CN111199440A

CN111199440A - Event prediction method and device and electronic equipment

Info

Publication number: CN111199440A
Application number: CN201811369404.2A
Authority: CN
Inventors: 唐甜田; 段明琪; 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: 2018-11-16
Filing date: 2018-11-16
Publication date: 2020-05-26

Abstract

The application provides an event pre-estimation method, an event pre-estimation device and electronic equipment, wherein the event pre-estimation method comprises the following steps: acquiring first order data in a target area within a specified time period; comparing the first order data with historical data in the target area, and judging whether the first order data is separated from the rule of the historical data in the target area; and obtaining an estimated result of whether the set event occurs in the target area according to the judgment result. According to the method and the device, the order condition of the current time is compared and analyzed with the order condition in the historical data, so that the estimation condition of the possible events which are going through in the target area is achieved.

Description

Event prediction method and device and electronic equipment

Technical Field

The application relates to the technical field of data processing, in particular to an event pre-estimation method and device and electronic equipment.

Background

The condition of each area is not constant, and occasionally, different types of events from those in the normal state occur. If an event occurs in an area, the traffic in the area will increase or decrease to some extent compared to the current period, resulting in a change in the services available in the area. In the prior art, social trending events are mostly acquired by crawling various web portals, and only a few events which are arranged in advance can be acquired in the mode.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide an event estimation method, an event estimation device, and an electronic device, which can solve the problem that the change of an order in an area and the existence of the area cannot be known in the prior art by comparing an order at the current time with historical data, so as to achieve the effect of estimating an event that may exist in a target area.

According to one aspect of the present 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 operated, the processor communicates with the storage medium through the bus, and the processor executes the machine readable instructions to perform one or more of the following operations:

acquiring first order data in a target area within a specified time period;

comparing the first order data with historical data in the target area, and judging whether the first order data is separated from the rule of the historical data in the target area;

and obtaining an estimated result of whether the set event occurs in the target area according to the judgment result.

According to the event estimation method provided by the embodiment of the application, the order condition at the current time is compared and analyzed with the order condition in the historical data, so that the possible events which are experienced in the target area can be estimated, and compared with the events which are disclosed from various gate house websites in the prior art, the estimation of the events which can cause order quantity change can be obtained.

In some embodiments, the step of comparing the first order data with the historical data in the target area and determining whether the first order data deviates from the rule of the historical data in the target area includes:

acquiring second order data from historical data, wherein the second order data comprises an order quantity generated by the target area in the same time period as the specified time period, and the specified time period represents a time period of the day;

calculating the first order data and the second order data to obtain a floating ratio;

and judging whether the floating ratio is larger than a first set value or not, if so, indicating that the first order data is separated from the rule of historical data in the target area.

In some embodiments, the calculating the first order data and the second order data to obtain the floating ratio is implemented by the following formula:

wherein Ratio represents a floating Ratio; d1 denotes first order data; d2 denotes second order data.

In some embodiments, the second order data comprises: and the target area is the same as the target area in the attribute day before the current day and the order amount corresponding to the specified time period, and the attribute day comprises a working day and a holiday.

Further, according to the event estimation method provided by the embodiment of the application, the comparison of the order quantity can be intuitively realized by comparing the first order data with the order quantity in the corresponding time period in the historical data, and whether an event causing the change of the order quantity exists in the target area can be more quickly determined. Further, if the holiday of a target area may be different from the original order amount of the working day, the same attribute day of the target area may be compared with the first order data of the current day to identify the target area more accurately, so that the more accurate identification of the difference of the order amount on different attribute days is realized.

acquiring third order data from the historical data, wherein the third order data comprises the order quantity generated by the target area in the same time period in the specified time period every day in the previous specified day;

calculating to obtain an order floating value of the last appointed day in an appointed time period according to the third order data;

and judging whether the first order data are in the limited range of the order floating value, if not, indicating that the first order data are separated from the rule of historical data in the target area.

Further, the event estimation method provided by the embodiment of the application can also obtain a floating range corresponding to the historical data through calculation of the historical data, and then separate the first order data from the floating range obtained through calculation, so that estimation of the time of the target area which may be happening can be realized.

In some embodiments, the step of calculating the order float value for the specified time period on the specified day of the past specified day according to the third order data comprises:

and carrying out weighted summation on the order quantity of the previous appointed day in the appointed time period every day to obtain an order floating value.

acquiring fourth order data from the historical data, wherein the fourth order data comprises the order quantity generated by the target area in the same time period as the specified time period on the previous specified day;

performing density peak clustering calculation on the fourth order data to judge a first clustering center in the historical time within the range of the target area;

performing density peak clustering on order data in the specified time period within the range of the target area including the first order data to calculate a second clustering center of the target area at the current time within the range of the target area;

and judging whether the position of the second clustering center deviates from the position of the first clustering center by more than a set distance, if so, indicating that the first order data is separated from the rule of historical data in the target area.

modeling and predicting the order quantity of the target area in the specified time period of the current day by using historical data to obtain a predicted value;

and comparing the predicted value with the first order data, and judging whether the order difference between the first order data and the predicted value exceeds a first set value, wherein if yes, the rule that the first order data is separated from historical data in the target area is represented.

In some embodiments, the modeling and predicting the order quantity of the target area in the specified time period of the current day by using the historical data, and obtaining the predicted value is implemented by:

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

wherein s is_iIs the smoothed order quantity over the time step i; x is the number of_iIs the actual order quantity in this time step, α represents an arbitrary value between 0 and 1, t_iRepresenting a trend, β being a variable controlling the trend, p_iRepresenting the amount of periodic partial orders; k represents the length of a period; γ is a variable controlling the periodicity;

and the predicted value of the time point of i + h is represented by the current time of i.

In some embodiments, the step of using the historical data to model and predict the order quantity of the target area in the specified time period of the current day to obtain the predicted value includes:

and modeling and predicting the order quantity of the target area in the specified time period of the current day by using an autoregressive integral moving average model and taking historical data as basic data to obtain a predicted value.

In some embodiments, the step of obtaining an estimated result of whether a set event occurs in the target area according to the determination result includes:

judging that the first order data is separated from the rule of the historical data in the target area and the first order data is higher than the order quantity corresponding to the historical data in the target area, and judging that the estimated result is a first type event which can cause the order quantity to increase; and/or the first and/or second light-emitting diodes are arranged in the light-emitting diode,

and judging that the first order data are separated from the rule of the historical data in the target area and the first order data are lower than the order quantity corresponding to the historical data in the target area, wherein the estimated result is a second type event, and the order quantity is reduced due to the second type event.

In some embodiments, the method further comprises:

matching a resource allocation strategy for the target area according to the estimation result;

and allocating resources to the target area according to the resource allocation strategy.

In some embodiments, the order is a network car booking order, and the resource allocation policy is a network car booking quantity matching policy; the step of allocating resources to the target area according to the resource allocation policy includes:

if the estimated result is a first type event, calling a network car booking driver to the target area;

and if the estimated result is a second type of event, the network car booking driver in the target area is dispatched to other areas.

if the first order data is compared with the historical data in the target area, judging whether the first order data is separated from the rule of the historical data in the target area, and obtaining at least two judgment results in at least two ways, carrying out weighted summation on each judgment result to obtain an event matching value;

and judging whether the event matching value is larger than a third set value, if so, indicating that a set event occurs.

In some embodiments, prior to the step of obtaining first order data within a specified time period in a target area, the method further comprises:

calculating the entropy of each region;

screening out the regions with the entropy larger than a fourth set value to obtain preselected regions;

and obtaining a target area from the preselected area.

In some embodiments, the calculation of the entropy for each region is implemented by the following formula:

entropy＝-∑p_ilog(p_i)；

wherein, C_iRepresenting the amount of orders generated by the ith individual over a period of time; c represents the total amount of orders generated in a time period; entropy represents the entropy of a region.

In some embodiments, the step of obtaining first order data in a target area within a specified time period comprises:

acquiring longitude and latitude data of an order in a target area within a specified time period;

calculating to obtain a geographic hash value of the target area according to the longitude and latitude data;

and obtaining the order quantity of different positions according to the geographic hash value of each order to obtain first order data.

In some embodiments, each order in the first order data carries a location attribute tag, and the method may further include:

and if the estimated result is characterized in that a set event occurs, determining the position attribute type causing the set event according to the position attribute label in each order in the first order data.

According to another aspect of the present application, there is provided an event prediction apparatus including:

the acquisition module is used for acquiring first order data in a target area within a specified time period;

the judging module is used for comparing the first order data with historical data in the target area and judging whether the first order data is separated from the rule of the historical data in the target area;

and the obtaining module is used for obtaining an estimated result of whether the set event occurs in the target area according to the judgment result.

In some embodiments, the determining module is further configured to:

In some embodiments, the determining module is further configured to:

In some embodiments, said calculating the order float value for said specified time period on said specified day from said third order data is performed by:

In some embodiments, the determining module is further configured to:

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

In some embodiments, the above modeling prediction of the order quantity of the target area in the specified time period of the current day by using the historical data is implemented by the following steps:

In some embodiments, the obtaining module is further configured to:

In some embodiments, the apparatus further comprises:

the matching module is used for matching a resource allocation strategy for the target area according to the estimation result;

and the allocation module is used for allocating resources to the target area according to the resource allocation strategy.

In some embodiments, the order is a network car booking order, and the resource allocation policy is a network car booking quantity matching policy; the allocation module is further configured to:

In some embodiments, the obtaining module is further configured to:

In some embodiments, the apparatus further comprises: a filtration module to:

calculating the entropy of each region;

and obtaining a target area from the preselected area.

entropy＝-∑p_ilog(p_i)；

In some embodiments, the obtaining module is further configured to:

In some embodiments, each order in the first order data may carry a location attribute tag, and the apparatus may further include:

and the determining module is used for determining the position attribute type causing the set event according to the position attribute label in each order in the first order data if the estimation result is characterized in that the set event occurs.

According to another aspect of the present application, this application embodiment further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of the time estimation method in the first aspect or any one of the possible implementation manners of the first aspect.

In order to make the aforementioned 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 required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

FIG. 1 is a schematic diagram illustrating an environment of a trajectory recognition system provided by an embodiment of the present application;

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

FIG. 3 is a flow chart illustrating a method for event prediction according to an embodiment of the present application;

fig. 4 shows a schematic structural diagram of an event prediction apparatus according to an embodiment of the present application.

Icon: 100-an event prediction system; 110-a server; 120-a network; 130-service request side; 140-service provider; 150-a database; 200-an electronic device; 210-a network port; 220-a processor; 230-a communication bus; 240-storage medium; 250-an interface; 401-an acquisition module; 402-a judgment module; 403-obtaining a module.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

To enable those skilled in the art to utilize the present disclosure, the following embodiments are presented in conjunction with a specific application scenario, "network appointment". It will be apparent to those skilled 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. Although the present application is described primarily in the context of a net appointment, it should be understood that this is only 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 terrestrial, marine, or airborne, among others, or any combination thereof. The vehicle of the transportation system may include a taxi, a private car, a windmill, a bus, a train, a bullet train, a high speed rail, a subway, a ship, an airplane, a spacecraft, a hot air balloon, or an unmanned vehicle, etc., or any combination thereof. The present application may also include any service system for order trading, for example, a system for sending and/or receiving couriers, a service system for business to and from parties. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The terms "passenger," "requestor," "service person," "service requestor," and "customer" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "driver," "provider," "service provider," and "provider" are used interchangeably in this application to refer to an individual, entity, or tool that can provide a service. The term "user" in this application may refer to an individual, entity or tool that requests a service, subscribes to a service, provides a service, or facilitates the provision of a service. For example, the user may be a passenger, a driver, an operator, etc., or any combination thereof. In the present application, "passenger" and "passenger terminal" may be used interchangeably, and "driver" and "driver terminal" may be used interchangeably.

The terms "service request" and "order" are used interchangeably herein to refer to a request initiated by a passenger, a service requester, a driver, a service provider, or a supplier, the like, or any combination thereof. Accepting the "service request" or "order" may be a passenger, a service requester, a driver, a service provider, a supplier, or the like, or any combination thereof. The service request may be charged or free.

The Positioning technology used in the present application may be based on a Global Positioning System (GPS), a Global Navigation Satellite System (GLONASS), a COMPASS Navigation System (COMPASS), a galileo Positioning System, a Quasi-Zenith Satellite System (QZSS), a Wireless Fidelity (WiFi) Positioning technology, or the like, or any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

One aspect of the present application relates to an event recognition system. The system may enable an estimate of whether or not a target area is likely to or is experiencing an event that causes an order change by matching the order conditions in the target area to the likely rules of the order for which historical data is available.

It is noted that before the application is filed, social trending events are obtained by crawling various web portals, and only a few events scheduled in advance can be obtained in this way. However, the event estimation method provided by the application can also realize estimation on unpublished events according to actual order conditions.

Example one

FIG. 1 is a block diagram of an event prediction system 100 according to some embodiments of the present application. For example, event prediction system 100 may be an online transportation service platform for transportation services such as taxis, designated driving services, express, carpooling, bus services, driver rentals, or regular bus services, or any combination thereof. The event prediction system 100 may include one or more of a server 110, a network 120, a service requester 130, a service provider 140, and a database 150, and the server 110 may include a processor for executing instructions.

In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can 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 130, the service provider 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 130, the service provider 140, and the database 150 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.

In some embodiments, the 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 herein. For example, the processor may determine the target vehicle based on a service request obtained from the service requester 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)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a 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 (e.g., server 110, service requester 130, service provider 140, and database 150) in event prediction system 100 may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, 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 (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a 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 event prediction system 100 may connect to network 120 to exchange data and/or information.

In some embodiments, the user of the service requestor 130 may be someone other than the actual demander of the service. For example, the user a of the service requester 130 may use the service requester 130 to initiate a service request for the actual service demander B (for example, the user a may call a car for his friend B), or receive service information or instructions from the server 110. In some embodiments, the user of the service provider 140 may be the actual provider of the service or may be another person other than the actual provider of the service. For example, user C of service provider 140 may use service provider 140 to receive a service request serviced by actual service provider D (e.g., user C may take an order for driver D employed by user C), and/or information or instructions from server 110. In some embodiments, "service requestor" and "service requestor" may be used interchangeably, and "service provider" may be used interchangeably.

In some embodiments, the service requester 130 may include a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., 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, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, control devices for smart electrical devices, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a 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, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or 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 130 may be a device having a location technology for locating the location of the service requester and/or the service requester.

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

Database 150 may store data and/or instructions. In some embodiments, the database 150 may store data obtained from the service requester 130 and/or the service provider 140. In some embodiments, database 150 may store data and/or instructions for the exemplary methods described herein. In some embodiments, database 150 may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, 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, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double Data Rate Synchronous Dynamic RAM (DDRSDRAM); static RAM (SRAM), Thyristor-based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, database 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

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

In some embodiments, one or more components (e.g., server 110, service requestor 130, service provider 140, etc.) in the event prediction system 100 may have access to the database 150. In some embodiments, one or more components in event pre-estimation system 100 may read and/or modify information related to a service requestor, 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 for one or more users after receiving a service request. As another example, the service provider 140 may access information related to the service requester when receiving the service request from the service requester 130, but the service provider 140 may not modify the related information of the service requester.

In some embodiments, the exchange of information by one or more components in event prediction system 100 may be accomplished by requesting a 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. Tangible products may include food, pharmaceuticals, commodities, chemical products, appliances, clothing, automobiles, homes, or luxury goods, and the like, or any combination thereof. The non-material 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 stand-alone host product, a network 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, programs, or systems of the mobile terminal, etc., or any combination thereof. The mobile terminal may include a tablet, a laptop, a mobile phone, a Personal Digital Assistant (PDA), a smart watch, a Point of sale (POS) device, a vehicle-mounted computer, a vehicle-mounted television, a wearable device, or the like, or any combination thereof. The internet product may be, for example, any software and/or application used in a computer or mobile phone. The software and/or applications may relate to social interaction, shopping, transportation, 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 dispatch software and/or applications, mapping software and/or applications, and the like. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a human powered vehicle (e.g., unicycle, bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, privatege, etc.), a train, a subway, a ship, an airplane (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), etc., or any combination thereof.

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

The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the event prediction method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms 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 a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, 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. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

Example two

The embodiment provides an event prediction 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 connected to the database 150.

FIG. 3 shows a flow chart of a trajectory identification method in one embodiment of the present application. The following describes the flow of the trajectory recognition method shown in fig. 3 in detail.

In step S301, first order data in a target area within a specified time period is obtained.

Before step S301, the corresponding regions may be filtered to select some regions where events may occur. In contrast, some personnel have stable conditions, area elimination which does not generate change of order quantity is avoided, and event prediction is not carried out any more. For example, the order size may be relatively stable in areas such as residential areas, remote suburban areas, etc., and the likelihood of some events occurring is relatively low.

In one embodiment, step S301 may be preceded by: calculating the entropy of each region; screening out the regions with the entropy larger than a fourth set value to obtain preselected regions; and obtaining the target area from the preselected area.

The calculation of the entropy for each region is realized by the following formula:

entropy＝-∑p_ilog(p_i)；

wherein, C_iRepresenting the amount of orders generated by the ith individual over a period of time; c represents the total amount of orders generated in a time period; entropy represents the entropy of a region; p is a radical of_iRepresenting an intermediate calculation.

Wherein, the larger the entropy value is, the more disordered the order in the area is, and the smaller the entropy value is, the relatively stable the order in the area is. Therefore, the stable situation of the order of one area can be expressed by the entropy.

The fourth setting value may be set according to attributes such as city density, city scale, and the like. In one example, the fourth set point may be any one of 3-3.5, for example, 3.2. And when the calculated entropy is larger than 3.2, the region is taken as a preselected region.

The following description will take the network appointment order as an example, C_iMay represent the number of times an ith individual takes a car over a period of time to an area where stability needs to be calculated; c_iIt may also indicate the number of times the ith individual takes a car over a period of time and leaves the area where stability needs to be calculated. C may represent the total number of times the car is hired to the area where stability needs to be calculated in the above time period; c may also represent the total number of times the car has left the area for which stability is to be calculated during the time period. In this example, the stability of the corresponding area can be obtained through the number of times of ordering the vehicle by the network appointment.

In another embodiment, the filtering may be performed by an attribute of each area, for example, the attribute is expressed as an area such as a school zone, a work circle, a recreation activity circle, or the like as a pre-selected area, and any area is selected from the pre-selected area as a target area.

In some embodiments, step S301 may further include: acquiring longitude and latitude data of an order in a target area within a specified time period; calculating to obtain a geographic hash value of the target area according to the longitude and latitude data; and obtaining the order quantity of different positions according to the geographic hash value of each order to obtain first order data.

In one example, the above-described geohash6 of the latitude and longitude of the order generated in the target area and the order generation time are obtained. And the geohash6 of the longitude and latitude of the order and the order generation time are input into the order statistical model, so that the order quantity generated at different time at different positions in a time period can be obtained.

In the case of a network appointment, the order may be an order to the destination area or an order to leave the destination area.

Step S302, comparing the first order data with the historical data in the target area, and determining whether the first order data is separated from the rule of the historical data in the target area.

The event estimation method may be implemented in various ways to determine the rule of whether the first order data is separated from the historical data in the target area.

In the first embodiment, the size comparison may be directly performed with specific values in the historical data, so that the rule of determining whether the first order data deviates from the historical data in the target area may be implemented.

In some embodiments, step S302 may include: acquiring second order data from historical data, and calculating the first order data and the second order data to obtain a floating ratio; and judging whether the floating ratio is larger than a first set value, if so, indicating that the first order data is separated from the rule of the historical data in the target area.

The second order data includes an order amount generated by the target area for a same time period as the designated time period.

The second order data comprises the order quantity of the target area corresponding to the same time period as the specified time period, and the specified time period represents a time period of the day. In one example, the specified time period may be ten to twelve am. Of course, the specified time period may be other time periods.

Specifically, the floating ratio obtained by calculating the first order data and the second order data is realized by the following formula:

Further, the first order data may be compared with the historical data in a ring ratio manner: in the same area (for example, the same geohash 6), assuming that the order quantity of a certain time period on the day is d1, and the order quantity of the same time period on the same attribute day (the same working day or the same holiday) which is the most recent day before the day is d21, the calculation formula of the ring ratio is:

for example, if today is tuesday, then yesterday (monday) was chosen for the loop ratio of the contemporaneous order quantity to the first order data. As another example, if today is Monday, the order quantity for the current period of Friday of the last week is selected for a ring ratio with the first order data.

Further, the first order data may be compared to historical data in a comparably manner: in the same area (for example, the same geohash 6), assuming that the order quantity of a certain time period on the day is d1, and the order quantity of the same time period on the same attribute day (the same working day or the same holiday) which is the latest day before the day is d22, the formula for calculating the equivalence ratio is:

for example, if today is tuesday, then the same period of order quantity for last tuesday is selected for the ring ratio with the first order data. For another example, if today is tuesday, but last tuesday is holiday (e.g., mid-autumn festival, afternoon festival, etc.), then the same period of the last tuesday's order quantity is selected for parity with the first order data.

In the second embodiment, a possible fluctuation range of the target area in the historical time period may be obtained by calculation according to multiple sets of data in the historical data, and determining whether the first order data falls within the fluctuation range may implement a rule for determining whether the first order data deviates from the historical data in the target area.

In some embodiments, step S302 may include: acquiring third order data from the historical data; calculating to obtain an order floating value of the last appointed day in an appointed time period according to the third order data; and judging whether the first order data are in the limited range of the order floating value, if not, indicating that the first order data are separated from the rule of historical data in the target area.

Wherein the third order data includes an order amount that the target area produced in a same time period in a specified time period each day on a previously specified day.

In one embodiment, the limited range of the order float value may be within a set ratio range of the order float value, for example, 70% -130% of the order float value.

In another possible implementation, the defined range of order float values may be within a range of a first endpoint and a second endpoint with the order float value as a built-in point, wherein the first endpoint is less than the order float value and the second endpoint is greater than the order float value, e.g., (order float value-500) - (order float value + 700).

The number of the above-mentioned specified days may be set as required, and for example, may be three days, seven days, fifteen days, and the like.

In some embodiments, the step of calculating the order floating value of the previously specified day in the specified time period according to the third order data includes: and carrying out weighted summation on the order quantity of the previous appointed day in the appointed time period every day to obtain an order floating value.

In one example, the previous specified day may be seven days prior to the current day, e.g., the order amounts for the same time period of the specified time period for the seven days may be: v1, v2, v3, v4, v5, v6, v7, the order float value may be calculated using the following formula:

v＝a1*v1+a2*v2+a3*v3+a4*v4+a5*v5+a6*v6+a7*v7；

the above parameters satisfy: a1+ a2+ a3+ a4+ a5+ a6+ a7 is 1;

wherein, a1, a2, a3, a4, a5, a6 and a7 respectively represent the weight of each day, and further, a1, a2, a3, a4, a5, a6 and a7 can be selected to be all non-negative numbers.

In one example, the order volume for each day of the previous specified day over a specified time period may be averaged, and the order float value may be expressed as:

v＝(v1+v2+v3+v4+v5+v6+v7)/7。

whether the event is occurring in the target area is estimated by directly using the order floating value, the calculated amount is relatively small, and in a scene of saving calculation resources, a second trial mode can be adopted to estimate whether the event is occurring in the target area.

In the third embodiment, the position point with higher order density in the larger area including the target area in the historical time period and the order data generated today can be obtained by calculation according to the historical data, that is, whether the position point with higher order density corresponding to the first order data is the same or similar can be realized to judge whether the first order data is separated from the historical data in the target area.

In some embodiments, step S302 may include: acquiring fourth order data from the historical data; performing density peak clustering calculation on the fourth order data to judge a first clustering center in the historical time within the range of the target area; performing density peak clustering on order data in the specified time period within the range of the target area including the first order data to calculate a second clustering center of the target area at the current time within the range of the target area; and judging whether the position of the second clustering center deviates from the position of the first clustering center by more than a set distance, if so, indicating that the first order data is separated from the rule of historical data in the target area.

Wherein the fourth order data includes an amount of orders that the target area produced in a same time period as the specified time period on a previously specified day.

For a data set, the cluster centers represent some bounding of low local density data points, and these low local density points are all at a greater distance from other high local density points.

The following describes in detail the acquisition of the cluster center by a specific formula:

the local density is expressed as:

wherein d is_cRepresents a truncation distance; d_ijRepresents the distance of position i from position j;

distance from high density point:

the meaning of the above formula is to find the minimum of the distances to position i among all the position points having a greater local density than position i.

Further, the data point with the greatest density may be generally expressed as:

further, those points having a larger distance δ and at the same time a larger local density ρ are defined as cluster centers.

The value of the position in the above formula can be expressed by an order quantity.

In the fourth embodiment, historical data can be used to estimate the today's order situation of the target area, and comparing the actual situation of the today's order with the order situation estimated from the historical data can realize the rule of determining whether the first order data deviates from the historical data in the target area.

In some embodiments, step S302 may include: modeling and predicting the order quantity of the target area in the specified time period of the current day by using historical data to obtain a predicted value; and comparing the predicted value with the first order data, and judging whether the order difference between the first order data and the predicted value exceeds a first set value, wherein if yes, the rule that the first order data is separated from historical data in the target area is represented.

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

In some embodiments, the step of using the historical data to model and predict the order quantity of the target area in the specified time period of the current day to obtain the predicted value includes: and modeling and predicting the order quantity of the specified time period of the current day of the target area by using an auto regression integration moving average model (ARIMA) and historical data as basic data to obtain a predicted value.

Specifically, the ARIMA (p, d, q) model can be expressed as:

wherein, L represents a Lag operator (Lag operator), d in Z, d > 0.

Further, the AR in the ARIMA (p, d, q) model can be expressed as:

AR (p) called p-order autoregressive model:

x_t＝δ+φ₁x_t-1+φ₂x_t-2+...+φ_px_t-p+u_t；

wherein u is_tRepresenting a white noise sequence; δ represents a constant; x is the number of_t-iRepresenting the order quantity at time t-i; phi denotes the parameters of the AR model.

Moving average of past white noise by MA:

x_t＝μ+u_t+θ₁u_t-1+θ₂u_t-2+...+θ_qu_t-q；

x_t-μ＝(1+θ₁L+θ₂L²+...+θ_qL^q)u_t-q＝Θ(L)u_t；

wherein, { u_tDenotes the white noise process; l represents a hysteresis operator; θ represents the parameters of the MA model.

The ARMA model is expressed as:

x_t＝δ+φ₁x_t-1+φ₂x_t-2+...+φ_px_t-p+u_t+θ₁u_t-1+θ₂u_t-2+...+θ_qu_t-q

Φ(L)x_t＝(1-φ₁L-φ₂L²-...-φ_pL^p)x_t

＝δ+(1+θ₁L+θ₁L²+...+θ_qL^q)u_t＝δ+Θ(L)u_t

Φ(L)x_t＝δ+Θ(L)u_t；

ARIMA model: the difference from the ARMA model is that x on the left side of the formula is changed into a difference operator, so that the stability of data is ensured.

Φ(L)Δ^dx_t＝δ+Θ(L)u_t；

The difference operator is represented as:

Δx_t＝x_t-x_t-1＝x_t-Lx_t＝(1-L)x_t；

Δ²x_t＝Δx_t-Δx_t-1＝(1-L)x_t-(1-L)x_t-1＝(1-L)²x_t；

Δ^dx_t＝(1-L)^dx_t；

order: w is a_t＝Δ^dx_t＝(1-L)^dx_t；

ARIMA may be expressed as:

w_t＝δ+φ₁w_t-1+φ₂w_t-2+...+φ_pw_t-p+u_t+θ₁u_t-1+θ₂u_t-2+...+θ_qu_t-q。

the amount of orders possible according to the rules of historical data at the current time can be estimated by using ARIMA. Whether there is an abnormality in the target area can be found by comparing the estimated order amount with the actual order amount (i.e., the first order data described above).

Step S303, obtaining an estimated result of whether the set event occurs in the target area according to the judgment result.

In some embodiments, step S303 comprises: judging that the first order data is separated from the rule of the historical data in the target area and the first order data is higher than the order quantity corresponding to the historical data in the target area, and judging that the estimated result is a first type event which can cause the order quantity to increase; or/and judging that the first order data are separated from the rule of the historical data in the target area and the first order data are lower than the order quantity corresponding to the historical data in the target area, wherein the estimated result is a second type event, and the order quantity is reduced due to the second type event.

In one example, the first type of event may be a shopping mall promotion, a concert hold, a regional literary activity, or the like that can result in a dramatic increase in the traffic of the target area.

In another example, the second type of event may be a mall closure, a sporting event that restricts automobile influx, or the like that may result in a reduction in the online appointment order.

if the first order data is compared with the historical data in the target area, judging whether the first order data is separated from the rule of the historical data in the target area, and obtaining at least two judgment results in at least two ways, carrying out weighted summation on each judgment result to obtain an event matching value; and judging whether the event matching value is larger than a third set value, if so, indicating that a set event occurs.

The following description will take the step S302 to obtain four determination results in the first embodiment, the second embodiment, the third embodiment and the fourth embodiment as an example:

and each embodiment assigns a variable to 1 when judging that the event occurs, and assigns the variable to 0 if the event does not occur.

In one example, the variables corresponding to the determination results in the first, second, third, and fourth embodiments are: b1, b2, b3, b 4;

the expression formula of the matching value can be expressed as:

b＝o1*b1+o2*b2+o3*b3+o4*b4；

wherein o1, o2, o3 and o4 represent weights of four embodiments, each weight is a non-negative number and satisfies: o1+ o2+ o3+ o4 is 1.

Further, when the weight corresponding to any embodiment is zero, the embodiment may not be used as a criterion for determining whether an event occurs. As can be understood from the above description, the event prediction in the embodiment of the present application may use one implementation to determine whether the first order data is separated from the historical data in the target area, or may use multiple implementations to determine whether the first order data is separated from the historical data in the target area. Specifically, different determination manners may be selected for different scenes.

In one example, if o1 is zero, the result of the event is determined by the determination results of the second, third and fourth embodiments. For example, when the calculated matching value b is greater than 0.7, it indicates that an event occurs in the target area. If the value of o2, o3, or o4 is 1/3, the target area is determined to have an event if any two of the values b2, b3, and b4 are 1.

In some embodiments, the method further comprises: matching a resource allocation strategy for the target area according to the estimation result; and allocating resources to the target area according to the resource allocation strategy.

If the event causes the order sharp increase, service resources can be called from other areas to the target area; if the order is lowered or even no event is caused, the service resources in the target area can be tuned away from the target area, and a prompt message is further sent to the service resources in other areas to inform the service resources in other areas not to go to the target area.

In some embodiments, the order is a network car booking order, and the resource allocation policy is a network car booking quantity matching policy; the step of allocating resources to the target area according to the resource allocation policy includes: if the estimated result is a first type event, calling a network car booking driver to the target area; and if the estimated result is a second type of event, the network car booking driver in the target area is dispatched to other areas.

By matching the resource allocation strategy for the target area according to the event estimation result, the target area can not lose the average service currently provided by the target area due to the occurrence of the event, and the user experience is improved.

Each order in the first order data carries a position attribute tag. By the attributes of each order, it can be identified which areas are likely to cause events to occur, or which types of areas are more likely to have set type events.

Based on the flowchart shown in fig. 3, in other embodiments, the event prediction method may further include: and if the estimated result is characterized in that a set event occurs, determining the position attribute type causing the set event according to the position attribute label in each order in the first order data.

The location attribute tags may be a mall name, office building name, school name, hospital name, park name, and the like.

The position attribute type causing the set event is determined through the position attribute tag, which type of area is easier to occur the set event can be obtained through the position attribute type of the occurrence of the historical events for a plurality of times, and relatively more or less resources can be provided for the area which is possible to occur the set event in advance under possible conditions, so that the speed of responding to the user is improved, and the satisfaction degree of the user is further improved.

EXAMPLE III

Based on the same application concept, an event prediction device corresponding to the event prediction method is further provided in the embodiment of the present application, and as the principle of solving the problem of the device in the embodiment of the present application is similar to that of the event prediction method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and repeated parts are not described again.

Fig. 4 is a block diagram illustrating an event prediction apparatus according to some embodiments of the present application, where the event prediction apparatus implements functions corresponding to the steps performed by the method. The device may be understood as the server or the processor of the server, or may be understood as a component that is independent of the server or the processor and implements the functions of the present application under the control of the server, as shown in the figure, the event prediction device may include: an obtaining module 401, a judging module 402, and an obtaining module 403, wherein:

the obtaining module 401 may be configured to obtain first order data in a target area within a specified time period;

a determining module 402, configured to compare the first order data with historical data in the target area, and determine whether the first order data is separated from a rule of the historical data in the target area;

the obtaining module 403 may be configured to obtain, according to the determination result, an estimated result of whether a set event occurs in the target area.

In some embodiments, the determining module may be further configured to:

In some embodiments, the determining module is further configured to:

and carrying out weighted summation on the order quantity of the specified days in the specified time period each day to obtain an order floating value.

In some embodiments, the determining module is further configured to:

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

In some embodiments, the obtaining module is further configured to:

In some embodiments, the apparatus further comprises:

In some embodiments, the obtaining module is further configured to:

In some embodiments, the apparatus further comprises: a filtration module to:

calculating the entropy of each region;

and obtaining a target area from the preselected area.

entropy＝-∑p_ilog(p_i)；

In some embodiments, the obtaining module is further configured to:

Each order in the first order data may carry a location attribute tag, and the event estimation device may further include:

The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, 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 description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.

According to the event estimation method and device, the order condition at the current time is compared and analyzed with the order condition in the historical data, so that the possible events which are experienced in the target area can be estimated, and compared with the events which are disclosed from all large portal websites in the prior art, the estimation of some events which can cause order quantity change can be achieved.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the event prediction method described in the above method embodiment.

The computer program product of the route planning method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the event prediction method in the above method embodiment, which may be referred to in the above method embodiment specifically, and are not described herein again.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into 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 such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An event prediction method, comprising:

acquiring first order data in a target area within a specified time period;

2. The method of claim 1, wherein comparing the first order data to historical data in the target area to determine whether the first order data deviates from the regularity of the historical data in the target area comprises:

3. The method of claim 2, wherein said calculating said first order data and said second order data as a floating ratio is accomplished by the formula:

4. The method of claim 2, wherein the second order data comprises: and the target area is the same as the target area in the attribute day before the current day and the order amount corresponding to the specified time period, and the attribute day comprises a working day and a holiday.

5. The method of claim 1, wherein comparing the first order data to historical data in the target area to determine whether the first order data deviates from the regularity of the historical data in the target area comprises:

6. The method of claim 5, wherein said step of calculating from said third order data an order float value for said previously specified day for a specified time period comprises:

7. The method of claim 1, wherein comparing the first order data to historical data in the target area to determine whether the first order data deviates from the regularity of the historical data in the target area comprises:

8. The method of claim 1, wherein comparing the first order data to historical data in the target area to determine whether the first order data deviates from the regularity of the historical data in the target area comprises:

9. The method of claim 8, wherein the modeling of the usage history data for the amount of orders for the specified time period of the current day for the target area is predicted, and wherein the obtaining of the predicted value is performed by:

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

10. The method of claim 8, wherein the step of using historical data to model and predict the amount of orders for the target area for the specified time period of the day to obtain a predicted value comprises:

11. The method according to any one of claims 1 to 10, wherein the step of obtaining an estimated result of whether the target area has a set event according to the determination result comprises:

12. The method of claim 11, wherein the method further comprises:

13. The method of claim 12, wherein the order is a network car booking order and the resource allocation policy is a network car booking quantity matching policy; the step of allocating resources to the target area according to the resource allocation policy includes:

14. The method according to any one of claims 1 to 10, wherein the step of obtaining an estimated result of whether the target area has a set event according to the determination result comprises:

15. The method of any of claims 1-10, wherein prior to the step of obtaining first order data within a target area for a specified period of time, the method further comprises:

calculating the entropy of each region;

and obtaining a target area from the preselected area.

16. The method of claim 15, wherein the calculation of entropy for each region is achieved by the following equation:

entropy＝-∑p_ilog(p_i)；

17. The method of any one of claims 1-10, wherein the step of obtaining first order data within a target area for a specified period of time comprises:

18. The method of claim 1, wherein each order in the first order data carries a location attribute tag, the method further comprising:

19. An event prediction apparatus, comprising:

20. The apparatus of claim 19, wherein the determining module is further configured to:

21. The apparatus of claim 20, wherein said calculating said first order data and said second order data as a floating ratio is accomplished by the equation:

22. The apparatus of claim 20, wherein the second order data comprises: and the target area is the same as the target area in the attribute day before the current day and the order amount corresponding to the specified time period, and the attribute day comprises a working day and a holiday.

23. The apparatus of claim 19, wherein the determining module is further configured to:

24. The apparatus of claim 23, wherein said calculating an order float value for said previously specified day for a specified time period based on said third order data is performed by:

25. The apparatus of claim 19, wherein the determining module is further configured to:

26. The apparatus of claim 19, wherein the determining module is further configured to:

27. The apparatus of claim 26, wherein the modeling of the usage history data to predict the amount of orders for the specified time period of the current day for the target area results in a predicted value by:

s_i＝α(x_i-p_i-k)+(1-α)(s_i-1+t_i-1)；

t_i＝β(s_i-s_t-1)+(1-β)t_i-1；

p_i＝γ(x_i-s_i)+(1-γ)p_i-k；

28. The apparatus of claim 26, wherein the model prediction of the order size for the specified time period of the current day for the target area using historical data is performed by:

29. The apparatus of any one of claims 19-28, wherein the means for obtaining is further configured to:

30. The apparatus of claim 29, wherein the apparatus further comprises:

31. The apparatus of claim 30, wherein the order is a network car booking order and the resource allocation policy is a network car booking quantity matching policy; the allocation module is further configured to:

32. The apparatus of any one of claims 19-28, wherein the means for obtaining is further configured to:

33. The apparatus of any one of claims 19-28, further comprising: a filtration module to:

calculating the entropy of each region;

and obtaining a target area from the preselected area.

34. The apparatus of claim 33, wherein the calculation of entropy for each region is achieved by:

entropy＝-∑p_ilog(p_i)；

35. The apparatus of any one of claims 19-28, wherein the obtaining module is further configured to:

36. The apparatus of claim 19, wherein each order in the first order data carries a location attribute tag, the apparatus further comprising:

37. 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 communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 18.

38. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 18.