of the target user according to the average value and the variance.

In some embodiments, the step of calculating a resource floating region of the target user from the mean and the variance comprises:

judging whether the variance is in a first numerical value interval or a second numerical value interval, wherein the right end point of the first numerical value interval is the left end point of the second numerical value interval;

if the variance is in the first numerical value interval, a first resource interval is calculated according to the average value and the variance by using a first calculation mode, and the first resource interval is used as a resource floating area;

and if the variance is in the second numerical range, calculating to obtain a second resource range according to the average value and the variance by using a second calculation mode, wherein the second resource range is in the first resource range, and the second resource range is used as a resource floating region.

In some embodiments, the step of calculating, using a first calculation method, a first resource interval according to the average value and the variance, and using the first resource interval as a resource floating region includes:

Obtaining a first left endpoint from the difference between the average value and the variance of the first multiple;

and obtaining a first right endpoint by the sum of the average value and the variance of the first multiple, wherein the first left endpoint and the first right endpoint form a first resource interval, and the first resource interval is used as a resource floating area.

In some embodiments, the step of calculating, using a second calculation method, a second resource interval according to the average value and the variance, where the second resource interval is within the first resource interval, and the second resource interval is used as a resource floating area includes:

obtaining a second left end point by the difference between the average value and the variance of a second multiple, wherein the second multiple is larger than the first multiple;

and obtaining a second right endpoint by the sum of the mean value and the variance of a second multiple, wherein the second left endpoint and the second right endpoint form a second resource interval, and the second resource interval is used as a resource floating area.

The resource floating region is obtained through the calculation of the average value and the variance of the order resource, the calculation mode is relatively simple, and the possible floating region of the resource can be well embodied.

In some embodiments, the step of calculating the resource floating area of the target user according to the order resource further includes:

Judging whether the variance is in a third numerical value interval or not, wherein the right end point of the second numerical value interval is the left end point of the third numerical value interval;

if yes, calculating to obtain a resource floating area by using a heuristic search algorithm based on the order data.

When the variance of the order resources is larger, the variance may indicate that the corresponding order resources of the user float more, and the resource float area of the target user can be found out by heuristic search, so that the method can be more in line with the resource float range of the user.

In some embodiments, the step of calculating a resource float region using a heuristic search algorithm based on the order data comprises:

obtaining the total order number of the target user and the order number in each search interval according to the order data;

calculating a first interval score corresponding to each search interval by using the total order quantity and the order quantity corresponding to each search interval;

and screening out the search interval with the highest score of the first interval as a resource floating area.

In some embodiments, the calculating the first interval score corresponding to each search interval by using the total number of orders and the number of orders corresponding to each search interval is implemented in the following manner:

wherein ,

b＝1-(m·a)；

wherein score1 _k A first interval score representing a kth search interval; subcnt _k Representing the amount of orders of the target user in the kth search interval; cnt represents the total number of orders of the target user; range1 _k A section length representing a kth search section; up _k 、low _k Representing the upper and lower bounds of the kth search interval; beta represents a piecewise function; m represents the total resourceThe number of cells divided between the source regions; finish (E) _i Representing the amount of orders of the target user among the ith cells; t represents a set magnification; n represents a set section length.

In some embodiments, the step of calculating the upper limit of the resource matched with the target user according to the resource floating area or/and the order data comprises the following steps:

and calculating the upper limit of the resources matched with the target user by using a heuristic search algorithm based on the upper limit of the resource floating region.

In some embodiments, the step of calculating an upper limit of resources matching the target user using a heuristic search algorithm based on the upper limit of the resource floating region includes:

acquiring the completed order quantity and the exploratory order quantity of the target user according to the order data, wherein the exploratory order represents an order which is not successfully placed;

Calculating a second interval score corresponding to each search interval by using the completed amount of orders and the exploratory amount of orders;

and screening out the right endpoint of the search interval with the highest score of the second interval as the upper limit of the resource.

In some embodiments, the calculating the second interval score corresponding to each search interval by using the completed amount of orders and the exploratory amount of orders is implemented in the following manner:

range2 _k ＝k-up_ctg+1；

up_ctg<k≤m；

wherein, subSend _k Indicating that the k-th search interval has completed the amount of orders; subBub _k Represents the kthSearching interval exploratory order quantity; range2 _k Representing the length of the kth search interval; lenSend _k Indicating the number of cells in which orders are completed in the kth search interval; m represents the number of cells divided between total resource intervals; alpha represents an energy storage value; gamma represents the excitation value.

In some embodiments, the stored energy value is calculated by:

if the ratio is subRatio _k-1 >subRatio _k The method comprises the steps of carrying out a first treatment on the surface of the Setting energy+ =1, setting α=1;

if the ratio is subRatio _k-1 <subRatio _k The method comprises the steps of carrying out a first treatment on the surface of the Make the following stepsJuxtaposition energy=1; subRatio _k-1 ＝subRatio _k Let α=1.

In some embodiments, the excitation value is calculated by:

if there is a completed order in the current search interval and γ is not zero, then γ=e, e represents a constant;

If no order has been completed in the current search interval, γ=γ -1 until γ decays to 0; gamma is set to 0.

In some embodiments, the order data includes a completed order and a exploratory order, and if the order data of the target user includes only exploratory orders, the step of calculating a resource upper limit matched with the target user according to the resource floating area or/and the order data includes:

acquiring a resource lower limit of the resource floating region;

and calculating the upper limit of the resources matched with the target user according to the lower limit of the resources.

If only exploratory orders are included, indicating that the target user is not yet engaged in the order, the user's possible habits may be known through some exploratory operations of the user, enabling knowledge of the non-engaged user.

In some embodiments, the method further comprises:

matching a sharing strategy for the target user according to the upper limit of the resource matched by the target user or the resource floating region, wherein the sharing strategy comprises a balance resource ticket;

and sending the sharing strategy to the terminal corresponding to the target user.

The sharing strategy is matched for the user according to the upper limit of the resource or the resource floating region of the user, so that the use requirement of the user can be better met, and the activity of the user on a corresponding platform can be improved.

In another aspect, an embodiment of the present application further provides an order data analysis apparatus, including:

the acquisition module is used for acquiring order data of the target user from the order server, wherein the order data comprises order resources;

the first calculation module is used for calculating a resource floating area of the target user according to the order resource;

and the second calculation module is used for calculating the upper limit of the resource matched with the target user according to the resource floating area or/and the order data.

In some embodiments, the first computing module is further to:

calculating an average value and variance of order resources in the order data;

In some embodiments, the first computing module is further to:

In some embodiments, the second computing module is further to:

wherein ,

b＝1-(m·a)；

wherein score1 _k A first interval score representing a kth search interval; subcnt _k Representing the amount of orders of the target user in the kth search interval; cnt represents the total number of orders of the target user; range1 _k A section length representing a kth search section; up _k 、low _k Representing the upper and lower bounds of the kth search interval; beta represents a piecewise function; m represents the number of cells divided between total resource intervals; finish (E) _i Representing the amount of orders of the target user among the ith cells; t represents a set magnification; n represents a set section length.

In some embodiments, the second computing module is further to:

range2 _k ＝k-up_ctg+1；

up_ctg<k≤m；

wherein, subSend _k Indicating that the k-th search interval has completed the amount of orders; subBub _k Representing the k search interval exploratory order quantity; lenSend _k Indicating the number of cells in which orders are completed in the kth search interval; m represents the number of cells divided between total resource intervals; alpha represents an energy storage value; gamma represents the excitation value.

In some embodiments, the stored energy value is calculated by:

In some embodiments, the excitation value is calculated by:

In some embodiments, the second computing module is further to:

acquiring a resource lower limit of the resource floating region;

In some embodiments, the apparatus further comprises:

In another aspect, embodiments of the present application also provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs the steps of the order data analysis method of any one of the possible embodiments described above.

In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a block diagram showing a data analysis system according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

FIG. 3 is a flow chart illustrating a method for analyzing order data according to an embodiment of the present application;

fig. 4 is a flowchart illustrating a specific method of step S302 in the order data analysis method according to the embodiment of the present application;

fig. 5 shows a schematic structural diagram of an order data analysis device according to an embodiment of the present application.

Illustration of: 100-a data analysis system; 110-a server; 120-network; 130-service request end; 140-a service providing end; 150-a database; 200-an electronic device; 210-network ports; 220-a processor; 230-a communication bus; 240-storage medium; 250-interface; 401-an acquisition module; 402-a first computing module; 403-a second calculation module.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

In order to enable those skilled in the art to use the present disclosure, the following embodiments are presented in connection with a specific application scenario "net car". It will be apparent to those having ordinary skill in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. While the application is primarily described in the context of a net cart, it should be understood that this is but one exemplary embodiment. The application can be applied to any other traffic type. For example, the present application may be applied to different transportation system environments, including land, sea, or air, or the like, or any combination thereof. The transportation means of the transportation system may include taxis, private cars, windmills, buses, trains, bullet trains, high speed railways, subways, ships, airplanes, spacecraft, hot air balloons, or unmanned vehicles, etc., or any combination thereof. The application may also include any service system for order service, e.g. a system for sending and/or receiving express, a service system for trading between buyers and sellers. Applications of the system or method of the present application may include web pages, plug-ins to a browser, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, etc., or any combination thereof.

It should be noted that the term "comprising" will be used in embodiments of the application to indicate the presence of the features stated hereafter, but not to exclude the addition of other features.

One aspect of the application relates to a data analysis system. According to the system, the situation of the use order of the user can be obtained through analysis of the order data of the user, and the adaptation state of the user to the order can be depicted, so that the user can be informed.

Example 1

FIG. 1 is a block diagram of a data analysis system 100 according to some embodiments of the application. For example, the data analysis system 100 may be an online transport services platform for transport services such as taxis, ride-on services, express, carpools, bus services, driver leases, or airliner services, or any combination thereof. The data analysis system 100 may include one or more of a server 110, a network 120, a service requester terminal 130, a service provider terminal 140, and a database 150, and a processor executing instruction operations may be included in the server 110.

In some embodiments, the server 110 may be a single server or a group of servers. The server farm may be centralized or distributed (e.g., server 110 may be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, the server 110 may access information and/or data stored in the service requester terminal 130, the service provider terminal 140, or the database 150, or any combination thereof, via the network 120. As another example, the server 110 may be directly connected to at least one of the service requester terminal 130, the service provider terminal 140, and the database 150 to access stored information and/or data. In some embodiments, server 110 may be implemented on a cloud platform; for example only, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud (community cloud), distributed cloud, inter-cloud (inter-cloud), multi-cloud (multi-cloud), and the like, or any combination thereof. In some embodiments, server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 of the present application.

In some embodiments, server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described in the present application. For example, the processor may determine the target vehicle based on a service request obtained from the service requester terminal 130. In some embodiments, a processor may include one or more processing cores (e.g., a single core processor (S) or a multi-core processor (S)). By way of example only, the Processor may include a central processing unit (Central Processing Unit, CPU), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), special instruction set Processor (Application Specific Instruction-set Processor, ASIP), graphics processing unit (Graphics Processing Unit, GPU), physical processing unit (Physics Processing Unit, PPU), digital signal Processor (Digital Signal Processor, DSP), field programmable gate array (Field Programmable Gate Array, FPGA), programmable logic device (Programmable Logic Device, PLD), controller, microcontroller unit, reduced instruction set computer (Reduced Instruction Set Computing, RISC), microprocessor, or the like, or any combination thereof.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components in the data analysis system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, and the database 150) may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester terminal 130 via the network 120. In some embodiments, network 120 may be any type of wired or wireless network, or a combination thereof. By way of example only, the network 120 may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a public switched telephone network (Public Switched Telephone Network, PSTN), a bluetooth network, a ZigBee network, a near field communication (Near Field Communication, NFC) network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of data analysis system 100 may connect to network 120 to exchange data and/or information.

In some embodiments, the user of the service requester terminal 130 may be a person other than the actual consumer of the service. For example, user a of service requester terminal 130 may use service requester terminal 130 to initiate a service request for service actual requester B (e.g., user a may call his own friend B), or receive service information or instructions from server 110, etc. In some embodiments, the user of the service provider terminal 140 may be the actual service provider or may be a person other than the actual service provider. For example, user C of service provider terminal 140 may use service provider terminal 140 to receive a service request for providing a service by service actual provider D (e.g., user C may pick up for driver D employed by himself), and/or information or instructions from server 110. In some embodiments, "service requester" and "service requester terminal" may be used interchangeably and "service provider" and "service provider terminal" may be used interchangeably.

In some embodiments, the service requester terminal 130 may include a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, or the like, or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, or an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device for a smart appliance device, a smart monitoring device, a smart television, a smart video camera, or an intercom, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, a smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, etc., or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), a gaming device, a navigation device, or a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include various virtual reality products, and the like. In some embodiments, the built-in devices in the motor vehicle may include an on-board computer, an on-board television, and the like. In some embodiments, the service requester terminal 130 may be a device having location technology for locating the location of the service requester and/or service requester terminal.

In some embodiments, the service provider terminal 140 may be a similar or identical device to the service requester terminal 130. In some embodiments, the service provider terminal 140 may be a device with positioning technology for locating the location of the service provider and/or service provider terminal. In some embodiments, the service requester terminal 130 and/or the service provider terminal 140 may communicate with other positioning devices to determine the location of the service requester, the service requester terminal 130, the service provider, or the service provider terminal 140, or any combination thereof. In some embodiments, the service requester terminal 130 and/or the service provider terminal 140 may send the positioning information to the server 110.

Database 150 may store data and/or instructions. In some embodiments, database 150 may store data obtained from service requester terminal 130 and/or service provider terminal 140. In some embodiments, database 150 may store data and/or instructions for the exemplary methods described in the present disclosure. In some embodiments, database 150 may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), or the like, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, magnetic tape, and the like; the volatile read-write memory may include random access memory (Random Access Memory, RAM); the RAM may include dynamic RAM (Dynamic Random Access Memory, DRAM), double data Rate Synchronous dynamic RAM (DDR SDRAM); static Random-Access Memory (SRAM), thyristor RAM (T-RAM) and Zero-capacitor RAM (Zero-RAM), etc. By way of example, ROM may include Mask Read-Only Memory (MROM), programmable ROM (Programmable Read-Only Memory, PROM), erasable programmable ROM (Programmable Erasable Read-Only Memory, PEROM), electrically erasable programmable ROM (Electrically Erasable Programmable Read Only Memory, EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, database 150 may be implemented on a cloud platform. For example only, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, or other similar, or the like, or any combination thereof.

In some embodiments, database 150 may be connected to network 120 to communicate with one or more components in data analysis system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.). One or more components in the data analysis system 100 may access data or instructions stored in the database 150 via the network 120. In some embodiments, database 150 may be directly connected to one or more components in data analysis system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.); alternatively, in some embodiments, database 150 may also be part of server 110.

In some embodiments, one or more components in the data analysis system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.) may have access to the database 150. In some embodiments, one or more components in the data analysis system 100 may read and/or modify information related to a service requester, a service provider, or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information of one or more users after receiving a service request. As another example, the service provider terminal 140 may access information related to the service requester upon receiving a service request from the service requester terminal 130, but the service provider terminal 140 may not modify the related information of the service requester.

In some embodiments, the exchange of information of one or more components in the data analysis system 100 may be accomplished through a request service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or a non-physical product. The tangible product may include a food, a pharmaceutical, a merchandise, a chemical product, an appliance, a garment, an automobile, a house, a luxury item, or the like, or any combination thereof. The non-substance product may include a service product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include a host product alone, a web product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The internet product may be used in software, a program, a system, etc. of the mobile terminal, or any combination thereof. The mobile terminal may include a tablet computer, a notebook computer, a mobile phone, a personal digital assistant (Personal Digital Assistant, PDA), a smart watch, a Point of sale (POS) device, a car computer, a car television, or a wearable device, or the like, or any combination thereof. For example, the internet product may be any software and/or application used in a computer or mobile phone. The software and/or applications may involve social, shopping, shipping, entertainment time, learning, or investment, or the like, or any combination thereof. In some embodiments, the transportation related software and/or applications may include travel software and/or applications, vehicle scheduling software and/or applications, drawing software and/or applications, and the like. In the vehicle scheduling software and/or applications, the vehicle may include horses, dollies, rickshaw (e.g., wheelbarrows, bicycles, tricycles, etc.), automobiles (e.g., taxis, buses, private cars, etc.), trains, subways, watercraft, aircraft (e.g., aircraft, helicopters, space shuttles, rockets, hot air balloons, etc.), and the like, or any combination thereof.

Fig. 2 shows a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester terminal 130, a service provider terminal 140, which may implement the inventive concepts according to some embodiments of the application. For example, a processor may be used on electronic device 200 and to perform functions in the present application.

The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the order data analysis method of the present application. Although only one computer is shown, the functionality described herein may be implemented in a distributed fashion across multiple similar platforms for convenience to balance processing loads.

For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and various forms of storage media 240, such as magnetic disk, ROM, or RAM, or any combination thereof. By way of example, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in the electronic device 200. It should be noted, however, that the electronic device 200 of the present application may also include multiple processors, and thus, steps performed by one processor described in the present application may also be performed jointly by multiple processors or separately. For example, if the processor of the electronic device 200 performs steps a and B, it should be understood that steps a and B may also be performed by two different processors together or performed separately in one processor. For example, the first processor performs step a, the second processor performs step B, or the first processor and the second processor together perform steps a and B.

Example two

The embodiment provides an order data analysis method. The method of this embodiment may be performed by the server 110 shown in fig. 1, or may be performed by a device communicatively coupled to the database 150. FIG. 3 illustrates a flow chart of a method of order data analysis in one embodiment of the application. The flow of the order data analysis method shown in fig. 3 is described in detail below.

In step S301, order data of a target user is acquired from an order server.

The order data includes order resources. The order server may be a server 110 with a database 150 as shown in fig. 1, or may be a server connected to the database 150.

The resources may be information or configuration parameters that are required to be consumed by the service requester in one order use, and for example, the resources may be user points, order use tickets, prices, money, and the like.

Taking the network about vehicle as an example, the order resource may be a resource contribution that the passenger needs to provide when riding the network about vehicle. In one example, the passenger side corresponds to resources such as points, usage coupons, etc., which are paid to the driver side providing the service after each travel on the net. The credit or ticket on the driver side is increased and the credit or ticket on the passenger side is decreased each time the network booking service is completed.

And step S302, calculating the resource floating area of the target user according to the order resource.

In some embodiments, as shown in fig. 4, step S302 may be implemented as:

step S3021, calculating an average value and variance of order resources in the order data;

step S3022, calculating a resource floating area of the target user according to the average value and the variance.

In one embodiment, step S3022: judging whether the variance is in a first numerical interval or a second numerical interval; and if the variance is in the first numerical value interval, calculating to obtain a first resource interval according to the average value and the variance by using a first calculation mode, and taking the first resource interval as a resource floating region.

The right end point of the first numerical value interval is the left end point of the second numerical value interval.

In one example, a first interval of values may be expressed as (0, a) and a second interval of values may be expressed as (a, b). Values for a and b may be selected as desired, embodiments of the present application are not limited to the particular values of a and b.

The step of calculating, using the first calculation method, a first resource interval according to the average value and the variance, and using the first resource interval as a resource floating region includes: obtaining a first left endpoint from the difference between the average value and the variance of the first multiple; and obtaining a first right endpoint by the sum of the average value and the variance of the first multiple, wherein the first left endpoint and the first right endpoint form a first resource interval, and the first resource interval is used as a resource floating area.

In one example, the average may be expressed as mean, the variance as std, and the first multiple as c. The first left endpoint may be denoted mean-c std and the first right endpoint may be denoted mean + c std. Specifically, the value of the first multiple c can be selected to be a proper value according to specific conditions; for example, the first multiple c may be set to 1; as another example, the first multiple c may be set to 2.

In another embodiment, step S3022 may be implemented as: and judging whether the variance is in a first numerical value interval or a second numerical value interval, if the variance is in the second numerical value interval, calculating to obtain a second resource interval according to the average value and the variance by using a second calculation mode, wherein the second resource interval is in the first resource interval, and the second resource interval is used as a resource floating region.

In some embodiments, the step of calculating, using the second calculation method, a second resource interval according to the average value and the variance, where the second resource interval is within the first resource interval, and the second resource interval is used as a resource floating area includes: obtaining a second left end point by the difference between the average value and the variance of a second multiple, wherein the second multiple is larger than the first multiple; and obtaining a second right endpoint by the sum of the mean value and the variance of a second multiple, wherein the second left endpoint and the second right endpoint form a second resource interval, and the second resource interval is used as a resource floating area.

In one example, the average may be expressed as mean, the variance as std, and the second multiple as d. The first left endpoint may be denoted mean-d std and the first right endpoint may be denoted mean + d std. Specifically, the value of the second multiple d may be selected appropriately according to the specific situation and the specific application scenario, for example, the second multiple d may be set to 3, and for example, the second multiple d may also be set to 4, etc.

Different from the above direct calculation of the resource floating region by the average and variance of the resource order resources; in some embodiments, the inventors have conducted studies to calculate the resource float region through other calculation methods in cases where the resource float is relatively large, and in particular, are described in detail below through some alternative implementations.

Optionally, step S302 may include: judging whether the variance is in a third numerical interval; if yes, calculating to obtain a resource floating area by using a heuristic search algorithm based on the order data.

The right end point of the second numerical value interval is the left end point of the third numerical value interval. Specifically, the second numerical interval example above may be used, and the third numerical interval may be represented as (b, x). Wherein x may be infinite or a larger value.

In some embodiments, the step of calculating the resource floating region based on the order data using a heuristic search algorithm includes: obtaining the total order number of the target user and the order number in each search interval according to the order data; calculating a first interval score corresponding to each search interval by using the total order quantity and the order quantity corresponding to each search interval; and screening out the search interval with the highest score of the first interval as a resource floating area.

wherein ,

/>

b＝1-(m·a)；

The above-described set section length n may be expressed as a search tendency length, and further, when the length of the search section is n, the score of the calculation section by the above-described formula is relatively higher.

In one example, the total resource interval may be divided into 101 intervals, denoted as:

interval No. 0 [0, 1); interval 1 [1, 2); interval No. 2 [2, 3); interval i [ i, i+1); …;100 # section [100, ].

In another example, the total resource interval may also be divided into 56 intervals, denoted as:

interval No. 0 [0, 2); interval 1 [2, 4); interval No. 2 [4, 6); i number interval [2i, 2i+2); …; interval No. 50 [100, ++).

Of course, the partitioning of the total resource interval may be partitioned according to specific requirements. In an alternative embodiment, the total resource interval may be divided into a corresponding number according to the received parameters.

In one example, the server performing the method in this embodiment is communicatively connected to a management terminal, which may send the parameter m to be set to the server. In another example, the method in this embodiment is performed by an electronic device that includes a display operation interface in which a window for receiving parameter inputs is provided, and the electronic device divides the total resource interval by obtaining the value received by the window.

In some embodiments, the total number of cells m, the set magnification t, and the set interval length n may be set as desired. The setting mode can be as follows: the manner in which the parameters entered by the user are received, or the manner in which the management terminal in communication with the electronic device executing the method in this embodiment is transmitted.

Each search section is formed by combining a plurality of continuous cells, or may be formed by combining one cell, so that the length of each formed search section may be equal to or greater than the length of each cell. In one example, each search interval may be represented as:

Interval No. 0, combination of interval No. 0 and interval No. 1, …, combination of interval No. 0 to interval No. i, …, combination of interval No. 0 to interval No. 100;

interval 1, combination of interval 1 and interval 2, …, combination of interval 1 to interval i, …, combination of interval 1 to interval 100; and so on until all interval divisions are completed.

In one example, the process of calculating the first interval score described above may be described as the following pseudocode:

by scoring and evaluating each search interval, a search interval with a relatively higher score can be obtained, and the search interval with the relatively higher score can relatively more accord with a possible resource floating region of a user, so that the resource floating region where the user is depicted is more accurate.

And step S303, calculating to obtain the upper limit of the resource matched with the target user according to the resource floating area or/and the order data.

In some embodiments, step S303 may include: and calculating the upper limit of the resources matched with the target user by using a heuristic search algorithm based on the upper limit of the resource floating region.

In an alternative embodiment, the step of calculating the upper limit of the resource matched with the target user by using a heuristic search algorithm based on the upper limit of the resource floating region includes: acquiring the completed amount of orders and the exploratory amount of orders of the target user according to the order data; calculating a second interval score corresponding to each search interval by using the completed amount of orders and the exploratory amount of orders; and screening out the right endpoint of the search interval with the highest score of the second interval as the upper limit of the resource.

range2 _k ＝k-up_ctg+1；

up_ctg<k≤m；

wherein, subSend _k Indicating that the k-th search interval has completed the amount of orders; subBub _k Representing the k search interval exploratory order quantity; range2 _k Representing the length of the kth search interval; lenSend _k Indicating the number of cells in which orders are completed in the kth search interval; m represents the number of cells divided between total resource intervals; alpha represents an energy storage value; gamma represents the excitation value.

In one example, the case of a search interval formed from interval No. 5 to interval No. 11 is currently being calculated. Then in this example lenSend _k The value of (2) is denoted 7.

The number of cells divided by the total resource interval when the upper limit of the computing resource is calculated may be the same as the number of cells divided by the total resource interval when the floating region of the resource is calculated, or may be different. The partitioning of the total resource interval may be partitioned according to specific requirements. In an alternative embodiment, the total resource interval may be divided into a corresponding number according to the received parameters.

In some embodiments, the stored energy value α is calculated by:

In some embodiments, the excitation value γ described above is calculated by:

When gamma decays to 0, indicating that no orders have been completed for consecutive e intervals, gamma is set to zero, i.e. the excitation of the excitation number gamma is turned off.

Specifically, that is score2 _k And when the value is maximum, the corresponding k value is used as the upper limit of the resource.

The e value described above can indicate that heuristic searches for the upper resource limit that the target user matches tend to find a search interval of length e where an order is completed. Wherein the value of e may be set as desired.

In one example, the above process of calculating the second interval score may be described as the following pseudocode:

The method can obtain the habit or the adaptable upper limit of the resources of the user by analyzing the completed order of the user. There are some unsuccessfully ordered users in the user group, and for such users, some exploratory operations of the user may be used to get user habits or an adaptable upper resource limit.

In an alternative embodiment, the order data of the target user includes the completed order and the exploratory order, and if the order data of the target user includes only the exploratory order, the step S303 includes: acquiring a resource lower limit of the resource floating region; and calculating the upper limit of the resources matched with the target user according to the lower limit of the resources.

If only exploratory orders are included in the order data of the target user, the left endpoint of the resource floating region may be represented as the lowest value of resources in the exploratory order and the right endpoint may be represented as the highest value of resources in the exploratory order.

Optionally, the above-mentioned upper limit of the resource matched with the target user according to the lower limit of the resource may be calculated by a calculation formula:

upl＝p*min；

wherein upl represents the upper resource limit matched for the target user; p represents the proportion of the user match; min represents the lower resource limit of the resource floating region.

In some alternative embodiments, p may be 1 or a positive number less than 1.

If the user and the order are successfully placed, the matching resource of the order provided for the user currently exceeds the expectation of the user, and the upper limit of the matching resource of the target user can be smaller than or equal to the lower limit of the resource floating area corresponding to the exploratory order so as to be more suitable for the possible requirement of the user.

The exploratory order described above may be represented as an order in which the user views the resource for which the order corresponds simply by entering defined parameters. Taking the network about car as an example, the exploratory order can be represented as a starting point and a finishing point which are input by a user, and the order which is not successfully placed after the price required for the order is displayed.

By the method, the upper limit of the resources which are required by the user can be explored, and further, a proper sharing strategy can be matched for the user according to the exploration result, so that the probability of using orders by the user is improved.

On the basis of steps S301 to S303, it may further include: matching a sharing strategy for the target user according to the upper limit of the resource matched by the target user or the resource floating region, wherein the sharing strategy comprises a balance resource ticket; and sending the sharing strategy to the terminal corresponding to the target user.

The sharing policy described above may include vouchers, coupons, and the like. And a coupon or discount coupon matching the upper limit of the resource or the floating area of the resource. In one example, a target user's consumption resources of an order reaching the target user's matching upper resource limit may use a mortgage, enabling the user to consume less resources. In another example, a coupon may be used where the consumed resources of an order of the target user reach the target user's matching upper resource limit, allowing the user to consume less resources

The target user corresponding terminal described above may be the service requester terminal 130 shown in fig. 1.

Example III

Based on the same application conception, the embodiment of the application also provides an order data analysis device corresponding to the order data analysis method, and because the principle of solving the problem by the device in the embodiment of the application is similar to that of the order data analysis method in the embodiment of the application, the implementation of the device can refer to the implementation of the method, and the repetition is omitted.

Fig. 5 is a block diagram illustrating an order data analysis device implementing functions corresponding to the steps performed by the above-described method according to some embodiments of the present application. The apparatus may be understood as the above server, or the processor of the server, or as a component, which is independent from the above server or processor and is controlled by the server, to implement the functions of the present application, and as shown in the figure, the order data analysis apparatus may include: an acquisition module 401, a first calculation module 402, a second calculation module 403, wherein,

an obtaining module 401, configured to obtain order data of a target user from an order server, where the order data includes order resources;

a first calculation module 402, configured to calculate a resource floating area of the target user according to the order resource;

A second calculating module 403, configured to calculate, according to the resource floating area or/and the order data, an upper resource limit matched with the target user.

In some embodiments, the first computing module 402 is further configured to:

calculating an average value and variance of order resources in the order data;

In some embodiments, the first computing module 402 is further configured to:

In some embodiments, the second computing module 403 is further configured to:

wherein ,

b＝1-(m·a)；

In some embodiments, the second computing module 403 is further configured to:

acquiring the completed amount of orders and the exploratory amount of orders of the target user according to the order data;

range2 _k ＝k-up_ctg+1；

up_ctg<k≤m；

In some embodiments, the stored energy value is calculated by:

In some embodiments, the excitation value is calculated by:

In some embodiments, the second computing module 403 is further configured to:

acquiring a resource lower limit of the resource floating region;

In some embodiments, the apparatus further comprises:

The modules may be connected or communicate with each other via wired or wireless connections. The wired connection may include a metal cable, optical cable, hybrid cable, or the like, or any combination thereof. The wireless connection may include a connection through a LAN, WAN, bluetooth, zigBee, or NFC, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.

The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.

Furthermore, the embodiment of the present application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor performs the steps of the order data analysis method described in the above method embodiment.

The computer program product of the order data analysis method provided by the embodiment of the present application includes a computer readable storage medium storing program codes, where the instructions included in the program codes may be used to execute the steps of the order data analysis method described in the above method embodiment, and specifically, reference may be made to the above method embodiment, and details are not repeated herein.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the method embodiments, and are not repeated in the present disclosure. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, and for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. A method of order data analysis, comprising:

acquiring order data of a target user from an order server, wherein the order data comprises order resources, the order resources comprise information or configuration parameters which are required to be consumed by the target user in one order use, the order data comprise completed orders and exploratory orders, or the order data only comprise exploratory orders, and the exploratory orders represent orders which are not successfully placed;

if the order data comprises a completed order and a exploratory order, calculating an upper limit of the resource matched with the target user by using a heuristic search algorithm based on the upper limit of the resource floating area;

The method for calculating the resource upper limit matched with the target user by using a heuristic search algorithm based on the upper limit of the resource floating region comprises the following steps:

calculating a second interval score corresponding to each search interval by using the upper limit of the resource floating area and the completed amount of orders and the exploratory amount of orders;

screening out the right endpoint of the search interval with the highest score of the second interval as the upper limit of resources;

if the order data of the target user only comprises exploratory orders, acquiring a resource lower limit of the resource floating area;

calculating to obtain an upper resource limit matched with the target user according to the lower resource limit;

matching a sharing strategy for the target user according to the upper limit of the resources matched by the target user, wherein the sharing strategy comprises a balance resource ticket;

2. The method of claim 1, wherein said step of calculating a resource float area for said target user based on said order resource comprises:

calculating an average value and variance of order resources in the order data;

3. The method of claim 2, wherein the step of calculating a resource floating region for the target user based on the average and the variance comprises:

and if the variance is in the second numerical range, calculating to obtain a second resource range according to the average value and the variance by using a second calculation mode, wherein the first resource range is in the second resource range, and the second resource range is used as a resource floating region.

4. The method of claim 3, wherein the step of calculating a first resource interval from the mean and the variance using a first calculation method, and using the first resource interval as a resource floating region comprises:

5. The method of claim 4, wherein the calculating, using the second calculation method, a second resource interval according to the average value and the variance, the first resource interval being within the second resource interval, and the step of using the second resource interval as a resource floating region comprises:

6. The method of claim 3 wherein said step of calculating a resource float area for said target user based on said order resource further comprises:

7. The method of claim 6, wherein the step of calculating a resource float region using a heuristic search algorithm based on the order data comprises:

8. The method of claim 7, wherein calculating the first interval score corresponding to each search interval using the total number of orders and the number of orders corresponding to each search interval is achieved by:

；

wherein ,；

；

wherein ,a first interval score representing a kth search interval; />Representing the amount of orders of the target user in the kth search interval; / >Representing the total order number of the target user; />A section length representing a kth search section; />Representing the upper and lower bounds of the kth search interval; />Representing a piecewise function; />Representing the number of cells of the total resource interval partition; />Representing the amount of orders of the target user among the ith cells; />Representing a set magnification; />Indicating a set interval length.

9. The method of claim 1, wherein the calculating the second interval score corresponding to each search interval using the upper limit of the resource floating area, the completed amount of orders, and the exploratory amount of orders is achieved by:

；

wherein ,indicating that the k-th search interval has completed the amount of orders; />Representing the k search interval exploratory order quantity; />Representing the length of the kth search interval; />Indicating the number of cells in which orders are completed in the kth search interval; />Representing the number of cells of the total resource interval partition; />Representing an energy storage value; />Representing an excitation value; />Representing an upper bound of the resource float region;

the energy storage value is calculated by the following method:

if it isThe method comprises the steps of carrying out a first treatment on the surface of the Put->Put->；

If it isThe method comprises the steps of carrying out a first treatment on the surface of the Make->Juxtaposing- >The method comprises the steps of carrying out a first treatment on the surface of the If->Put in ；

wherein ,representing excitation energy;

the excitation value is calculated by:

if there is a completed order in the current search interval, andif not zero, put ∈>，/>Representing a constant;

if no order is completed in the current search interval, thenUp to->Decaying to 0; />Setting to 0;

when (when)Decay to 0, < ->The length is set to 0.

10. An order data analysis device, comprising:

the acquisition module is used for acquiring order data of a target user from the order server, wherein the order data comprises order resources, the order resources comprise information or configuration parameters which are required to be consumed by the target user in one order use, the order data comprise completed orders and exploratory orders, or the order data only comprise exploratory orders, and the exploratory orders represent orders which are not successfully placed;

a second calculation module for:

the apparatus further comprises:

means for matching a sharing policy for the target user based on the target user's matching resource cap, the sharing policy comprising a vouchers of repudiated resources;

and the module is used for sending the sharing strategy to the terminal corresponding to the target user.

11. The apparatus of claim 10, wherein the first computing module is further to:

calculating an average value and variance of order resources in the order data;

12. The apparatus of claim 11, wherein the first computing module is further to:

13. The apparatus of claim 12, wherein the computing a first resource interval from the mean and variance using a first computing means, the first resource interval being a resource floating region, comprises:

14. The apparatus of claim 13, wherein the calculating, using the second calculation means, a second resource interval from the average and the variance, the first resource interval being within the second resource interval, the second resource interval being a resource floating region, comprises:

15. The apparatus of claim 12, wherein the first computing module is further to:

16. The apparatus of claim 15, wherein the first computing module is further to:

17. The apparatus of claim 16, wherein the calculating the first interval score corresponding to each search interval using the total number of orders and the number of orders corresponding to each search interval is achieved by:

；

wherein ,；

；

wherein ,a first interval score representing a kth search interval; />Representing the amount of orders of the target user in the kth search interval; />Representing the total order number of the target user; />A section length representing a kth search section; />Representing the upper and lower bounds of the kth search interval; />Representing a piecewise function; />Representing the number of cells of the total resource interval partition; />Representing the amount of orders of the target user among the ith cells; />Representing a set magnification; />Indicating a set interval length.

18. The apparatus of claim 10, wherein the calculating of the second interval score corresponding to each search interval using the upper limit of the resource floating area, the completed amount of orders, and the exploratory amount of orders is achieved by:

；

the energy storage value is calculated by the following method:

If it isThe method comprises the steps of carrying out a first treatment on the surface of the Make->Juxtaposing->The method comprises the steps of carrying out a first treatment on the surface of the If->Put in ；

wherein ,representing excitation energy;

the excitation value is calculated by:

when (when)Decay to 0, < ->The length is set to 0.

19. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 9.

20. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any of claims 1 to 9.