CN112465331A

CN112465331A - Riding safety control method, model training method, device, equipment and medium

Info

Publication number: CN112465331A
Application number: CN202011323848.XA
Authority: CN
Inventors: 苏红
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2021-03-09

Abstract

The application provides a riding safety control method, a model training method, a device, equipment and a medium, which relate to the technical field of data processing, and the riding safety control method comprises the following steps: acquiring historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified and service information of the service provider to be identified; determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified; and if the risk level is higher than the preset level, monitoring the service of the service provider to be identified. By monitoring the service of the service provider with higher risk level obtained by model prediction in real time and pushing preset education materials in advance, dangerous events can be effectively prevented, the safety of a service requester is ensured, and the service experience is improved.

Description

Riding safety control method, model training method, device, equipment and medium

Technical Field

The application relates to the technical field of data processing, in particular to a riding safety control method, a model training method, a device, equipment and a medium.

Background

In the current riding service process, some related accidents sometimes happen, and part of dangerous service providers with sexual disturbance or even sexual criminal intention are causing great threat to the personal safety of female service requesters. How to better achieve 'intervention in advance and occurrence reduction', improve the use experience of the platform, and effectively protect the safety of a service requester becomes more important.

In the prior art, security management and control are performed from the dimension of a service request, that is, the security of the service requester is determined according to the relevant service information of the service request initiated by the service requester, and a protection measure is executed under the condition of low security.

However, the safety control performed from the service request dimension is likely to cause misjudgment on part of dangerous service providers, so that the riding safety control effect is poor.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide a method, a device and a medium for controlling vehicle safety, so as to solve the problem in the prior art that the vehicle safety control effect is poor.

In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:

in a first aspect, an embodiment of the present application provides a riding safety control method, including:

acquiring historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified and service information of the service provider to be identified;

determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified;

and if the risk level is higher than a preset level, monitoring the service of the service provider to be identified.

Optionally, the attribute information of the service provider to be identified includes at least one of: the certificate information of the service provider to be identified, the vehicle-mounted equipment information of the service provider to be identified and the function information of the service provider to be identified;

the service information of the service provider to be identified comprises at least one of the following information: the method comprises the steps of counting total services in a preset time period, counting negative evaluation services in the preset time period, counting services in a target time period in the preset time period, counting services of a target service point in the preset time period, counting services with target voice information in the preset time period, and counting average distance and average duration of each service in the preset time period.

Optionally, the determining the risk level of the service provider to be identified by using a risk prediction model obtained by pre-training according to the historical service feature information of the service provider to be identified includes:

inputting the historical service characteristic information of the service provider to be identified into the risk prediction model to obtain a risk probability value of the service provider to be identified;

and determining the risk level of the service provider to be identified according to the risk probability value.

Optionally, the determining the risk level of the service provider to be identified according to the risk probability value includes:

acquiring risk probability values of a plurality of reference service providers except the service provider to be identified;

sequencing the service provider to be identified and the plurality of reference service providers according to the risk probability value of the service provider to be identified and the risk probability values of the plurality of reference service providers to obtain a sequencing result;

and determining the risk level of the service provider to be identified according to the sequencing result.

Optionally, the monitoring the service of the service provider to be identified includes:

acquiring service data of current service of the service provider to be identified, wherein the service data of the current service comprises: service time, service place, identity information of a service requester and identity information of a service provider;

determining whether the current service is suspected risk service according to the service data of the current service;

and if so, generating and outputting a processing request, wherein the processing request is used for requesting a service handling user to carry out risk judgment on the current service.

Optionally, after generating and outputting the processing request, the method further includes:

receiving a treatment result input by the service treatment user, wherein the treatment result is used for indicating a risk value of the current service;

if the risk value of the current service reaches a preset threshold value, adding the service provider to be identified to a training positive sample of the risk prediction model, and performing safety intervention processing on the current service, wherein the safety intervention processing comprises at least one of the following steps: and calling the service provider to be identified by voice, and sending alarm information or prompt information to the service provider to be identified.

Optionally, the method further comprises:

and sending target push information to a terminal associated with the service provider to be identified, wherein the target push information comprises preset education materials.

In a second aspect, an embodiment of the present application provides a risk prediction model training method, including:

acquiring historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the positive sample service providers are marked with risk labels, the negative sample service providers are marked with security labels, and the historical service characteristic information comprises: attribute information of a service provider and service information of the service provider;

and training and acquiring a risk prediction module according to the historical service characteristic information of the positive sample service providers and the historical service characteristic information of the negative sample service providers, wherein the risk prediction module is used for determining the risk probability value of the service provider to be identified.

Optionally, the attribute information of the service provider includes at least one of: certificate information of the service provider, vehicle-mounted equipment information of the service provider and function information of the service provider;

the service information of the service provider includes at least one of the following information: the method comprises the steps of counting total services in a preset time period, counting negative evaluation services in the preset time period, counting services in a target time period in the preset time period, counting services of a target service point in the preset time period, counting services with target voice information in the preset time period, and counting average distance and average duration of each service in the preset time period.

Optionally, the service information of the service provider further includes at least one of the following information: the total service quantity in the preset time period is divided according to the gender of the service requester to obtain service information, the ratio of the service quantity in the target time period to the total service quantity in the preset time period, and the gender difference information of the service requester corresponding to the negative evaluation service quantity in the preset time period.

In a third aspect, an embodiment of the present application provides a ride safety control device, including: the device comprises an acquisition module, a determination module and a processing module;

the acquisition module is configured to acquire historical service feature information of a service provider to be identified, where the historical service feature information includes: attribute information of the service provider to be identified and service information of the service provider to be identified;

the determining module is used for determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified;

and the processing module is used for monitoring the service of the service provider to be identified if the risk level is higher than a preset level.

Optionally, the determining module is specifically configured to input historical service feature information of the service provider to be identified into the risk prediction model, so as to obtain a risk probability value of the service provider to be identified; and determining the risk level of the service provider to be identified according to the risk probability value.

Optionally, the determining module is specifically configured to obtain risk probability values of a plurality of reference service providers other than the service provider to be identified; sequencing the service provider to be identified and the plurality of reference service providers according to the risk probability value of the service provider to be identified and the risk probability values of the plurality of reference service providers to obtain a sequencing result; and determining the risk level of the service provider to be identified according to the sequencing result.

Optionally, the obtaining module is further configured to obtain service data of a current service of the service provider to be identified, where the service data of the current service includes: service time, service place, identity information of a service requester and identity information of a service provider;

the determining module is further configured to determine whether the current service is a suspected risk service according to the service data of the current service;

and the processing module is further used for generating and outputting a processing request if the current service is the service, wherein the processing request is used for requesting a service handling user to carry out risk judgment on the current service.

Optionally, the apparatus further comprises: a receiving module;

the receiving module is used for receiving a treatment result input by the service treatment user, wherein the treatment result is used for indicating a risk value of the current service;

the processing module is further configured to add the service provider to be identified to a training positive sample of the risk prediction model if the risk value of the current service reaches a preset threshold, and perform a safety intervention process on the current service, where the safety intervention process includes at least one of: and calling the service provider to be identified by voice, and sending alarm information or prompt information to the service provider to be identified.

Optionally, the apparatus further comprises: a sending module;

the sending module is used for sending target pushing information to a terminal associated with the service provider to be identified, wherein the target pushing information comprises preset education materials.

In a fourth aspect, an embodiment of the present application provides a risk prediction model training apparatus, including: the system comprises an acquisition module and a training module;

the acquisition module is used for acquiring historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the positive sample service providers are marked with risk tags, the negative sample service providers are marked with security tags, and the historical service characteristic information comprises: attribute information of a service provider and service information of the service provider;

the training module is used for training and acquiring a risk prediction module according to the historical service characteristic information of the positive sample service providers and the historical service characteristic information of the negative sample service providers, and the risk prediction model is used for determining the risk probability value of the service provider to be identified.

In a fifth aspect, an embodiment of the present application provides an electronic device, including: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method according to the first or second aspect.

In a sixth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method according to the first or second aspect.

The beneficial effect of this application:

the embodiment of the application provides a riding safety control method, a model training method, a device, equipment and a medium, wherein the riding safety control method comprises the following steps: acquiring historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified and service information of the service provider to be identified; determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified; and if the risk level is higher than the preset level, monitoring the service of the service provider to be identified. According to the scheme, the risk prediction model is adopted to predict the risk level of the service provider to be identified according to the historical service characteristic information of the service provider to be identified, so that the service provider with higher risk level can be selected from a large number of service providers according to the risk level to pay extra attention and monitor services, and huge data processing amount generated by monitoring each service provider is avoided. By carrying out preliminary suspected risk service judgment according to the service data of the current service, the service disposal user is handed to carry out risk judgment on the current service only when the current service is suspected risk service, the data processing amount of the service disposal user can be reduced to a certain extent, and the labor cost caused by judging all services of a service provider to be identified with a high risk level is avoided. And by monitoring the service in real time, dangerous events can be effectively prevented, the safety of a service requester is ensured, and the service experience is improved.

In addition, for a service provider with a high risk level, preset education materials can be pushed in advance to play a role in warning the service provider, so that the possibility of danger caused by the service provider is reduced.

The risk prediction model training method comprises the following steps: the method comprises the following steps of acquiring historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the positive sample service providers are marked with risk labels, the negative sample service providers are marked with safety labels, and the historical service characteristic information comprises the following steps: attribute information of a service provider and service information of the service provider; and training and acquiring a risk prediction module according to the historical service characteristic information of the positive sample service providers and the historical service characteristic information of the negative sample service providers, wherein the risk prediction module is used for determining the risk probability value of the service provider to be identified. According to the method, the risk prediction model is trained by acquiring the historical service characteristic information of the positive sample service provider and the negative sample service provider, so that the risk grade of the service provider to be identified can be predicted based on the trained risk prediction model, service monitoring and advanced education can be realized for the service provider according to the risk grade, and dangerous events can be prevented. The acquired historical feature information comprises both attribute information and service information of the service provider, the service information also comprises voice data acquired from a recording text in a service travel, and in addition, the acquired historical feature information also comprises a plurality of proportional features derived according to the service information, so that a plurality of pieces of service feature information are integrated, the identification precision of a trained risk prediction model is high, and the risk grade of the service provider to be identified is accurately predicted.

Drawings

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

Fig. 1 is a block diagram of a ride safety control system according to an embodiment of the present disclosure;

FIG. 2 is a diagram of exemplary hardware and software components of an electronic device that may implement the concepts of the present application, provided by an embodiment of the present application;

fig. 3 is a schematic flow chart of a riding safety control method according to an embodiment of the present application;

fig. 4 is a schematic flow chart of another ride safety control method according to an embodiment of the present application;

fig. 5 is a schematic flow chart of another ride safety control method according to an embodiment of the present disclosure;

fig. 6 is a schematic flow chart of another ride safety control method according to an embodiment of the present application;

fig. 7 is a schematic flow chart of another ride safety control method according to an embodiment of the present application;

fig. 8 is a risk prediction model training method provided in an embodiment of the present application;

fig. 9 is a schematic view of a ride safety control apparatus according to an embodiment of the present application;

FIG. 10 is a schematic diagram of a risk prediction model training apparatus according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

To enable those skilled in the art to utilize the present disclosure, the following embodiments are presented in conjunction with a specific application scenario, "network appointment service. It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is described primarily in the context of a net appointment, it should be understood that this is only one exemplary embodiment. The application can be applied to any other traffic type. For example, the present application may be applied to different transportation system environments, including terrestrial, marine, or airborne, among others, or any combination thereof. The method and the system can also be used for take-out service, express delivery service and the like. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

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

The terms "passenger," "requestor," "service person," "service requestor," and "customer" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "driver," "provider," "service provider," and "provider" are used interchangeably in this application to refer to an individual, entity, or tool that can provide a service.

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

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

Before the application is filed, the prior technical scheme is as follows: and judging whether the sex of the service provider and the service requester of the current service is the same or not based on the related data of the service currently executed by the service provider, if so, judging the risk level of the service by using the data associated with the current service and/or the state data when the current service is executed, and executing the set operation.

The technical problems caused by the method are as follows: intervention in the future is carried out from the service data dimension of the current service, advance prevention cannot be achieved, and due to misjudgment of partial dangerous service providers, the riding safety control effect is poor.

In order to solve the technical problem, the riding safety control method provided by the application has the core idea that: according to the method, a risk prediction model is trained from the dimensionality of a service provider, the risk level of the service provider is determined by the risk prediction model, the service of the service provider is monitored when the service provider is determined to be in a high risk level, and education materials are pushed in advance.

The technical solution of the present application is explained below by means of possible implementations.

Fig. 1 is a block diagram of a ride safety control system according to an embodiment of the present application. For example, the ride safety control system 100 may be an online transportation service platform for transportation services such as taxi, net appointment, designated drive service, express, pool, bus service, driver rental, or regular service, or any combination thereof. The ride safety control system 100 may include one or more of a server 110, a network 120, a service requester terminal 130, a service provider terminal 140, and a database 150, and the server 110 may include a processor therein that performs the operations of the instructions.

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

In some embodiments, the server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processor may obtain the service data based on a service request initiated from a service requestor. In some embodiments, a processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set computer (Reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.

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

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

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

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

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

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

In some embodiments, one or more components (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc.) in the ride safety control system 100 may have access to the database 150. In some embodiments, one or more components in the ride safety control system 100 may read and/or modify information related to the service requester, the service provider, or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information for one or more users after receiving a service request.

Fig. 2 is a schematic diagram of exemplary hardware and software components of an electronic device that can implement the concepts of the present application according to an embodiment of the present application. For example, the processor 220 may be used on the electronic device 200 and to perform the functions herein.

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

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

For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

Fig. 3 is a schematic flowchart of a riding safety control method provided in an embodiment of the present application, where an execution subject of the method may be a server, a processor, or a management terminal of a service platform, and as shown in fig. 3, the method of the present application may include:

s301, obtaining historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified, and service information of the service provider to be identified.

It should be noted that, in the embodiments of the present application, the service provider may refer to a driver in the driver-and-passenger service, the service requester may refer to a passenger in the driver-and-passenger service, the service provider may refer to a takeout rider in the takeout service, and the service requester may refer to a meal ordering user in the takeout service. Of course, the application scenarios are not limited to the above-mentioned example, and the users designated by the service provider and the service requester may be adaptively changed when different application scenarios are corresponded. Wherein the service requester and the service provider correspond to the same service.

In this embodiment, a description is given of a method using a network car booking service as an application scenario. Alternatively, historical service characteristic information of the service provider to be identified can be obtained from a database of the network appointment platform, and the historical service characteristic information can refer to characteristic information related to riding services provided by the service provider to be identified in a historical time period. Optionally, it may include: attribute information of the service provider to be identified, which may refer to basic features of the service provider to be identified, such as: identity information, etc. The service information of the service provider to be identified may refer to service data of the ride service it has completed, such as: the time of the ride, the identity information of the service requester, etc.

S302, determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified.

In the network car booking service process, related events (sexual disturbance, sexual crime and the like) with different risk degrees occur almost every day, personal safety threats and even life dangers with different degrees are caused to service requesters (especially female service providers), and the image of a network car booking platform is also seriously influenced.

In this embodiment, a risk level of the service provider to be identified may be determined by using a risk prediction model trained in advance according to the acquired historical service feature information of the service provider to be identified, where the risk level may be used to indicate a level of possibility that the service provider to be identified may cause harm to the service requester, and the higher the risk level is, the greater the risk of the service provider to be identified is, the more difficult the safety of the service requester is to be ensured.

And S303, if the risk level is higher than the preset level, monitoring the service of the service provider to be identified.

Alternatively, the service of the service provider to be identified may refer to a service that the service provider is currently performing, or each service after the current service, for which the risk level of the service provider to be identified is higher than a preset level limit.

In some embodiments, since the risk level of the service provider to be identified can be continuously predicted and updated in real time through the risk prediction model, when the risk level is lower than the preset level within a certain time, the monitoring processing on the service may not be performed.

In some embodiments, if it is determined that the risk level of the service provider to be identified is higher than the preset level, the service provider to be identified may be considered as a high-risk service provider and is likely to generate a risk service, and then, the monitoring process may be performed on future services of the service provider to be identified. That is, in the process of providing services by the service provider to be identified, monitoring and safety interference are performed on the services of the service provider to be identified by combining the manual system according to the recording data and the video data of the services, so as to prevent dangerous events.

By predicting the risk level of the service provider to be identified according to the historical service characteristic information of the service provider to be identified, the service provider with higher risk level can be selected from a large number of service providers cooperated by the platform to pay extra attention and monitor the service, and huge data processing amount generated by monitoring each service provider is avoided. And by monitoring the service of the service provider with higher risk level in real time, the occurrence of dangerous events can be effectively prevented, the safety of the service requester is ensured, and the service experience is improved.

In summary, the riding safety control method provided in this embodiment includes: acquiring historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified and service information of the service provider to be identified; determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified; and if the risk level is higher than the preset level, monitoring the service of the service provider to be identified. According to the scheme, the risk prediction model is adopted to predict the risk level of the service provider to be identified according to the historical service characteristic information of the service provider to be identified, so that the service provider with higher risk level can be selected from a large number of service providers according to the risk level to pay extra attention and monitor services, and huge data processing amount generated by monitoring each service provider is avoided. And by monitoring the service of the service provider with higher risk level in real time, the occurrence of dangerous events can be effectively prevented, the safety of the service requester is ensured, and the service experience is improved.

Optionally, the attribute information of the service provider to be identified includes at least one of: certificate information of the service provider to be identified, vehicle-mounted equipment information of the service provider to be identified, and function information of the service provider to be identified.

The certificate information of the provider to be served may include: identity card, driving license, network appointment platform working card, etc. The on-vehicle device information of the service provider to be identified may refer to whether or not the vehicle driven by the service provider to be identified is mounted with the monitoring device. The functional information of the service provider to be identified may indicate whether the service provider belongs to a part-time service or a full-time service.

Generally, a service provider having certificate information, a driven vehicle equipped with a monitoring device, and belonging to full-time service may be considered to have a high possibility of belonging to a compliant service provider, while a safety factor may be relatively high and a risk level may be relatively low for a compliant service provider.

And the service information of the service provider to be identified may include at least one of the following: the method comprises the steps of counting the total service quantity in a preset time period, counting the negative evaluation service quantity in the preset time period, counting the service quantity of a target time interval in the preset time period, counting the service quantity of a target service point in the preset time period, counting the service quantity with target voice information in the preset time period, and counting the average distance and the average duration of each service in the preset time period.

The preset time period may be 180 days, 90 days, 60 days, and the like, and the total service number of the service provider to be identified in different preset time periods may be obtained. The negative evaluation service number in the preset time period may refer to the number of services that are badly evaluated, complained, or blackened in the preset time period. Generally, when the number of negative evaluation services of the service provider to be identified is large, the service quality of the service provider to be identified may be considered poor, and for such service provider, the risk level may be relatively high. The service amount of the target period in the preset time period may refer to the late-night service amount in the preset time period, wherein the target period may refer to the service provider who prefers the late-night service after 12 pm in each day, and the risk level of the service provider is considered to be high. The service number of the target service point in the preset time period may refer to the service number of some sensitive places in the preset time period, for example: the destination or starting place of the service is a dangerous event high-occurrence place such as a bar, a KTV, a bathing center and the like. When the number of services at the target service point is large, the risk level of the service provider can be considered to be high. The number of services having the target voice information in the preset time period may refer to that the trip record obtained during the service driving process includes the target voice information, where the target voice information may be, for example: abuse, talking about privacy, touching me, etc. The risk level of the service provider may be considered higher when the number of services having the target voice information is larger. Alternatively, the feature of the service number having the target voice information in the preset time period may be extracted from the recording text of the service schedule. The average distance and average duration of each service in the preset time period refer to the average distance and average duration of each service, and generally, a service provider with a longer service distance and a longer service duration may be considered to have a higher risk level.

Of course, the above is only an exemplary list of some possible and relatively important historical service feature information, and in practical applications, the historical service feature information may not be limited to the above, and the accuracy of the determined risk level of the service provider is higher when the obtained historical service feature information is more comprehensive.

Fig. 4 is a schematic flow chart of another ride safety control method according to an embodiment of the present application; optionally, in step S302, determining the risk level of the service provider to be identified by using a risk prediction model obtained through pre-training according to the historical service feature information of the service provider to be identified may include:

s401, inputting historical service characteristic information of the service provider to be identified into a risk prediction model, and obtaining a risk probability value of the service provider to be identified.

Optionally, the acquired historical service characteristic information of the service provider to be identified may be used as an input of a risk prediction model, and a risk probability value of the service provider to be identified may be obtained through calculation of the model.

S402, determining the risk level of the service provider to be identified according to the risk probability value.

In some embodiments, after obtaining the risk probability value of the service provider to be identified based on the risk prediction model, the risk prediction model may further determine the risk level of the service provider to be identified according to the risk probability value, and finally output a risk level result.

In yet other embodiments, the risk prediction model may output a calculated risk probability value for the service provider to be identified, such as: output to a processor or the like external to the risk prediction model such that the calculation of the risk level is performed in the processor.

Fig. 5 is a schematic flow chart of another ride safety control method according to an embodiment of the present disclosure; optionally, in step S402, determining the risk level of the service provider to be identified according to the risk probability value may include:

s501, risk probability values of a plurality of reference service providers except the service provider to be identified are obtained.

Alternatively, the predictive risk model may be employed to calculate risk probability values for a plurality of reference service providers other than the service provider to be identified. Wherein the plurality of reference service providers may be service providers that are servicing the same day as the service provider to be identified. For example: and the service provider to be identified performs the service today, then historical service characteristic information of other multiple service providers which perform the service today can be obtained, and risk probability calculation is performed. To extract high risk level service providers for listening from a plurality of service providers that are serving each day.

S502, sequencing the service provider to be identified and the plurality of reference service providers according to the risk probability value of the service provider to be identified and the risk probability values of the plurality of reference service providers to obtain a sequencing result.

Optionally, the service providers to be identified and the plurality of reference service providers may be ranked according to the risk probability values of the service providers, where the ranking results may be obtained by ranking the risk probability values from large to small.

And S503, determining the risk level of the service provider to be identified according to the sequencing result.

Optionally, risk ranking may be performed for a plurality of service providers according to the ranking result, for example: the top 500 of the ranking is determined as the high risk level, 501 and 1000 are determined as the medium risk level, and 1001 are determined as the low risk level. Of course, the partition threshold may be adaptively adjusted.

Alternatively, if the risk probability value of the service provider to be identified is the top 500 in the top rank, then its risk level may be determined to be a high risk level.

In addition to the method, the comparison may also be performed directly according to the risk probability value of the service provider to be identified and the preset risk probability value, and if the risk probability value of the service provider to be identified is greater than the preset risk probability value, it is determined that the service provider to be identified is in a high risk level.

Fig. 6 is a schematic flow chart of another ride safety control method according to an embodiment of the present application; alternatively, as shown in fig. 6, in step S303, performing a listening process on the service of the service provider to be identified may include:

s601, acquiring service data of current service of a service provider to be identified, wherein the service data of the current service comprises: service time, service location, identity information of the service requester, identity information of the service provider.

Optionally, the service data of the current service of the service provider to be identified may be obtained according to a service request initiated by the service requester or a service request received by the service provider to be identified. The service time, the service location, the identity information of the service requester may refer to the gender of the service requester, and the identity information of the service provider may refer to the gender of the service provider to be identified. Of course, the service data may not be limited to the listed ones, and may include, for example, service environment data and the like.

S602, determining whether the current service is suspected risk service according to the service data of the current service.

Optionally, whether the current service is a suspected risk service may be determined according to the acquired service data through a preset determination rule. For example: the service places are as follows: sensitive places such as bars and KTVs, which are served late at night, which are served by a service requester and a service provider, which are served by a service provider, etc., it may be determined whether the current service is a suspected risk service.

And S603, if so, generating and outputting a processing request, wherein the processing request is used for requesting a service handling user to carry out risk judgment on the current service.

Of course, the above judgment by the preset rule can only make a preliminary judgment on the current service, and the current service is only suspected risk service. In this embodiment, after determining that the current service is a suspected risk service, a processing request may be further generated and output to instruct a service handling user to perform risk judgment on the current service, so as to determine a risk level of the current service. Among them, the service handling user may refer to a worker of the service platform, which may make an accurate judgment on the current service.

By carrying out preliminary suspected risk service judgment according to the service data of the current service, the service disposal user is handed to carry out risk judgment on the current service only when the current service is suspected risk service, the data processing amount of the service disposal user can be reduced to a certain extent, and the labor cost caused by judging all services of a service provider to be identified with a high risk level is avoided.

Optionally, the risk level of the current service may be determined according to the service monitoring video and audio information of the service provider to be identified, which is obtained in real time. For example: the service provider makes a verbal disturbance to the service requester, and then can determine that the risk level is relatively low, and when the service provider makes a physical touch to the service requester, then can determine that the risk level is relatively high.

Fig. 7 is a schematic flow chart of another ride safety control method according to an embodiment of the present application; optionally, as shown in fig. 7, after the processing request is generated and output in step S603, the method of the present application may further include:

s701, receiving a treatment result input by a service treatment user, wherein the treatment result is used for indicating a risk value of the current service.

Optionally, the risk value of the current service may also refer to the risk level of the current service, and the higher the risk value, the higher the risk level may be considered.

S702, if the risk value of the current service reaches a preset threshold value, adding a service provider to be identified into a training positive sample of the risk prediction model, and performing safety intervention processing on the current service, wherein the safety intervention processing comprises at least one of the following steps: and calling the service provider to be identified by voice, and sending alarm information or prompt information to the service provider to be identified.

In some embodiments, when it is determined that the risk value of the current service reaches the preset threshold, it is determined that the current service is really a risk service, that is, the current service is really weighted, the service provider to be identified may be added to the database, and is marked as a training positive sample of the risk prediction model, so as to use the training positive sample as a source of the training positive sample of the risk prediction model, and further optimize the training model, thereby improving the identification accuracy of the trained risk prediction model.

Meanwhile, for services with different risk levels, corresponding safety intervention processing can be adopted so as to reduce the damage to the service requester. For example: if the risk level of the current service is low, alarm information or prompt information can be sent to the service provider to be identified, for example: prompt information is broadcasted through voice in the service vehicle, so that the service provider to be identified can correct own attitude, and the vehicle is driven safely. And if the risk level of the current service is medium, the service provider to be identified can be called by voice, wherein the service provider can be called by a worker of the service platform to perform early warning. And if the risk level of the current service is higher, the service provider to be identified can be called by voice, wherein the call warning can be carried out on the service provider through the intervention of the police. It is even possible to instruct the police closer to the service provider to be identified to take control.

Optionally, the method of the present application may further include: and sending target push information to a terminal associated with the service provider to be identified, wherein the target push information comprises preset education materials.

In some embodiments, after determining that the service provider to be identified is at a high risk level, the preset education material may be displayed on the display screen of the associated vehicle-mounted terminal or mobile phone terminal before the service provider to be identified performs the service. To alert the service provider to be identified.

In summary, the riding safety control method provided in this embodiment includes: acquiring historical service characteristic information of a service provider to be identified, wherein the historical service characteristic information comprises: attribute information of the service provider to be identified and service information of the service provider to be identified; determining the risk level of the service provider to be identified by adopting a risk prediction model obtained by pre-training according to the historical service characteristic information of the service provider to be identified; and if the risk level is higher than the preset level, monitoring the service of the service provider to be identified. According to the scheme, the risk prediction model is adopted to predict the risk level of the service provider to be identified according to the historical service characteristic information of the service provider to be identified, so that the service provider with higher risk level can be selected from a large number of service providers according to the risk level to pay extra attention and monitor services, and huge data processing amount generated by monitoring each service provider is avoided. By carrying out preliminary suspected risk service judgment according to the service data of the current service, the service disposal user is handed to carry out risk judgment on the current service only when the current service is suspected risk service, the data processing amount of the service disposal user can be reduced to a certain extent, and the labor cost caused by judging all services of a service provider to be identified with a high risk level is avoided. And by monitoring the service in real time, dangerous events can be effectively prevented, the safety of a service requester is ensured, and the service experience is improved.

Fig. 8 is a risk prediction model training method provided in an embodiment of the present application, and as shown in fig. 8, the method may include:

s801, acquiring historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the positive sample service providers are marked with risk labels, the negative sample service providers are marked with safety labels, and the historical service characteristic information comprises: attribute information of the service provider, service information of the service provider.

The positive sample service provider may refer to a service provider who has caused a dangerous event before, and the negative sample service provider may refer to a service provider who has not caused a dangerous event before. Alternatively, historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers may be obtained from the data. And, each positive exemplar service provider is labeled with an at-risk label and each negative exemplar service provider is labeled with a security label. And the historical service characteristic information may be the historical service characteristic information of the service provider to be identified, which is acquired in the above model application process.

S802, training and acquiring a risk prediction module according to historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the risk prediction module is used for determining the risk probability value of the service provider to be identified.

Optionally, the historical service feature information of a plurality of positive sample service providers and the risk label marked by each positive sample service provider, and the historical service feature information of a plurality of negative sample service providers and the risk label marked by each negative sample service provider may be used as input data, input into the risk prediction model to be trained, and a preset classification algorithm is adopted, so as to train and obtain the risk prediction model.

The preset classification algorithm may adopt a machine learning classification algorithm, for example, xgboost (eXtreme Gradient Boosting), SVM (support vector machines), RF (Random Forest), and other methods.

In summary, the risk prediction model training method provided in this embodiment includes: the method comprises the following steps of acquiring historical service characteristic information of a plurality of positive sample service providers and historical service characteristic information of a plurality of negative sample service providers, wherein the positive sample service providers are marked with risk labels, the negative sample service providers are marked with safety labels, and the historical service characteristic information comprises the following steps: attribute information of a service provider and service information of the service provider; and training and acquiring a risk prediction module according to the historical service characteristic information of the positive sample service providers and the historical service characteristic information of the negative sample service providers, wherein the risk prediction module is used for determining the risk probability value of the service provider to be identified. According to the method, the risk prediction model is trained by acquiring the historical service characteristic information of the positive sample service provider and the negative sample service provider, so that the risk grade of the service provider to be identified can be predicted based on the trained risk prediction model, service monitoring and advanced education can be realized for the service provider according to the risk grade, and dangerous events can be prevented. The acquired historical feature information comprises both attribute information and service information of the service provider, the service information also comprises voice data acquired from a recording text in a service travel, and in addition, the acquired historical feature information also comprises a plurality of proportional features derived according to the service information, so that a plurality of pieces of service feature information are integrated, the identification precision of a trained risk prediction model is high, and the risk grade of the service provider to be identified is accurately predicted.

Optionally, the attribute information of the service provider includes at least one of: certificate information of a service provider, vehicle-mounted equipment information of the service provider and function information of the service provider;

the service information of the service provider includes at least one of the following information: the method comprises the steps of counting the total service quantity in a preset time period, counting the negative evaluation service quantity in the preset time period, counting the service quantity of a target time interval in the preset time period, counting the service quantity of a target service point in the preset time period, counting the service quantity with target voice information in the preset time period, and counting the average distance and the average duration of each service in the preset time period.

The specific description of the part of the feature information is described in the foregoing embodiments, and is not repeated here.

Optionally, the service information of the service provider further includes at least one of the following information: the method comprises the steps of splitting the total service quantity in a preset time period according to the gender of a service requester to obtain service information, comparing the total service quantity in a target time period with the total service quantity in the preset time period, and evaluating the gender difference information of the service requester corresponding to the service quantity in a negative direction in the preset time period.

In some embodiments, the service information used for risk prediction model training may also be some feature information derived on the basis of the service information provided above.

The service information obtained by splitting the total service quantity in the preset time period according to the gender of the service requester can refer to the service quantity of the male service requester and the service quantity of the female service requester in the total service quantity. In the referred scenario, if the service behaviors of the service provider for the female service requester and the service behaviors of the service provider for the male service requester are different, and the number of services of the female service requester is relatively large, extra attention needs to be paid to the service provider, and the possibility of high risk level is high. The ratio of the service quantity in the target time period to the total service quantity in the preset time period may be a ratio of the late-night service quantity to the total service quantity, and if the ratio is large, that is, the service provider likes to provide services at late night, the risk level is high. The sex difference information of the service requester corresponding to the negative-direction evaluation service quantity in the preset time period may refer to a ratio of the negative-direction evaluation service quantity of the male service requester to the negative-direction evaluation service quantity of the female service requester, and the smaller the ratio is, that is, the more the negative-direction evaluation service quantity of the female service requester is, the higher the risk level of the service provider is.

Optionally, through the analysis of some of the above-mentioned proportional characteristics, the essential behavior of the service provider can be more accurately characterized, for example: late-night service preferences, female service preferences, etc. Therefore, the recognition accuracy of the trained risk prediction model is high.

The following describes devices, apparatuses, and storage media corresponding to the riding safety control method and the risk prediction model training method provided in the present application, and specific implementation processes and technical effects thereof are referred to above and will not be described again below.

Fig. 9 is a schematic diagram of a vehicle safety control device according to an embodiment of the present application, where functions implemented by the vehicle safety control device correspond to steps executed by the vehicle safety control method. The apparatus may be understood as the server or the processor of the server, or may be understood as a component that is independent of the server or the processor and implements the functions of the present application under the control of the server, as shown in fig. 9, the apparatus may include: an obtaining module 910, a determining module 920 and a processing module 930;

the obtaining module 910 is configured to obtain historical service feature information of a service provider to be identified, where the historical service feature information includes: attribute information of the service provider to be identified and service information of the service provider to be identified;

a determining module 920, configured to determine a risk level of the service provider to be identified by using a risk prediction model obtained through pre-training according to historical service feature information of the service provider to be identified;

the processing module 930 is configured to monitor the service of the service provider to be identified if the risk level is higher than the preset level.

Optionally, the attribute information of the service provider to be identified includes at least one of: certificate information of a service provider to be identified, vehicle-mounted equipment information of the service provider to be identified, and function information of the service provider to be identified;

the service information of the service provider to be identified includes at least one of the following information: the method comprises the steps of counting the total service quantity in a preset time period, counting the negative evaluation service quantity in the preset time period, counting the service quantity of a target time interval in the preset time period, counting the service quantity of a target service point in the preset time period, counting the service quantity with target voice information in the preset time period, and counting the average distance and the average duration of each service in the preset time period.

Optionally, the determining module 920 is specifically configured to input historical service feature information of the service provider to be identified into the risk prediction model, so as to obtain a risk probability value of the service provider to be identified; and determining the risk level of the service provider to be identified according to the risk probability value.

Optionally, the determining module 920 is specifically configured to obtain risk probability values of a plurality of reference service providers except the service provider to be identified; sequencing the service provider to be identified and a plurality of reference service providers according to the risk probability value of the service provider to be identified and the risk probability values of the reference service providers to obtain a sequencing result; and determining the risk level of the service provider to be identified according to the sequencing result.

Optionally, the obtaining module 910 is further configured to obtain service data of a current service of the service provider to be identified, where the service data of the current service includes: service time, service place, identity information of a service requester and identity information of a service provider;

the determining module 920 is further configured to determine whether the current service is a suspected risk service according to the service data of the current service;

the processing module 930 is further configured to generate and output a processing request if the current service is determined to be a risk of the service handling user.

Optionally, the apparatus further comprises: a receiving module;

the receiving module is used for receiving a treatment result input by a service treatment user, and the treatment result is used for indicating a risk value of the current service;

the processing module 930 is further configured to, if the risk value of the current service reaches a preset threshold, add a service provider to be identified to a training positive sample of the risk prediction model, and perform a security intervention on the current service, where the security intervention includes at least one of: and calling the service provider to be identified by voice, and sending alarm information or prompt information to the service provider to be identified.

Optionally, the apparatus further comprises: a sending module;

and the sending module is used for sending target push information to a terminal associated with the service provider to be identified, wherein the target push information comprises preset education materials.

Fig. 10 is a schematic diagram of a risk prediction model training device according to an embodiment of the present application, where functions implemented by the risk prediction model training device correspond to steps executed by the risk prediction model training method. The apparatus may be understood as the server or the processor of the server, or may be understood as a component that is independent of the server or the processor and implements the functions of the present application under the control of the server, as shown in fig. 10, the apparatus may include: an acquisition module 1110, a training module 1120;

the collecting module 1110 is configured to collect and obtain historical service feature information of a plurality of positive sample service providers and historical service feature information of a plurality of negative sample service providers, where the positive sample service providers are labeled with risk tags, the negative sample service providers are labeled with security tags, and the historical service feature information includes: attribute information of a service provider and service information of the service provider;

the training module 1120 is configured to train and acquire a risk prediction module according to the historical service feature information of the positive sample service providers and the historical service feature information of the negative sample service providers, where the risk prediction module is configured to determine a risk probability value of the service provider to be identified.

The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

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

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

It should be noted that the above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, the modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 11, the electronic device may include: a processor 901 and a memory 902, wherein:

the memory 902 is used for storing programs, and the processor 901 calls the programs stored in the memory 902 to execute the above method embodiments. The specific implementation and technical effects are similar, and are not described herein again.

Optionally, the present application also provides a program product, such as a computer readable storage medium, comprising a program which, when being executed by a processor, is adapted to carry out the above-mentioned method embodiments.

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

Claims

1. A ride safety control method is characterized by comprising the following steps:

2. The method of claim 1, wherein the attribute information of the service provider to be identified comprises at least one of: the certificate information of the service provider to be identified, the vehicle-mounted equipment information of the service provider to be identified and the function information of the service provider to be identified;

3. The method according to claim 1, wherein the determining the risk level of the service provider to be identified by using a risk prediction model trained in advance according to the historical service feature information of the service provider to be identified comprises:

4. The method of claim 3, wherein the determining the risk level of the service provider to be identified according to the risk probability value comprises:

5. The method according to any of claims 1-4, wherein the listening processing for the service of the service provider to be identified comprises:

6. The method of claim 5, wherein after generating and outputting the processing request, further comprising:

7. The method according to any one of claims 1-4, further comprising:

8. A risk prediction model training method is characterized by comprising the following steps:

9. The method of claim 8, wherein the attribute information of the service provider comprises at least one of: certificate information of the service provider, vehicle-mounted equipment information of the service provider and function information of the service provider;

10. The method of claim 9, wherein the service information of the service provider further comprises at least one of the following information: the total service quantity in the preset time period is divided according to the gender of the service requester to obtain service information, the ratio of the service quantity in the target time period to the total service quantity in the preset time period, and the gender difference information of the service requester corresponding to the negative evaluation service quantity in the preset time period.

11. A ride safety control device, comprising: the device comprises an acquisition module, a determination module and a processing module;

12. A risk prediction model training device, comprising: the system comprises an acquisition module and a training module;

13. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 10.

14. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 10.