CN111598372A

CN111598372A - Risk prevention method and system

Info

Publication number: CN111598372A
Application number: CN201910132784.6A
Authority: CN
Inventors: 何冠乔; 张威; 张佳林
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-02-21
Filing date: 2019-02-21
Publication date: 2020-08-28

Abstract

The application discloses a risk prevention method. The method comprises the following steps: acquiring relevant data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution; when receiving information of an order ending sent by a terminal associated with a current order and a first position of a vehicle associated with the current order when the order is ended is not matched with a travel destination, determining a risk judgment result in the execution process of the current order at least based on relevant data of the order; and executing the set operation based on the risk judgment result. According to the method and the system, when the information of the terminal end order related to the current order is received and the first position where the vehicle is located is not matched with the travel destination, the system platform can timely perform risk judgment and timely perform corresponding processing on the potential risk order.

Description

Risk prevention method and system

Technical Field

The present application relates to the field of public transportation, and in particular, to a method and system for risk prevention.

Background

With the development of social economy and the improvement of the living standard of people, the quantity of automobiles in China is continuously increased. How to guarantee the safety of drivers and passengers is the primary problem of the platform by the network car booking platform, but a relatively effective monitoring measure is lacked at present. Therefore, how to provide a method and system for risk prevention to improve the safety of drivers and passengers has become one of the technical problems to be solved urgently.

Disclosure of Invention

One of the embodiments of the present application provides a method for risk prevention. The method comprises the following steps: acquiring relevant data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution; when receiving information of an order ending sent by a terminal associated with a current order and a first position of a vehicle associated with the current order when the order is ended is not matched with a travel destination, determining a risk judgment result in the execution process of the current order at least based on relevant data of the order; and executing the set operation based on the risk judgment result.

In some embodiments, the terminals include in-vehicle terminals, service provider terminals, and/or service requester terminals.

In some embodiments, the order characteristics include at least one of: identity information of the service provider, identification information of a vehicle associated with the service provider, service time, trip start point, trip destination, trip path, and identity information of the service requester; the state data in the order execution process at least comprises one of the following data: the data processing system comprises positioning data of a terminal, state data of the terminal, state data of a vehicle, environment data inside the vehicle and real-time state data of an external environment.

In some embodiments, said determining a risk determination result during execution of the current order based on at least the relevant data of the order further comprises: and determining a risk judgment result in the current order execution process based on the distance between the first position and the travel destination.

In some embodiments, said determining a risk determination result during execution of the current order based on at least the relevant data of the order further comprises: and determining a risk judgment result in the current order execution process based on whether the first position is located in a preset safe area.

In some embodiments, said determining a risk determination result during execution of the current order based on at least the correlation number of the order further comprises: based on the relevant data of the order, extracting traffic flow data within a certain distance range from the first position; determining a risk judgment result of the order based on the traffic flow data within a certain distance range from the first position; the traffic data within a certain distance range from the first position comprise real-time traffic data and/or estimated traffic data.

In some embodiments, said determining a risk determination result during execution of the current order based on at least the relevant data of the order further comprises: determining data reflecting user behavior based on relevant data of the order; determining a risk judgment result in the current order execution process based on the data reflecting the user behavior.

In some embodiments, the data reflecting the user behavior includes an operation behavior of the user on the service platform or a movement track of the user after the current order is finished.

In some embodiments, the operational behavior of the user on the service platform includes: whether the service provider accepts or executes a new order through its terminal or whether the service requester initiates a new order through its terminal.

In some embodiments, said determining a risk determination result during execution of the current order based on at least the relevant data of the order further comprises: and processing the order related data by utilizing the trained risk judgment model to determine a risk judgment result.

In some embodiments, the operation of setting comprises at least one of: risk ranking operations, risk confirmation operations, risk handling operations, and continuous monitoring operations.

One of the embodiments of the present application provides a risk prevention system. The system comprises: the data acquisition module is used for acquiring related data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution; the risk judgment module is used for determining a risk judgment result in the execution process of the current order at least based on relevant data of the order when the information that the terminal associated with the current order sends the order is received and the first position of the vehicle associated with the current order when the order is ended is not matched with the travel destination; and the risk handling module is used for executing set operation based on the risk judgment result.

In some embodiments, the order characteristics include at least one of: identity information of the service provider, identification information of a vehicle associated with the service provider, service time, trip start point, trip destination, trip path, and identity information of the service requester; the state data in the order execution process at least comprises one of the following data: positioning data of terminal, status data of vehicle, environmental data of vehicle interior and real-time status data of external environment

In some embodiments, the risk determination module is further configured to: and determining a risk judgment result in the current order execution process based on the distance between the first position and the travel destination.

In some embodiments, the risk determination module is further configured to: and determining a risk judgment result in the current order execution process based on whether the first position is located in a preset safe area.

In some embodiments, the risk determination module is further configured to: based on the relevant data of the order, extracting traffic flow data within a certain distance range from the first position; determining a risk judgment result of the order based on the traffic flow data within a certain distance range from the first position; the traffic data within a certain distance range from the first position comprise real-time traffic data and/or estimated traffic data.

In some embodiments, the risk determination module is further configured to: determining data reflecting user behavior based on relevant data of the order; determining a risk judgment result in the current order execution process based on the data reflecting the user behavior.

In some embodiments, the data reflecting the user behavior includes an operation behavior of the user on the service platform or a movement track of the user after the current order is finished. Whether the service provider accepts or executes the new order through its terminal.

In some embodiments, the operational behavior of the user on the service platform includes: whether the service provider accepts or executes a new order through its terminal or whether a new order is initiated in the service request through its terminal.

In some embodiments, the risk determination module is further configured to: and processing the order related data by utilizing the trained risk judgment model to determine a risk judgment result.

One of the embodiments of the present application provides a risk prevention device. The apparatus comprises at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the method for risk prevention.

One of the embodiments of the present application provides a computer-readable storage medium. The storage medium stores computer instructions that, when executed by a processor, implement the method of risk prevention.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a risk prevention system 100 according to some embodiments of the present application;

FIG. 2 is a block diagram of a user terminal 200 according to some embodiments of the present application;

FIG. 3 is a block diagram of a processing device 110 according to some embodiments of the present application;

FIG. 4 is an exemplary flow chart of a method 400 of risk prevention according to some embodiments of the present application;

FIG. 5 is an exemplary flow chart of another risk prevention method 500 according to some embodiments of the present application;

FIG. 6 is an exemplary flow chart of a risk determination method 600 according to some embodiments of the present application;

FIG. 7 is an exemplary flow chart of another risk determination method 700 shown in some embodiments herein;

FIG. 8 is an exemplary flow chart of another risk determination method 800 according to some embodiments of the present application;

FIG. 9 is an exemplary flow chart of another risk determination method 900 according to some embodiments of the present application;

FIG. 10 is an exemplary flow chart of another method 1000 of risk prevention according to some embodiments of the present application;

FIG. 11 is an exemplary flow diagram of a method 1100 of training a risk decision model according to some embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Embodiments of the present application may be applied to different transportation systems including, but not limited to, one or a combination of terrestrial, marine, aeronautical, aerospace, and the like. For example, taxis, special cars, tailplanes, buses, designated drives, trains, railcars, high-speed rails, ships, airplanes, hot air balloons, unmanned vehicles, receiving/sending couriers, and the like, employ managed and/or distributed transportation systems. The application scenarios of the different embodiments of the present application include, but are not limited to, one or a combination of several of a web page, a browser plug-in, a client, a customization system, an intra-enterprise analysis system, an artificial intelligence robot, and the like. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these figures. For example, other similar guided user parking systems.

The terms "passenger", "passenger end", "user terminal", "customer", "demander", "service requester", "service demander", "consumer side", "user demander" and the like are used interchangeably and refer to a party that needs or orders a service, either a person or a tool. Similarly, "driver," "provider," "service provider," "server," and the like, as described herein, are interchangeable and refer to an individual, tool, or other entity that provides a service or assists in providing a service. In addition, a "user" as described herein may be a party that needs or subscribes to a service, or a party that provides or assists in providing a service.

One aspect of the invention relates to a risk prevention method. The processing equipment can acquire data related to the travel and the user before and after the order in real time, judge whether the order is risky according to a certain rule, take corresponding risk countermeasures based on a judgment result, and dynamically adjust the judgment rule and algorithm according to the result and feedback.

Fig. 1 is a schematic diagram of an application scenario of a risk prevention system 100 according to some embodiments of the present application.

The risk prevention system 100 may determine the risk of a safety event on the trip and take countermeasures to reduce injury to the user. The risk prevention system 100 may be used in a service platform for the internet or other networks. For example, the risk prevention system 100 may be an online service platform that provides services for transportation. In some embodiments, the risk prevention system 100 may be applied to a network appointment service, such as a taxi call, a express call, a special call, a mini-bus call, a car pool, a bus service, a driver hiring and pick-up service, and the like. In some embodiments, the risk prevention system 100 may also be applied to designated drives, couriers, takeoffs, and the like. In other embodiments, the risk prevention system 100 may be applied to the fields of housekeeping services, travel (e.g., tourism) services, education (e.g., offline education) services, and the like. As shown in FIG. 1, the risk prevention system 100 may include a processing device 110, one or more terminals 120, a storage device 130, a network 140, and an information source 150.

In some embodiments, processing device 110 may process data and/or information obtained from terminal 120, storage device 130, and/or information source 150. For example, the processing device 110 may obtain location/trajectory information for the plurality of terminals 120 and/or characteristic information of parties (e.g., drivers and passengers) associated with the trip. Processing device 110 may process the information and/or data obtained as described above to perform one or more functions described herein. For example, the processing device 110 may determine the security risk based on the risk determination rule and/or risk determination model and determine to take corresponding countermeasures, such as alarming and/or providing offline support, according to the determination result.

In some embodiments, the processing device 110 may obtain data related to at least one current order; the relevant data of the order comprises at least one of: order characteristics, status data during order execution, and a history associated with at least one data in the order. In some embodiments, the processing device 110 may determine a risk determination result during execution of the current order based on at least the relevant data of the order when the first location where the vehicle is located does not match the travel destination when the information of the terminal end order associated with the current order is received. In some embodiments, the processing device 110 may perform the set operation based on the risk determination result.

In some embodiments, the processing device 110 may be a stand-alone server or a group of servers. The set of servers may be centralized or distributed (e.g., processing device 110 may be a distributed system). In some embodiments, the processing device 110 may be local or remote. For example, the processing device 110 may access information and/or material stored in the terminal 120, the storage device 130, and/or the information source 150 via the network 140. In some embodiments, the processing device 110 may be directly connected to the terminal 120, the storage device 130, and/or the information source 150 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like. In other embodiments, the processing device 110 may be one of the terminals 120 at the same time.

In some embodiments, processing device 110 may include one or more sub-processing devices (e.g., a single-core processor or a multi-core processor). By way of example only, processing device 110 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 circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

In some embodiments, the terminal 120 may be a device with data acquisition, storage, and/or transmission capabilities, and may include any user or terminal that does not directly participate in a service, a service provider terminal, a service requester terminal, and/or a vehicle mounted terminal. The service provider may be an individual, tool, or other entity that provides the service. The service requester may be an individual, tool or other entity that needs to obtain or is receiving a service. For example, for a car-order-on-the-net service, the service provider may be a driver, a third-party platform, and the service requester may be a passenger or other person or device (e.g., an internet-of-things device) that receives similar services. In some embodiments, the terminal 120 may be used to collect various types of data, including but not limited to data related to services. For example, the data collected by the terminal 120 may include data related to an order (e.g., order request time, start and end points, passenger information, driver information, vehicle information, etc.), data related to vehicle driving conditions (e.g., current speed, current acceleration, attitude of the device, road conditions, etc.), data related to a service trip (e.g., preset trip path, actual travel path, cost, etc.), data related to a service participant (service provider/service requester) (e.g., personal information of the participant, handling information of the terminal 120 by the service provider/service requester, various related data of the terminal device, etc.), and the like or any combination thereof. The collected data may be real-time or various types of historical data such as past usage history of the user, etc. The data may be collected by the terminal 120 through its own sensor, may also collect data acquired by an external sensor, may also read data stored in its own memory, and may also read data stored in the storage device 130 through the network 140.

In some embodiments, the sensor may include a pointing device, a sound sensor, an image sensor, a temperature and humidity sensor, a position sensor, a pressure sensor, a distance sensor, a velocity sensor, an acceleration sensor, a gravity sensor, a displacement sensor, a moment sensor, a gyroscope, or the like, or any combination thereof, or the like. Various types of data collected by the terminal 120 may be used to determine malignancy and/or abnormal conditions that may occur during subsequent service execution. For example, it may be determined whether there is a stay abnormality at a certain place (including during service execution and/or after completion of service), whether a signal is lost at a certain route section, whether service is ended in advance without reaching a service destination, whether there is a preset route, whether there is a travel to a remote area, whether there are stops in a trip for a plurality of times, whether a travel speed is slow, whether there is an offset route period, whether a travel time exceeds a threshold value, and the like, based on trajectory data. For example, it is possible to determine whether or not the vehicle is in danger of driving, such as a collision or a rollover, based on changes in the posture, speed, and/or acceleration of the vehicle.

In some embodiments, the terminal 120 may include one or a combination of desktop computer 120-1, laptop computer 120-2, in-vehicle device 120-3, mobile device 120-4, and/or the like. In some embodiments, mobile device 120-4 may include a smart home device, a wearable device, a smart mobile device, an augmented reality device, and the like, or a combination thereof. In some embodiments, the wearable device may include a smart bracelet, smart footwear, smart glasses, smart helmet, smart watch, smart clothing, smart backpack, smart accessory, or 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, a POS machine, or the like, or a combination thereof. In some embodiments, the in-vehicle device 120-3 may include an on-board computer, an automotive data recorder, an on-board human-computer interaction (HCI) system, a tachograph, an on-board television, and so forth. In some embodiments, the on-board embedded device 120-3 may acquire various component data and/or operational data of the vehicle, such as speed, acceleration, direction of travel, component status, vehicle surroundings, and the like. The acquired data may be used to determine whether a driving accident (e.g., a rollover, a crash), a driving malfunction (e.g., an engine or transmission malfunction causing the vehicle to be unable to move), etc.

In some embodiments, the terminal 120 may be a device having a positioning technology for locating the position of the terminal 120. In some embodiments, the terminal 120 may transmit the collected data/information to the processing device 110 via the network 140 for subsequent steps. The terminal 120 may also store the collected data/information in its own memory or transmit it to the storage device 130 via the network 140 for storage. The terminal 120 may also receive and/or display notifications related to risk prevention generated by the processing device 110. In some embodiments, multiple terminals may be connected to each other, and various types of data may be collected together and preprocessed by one or more terminals. The in-vehicle terminal (e.g., in-vehicle built-in device 120-3) may acquire data of various sensors in the vehicle. For example, the vehicle position may be acquired by a positioning device, the vehicle acceleration may be acquired by an acceleration sensor, the vehicle speed may be acquired by a speed sensor, and the like.

Storage device 130 may store data and/or instructions. In some embodiments, storage device 130 may store data/information obtained by terminal 120. For example, the storage device 130 may store a history of the driver or passenger, such as historical orders, driver or passenger related information, and the like. The storage device 130 may also store historical transportation service data for historical events, such as order data for historical service orders for some events, service participant data, vehicle-related data, and the like, and trip data, and the like. In some embodiments, storage device 130 may store data and/or instructions for execution by, or used by, processing device 110 to perform the exemplary methods described in this application. For example, the storage device 130 may store a risk determination model that may determine whether a transportation service is at risk based on data/information related to the transportation service acquired by the processing device 110. In some embodiments, the storage device 130 may store various types of real-time or historical data of the user terminal, for example, historical records of the user related to historical services, such as historical ratings, and the like. In some embodiments, the storage device 130 may be part of the processing device 110 or the terminal 120. In some embodiments, storage 130 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read-only memory can include Random Access Memory (RAM). Exemplary RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDRSDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance RAM (Z-RAM), and the like. Exemplary ROMs may include Mask ROM (MROM), Programmable ROM (PROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, storage device 130 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, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof. For example, some risk judgment algorithms or data in the present invention may be stored on a certain cloud platform, and are periodically updated, and the processing device 110 accesses these algorithms or data through a network, so as to implement unification and interaction of the algorithms or data of the whole platform. In particular, some historical data may be uniformly stored on one cloud platform of the platform so that a plurality of processing devices 110 or terminals 120 can access or update the data, thereby ensuring real-time performance and cross-platform use of the data. For example, the terminal 120 may issue its speed and positioning information to a certain cloud platform at any time, and the system may determine whether an abnormal condition occurs according to the feedback of multiple terminals 120.

In some embodiments, the storage device 130 may be connected to the network 140 to communicate with one or more components (e.g., the processing device 110, the terminal 120, the information source 150) in the risk prevention system 100. One or more components in the risk prevention system 100 may access data or instructions stored in the storage device 130 through the network 140. In some embodiments, the storage device 130 may be directly connected or in communication with one or more components (e.g., the processing device 110, the terminal 120, the information source 150) in the risk prevention system 100. In some embodiments, the storage device 130 may be part of the processing device 110.

Network 140 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the risk prevention system 100 (e.g., the processing device 110, the terminal 120, the storage device 130, and the information source 150) may send and/or receive information and/or data to/from other components in the risk prevention system 100 via the network 140. For example, the processing device 110 may obtain data/information related to a transportation service from the terminal 120 and/or the information source 150 via the network 140. As another example, the terminal 120 may obtain a determination model for determining whether the transportation service is at risk from the processing device 110 or the storage device 130 via the network 140. The obtained decision model may be implemented in application software of the terminal 120. After acquiring the data/information related to the transportation service, the terminal 120 may determine whether the transportation service has a risk and perform a risk handling operation, such as initiating a telephone alarm. In some embodiments, the network 140 may be any form or combination of wired or wireless network. By way of example only, network 140 may include a cable network, a wireline 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, a Global System for Mobile communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a General Packet Radio Service (GPRS) network, an enhanced data rates for GSM evolution (EDGE) network, a Wideband Code Division Multiple Access (WCDMA) network, a High Speed Downlink Packet Access (HSDPA) network, a Long Term Evolution (LTE) network, a User Datagram Protocol (UDP) network, a Transmission control protocol/Internet protocol (TCP/IP) network, a Short Message Service (SMS), A Wireless Application Protocol (WAP) network, an ultra-wideband (UWB) network, a mobile communication (1G, 2G, 3G, 4G, 5G) network, Wi-Fi, Li-Fi, narrowband Internet of things (NB-IoT), and the like, or any combination thereof. In some embodiments, the risk prevention system 100 may include one or more network access points. For example, risk prevention system 110 may include wired or wireless network access points, such as base stations and/or wireless access points 140-1, 140-2, through which one or more components of risk prevention system 100 may connect to network 140 to exchange data and/or information.

The information source 150 may be used to provide a source of information for the risk prevention system 100. In some embodiments, the information source 150 may be used to provide the risk prevention system 100 with information related to transportation services, such as weather conditions, traffic information, geographic information, legal information, news events, life information, life guide information, and the like. In some embodiments, the information source 150 may also be other third party platforms that may provide credit records, such as credit records, for the service requester and/or the service provider. In some embodiments, the information source 150 may be used to provide risk prevention system 100 with information related to risk prevention, such as driving safety tips, personal safety tips, property safety tips, and the like. The information source 150 may be implemented in a single central server, multiple servers connected by communication links, or multiple personal devices. When the information source 150 is implemented in multiple personal devices, the personal devices may generate content (e.g., referred to as "user-generated content"), for example, by uploading text, voice, images, and video to a cloud server. The information source may be generated by a plurality of personal devices and a cloud server. The storage device 130, the processing device 110 and the terminal 120 may also be sources of information. For example, the speed and positioning information fed back by the terminal 120 in real time may be used as an information source to provide traffic condition information for other devices to obtain.

Fig. 2 is a diagram illustrating exemplary hardware and/or software components of a mobile device 200 on which terminal 120 may be implemented according to some embodiments of the present application.

As shown in fig. 2, mobile device 200 may include a communication unit 210, a display unit 220, a Graphics Processing Unit (GPU)230, a Central Processing Unit (CPU)240, input/output 250, memory 260, storage 270, and sensors 280. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in mobile device 200.

In some embodiments, the operating system 262 (e.g., IOS) is moved^TM、Android^TM、Windows Phone^TMEtc.) and one or more application programs 264 may be loaded from storage 290 into memory 260 for execution by CPU 240. The applications 264 may include a browser or any other suitable mobile application for sending data/information associated with transportation services and receiving and presenting processing or other related information from the risk prevention system 100. For example, application 264 may be an online taxi appointment travel platform (e.g., a drip line)^TM) The user (e.g., service requester) may request the transportation service through the application 264 and send the request information to the backend server. User interaction with the information flow may be accomplished via input/output 250 and provided to processing device 110 and/or other components of risk prevention system 100 via network 140.

In some embodiments, mobile device 200 may also include a plurality of sensors 280. The sensors 280 may acquire data related to service participants (e.g., drivers/passengers), vehicles, and/or travel, etc. In some embodiments, the sensor may include a sound sensor, an image sensor, a temperature and humidity sensor, a position sensor, a pressure sensor, a distance sensor, a velocity sensor, an acceleration sensor, a gravity sensor, a displacement sensor, a moment sensor, a gyroscope, or the like, or any combination thereof. In some embodiments, the data acquired by the sensors may be used to subsequently determine whether a risk occurs and/or what risk occurs. For example, the sound sensor and the image sensor may collect conversations between service participants and real-time scenes in the vehicle for determining whether a driver conflict or a property/personal safety event occurs, such as a physical conflict, drunk driving, robbery, sexual assault, sexual disturbance, etc. For another example, the position sensor and the displacement sensor may collect real-time position of the vehicle and/or travel track data of the vehicle, so as to determine whether a travel abnormality occurs, such as an abnormal stop, a travel deviation, an abnormal travel time, and the like. Also for example, the speed sensor, the acceleration sensor and the gyroscope may acquire a real-time speed, a real-time acceleration, a deflection amount, a deflection frequency and the like of the vehicle, so as to determine whether a driving safety accident, such as a collision, a rollover and the like, occurs in the vehicle.

In some embodiments, the mobile device 200 may also communicate with the vehicle, for example, bluetooth communication, to acquire data collected by vehicle-mounted sensors installed inside or outside the vehicle, such as current state data and driving data of the vehicle, and combine the data acquired by the own sensors and the data acquired by the vehicle-mounted sensors for subsequent risk determination.

In some embodiments, the mobile device 200 may send the acquired data/information, including data acquired by its own sensors and data acquired by in-vehicle sensors, to the processing device 110 of the risk prevention system 100 via the network 140 for risk determination and handling. In some embodiments, mobile device 200 may make risk determinations and treatments directly. For example, the application 264 may have a code or a module for risk assessment built therein, and may directly perform risk assessment and treatment. In some embodiments, the processing device 110 and/or the mobile device 200 of the risk prevention system 100 may also generate a security notification instruction according to the risk determination and/or treatment result. The mobile device 200 may remind the user of the current security status by receiving and executing the security notification command. For example, the mobile device 200 may implement the security notification by way of voice (e.g., through a speaker), vibration (e.g., through a vibrator), text (e.g., through a text message or a social application), flashing lights (e.g., through a flashing light or the display unit 220), or the like, or a combination thereof, for the purpose of alerting the user.

In some embodiments, a user of mobile device 200, e.g., a driver and/or passenger, may perform the risk determination process on their own. In particular, the driver and/or passenger may actively report the risk through the application 264 in the mobile device 200. For example, performing a particular operation on the mobile device 200, such as shaking or throwing, may initiate an alarm procedure. As another example, the interface of the application 264 may include a quick entry (e.g., alarm button, help button) that communicates directly with the back-end security platform, and the user may alert the police by clicking on the alarm button when determining that the user is in a dangerous situation. After alerting, the application 264 may also send the current location and travel information of the alerting user to the police to assist in rescue.

To implement the various modules, units, and functions thereof described herein, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface components may be used to implement a Personal Computer (PC) or any other type of workstation or terminal device. A computer can also function as a system if the computer is appropriately programmed.

Fig. 3 is a block diagram of an exemplary processing device 110 shown in accordance with some embodiments of the present application.

The processing device 110 may obtain data related to the transportation service for processing to determine a risk determination for the transportation service, and further determine a risk coping method according to a result of the risk determination. In some embodiments, the processing device 110 may further update the methods, such as rules, algorithms, models, and the like, used in the risk determination and handling process according to the risk confirmation and handling results, so as to achieve the optimal risk prevention and handling effects. As shown in fig. 3, processing device 110 may include a data acquisition module 310, a risk determination module 320, a risk management module 330, and an update module 340.

The data acquisition module 310 may be used to acquire data.

In some embodiments, the data acquisition module 310 may acquire data related to at least one service order. The service order may be a transportation service order, such as a freight transportation order, a travel service order, and/or the like, that is requested, executed, and/or completed at the current time. The data related to the service order may include an order characteristic of the service order, status data during execution of the order, and a history associated with at least one data in the service order. The order characteristics may be information directly documented in the service order including, but not limited to, identity information of the service provider, identification information of the vehicle associated with the service order, service time, trip origin, trip destination, trip path, identity information of the service requester, and the like, or any combination thereof. The status data during order execution may refer to status data of equipment related to the order during service order execution and/or environmental data of the user or the vehicle surroundings during order execution, including but not limited to location data of the terminal related to the service order, status data of the vehicle, environmental data of the vehicle interior and environmental data of the vehicle surroundings, and the like, or any combination thereof. The history record related to at least one data in the service order may be understood as a history record corresponding to a certain data in the current service order, for example, a record of an execution history service order of a service provider, a credit investigation record of a service provider, a record of a participation history service order of a service requester, a credit investigation record of a service requester, and the like, or any combination thereof.

In some embodiments, the data acquisition module 310 may communicate with the terminal 120, the storage device 130, and/or the information source 150 via the network 140 to acquire the data. After acquisition, the data acquisition module 310 may transmit the data to the risk determination module 320 for various types of risk determinations.

In some embodiments, the data acquisition module 310 may also acquire historical order data, which may include traffic transportation service related data for which a risk event occurred. The historical data may be similar to the real-time data described above, while also including the specific types of risk events that occur corresponding to a particular transportation service. The risk event types may include robbery, personal safety events, service cancellation exceptions, stay in journey exceptions, stay after journey exceptions, loss exceptions, miss exceptions, journey exceptions, driving hazards, and the like, or any combination thereof. In some embodiments, the historical order data may be used as training data to train a risk decision model or to determine risk decision rules. The resulting risk decision model or risk decision rule may be used to decide on the service order data to determine if there is risk.

In some embodiments, the historical order data may be stored in the storage device 130, and the data acquisition module 310 may communicate with the storage device 130 over the network 140 to read the historical order data stored therein.

The risk determination module 320 may make a risk determination based on the acquired data.

In some embodiments, the risk determination module 320 may use the determination rules to make a risk determination for the current state of the service order. In some embodiments, the decision rule may be a condition and/or threshold set based on the historical order data and/or experience. The threshold setting of the decision rule may be determined according to data statistics, and an intermediate result obtained in a training process of the risk decision model may also be used as a decision threshold. For example, the determination rule may be set to determine the risk of robbery and/or the risk of female safety incident based on preset conditions such as whether the time of departure is late at night, whether the departure point is remote, whether the driver and/or passenger have a relevant history, whether the number of occurrences of sensitive words in the sensed data exceeds a preset value, and the like. For another example, it may be determined whether the vehicle has a driving risk such as a collision or a rollover based on sensor data (e.g., acceleration due to gravity) exceeding a preset threshold.

In some embodiments, the risk assessment module 320 may use a risk assessment model to make a risk assessment of the current state of the transportation service. The risk determination model may be a machine learning model, such as a decision tree, trained via the acquired historical order data. For example, the model may be trained using data associated with the transportation service in the historical order data as input, and the type of risk that the transportation service is occurring as the correct criteria (group Truth). In some embodiments, the risk determination model may be a single overall determination model for determining whether one or more types of risks exist, including robbery, personal safety incident, cancellation exception, stop-in-journey exception, stop-after-journey exception, loss exception, miss-delivery exception, journey exception, driving hazard, and the like, or any combination thereof. In some embodiments, the risk decision model may include multiple models that are each specific to a particular risk event. For example, for the determination of the robbery risk, there may be a special robbery determination model to determine the current state of the transportation service. Similarly, other risk determinations may be performed with a specific corresponding model. The risk determination module 320 may utilize a combination of models to determine one or more risks. The combination mode of the models can be determined according to actual requirements. For example, in areas with poor security (e.g., urban and rural junctions), the decision may be made with emphasis on robbery and personal safety incidents. In a region with dense traffic flows such as a city center, the determination of the travel abnormality can be emphasized.

In some embodiments, the determination result of the risk determination module 320 may include the presence or absence of risk and a quantitative representation of risk. For example only, the determination may be risk-free. Alternatively, the determination result may be the existence risk and the type of risk, a value representing the risk level, the risk probability, etc., for example, the determination result is (risk, robbery-5 level) or (risk, robbery-56%, abnormal stay-87%). In some embodiments, the risk assessment module 320 may aggregate the overall risk level and/or probability and output a assessment corresponding to the aggregate risk assessment, e.g., the assessment is (at risk, 74%). It should be noted that the form of the determination result described above is for illustrative purposes only, and the present application does not limit the form of the determination result.

In some embodiments, the risk determination module 320 may be configured to determine a risk determination result during execution of the current order based on at least relevant data of the order when information that the terminal associated with the current order transmits the end order is received and the first location where the vehicle associated with the current order was located when the order was ended does not match the travel destination.

In some embodiments, the risk determination module 320 is further configured to: and determining a risk judgment result in the current order execution process based on the distance between the first position and the travel destination.

In some embodiments, the risk determination module 320 is further configured to: and determining a risk judgment result in the current order execution process based on whether the first position is located in a preset safe area.

In some embodiments, the risk determination module 320 is further configured to: based on the relevant data of the order, extracting traffic flow data within a certain distance range from the first position; determining a risk judgment result of the order based on the traffic flow data within a certain distance range from the first position; the traffic data within a certain distance range from the first position comprise real-time traffic data and/or estimated traffic data.

In some embodiments, the risk determination module 320 is further configured to: determining data reflecting user behavior based on relevant data of the order; determining a risk judgment result in the current order execution process based on the data reflecting the user behavior.

In some embodiments, the data reflecting user behavior includes whether the service provider accepts or executes a new order through its terminal.

In some embodiments, the data reflecting user behavior further comprises: after the current order is finished, the movement track of the service provider and the movement track of the service requester.

In some embodiments, the risk determination module 320 is further configured to: and processing the order related data by utilizing the trained risk judgment model to determine a risk judgment result.

In some embodiments, the risk determination includes a risk level of the order; the risk coping module 320 is further configured to: determining an ordering of the order in the pending risk order based on the risk level of the order; and determining whether to execute at least one risk confirmation operation based on the sequencing result of the order.

In some embodiments, the risk determination result includes at least whether a risk exists; the risk coping module 320 is further configured to: and when the order is at risk, performing at least one risk confirmation operation on the order.

The risk coping module 330 may perform a risk coping operation based on the risk determination result.

In some embodiments, risk management module 330 may further include a risk ranking unit 332, a risk confirmation unit 334, a risk handling unit 336, and a continuous monitoring unit 338. The risk ranking unit 332 may rank the risk determination results based on a ranking rule. The ranking rules may be based on one or more risk parameters of different risks (e.g., distance of vehicle location from travel destination when the order is ended, overlap of movement trajectories of driver and passenger after the order is ended, dwell time in risk of dwell anomaly, etc.) for ranking. The ranking rule may also rank the risk probabilities and/or the levels according to the determination results. The sorting rule may also be setting a sorting result threshold (e.g., a level threshold, a probability threshold, etc.), and sorting the risk determination results that meet different thresholds respectively. The ranking rule may also be based on the magnitude of some operation result (e.g., a weighted average) of a plurality of risk parameters.

In some embodiments, the risk ranking unit 332 may rank the risk determination results using a ranking model. The ranking model may be a mathematical model, and the risk ranking results may be formulated (e.g., weighted) based on the eigenvalues in the different risk categories and/or the eigenvalues of all risks, respectively. The ranking model may also be a machine learning model, which may be obtained after training based on feature data of the trigger risk. The risk ranking unit 332 may input the risk determination result corresponding to the transportation service order into the trained risk ranking model to determine the ranking result. In some embodiments, the ranking results may represent a risk level ranking for the service order. In some embodiments, the ranking results may represent a risk probability level ranking of the service orders. In some embodiments, the ranking results determine subsequent countermeasures.

In some embodiments, risk ranking unit 332 may rank the different risks separately. For example, all orders with the same risk are ranked, and ranking results of different risks are obtained respectively. In some embodiments, the risk ranking unit 332 may also rank all risks comprehensively. For example, weights may be set for different risks, and orders with different risks may be comprehensively ranked according to the weights.

Risk confirmation unit 334 may perform risk confirmation. In some embodiments, risk validation unit 334 may validate the risk based on the ranking results of risk ranking unit 332. For example, a preset number of orders may be selected among the higher risk ranked orders for risk confirmation. In some embodiments, the risk confirmation unit 334 may confirm the risk directly based on the determination result of the risk determination module 320. For example, risk confirmation is performed for orders for which the risk determination module 320 determines that the results (e.g., risk level, risk probability, etc.) are within a preset range. In some embodiments, risk confirmation element 334 may risk confirm all service orders directly.

In some embodiments, the risk confirmation operation may include risk confirmation by interacting with user information, risk confirmation by a worker going to the field, risk confirmation by obtaining in-vehicle audio or image information, risk confirmation based on traffic system broadcast information confirmation, and the like, or any combination thereof. The risk confirmation unit 334 may perform risk confirmation manually. For potentially risky orders, the risk prevention system 100 may present information associated with the risk order and further determine the associated risk information manually (e.g., by human customer service). In some embodiments, risk confirmation unit 334 may perform risk confirmation in an automated manner. For potentially risky orders, the automatic risk confirmation unit 334 may confirm the risk by means including Interactive Voice Response (IVR) outbound call, terminal screen pop, text application, Voice inquiry or Voice monitoring of the driver and/or passenger in the vehicle, in-vehicle Voice reporting, etc. In some embodiments, risk confirmation element 334 may also perform risk confirmation by way of human interaction with automation. For potentially risky orders, the risk confirmation unit 334 may perform risk confirmation by way of telephonic interaction.

Risk handling unit 336 may perform risk handling operations. The risk handling operations may include notifying emergency contacts, initiating driver-side and/or passenger-side data reporting, special person follow-up alerts, and the like, or any combination thereof. In some embodiments, risk handling unit 336 may determine the risk handling operation based directly on the risk determination result. For example, the risk handling unit 336 may perform risk handling on high risk orders and take different actions depending on the risk probability. For example, according to the algorithm, when the risk probability exceeds 20%, some action is taken, such as sending a prompt message to the user terminal to remind the user (driver or passenger) that there is some risk and request the user's attention. When the risk probability is higher (e.g. 90%), the termination of the service may be directly required. In some embodiments, risk handling unit 336 may determine a risk handling operation based on the system multiple risk ranking results. For example, the risk handling unit 336 may perform risk handling, such as person to hand, on orders with a risk ranking order in the top 30%. In some embodiments, risk handling unit 336 may also determine a risk handling operation based on the risk confirmation result. For example, the risk handling unit 336 may perform a risk handling operation on an order that is confirmed to be at risk. The criteria and thresholds for system risk handling may be dynamically adjusted according to real-time conditions and historical data and feedback in conjunction with the update unit.

In some embodiments, risk handling unit 336 may handle risks by methods of risk study. The risk handling unit 336 may obtain the service orders and the related service order data that satisfy the risk study condition, obtain the risk determination result of the service orders and the risk information related to various aspects of the service orders, and determine whether a risk event occurs in the service orders based on the risk determination result and the risk information.

In some embodiments, risk handling unit 336 may handle risks by means of risk rescues. The risk handling unit 336 may determine whether the service order satisfies the risk rescue condition based on the risk determination result, generate rescue information for satisfying the risk rescue condition, and transmit the rescue information. For example, for an order determined to be at risk, risk information (e.g., risk type, risk level, etc.) thereof may be acquired, and for an order whose risk level satisfies a preset threshold, rescue information may be generated to notify surrounding drivers to go for help or view.

The continuous monitoring unit 338 may perform continuous monitoring of the service orders. The continuous monitoring may be performed for service orders determined to be risk-free in the risk determination, or for service orders at the end of the risk ranking, or for service orders that are risk-free after risk confirmation. In some embodiments, the continuous monitoring unit 338 may determine the terminal associated with the service order to be continuously monitored based on information about the service order. The terminal may be a service provider terminal, a service requester terminal, a vehicle-mounted terminal, etc. The continuous monitoring unit 338 may obtain text, sound and/or image data reflecting the service order execution live through the terminal. Data acquisition may be achieved through various sensors installed on the terminal. For example, audio data may be acquired by a sound sensor (e.g., a microphone) and video data may be acquired by an image sensor (e.g., a camera). The acquired data may be used for risk determination and handling at a next time, e.g., after 10 s.

In some embodiments, the risk handling module 330 may be configured to perform a set operation based on the risk determination result. The risk handling module 330 is further configured to: based on the risk determination result, at least one risk handling operation is performed.

The update module 340 may update rules and/or models based on the risk handling operation results. The updated rules may include risk decision rules, risk ranking rules, and the like. The updated models may include a risk determination model, a risk ranking model, and the like. In some embodiments, the update module 340 may compare the risk confirmation result and/or the risk treatment result with the risk determination result/risk ranking result to obtain the difference therebetween. And updating a risk parameter and/or a risk parameter value in the decision/ranking rule according to the difference. In some embodiments, the update module 340 may retrain the risk determination model as new sample data to update parameters in the model for orders determined to have a risk event in the risk confirmation operation and/or the risk treatment operation. Meanwhile, the updating module 340 may retrain the risk ranking model according to the feature data of each order of the actual ranking result obtained by risk confirmation or risk response. In some embodiments, updates to the rules and models may be made at predetermined intervals, such as a day, a week, a month, a quarter, and so forth. In some embodiments, the update module 340 may force the system to update in an active push manner.

In some embodiments, the processing device 110 may further include a training module to train the risk decision model. The training step may include: obtaining a plurality of sample orders; extracting order related data of a sample order when a first vehicle position of the sample order when the current order is finished at a terminal is not coincident with a travel destination, and an actual risk result corresponding to the sample order; training a pre-constructed initial model based on order related data of the sample order and an actual risk result thereof to obtain the risk judgment model.

It should be understood that the system and its modules shown in FIG. 3 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description is merely for convenience and should not be taken as limiting the scope of the present application. It will be understood by those skilled in the art that, having the benefit of the teachings of this system, various modifications and changes in form and detail may be made to the field of application for which the method and system described above may be practiced without departing from this teachings.

FIG. 4 is an exemplary flow chart of a risk prevention method 400 according to some embodiments of the present application.

In some embodiments, one or more steps of method 400 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 400 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

At step 410, data associated with at least one service order is obtained. Step 410 may be performed by data acquisition module 310.

In some embodiments, the service order may be a transportation service order, such as a cargo transportation order, a travel service order, and/or the like, that is requested, executed, and/or completed at the present time. The data related to the service order may include service order characteristics of the service order, real-time status data during execution of the service order, and a history associated with at least one data in the service order. In some embodiments, the service order characteristics further may include identity information of the service provider, identification information of a vehicle associated with the service order, time associated with the service, starting point of the service, destination of the service, path of the service, identity information of the service requester, and estimated cost of the service. The service provider information may include age, gender, facial portrayal, contact, educational level, identification number, driver's license number, etc. The identification information of the vehicle associated with the service order may include a license plate number, a vehicle type, a vehicle brand, a vehicle body color, a vehicle age, a load capacity, and the like. The service related time may include a service order request time and/or a service order execution time. The service order request time may be a time when the service requester makes an order request, and the service order execution time may be a time when the service provider starts executing a service order. The identity information of the service requester may include age, gender, facial portrayal, contact details, education level, identification number, etc. The order characteristics can also include estimated order completion duration, estimated order completion time, estimated service cost and the like. In some embodiments, the real-time status data during order fulfillment further may include real-time status data of an external environment during said service order fulfillment, positioning data associated with the service order, status data of a vehicle associated with the service order, and environmental data of an interior of said vehicle. The real-time status data of the external environment during the service order execution process may include real-time road conditions, traffic flow, road types, road event information, current location and location characteristics, and the like. The status data during the order execution may further include the operation content of the terminal by the user of the terminal (e.g., the service requester and/or the service provider) with respect to the terminal, and the positioning data related to the service order may include the positioning position, the moving path, and the like of the terminal (e.g., the terminal device used by the service provider/service requester) related to the service participant. The status data related to the service order may include power level of the terminal, communication signal strength, sensor operating status, running status of an application on the terminal, and the like. The status data of the vehicle associated with the service order may include vehicle position, vehicle speed, vehicle acceleration, vehicle attitude, travel trajectory, motion status (e.g., whether parked or not stationary), and the like. The vehicle interior environment data may include in-vehicle audio data, in-vehicle image data, and the like. In some embodiments, the history associated with the at least one data in the service order further may include a record of other service orders of the service provider (e.g., number of completed orders, historical rating), credit record of the service provider (e.g., loan record, consumption record, etc.), records of other service orders of the service requester (e.g., order complete, historical rating), credit record of the service requester, identification information of vehicles of other service orders of the service provider, service related time of other service orders of the service provider, service start points of other service orders of the service provider, service destinations of other service orders of the service provider, service paths of other service orders of the service provider, identification information of vehicles of other service orders of the service requester, service related time of other service orders of the service requester, historical rating of other service orders of the service requester, historical data of, One or more of a service start point of the service requester other service order, a service destination of the service requester other service order, a service path of the service requester other service order, a cost of the service requester other service order, and a payment record of the service requester other service order, etc. The records of the service provider's other service orders may include accumulated service completion times, accumulated service cancellation times, complaint times, banned times, reputation scores, rating levels, historical rating content, and the like. The records of other service orders of the service requester may include accumulated service request times, accumulated service cancellation times, accumulated service completion times, service fee payment conditions, credit scores, rating levels, historical rating contents, and the like. The credit investigation records of the service provider/service requester may include credit records relating to debits, credit card consumptions, and the like. In some embodiments, the data acquisition module 210 may acquire the service order data by communicating with the terminal 120, the storage device 130, and/or the information source 150. For example, the terminal 120 may acquire sensing data and operation contents of the terminal 120 by the user in real time through various sensors installed thereon. The data acquisition module 410 may perform data acquisition after communicating with the terminal 120. As another example, the data acquisition module 410 may access to read user characteristic data stored on the terminal 120 or the storage device 130. Also for example, the data acquisition module 410 may communicate with the information source 150 to acquire external association data.

It should be noted that the service order data is acquired for a particular point in time. The data acquisition module 410 may continuously acquire real-time data associated therewith for the same transportation service order, and the acquired data may be different at different points in time. Meanwhile, the data acquisition module 410 may transmit the acquired data of the transportation service order to other modules of the processing device 110, such as the risk determination module 320, in real time to perform a risk determination operation for risk monitoring of all different phases of the order.

Step 420, processing the relevant data of the service order, and performing risk judgment on the service order. Step 420 may be performed by risk determination module 320.

In some embodiments, the risk determination may be a determination of whether the service order has a malignancy and/or anomaly occurring at the current time. The malignant event and/or abnormal condition may include robbery, personal safety event, order cancellation abnormality, stay-in-journey abnormality, stay-after-journey abnormality, position loss abnormality, miss-delivery abnormality, journey abnormality, driving danger, or any combination thereof. In some embodiments, the risk determination module 320 may make a risk determination for the service order based on a determination rule. The decision rule may be a condition and/or threshold set based on historical order data and/or experience. The historical order data may include order data for historical transportation services in which a malignancy and/or abnormal condition occurred. The historical order data categories may be the same or similar to the service order data described above, and may also include specific types of adverse events and/or anomalies that occur with respect to a transportation service order. In some embodiments, through statistical analysis of the historical order data, decision rules for a particular malignancy and/or abnormal situation may be determined. For example, statistical analysis is performed on historical order data of a robbery malignant event, so that characteristics of low evaluation of service participants (such as passengers), late night order issuing time, remote order starting point position and the like can be obtained. Then, for the determination of the robbery malignancy, a determination rule such as an evaluation threshold, an issue time threshold, a start point position range threshold, etc. may be set. In some embodiments, the threshold setting of the decision rule may be determined from data statistics. Still referring to the above example, assume that through statistical analysis, historical service orders for a robbery malignancy occurred, with the time of issuance centered at 1 point in the morning. The invoice time threshold may be set to 1 am. The risk determination module 320 may compare the determination rules with the corresponding data of the obtained service orders and determine orders that exceed a threshold as risk orders. In some embodiments, there may be one or more decision rules for each type of malignancy or anomaly. When the risk determination module 320 performs the determination using the rule, the determination may be performed using a single rule, or may be performed using a combination of a plurality of rules, or may be performed using all the rules, which is not specifically limited in the present application. For example, when the terminal end order information associated with the current order is received and the first location of the vehicle does not match the travel destination, the risk determination module 320 may determine the risk determination result during execution of the current order based at least on the relevant data of the order. The description of determining the risk judgment result in the execution of the current order based on at least the relevant data of the order when the first position where the vehicle is located does not match the travel destination when the information of the terminal end order associated with the current order is received can be referred to the following description of the drawings (for example, fig. 5)

In some embodiments, the risk determination module 320 may make a risk determination for the service order based on a risk determination model. The risk determination model may be a Machine learning model, including but not limited to a Classification and Logistic Regression (Logistic Regression) model, a K-Nearest Neighbor algorithm (K-Nearest Neighbor, KNN) model, a Naive Bayes (Naive Bayes, NB) model, a Support Vector Machine (SVM), a Decision Tree (Decision Tree, DT) model, a Random Forest (RF) model, a Regression Tree (Classification and Regression Trees, CART) model, a Gradient Boosting Decision Tree (GBDT) model, an xgboost (xtransition Gradient), a light Gradient Boosting Machine (LightGradient Boosting, lightm), a Gradient Boosting Machine (Boosting, software), a so (abstract software, software), and an Artificial Neural network (Artificial Neural network, so). The risk assessment model may be trained from data associated with the historical service order. For example only, the model may be trained with relevant data of historical service orders as input, and categories of corresponding specific malignancy or abnormal conditions as the correct criteria (Ground Truth). While the model parameters may be adjusted back according to the difference between the predicted output of the model (e.g., the predicted risk category) and the correct criteria. When a predetermined condition is satisfied, for example, the number of training samples reaches a predetermined number, the predicted accuracy of the model is greater than a predetermined accuracy threshold, or the value of the Loss Function (Loss Function) is less than a predetermined value, the training process is stopped, and the trained model is designated as the risk determination model. In some embodiments, the risk decision model may be a decision model for all types of malignancy or abnormal situations. The risk assessment module 320 may process the service order using the risk assessment model to determine if one or more types of malignancy or anomaly are present. In some embodiments, there may be one risk determination model for each type of malignancy or abnormality. For example, for the judgment of the robbery risk, a special robbery judgment model can be used for judging. Similarly, other risk determinations may be performed with a specific corresponding model. The risk determination module 320 may utilize a combination of models to determine one or more risks. The combination mode of the models can be determined according to actual requirements. For example, in areas with poor security (e.g., urban and rural junctions), the decision may be made with emphasis on robbery and personal safety incidents. In a region with dense traffic flows such as a city center, the determination of the travel abnormality can be emphasized. For more details on the risk determination rule and the risk determination model, refer to fig. 5 and the description thereof, which are not repeated herein.

In some embodiments, the intermediate results generated during the training of the risk decision model may be used as decision thresholds for the decision rules. For example, taking training a decision tree model for determining a robbery event as an example, the dispatching time selected when the root node is forked is taken as an optimal feature for forking. The bifurcation threshold of the time node of issuance can be used as a decision threshold of the decision model when the bifurcation threshold reaches a stable value (i.e., the data of the root node can be divided into two correct classes) after repeated correction of multiple training.

In some embodiments, the determination of the risk determination for the service order may include the presence or absence of risk and a quantitative representation of risk. For example only, the determination may be risk-free. Alternatively, the determination result may be the existence of risk and a value indicating the risk level, the risk probability, etc., for example, the determination result is (risk, robbery-5 level) or (risk, robbery-56%, abnormal stay-87%). In some embodiments, the risk assessment module 320 may aggregate the overall risk level and/or probability and output a assessment corresponding to the aggregate risk assessment, e.g., the assessment is (at risk, 74%). It should be noted that the form of the determination result described above is for illustrative purposes only, and the present application does not limit the form of the determination result.

Based on the risk determination result, a risk coping operation is performed for each service order, step 430. Step 430 may be performed by risk handling module 330.

In some embodiments, the risk coping module 330 may perform different risk coping operations according to the risk determination result in step 420, which may include risk ranking operations, risk confirmation operations, risk handling operations, continuous monitoring, or any combination thereof.

The processing device 110 needs to process multiple service orders at the same time, and when the number of the orders to be processed is large, the multiple orders need to be sorted to ensure that the orders with higher risk degree are processed in time. In some embodiments, the risk assessment results of the service orders may be ranked, and in particular, one or more risk parameters may be determined based on the risk assessment results, and the ranking may be based on the risk parameters. The risk parameter may be some data in the relevant data of the service order (for example, a characteristic value such as a stay time in the risk of the stay abnormality, and the longer the stay time is, the more dangerous the risk is), or may be a risk type, a risk level, or a risk probability in the risk determination result.

In some embodiments, the risk ranking operation may be based on a ranking rule. The ranking rule may also rank the risk probabilities and/or the levels according to the determination results. The sorting rule may also be setting a sorting result threshold (e.g., a level threshold, a probability threshold, etc.), and sorting the risk determination results that meet different thresholds respectively. The ranking rule may be a ranking directly according to the magnitude of the risk probability contained in the risk decision result. The ranking rule may also be based on the magnitude of some operation result (e.g., a weighted average) of a plurality of risk parameters.

In some embodiments, the risk ranking operation may be performed based on a ranking model. The ranking model may be a mathematical statistical model, and the risk ranking results may be derived by formula calculations (e.g., weight calculations) based on the eigenvalues in the different risk categories and/or the eigenvalues of all risks, respectively. The ranking model may also be a Machine learning model including, but not limited to, a Classification and Logistic Regression (Logistic Regression) model, a K-Nearest Neighbor algorithm (K-Nearest Neighbor, KNN) model, a Naive Bayes (Naive Bayes, NB) model, a Support Vector Machine (SVM), a Decision Tree (Decision Tree, DT) model, a Random Forest (RF) model, a Regression Tree (Classification and Regression Trees, CART) model, a Gradient Boosting Decision Tree (GBDT) model, an xgboost (xtransition Gradient), a Light Gradient Boosting Machine (Light Gradient Boosting, lightm), a Gradient Boosting Machine (Boosting, summary), a so (abstract and Neural network), an Artificial Neural network (Artificial Neural network, so, and so). The model can be obtained after training based on the characteristic data of the trigger risk. The risk handling module 330 may input the risk determination results of the plurality of service orders into the trained risk ranking model to determine the ranking results. In some embodiments, the risk handling module 330 may input some or all of the relevant data of the plurality of service orders with risks as the risk determination result into the trained risk ranking model to determine the ranking result. Depending on the sample data form of the model training.

In some embodiments, the risk handling module 330 may sort each type of risk separately, resulting in sorting results under different risk types. In some embodiments, risk handling module 330 may rank all risks comprehensively. For example, weights may be set for different risk categories, and orders with different risks may be comprehensively ranked according to the weights, so as to determine a risk ranking result for all service orders. In some embodiments, risk coping module 330 may rank the service orders for which the risk determination results belong to a certain risk type combination. For example, service orders with risk determination results of robbery and personal safety events may be comprehensively ordered.

In some embodiments, risk coping module 330 may skip the risk ranking operation, processing each service order directly, including risk confirmation, risk handling, and/or continuous monitoring. It should be noted that the operations performed by risk handling model 330 may be different for different risk decision result service orders. For example, for high risk orders (e.g., with a risk probability greater than 50%), the risk coping module 330 can perform risk handling operations, alert the user, and/or directly alert. For another example, the risk coping model 330 may perform risk confirmation on service orders other than high risk orders and immediately perform alarm and/or rescue coping when the true risk is confirmed. While for non-risky service orders, or non-risky orders after risk confirmation, the risk response model 330 may perform continuous monitoring to discover risk at a first time. In some embodiments, the risk handling model 330 may also be the same way all orders are processed. For example, all service orders are first risk confirmed and then subsequent operations are performed, or directly disposed of.

In some embodiments, the purpose of risk confirmation may be to determine the actual condition of the service order and/or to determine whether it is consistent with the decision made through the risk decision operation. In some embodiments, the risk confirmation operation may include risk confirmation by interacting with user information, risk confirmation by a worker going to the field, risk confirmation by obtaining in-vehicle audio or image information, risk confirmation based on traffic system broadcast information confirmation, and the like, or any combination thereof. The user may refer to a party to a service order, including a service provider and/or a service requester. The risk confirmation through the interaction with the user information may be confirmation of the risk through ways including Interactive Voice Response (IVR) outbound call, terminal display screen pop-up, application text/Voice query, telephone interaction, and the like. For example, the user may be called out through an IVR to enter information, such as a cell phone number, on the user's terminal (e.g., terminal 120) to confirm that the user is in a safe state. The telephone interaction may be a communication by placing a call to the user to confirm the risk. The risk handling module 330 may obtain the telephone interactive content, and confirm whether the telephone receiver is the user, whether dangerous words appear in the telephone interactive content of the voice of the receiver, and the like through voice recognition, semantic recognition, tone recognition, and the like, so as to perform risk confirmation. For example, telephone communication with the driver and/or passenger may be used to confirm whether the driver or passenger is at risk. For another example, the department voice information may be collected by making an anonymous call (e.g., insurance promotion, house promotion, telephone shopping, etc.), and risk confirmation may be performed by recognizing the party's voice (e.g., anger, background sound, personal voiceprint, etc.). Also for example, non-risk parties may also be communicated telephonically (e.g., a driver may be considered telephonically interactive when determining that a passenger is at risk) to confirm risk. The confirmation of risk by staff to the field may be based on the location of the vehicle or participant of the service order, notifying staff nearby the location to go to confirmation. The risk confirmation by acquiring the audio or image information in the vehicle may be performed by automatically or manually confirming the risk after acquiring the audio and video in the vehicle through a sensor (e.g., an image sensor, a sound sensor, etc.) installed on a terminal (including a service provider terminal, a service requester terminal, a vehicle-mounted terminal, etc.). The risk confirmation based on the traffic system broadcast information confirmation can be that the service order to be subjected to risk confirmation is subjected to risk occurrence authenticity confirmation through the event occurrence place, time and event type in the traffic system broadcast information. In some embodiments, the risk confirmation operation may further include by manual confirmation. The manual risk confirmation may be to display various information of the service order requiring risk confirmation to the background safety confirmation staff, such as a driving track, video and audio in the vehicle, the current position of the user, historical risk data of the user, historical risk cause, and the like, and the safety confirmation staff determines relevant risk information, such as where the vehicle has stopped, a plurality of times of stopping, whether the driving track disappears, whether there is a collision of body and/or language between the users, and the like.

In some embodiments, the risk handling operations may include notifying emergency contacts, initiating driver-side and/or passenger-side data reporting, special person follow-up alerts, and the like, or any combination thereof. The emergency contact may be contact information (e.g., cell phone number) of a first-order contact that the passenger and/or driver added during registration and/or use of the on-demand service (e.g., via the passenger and/or driver's terminal, mobile application, etc.) if the passenger and/or driver encounters a hazard. For example, a quick portal (e.g., contact emergency contacts button, alarm button, help button) may be provided on the user terminal that communicates with the back-end security platform. When the user is in a dangerous condition, the user can click the emergency contact button, the terminal can automatically send help-seeking voice or text information to the emergency contact after detecting that the button is triggered, and the current positioning information of the terminal can be automatically added into the information. Or the user can alert the police by clicking the alert button. After alarming, the terminal can also send the current position and the travel information of the alarming user to the police to assist rescue. The driver-side and/or passenger-side data may be audio, video, image, etc. data obtained by various sensors mounted on the mobile device of the driver and/or passenger, e.g., the terminal 120 or the mobile device 200. The processing device 110 may automatically retrieve the data. The user can also actively report the data. The special person follow-up alarm may be processing of alarming and the like in a way that a special person (e.g., a manual customer service) follows up. In some embodiments, risk handling module 330 may also perform risk handling operations on the service orders that have been risk validated. For example, assuming that an order is identified as being at risk, risk handling module 330 may perform a risk handling operation of alerting.

In some embodiments, the risk treatment may include risk studies. The risk coping module 330 may obtain the service orders and the related service order data thereof meeting the risk research and judgment condition, and obtain the risk judgment result of the service orders and the risk information related to various aspects of the service orders. The risk coping module 330 may send the above data to a processing device associated with the judge and obtain a manual judge result through the processing device associated with the judge. The risk judging condition may include that the service order has risk, the risk level or risk probability exceeds a judging threshold, the service order has not been risk confirmed, the service order has no risk after risk confirmation at a previous time (for example, "temporary safety" or "temporary alarm") but is judged to have risk at the current time, and the like. For a service order satisfying the risk judgment condition, the risk coping module 330 may obtain a risk judgment result of the service order (e.g., based on step 420) and risk information related to various aspects of the service order, including user information (e.g., current location, number of complaints of the user, etc.), vehicle location (e.g., remote area in the environment, etc.), trajectory data (e.g., deviation of the route from a common route, too long stay in a certain location, etc.), vehicle environment extraction information (e.g., audio recording, video, call, image, etc.), external association information (e.g., traffic flow, etc.). After obtaining the information, risk management module 330 may send the data to a processing device associated with the judge. The processing device associated with the judge may, upon receiving the data, automatically judge the service order to determine whether a malignancy and/or an abnormal situation has occurred, or the judge may operate the processing device to make the judgment. In some embodiments, the risk management module 330 may generate a job order and assign the job order to a plurality of processing devices associated with a job person for a job to determine a result of the job. The decision work order may be presented in a predetermined form (e.g., a list) in an interface (e.g., a processing interface of a processing device associated with the decision worker), and the background security decision worker may select or click on the list to view information contained in the decision work order, for example, to generate a risk determination result of a service order of the decision work order and risk information related to various aspects of the service order, and determine whether a malignant event and/or an abnormal situation occurs. Meanwhile, the information may be in a highlighted form, for example, a change in font color and thickness. In some embodiments, the risk management module 330 may first make a determination of a service order that satisfies the criteria and send the determination in the form of a systematic opinion along with the criteria work order to a processing device associated with the reviewer to assist in the determination.

In some embodiments, the risk disposition may also include risk rescue. The risk handling module 330 may generate rescue information based on the information related to the service order to be risk-disposed and the risk determination result. Specifically, the risk coping module 330 can determine whether the service order satisfies the risk rescue condition based on the risk determination result. The risk coping module 330 may determine that the service order in the risk determination result whose risk level and/or risk probability exceeds a rescue threshold, such as 80%, 85%, or 90%, meets the risk rescue condition. For service orders that satisfy the rescue conditions, risk coping module 330 can generate rescue information based on information related to the service orders. For example, the risk handling module 330 may generate rescue information based on the position of the vehicle, the vehicle information, the type of risk determined to occur, and the like, for example, a white vehicle with a license plate number of jing a12345, which is located near the east door of the central park, has an abnormal parking condition, is suspected of having a robbery event, and asks you to look up the rescue. After generating the rescue information, risk coping module 330 sends the rescue information to a processing device associated with the police, a terminal associated with the emergency contact, and/or a terminal associated with another service provider. When the processing device associated with the police sends rescue information, the police may be alerted at the same time. When the rescue information is sent to the terminal associated with the emergency contact, the reminding information can be sent at the same time to remind the emergency contact to give an alarm to the police, or the personal safety is ensured when checking and/or rescuing. The other service providers include service providers that are no more than a set distance threshold from a current execution location of a service order to be risk disposed. The current execution location may refer to a current time, a relevant party of the service order to be risk-disposed, including a location of the user, the vehicle. In some embodiments, while the rescue information is being sent, a subsidy or reward information may also be sent, prompting the service provider (e.g., the driver) that the subsidy or reward may be obtained if the driver goes to review and/or rescue. In some embodiments, different numbers and types of drivers may be notified for different risk events. For example, the number of drivers notified of a rescue visit due to an abnormal stay event is much smaller than for a robbery event. While informing drivers who are heading for a rescue robbery event may be young drivers. In some embodiments, the rescue information may be sent in consideration of the distance of other drivers from the location where the risk event occurred and the road conditions.

In some embodiments, the risk coping process may delay processing. By collecting the user's security activities over the delay time, stress and impact on the risk processing devices (e.g., processing device 110) may be reduced. Because the processing device 110 needs to process multiple service orders at the same time, the delay processing can reduce the load of the processing device 110 and increase the processing speed of the orders. In some embodiments, after determining that the at-risk service order is ended, the risk coping module 330 may obtain data reflecting user behavior associated with the service order and determine whether the user associated with the service order performs security behavior based on the data reflecting user behavior associated with the service order. Cancelling the determination that the service order is at risk if a security action occurs for a user associated with the service order. For example, if the service order determined to have an abnormal parking risk in step 420 is a general risk level (e.g., risk level, risk probability are within a preset threshold range), the order may be continuously monitored, and if the driver continues to receive orders and/or the passenger continues to issue orders normally after the order is ended, the determination that an abnormal parking risk exists may be cancelled, and the driver and/or passenger safety may be determined. In some embodiments, orders determined to be at high risk may also be validated during the delay phase. For example, the verification may be performed by manual verification, automatic verification, phone-based interactive verification, etc., for example, to guide the passenger to confirm whether there is a security risk on the passenger terminal (e.g., send information to be answered in APP, initiate a red envelope robbing activity, etc.), automatically dial a service call, indirectly dial a call (e.g., obtain relevant information by dialing a financial service call, etc.), contact relatives and friends verification, etc.

In some embodiments, a user may autonomously determine and report security risks. For example, a quick portal (e.g., an alarm button, a help button) may be included in the interface of the application 380 that communicates directly with the on-demand service platform through which the user may report risk. As another example, the user may perform a particular operation on the mobile device 200, such as pressing, shaking, or throwing. A sensor (e.g., a sound sensor, an image sensor, a pressure sensor, a velocity sensor, an acceleration sensor, a gravity sensor, a displacement sensor, a gyroscope, etc., or any combination thereof) installed in the mobile device 200 detects that the specific operation is, an alarm procedure may be initiated to report a security risk. After receiving the report, the risk handling module 330 may determine the accuracy of the reported security risk (e.g., whether there is noise, etc.) and perform risk confirmation and risk handling.

In some embodiments, the risk management may also include continuous monitoring. The continuous monitoring may be performed for the service orders determined to be risk-free in step 420, or for the service orders at the end of the risk ranking, or for the service orders that are risk-free after risk confirmation. In some embodiments, risk handling module 330 may determine a terminal associated with a service order to be continuously monitored based on information about the service order. The terminal may be a service provider terminal, a service requester terminal, a vehicle-mounted terminal, etc. Risk management module 330 may obtain text, audio, and/or image data reflecting the service order execution live via the terminal. Data acquisition may be achieved through various sensors installed on the terminal. For example, audio data may be acquired by a sound sensor (e.g., a microphone) and video data may be acquired by an image sensor (e.g., a camera). The acquired data may be used for risk determination and handling at a next time, e.g., after 10 s.

It should be noted that risk determination and handling for an order is an ongoing process. When a particular order is determined to be safe at the current time or is determined to be safe during a risk coping operation (e.g., a risk confirmation operation), continuous monitoring is still performed, and risk determination and coping are repeated to determine whether a risk event will occur subsequently, for example, risk determination and subsequent steps are performed every preset time (e.g., 10 seconds). The risk assessment and handling process for the order may be ended until a threshold time after the completion of the specific order is reached, for example, 10 minutes, 20 minutes, or 30 minutes after the order is completed. Meanwhile, for a service order with risk determination result obtained in step 420 being risk-free, the risk handling module 330 may continuously monitor the service order.

Likewise, it will be appreciated that the processing operations in the risk management pair may be performed selectively. In some embodiments, the risk handling module 330 may sort all the service orders based on the risk determination result, and then selectively perform the subsequent operation according to the sorted result. For example, risk management module 330 may select the top ranked service order to perform risk handling operations, perform risk handling operations for the medium ranked service order, and perform continuous monitoring operations for the bottom ranked service order. In some embodiments, risk coping module 330 may skip the sorting step, perform risk validation directly on all service orders and perform subsequent handling operations based on validation results. For example, non-risk service orders after risk confirmation can be continuously monitored, and corresponding to risky orders, users can be reminded (such as abnormal parking of vehicles) or direct alarms (such as robbery) can be selected according to the risk. In some embodiments, risk handling module 330 may handle all service orders based directly on the risk determination results. For example, risk handling module 330 may send an alert to an associated user of a service order for which the risk determination is low risk. For service orders with a high risk as a result of the risk determination, the risk coping module 330 may directly notify the police. For non-risky service orders, however, the risk management module 330 may continue to monitor to prevent subsequent risks from being discovered in the shortest amount of time. In some embodiments, risk coping module 330 may rank the service orders based on the risk determination results and directly handle the service orders based on the ranking results. For example, the risk response module 330 may first process the top ranked service orders (e.g., high risk orders) and continue processing the bottom ranked orders (e.g., low risk orders) after completion. In some embodiments, the risk response module 330 may delay processing of the service order based on the risk determination. For example, the risk handling module 330 monitors for service orders that are determined to be at risk. Upon completion thereof, risk handling module 330 may obtain behavioral data of the user associated with the order. If a user has a security action, such as the user associated with a high risk order continues to request transportation services after the order is completed, the risk handling module 330 may confirm the at-risk service order as a security order.

At step 440, rules and/or models are updated based on the risk response operation results. Step 440 may be performed by update module 340.

In some embodiments, the updated rules may include risk decision rules, risk ranking rules, etc., and the updated models may include risk decision models, risk ranking models, etc. In some embodiments, the update module 340 may obtain the difference based on the comparison between the risk confirmation result and/or the risk treatment result and the risk determination result. And updating the risk parameter value in the decision rule according to the difference. For example, the judgment rule for judging the robbery event may be to judge according to the invoice time and the starting point, and set that the invoice time exceeds 12 pm and the travel destination is located in the neighborhood of city and county, so that the robbery risk may occur. If the risk of the order with the robbery risk is confirmed, the order with the order issuing time between 12 o 'clock and 12 o' clock in the evening is found, and the robbery event does not occur. The updating module can change the judgment rule for judging the robbery time into the condition that the invoice sending time exceeds 12 o' clock and a half night and the travel destination is located in the adjacent city and county, so that the robbery risk is possible. For another example, the determination rule for determining the risk order may be whether the distance between the vehicle position and the driving destination when the terminal finishes the order is less than a certain threshold, for example, the threshold is set to 100 meters; and if the risk confirmation is carried out on the order judged to have the risk, the situation that the risk exists when the distance between the vehicle position and the driving destination when the terminal finishes the order is found to be less than 200m through statistics. The update module may change the threshold from 100 meters to 200 meters. In some embodiments, the update module 340 may retrain the risk determination model as new sample data to update parameters in the model for orders determined to have a risk event in the risk confirmation operation and/or the risk treatment operation. Similarly, for training of the risk ranking rules and risk ranking models, the update module 340 may also compare the risk ranking results with the risk confirmation results and/or the risk treatment results to obtain differences and update. For example, a high risk order that is first in the rank in the sequence is determined to be risk free in a subsequent risk confirmation operation, the update module 340 may update the risk parameters used by the sequence. For the update of the risk ranking model, the updating module 340 may retrain the risk ranking model according to the feature data of each order of the actual ranking result obtained by risk confirmation or risk response, so as to achieve the purpose of updating. In some embodiments, updates to the rules and models may be made at predetermined intervals, such as a day, a week, a month, a quarter, and so forth.

It should be noted that the foregoing description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Many variations and modifications may be made to the teachings of the present invention by those of ordinary skill in the art in light of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. In some embodiments, one or more other optional operations may be omitted in exemplary method 400. For example, for a service order with a high risk (e.g., a risk level, a risk probability, etc. higher than a preset threshold) as a result of the risk determination, the risk ranking operation and the risk confirming operation may be omitted, and the risk handling operation (e.g., alarming or transferring to a security personnel for judgment) may be performed directly. For another example, for a service order with a low risk (for example, the risk level, the risk probability, and the like are lower than a preset threshold) as a result of the risk determination, a monitoring waiting process may be performed (for example, data acquisition is continuously performed, and the risk determination is performed again after a preset time).

FIG. 5 is an exemplary flow chart of another method 500 of risk prevention according to some embodiments of the present application.

In some embodiments, one or more steps of method 500 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 500 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 510, acquiring relevant data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution. Step 510 may be performed by data acquisition module 310.

In some embodiments, the current order may be a service order (e.g., a transportation service order) that is requested, executed, and/or completed at the current time. In some embodiments, the order characteristics may include identity information of the service provider, identification information of a vehicle associated with the service provider, service time, trip start point, trip destination, trip path, identity information of the service requester, and the like. In some embodiments, the status data during the order execution process may include positioning data of the terminal, status data of the vehicle, environmental data of the interior of the vehicle, and real-time status data of the external environment. More detailed description of the order characteristics, real-time status data during order execution can be found in the description of fig. 3 and 4.

And step 520, when the information that the terminal associated with the current order sends the order is received and the first position of the vehicle associated with the current order when the order is finished is not matched with the travel destination, determining a risk judgment result in the execution process of the current order at least based on the relevant data of the order. Step 520 may be performed by risk determination module 320.

In some embodiments, the terminals include in-vehicle terminals, service provider terminals, and/or service requester terminals. For example, the vehicle-mounted terminal may include a vehicle-mounted device such as a vehicle event recorder and a vehicle-mounted control terminal. The service requester terminal may include a mobile phone, a tablet computer, a laptop computer, a smart device, etc., or any combination thereof, of the service requester (e.g., passenger). The service provider terminal may include a service provider (e.g., driver) mobile phone, tablet, laptop, smart device, and the like, or any combination thereof. For example, the terminal associated with the current order may be a mobile phone used in the process of a passenger requesting the order or a driver accepting the order.

In some embodiments, the first location may refer to a location where the vehicle was located at the end of the order. The first location may be a latitude and longitude data point or may be an area described by a series of latitude and longitude data points. The first position may be obtained by a positioning module (e.g., an in-vehicle device) provided on the vehicle, a positioning system (e.g., a GPS positioning system) on the terminal. For example, the first location may include longitude data, latitude data, positioning information, ambient environment information, and the like, or any combination thereof. The positioning module can realize the positioning of the vehicle through a Global Positioning System (GPS), a global satellite navigation system (GLONASS), a Beidou navigation system (COMPASS), a Galileo positioning system, a quasi-zenith satellite system (QZSS), a wireless fidelity (Wi-Fi) positioning technology and the like or any combination thereof.

In some embodiments, when the terminal (e.g., the driver's mobile phone) associated with the current order finishes the order, the processing device 110 may receive the order-finished information over the network 140 and then obtain data related to the at least one current order.

Based on the relevant data, the processing device 110 may further determine whether the first position of the vehicle matches the travel destination. For example, the processing device 110 may determine whether the first location of the vehicle matches the travel destination based on the first location information of the vehicle matching the destination location information stored by the storage device 130. Specifically, the storage device 130 may store therein location information of each destination, such as latitude and longitude information. When the first position information of the vehicle is not included in the range of the travel destination, the vehicle is considered not to match the travel destination. Alternatively, the processing device 110 may calculate a distance between the first location of the vehicle and the driving destination, and consider the vehicle not to match the travel destination when the distance is greater than or equal to a set threshold, such as 50 meters, 100 meters, 200 meters, 300 meters, and so on.

In some embodiments, the processing device 110 further determines a risk determination result during execution of the current order based on the related data when the first location at which the vehicle is located does not match the travel destination. The risk determination result may include whether the order is at risk, risk category, risk level, and the like. An order that is at risk may be referred to as a risk order. For example, when the processing device 110 determines that the first location of the vehicle does not match the travel destination when the current order is over, it may further determine whether the current order is at risk during execution, a risk category and a risk level of the order based on the relevant data of the order. In some embodiments, the risk determination may be made based on one or more rules, or by setting different rules for orders of different risk categories. The detailed description may refer to the description of other figures (e.g., fig. 6-9). In other embodiments, the order-related data may be processed using a trained risk assessment model to determine a risk assessment result. For example, a feature vector is constructed by the relevant data of the order, and the feature vector is input into a risk judgment model, and a specific risk judgment result is output.

In step 530, a set operation is performed based on the risk determination result. Step 530 may be performed by risk handling module 330.

In some embodiments, the performing the set operation may include performing the set operation including a risk ranking operation, a risk confirming operation, a risk disposing operation, a continuous monitoring operation, and the like, based on the risk determination result. For a more detailed description of the setting operation, reference may be made to fig. 3 and the related description of the risk countermeasure in fig. 4, and details are not repeated here.

It should be noted that the above description of method 500 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and alterations to method 500 will be apparent to those skilled in the art in light of the present application. However, such modifications and variations are intended to be within the scope of the present application. For example, the risk confirmation operation is not a necessary operation, and the risk confirmation operation may determine whether the risk is required according to the risk level, for example, an order with a particularly high risk level may omit the risk confirmation operation and directly perform risk handling.

Fig. 6 is an exemplary flow diagram of a risk determination method 600 according to some embodiments of the present application.

In some embodiments, one or more steps of method 600 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 600 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 610, obtaining a distance between a first position where a vehicle in at least one current order is located and a travel destination.

In some embodiments, the distance between the first location and the travel destination may include a navigation distance, a route distance, a straight line distance, the like, or any combination thereof, between the first location and the travel destination. In some embodiments, the navigation distance may refer to a length of one navigation path from the first location to the travel destination, or an average of lengths of a plurality of navigation paths. The route distance may refer to an actual length of a route from the first location to the travel destination, and the route distance may be a distance of any one of the one or more routes or an average of total distances of the one or more routes. The straight distance may refer to a length of a line connecting the first location to the travel destination, and may be calculated by, for example, latitude and longitude of the first location and the travel destination.

In some embodiments, the processing device 110 may obtain data related to the current order from a service provider terminal (e.g., a mobile phone of a driver), a service requester terminal (e.g., a mobile phone of a passenger), and a vehicle terminal (vehicle center) via the network 140. For example, the processing device 110 may obtain destination information of a passenger's current order from a service requester terminal through the network 140. As another example, the processing device 110 may obtain, from the service provider terminal, a navigation path, an actual travel path, a first location, and the like, selected by the driver, planned by the processing device 110 via the network 140. As another example, the processing device 110 may obtain other relevant information for the first location and the travel destination from the internet via the network 140. In some embodiments, based on the obtained data related to the current order, the processing device 110 may calculate a distance between a first location at which the vehicle in the current order is located and the travel destination. For example, the processing device 110 may calculate a distance between the latitude and longitude at the first location and the latitude and longitude at the destination. As another example, the processing device 110 may calculate an actual length of the navigation path from the first location to the travel destination.

Step 620, determining a risk judgment result in the current order execution process based on the distance between the first position and the travel destination.

In some embodiments, the risk determination result may include whether the current terminal has a risk, a risk category, a risk level, and the like. An order that is at risk may be referred to as a risk order. Specifically, the risk category may refer to which type of risk the risk belongs, for example, a non-delivery destination exception risk, a loss of signal exception risk, a trip exception risk, a stop exception risk, and an order cancellation exception risk, and the like. The risk level may refer to a level classified according to the severity of risk or the probability of risk occurrence. For example, high risk orders, medium risk orders, low risk orders, and risk free orders, among others.

In some embodiments, processing device 110 may make a risk determination based on one or more rules. For example, when the distance between the first location and the travel destination is greater than or equal to a preset threshold (e.g., 100 meters, 200 meters, 500 meters, 1 kilometer, etc.), the processing device 110 determines that the risk determination result in the current order execution process is a risk order; when the distance between the first position and the travel destination is smaller than a preset threshold, the processing device 110 determines that the risk determination result in the current order execution process is a non-risk order. Further, the risk determination result may be divided into several risk levels (e.g., three levels, high, medium, and low), and the processing device 110 may determine the risk levels according to the magnitude of the multiple between the distance between the first location and the travel destination and the preset threshold. For example, a high risk order with a distance between the first location and the travel destination that is 5 times or more of the preset distance; 1-4 times of the risk order; 0-1 times low risk orders.

It should be noted that the above description of operation 600 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and changes to operation 600 will be apparent to those skilled in the art in light of the present disclosure. However, such modifications and variations are intended to be within the scope of the present application.

Fig. 7 is an exemplary flow diagram of another risk determination method 700 according to some embodiments of the present application.

In some embodiments, one or more steps of method 700 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 700 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 710, determining whether the first position is located in a preset safe area.

In some business circles (e.g., core business circles) with high order density, blocks with high traffic flow, areas around platforms (e.g., bus stations, subway stations, train stations, etc.), hot spots, traffic control areas (e.g., sections near schools, sections with traffic accidents), and sections with high traffic flow, the probability of generating risks is usually very low, so the processing device 110 can preset these areas as a safety range. In some embodiments, the security zone may be a latitude and longitude data point or a zone range described by a series of latitude and longitude data points. For example only, the security zones may be preset statistics and settings and stored in the storage device 130. In some embodiments, the processing device 110 may match a first location where the vehicle is located with a location of at least one preset safe area, and determine whether the first location is located in the preset safe area based on the matching result. For example, when the processing device 110 detects that the order is ended, first position information where the vehicle is located is matched with position information of a preset safety area. Specifically, when the first position information in which the vehicle is located is included in a preset safety region, the first position is considered to be located in the preset safety region.

And 720, determining a risk judgment result in the current order execution process based on the judgment result.

In some embodiments, processing device 110 may make a risk determination based on one or more rules. For example, when the first location is located in a preset safe area, the processing device 110 determines that the risk determination result in the current order execution process is a non-risk order; when the first position is not located in the preset safe area, the processing device 110 determines that the risk determination result in the current order execution process is a risk order. Further, the risk determination result may be divided into several levels (e.g., three levels, i.e., high, medium, and low), and the processing device 110 may determine the risk level according to a multiple of a distance between the first location and a certain location (e.g., a preset location, a center location, etc.) within a preset safety area and a preset threshold. For example, a high risk order with a distance between the first location and the travel destination that is 5 times or more of the preset distance; 1-4 times of the risk order; 0-1 times low risk orders.

In some embodiments, the processing device 110 may determine the risk determination result during the current order execution by constructing a risk determination model. For example, a feature vector is constructed by the distance between the first position and the position of a preset safe area, and the feature vector is input into a risk judgment model, and specific risk judgment results such as high risk, medium risk, low risk and no risk are output.

It should be noted that the above description of operation 700 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and changes to operation 700 will be apparent to those skilled in the art in light of the present disclosure. However, such modifications and variations are intended to be within the scope of the present application. For example, operation 700 may jointly determine a risk determination result in conjunction with operation 600. As another example, either of

operations

700 and 600 may determine that the risk determination for an order is a risk order, and that the order is a risk order.

Fig. 8 is an exemplary flow diagram of another risk determination method 800 according to some embodiments of the present application.

In some embodiments, one or more steps of method 800 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 800 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 810, based on the relevant data of the order, extracting traffic flow data within a certain distance range from the first position.

In some embodiments, the traffic data within a distance range from the first location comprises real-time traffic data and/or estimated traffic data. Specifically, the certain distance may be a preset value, or may be adaptively adjusted according to an actual situation. The adaptive adjustment can be performed according to data such as city scale, city road network data and the like. For example, the certain distance may be different according to the classification of the scale of cities, and may be larger, such as 200m, for a first-line city, and smaller, such as 100m, for a four-line city. For another example, the certain distance may be different according to the grade of the road adjacent to the current first position, and the certain distance may be larger, such as 200m, for the main road, and smaller, such as 100m, for the branch road.

In some embodiments, the real-time traffic data may be estimated from real-time traffic data from traffic authorities, or from real-time order data, for example, based on the location of other vehicles within a certain distance from the first location. In some embodiments, the estimated traffic flow data may be estimated according to historical traffic flow data of the traffic control department, or may be estimated based on traffic flow data within a certain distance range from the first location in the historical order. For example, the processing device 110 may count traffic data within a certain distance range from the first location in all historical orders in a certain time period, to obtain a historical traffic of the first location, which is estimated traffic data.

Step 820, determining a risk judgment result of the order based on the traffic flow data within a certain distance range from the first position.

In some embodiments, processing device 110 may make a risk determination based on one or more rules. For example, when the traffic data within a certain distance range from the first position is greater than or equal to a preset threshold, the processing device 110 determines that the risk determination result of the order is a non-risk order; when the traffic data within a certain distance range from the first position is smaller than a preset threshold, the processing device 110 determines that the risk determination result of the order is a risk order. Further, the risk determination result may be divided into several levels (for example, three levels, i.e., high, medium, and low), and the processing device 110 may determine the risk level according to a magnitude of a multiple that traffic data within a certain distance range from the first location is smaller than a preset threshold. For example, a low risk order is given when the traffic data within a certain distance range from the first position is more than 3 times greater than a preset threshold; less than 3 times and more than 2 times an at-risk order; less than 2 times and more than 1 times high risk orders.

It should be noted that the above description of operation 800 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and changes to operation 800 will be apparent to those skilled in the art in light of this disclosure. However, such modifications and variations are intended to be within the scope of the present application. For example, operation 800 may jointly determine a risk determination result in conjunction with

operations

700 and 600. As another example, any of

operations

800, 700, and 600 may determine that the risk determination result for an order is a risk order, and that the order is a risk order.

Fig. 9 is an exemplary flow diagram of another risk determination method 900 according to some embodiments of the present application.

In some embodiments, one or more steps of method 900 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 900 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

At step 910, data reflecting user behavior is determined based on the relevant data of the order.

In some embodiments, the user may include a service provider and a service requester. In some embodiments, the data reflecting the user behavior includes an operation behavior of the user on the service platform or a movement track of the user after the current order is finished. A service platform may refer to a system including software and hardware devices that provides a service to a user. For example, it may include software systems, servers, and databases. The terminal can access the service platform through the network, for example, application software pre-installed on the terminal, for example, APP installed on a mobile phone, so that a user can access the service platform through the application software and perform related operations. In some embodiments, the operational behavior of the user on the service platform includes clicking on a red envelope, posting a comment (e.g., a comment on a driver or a comment on a passenger, clicking on a connection pushed by the service platform), whether the service provider accepts or executes a new order through its terminal, or whether the service requester initiates a new order through its terminal, etc. In some embodiments, the user's movement trajectory includes a service provider's movement trajectory and a service requester's movement trajectory. After the current order is finished, the processing device 110 may obtain the movement trace data of the service provider, the service requester, and the movement trace data of the vehicle through the network 140, respectively. For example, the movement trajectory of the driver, the movement trajectory of the vehicle, and the movement trajectory of the passenger. Specifically, the movement track may be obtained by a positioning system built in the terminal. The positioning technology may include Global Positioning System (GPS), global satellite navigation system (GLONASS), COMPASS navigation system (COMPASS), galileo positioning system, quasi-zenith satellite system (QZSS), wireless fidelity (Wi-Fi) positioning technology, and the like, or any combination thereof.

Step 920, determining a risk judgment result in the current order execution process based on the data reflecting the user behavior.

In some embodiments, processing device 110 may make a risk determination based on one or more rules and the data reflecting user behavior.

It should be noted that the above description of operation 900 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and changes to operation 900 may occur to those skilled in the art upon review of the present application. However, such modifications and variations are intended to be within the scope of the present application. For example, operation 900 may jointly determine a risk determination result in conjunction with

operations

800, 700, and 600. As another example, any of

operations

900, 800, 700, and 600 may determine that the risk determination for an order is a risk order and that the order is a risk order. For another example, operation 800, operation 700, and operation 600 determine that the risk determination result for an order is a risk order, and operation 900 determines that the order is a non-risk order, and the order is a non-risk order.

FIG. 10 is an exemplary flow chart of another method 1000 of risk prevention according to some embodiments of the present application.

In some embodiments, one or more steps of method 1000 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 1000 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 1010, acquiring relevant data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution.

Step 1010 is similar to step 510, and the detailed description can be found in the corresponding description of step 510 in FIG. 5.

And step 1020, when the information that the terminal associated with the current order sends the order is received and the first position of the vehicle associated with the current order when the order is finished is not matched with the travel destination, processing the order related data by using the trained risk judgment model and determining a risk judgment result.

In some embodiments, the processing device 110 may extract feature values in the relevant data of the at least one current order. The characteristic values may include an order characteristic, status data during execution of the order, a history associated with at least one data in the order, and reflective user behavior data determined based on the associated data of the order. By way of example only, the characteristic values may include a distance from a first position of the vehicle to a driving destination, whether the first position of the vehicle is located in a preset safety zone, a travel destination, a traffic volume within a certain distance range from the first position, a travel speed, whether a service provider accepts or executes a new order through its terminal after the order is ended, a degree of coincidence of a movement trajectory of the service provider and a movement trajectory of the service requester after the order is ended, and the like. Further, the processing device 110 may construct a feature vector based on feature values in the relevant data of the at least one current order. After the feature vector is constructed, the feature vector is input into a risk judgment model, and a risk judgment result of the order at the current moment can be obtained.

In some embodiments, the risk assessment model includes a neural network and a classifier, the feature vector is input into the neural network, a target feature vector can be obtained, the target feature vector is input into the classifier, and a risk assessment result is output. For example, an output value of "1" indicates that there is a risk, and an output value of "0" indicates that there is no risk. Alternatively, a larger output value indicates a higher risk level, and a smaller output value indicates a lower risk level.

Step 1030, based on the risk determination result, executing the set operation.

Step 1030 is similar to step 530 and the detailed description can be found in the corresponding description of step 530 in fig. 5.

In some embodiments, one or more steps of method 1100 may be implemented in system 100 shown in FIG. 1. For example, one or more steps of method 1100 may be stored as instructions in storage device 130 and/or memory 270 and invoked and/or executed by processing device 110.

Step 1110, obtain a plurality of sample orders

In some embodiments, the sample order may include a positive sample and a negative sample. The positive sample may refer to a historical risk order. The negative examples may refer to historical non-risk orders. In some embodiments, the processing device 110 may obtain sample orders from service provider terminals, service requester terminals, service platforms (e.g., taxi taking platforms), APPs, storage devices 130, information sources 150, external data sources, and the like, via the network 140.

Step 1120, extracting the order related data of the sample order when the first vehicle position of the terminal when the current order is finished does not coincide with the travel destination, and the actual risk result corresponding to the sample order.

In some embodiments, the processing device 110 extracts, from the positive and negative examples, data relating to the order for which the first position of the vehicle does not coincide with the travel destination at the time the terminal ended the current order, and an actual risk determination result corresponding to the order. The order in which the first position of the vehicle does not coincide with the travel destination may include an order in which a distance between the first position of the vehicle and the travel destination is abnormal, an order in which the first position is not located in a preset safe area, an order in which traffic data in a certain distance range of the first position is abnormal, an order in which data reflecting user behavior is abnormal, and the like, in any combination thereof. The relevant data of the order comprises at least one of: order characteristics, status data during order execution, and a history associated with at least one data in the order. The actual risk determination result may include whether or not there is a risk, a risk category, a risk level, and the like.

Step 1130, training a pre-constructed initial model based on order related data of the sample order and an actual risk result thereof, and obtaining the risk judgment model.

In some embodiments, the risk assessment initial model may be a Machine learning model, including but not limited to a Classification and Logistic Regression (Logistic Regression) model, a K-Nearest Neighbor algorithm (K-Nearest Neighbor, KNN) model, Naive Bayes (Naive Bayes, NB) model, Support Vector Machine (SVM), Decision Tree (Decision Tree, DT) model, Random forest (Random forest, RF) model, Regression Tree (Classification and Regression Trees, CART) model, Gradient Boosting Decision Tree (GBDT) model, xgboost (electronic Gradient Boosting), Light Gradient Boosting Machine (Light Gradient Boosting Machine, Light GBM), Gradient Boosting Machine (Boosting, abstract), Artificial gbtensile class (noise, and noise), and so on.

In some embodiments, the output of the risk decision model may include the presence or absence of risk and a quantitative representation of risk. For example only, the output result may be risk-free. Alternatively, the output may be a value representing risk and representing a risk level, a risk probability, etc., such as (at risk, high risk order) or (at risk, risk probability 80%).

And continuously adjusting the training parameters of the pre-constructed initial model according to the risk judgment result of each sample order and the risk judgment result corresponding to the sample order, and obtaining the risk judgment model through multiple rounds of training. In some embodiments, the training parameters of the pre-constructed initial model may be adjusted by comparing the risk determination result obtained by the pre-constructed initial model based on the order-related data of the input sample order with the actual risk result, and the training of the pre-constructed initial model is completed until all sample orders are trained.

It should be noted that the form of the determination result described above is for illustrative purposes only, and the present application does not limit the form of the determination result.

It should be noted that the above description of operation 1100 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and changes to operation 1100 will be apparent to those skilled in the art in light of this disclosure. However, such modifications and variations are intended to be within the scope of the present application.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) risk orders are found more quickly and early warning is carried out on users in time; (2) the risk event judgment method has more accurate risk event judgment rate, can save risk judgment and processing time, and reduces harm to users.

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of risk prevention, comprising:

acquiring relevant data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution;

when receiving information of an order ending sent by a terminal associated with a current order and a first position of a vehicle associated with the current order when the order is ended is not matched with a travel destination, determining a risk judgment result in the execution process of the current order at least based on relevant data of the order;

and executing the set operation based on the risk judgment result.

2. The method of claim 1, wherein the terminal comprises an in-vehicle terminal, a service provider terminal, and/or a service requester terminal.

3. The method of claim 1, wherein:

the order characteristics include at least one of:

identity information of the service provider, identification information of a vehicle associated with the service provider, service time, trip start point, trip destination, trip path, and identity information of the service requester;

the state data in the order execution process at least comprises one of the following data:

the data processing system comprises positioning data of a terminal, state data of the terminal, state data of a vehicle, environment data inside the vehicle and real-time state data of an external environment.

4. The method of claim 1, wherein determining a risk determination result during execution of a current order based at least on the data related to the order further comprises:

and determining a risk judgment result in the current order execution process based on the distance between the first position and the travel destination.

5. The method of claim 1, wherein determining a risk determination result during execution of a current order based at least on the data related to the order further comprises:

and determining a risk judgment result in the current order execution process based on whether the first position is located in a preset safe area.

6. The method of claim 1, wherein determining a risk determination result during execution of a current order based at least on the correlation of the order further comprises:

based on the relevant data of the order, extracting traffic flow data within a certain distance range from the first position;

determining a risk judgment result of the order based on the traffic flow data within a certain distance range from the first position;

the traffic data within a certain distance range from the first position comprise real-time traffic data and/or estimated traffic data.

7. The method of claim 1, wherein determining a risk determination result during execution of a current order based at least on the data related to the order further comprises:

determining data reflecting user behavior based on relevant data of the order;

determining a risk judgment result in the current order execution process based on the data reflecting the user behavior.

8. The method of claim 7, wherein the data reflecting the user behavior comprises an operation behavior of the user on the service platform or a movement track of the user after the current order is finished.

9. The method of claim 8, wherein the operational behavior of the user on the service platform comprises: whether the service provider accepts or executes a new order through its terminal or whether the service requester initiates a new order through its terminal.

10. The method of claim 1, wherein determining a risk determination result during execution of a current order based at least on the data related to the order further comprises:

and processing the order related data by utilizing the trained risk judgment model to determine a risk judgment result.

11. The method of claim 1, wherein the setting operation comprises at least one of: risk ranking operations, risk confirmation operations, risk handling operations, and continuous monitoring operations.

12. A system for risk prevention, comprising:

the data acquisition module is used for acquiring related data of at least one current order; the relevant data of the order comprises at least one of: order characteristics, real-time status data during order execution;

the risk judgment module is used for determining a risk judgment result in the execution process of the current order at least based on relevant data of the order when the information that the terminal associated with the current order sends the order is received and the first position of the vehicle associated with the current order when the order is ended is not matched with the travel destination;

and the risk handling module is used for executing set operation based on the risk judgment result.

13. The system of claim 12, wherein the terminal comprises an in-vehicle terminal, a service provider terminal, and/or a service requester terminal.

14. The system of claim 12, wherein:

the order characteristics include at least one of:

15. The system of claim 12, wherein the risk determination module is further to:

16. The system of claim 12, wherein the risk determination module is further to:

17. The system of claim 12, wherein the risk determination module is further to:

18. The system of claim 12, wherein the risk determination module is further to:

determining data reflecting user behavior based on relevant data of the order;

19. The system of claim 18, wherein the data reflecting user behavior comprises user operation behavior on the service platform or a movement track of the user after a current order is finished. Whether the service provider accepts or executes the new order through its terminal.

20. The system of claim 19, wherein the operational behavior of the user on the service platform comprises: whether the service provider accepts or executes a new order through its terminal or whether a new order is initiated in the service request through its terminal.

21. The system of claim 12, wherein the risk determination module is further to:

22. The system of claim 12, wherein the operation of setting comprises at least one of: risk ranking operations, risk confirmation operations, risk handling operations, and continuous monitoring operations.

23. An apparatus for risk prevention, the apparatus comprising at least one processor and at least one memory;

the at least one memory is for storing computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the operations of any of claims 1 to 11.

24. A computer-readable storage medium, characterized in that the storage medium stores computer instructions which, when executed by a processor, carry out the operations of any one of claims 1 to 11.