Ride management method and device, electronic device, and storage medium

Abstract

Embodiments of the present application provide a ride management method and device, electronic equipment and storage medium, belonging to the field of public transportation. The method comprises: calling a long-term risk assessment sub-model of a pre-trained risk assessment model to perform long-term risk assessment on a balance fare ratio, a device risk probability and a long-term payment failure probability, to obtain a long-term payment risk score; calling a short-term risk assessment sub-model of the pre-trained risk assessment model to perform short-term risk assessment on a scan code time interval sequence and a short-term payment failure probability, to obtain a short-term payment risk score; determining a target payment risk score according to a preset weight coefficient, the long-term payment risk score and the short-term payment risk score; performing risk level mapping on the target payment risk score according to a preset risk level mapping table, to obtain a target risk level of a target object, and performing ride management on the target object according to the target risk level. The embodiments of the present application can improve the accuracy of ride management on users.

Description

Technical Field

[0001] This application relates to the field of public transportation technology, and in particular to a passenger management method and device, electronic equipment and storage medium. Background Technology

[0002] In the development of modern cities, QR code payment has become the mainstream payment method due to its convenience, efficiency, and security. For example, in the public transportation sector, QR code payment, as a type of QR code payment, is gradually replacing card payment and becoming the preferred choice for people's travel due to its convenience of not needing to purchase a card in advance.

[0003] In scenarios where QR codes for public transportation are widely used, operators often need to address the risk of user non-payment. For example, if a user's account balance is insufficient or they intentionally default, the QR code payment may fail, resulting in financial losses for the operator and potentially causing credit issues. Credit issues often indicate increased user payment risk, making it difficult for operators to accurately predict a user's payment ability, thus increasing operational uncertainty and potential losses. Therefore, operators need to conduct payment risk assessments for users and then manage their rides accordingly based on the assessment results to ensure operational efficiency and user experience.

[0004] However, in existing technologies, the accuracy of payment risk assessment is low, which leads to a decrease in the accuracy of user ride management. Summary of the Invention

[0005] The main objective of this application is to provide a ride management method, device, electronic device, and storage medium, which aims to improve the accuracy of ride management for users.

[0006] To achieve the above objectives, a first aspect of this application proposes a passenger management method, the method comprising:

[0007] In response to a ride payment operation by a target user, the system obtains the target user's balance-to-fare ratio at the current time, obtains the target user's QR code scanning time interval sequence and short-term payment failure probability within a first time interval prior to the current time, and obtains the target user's device risk probability and long-term payment failure probability within a second time interval prior to the current time; wherein the second time interval is greater than the first time interval.

[0008] The long-term risk assessment sub-model of the pre-trained risk assessment model is invoked to perform a long-term risk assessment on the balance-to-fare ratio, the equipment risk probability, and the long-term payment failure probability, thereby obtaining a long-term payment risk score.

[0009] The short-term risk assessment sub-model of the pre-trained risk assessment model is invoked to perform a short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, thereby obtaining a short-term payment risk score.

[0010] The target payment risk score of the target object is determined based on the preset weighting coefficient, the long-term payment risk score, and the short-term payment risk score;

[0011] The target payment risk score is mapped to a risk level according to a preset risk level mapping table to obtain the target risk level of the target object, and the target object is managed for travel based on the target risk level; wherein, the preset risk level mapping table is used to indicate the mapping relationship between the risk level and the payment risk score.

[0012] In some embodiments, the step of managing the passenger transport of the target object according to the target risk level includes:

[0013] If the target risk level is low risk, control the target object's ride payment data to be in normal use status;

[0014] If the target risk level is medium risk, send reminder data to the target object according to the preset reminder method, and / or control the target object's ride payment data to be in a limited usage state;

[0015] If the target risk level is high risk, the travel payment data of the target object will be restricted from use.

[0016] In some embodiments, after managing the passenger transport of the target object according to the target risk level, the method further includes:

[0017] If the target risk level is the high risk level, obtain the actual payment risk score of the target object at the current time, and construct training sample data based on the actual payment risk score, the scanning time interval sequence, the short-term payment failure probability, the device risk probability, and the long-term payment failure probability;

[0018] The risk assessment model is optimized and trained based on the training sample data to obtain the optimized risk assessment model.

[0019] In some embodiments, determining the target payment risk score of the target object based on preset weighting coefficients, the long-term payment risk score, and the short-term payment risk score includes:

[0020] The current long-term risk data is constructed based on the balance-to-fare ratio, the equipment risk probability, and the long-term payment failure probability. The historical long-term risk data of the target object is obtained. The stability of the current long-term risk data is evaluated based on the historical long-term risk data to obtain a long-term stability score.

[0021] The current short-term risk data is constructed based on the scanning time interval sequence and the short-term payment failure probability. The historical short-term risk data of the target object is obtained. The stability of the current short-term risk data is evaluated based on the historical short-term risk data to obtain a short-term stability score.

[0022] The first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score are determined based on the long-term stability score, the short-term stability score, and the preset weighting coefficient; wherein, the preset weighting coefficient is used to indicate the sum of the first sub-weighting coefficient and the second sub-weighting coefficient;

[0023] The target payment risk score of the target object is determined based on the long-term payment risk score, the first sub-weighting coefficient, the short-term payment risk score, and the second sub-weighting coefficient.

[0024] In some embodiments, determining the first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score based on the long-term stability score, the short-term stability score, and the preset weighting coefficient includes:

[0025] A total stability score is determined based on the sum of the long-term stability score and the short-term stability score, and a weighting adjustment factor for the long-term payment risk score is determined based on the ratio of the long-term stability score to the total stability score.

[0026] Based on the weight adjustment factor, the preset weight coefficient is adjusted to obtain the first sub-weight coefficient corresponding to the long-term payment risk score;

[0027] The second sub-weighting coefficient corresponding to the short-term payment risk score is determined based on the preset weighting coefficient and the first sub-weighting coefficient.

[0028] In some embodiments, the step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object includes:

[0029] Obtain the system security status at the current time;

[0030] If the system security status is under attack, the parameters of the preset risk level mapping table are adjusted according to the first adjustment range to obtain the first adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the first adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table.

[0031] The target payment risk score is mapped to a risk level based on the first adjusted risk level mapping table to obtain the target risk level of the target object.

[0032] In some embodiments, the step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes:

[0033] If the system security status is normal, obtain the geographical location information of the target object at the current time;

[0034] If the geographical location information is within a preset high-risk area, the parameters of the preset risk level mapping table are adjusted according to the second adjustment range to obtain a second adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the second adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table; and the second adjustment range is less than the first adjustment range.

[0035] The target payment risk score is mapped to a risk level based on the second adjusted risk level mapping table to obtain the target risk level of the target object.

[0036] In some embodiments, the step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes:

[0037] If the geographical location information is outside the preset high-risk area and the current time is within the preset peak period, the parameters of the preset risk level mapping table are adjusted according to the third adjustment range to obtain the third adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the third adjusted risk level mapping table is greater than the maximum risk score of the corresponding risk level in the preset risk level mapping table; and the third adjustment range is less than the second adjustment range.

[0038] The target payment risk score is mapped to a risk level based on the third adjusted risk level mapping table to obtain the target risk level of the target object.

[0039] To achieve the above objectives, a second aspect of this application provides a passenger management device, the device comprising:

[0040] The data acquisition unit is used to respond to the ride payment operation of the target object, acquire the balance-to-fare ratio of the target object at the current time, acquire the scan time interval sequence and short-term payment failure probability of the target object in a first time interval before the current time, and acquire the device risk probability and long-term payment failure probability of the target object in a second time interval before the current time; wherein, the second time interval is greater than the first time interval;

[0041] The long-term risk assessment unit is used to call the long-term risk assessment sub-model of the pre-trained risk assessment model to conduct a long-term risk assessment of the balance fare ratio, the equipment risk probability and the long-term payment failure probability, and obtain a long-term payment risk score.

[0042] The short-term risk assessment unit is used to call the short-term risk assessment sub-model of the pre-trained risk assessment model to perform a short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, and obtain a short-term payment risk score.

[0043] A risk scoring unit is used to determine the target payment risk score of the target object based on a preset weighting coefficient, the long-term payment risk score, and the short-term payment risk score.

[0044] The ride management unit is used to map the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object, and to manage the ride of the target object according to the target risk level; wherein, the preset risk level mapping table is used to indicate the mapping relationship between the risk level and the payment risk score.

[0045] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0046] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0047] The ride management method, device, electronic equipment, and storage medium proposed in this application, in response to a ride payment operation by a target user, obtains the target user's balance-to-fare ratio at the current time, the scan interval sequence and short-term payment failure probability within a first time interval prior to the current time, and the device risk probability and long-term payment failure probability within a second time interval prior to the current time. Then, it calls a long-term risk assessment sub-model of a pre-trained risk assessment model to perform a long-term risk assessment on the balance-to-fare ratio, device risk probability, and long-term payment failure probability, obtaining a long-term payment risk score. Furthermore, it calls a short-term risk assessment sub-model of the pre-trained risk assessment model to perform a short-term risk assessment on the scan interval sequence and short-term payment failure probability, obtaining a short-term payment risk score. Further, it determines the target user's target payment risk score based on preset weighting coefficients, the long-term payment risk score, and the short-term payment risk score. Finally, it maps the target payment risk score to a risk level according to a preset risk level mapping table, obtaining the target user's target risk level, and manages the ride based on the target risk level.

[0048] This application utilizes a pre-trained risk assessment model's long-term risk assessment sub-model to perform a long-term risk assessment of the device risk probability, long-term payment failure probability, and current balance-to-fare ratio within a second time interval, obtaining a long-term payment risk score for the target object. Simultaneously, it uses a pre-trained risk assessment model's short-term risk assessment sub-model to perform a short-term risk assessment of the scanning time interval sequence and short-term payment failure probability within a first time interval, obtaining a short-term payment risk score for the target object. Based on the long-term and short-term payment risk scores and preset weighting coefficients, a target payment risk score is determined. Finally, a risk level mapping is performed on the target payment risk score using a preset risk level mapping table to obtain the target risk level for the target object. This allows for the management of the target object's ride based on the target risk level. In this way, it enables comprehensive analysis of the target object's multi-dimensional data through two different models, reducing the potential for insufficient data utilization from single-dimensional data and avoiding the limitation of a single model in simultaneously predicting both short-term and long-term payment risks. This improves the accuracy of payment risk assessment, allowing for a more accurate reference when determining the target object's target risk level. In other words, this application enhances the accuracy of user ride management. Attached Figure Description

[0049] Figure 1 This is a flowchart of the passenger management method provided in the embodiments of this application;

[0050] Figure 2 This is a flowchart of the first method for determining the target risk level provided in the embodiments of this application;

[0051] Figure 3 This is a flowchart illustrating the second method for determining a target risk level, as provided in the embodiments of this application.

[0052] Figure 4 This is a flowchart illustrating the third method for determining the target risk level provided in this application embodiment;

[0053] Figure 5 This is a schematic diagram of the passenger management device provided in the embodiments of this application;

[0054] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0058] First, let's analyze some of the terms used in this application:

[0059] Neural network models are computational models inspired by biological neural networks, used in machine learning and artificial intelligence. A neural network model consists of a hierarchical structure of multiple neurons, each connected to neurons in the next layer. These connections have weights, and through these weights and activation functions, the neural network can learn complex patterns and relationships in the input data.

[0060] A transit code is a credential used to ride public transportation. Users can generate one using a relevant application and present it to the onboard scanning device for scanning. The transit code contains the user's identity information, account information, and travel history data. By integrating with the public transportation payment system, it enables fast fare deduction, making travel more convenient for users.

[0061] The widespread application of ride management methods has provided technical support for public transportation operations, effectively improving operational efficiency and user experience. However, existing ride management methods still have shortcomings in assessing user payment risk, which significantly affects the accuracy of ride management and leads to a decreased user experience. For example, related technologies typically use fixed rules (such as account balance thresholds and historical arrears) to control user arrears risk. However, this approach cannot dynamically assess user payment risk in real time, potentially leading to misjudgments (e.g., high-credit users being blocked due to insufficient temporary balance) or omissions (e.g., users with frequent arrears not being restricted). Alternatively, related technologies may employ manual review for payment risk assessment. However, this method is inefficient, struggles to handle high-concurrency scenarios, and degrades the user experience. Furthermore, related technologies may use simple rule engines to integrate limited user data. However, this approach fails to effectively utilize multi-dimensional data for comprehensive analysis, resulting in insufficient risk assessment and reduced accuracy in payment risk assessment, which in turn reduces the accuracy of ride management. Based on this, embodiments of this application provide a ride management method and apparatus, electronic device and storage medium, aiming to improve the accuracy of payment risk assessment, thereby improving the accuracy of ride management for users and ultimately enhancing the user experience.

[0062] The ride management method provided in this application relates to the field of public transportation technology. The ride management method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the ride management method, but is not limited to the above forms.

[0063] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0064] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0065] Figure 1 This is an optional flowchart of the ride management method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105:

[0066] Step S101: In response to the ride payment operation of the target object, obtain the balance-to-fare ratio of the target object at the current time, obtain the scan time interval sequence and short-term payment failure probability of the target object in the first time interval before the current time, and obtain the device risk probability and long-term payment failure probability of the target object in the second time interval before the current time.

[0067] Step S102: Call the long-term risk assessment sub-model of the pre-trained risk assessment model to conduct a long-term risk assessment of the balance fare ratio, equipment risk probability and long-term payment failure probability, and obtain a long-term payment risk score.

[0068] Step S103: Call the short-term risk assessment sub-model of the pre-trained risk assessment model to perform short-term risk assessment on the scanning time interval sequence and short-term payment failure probability to obtain a short-term payment risk score.

[0069] Step S104: Determine the target payment risk score of the target object based on the preset weighting coefficient, long-term payment risk score, and short-term payment risk score;

[0070] Step S105: Map the target payment risk score to a risk level according to the preset risk level mapping table to obtain the target risk level of the target object, and manage the ride of the target object according to the target risk level.

[0071] Steps S101 to S105 of this embodiment involve calling the long-term risk assessment sub-model of the trained risk assessment model to perform a long-term risk assessment on the device risk probability, long-term payment failure probability, and current balance-to-fare ratio within the second time interval, thereby obtaining a long-term payment risk score for the target object. Then, the short-term risk assessment sub-model of the trained risk assessment model is called to perform a short-term risk assessment on the scanning time interval sequence and short-term payment failure probability within the first time interval, thereby obtaining a short-term payment risk score for the target object. Finally, the target payment risk score is determined based on the long-term payment risk score, the short-term payment risk score, and a preset weighting coefficient. Finally, the target payment risk score is mapped to a risk level according to a preset risk level mapping table to obtain the target risk level for the target object, thus enabling ride management for the target object based on the target risk level. In this way, it is possible to comprehensively analyze the multidimensional data of the target object through two different models, reduce the problem of insufficient data utilization that may be caused by single-dimensional data, and avoid the problem that a single model may not be able to simultaneously take into account the prediction of short-term payment risk and long-term payment risk, thereby improving the accuracy of payment risk assessment. As a result, when determining the target risk level of the target object, a more accurate payment risk score can be referenced. In other words, this application can improve the accuracy of user ride management.

[0072] In step S101 of some embodiments, the target object may refer to an object that requires payment risk assessment and whose corresponding risk level is determined based on the payment risk assessment results. For example, the target object may be a user who needs to take public transportation. The payment operation may refer to the action of the target object controlling the application related to public transportation to generate a ride code. The current time may refer to the point in time when the target object controls the application related to public transportation to generate the ride code. For example, if the target object performs a payment operation at 8:00, then the current time is 8:00. The balance-to-fare ratio may refer to the ratio of the target object's account balance at the current time to the estimated ride cost. For example, if the target object's account balance at the current time is 20 yuan and the estimated ride cost is 5 yuan, then the balance-to-fare ratio is 4; or, if the target object's account balance at the current time is 1 yuan and the estimated ride cost is 2 yuan, then the balance-to-fare ratio is 0.5. It should be noted that the estimated ride cost may refer to the cost comprehensively assessed based on the target object's historical travel records (such as historical travel routes, historical ride costs, etc.). For example, if the fare for the public transportation that the target person usually takes is 2 yuan, then the estimated cost of the ride can be estimated as 2 yuan.

[0073] The first time interval can refer to a specific time range preceding the current time. For example, if the current time is 8:00, the first time interval can refer to the one hour from 7:00 to 8:00; or, if the current time is March 6th, the first time interval can refer to the one day from March 5th to March 6th. Understandably, the length of the first time interval can be adjusted according to actual needs. The scanning time interval sequence can refer to the sequence of time intervals corresponding to the target object's scanning operation using the transit code within the first time interval. For example, if the first time interval is the one hour from 7:00 to 8:00, then the scanning time interval sequence can be the time interval between each scanning operation within that one hour, such as [5s, 4s, 300s]. The short-term payment failure probability can refer to the ratio of the number of payment failures to the total number of payments after the target object scans the transit code within the first time interval. For example, if the first time interval is the one hour from 7:00 to 8:00, and the target object makes a total of 4 payments within that one hour, with 2 payments failing, then the short-term payment failure probability is 0.5.

[0074] The second time interval can refer to another specific time range preceding the current time. For example, if the current time is September, the second time interval could refer to the month corresponding to August and September. Or, if the current time is the 8th week, the second time interval could refer to the 5 weeks corresponding to the 8th week and the 3rd week. It is understood that the length of the second time interval can be adjusted according to actual needs, but it must be ensured that the length of the second time interval is longer than the length of the first time interval. Device risk probability can refer to the risk value comprehensively assessed based on the usage of the target's terminal device (such as a mobile phone) within the second time interval. For example, if the target frequently changes its terminal device or the device's IP address changes abnormally within the second time interval, the device risk probability can be set to a higher value, such as 0.8; or, if the target uses the same terminal device and the IP address is stable within the second time interval, the device risk probability can be set to a lower value, such as 0.1. It is understood that the assessment method for device risk probability can be adjusted according to actual needs. Long-term payment failure probability can refer to the ratio of the number of payment failures to the total number of payments made by the target after using the transit code for scanning within the second time interval. For example, if the second time interval is one month from August to September, and the target object makes a total of 80 payments within one month, with 20 of them failing, then the long-term payment failure probability is 0.25.

[0075] In step S102 of some embodiments, the pre-trained risk assessment model can refer to a trained deep learning model with payment risk assessment capabilities. The risk assessment model includes a long-term risk assessment sub-model. The long-term risk assessment sub-model can refer to the model component in the risk assessment model used to assess long-term payment risk. For example, the long-term risk assessment sub-model can be a Random Forest (RF) model or a Gradient Boosting Decision Tree (GBDT) model, without specific limitations. Long-term risk assessment can refer to the process of calling the long-term risk assessment sub-model to comprehensively analyze the balance-to-fare ratio, equipment risk probability, and long-term payment failure probability to predict the long-term payment risk score of the target object. The long-term payment risk score can refer to the numerical value obtained after calling the long-term risk assessment sub-model to perform a long-term risk assessment of the balance-to-fare ratio, equipment risk probability, and long-term payment failure probability, used to quantify the long-term payment risk of the target object.

[0076] In step S103 of some embodiments, the risk assessment model includes a short-term risk assessment sub-model. The short-term risk assessment sub-model can refer to a model component in the risk assessment model used to assess short-term payment risk. For example, the short-term risk assessment sub-model can be a Long Short-Term Memory (LSTM) model or a Gated Recurrent Unit (GRU) model, without specific limitations. Short-term risk assessment can refer to the process of calling the short-term risk assessment sub-model to comprehensively analyze the scanning time interval sequence and the short-term payment failure probability to predict the short-term payment risk score of the target object. The short-term payment risk score can refer to the numerical value obtained after calling the short-term risk assessment sub-model to perform a short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, used to quantify the short-term payment risk of the target object.

[0077] In step S104 of some embodiments, the preset weighting coefficient refers to a pre-defined weight value, including the weights corresponding to the long-term payment risk score and the short-term payment risk score, respectively. For example, if the preset weighting coefficient can be represented as 1, and the long-term payment risk score is more important than the short-term payment risk score, then the weight of the long-term payment risk score can be set to 0.7, and the weight of the short-term payment risk score can be set to 0.3; or, if the short-term payment risk score is more important than the long-term payment risk score, then the weight of the short-term payment risk score can be set to 0.6, and the weight of the long-term payment risk score can be set to 0.4. The target payment risk score can refer to the comprehensive score obtained by weighting the long-term payment risk score and the short-term payment risk score according to the preset weighting coefficient. For example, if the long-term payment risk score is 0.8, the short-term payment risk score is 0.5, the weight of the long-term payment risk score is set to 0.7, and the weight of the short-term payment risk score is set to 0.3, then the target payment risk score is 0.7×0.8 + 0.3×0.5 = 0.61; or, if the long-term payment risk score is 0.5, the short-term payment risk score is 0.2, the weight of the long-term payment risk score is set to 0.4, and the weight of the short-term payment risk score is set to 0.6, then the target payment risk score is 0.4×0.5 + 0.6×0.2 = 0.32.

[0078] In step S105 of some embodiments, risk level mapping can refer to the process of mapping a target payment risk score to a corresponding risk level according to a preset risk level mapping table. The preset risk level mapping table can be a predefined table used to indicate the mapping relationship between risk levels and payment risk scores. For example, as shown in Table 1, Table 1 is an example of a preset risk level mapping table provided in this application embodiment, where the payment risk score is divided into three intervals: 0-0.4 corresponds to low risk, 0.4-0.7 corresponds to medium risk, and greater than 0.7 corresponds to high risk. The target risk level can refer to the level used to indicate the payment risk level of the target object after mapping the target payment risk score according to the preset risk level mapping table. For example, if the target payment risk score is 0.6, then according to the preset risk level mapping table in Table 1, the score falls within the interval of 0.4-0.7, and the corresponding risk level is medium risk. Or, if the target payment risk score is 0.8, then according to the preset risk level mapping table in Table 1, the score is greater than 0.7, and the corresponding risk level is high risk.

[0079]

[0080] Ride management can refer to the process of taking corresponding management measures based on the target risk level of the target group in order to control and reduce payment risks in public transportation operations. For example, for target groups with low risk levels, they can be allowed to use the ride code normally; for target groups with medium risk levels, their ride amount or number of rides can be restricted; for target groups with high risk levels, their ride service can be suspended or they can be required to top up their accounts in advance to ensure their payment ability.

[0081] In some embodiments, the target payment risk score of the target object is determined based on preset weighting coefficients, long-term payment risk scores, and short-term payment risk scores, including:

[0082] The current long-term risk data is constructed based on the balance-to-fare ratio, equipment risk probability, and long-term payment failure probability. The historical long-term risk data of the target object is obtained. The stability of the current long-term risk data is assessed based on the historical long-term risk data to obtain a long-term stability score.

[0083] The current short-term risk data is constructed based on the scan time interval sequence and the short-term payment failure probability. The historical short-term risk data of the target object is obtained. The stability of the current short-term risk data is evaluated based on the historical short-term risk data to obtain a short-term stability score.

[0084] The first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score are determined based on the long-term stability score, the short-term stability score, and the preset weighting coefficient; wherein, the preset weighting coefficient is used to indicate the sum of the first sub-weighting coefficient and the second sub-weighting coefficient;

[0085] The target payment risk score of the target object is determined based on the long-term payment risk score, the first sub-weight coefficient, the short-term payment risk score, and the second sub-weight coefficient.

[0086] In this embodiment, current long-term risk data can refer to a dataset constructed based on the balance-to-fee ratio, equipment risk probability, and long-term payment failure probability, used to reflect the current long-term payment risk status of the target object. Historical long-term risk data can refer to a dataset constructed based on the historical balance-to-fee ratio, historical equipment risk probability, and historical long-term payment failure probability of the target object, used to reflect the historical long-term payment risk status of the target object. It should be noted that the historical long-term risk data and current long-term risk data contain the same data types, and the reference time length when obtaining the required data is also the same, to ensure consistency and comparability when conducting stability assessments. Stability assessment can refer to the process of comparing the balance-to-fee ratio, equipment risk probability, and long-term payment failure probability included in the current long-term risk data with the historical balance-to-fee ratio, historical equipment risk probability, and historical long-term payment failure probability included in the historical long-term risk data to assess data consistency and volatility. Long-term stability score can refer to the quantitative score obtained after conducting a stability assessment of the current long-term risk data based on the historical long-term risk data, used to indicate the long-term payment risk stability of the target object.

[0087] Current short-term risk data refers to a dataset constructed based on the scan interval sequence and short-term payment failure probability, reflecting the current short-term payment risk status of the target entity. Historical short-term risk data refers to a dataset constructed based on the historical scan interval sequence and historical short-term payment failure probability of the target entity, reflecting its historical short-term payment risk status. It should be noted that historical and current short-term risk data contain the same data types and use the same reference time period when acquiring the required data to ensure consistency and comparability in stability assessment. Stability assessment involves comparing the scan interval sequence and short-term payment failure probability in the current short-term risk data with their corresponding historical scan interval sequence and historical short-term payment failure probability in the historical short-term risk data to evaluate data consistency and volatility. The short-term stability score refers to a quantitative score obtained after stability assessment, indicating the short-term payment risk stability of the target entity.

[0088] The first and second sub-weighting coefficients can be determined comprehensively based on the long-term stability score, the short-term stability score, and the preset weighting coefficients. The preset weighting coefficients refer to the pre-defined weight ratios used to indicate the sum of the first and second sub-weighting coefficients. For example, if the long-term stability score is 0.9, the short-term stability score is 0.6, and the preset weighting coefficient is 1, then the long-term payment risk score is considered to be relatively stable, and the first sub-weighting coefficient can be set to 0.7 and the second sub-weighting coefficient to 0.3. Alternatively, if the long-term stability score is 0.6, the short-term stability score is 0.8, and the preset weighting coefficient is 1, then the short-term payment risk score is considered to be relatively stable, and the first sub-weighting coefficient can be set to 0.4 and the second sub-weighting coefficient to 0.6.

[0089] The target payment risk score can be defined as a composite score obtained by multiplying the long-term payment risk score by a first sub-weighting coefficient, and adding the short-term payment risk score multiplied by a second sub-weighting coefficient. For example, if the long-term payment risk score is 0.8, the first sub-weighting coefficient is 0.7, the short-term payment risk score is 0.5, and the second sub-weighting coefficient is 0.3, then the target payment risk score is 0.8 × 0.7 + 0.5 × 0.3 = 0.61.

[0090] Understandably, this application assesses the stability of current long-term risk data constructed based on the balance-to-fare ratio, equipment risk probability, and long-term payment failure probability, compared with corresponding historical long-term risk data, to obtain a long-term stability score. Simultaneously, it assesses the stability of current short-term risk data constructed based on the scan interval sequence and short-term payment failure probability, compared with corresponding historical short-term risk data, to obtain a short-term stability score. Then, based on the long-term stability score, short-term stability score, and preset weighting coefficients, it determines the first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score. In this way, the target payment risk score can be obtained by weighting the long-term and short-term payment risk scores with their respective weighting coefficients, reducing the bias that may be caused by pre-allocated weights, mitigating the impact of unstable data on payment risk prediction, improving the accuracy of payment risk assessment, and thus improving the accuracy of ride management.

[0091] In some embodiments, determining a first sub-weighting coefficient corresponding to the long-term payment risk score and a second sub-weighting coefficient corresponding to the short-term payment risk score based on a long-term stability score, a short-term stability score, and a preset weighting coefficient includes:

[0092] The total stability score is determined by summing the long-term stability score and the short-term stability score, and the weighting adjustment factor for the long-term payment risk score is determined by the ratio of the long-term stability score to the total stability score.

[0093] The first sub-weight coefficient corresponding to the long-term payment risk score is obtained by adjusting the parameters of the preset weight coefficient based on the weight adjustment factor.

[0094] The second sub-weight coefficient corresponding to the short-term payment risk score is determined based on the preset weight coefficient and the first sub-weight coefficient.

[0095] In this embodiment, the overall stability score can refer to the sum of the long-term stability score and the short-term stability score. For example, if the long-term stability score is 0.7 and the short-term stability score is 0.3, then the overall stability score is 1.0. The weight adjustment factor can refer to the ratio of the long-term stability score to the overall stability score, used to measure the relative contribution of the long-term payment risk score to overall stability. For example, if the long-term stability score is 0.7 and the overall stability score is 1.0, then the weight adjustment factor is 0.7 / 1.0 = 0.7. Parameter adjustment can refer to the process of adjusting a preset weight coefficient according to the weight adjustment factor to determine the first sub-weight coefficient. The first sub-weight coefficient can refer to the weight value corresponding to the long-term payment risk score obtained after adjusting the preset weight coefficient based on the weight adjustment factor. For example, if the weight adjustment factor is 0.7 and the preset weight coefficient is 1, then the first sub-weight coefficient can be 0.7 × 1 = 0.7. The second sub-weight coefficient can refer to the weight value corresponding to the short-term payment risk score determined according to the preset weight coefficient and the first sub-weight coefficient. For example, if the preset weight coefficient is 1 and the first sub-weight coefficient is 0.7, then the second sub-weight coefficient is 0.3.

[0096] Understandably, this application determines the total stability score by summing the long-term stability score and the short-term stability score. It then determines the weight adjustment factor for the long-term payment risk score based on the ratio of the long-term stability score to the total stability score. Next, it determines the first sub-weight coefficient for the long-term payment risk score based on the weight adjustment factor and a preset weight coefficient. Finally, it determines the second sub-weight coefficient for the short-term payment risk score based on the first sub-weight coefficient and the preset weight coefficient. This allows for automatic adjustment of the weight values ​​corresponding to the long-term and short-term payment risk scores, making weight allocation more flexible and accurate, reducing manual intervention, improving the efficiency of payment risk assessment, and consequently improving the efficiency of ride management.

[0097] It should be noted that, in order to reduce the computational resources required to calculate the weight values ​​corresponding to the long-term payment risk score and the short-term payment risk score, this embodiment of the application can also pre-set a stability score threshold, for example, 0.8. When the long-term stability score or the short-term stability score is higher than this stability score threshold, the corresponding weight coefficient can be directly determined to a preset value, for example, 0.7, and another weight coefficient can be determined based on the preset weight coefficient. When both the long-term stability score and the short-term stability score are higher than or lower than this stability score threshold, the corresponding weight coefficients can be directly determined to another preset value, for example, 0.5. The size of the stability score threshold and the preset weight coefficient can be freely adjusted according to actual needs.

[0098] In some embodiments, the target payment risk score is mapped to a risk level according to a preset risk level mapping table to obtain the target risk level of the target object, including:

[0099] Get the system security status at the current time;

[0100] If the system security status is under attack, the parameters of the preset risk level mapping table are adjusted according to the first adjustment range to obtain the first adjusted risk level mapping table;

[0101] Based on the first adjusted risk level mapping table, the target payment risk score is mapped to a risk level to obtain the target risk level of the target object.

[0102] In this embodiment, the system security status can refer to the current security status of the system. The system security status includes an attacked state and a normal state. An attacked state can mean that the system is currently under security threat or attack. A normal state can mean that the system is not under security threat and is operating stably at the current time. The first adjustment range can refer to a pre-set parameter used to adjust the maximum value of the payment risk score in the preset risk level mapping table. For example, the first adjustment range can be 0.5 or 0.4, and is not specifically limited. Parameter adjustment can refer to the process of changing the maximum risk score corresponding to each risk level in the preset risk level mapping table according to the first adjustment range. The first adjusted risk level mapping table can refer to the risk level mapping table obtained after adjusting the parameters of the preset risk level mapping table according to the first adjustment range.

[0103] It is understandable that the maximum risk score corresponding to each risk level in the first adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table. For example, as shown in Table 2, which is an example of a first adjusted risk level mapping table provided in this application embodiment, this table is obtained by adjusting the parameters of the preset risk level mapping table shown in Table 1 when the first adjustment range is 0.5. The payment risk score is divided into three intervals: 0-0.2 corresponds to low risk, 0.2-0.35 corresponds to medium risk, and greater than 0.35 corresponds to high risk.

[0104]

[0105] The target risk level can refer to the level used to indicate the payment risk level of the target object after mapping the target payment risk score according to the first adjusted risk level mapping table. For example, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The flowchart for determining a target risk level provided in this application embodiment specifically includes: First, obtaining the current system security status. Then, determining whether the system security status is under attack. If the system security status is under attack, adjusting the parameters of a preset risk level mapping table according to a first adjustment range to obtain a first adjusted risk level mapping table. Finally, mapping the target payment risk score to a risk level according to the first adjusted risk level mapping table to obtain the target risk level of the target object; if the system security status is not under attack (i.e., normal status), mapping the target payment risk score to a risk level according to the preset risk level mapping table to obtain the target risk level of the target object.

[0106] It is understood that the embodiments of this application dynamically adjust the preset risk level mapping table based on the current security status of the system. This allows for timely increases in the rigor of risk assessment when the system is under attack, reducing the possibility of overlooking potential risks when assessing user payment risks, improving the accuracy of payment risk assessment, and consequently enhancing the accuracy of ride management.

[0107] In some embodiments, mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes:

[0108] If the system security status is normal, obtain the target object's geographical location information at the current time;

[0109] If the geographical location information is within the preset high-risk area, the parameters of the preset risk level mapping table are adjusted according to the second adjustment range to obtain the second adjusted risk level mapping table;

[0110] The target payment risk score is mapped to a risk level based on the second adjusted risk level mapping table to obtain the target risk level of the target object.

[0111] In this embodiment, geographic location information can refer to the geographic location of the target object at the current time. For example, geographic location information can be the latitude and longitude coordinates of the target object; or, geographic location information can be a combination of the city and street names of the target object, without specific limitations. The preset high-risk area can refer to a pre-defined high-risk geographical area. When the geographic location information is within the preset high-risk area, the parameters of the preset risk level mapping table can be adjusted according to the second adjustment range to obtain a second adjusted risk level mapping table. The second adjustment range can refer to a pre-set parameter used to adjust the maximum value of the payment risk score in the preset risk level mapping table. For example, the second adjustment range can be 0.2 or 0.3. It is understood that the second adjustment range can be freely adjusted according to actual needs, but it must be ensured that the second adjustment range is less than the first adjustment range. Parameter adjustment can refer to the process of changing the maximum risk score corresponding to each risk level in the preset risk level mapping table according to the second adjustment range. The second adjusted risk level mapping table can refer to the risk level mapping table obtained after adjusting the parameters of the preset risk level mapping table according to the second adjustment range.

[0112] It is understandable that the maximum risk score corresponding to each risk level in the second adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table. For example, as shown in Table 3, which is an example of a second adjusted risk level mapping table provided in this application embodiment, this table is obtained by adjusting the parameters of the preset risk level mapping table shown in Table 1 when the second adjustment range is 0.2. The payment risk score is divided into three intervals: 0-0.32 corresponds to low risk, 0.32-0.56 corresponds to medium risk, and greater than 0.56 corresponds to high risk.

[0113]

[0114] The target risk level can refer to the level used to indicate the payment risk level of the target object after mapping the target payment risk score according to the second adjusted risk level mapping table. For example, please refer to [link to relevant documentation]. Figure 3 , Figure 3The second flowchart for determining the target risk level provided in this application embodiment specifically includes: First, obtaining the system security status at the current time. Then, determining whether the system security status is under attack. If the system security status is under attack, adjusting the parameters of a preset risk level mapping table according to a first adjustment range to obtain a first adjusted risk level mapping table. Finally, mapping the target payment risk score to a risk level according to the first adjusted risk level mapping table to obtain the target risk level of the target object. If the system security status is not under attack (i.e., normal status), obtaining the geographical location information of the target object at the current time. Further, determining whether the geographical location information is within a preset high-risk area. If the geographical location information is within a preset high-risk area, adjusting the parameters of the preset risk level mapping table according to a second adjustment range to obtain a second adjusted risk level mapping table. Finally, mapping the target payment risk score to a risk level according to the second adjusted risk level mapping table to obtain the target risk level of the target object. If the geographical location information is not within a preset high-risk area, mapping the target payment risk score to a risk level according to the preset risk level mapping table to obtain the target risk level of the target object.

[0115] It is understood that, in the embodiments of this application, when the system security status is normal, the preset risk level mapping table can be dynamically adjusted based on the geographical location information of the target object at the current time. In this way, when the system is in a normal state but the target object is located in a high-risk area, the stringency of risk assessment can be appropriately increased, reducing potential risk omissions due to geographical location, improving the accuracy of payment risk assessment, and thus improving the accuracy of ride management.

[0116] In some embodiments, mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes:

[0117] If the geographical location information is outside the preset high-risk area and the current time is within the preset peak period, the parameters of the preset risk level mapping table are adjusted according to the third adjustment range to obtain the third adjusted risk level mapping table.

[0118] The target payment risk score is mapped to the target risk level based on the third adjustment risk level mapping table to obtain the target risk level of the target object.

[0119] In this embodiment, the preset peak period can refer to a pre-defined time period with high public transportation usage. For example, the preset peak period can be from 7:00 AM to 9:00 AM or from 5:00 PM to 7:00 PM, and the specific time is not limited. When the geographical location information of the target object is outside the preset high-risk area and the current time is within the preset peak period, the parameters of the preset risk level mapping table can be adjusted according to the third adjustment range to obtain the third adjusted risk level mapping table. The third adjustment range can refer to a pre-defined parameter used to adjust the maximum value of the payment risk score in the preset risk level mapping table. For example, the third adjustment range can be 0.05 or 0.1. It is understood that the third adjustment range can be freely adjusted according to actual needs, but it must be ensured that the third adjustment range is less than the second adjustment range. Parameter adjustment can refer to the process of changing the maximum risk score corresponding to each risk level in the preset risk level mapping table according to the third adjustment range. The third adjusted risk level mapping table can refer to the risk level mapping table obtained after adjusting the parameters of the preset risk level mapping table according to the third adjustment range.

[0120] It is understandable that the maximum risk score corresponding to each risk level in the third adjusted risk level mapping table is greater than the maximum risk score of the corresponding risk level in the preset risk level mapping table. For example, as shown in Table 4, which is an example of a third adjusted risk level mapping table provided in this application embodiment, this table is obtained after adjusting the parameters of the preset risk level mapping table shown in Table 1 with a third adjustment range of 0.1. The payment risk score is divided into three intervals: 0-0.32 corresponds to low risk, 0.32-0.56 corresponds to medium risk, and greater than 0.56 corresponds to high risk.

[0121]

[0122] The target risk level can refer to the level used to indicate the payment risk level of the target object after mapping the target payment risk score according to the third adjusted risk level mapping table. For example, please refer to [link to relevant documentation]. Figure 4 , Figure 4The flowchart for determining the target risk level provided in this application embodiment specifically includes: First, obtaining the system security status at the current time. Then, determining whether the system security status is under attack. If the system security status is under attack, adjusting the parameters of the preset risk level mapping table according to the first adjustment range to obtain the first adjusted risk level mapping table, and finally mapping the target payment risk score to the target risk level of the target object according to the first adjusted risk level mapping table to obtain the target risk level of the target object; if the system security status is not under attack (i.e., normal status), obtaining the geographical location information of the target object at the current time. Further, determining whether the geographical location information is within a preset high-risk area. If the geographical location information is within the preset high-risk area, adjusting the parameters of the preset risk level mapping table according to the second adjustment range to obtain the second adjusted risk level mapping table, and finally mapping the target payment risk score to the target risk level of the target object according to the second adjusted risk level mapping table to obtain the target risk level of the target object; if the geographical location information is not within the preset high-risk area, further determining whether the current time is within a preset peak period. If the current time falls within the preset peak period, the parameters of the preset risk level mapping table are adjusted according to the third adjustment range to obtain the third adjusted risk level mapping table. The target payment risk score is then mapped to a risk level based on this third adjusted risk level mapping table to obtain the target risk level of the target object. If the current time does not fall within the preset peak period, the target payment risk score is mapped to a risk level based on the preset risk level mapping table to obtain the target risk level of the target object.

[0123] It is understood that, in this embodiment of the application, when the system security status is normal and the current geographical location information is not within the preset high-risk area, the preset risk level mapping table can be dynamically adjusted according to the current time and preset peak period. In this way, when in a non-high-risk area but during peak hours, the payment risk assessment standards can be appropriately relaxed, reducing payment restrictions on users and improving user experience.

[0124] It should be noted that, in this embodiment, the adjustment range of the preset risk level mapping table can also be determined by comprehensively considering the current time, the system security status at the current time, and the geographical location information of the target object at the current time. Then, the parameters of this preset risk level mapping table are adjusted according to the adjustment range. Finally, the target payment risk score is mapped to a risk level based on the adjusted preset risk level mapping table to obtain the target risk level of the target object. For example, if the system security status is under attack, the geographical location information is within a preset high-risk area, and the current time is within a preset peak period, and the first adjustment range is 0.5, the second adjustment range is 0.2, and the third adjustment range is 0.1, the final adjustment range is 0.5 + 0.2 - 0.1 = 0.6. Based on this adjustment range, the maximum risk score corresponding to each risk level in the preset risk level mapping table can be reduced.

[0125] In some embodiments, managing the travel of target individuals based on target risk levels includes:

[0126] If the target risk level is low, control the target's ride payment data to be in normal use status;

[0127] If the target risk level is medium risk, send reminder data to the target object according to the preset reminder method, and / or control the target object's ride payment data to a limited usage status;

[0128] If the target risk level is high, the travel payment data of the target person will be restricted from use.

[0129] In this embodiment, ride payment data can refer to data generated when the target user uses a ride code for payment. For example, ride payment data can be the ride code generation time, payment amount, number of payments, or payment method, etc., without specific limitations. Normal usage status can refer to the normal use of the ride code's payment function. For example, normal usage status can mean generating a ride code at any time; or, normal usage status can also mean completing payment according to the regular payment process; or, normal usage status can also mean no limit on the number of payments or payment amount. Preset reminder method can refer to a method of sending notifications to the target user through various communication channels. For example, preset reminder method can be sending notifications via SMS; or, preset reminder method can also be sending notifications via application push notifications, without specific limitations. Reminder data can refer to risk warning information containing the target user's risk level and the corresponding ride management method. Limited usage status can refer to limiting the ride code payment amount, number of rides, or the ride code generation time. For example, a usage limit could mean restricting the payment amount for a single ride to no more than 5 yuan; or it could mean limiting the number of rides per day to no more than 3; or it could mean restricting the generation of the transit code during peak hours—the specifics are not limited. A usage restriction could also mean disabling the transit code's payment function. For example, a usage restriction could mean permanently disabling the transit code's payment function; or it could mean temporarily disabling the transit code's payment function—the specifics are not limited.

[0130] It should be noted that, to better manage ride-hailing for high-risk individuals, this embodiment of the application can also set a payment risk score threshold (e.g., 0.9). When an individual's target payment risk score exceeds the threshold, the payment function of their ride-hailing code is permanently suspended. When the target payment risk score does not exceed the threshold, the payment function is only temporarily suspended, such as restricting the individual from using the payment function for one week after the current time. If the individual needs to continue using the service at the current time, a credit recovery mechanism can be established, allowing them to apply for credit recovery by paying the outstanding fares, thereby reducing their risk level.

[0131] It is understood that the embodiments of this application can adopt corresponding ride management methods based on the risk level of the target object. When the risk level of the target object is high, a preset reminder method can be used to send reminder data containing the risk level and the corresponding ride management method, while controlling the use of its ride payment data to a limited state; when the risk level of the target object is extremely high, the use of the ride code payment function can be directly restricted. In this way, dynamic management of target objects with different risk levels can be achieved, avoiding the potential risks brought about by high-risk target objects using the ride code at will, reducing risk events in the ride payment process, thereby ensuring the operator's operational efficiency and improving the user experience.

[0132] In some embodiments, after managing the travel of target individuals based on the target risk level, the method further includes:

[0133] If the target risk level is high risk level, obtain the actual payment risk score of the target object at the current time, and construct training sample data based on the actual payment risk score, the scanning time interval sequence, the short-term payment failure probability, the device risk probability, and the long-term payment failure probability.

[0134] The risk assessment model is optimized and trained based on the training sample data to obtain the optimized risk assessment model.

[0135] In this embodiment, the actual payment risk score can refer to a score reflecting the actual risk level of the target object. It is understood that the actual payment risk score can be obtained through manual assessment; alternatively, it can be obtained through automatic system assessment, without specific limitations. Training sample data can refer to a dataset formed by combining the actual payment risk score, scanning time interval sequence, short-term payment failure probability, device risk probability, and long-term payment failure probability. The training sample data can be used to train and optimize the risk assessment model. Optimization training can refer to the process of further training the risk assessment model using the constructed training sample data to improve model performance. The optimized risk assessment model can refer to a risk assessment model with higher performance obtained after optimizing the risk assessment model based on the training sample data.

[0136] It is understood that the embodiments of this application can obtain the actual payment risk score of the target object at the current time, construct training sample data based on this score, and then optimize and train the risk assessment model based on the training sample data. In this way, when encountering a new high-risk target object, the model can be incrementally learned through the constructed training sample data, avoiding a complete update of the risk assessment model, thereby improving the efficiency and accuracy of model updates and enhancing the model's ability to identify high-risk characteristics.

[0137] Please see Figure 5This application also provides a passenger management device that can implement the above-described passenger management method. The device includes:

[0138] The data acquisition unit 501 is used to respond to the ride payment operation of the target object, acquire the balance-to-fare ratio of the target object at the current time, acquire the scan time interval sequence and short-term payment failure probability of the target object in the first time interval before the current time, and acquire the device risk probability and long-term payment failure probability of the target object in the second time interval before the current time; wherein, the second time interval is greater than the first time interval;

[0139] Long-term risk assessment unit 502 is used to call the long-term risk assessment sub-model of the pre-trained risk assessment model to conduct long-term risk assessment on the balance-to-fare ratio, equipment risk probability and long-term payment failure probability, and obtain a long-term payment risk score.

[0140] The short-term risk assessment unit 503 is used to call the short-term risk assessment sub-model of the pre-trained risk assessment model to perform short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, and obtain a short-term payment risk score.

[0141] Risk scoring unit 504 is used to determine the target payment risk score of the target object based on preset weighting coefficients, long-term payment risk score and short-term payment risk score;

[0142] The ride management unit 505 is used to map the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object, and to manage the ride of the target object according to the target risk level; wherein, the preset risk level mapping table is used to indicate the mapping relationship between the risk level and the payment risk score.

[0143] The specific implementation of this passenger management device is basically the same as the specific implementation of the passenger management method described above, and will not be repeated here.

[0144] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described passenger management method. This electronic device can be any smart terminal, including a tablet computer, an in-vehicle computer, or similar device.

[0145] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0146] The processor 601 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0147] The memory 602 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the vehicle management method of the embodiments of this application.

[0148] The input / output interface 603 is used to implement information input and output;

[0149] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0150] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0151] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0152] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described passenger management method.

[0153] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0154] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0155] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0156] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0158] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0159] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0161] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for managing passenger transport, characterized in that, The method includes: In response to a ride payment operation by a target user, the system obtains the target user's balance-to-fare ratio at the current time, obtains the target user's QR code scanning time interval sequence and short-term payment failure probability within a first time interval prior to the current time, and obtains the target user's device risk probability and long-term payment failure probability within a second time interval prior to the current time; wherein the second time interval is greater than the first time interval. The long-term risk assessment sub-model of the pre-trained risk assessment model is invoked to perform a long-term risk assessment on the balance-to-fare ratio, the equipment risk probability, and the long-term payment failure probability, thereby obtaining a long-term payment risk score. The short-term risk assessment sub-model of the pre-trained risk assessment model is invoked to perform a short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, thereby obtaining a short-term payment risk score. The target payment risk score of the target object is determined based on the preset weighting coefficient, the long-term payment risk score, and the short-term payment risk score; The target payment risk score is mapped to a risk level according to a preset risk level mapping table to obtain the target risk level of the target object, and the target object is managed for travel based on the target risk level; wherein, the preset risk level mapping table is used to indicate the mapping relationship between the risk level and the payment risk score; The step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object includes: Obtain the system security status at the current time; If the system security status is under attack, the parameters of the preset risk level mapping table are adjusted according to the first adjustment range to obtain the first adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the first adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table. The target payment risk score is mapped to a risk level based on the first adjusted risk level mapping table to obtain the target risk level of the target object.

2. The method according to claim 1, characterized in that, The step of managing the travel of the target object according to the target risk level includes: If the target risk level is low risk, control the target object's ride payment data to be in normal use status; If the target risk level is medium risk, send reminder data to the target object according to the preset reminder method, and / or control the target object's ride payment data to be in a limited usage state; If the target risk level is high risk, the travel payment data of the target object will be restricted from use.

3. The method according to claim 2, characterized in that, After implementing the vehicle management for the target object based on the target risk level, the method further includes: If the target risk level is the high risk level, obtain the actual payment risk score of the target object at the current time, and construct training sample data based on the actual payment risk score, the scanning time interval sequence, the short-term payment failure probability, the device risk probability, and the long-term payment failure probability; The risk assessment model is optimized and trained based on the training sample data to obtain the optimized risk assessment model.

4. The method according to claim 1, characterized in that, The step of determining the target payment risk score of the target object based on the preset weighting coefficient, the long-term payment risk score, and the short-term payment risk score includes: The current long-term risk data is constructed based on the balance-to-fare ratio, the equipment risk probability, and the long-term payment failure probability. The historical long-term risk data of the target object is obtained. The stability of the current long-term risk data is evaluated based on the historical long-term risk data to obtain a long-term stability score. The current short-term risk data is constructed based on the scanning time interval sequence and the short-term payment failure probability. The historical short-term risk data of the target object is obtained. The stability of the current short-term risk data is evaluated based on the historical short-term risk data to obtain a short-term stability score. The first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score are determined based on the long-term stability score, the short-term stability score, and the preset weighting coefficient; wherein, the preset weighting coefficient is used to indicate the sum of the first sub-weighting coefficient and the second sub-weighting coefficient; The target payment risk score of the target object is determined based on the long-term payment risk score, the first sub-weighting coefficient, the short-term payment risk score, and the second sub-weighting coefficient.

5. The method according to claim 4, characterized in that, The step of determining the first sub-weighting coefficient corresponding to the long-term payment risk score and the second sub-weighting coefficient corresponding to the short-term payment risk score based on the long-term stability score, the short-term stability score, and the preset weighting coefficient includes: A total stability score is determined based on the sum of the long-term stability score and the short-term stability score, and a weighting adjustment factor for the long-term payment risk score is determined based on the ratio of the long-term stability score to the total stability score. Based on the weight adjustment factor, the preset weight coefficient is adjusted to obtain the first sub-weight coefficient corresponding to the long-term payment risk score; The second sub-weighting coefficient corresponding to the short-term payment risk score is determined based on the preset weighting coefficient and the first sub-weighting coefficient.

6. The method according to claim 1, characterized in that, The step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes: If the system security status is normal, obtain the geographical location information of the target object at the current time; If the geographical location information is within a preset high-risk area, the parameters of the preset risk level mapping table are adjusted according to the second adjustment range to obtain a second adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the second adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table; and the second adjustment range is less than the first adjustment range. The target payment risk score is mapped to a risk level based on the second adjusted risk level mapping table to obtain the target risk level of the target object.

7. The method according to claim 6, characterized in that, The step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object further includes: If the geographical location information is outside the preset high-risk area and the current time is within the preset peak period, the parameters of the preset risk level mapping table are adjusted according to the third adjustment range to obtain the third adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the third adjusted risk level mapping table is greater than the maximum risk score of the corresponding risk level in the preset risk level mapping table; and the third adjustment range is less than the second adjustment range. The target payment risk score is mapped to a risk level based on the third adjusted risk level mapping table to obtain the target risk level of the target object.

8. A passenger management device, characterized in that, The device includes: The data acquisition unit is used to respond to the ride payment operation of the target object, acquire the balance-to-fare ratio of the target object at the current time, acquire the scan time interval sequence and short-term payment failure probability of the target object in a first time interval before the current time, and acquire the device risk probability and long-term payment failure probability of the target object in a second time interval before the current time; wherein, the second time interval is greater than the first time interval; The long-term risk assessment unit is used to call the long-term risk assessment sub-model of the pre-trained risk assessment model to conduct a long-term risk assessment of the balance fare ratio, the equipment risk probability and the long-term payment failure probability, and obtain a long-term payment risk score. The short-term risk assessment unit is used to call the short-term risk assessment sub-model of the pre-trained risk assessment model to perform a short-term risk assessment on the scanning time interval sequence and the short-term payment failure probability, and obtain a short-term payment risk score. A risk scoring unit is used to determine the target payment risk score of the target object based on a preset weighting coefficient, the long-term payment risk score, and the short-term payment risk score. The ride management unit is used to map the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object, and to manage the ride of the target object according to the target risk level; wherein, the preset risk level mapping table is used to indicate the mapping relationship between the risk level and the payment risk score; The step of mapping the target payment risk score to a preset risk level mapping table to obtain the target risk level of the target object includes: Obtain the system security status at the current time; If the system security status is under attack, the parameters of the preset risk level mapping table are adjusted according to the first adjustment range to obtain the first adjusted risk level mapping table; wherein, the maximum risk score corresponding to each risk level in the first adjusted risk level mapping table is less than the maximum risk score of the corresponding risk level in the preset risk level mapping table. The target payment risk score is mapped to a risk level based on the first adjusted risk level mapping table to obtain the target risk level of the target object.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the passenger management method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the passenger management method according to any one of claims 1 to 7.

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