Equipment operation management and risk control model training method, equipment, medium and program

Abstract

The embodiment of the invention discloses an equipment operation management and risk control model training method, equipment, a medium and a program, and relates to the field of biological recognition. The method comprises the following steps: acquiring social feature data of a target customer, and determining an operation behavior of the target customer when the target customer operates on local equipment; calling a target differentiated risk control model to perform personalized analysis on the social feature data of the target customer to obtain a risk control index of the target customer; and managing the operation behavior of the target customer on the local equipment according to the risk control index of the target customer. According to the technical scheme provided by the embodiment of the invention, the customer risk control auditing efficiency of the currency exchange equipment can be improved, and personalized risk control strategies are formulated for different customers, so that the effective management of customer operation behaviors in the currency exchange equipment is realized.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of biometrics, and in particular to a method, device, medium, and program for device operation management and risk control model training. Background Art

[0002] With the accelerated development of global economic integration and the increasing frequency of cross-border exchanges, the demand for currency exchange has shown a rapid growth trend. In order to meet this demand, payment facilitation measures are constantly being promoted. The traditional manual currency exchange method at the counter can no longer meet the growing demand for currency exchange, nor can it provide a convenient, fast and easy-to-use user experience. In particular, in terms of customer identity verification and risk control, the manual method has many disadvantages: (1) The traditional manual processing model relies on the account manager to query various customer information and compare them one by one, which has low audit efficiency; (2) There is a lack of personalized risk control strategies for different customers, and it is impossible to dynamically adjust the currency exchange quota. Summary of the Invention

[0003] The embodiments of the present invention provide a device operation management and risk control model training method, device, medium and program, which can improve the efficiency of currency exchange equipment in customer risk control review and formulate personalized risk control strategies for different customers, thereby realizing effective management of customer operation behavior in currency exchange equipment.

[0004] According to one aspect of the present invention, there is provided a device operation management method applied to a currency exchange device, comprising:

[0005] Acquire social profile data of target customers, and determine operation behaviors of the target customers when performing operations on the local device;

[0006] Calling a target differentiated risk control model to perform personalized analysis on the social profile data of the target customer to obtain the risk control index of the target customer;

[0007] The operating behavior of the target customer on the local device is managed according to the risk control index of the target customer.

[0008] According to another aspect of the present invention, a risk control model training method is provided, comprising:

[0009] Obtaining sample data of historical operational behaviors of customers on currency exchange equipment and sample data of social profiles of said customers;

[0010] Using the customer's historical operational behavior sample data and the social outlook sample data as risk control model training sample data, and training the risk control model based on the risk control model training sample data to obtain a target differentiated risk control model;

[0011] The target differentiated risk control model is applied to the device operation management method described in any embodiment of the present invention, and is used to perform personalized analysis on the social profile data of the target customers to obtain the risk control indicators of the target customers.

[0012] According to another aspect of the present invention, there is provided a device operation management apparatus, configured for a currency exchange device, comprising:

[0013] A data acquisition module, configured to acquire social profile data of a target customer and determine an operation behavior of the target customer when performing an operation on the local device;

[0014] A risk control indicator determination module is used to call a target differentiated risk control model to perform personalized analysis on the social profile data of the target customer to obtain the risk control indicator of the target customer;

[0015] The operation behavior management module is used to manage the operation behavior of the target customer on the local device according to the risk control index of the target customer.

[0016] According to another aspect of the present invention, a risk control model training device is provided, comprising:

[0017] A sample data acquisition module, configured to acquire sample data of a customer's historical operating behavior on a currency exchange device and sample data of the customer's social profile;

[0018] a risk control model training module, configured to use the customer's historical operational behavior sample data and the social profile sample data as risk control model training sample data, and train a risk control model based on the risk control model training sample data to obtain a target differentiated risk control model;

[0019] The target differentiated risk control model is applied to the device operation management method described in any embodiment of the present invention, and is used to perform personalized analysis on the social profile data of the target customers to obtain the risk control indicators of the target customers.

[0020] According to another aspect of the present invention, an electronic device is provided, comprising:

[0021] at least one processor; and

[0022] a memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the equipment operation management method or risk control model training method described in any embodiment of the present invention.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the device operation management method or risk control model training method described in any embodiment of the present invention when executed.

[0025] According to another aspect of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the device operation management method or risk control model training method described in any embodiment of the present invention.

[0026] In an embodiment of the present invention, a currency exchange device is used to obtain the social profile data of a target customer and determine the target customer's operational behavior when operating on the local device. Furthermore, the currency exchange device uses a target differentiated risk control model to perform personalized analysis on the target customer's social profile data, obtaining the target customer's risk control index, and thereby managing the target customer's operational behavior on the local device based on the target customer's risk control index. Before invoking the target differentiated risk control model, the device obtains sample data on the customer's historical operational behavior on the currency exchange device and sample data on the customer's social profile. The customer's historical operational behavior sample data and social profile sample data are used as sample data for training the risk control model. The risk control model is then trained based on the risk control model training data to obtain a target differentiated risk control model. In this solution, the currency exchange device uses the target differentiated risk control model to perform personalized risk control management on the customer's operational behavior, addressing the inefficiency of traditional manual review methods and the lack of personalized risk control strategies for different customers. This solution improves the efficiency of the currency exchange device's risk control review of customers and allows the development of personalized risk control strategies for different customers, thereby effectively managing customer operational behavior within the currency exchange device.

[0027] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 This is a flow chart of a device operation management method provided by the first embodiment of the present invention;

[0030] Figure 2This is a flow chart of a device operation management method provided by Embodiment 2 of the present invention;

[0031] Figure 3 This is a flow chart of a risk control model training method provided in Example 3 of the present invention;

[0032] Figure 4 This is a schematic diagram of a device operation management apparatus provided by a fourth embodiment of the present invention;

[0033] Figure 5 Schematic diagram of a risk control model training device provided by the fifth embodiment of the present invention;

[0034] Figure 6 This is a structural diagram of an electronic device provided in Example 6 of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "objective", "history", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Example 1

[0038] Figure 1 This is a flow chart of a device operation management method provided by the first embodiment of the present invention. This embodiment is applicable to the case where a differentiated risk control model is used to perform personalized risk control management on a customer's operation behavior on a currency exchange device. The method can be executed by a device operation management device, which can be implemented by software and / or hardware and can generally be integrated into an electronic device, which can be a currency exchange device. Accordingly, if Figure 1 As shown, the method includes the following operations:

[0039] S110: Acquire the social profile data of the target customer, and determine the operation behavior of the target customer when operating on the local device.

[0040] The target customer may be any customer who performs currency exchange operations on a currency exchange device. The local device may be any currency exchange device. For example, the local device may be a currency exchange device placed outdoors at Airport A or indoors at Bank B. The embodiment of the present invention does not limit the specific location of the currency exchange device. The social profile data may be a data set reflecting the various characteristics, behaviors, attitudes, and status of the target customer in social life. For example, the social profile data may include but is not limited to the nationality, ethnicity, cultural background, and occupational status of the target customer. The embodiment of the present invention does not limit the specific content included in the social profile data. The operation behavior may be various operation actions of the target customer on the local currency exchange device, such as but not limited to login actions, click actions, and input actions. As long as it is an operation of the target customer on the local currency exchange device, the embodiment of the present invention does not limit the specific type of operation behavior.

[0041] In an embodiment of the present invention, when the target customer operates on the currency exchange device, the currency exchange device can obtain the target customer's social profile data and the target customer's operating behavior when operating on the local device as reference data for subsequent verification of the customer's identity and risk control management of the customer.

[0042] S120: Calling a target differentiated risk control model to perform personalized analysis on the social profile data of the target customer to obtain the risk control index of the target customer.

[0043] The target-differentiated risk control model can be a model that develops personalized risk control strategies based on risk characteristics and business needs, and can be used to calculate personalized risk control indicators for different customers. Risk control indicators can be a series of quantitative standards and parameters used to assess the credit risk of target customers, including but not limited to the target customer's credit score, credit limit utilization rate, and behavioral score. The embodiments of the present invention do not limit the specific types of risk indicators.

[0044] Accordingly, after the currency exchange device obtains the target customer's social profile data and determines the target customer's operational behavior on the local device, it can conduct a risk assessment on the target customer. Specifically, the currency exchange device can use a target-differentiated risk control model to perform a personalized analysis of the target customer's social profile data, thereby obtaining the target customer's risk control indicators.

[0045] S130: Manage the target customer's operating behavior on the local device according to the target customer's risk control indicator.

[0046] Accordingly, after the currency exchange device obtains the target customer's risk control indicators, it can manage the target customer's operating behavior on the local device based on the target customer's risk control indicators, for example, it can adaptively adjust the target customer's currency exchange limit and whether to allow the target customer to exchange currency.

[0047] In a specific example, assuming that the currency exchange device determines that the target customer's risk control indicator is intermediate, the currency exchange limit of the target customer can be reduced; assuming that the currency exchange device determines that the target customer's risk control indicator is advanced, the target customer can be prohibited from exchanging currency on the currency exchange machine, and the target customer can be prompted to go to the manual counter for consultation.

[0048] The embodiment of the present invention utilizes a currency exchange device to obtain the social profile data of a target customer and determines the target customer's operational behavior when operating on the local device. Furthermore, the currency exchange device calls a target differentiated risk control model to perform personalized analysis on the target customer's social profile data to obtain the target customer's risk control index, thereby managing the target customer's operational behavior on the local device based on the target customer's risk control index. In the above scheme, the currency exchange device calls a target differentiated risk control model to perform personalized risk control management on the customer's operational behavior, solving the problems of low efficiency of traditional manual review methods and lack of personalized risk control strategies for different customers. It can improve the efficiency of the currency exchange device's risk control review of customers and formulate personalized risk control strategies for different customers, thereby achieving effective management of customer operational behavior in the currency exchange device.

[0049] Example 2

[0050] Figure 2 This is a flow chart of a device operation management method provided by the second embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, multiple specific optional implementation methods are provided for obtaining the social profile data of the target customer and performing personalized analysis on the social profile data of the target customer by calling the target differentiated risk control model to obtain the risk control indicators of the target customer. Figure 2 As shown, the method of this embodiment may include:

[0051] S210: Obtain facial information data of the target customer.

[0052] The facial information data may be image or video data of the target customer's face acquired through a camera or other image acquisition device.

[0053] In an embodiment of the present invention, when a target customer wants to perform a currency exchange operation on a currency exchange device, the target customer's facial information data can first be collected by an image acquisition device on the currency exchange device to authenticate the target customer.

[0054] Optionally, when the target customer's face is obscured or the target customer has a darker skin tone, the image capture device on the currency exchange device can adaptively adjust the aperture, shutter speed, and sensitivity of the capture device to increase the exposure of the captured image, thereby increasing image brightness and improving the clarity of image details. After image capture, the image processing model can also be used to adjust the image's brightness and contrast to improve image visibility.

[0055] Optionally, facial data from customers can be collected from different angles for currency exchange devices placed in different locations, such as indoors, outdoors, and in remote areas. This data can be used as training sample data for the image processing model to improve its image processing capabilities for facial data. This allows the model to be used to perform a series of image optimization operations, including but not limited to denoising, contrast enhancement, and reducing sensitivity to lighting changes, when processing facial data. These measures can effectively address the challenges of currency exchange devices facing multiple light sources and complex backgrounds when capturing images in different scenarios. These processing steps can significantly reduce the impact of lighting conditions on facial recognition accuracy, improving the robustness of portrait photos.

[0056] S220: Analyze the facial information data of the target customer using an improved image feature extraction algorithm to obtain facial analysis data of the target customer.

[0057] The improved image feature extraction algorithm may be an algorithm that can adaptively adjust the weight of each facial feature based on the facial information data of the target customer. The facial analysis data may be data obtained by analyzing the facial information data using the improved image feature extraction algorithm.

[0058] Accordingly, after the currency exchange device obtains the facial information data of the target customer, the facial information data of the target customer can be analyzed using an improved image feature extraction algorithm, so as to extract key features in the facial data as the facial analysis data of the target customer, which may include but is not limited to the eyes, nose and mouth of the target customer. The embodiment of the present invention does not limit the specific type of facial analysis data.

[0059] In an optional embodiment of the present invention, the use of an improved image feature extraction algorithm to analyze the facial information data of the target customer to obtain the facial analysis data of the target customer may include: dynamically adjusting the facial feature weight of the facial information data in the improved image feature extraction algorithm based on the facial information data of the target customer; and analyzing the facial information data of the target customer based on the facial feature weight of the facial information data in the improved image feature extraction algorithm to obtain the facial analysis data of the target customer.

[0060] Among them, facial features can be the features of each face in the improved image feature extraction algorithm, for example, can include but not be limited to eyes, mouth and nose, etc. The embodiment of the present invention does not limit the specific content of facial features.

[0061] In an embodiment of the present invention, when a currency exchange device analyzes a target customer's facial information data using an improved image feature extraction algorithm to obtain the target customer's facial analysis data, the weights of the facial features in the improved image feature extraction algorithm can be dynamically adjusted based on the target customer's facial information data. For example, if the target customer's face is obscured or has a darker complexion, the weight of the eyes in the improved image feature extraction algorithm can be adaptively increased to improve the facial recognition rate for this type of person.

[0062] S230: Based on the facial analysis data of the target customer, a weighted chain multi-dimensional face recognition algorithm is used to verify the identity of the target customer.

[0063] Among them, the weighted chain multi-dimensional face recognition algorithm can be a face recognition method that combines feature weighting, multi-dimensional feature extraction and chain structure optimization.

[0064] Accordingly, after the currency exchange device obtains the target customer's facial analysis data, it can use a weighted chain multi-dimensional facial recognition algorithm to perform facial recognition on the target customer based on the facial analysis data and compare the facial recognition data with the target customer's identification information. For example, identification information may include, but is not limited to, the target customer's passport and ID card. The embodiments of the present invention do not limit the specific type of identification information. The above method uses a weighted chain multi-dimensional facial recognition algorithm to verify the target customer's identity, eliminating manual verification of the target customer's identity and improving verification efficiency.

[0065] S240: Determine whether the target customer has passed the identity verification. If so, execute S250; otherwise, prompt that the target customer's identity verification has failed.

[0066] Accordingly, if the target customer's identification information is consistent with the facial recognition result, it means that the target customer has passed the identity verification; if the target customer's identification information is inconsistent with the facial recognition result, it means that the target customer has failed the identity verification. If the currency exchange device determines that the target customer has failed the identity verification, it can prompt the target customer that the identity verification has failed.

[0067] S250: Obtain pre-stored social profile data corresponding to the target customer, and determine the operation behavior of the target customer when performing operations on the local device.

[0068] Specifically, when it is determined that the target customer has passed the identity verification, the currency exchange device can obtain the target customer's corresponding pre-stored social profile data and the target customer's operating behavior when operating on the local device as reference data for subsequent verification of user identity and risk control management of the target customer's operating behavior.

[0069] S260: Retrieve the historical operation behavior data of the target customer according to the social profile data of the target customer.

[0070] The historical operation behavior data may be the target customer's operation behavior on various currency exchange devices before the current operation.

[0071] Accordingly, after obtaining the target customer's social profile data, the currency exchange device can call up the target customer's operating behavior on various currency exchange devices before the target customer's current operation based on the target customer's social profile data as a reference for evaluating the target customer's risk indicators.

[0072] S270. Dynamically adjust the weights of the social profile characteristics and the operational behavior characteristics in the target differentiated risk control model according to the social profile data of the target customer and the historical operational behavior data.

[0073] The social profile characteristics may be various attributes in the target-differentiated risk control model that reflect the characteristics of the customer in social life. The operational behavior characteristics may be various attributes in the target-differentiated risk control model that reflect the operational behavior of the customer on the currency exchange device.

[0074] Accordingly, in order to achieve personalized risk control for different customers, the target differentiated risk control model can analyze the social profile data and historical operational behavior data of the target customers. Furthermore, the target differentiated risk control model can adaptively adjust the weights of the social profile characteristics and operational behavior characteristics based on the analysis results of the target customers' social profile data and historical operational behavior data. In a specific example, special weights can be given to currency exchange transactions in the form of specific time points and special amounts. For example, based on the target customer's historical operational behavior data, it is determined that the target customer has recently frequently gone to currency exchange equipment in remote suburban areas at 12 o'clock in the middle of the night to conduct small currency exchange transactions. It can be considered that the target customer's behavior is relatively high-risk. Therefore, the weight of this operational behavior feature in the target differentiated risk control model can be increased.

[0075] Optionally, when a target customer uses a currency exchange device for the first time, the target-differentiated risk control model can adaptively adjust the weight of the social profile feature based solely on the target customer's social profile data. Furthermore, the target customer's operational behavior data on the currency exchange device can be collected and used as historical operational behavior data for the target customer's next operation of the currency exchange device. Alternatively, the target-differentiated risk control model can also use the target customer's operational behavior data on other currency exchange devices as historical operational behavior data for the target customer.

[0076] S280: Call the target differentiated risk control model after weight adjustment to perform risk assessment on the target customer to obtain the risk control index of the target customer.

[0077] Accordingly, after dynamically adjusting the weights of the social profile characteristics and operational behavior characteristics in the target differentiated risk control model based on the target customers' social profile data and historical operational behavior data, the target differentiated risk control model can use the target differentiated risk control model with adjusted weights to conduct risk assessment on the target customers and obtain the risk control indicators of the target customers, so as to continuously adjust the risk control indicators of the target customers according to customers with different social profiles and their recent transactions.

[0078] S290: Manage the target customer's operating behavior on the local device according to the target customer's risk control indicator.

[0079] In an optional embodiment of the present invention, the management of the target customer's operating behavior on the local device based on the target customer's risk control indicators may include: determining the target customer's risk level based on the target customer's risk control indicators; when it is determined that the target customer's risk level is high risk, prohibiting the target customer from operating on the local device and providing a risk warning to the target customer; when it is determined that the target customer's risk level is not high risk, adaptively adjusting the target customer's current currency exchange amount based on the target customer's risk level.

[0080] The risk level may be the result of an assessment and classification of the potential risk level of a target customer, including, but not limited to, high risk, medium risk, and low risk. The present embodiment does not limit the risk level classification. A risk warning may be used to inform the target customer of potential risks. The current currency exchange limit may be the maximum amount that the target customer may exchange currency within a certain period of time.

[0081] In an embodiment of the present invention, in the process of managing the target customer's operating behavior on the local device according to the target customer's risk control indicators, the target customer's risk level can be determined based on the target customer's risk control indicators, and the target customer's operating behavior on the local device can be managed based on the target customer's risk level. Specifically, if the target customer's risk level is determined to be high risk, the currency exchange device can refuse to provide currency exchange services to the target customer, and prompt the target customer that the current risk level is assessed as high risk, and the target customer can go to the manual counter for consultation and currency exchange; if the target customer's risk level is not high risk, the currency exchange device can adaptively adjust the target customer's current currency exchange amount according to the target customer's risk level, and provide currency exchange services to the target customer, thereby avoiding manual one-by-one comparison of customer information, improving the efficiency of risk control review, and realizing personalized risk control strategies for different customers.

[0082] In an optional embodiment of the present invention, after managing the target customer's operating behavior on the local device according to the target customer's risk control indicators, it may also include: real-time monitoring of the target customer's operating behavior data on the local device; storing the target customer's operating behavior data on the local device as the target customer's historical operating behavior data.

[0083] The operation behavior data may be a data set of all operation behaviors of the target customer on the currency exchange device.

[0084] In an embodiment of the present invention, after the currency exchange device manages the target customer's operating behavior on the local device according to the target customer's risk control indicators, it can also monitor the target customer's operating behavior data on the currency exchange device in real time and store it as the target customer's historical operating behavior data, so that the target differentiated risk control model can continuously adjust the target customer's risk control indicators according to the target customer's recent transaction behavior.

[0085] In an embodiment of the present invention, a currency exchange device acquires facial information data of a target customer and analyzes the facial information data using an improved image feature extraction algorithm to obtain facial analysis data of the target customer. Furthermore, the currency exchange device uses a weighted chain multi-dimensional face recognition algorithm to verify the identity of the target customer based on the facial analysis data. If the target customer's identity is verified, the currency exchange device acquires pre-stored social profile data corresponding to the target customer and determines the target customer's operational behavior when operating on the local device. Furthermore, the currency exchange device retrieves the target customer's historical operational behavior data based on the target customer's social profile data and dynamically adjusts the weights of the social profile features and operational behavior features in a target differentiated risk control model based on the target customer's social profile data and historical operational behavior data. The target differentiated risk control model, after weight adjustment, is then used to conduct a risk assessment on the target customer and obtain a risk control index for the target customer. After obtaining the target customer's risk control index, the target customer's operational behavior on the currency exchange device is managed based on the target customer's risk control index. In the above solution, the currency exchange device calls the target differentiated risk control model to perform personalized risk control management on the customer's operating behavior, which solves the problems of low efficiency of traditional manual review methods and lack of personalized risk control strategies for different customers. It can improve the efficiency of currency exchange equipment in customer risk control review and formulate personalized risk control strategies for different customers, thereby realizing effective management of customer operating behavior in currency exchange equipment.

[0086] Example 3

[0087] Figure 3 This is a flow chart of a risk control model training method provided by the third embodiment of the present invention. This embodiment can be applied to train the risk control model based on historical operation behavior sample data and social outlook sample data to achieve personalized risk control for different customers. The method can be executed by a risk control model training device, which can be implemented by software and / or hardware and can generally be integrated into an electronic device. The electronic device can be a terminal device or a server device. As long as it can execute the risk control model training method, the embodiment of the present invention does not limit the specific device type of the electronic device. Accordingly, if Figure 3 As shown, the method includes the following

[0088] S310: Obtain sample data of the customer's historical operation behavior on the currency exchange device and sample data of the customer's social profile.

[0089] The historical operation behavior sample data may be a data set recording various operation behaviors performed by multiple customers in the past period of time. The social profile sample data may be a collection of social profile data of multiple customers.

[0090] In an embodiment of the present invention, in order to implement personalized risk control for different customers, the historical operation behaviors of multiple customers on currency exchange devices can be obtained as historical operation behavior sample data and the social profile data of multiple customers can be obtained as social profile sample data.

[0091] S320: Use the customer's historical operation behavior sample data and the social outlook sample data as risk control model training sample data, and train the risk control model based on the risk control model training sample data to obtain a target differentiated risk control model.

[0092] Among them, the risk control model training sample data can be a data set used to train and optimize the risk control model.

[0093] Correspondingly, after obtaining the customer's historical operation behavior sample data on the currency exchange device and the customer's social appearance sample data, the customer's historical operation behavior sample data and social appearance sample data can be used as risk control model training sample data, and the risk control model can be trained using the risk control model training sample data, so as to obtain a target differentiated risk control model for personalized risk control for different customers.

[0094] In an optional embodiment of the present invention, training the risk control model based on the risk control model training sample data may include: classifying historical operational behavior characteristics and social appearance characteristics, and calculating the importance scores of the classified historical operational behavior characteristics and social appearance characteristics in the risk control model; dynamically adjusting the weights of the historical operational behavior characteristics and social appearance characteristics in the risk control model based on the importance scores of the classified historical operational behavior characteristics and social appearance characteristics in the risk control model.

[0095] Among these, historical operational behavior features can be various attributes in a risk control model that reflect a customer's operational behavior on currency exchange devices. Social profile features can be various attributes in a risk control model that reflect a customer's social life characteristics. Importance scores can be indicators that measure the contribution of each feature to the model's predictive ability.

[0096] In an embodiment of the present invention, in the process of training a risk control model based on risk control model training sample data, the historical operational behavior characteristics and social appearance characteristics can first be classified. For example, the historical operational behavior characteristics and social appearance characteristics can be classified according to the customer's nationality, cultural background, appearance characteristics, and transaction behavior. After completing the classification of the historical operational behavior characteristics and social appearance characteristics, the risk control model can calculate the importance score of the classified historical operational behavior characteristics and social appearance characteristics based on the historical operational behavior sample data and social appearance sample data, so as to obtain the contribution of each feature to the predictive ability of the risk control model. In a specific example, a decision tree model can be used to calculate the Gini importance of each feature or a neural network can be used to analyze the weight of each neuron in the neural network and calculate the gradient of each feature to the loss function. After calculating the importance score of the risk control model for the classified historical operational behavior characteristics and social appearance characteristics, the weight of each feature in the risk control model can be dynamically adjusted according to the importance score of each feature, thereby improving the accuracy and robustness of the risk control model.

[0097] In a specific example, features with high importance scores can have their weights increased to enhance their role in the risk control model; features with low importance scores can have their weights reduced to mitigate their impact on the risk control model. It is understood that the importance scores of each feature can be recalculated regularly, with their weights adjusted based on the latest data and the performance of the target-differentiated risk control model. Furthermore, during the operation of the target-differentiated risk control model, the weights of each feature can be dynamically adjusted based on each customer's historical operational behavior data, thereby implementing personalized risk control strategies based on the customer's recent trading behavior.

[0098] An embodiment of the present invention obtains sample data on a customer's historical operational behavior on a currency exchange device and sample data on their social profile, and uses these data as sample data for risk control model training. This method trains a risk control model based on the sample data, resulting in a target-differentiated risk control model. This risk control model training method can improve the precision of the target-differentiated risk control model and the accuracy of risk control indicator calculations. The trained target-differentiated risk control model can perform personalized analysis of a customer's social profile data, thereby implementing personalized risk control strategies for different customers.

[0099] In the technical solution disclosed herein, the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0100] It should be noted that, in the embodiment of the present invention, a corresponding operation entry can be provided for the user to choose to agree or reject the automated decision result; if the user chooses to reject, the expert decision process will be entered.

[0101] It should be noted that any arrangement and combination of the technical features in the above embodiments also falls within the protection scope of the present invention.

[0102] Example 4

[0103] Figure 4 Schematic diagram of a device operation management apparatus provided by the fourth embodiment of the present invention, such as Figure 4 As shown, the device includes: a data acquisition module 410, a risk control indicator determination module 420 and an operation behavior management module 430, wherein:

[0104] The data acquisition module 410 is used to acquire the social profile data of the target customer and determine the operation behavior of the target customer when performing operations on the local device.

[0105] The risk control index determination module 420 is used to call the target differentiated risk control model to perform personalized analysis on the social profile data of the target customer to obtain the risk control index of the target customer.

[0106] The operation behavior management module 430 is used to manage the operation behavior of the target customer on the local device according to the risk control index of the target customer.

[0107] The embodiment of the present invention utilizes a currency exchange device to obtain the social profile data of a target customer and determines the target customer's operational behavior when operating on the local device. Furthermore, the currency exchange device calls a target differentiated risk control model to perform personalized analysis on the target customer's social profile data to obtain the target customer's risk control index, thereby managing the target customer's operational behavior on the local device based on the target customer's risk control index. In the above scheme, the currency exchange device calls a target differentiated risk control model to perform personalized risk control management on the customer's operational behavior, solving the problems of low efficiency of traditional manual review methods and lack of personalized risk control strategies for different customers. It can improve the efficiency of the currency exchange device's risk control review of customers and formulate personalized risk control strategies for different customers, thereby achieving effective management of customer operational behavior in the currency exchange device.

[0108] Optionally, the data acquisition module 410 is specifically used to: obtain the facial information data of the target customer; analyze the facial information data of the target customer using an improved image feature extraction algorithm to obtain the facial analysis data of the target customer; based on the facial analysis data of the target customer, use a weighted chain multi-dimensional face recognition algorithm to verify the identity of the target customer; and when it is determined that the identity verification of the target customer is passed, obtain the corresponding pre-stored social appearance data of the target customer.

[0109] Optionally, the data acquisition module 410 is also used to: dynamically adjust the facial feature weight of the facial information data in the improved image feature extraction algorithm based on the facial information data of the target customer; analyze the facial information data of the target customer based on the facial feature weight of the facial information data in the improved image feature extraction algorithm to obtain the facial analysis data of the target customer.

[0110] Optionally, the risk control indicator determination module 420 is specifically used to: call the historical operational behavior data of the target customer based on the social profile data of the target customer; dynamically adjust the weights of the social profile characteristics and operational behavior characteristics in the target differentiated risk control model based on the social profile data of the target customer and the historical operational behavior data; call the target differentiated risk control model after weight adjustment to perform risk assessment on the target customer to obtain the risk control indicator of the target customer.

[0111] Optionally, the operation behavior management module 430 is specifically used to: determine the risk level of the target customer based on the risk control indicators of the target customer; when it is determined that the risk level of the target customer is high risk, prohibit the target customer from performing operations on the local device, and provide risk warnings to the target customer; when it is determined that the risk level of the target customer is not high risk, adaptively adjust the current currency exchange amount of the target customer according to the risk level of the target customer.

[0112] Optionally, the above-mentioned device may further include a historical operation behavior data acquisition module, which is used to: monitor the operation behavior data of the target customer on the local device in real time; and store the operation behavior data of the target customer on the local device as the historical operation behavior data of the target customer.

[0113] The above-mentioned equipment operation management device can execute the equipment operation management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the equipment operation management method provided by any embodiment of the present invention.

[0114] Since the device operation management apparatus described above is a device that can execute the device operation management method in the embodiment of the present invention, and based on the device operation management method described in the embodiment of the present invention, those skilled in the art can understand the specific implementation of the device operation management apparatus of this embodiment and its various variations, so how the device operation management apparatus implements the device operation management method in the embodiment of the present invention will not be described in detail here. As long as those skilled in the art can implement the device used in the device operation management method in the embodiment of the present invention, it falls within the scope of protection of this application.

[0115] Example 5

[0116] Figure 5 Schematic diagram of a risk control model training device provided by the fifth embodiment of the present invention. Figure 5 As shown, the device includes: a sample data acquisition module 510 and a risk control model training module 520, wherein:

[0117] The sample data acquisition module 510 is used to acquire sample data of the customer's historical operation behavior on the currency exchange device and sample data of the customer's social profile.

[0118] The risk control model training module 520 is used to use the customer's historical operation behavior sample data and the social outlook sample data as risk control model training sample data, and train the risk control model based on the risk control model training sample data to obtain a target differentiated risk control model.

[0119] The target differentiated risk control model is applied to the device operation management method described in any embodiment of the present invention, and is used to perform personalized analysis on the social profile data of the target customers to obtain the risk control indicators of the target customers.

[0120] An embodiment of the present invention obtains sample data on a customer's historical operational behavior on a currency exchange device and sample data on their social profile, and uses these data as sample data for risk control model training. This method trains a risk control model based on the sample data, resulting in a target-differentiated risk control model. This risk control model training method can improve the precision of the target-differentiated risk control model and the accuracy of risk control indicator calculations. The trained target-differentiated risk control model can perform personalized analysis of a customer's social profile data, thereby implementing personalized risk control strategies for different customers.

[0121] The above-mentioned risk control model training device can execute the risk control model training method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the risk control model training method provided by any embodiment of the present invention.

[0122] Since the risk control model training device introduced above is a device that can execute the risk control model training method in the embodiment of the present invention, based on the risk control model training method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation method of the risk control model training device of this embodiment and its various variations, so how the risk control model training device implements the risk control model training method in the embodiment of the present invention will not be introduced in detail here. As long as technical personnel in this field implement the device used in the risk control model training method in the embodiment of the present invention, it falls within the scope of protection of this application.

[0123] Example 6

[0124] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0125] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0126] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0127] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the device operation management method or the risk control model training method.

[0128] In some embodiments, the device operation management method or the risk control model training method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the device operation management method or the risk control model training method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the device operation management method or the risk control model training method in any other appropriate manner (for example, by means of firmware).

[0129] Optionally, a device operation management method, applied to a currency exchange device, may include: obtaining the social profile data of a target customer, and determining the operational behavior of the target customer when operating on the local device; calling a target differentiated risk control model to perform personalized analysis on the social profile data of the target customer, and obtaining the risk control index of the target customer; and managing the operational behavior of the target customer on the local device according to the risk control index of the target customer.

[0130] Optionally, the risk control model training method may include: obtaining historical operational behavior sample data of the customer on the currency exchange device and social profile sample data of the customer; using the historical operational behavior sample data and the social profile sample data of the customer as risk control model training sample data, and training the risk control model based on the risk control model training sample data to obtain a target differentiated risk control model; wherein, the target differentiated risk control model is applied to the device operation management method described in any embodiment of the present invention, and is used to perform personalized analysis of the social profile data of the target customer to obtain the risk control indicators of the target customer.

[0131] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0136] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0137] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0138] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A device operation management method, characterized in that: Applied to currency exchange equipment, including: Acquire social profile data of target customers, and determine operation behaviors of the target customers when performing operations on the local device; Calling a target differentiated risk control model to perform personalized analysis on the social profile data of the target customer to obtain the risk control index of the target customer; The operating behavior of the target customer on the local device is managed according to the risk control index of the target customer.

2. The method according to claim 1, characterized in that The acquisition of target customer's social profile data includes: Obtaining facial information data of the target customer; Analyzing the facial information data of the target customer using an improved image feature extraction algorithm to obtain facial analysis data of the target customer; Based on the facial analysis data of the target customer, a weighted chain multi-dimensional face recognition algorithm is used to verify the identity of the target customer; When it is determined that the identity of the target customer has been verified, the pre-stored social profile data corresponding to the target customer is obtained.

3. The method according to claim 2, characterized in that The step of analyzing the target customer's facial information data using an improved image feature extraction algorithm to obtain the target customer's facial analysis data includes: Dynamically adjusting the facial feature weight of the facial information data in the improved image feature extraction algorithm according to the facial information data of the target customer; The facial information data of the target customer is analyzed according to the facial feature weights of the facial information data in the improved image feature extraction algorithm to obtain facial analysis data of the target customer.

4. The method according to claim 1, wherein The target differentiated risk control model is called to perform personalized analysis on the social profile data of the target customer to obtain the risk control indicators of the target customer, including: Retrieving the historical operation behavior data of the target customer based on the social profile data of the target customer; Dynamically adjusting the weights of the social profile characteristics and the operational behavior characteristics in the target differentiated risk control model according to the social profile data and the historical operational behavior data of the target customer; The target differentiated risk control model after weight adjustment is called to perform risk assessment on the target customer to obtain the risk control index of the target customer.

5. The method according to claim 1, wherein Managing the target customer's operating behavior on the local device according to the target customer's risk control indicator includes: Determine the risk level of the target customer based on the risk control indicators of the target customer; If the risk level of the target customer is determined to be high risk, prohibiting the target customer from operating on the local device and providing a risk warning to the target customer; When it is determined that the risk level of the target customer is not high risk, the current currency exchange limit of the target customer is adaptively adjusted according to the risk level of the target customer.

6. The method according to claim 1, characterized in that After managing the target customer's operation behavior on the local device according to the target customer's risk control indicator, the method further includes: Real-time monitoring of the target customer's operating behavior data on the local device; The operation behavior data of the target customer on the local device is stored as the historical operation behavior data of the target customer.

7. A risk control model training method, characterized in that: include: Obtaining sample data of historical operational behaviors of customers on currency exchange equipment and sample data of social profiles of said customers; Using the customer's historical operational behavior sample data and the social outlook sample data as risk control model training sample data, and training the risk control model based on the risk control model training sample data to obtain a target differentiated risk control model; Among them, the target differentiated risk control model is applied to the equipment operation management method described in any one of claims 1-6, and is used to perform personalized analysis on the social profile data of the target customers to obtain the risk control indicators of the target customers.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the equipment operation management method described in any one of claims 1-6, or execute the risk control model training method described in claim 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a processor to implement the device operation management method described in any one of claims 1 to 6, or to execute the risk control model training method described in claim 7 when executed.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by the processor, the device operation management method described in any one of claims 1 to 6 is implemented, or the risk control model training method described in claim 7 is executed.

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