Data processing method and device, computer equipment and program

Abstract

This application provides a data processing method and apparatus, a computer device, and a program. The method includes the steps of: acquiring biometric recognition data collected for a predetermined object in a business scenario; performing an ID recognition process on the predetermined object based on the biometric recognition data to obtain a recognition result for the predetermined object; acquiring a first credit metric corresponding to the predetermined object in the business scenario; and performing a grade adjustment process on the first credit metric based on the recognition result to obtain a second credit metric, where the second credit metric is different from the first credit metric, and a risk management strategy associated with the second credit metric in the business scenario is different from the risk management strategy associated with the first credit metric in the business scenario. This application enables flexibly adjusting the risk management strategy of the user in the business scenario by adjusting the grade of the user's credit metric in the business scenario.

Description

[Technical Field]

[0001] This application claims priority from a Chinese patent application filed with the China Patent Office on June 13, 2023, bearing application number 2023107006506 and entitled "Data processing method, apparatus, computer equipment, storage medium and product," the entire contents of which are incorporated herein by reference.

[0002] The present application relates to the field of computer technology, and in particular to a data processing method and apparatus, and a computer device and program. [Background technology]

[0003] For example, various business scenarios, such as payment scenarios, access control scenarios, and game scenarios, often involve a large number of business requests (e.g., a payment scenario involves a request to pay assets, or an access control scenario involves a request to open a door). Since a large number of business requests involves a large number of data interactions, business scenarios need to have corresponding risk management strategies to avoid abnormal data interactions, such as malicious transactions by users.

[0004] Currently, adjusting risk management strategies in business scenarios generally requires manual requests, and initiating a request for risk management strategies requires finding a corresponding request method, which is not flexible enough. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments of the present application aim to provide a data processing method, device, computer equipment, and program that can flexibly adjust a user's risk management strategy in a business scenario by adjusting the grade / level of the user's credit indicators in the business scenario. [Means for solving the problem]

[0006] According to one aspect, an embodiment of the present application provides a data processing method, the method comprising: Acquire biometric data collected about a specified object (specified target) in a business scenario; performing ID recognition processing on the predetermined object based on the biometric recognition data to obtain an identification result of the predetermined object; and The method includes: obtaining a first trust index corresponding to a predetermined object in a business scenario; and performing a grade adjustment process on the first trust index based on the identification result to obtain a second trust index; Among them, the second credit indicator is different from the first credit indicator, and in a business scenario, the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator.

[0007] According to one aspect, an embodiment of the present application provides a data processing apparatus, the apparatus comprising: an acquisition unit for acquiring biometric data collected about a predetermined object in a business scenario; and a processing unit for performing ID recognition processing on a predetermined object based on biometric recognition data to obtain an identification result of the predetermined object; The processing unit is further used for obtaining a first trust index corresponding to a predetermined object in the business scenario, and performing a grade adjustment process on the first trust index according to the identification result to obtain a second trust index; Among them, the second credit indicator is different from the first credit indicator, and in a business scenario, the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator.

[0008] According to one aspect, an embodiment of the present application provides a computing device, the computing device including a memory and a processor, the memory storing a computer program, the computer program, when executed by the processor, causing the processor to perform the data processing method described above.

[0009] According to one aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being capable of causing the computer device to perform the data processing method described above when read and executed by a processor of the computer device.

[0010] According to one aspect, an embodiment of the present application provides a computer program product or a computer program, the computer program product or the computer program comprising computer instructions stored on a computer-readable storage medium, the computer instructions being read by a processor of a computing device from the computer-readable storage medium and executed by the processor to cause the computing device to perform the data processing method described above. [Effects of the Invention]

[0011] In the embodiments of the present application, biometric recognition data collected for a specific object in a business scenario can be obtained, and then ID recognition processing can be performed on the specific object based on the biometric recognition data to obtain an identification result for the specific object. Since ID recognition requests are often closely related to specific business scenarios, the present application can improve the adaptability of ID recognition scenarios by performing ID recognition processing on the current user according to the user's biometric recognition data in specific business scenarios (e.g., payment scenarios, access control scenarios, game scenarios, etc.). In addition, a first credit indicator corresponding to a specific object in the business scenario can be obtained, and a grade adjustment processing can be performed on the first credit indicator based on the identification result to obtain a second credit indicator, wherein the second credit indicator is different from the first credit indicator, and the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator. As can be seen from this, the present application performs ID recognition processing on a user in a business scenario and flexibly adjusts the user's credit index according to the identification result, so that the risk management strategy for the user can also change along with changes in the user's credit index (e.g., changes from a first credit index to a second credit index), and therefore the present application can flexibly adjust the corresponding risk management strategy based on the index adjustment method in a specific business scenario. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 illustrates the architecture of a data processing system provided in an embodiment of the present application. [Figure 2] FIG. 1 illustrates the architecture of a data processing scenario provided in an embodiment of the present application. [Figure 3] 1 is a flowchart of a data processing method provided in an embodiment of the present application. [Figure 4] 4 is a flowchart of another data processing method provided in an embodiment of the present application. [Figure 5a]FIG. 10 is a diagram illustrating an interface of the grade adjustment process provided in an embodiment of the present application. [Figure 5b] FIG. 10 is a diagram showing an interface of another grade adjustment process provided in an embodiment of the present application. [Figure 6] 1 is a flowchart of a data processing scenario provided in an embodiment of the present application; [Figure 7a] 1 is a flowchart of a palm payment scenario provided in an embodiment of the present application. [Figure 7b] 1 is a flowchart of another Palm payment scenario provided in an embodiment of the present application. [Figure 8] 1 is a block diagram of a data processing device provided in an embodiment of the present application; [Figure 9] FIG. 1 is a configuration diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0013] This application provides a data processing scheme, which is applicable to business scenarios such as payment scenarios, access control scenarios, and game scenarios, and performs identity verification on users in the above-mentioned business scenarios, and flexibly adjusts the user's security credit rating (i.e., credit index) according to the verification result, thereby changing the user's risk management strategy in the corresponding business scenario, so as to meet more business requirements in the business scenarios. Specifically, the principle of the data processing scheme is generally as follows:

[0014] (1) When an ID recognition request for a predetermined object is detected in a business scenario (for example, when a predetermined object needs to make a payment by facial recognition in a payment scenario, or when a predetermined object needs to perform ID verification in an access control scenario), biometric data collected for the predetermined object is acquired. The business scenario here may be any one of a payment scenario, an ID verification scenario (for example, an access control scenario), and a game scenario, and the predetermined object is any one object (usually a person) that needs to undergo ID verification or ID recognition. The biometric data here is data that can be used to reflect the ID (identification) of the predetermined object, such as palm data, fingerprint data, face data, pupil data, etc.

[0015] (2) Perform ID recognition processing on a predetermined object based on biometric recognition data to obtain an identification result for the predetermined object, where the identification result is used to reflect whether ID recognition for the predetermined object is successful or not, so the identification result includes recognition success and recognition failure, where so-called recognition success refers to the ID recognition for the predetermined object being successful, and recognition failure refers to the ID recognition for the predetermined object being unsuccessful.

[0016] (3) Obtain a first credit indicator corresponding to a predetermined object in a business scenario, and perform a grade adjustment process on the first credit indicator based on the recognition result to obtain a second credit indicator, wherein the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator. It should be understood that the predetermined object refers to an object whose ID has been registered in advance in the business scenario, so that the predetermined object can be assigned a corresponding first credit indicator after ID registration in the business scenario, and then adjust the first credit indicator based on the recognition result obtained after ID recognition is performed on the predetermined object in the real-time business scenario to adjust the risk management strategy of the predetermined object.

[0017] As can be seen from this, the present application performs ID recognition processing on a user (i.e., a specified object) that has an ID recognition request in a business scenario, thereby flexibly adjusting the user's credit index according to the recognition result obtained by the ID recognition processing; and since different credit indexes can be associated with different risk management strategies in a business scenario, the risk management strategy for the user also changes as the user's credit index changes (e.g., from a first credit index to a second credit index). In other words, the present application can flexibly adjust the corresponding risk management strategy based on the index adjustment method in a specific business scenario, and for the same user, the user's risk management strategy can be flexibly adjusted, making the risk management method more flexible.

[0018] The technical terms used in the examples of this application are introduced below.

[0019] 1. Business scenarios and specified objects A business scenario refers to a scenario that provides a business service and can support biometric recognition (or ID recognition) for a predetermined object, where the predetermined object refers to any target in the business scenario or a designated target in the business scenario (e.g., a target to which payment needs to be made in a payment scenario). For example, if the business service is a payment service, the business scenario is a payment scenario, and ID recognition is permitted for the payment target in the payment scenario. For example, if the business service is an ID verification service, the business scenario is an access control scenario, and ID recognition is permitted for the access control scenario in the ID verification target. For example, if the business service is a game service, the business scenario is a game scenario, and ID recognition is permitted for the game target in the game scenario.

[0020] 2. Biometric data Biometric recognition data refers to data for ID recognition or biometric recognition of a user (i.e., a specific object). ID recognition includes at least palm recognition, fingerprint recognition, face recognition, and pupil recognition. Among them, the biometric recognition data may include, but is not limited to, palm data, fingerprint data, face data, and pupil data. Specifically, palm recognition is a technology for ID recognition of a user based on palm data, fingerprint recognition is a technology for ID recognition of a user based on fingerprint data, face recognition is a technology for ID recognition of a user based on face data, and pupil recognition is a technology for ID recognition of a user based on pupil data.

[0021] This application mainly relates to performing ID recognition processing on a predetermined object based on palm recognition technology to obtain a recognition result for the predetermined object, which may include successful recognition and unsuccessful recognition. Specifically, the palm recognition technology can be applied to various business scenarios, such as payment scenarios, access control scenarios, and game scenarios. For example, in a payment scenario, palm data of a predetermined object can be collected and ID recognition can be performed on the predetermined object based on the palm data. If the recognition is successful, subsequent operations such as payment processing can be performed. For example, in an access control scenario, palm data of a predetermined object can be collected and ID recognition can be performed on the predetermined object based on the palm data. If the recognition is successful, subsequent operations such as opening a door can be performed.

[0022] 3. First and second credit indicators The so-called trust index may be an index assigned to different objects in the same business scenario based on a predetermined management rule. For example, a trust index corresponding to the user can be obtained after analyzing the user's ID information and operation data in the business scenario according to the management rule. Optionally, the trust index is an index for indicating the security credit grade of the user (i.e., a specific object) in the business scenario. The so-called security credit grade, as the name suggests, refers to a grade representing the security credit of the object in the business scenario. The data format of the security credit grade may be Chinese, English, text, etc. Specifically, the data format of the security credit grade indicated by the corresponding trust index in different business scenarios may be the same or different. The types and quantities of security credit grades specified in different business scenarios may be the same or different, and the specific quantities and types may vary depending on the management rule. For example, a payment scenario has four types of security credit grades, and an access control scenario has three types of security credit grades. For example, in a payment scenario, the safety credit grade indicated by the corresponding trust indicator may be expressed as beginner, medium, high, or super high; for example, in an access control scenario, the safety credit grade indicated by the corresponding trust indicator may be expressed as 80%, 90%, or 100%; for example, in a game scenario, the safety credit grade indicated by the corresponding trust indicator may be expressed as grade 1, grade 2, or grade 3. In the present application, the first trust indicator and the second trust indicator are different, and for example, the safety credit grade indicated by the first trust indicator (e.g., high) may be higher than the safety credit grade indicated by the second trust indicator (e.g., medium), or for example, the safety credit grade indicated by the first trust indicator (e.g., high) may be lower than the safety credit grade indicated by the second trust indicator (e.g., super high).

[0023] 4. Risk management strategy A risk management strategy is a strategy for eliminating or mitigating the possibility of risk events occurring by taking various measures and methods. Specifically, different business scenarios require different risk management strategies. For example, a risk management strategy for a payment scenario may include a strategy for risk management of payment amounts, payment frequency, etc., and a risk management strategy for an ID verification scenario may include a strategy for risk management of ID tampering, information theft, etc.

[0024] In the present application, the risk management strategy in the business scenario is dynamically adjusted according to the change in the credit index of a specific object, and specifically, the risk management strategy associated with the second credit index is different from the risk management strategy associated with the first credit index. For example, in a payment scenario, when the credit index of a specific object is the first credit index (e.g., the safety credit grade indicated by the first credit index may be medium), the risk management strategy associated with the first credit index may include a maximum payment amount of 1000 for one order, and when the credit index adjustment of the specific object is the second credit index (e.g., the safety credit grade indicated by the second credit index may be high), the risk management strategy associated with the second credit index may include a maximum payment amount of 2000 for one order.

[0025] 5. Cloud Technology In this application, the above-mentioned data processing scheme mainly includes processes such as obtaining biometric data collected for a specific object in a business scenario, performing ID recognition processing for the specific object based on the biometric data, and performing a grade adjustment processing for the first trust index based on the recognition result. These processes often involve large amounts of data calculation and data storage services, requiring large amounts of computer operation costs. Therefore, all of the data calculation, data storage, and other services related to this application can be realized using cloud storage technology in cloud technology. For example, a blockchain can be stored in the "cloud" using cloud storage technology. When transaction data generated in a transaction process in a business system needs to be stored in the blockchain, the transaction data can be uploaded to the blockchain in the "cloud" using cloud storage technology. When the transaction data needs to be read, the data can be read from the blockchain in the "cloud" at any time, thereby reducing the storage requirements for computer equipment and expanding the scope of blockchain application.

[0026] Cloud technology, a collective term for networking, information, integration, management platform, and application technologies based on cloud computing business models and applications, allows for the formation of resource pools and on-demand usage, providing flexibility and convenience. Cloud computing technology is an important support. Cloud technology can also include cloud storage technology, and so-called cloud storage is a new concept that expands and develops on the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as a storage system) refers to a storage system that uses cluster applications, grid technology, distributed storage file systems, and other features to integrate a large number of different types of storage devices on a network through application software or application interfaces, working together to provide external data storage and business access.

[0027] 6. Artificial Intelligence Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or digital computer-controlled devices to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use that knowledge to achieve optimal results. AI technology is an interdisciplinary field that encompasses a wide range of disciplines and combines both hardware-level and software-level technologies. Basic AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, large-scale model training technology, operation / interaction systems, mechatronics, and other technologies. AI software technology primarily encompasses several major areas, such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0028] The data processing schemes provided in the embodiments of this application are primarily related to their combination with machine learning techniques in the field of artificial intelligence. For example, a feature extraction model can be trained based on machine learning techniques, and the trained feature extraction model (e.g., a first feature extraction model and a second feature extraction model) can be used to perform feature extraction processing on biometric recognition data of a specific object to obtain the biometric features of the specific object. Then, identity recognition can be performed on the specific object based on the biometric features of the specific object. Machine learning (ML) is a multidisciplinary field that encompasses many fields, including probability theory, statistics, proximity theory, convex analysis, and algorithmic complexity theory. It focuses on studying how computers can simulate or realize human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and a fundamental method for endowing computers with intelligence, and its applications are widespread in all fields of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and supervised learning.

[0029] 7. Blockchain Blockchain is a new application mode of computer technology, including distributed data storage, P2P (Peer to Peer) transfer, consensus mechanism, and encryption algorithm. Blockchain is essentially a decentralized database, a series of data blocks (also called blocks) generated through cryptographic techniques, each containing information from a batch of network transactions, which is used to verify the validity (anti-forgery) of the information and generate the next data block. Blockchain uses encryption to ensure that data cannot be tampered with or forged.

[0030] In the present application, the data processing process involves a lot of data, such as biometric recognition data, recognition results, first credit indicators, second credit indicators, risk management strategies, etc. Optionally, the present application can transmit the above data to a blockchain for storage, and based on the properties of the blockchain such as immutability and traceability, data tampering or leakage can be avoided, thereby improving the security and reliability of the data processing process.

[0031] In addition, in this application, relevant data related to the data processing process may include, for example, biometric recognition data, recognition results, first credit indicators, second credit indicators, risk management strategies, etc. Furthermore, when the embodiments of this application are applied to a specific product or technology, user permission or consent must be obtained, and the collection, use, and processing of relevant data must comply with relevant national or regional laws, regulations, and standards, and conform to the principles of lawfulness, legitimacy, and necessity, and not involve the acquisition of data prohibited or restricted by law. In some alternative embodiments, the relevant data related to the embodiments of this application is acquired with the individual permission of the subject, and the use of such relevant data is indicated to the subject when the individual permission of the subject is obtained.

[0032] The data processing system provided in the embodiment of the present application will be described in detail below in conjunction with FIGS.

[0033] Referring to Fig. 1, Fig. 1 is a diagram illustrating the architecture of a data processing system provided in an embodiment of the present application. The architecture of the data processing system includes a server 104 and a terminal device group, where the terminal device group includes a plurality of terminal devices, such as terminal device 101, terminal device 102, terminal device 103, etc. Any one of the terminal devices in the terminal device group may be directly or indirectly connected to the server 104 via wired or wireless communication.

[0034] Each terminal device in the terminal device group may be a smartphone, a tablet computer, a laptop computer, a palm computer, a mobile internet device (MID), an in-vehicle device, an aircraft, a wearable device (e.g., a smart device such as a smart watch, a smart bracelet, or a pedometer), a virtual reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device), etc. As can be understood, the types of each terminal device in the terminal device group may be the same or different. For example, terminal device 101 may be a smartphone, and terminal device 102 may also be a smartphone. For example, terminal device 101 may be a tablet computer, and terminal device 103 may be an in-vehicle device. This application does not limit the number and types of terminal devices in the terminal device group.

[0035] The server 104 may be an independent physical server, a server cluster or a distributed system consisting of multiple physical servers, or may be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDNs (Content Delivery Networks), big data, and artificial intelligence platforms.

[0036] Next, an interaction process between any one terminal device (for example, terminal device 101) in the data processing system will be described as an example.

[0037] (1) The terminal device 101 responds to an ID recognition request of a predetermined object, and then acquires biometric recognition data collected for the predetermined object in a business scenario. Specifically, in a payment scenario, the ID recognition request may be a payment request submitted by the predetermined object, and in an access control scenario, the ID recognition request may be an ID verification request submitted by the predetermined object.

[0038] (2) The terminal device 101 transmits the acquired biometric recognition data to the server 104. After receiving the biometric recognition data of the predetermined object, the server 104 performs ID recognition processing on the predetermined object based on the biometric recognition data, thereby obtaining the recognition result of the predetermined object.

[0039] (3) The server 104 obtains a first reputation index corresponding to a predetermined object in a business scenario, and performs a grade adjustment process on the first reputation index based on the recognition result, thereby obtaining a second reputation index.

[0040] (4) The server 104 can send a second credit indicator to the terminal device 101, in which the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator, and then the terminal device 101 can perform business management for a specific object in a business scenario based on the risk management strategy indicated by the second credit indicator.

[0041] It should be noted that the above-described data processing interaction process is merely an example and does not limit the specific execution processes of the terminal device and the server. For example, obtaining a first trust indicator corresponding to a predetermined object in a business scenario may be executed by any one terminal device. Optionally, all processes included in the above-described data processing process of the present application, such as the process of obtaining biometric recognition data, the process of performing ID recognition processing on the predetermined object based on the biometric recognition data, and the process of adjusting the grade of the first trust indicator based on the recognition result, may be independently executed by any one terminal device or server.

[0042] In one possible implementation, the data processing system provided in the embodiments of the present application can be deployed in a blockchain system. For example, the server 104 and the terminal devices included in the terminal device group (e.g., terminal device 101, terminal device 102, terminal device 103, etc.) can all serve as blockchain node devices to jointly form a blockchain network. Therefore, the processes included in the data processing process in the embodiments of the present application, such as the process of acquiring biometric data, the process of performing ID recognition processing on a predetermined object based on the biometric data, and the process of performing a grade adjustment processing on a first trust index based on the recognition result, can all be executed on the blockchain. In this way, the fairness and impartiality of the data processing process can be guaranteed, and the data processing process can be made traceable, ensuring the security of the data in the data processing process, thereby improving the security and reliability of the entire data processing process.

[0043] The data processing system provided in this application can be applied to various business scenarios, such as payment scenarios, ID verification scenarios, game scenarios, etc. Next, taking the business scenario as a palm recognition scenario (e.g., payment scenario and ID verification scenario) as an example, a specific architecture related to the palm recognition scenario will be described in detail.

[0044] Please refer to Fig. 2, which is a diagram illustrating the architecture of a palm recognition scenario provided in an embodiment of the present application. As shown in Fig. 2, the palm recognition scenario includes a palm recognition device A, a palm recognition device B, a palm recognition device C, and a back-end server. Optionally, the palm recognition device A may be the terminal device 101 in the data processing system shown in Fig. 1, the palm recognition device B may be the terminal device 102 in the data processing system shown in Fig. 1, the palm recognition device C may be the terminal device 103 in the data processing system shown in Fig. 1, and the back-end server may be the server 104 in the data processing system shown in Fig. 1. Among them, palm recognition device A, palm recognition device B, and palm recognition device C each run a palm recognition client (APP), and the business scenarios applied to different palm recognition devices may be the same or different, for example, palm recognition device A is applied to an ID verification scenario, and palm recognition device B is applied to a small payment scenario (payment amount is within 10,000), and palm recognition device C is applied to a large payment scenario (payment amount is greater than 10,000), and the back-end server is used to provide back-end services to each of the above-mentioned palm recognition devices (palm recognition device A, palm recognition device B, and palm recognition device C). Next, the palm recognition device (e.g., palm recognition device A) and the back-end server will be described in detail.

[0045] 1. Palm recognition device A The palm recognition device A is equipped with a collection device and a palm recognition client / APP (Application). The collection device may be, for example, a 3D camera head (a conventional camera head with added software and hardware for biometric detection, including a depth camera and an infrared camera, ensuring information security). The 3D camera head mainly includes a color sensor (RGB Sensor) and an infrared sensor (IR Sensor), and the palm recognition client is equipped with a palm recognition module, a scenario setting module, and a payment application (APP) module. Each module in the palm recognition device is described below.

[0046] (1) RGB Sensor and IR Sensor: Collectively referred to as collection devices, they can be used to collect biological streaming data of a given object. Among them, the RGB Sensor is used to capture color images, and the IR Sensor is used to capture infrared images. The biological streaming data of a given object can be generated based on the color images captured by the RGB Sensor and the infrared images captured by the IR Sensor.

[0047] (2) Palm recognition module: Used to receive the biological streaming data collected by the collecting device (RGB sensor and IR sensor) and perform data processing on the biological streaming data to obtain biometric recognition data. The data processing may include optimization and biopsy processing. The optimization processing refers to performing selection processing on multiple biometric images included in the biological streaming data according to predetermined selection conditions (such as clarity, image contrast, image brightness, etc.) to obtain the optimal image. The biopsy processing refers to identifying whether a predetermined object is a living organism.

[0048] (3) Scenario setting module: Used to obtain shipping information of palm recognition device, generate scenario setting information of palm recognition device A based on the shipping information, and then send the scenario setting information of palm recognition device A to the backend server for recording and storage. Among them, the scenario setting information of palm recognition device A may include, but is not limited to, device identification (MCH_ID), SN (Serial Number), safety verification level of the business scenario where the palm recognition device is located, on_auto_type (used to indicate whether it is an unmanned self-service, i.e., unmanned monitoring scenario), and app_type (business scenario, such as payment, ID verification).

[0049] (4) Payment application module: Used to determine the business scenario in which the current palm recognition device is located based on the scenario setting information of the current palm recognition device and start the corresponding business service. Specifically, different business services can be started in different business scenarios based on the scenario setting information. For example, if the business scenario is a payment scenario, the business service started by the payment application module after the user passes palm recognition is to return a payment code. For example, if the business scenario is an access control scenario, the business service started by the payment application module after the user passes palm recognition is to return door open instruction information (OPENID), etc. Optionally, when the user is subject to risk management, that is, when it is not confirmed that the current user has a security credit rating that meets the current business scenario, a prompt can be displayed. The prompt can indicate that the current user does not meet the business conditions and can be used to guide the user to retry by scanning a code with a smartphone, or to retry after upgrading the rating in another business scenario where the rating can be upgraded after authentication.

[0050] Second, back-end server The back-end server is used to provide back-end services to the palm recognition device A, and the details of the back-end services may include palm recognition services, scenario management control services, credit rating management control services, payment services, etc.

[0051] (1) Palm recognition service: receives biometric data (e.g., palm data) collected by a collection device in a palm recognition device, and performs feature extraction on the palm data to perform ID recognition processing on a predetermined object. The feature extraction here may include palm print feature extraction and palm vein feature extraction, thereby obtaining the palm print feature and palm vein feature of a predetermined object, and then performing ID recognition processing on the predetermined object based on the palm print feature and palm vein feature. The two-element feature (palm print feature and palm vein feature) adopted in this application is more abundant and accurate than a single-element feature, thereby improving the accuracy of ID recognition and ensuring the security and reliability of the data processing process.

[0052] (2) Scenario management and control service: Used to mark the security level required for each business scenario and record the business scenarios of each palm recognition device. Among them, the business scenarios may include payment scenarios, ID verification scenarios (such as access control scenarios), and game scenarios. For example, the business scenario of palm recognition device A is the ID verification scenario, and the security level required for the ID verification scenario is level 1. For example, the business scenario of palm recognition device B is the small payment scenario, and the security level required for the small payment scenario is level 2. For example, the business scenario of palm recognition device C is the large payment scenario, and the security level required for the large payment scenario is level 3.

[0053] (3) Credit rating management and control service: Used to record the current user's credit index (or safety credit grade), assign a unique risk management strategy, and provide functional guidance when the safety credit grade indicated by the user's current credit index does not meet the requirements of the scenario. Among them, one credit index is associated with one risk management strategy, and the risk management strategies corresponding to different business scenarios are different.

[0054] (4) Payment service: After performing ID recognition processing on a specified object and obtaining a recognition result, if the recognition result is successful, the payment service can be called to complete the subsequent asset transfer processing (e.g., transferring assets owned by the specified object to the recipient's account).

[0055] In an embodiment of the present application, by performing ID recognition processing on a user (i.e., a specified object) with an ID recognition request in a business scenario, the user's credit index can be flexibly adjusted based on the recognition result obtained by the ID recognition processing. In addition, since different credit indexes can be associated with different risk management strategies in a business scenario, the risk management strategy for the user also changes with a change in the user's credit index (e.g., a change from a first credit index to a second credit index). In other words, the present application flexibly adjusts the corresponding risk management strategy based on the index adjustment method in a specific business scenario, and flexibly adjusts the user's risk management strategy for the same user, making the risk management method more flexible.

[0056] It is to be understood that the diagrams illustrating the system architectures described in the embodiments of the present application are intended to more clearly explain the technical solutions provided in the embodiments of the present application, and are not intended to limit the technical solutions provided in the embodiments of the present application. Furthermore, as will be apparent to those skilled in the art, with the evolution of system architectures and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application can be similarly applied to similar technical problems.

[0057] Next, the data processing methods provided in the embodiments of the present application will be described in detail.

[0058] Please refer to Fig. 3, which is a flowchart of a data processing method provided in an embodiment of the present application. The data processing method may be performed by a computer device, which may be a terminal device or a server in the data processing system shown in Fig. 1. The data processing method mainly includes, but is not limited to, steps S301 to S303 as follows:

[0059] S301: Biometric recognition data collected for a predetermined object in a business scenario is acquired.

[0060] In one possible implementation, the biometric data may be data collected in real time in a business scenario. Specifically, a specific object responds to an ID recognition request submitted in the business scenario, and the ID recognition request is used to request ID recognition for the specific object. Then, an ID recognition technology (e.g., palm recognition technology, fingerprint recognition technology, face recognition technology, pupil recognition technology, etc.) is employed to collect data for the specific object to obtain the biometric data of the specific object. Specifically, the types of biometric data collected using different types of ID recognition technologies are different. For example, biometric data collected using palm recognition technology is palm data; similarly, biometric data collected using fingerprint recognition technology is fingerprint data; and biometric data collected using face recognition technology is face data. In this implementation, the biometric data is collected in real time in a business scenario, and the real-time collected data is advantageous for performing more accurate ID recognition processing for the specific object.

[0061] In another possible implementation, the biometric data may be historical (past / history) collected data obtained from a database. Specifically, the biometric data of a specific object is obtained from the database, and the biometric data is data previously collected in a business scenario. In this implementation, the computer device can quickly obtain past data collected for the specific object from the database, where past data refers to data collected within a past time. The past time is referenced to the current system time, and the past time refers to the time before the current system time.

[0062] The biometric data collection process will be described in detail below, taking the example of collecting data in real time.

[0063] In one possible implementation, the acquisition of biometric data collected by a computer device for a specific object in a business scenario may include the following steps: (1) calling a collection device to collect biological streaming data of the specific object, where the biological streaming data includes multiple biometric images collected from the specific object, including but not limited to video, image, and audio data. Specifically, the biological streaming data of the specific object may be multiple biometric images collected by calling a collection device within a predetermined period (e.g., 30 seconds, 60 seconds). Different scenarios may collect different types of biometric images. For example, in a palm recognition scenario, the biometric images collected by calling a collection device are palm images; in a fingerprint recognition scenario, the biometric images collected by calling a collection device are fingerprint images; and in a face recognition scenario, the biometric images collected by calling a collection device are face images.

[0064] (2) A selection process is performed on multiple biometric images according to predetermined selection conditions to obtain biometric recognition data for a predetermined object. Here, the predetermined selection conditions include any one or more of image size (e.g., image length, width, etc.), shooting angle (e.g., 90 degrees, 45 degrees), image contrast, image brightness, and sharpness. Optionally, when multiple biometric images are selected according to the predetermined selection conditions, a reselection process can be performed from among the multiple biometric images. This reselection process can include selection methods such as random selection or selection based on collection time. For example, if three biometric images, i.e., img1, img2, and img3, are obtained by selecting according to the predetermined selection conditions, and the collection times of these three biometric images are sequentially greater than img1 > img2 > img3, img1 can be used as the final optimized biometric recognition data. Optionally, after obtaining biometric images by selecting according to the predetermined selection conditions, further image processing (e.g., image enhancement) can be performed on the selected biometric images, and the processed biometric images can be used as biometric recognition data for the predetermined object.

[0065] According to the above method, after performing optimization processing on the collected biological streaming data, biometric recognition data for ID recognition is obtained, which can improve the accuracy and reliability of the biometric recognition data.

[0066] S302: An ID recognition process is performed on a predetermined object based on the biometric recognition data to obtain a recognition result for the predetermined object.

[0067] Specifically, performing ID recognition processing on a predetermined object essentially recognizes the ID of the predetermined object, and generally speaking, biometric features are features that can uniquely identify a user's ID. Therefore, in this application, performing ID recognition processing on a predetermined object based on the biometric recognition data of the predetermined object specifically includes the following: obtaining the type of biometric recognition data, and then employing corresponding ID recognition technologies to perform ID recognition according to different types of biometric recognition data (wherein one type of biometric recognition data corresponds to one ID recognition technology), thereby recognizing and authenticating the user ID. For example, if the biometric recognition data is palm data, palm recognition technology can be employed to perform palm recognition processing on the palm data; if the biometric recognition data is fingerprint data, fingerprint recognition technology can be employed to perform fingerprint recognition processing on the fingerprint data; and if the biometric recognition data is face data, face recognition technology can be employed to perform face recognition processing on the face data.

[0068] The specific process of how to perform ID recognition processing for a predetermined object will be described in detail below.

[0069] In one possible implementation, the computer device may perform ID recognition processing on a predetermined object based on biometric recognition data to obtain a recognition result for the predetermined object, which may include the following steps: (1) performing a feature extraction processing on the biometric recognition data to obtain biometric features of the predetermined object. For example, if the biometric recognition data is palm data, the biometric features obtained by feature extraction may be palm features. For example, if the biometric recognition data is fingerprint data, the biometric features obtained by feature extraction may be fingerprint features. Furthermore, if the biometric recognition data is face data, the biometric features obtained by feature extraction may be face features. (2) obtaining enrollment data for the predetermined object, the enrollment data is generated after the predetermined object has successfully registered its ID in a business scenario, and the enrollment data includes the enrollment features. Note that if the predetermined object registers its ID based on a palm registration method, the enrollment features of the predetermined object include palm features. Similarly, if the predetermined object registers its ID based on a face registration method, the enrollment features of the predetermined object include face features. (3) performing ID recognition processing on the predetermined object based on the biometric features of the predetermined object and the enrollment features of the predetermined object to obtain a recognition result for the predetermined object, which may include successful recognition and failed recognition. Specifically, by performing feature matching between biometric features and registered features, ID recognition can be performed for a specified object. Specifically, the feature similarity between the biometric features and registered features of a specified object can be calculated. If the feature similarity is greater than or equal to a similarity threshold, it can be determined that ID recognition for the specified object is successful, i.e., the recognition result is determined to be recognition success. If the feature similarity is less than the similarity threshold, it can be determined that ID recognition for the specified object has failed, i.e., the recognition result is determined to be recognition failure.

[0070] Next, taking the biometric recognition data as palm data as an example, the specific process of performing feature extraction processing on palm data will be described in detail. In one possible implementation, the computer device performing feature extraction processing on the biometric recognition data to obtain the biometric features of a predetermined object may include the following steps:

[0071] (1) Performing palm vein feature extraction processing on palm data to obtain palm vein features of a predetermined object, and performing palm print feature extraction processing on palm data to obtain palm print features of the predetermined object. Specifically, a first feature extraction model can be employed to extract palm print features of the predetermined object, and a second feature extraction model can be employed to extract palm vein features of the predetermined object. The first feature extraction model and the second feature extraction model can be the same or different. For example, the first feature extraction model or the second feature extraction model can be a neural network model. The neural network model can include, for example, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a gated recurrent unit (GRU) model, etc. However, the embodiments of the present application do not limit the model structure of the neural network model.

[0072] (2) Performing feature fusion processing on palm print features and palm vein features to obtain biometric features of a predetermined object, where the feature fusion processing includes at least one of the following: feature weighting processing, feature alignment processing, and feature calculation processing. For example, a weighting processing may be performed on palm print features and palm vein features to obtain biometric features of a predetermined object; for example, an averaging processing may be performed on palm print features and palm vein features to obtain biometric features of a predetermined object; or, for example, a feature alignment processing may be performed on palm print features and palm vein features (for example, when the feature dimension of the palm print feature is k1×k2 and the feature dimension of the palm vein feature is k2×k3, the feature dimension of the palm vein feature and the feature dimension of the palm print feature can be aligned. After the feature alignment processing, the feature dimensions of both features are the same, which facilitates subsequent feature processing).

[0073] According to the above-mentioned method, when the biometric data of a predetermined object is palm data, the ID can be recognized by extracting two-element features of the palm print and palm vein of the predetermined object (palm vein feature and palm print feature). The advantage of two-element features over one-element features (palm vein feature or palm print feature) is that they have higher accuracy and security during ID recognition. In addition, because palm vein features are difficult to recognize and are not easily forged or simulated, the two-element feature extraction method can improve the accuracy and reliability of ID recognition for a predetermined object.

[0074] S303: Obtain a first credit indicator corresponding to a specified object in a business scenario, and perform a grade adjustment process on the first credit indicator based on the recognition result to obtain a second credit indicator, wherein, in the business scenario, the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator.

[0075] Specifically, in a business scenario, the risk management strategy of the same user can be dynamically adjusted according to the adjustment of the credit index grade, i.e., for a given object, the risk management strategy associated with the second credit index is different from the risk management strategy associated with the first credit index. For example, if the business scenario is a payment scenario, the risk management strategy associated with the safety credit grade (e.g., medium) indicated by the first credit index of a given object may be a strategy to be adopted when the maximum payment amount of any single order paid by the given object exceeds 1000, and the risk management strategy associated with the safety credit grade (e.g., high) indicated by the first credit index of a given object may be a strategy to be adopted when the maximum payment amount of any single order paid by the given object exceeds 2000. In other words, in the payment scenario, the risk management strategy is used to control the maximum amount a user will pay for an order.

[0076] In one possible implementation, the computer device performing a grade adjustment process on the first trust index based on the recognition result to obtain a second trust index may include: if the recognition result is successful, performing a grade upgrade process on the first trust index according to a first adjustment method to obtain a second trust index, where the first adjustment method is used to indicate that a grade upgrade process is performed on the safety trust grade indicated by the first trust index, for example, if the safety trust grade is grade 1, the first adjustment method is used to adjust the specified object from grade 1 to grade 2 to obtain a second trust index of the specified object.

[0077] In another possible implementation, the computer device performing a grade adjustment process on the first trust index based on the recognition result to obtain a second trust index may further include: obtaining scenario setting information of a business scenario, the scenario setting information including a safety verification grade of the business scenario; if the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is successful, performing a grade upgrade process on the first trust index according to the first adjustment method to obtain a second trust index; wherein the safety verification grade may be used to indicate whether the business scenario has a monitoring target; if the business scenario has a monitoring target, the safety verification grade of the business scenario is grade 1; and if the business scenario has no monitoring target, the safety verification grade of the business scenario is grade 2.

[0078] The specific process of adjusting the grade of the first credit index will be described in detail below.

[0079] First, perform a grade adjustment process on the first credit index according to the recognition result.

[0080] In one possible implementation manner, the computer device's performing a grade adjustment process on the first trust index based on the recognition result may include the following several methods: (1) if the recognition result is successful, performing a grade upgrade process on the first trust index according to the first adjustment method to obtain a second trust index, for example, upgrading a specified object from grade 2 (first trust index) to grade 3 (second trust index); (2) if the recognition result is unsuccessful, performing a grade downgrade process on the first trust index according to the second adjustment method to obtain a third trust index, for example, downgrading a specified object from grade 2 (first trust index) to grade 1 (third trust index); (3) if the recognition result is unsuccessful, performing a grade maintenance process on the first trust index, that is, maintaining the grade 2 (first trust index) of the specified object. Among them, the first adjustment method and the second adjustment method are different, and here, the difference between the first adjustment method and the second adjustment method includes the difference in the grade adjustment range for the first credit index (i.e., the grade adjustment range indicated by the first adjustment method is different from the grade adjustment range indicated by the second adjustment method), and the difference in the grade adjustment method for the first credit index.

[0081] Specifically, when the safety credit grade indicated by the first trust index is medium, the adjustment range indicated by the first adjustment method may be an upgrade of one grade, in which case the trust index of the specified object can be upgraded from medium to high; when the safety credit grade indicated by the first trust index is medium, the adjustment range indicated by the second adjustment method may be a downgrade of one grade, in which case the trust index of the specified object can be downgraded from medium to low. Note that the adjustment range of the first adjustment method and the adjustment range of the second adjustment method may be the same or different. For example, the grade adjustment process may include the following: when upgrading the grade, upgrading by one grade, and when downgrading the grade, downgrading by one grade as well; or, for example, the grade adjustment process may further include the following: when upgrading the grade, upgrading by two grades, and when downgrading the grade, downgrading by two grades as well. In this implementation method, adjustment processing can be performed on the trust index according to the recognition result, and if the recognition is successful, the safety trust grade of the trust index of the specified object can be upgraded, and if the recognition is unsuccessful, the safety trust grade of the trust index of the specified object can be downgraded.

[0082] Second, the first credit index is subjected to a comprehensive grade adjustment process according to the business scenario and the recognition result.

[0083] In one possible implementation manner, the computer device performing the grade adjustment process on the first trust index according to the recognition result and the scenario setting information of the business scenario may include the following several manners:

[0084] (1) When the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is successful, a grade upgrade process is performed on the first trust index according to the first adjustment method to obtain a second trust index. For example, when the business scenario is a scenario that has an object to be monitored (i.e., the safety verification grade of the business scenario is grade 1) and the recognition result is successful, the specified object can be upgraded from grade 2 (first trust index) to grade 3 (second trust index).

[0085] (2) If the safety verification grade of the business scenario does not satisfy the grade adjustment condition or the recognition result is a recognition failure, a grade downgrade process is performed on the first trust index according to the second adjustment method to obtain a third trust index. For example, if the business scenario is a scenario without a monitoring target (i.e., the safety verification grade of the business scenario is grade 2), or the recognition result is a recognition failure, the specified object can be downgraded from grade 2 (first trust index) to grade 1 (third trust index).

[0086] (3) If the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is a recognition failure, a grade maintenance process is performed on the first trust index. For example, if the business scenario is a scenario that includes an object to be monitored (i.e., the safety verification grade of the business scenario is grade 1) and the recognition result is a recognition success, the grade 2 (first trust index) of the specified object can be maintained.

[0087] (4) If the safety verification grade of the business scenario does not satisfy the grade adjustment condition and the recognition result is successful, a grade maintenance process is performed on the first trust index. For example, if the business scenario is a scenario without a monitoring target (i.e., the safety verification grade of the business scenario is grade 2) and the recognition result is successful, the grade 2 (first trust index) of the specified object can be maintained.

[0088] In the above methods (1) to (4), the grade adjustment condition may include the condition that there is a monitored object. For example, if the safety verification grade of a business scenario is grade 1, the business scenario satisfies the grade adjustment condition. Also, for example, if the safety verification grade of a business scenario is grade 2, the business scenario does not satisfy the grade adjustment condition. In other words, the grade adjustment condition is used to indicate that grade adjustment can be performed in a scenario where there is a monitored object.

[0089] Third, the first credit index is subjected to a comprehensive grade adjustment process according to the business scenario, past business data, and the recognition results.

[0090] In one possible implementation, if the security verification grade of a business scenario does not satisfy the grade adjustment condition (i.e., the business scenario is a scenario without a monitored object), historical business data of the specified object can be acquired, and a grade adjustment process can be performed on the first trust index of the specified object based on the acquired historical business data and the recognition result. The historical business data can include at least the following: business data generated by the specified object in the business scenario within the past period, and data generated by the specified object based on another business scenario (e.g., a scenario with a monitored object) within the past period. For example, in a payment scenario, the historical business data can include past payment data generated by the specified object within the past month (i.e., the month before the current time). A computer device can use the acquired past payment data of the specified object as sample data and train a grade recognition model based on the sample data, which can be used to recognize the security credit grade of the specified object in the business scenario. The grade recognition model can be a neural network model with any network structure, and is not limited thereto in the embodiments of the present application.Then, a grade recognition process can be performed on a specified object based on the trained grade recognition model, where the grade recognition process refers to recognizing the safety trust grade of the specified object. (1) For example, if the safety trust grade obtained by recognition satisfies a trust grade threshold (e.g., high) and the recognition result is successful, a grade upgrade process can be performed on the specified object, that is, the specified object can be upgraded from the first trust index to the second trust index. (2) For example, if the safety trust grade obtained by recognition satisfies a trust grade threshold (e.g., high) or the recognition result is unsuccessful, a grade maintenance process can be performed on the specified object. (3) For example, if the safety trust grade obtained by recognition does not satisfy the trust grade threshold and the recognition result is unsuccessful, a grade downgrade process can be performed on the specified object, that is, the specified object can be downgraded from the first trust index to the third trust index.

[0091] Specifically, the past payment data may include data such as payment time, payment quantity, and payment amount, and the process of a computer device training a grade recognition model based on the past payment data may specifically include the following: first, perform data analysis on the past payment data of the specified object to extract payment features (e.g., features such as whether payments are made on time, whether payments are frequent, and whether there are abnormalities in the payment amount), and then train the grade recognition model based on the extracted payment features. By adopting this method, the grade recognition model is trained based on the user's past payment data, and the trained grade recognition model is used to evaluate the user's safety credit grade, thereby meeting the special business requirement of being able to adjust the grade based on an individual's past business data when the business scenario is in a long-term unattended monitoring state. Furthermore, because the grade recognition model is trained based on the user's past business data, the safety credit grade obtained by using the grade recognition model to recognize the user is highly reliable, and grade adjustment can be made accurately and reliably based on the user's credit index.

[0092] In the embodiments of the present application, an ID recognition process can be performed for a user (i.e., a specified object) with an ID recognition request in a business scenario, so that the user's credit index can be flexibly adjusted according to the recognition result obtained by the ID recognition process; and since different credit indexes can be associated with different risk management strategies in a business scenario, with a change in a user's credit index (e.g., a change from a first credit index to a second credit index), the risk management strategy for the user also changes; that is, the present application can flexibly adjust the corresponding risk management strategy based on the index adjustment method in a specific business scenario, so that for the same user, the user's risk management strategy can be flexibly adjusted, making the risk management method more flexible.

[0093] Referring to Fig. 4, Fig. 4 is a flowchart of another data processing method provided in an embodiment of the present application. The data processing method may be performed by a computer device, which may be a terminal device or a server in the data processing system shown in Fig. 1. The data processing method mainly includes, but is not limited to, steps S401 to S404 as follows:

[0094] S401: Biometric recognition data collected for a predetermined object in a business scenario is acquired.

[0095] In one possible implementation, the acquisition of biometric data collected by a computer device for a specific object in a business scenario may include the following steps: (1) calling a collection device to collect biological streaming data of the specific object, where the biological streaming data includes multiple biometric images collected for the specific object, including, but not limited to, video, image, and audio data. Specifically, the biological streaming data of the specific object may be multiple biometric images collected by the collection device within a specific period (e.g., 30 seconds, 60 seconds). Different scenarios may collect different types of biometric images. For example, in a palm recognition scenario, the biometric images collected by the collection device are palm images; in a fingerprint recognition scenario, the biometric images collected by the collection device are fingerprint images; and in a face recognition scenario, the biometric images collected by the collection device are face images. (2) performing a selection process on the multiple biometric images according to specific selection conditions to obtain biometric data for the specific object. The predetermined selection criteria here include any one or more of image size (e.g., image length, width, etc.), shooting angle (e.g., 90 degrees, 45 degrees), image contrast, image brightness, and sharpness. Optionally, when there are multiple biometric images selected according to the predetermined selection criteria, a reselection process can be performed from among the multiple biometric images. The reselection process here can include methods such as random selection or selection based on collection time. For example, if three biometric images, i.e., img1, img2, and img3, are obtained by selection according to the predetermined selection criteria and the collection times of these three biometric images are sequentially img1 > img2 > img3, img1 can be selected as the final optimized biometric recognition data. Optionally, after obtaining the biometric images by selection according to the predetermined selection criteria, further image processing (e.g., image enhancement) can be performed on the selected biometric images, and the processed biometric images can be used as the biometric recognition data of a predetermined object.

[0096] According to the above method, after performing optimization processing on the collected biological streaming data, biometric recognition data for ID recognition can be obtained, so that the biometric recognition data can be made more accurate and reliable.

[0097] S402: An ID recognition process is performed on a predetermined object based on the biometric recognition data to obtain a recognition result for the predetermined object.

[0098] Specifically, performing ID recognition processing on a predetermined object essentially recognizes the ID of the predetermined object, and generally speaking, biometric features are features that can uniquely identify a user ID. Therefore, the present application can perform ID recognition processing on a predetermined object based on the biometric recognition data of the predetermined object. Specifically, ID recognition can be performed by adopting corresponding technologies according to different types of biometric recognition data, thereby recognizing and authenticating a user ID. For example, if the biometric recognition data is palm data, palm recognition technology can be adopted to perform palm recognition processing on the palm data. For example, if the biometric recognition data is fingerprint data, fingerprint recognition technology can be adopted to perform fingerprint recognition processing on the fingerprint data. Furthermore, for example, if the biometric recognition data is face data, face recognition technology can be adopted to perform face recognition processing on the face data.

[0099] In the embodiment of the present application, for details of the operations performed in steps S401 to S402, reference can be made to the operations performed in steps S301 to S302 in the embodiment of FIG. 3, and detailed description thereof will be omitted here.

[0100] In one possible implementation, after the computing device performs ID recognition processing on a specified object based on biometric recognition data and obtains a recognition result of the specified object, the following step may be further included: if the recognition result is successful, outputting a presentation interface, the presentation interface including a presentation area and an adjustment control, the presentation area is used to present whether the specified object has authorized the grade adjustment processing for the first trust index, and the adjustment control is used to receive a confirmation operation for the grade adjustment processing of the first trust index, and according to the authorization confirmation operation triggered by the specified object in the presentation area, triggering to obtain a first trust index corresponding to the specified object in the business scenario and to perform a grade adjustment processing for the first trust index based on the recognition result. Specifically, if the recognition result is a successful recognition, the first trust index is upgraded to a second trust index according to the authorization confirmation operation triggered for the first control in the presentation area; if the recognition result is a failure, the first trust index is downgraded to a third trust index according to the authorization confirmation operation triggered for the second control in the presentation area; if the recognition result is a successful recognition or a failure, the grade of the first trust index is maintained according to the authorization confirmation operation triggered for the third control in the presentation area.

[0101] The following will be a corresponding description of the interface process of how to adjust the grade in conjunction with Fig. 5a and Fig. 5b.

[0102] 5a, which is a diagram illustrating a grade adjustment processing interface provided in an embodiment of the present application. As shown in FIG. 5a, the presentation interface S501 is provided with a presentation area 5011, which displays presentation information. The presentation information is used to present whether a certain object has authorized a grade adjustment processing for a first credit index, and the grade adjustment processing includes any one or more of a grade downgrade processing, a grade upgrade processing, and a grade maintenance processing. For example, the presentation information 5012 may be as follows: "A grade upgrade processing can be performed for the current grade. Please confirm whether to perform a grade upgrade!" The presentation interface S501 further includes an adjustment control, which is used to receive a confirmation operation for the grade adjustment processing of the first credit index. Among them, the adjustment controls include a first control 5013, a second control 5014 and a third control 5015, and the presentation interface S501 further includes a cancel control 5016, where the first control 5013 is used to receive a confirmation operation for the grade upgrade process, the second control 5014 is used to receive a confirmation operation for the grade downgrade process, the third control 5015 is used to receive a confirmation operation for the grade downgrade process, and the cancel control 5016 is used to receive a cancellation operation for the grade adjustment process.For example, when the presented information displays that a grade upgrade process is to be performed on a first credit index, the user can upgrade the grade of the first credit index to a second credit index by clicking the first control 5013. At this time, since the grade adjustment process is a grade upgrade process, the states of the second control 5014 and the third control 5015 are all set to a non-clickable state, that is, neither the second control 5014 nor the third control 5015 can respond to a user's click operation (e.g., any one operation such as a single click, a double click, or a long press). In this case, the user is allowed to click the first control 5013 and the cancel control 5016, but is not allowed to click the second control 5014 and the third control 5015. Similarly, when the presented information displays that a grade downgrade process is to be performed on a first credit index, the user is allowed to click the second control 5014 and the cancel control 5016, but is not allowed to click the first control 5013 and the third control 5015.

[0103] 5b, which is a diagram illustrating another grade adjustment interface provided in an embodiment of the present application. As shown in FIG. 5b, the presentation interface S502 includes a presentation area 5021, which displays presentation information for presenting whether a predetermined object has authorized a grade adjustment process for a first credit index. The grade adjustment process includes any one or more of a grade downgrade process, a grade upgrade process, and a grade maintenance process. For example, the presentation information 5022 may be as follows: "Please confirm whether you want to adjust the grade!" The presentation interface S502 further includes an adjustment control 5023 and a cancel control 5024. If the user clicks the cancel control 5024, it means that the user does not agree to the grade adjustment. If the user clicks the adjustment control 5023, the presentation interface S503 can be displayed. Wherein, the presentation interface S503 displays presentation information 5031, for example, the presentation information 5031 may be as follows, namely, "You can perform a grade upgrade process for your current grade, please confirm whether you want to upgrade your grade!" In this case, the presentation interface S503 displays a first control 5032 and a cancel control 5033, and when the user clicks the first control 5031, it means that the user agrees to the grade adjustment, and the grade of the first credit indicator can be upgraded to the second credit indicator.

[0104] According to the above description, after ID recognition processing for the user (predetermined object), a presentation interface can be output, allowing the user to self-define whether to adjust the grade. This method can meet special business requirements where users do not want to adjust the grade, thereby improving the user experience in business scenarios.

[0105] S403: Obtain a first trust index corresponding to a predetermined object in the business scenario.

[0106] In one possible implementation manner, the first trust index refers to the trust index assigned to the specified object after the specified object completes ID registration in the business scenario. If the business scenario is a payment scenario, before the computer device obtains the biometric recognition data collected for the specified object in the business scenario, it further includes: obtaining scenario setting information for the payment scenario in response to the ID registration request in the payment scenario of the specified object, the scenario setting information including a security verification grade for the payment scenario; if the security verification grade of the payment scenario satisfies the grade adjustment conditions, the payment scenario assigns a trust index to the specified object according to the security verification grade of the payment scenario; if the security verification grade of the payment scenario does not satisfy the grade adjustment conditions, the payment scenario determines the reference trust index as the trust index to be assigned to the specified object.

[0107] Specifically, if a payment scenario is one that includes a monitored object (i.e., the safety verification level of the payment scenario is first, for example, first is high), the safety verification level (high) of the payment scenario can be set as the first trust index (high) of the specified object, and if a payment scenario is one that does not include a monitored object (i.e., the safety verification level of the payment scenario is second), the first trust index of the user can be implicitly regarded as low (reference trust index). By adopting this method, a corresponding trust index can be assigned to a specified object according to the safety verification level of the business scenario at the time of ID registration (i.e., whether there is a monitored object), so that the trust index of the specified object can be made to better match the requirements of the scenario.

[0108] S404: Obtain scenario setting information of the business scenario, and perform a grade adjustment process on the first credit index based on the scenario setting information and the recognition result to obtain a second credit index.

[0109] In one possible implementation, the computer device performing a grade adjustment process on the first trust index based on the scenario setting information and the recognition result to obtain the second trust index may include: if the safety verification level of the business scenario satisfies the grade adjustment condition and the recognition result is successful, performing a grade upgrade process on the first trust index according to the first adjustment method to obtain the second trust index. The safety verification level is used to indicate whether the business scenario has a monitored object, and if the business scenario has a monitored object, the safety verification level of the business scenario is grade 1, and if the business scenario has no monitored object, the safety verification level of the business scenario is grade 2. The grade adjustment condition may include a condition that a monitored object exists. For example, if the safety verification level of a business scenario is grade 1, the business scenario satisfies the grade adjustment condition, and if the safety verification level of a business scenario is grade 2, the business scenario does not satisfy the grade adjustment condition. For example, if a business scenario is a scenario that is subject to monitoring (i.e., the safety verification level of the business scenario is level 1) and the recognition result is successful, the specified object can be upgraded from level 2 (first trust index) to level 3 (second trust index).

[0110] It can be understood that the data processing method provided in the embodiments of the present application can be applied to business scenarios such as payment scenarios, access control scenarios, and game scenarios, by performing ID authentication on users in these business scenarios and flexibly adjusting the safety credit grade (i.e., credit index) of the users according to the authentication results, thereby changing the risk management strategy of the users in the corresponding business scenarios, and thus meeting more business requirements under the business scenarios.

[0111] The complete process of the data processing scenario is described in detail below in conjunction with Figure 6.

[0112] Please refer to Figure 6, which is a flowchart of a data processing scenario provided in an embodiment of the present application. As shown in Figure 6, the process of the data processing scenario mainly involves palm recognition device A, palm recognition device B, palm recognition device C, and a back-end server in the data processing system shown in Figure 2. Among them, the process of the data processing scenario mainly includes, but is not limited to, steps S601 to S611 as follows. Specifically, the process of the data processing scenario mainly includes a scenario setting process, an ID registration process, and a palm recognition process. The specific steps of these three processes will be described in order below.

[0113] 1. Scenario setting process (S601~S603) S601: Palm recognition device A submits (sends) a first scenario setting request for unmanned monitoring scenario A to the backend server.

[0114] Specifically, in this embodiment, the business scenario A to which the palm recognition device A belongs is a scenario without a monitoring target (i.e., unmanned monitoring scenario A), and the business scenario A is an ID verification scenario. The palm recognition device A generates a first scenario setting request for a specific object in the business scenario A, and then sends the first scenario setting request to the backend server. The first scenario setting request includes the scenario setting information of the business scenario A. For example, the scenario setting information may include, but is not limited to, the safety verification level of the business scenario A (used to indicate whether a monitoring target exists / whether manned monitoring is performed).

[0115] Optionally, the backend server may respond to the first scenario configuration request and perform scenario configuration for business scenario B based on the scenario configuration information of business scenario A to obtain a configuration result, where the configuration result may include successful configuration or failed configuration. Then, the backend server may return the configuration result to palm-recognized device A (as shown in step S6011).

[0116] S602: The palm recognition device B sends a second scenario setting request for manned monitoring scenario B to the backend server.

[0117] Specifically, when the business scenario B to which the palm recognition device B belongs is a scenario with a monitored object (i.e., manned monitoring scenario B), the business scenario is a small payment scenario (i.e., the payment amount is smaller than the first payment amount threshold), and the palm recognition device B can generate a second scenario setting request for a specific object in the business scenario B and send the second scenario setting request to the backend server. The second scenario setting request includes scenario setting information for the business scenario B, and for example, the scenario setting information may include, but is not limited to, the safety verification level of the business scenario B (used to indicate whether a monitored object exists / whether manned monitoring is performed).

[0118] Optionally, the backend server may respond to the second scenario setting request and perform scenario setting for business scenario B based on the scenario setting information of business scenario B to obtain a setting result, where the setting result may include setting success or setting failure, and then return the setting result to palm-recognized device B (as shown in step S6021).

[0119] S603: The palm recognition device C sends a third scenario setting request for manned monitoring scenario C to the backend server.

[0120] Specifically, in this embodiment, the business scenario C to which the palm recognition device C belongs is a scenario in which there is a monitored object (i.e., manned monitoring scenario C), and the business scenario C is a high-value payment scenario (i.e., the payment amount is equal to or greater than the second payment amount threshold, and the second payment amount threshold is greater than the first payment amount threshold). The palm recognition device C generates a third scenario setting request for a specific object in the business scenario C, and sends the third scenario setting request to the backend server. The third scenario setting request includes scenario setting information for the business scenario C. For example, the scenario setting information may include, but is not limited to, the safety verification level of the business scenario C (used to indicate whether there is a monitored object / manned monitoring).

[0121] Optionally, the backend server may respond to the third scenario setting request and perform scenario setting for business scenario C based on the scenario setting information of business scenario C to obtain a setting result, where the setting result may include setting success or setting failure. Then, the backend server may return the setting result to the palm-recognized device C (as shown in step S6031).

[0122] 2. ID Registration Process and Palm Recognition Process (S604~S611) S604: The palm recognition device A receives an ID registration request for palm ID confirmation of a predetermined object.

[0123] In concrete implementation, a predetermined object can perform ID registration in business scenario A (i.e., access control scenario for palm ID verification) to which palm recognition device A belongs, and generate an ID registration request, and the ID registration request may include ID information of the predetermined object, for example, the ID information may include, but is not limited to, data for uniquely identifying a user ID, such as an ID tag (identifier), ID, etc. Since this application mainly relates to palm recognition, the ID information of the predetermined object may include palm data of the predetermined object.

[0124] S605: The palm recognition device A performs ID registration with the backend server based on the ID registration request.

[0125] In particular, after receiving an ID registration request, the backend server performs ID registration for the specified object according to the ID information of the specified object. After the ID registration is completed, the backend server assigns a trust index (e.g., a first trust index) for the specified object in business scenario A, thereby generating a registration result, which may include registration success or registration failure.

[0126] Optionally, the backend server can return the registration result to the palm-recognized device A (as shown in step S6051), and the palm-recognized device A can return the registration result to a specified object (as shown in step S6052).

[0127] S606: The palm recognition device A receives a palm ID confirmation request for a predetermined object.

[0128] In specific implementation, when a specific object needs to request the opening of a door in business scenario A (i.e., a palm ID verification access control scenario), it can submit a palm ID verification request to palm recognition device A, and the palm ID verification request is used to request ID verification (e.g., palm recognition) for the specific object.

[0129] S607: The palm recognition device A sends a palm ID confirmation request to the backend server.

[0130] In a specific implementation, the palm recognition device A responds to the palm ID confirmation request, calls the collection device in business scenario A to have it collect biometric recognition data of a specified object, and then sends the collected biometric recognition data to the backend server.

[0131] Optionally, the backend server performs ID recognition processing on the predetermined object based on the biometric recognition data to obtain the recognition result (recognition success or recognition failure) of the predetermined object, and then returns the recognition result to the palm recognition device A (as shown in step S6071). After that, the palm recognition device can return the recognition result to the predetermined object (as shown in step S6072).

[0132] S608: The palm recognition device B acquires a palm payment request (small amount) for the predetermined object.

[0133] In a specific implementation, the palm payment request is used to request the processing of a small-value asset, where the small-value asset refers to an asset whose amount is less than a first payment amount threshold.

[0134] S609: The palm-recognized device B requests the backend server to make a palm payment (small amount).

[0135] In specific implementation, the backend server responds to a small-amount Palm payment request for a specific object, and performs Palm recognition processing for the specific object. If the Palm recognition for the specific object is successful, the backend server can call a payment service to perform an asset transfer processing (payment processing) to generate a payment result, which may include successful payment or unsuccessful payment.

[0136] Optionally, the backend service can return the payment result to the palm-recognized device B (as shown in step S6091), and the palm-recognized device B can return the payment result to a specified object (as shown in step S6092).

[0137] S610: The palm recognition device C acquires a palm payment request (high value) for a predetermined object.

[0138] In specific implementation, the palm settlement request requests the processing of high-value assets, where high-value assets refer to assets whose value is equal to or greater than a second payment amount threshold, and the second payment amount threshold is greater than the first payment amount threshold.

[0139] S611: The palm recognition device C requests the backend server to perform a palm payment (high value).

[0140] In specific implementation, the backend server responds to a high-value palm payment request for a specific object, and performs palm recognition processing on the specific object. If the palm recognition performed on the specific object is successful, the backend server can call a payment service to perform asset transfer processing (payment processing) to generate a payment result, which may include payment success or payment failure.

[0141] Optionally, the backend service can return the payment result to the palm-recognized device C (as shown in step S6111), and the palm-recognized device C can return the payment result to a predetermined object (as shown in step S6112).

[0142] Below, we will take a payment scenario as an example to explain the palm recognition process in detail.

[0143] In one possible implementation, the business scenario is a payment scenario, and the risk management strategy in the payment scenario is used to indicate that one credit indicator corresponds to one payment amount. After the computer device performs a grade adjustment process from the first credit indicator for the specified object based on the recognition result to obtain a second credit indicator, the following steps are further included: detect the asset amount requested to be processed in the payment scenario; if the payment amount corresponding to the second credit indicator is greater than or equal to the asset amount requested to be processed, call a payment service to perform a transfer process for the asset corresponding to the asset amount; if the payment amount corresponding to the second credit indicator is less than the asset amount requested to be processed, output presentation information, and the presentation information is used to trigger a grade adjustment process for the second credit indicator for the specified object, so that the payment amount corresponding to the second credit indicator after the grade adjustment process is greater than or equal to the asset amount requested to be processed.

[0144] 7a, which is a flowchart of a Palm payment scenario provided in an embodiment of the present application. As shown in FIG. 7a, a predetermined object can perform Palm recognition in a payment scenario, call a Palm recognition device to collect Palm data of the predetermined object, and then display the Palm data in the Palm recognition interface, and perform recognition processing on the Palm data. If the recognition is successful and the payment amount corresponding to the second credit indicator of the predetermined object is equal to or greater than the asset amount requested to be processed in the current payment scenario, a payment processing interface is displayed, and a payment service is called to perform payment processing for the asset corresponding to the asset amount. 7b, which is a flowchart of another Palm payment scenario provided in an embodiment of the present application. As shown in Figure 7b, a predetermined object can perform palm recognition in a payment scenario, and if the payment amount corresponding to the second credit indicator of the predetermined object is smaller than the asset amount requested for processing in the current payment scenario, the display device outputs prompting information, such as "Your safety credit rating does not meet the current payment conditions, please try again later!", so that the second credit indicator can be upgraded in another subsequent authentication-enabled business scenario before payment can be made again. This method allows for flexible adjustment of risk management strategies in accordance with the safety credit rating indicated by the user's credit indicator in the payment scenario, thereby ensuring the reliability and safety of related business processes.

[0145] In the embodiment of the present application, different palm recognition devices need to be configured for different business scenarios, so that users can conveniently complete ID registration in the corresponding pre-configured business scenarios (e.g., palm ID verification scenario, payment scenario, etc.). After that, users can perform related business processing operations such as door opening and payment in the business scenarios. The present application relies on users to register scenarios themselves, and, combined with a constantly adjusted and sophisticated risk management strategy, performs step-by-step verification and credit upgrade of the user's palm, ensuring security while gradually improving the user's experience in various business scenarios.

[0146] The method in the embodiment of the present application has been described in detail above, and in order to better implement the above-mentioned scheme in the embodiment of the present application, the corresponding apparatus in the embodiment of the present application is provided below. Subsequently, the corresponding description of the related apparatus in the embodiment of the present application will be given in conjunction with the data processing scheme provided in the embodiment of the present application.

[0147] Please refer to FIG. 8, which is a block diagram of a data processing device provided in the embodiments of the present application. As shown in FIG. 8, the data processing device 800 can be applied to the computer equipment (e.g., terminal equipment or server) mentioned in the above embodiments. Specifically, the data processing device 800 can be a computer program (including program code) executed on a computer equipment, for example, the data processing device 800 can be an application software, and the data processing device 800 can be used to perform corresponding steps in the data processing method provided in the embodiments of the present application. In specific implementation, the data processing device 800 can specifically include the following: An acquisition unit 801: used to acquire biometric recognition data collected about a predetermined object in a business scenario; and Processing unit 802: Used to perform ID recognition processing on a predetermined object based on biometric recognition data and obtain a recognition result of the predetermined object.

[0148] The processing unit 802 is further used for obtaining a first trustworthiness index corresponding to a predetermined object in the business scenario, and performing a grade adjustment process on the first trustworthiness index according to the recognition result to obtain a second trustworthiness index; Among them, the second credit indicator is different from the first credit indicator, and in a business scenario, the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator.

[0149] In one possible implementation, the processing unit 802 performs a grade adjustment process on the first trust index according to the recognition result to obtain a second trust index, which includes the following operations: Obtain scenario setting information of the business scenario, the scenario setting information including a safety verification grade of the business scenario; and If the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is successful, a grade upgrade process is performed on the first trust index according to the first adjustment method to obtain a second trust index.

[0150] In one possible implementation, the recognition result includes successful recognition or failed recognition, and the processing unit 802 is further used to perform any one of the following operations: If the safety verification grade of the business scenario does not meet the grade adjustment conditions or the recognition result is a recognition failure, perform a grade downgrade process on the first trust indicator according to the second adjustment method to obtain a third trust indicator; When the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is a recognition failure, perform a grade maintenance process on the first trust index; and If the safety verification grade of the business scenario does not satisfy the grade adjustment condition and the recognition result is successful, a grade maintenance process is performed on the first trust index.

[0151] In one possible implementation, the processing unit 802 performs a grade adjustment process on the first trust index according to the recognition result to obtain a second trust index, which includes the following operations: If the safety verification grade of the business scenario does not satisfy the grade adjustment condition, obtain past business data of the specified object, and use the past business data to train a grade recognition model; Invoke the trained grade recognition model to perform grade recognition processing on the given object to obtain the safety trust grade of the given object; and According to the safety trust grade of the predetermined object and the recognition result, a grade adjustment process is performed on the first trust index to obtain a second trust index.

[0152] In one possible implementation, the processing unit 802 is further configured to perform the following operations after performing ID recognition processing on the predetermined object based on the biometric recognition data to obtain the recognition result of the predetermined object: If the recognition result is successful, output a presentation interface, the presentation interface including a presentation area and an adjustment control, the presentation area is used to present whether the predetermined object has authorized the grade adjustment process for the first trust index, and the adjustment control is used to receive a confirmation operation for the grade adjustment process for the first trust index; and According to the authorization confirmation operation triggered in the presentation area, a first trust index corresponding to a specified object in the business scenario is obtained, and a grade adjustment process is performed on the first trust index based on the recognition result, thereby triggering the execution of a step of obtaining a second trust index.

[0153] In one possible implementation, the grade adjustment process includes any one or more of a grade downgrade process, a grade upgrade process, and a grade maintenance process, and the adjustment control includes a first control, a second control, and a third control, of which the first control is used to receive a confirmation operation for the grade upgrade process, the second control is used to receive a confirmation operation for the grade downgrade process, and the third control is used to receive a confirmation operation for the grade maintenance process.

[0154] The processing unit 802 may further be used to perform any one of the following operations: When the recognition result is successful, upgrade the first trust indicator to a second trust indicator according to an authorization confirmation operation triggered for the first control in the presentation area; When the recognition result is a recognition failure, downgrade the first trust indicator to a third trust indicator according to an authorization confirmation operation triggered for the second control in the presentation area; and When the recognition result is success or failure, a grade maintenance process is performed on the first trust index according to an authorization confirmation operation triggered on the third control in the presentation area.

[0155] In one possible implementation, the processing unit 802 performs ID recognition processing on the predetermined object based on the biometric recognition data to obtain the recognition result of the predetermined object, which includes the following operations: Performing feature extraction processing on the biometric recognition data to obtain biometric features of a predetermined object; Obtaining registration data of a predetermined object, the registration data being generated after the predetermined object has successfully registered its ID in a business scenario, the registration data including registration characteristics; and An ID recognition process is performed on the predetermined object based on the biometric features of the predetermined object and the registered features of the predetermined object, and a recognition result of the predetermined object is obtained.

[0156] In one possible implementation, the biometric recognition data includes palm data, and the processing unit 802 performs feature extraction processing on the palm recognition data to obtain the biometric features of a predetermined object, which includes the following operations: Performing palm vein feature extraction processing on the palm data to obtain palm vein features of a given object; Performing palm print feature extraction processing on the palm data to obtain palm print features of a predetermined object; and performing a feature fusion process on the palm print feature and the palm vein feature to obtain a biometric feature of a predetermined object; Among them, the feature fusion process includes at least one of the following processes: feature weighting process, feature alignment process, and feature calculation process.

[0157] In one possible implementation, the acquisition unit 801 acquires the biometric data collected for a predetermined object in a business scenario by performing the following operations: Invoking the acquisition device to acquire biological streaming data of the predetermined object, the biological streaming data including a plurality of biological images acquired from the acquisition of the predetermined object; and performing a selection process on a plurality of biometric images in accordance with predetermined selection conditions to obtain biometric recognition data of a predetermined object; The predetermined selection conditions include any one or more of image size, shooting angle, image contrast, image brightness, and clarity.

[0158] In one possible implementation, the business scenario is a payment scenario, and in the payment scenario, the risk management strategy is used to indicate that one credit indicator corresponds to one payment amount.

[0159] The processing unit 802 is further used to perform the following operations after performing a grade adjustment process from the first trust index to obtain a second trust index for the predetermined object according to the recognition result: Detect the asset amount requested for processing in payment scenarios; If the payment amount corresponding to the second credit indicator is equal to or greater than the asset amount requested for processing, calling the payment service to transfer the asset corresponding to the asset amount; and When the payment amount corresponding to the second credit indicator is smaller than the asset amount requested for processing, presentation information is output, and the presentation information is used to trigger a grade adjustment process of the second credit indicator for the specified object so that the payment amount corresponding to the second credit indicator after the grade adjustment process is equal to or greater than the asset amount requested for processing.

[0160] In one possible implementation manner, the first trust indicator refers to the trust indicator assigned to the predetermined object after the predetermined object completes ID registration in a business scenario, where the business scenario is a payment scenario, and before the processing unit 802 obtains the biometric recognition data collected for the predetermined object in the business scenario, the following is further included: In response to an ID registration request for a payment scenario of a predetermined object, obtain scenario setting information of the payment scenario, where the scenario setting information includes a security verification grade of the payment scenario; When the safety verification grade of the payment scenario satisfies the grade adjustment condition, the payment scenario assigns a trust index to the given object according to the safety verification grade of the payment scenario; and If the safety verification grade of the payment scenario does not satisfy the grade adjustment condition, the payment scenario determines the reference trust index as the trust index to be assigned to the predetermined object.

[0161] In the embodiments of the present application, for the specific implementation of the operations performed by each unit of the data processing device and the corresponding effects that can be achieved, reference can be made to the relevant descriptions of the above-mentioned embodiments, and detailed descriptions thereof will be omitted here.

[0162] Please refer to Figure 9, which is a block diagram of a computer device provided in an embodiment of the present application. The computer device 900 is used to perform the steps performed by the terminal device or server in the above-mentioned method embodiment, and includes one or more processors 901, one or more input devices 902, one or more output devices 903, and a memory 904. The processor 901, input device 902, output device 903, and memory 904 are connected via a bus 905. Specifically, the memory 904 is used to store a computer program, which includes program instructions, and the processor 901 is used to call the program instructions stored in the memory 904 to perform the following operations: Acquire biometric data collected about a given object in a business scenario; performing ID recognition processing on a predetermined object based on biometric recognition data to obtain a recognition result for the predetermined object; and Obtain a first trust index corresponding to a predetermined object in the business scenario, and perform a grade adjustment process on the first trust index based on the recognition result to obtain a second trust index; Among them, the second credit indicator is different from the first credit indicator, and in a business scenario, the risk management strategy associated with the second credit indicator is different from the risk management strategy associated with the first credit indicator.

[0163] In one possible implementation, the processor 901 performs a grade adjustment process on the first trustworthiness index according to the recognition result to obtain the second trustworthiness index, which can be performed as follows: Obtain scenario setting information of the business scenario, the scenario setting information including a safety verification grade of the business scenario; and If the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is successful, a grade upgrade process is performed on the first trust index according to the first adjustment method to obtain a second trust index.

[0164] In one possible implementation, the recognition result includes recognition success or recognition failure, and the processor 901 is further used to perform any one of the following operations: If the safety verification grade of the business scenario does not meet the grade adjustment conditions or the recognition result is a recognition failure, perform a grade downgrade process on the first trust indicator according to the second adjustment method to obtain a third trust indicator; When the safety verification grade of the business scenario satisfies the grade adjustment condition and the recognition result is a recognition failure, perform a grade maintenance process on the first trust index; and If the safety verification grade of the business scenario does not satisfy the grade adjustment condition and the recognition result is successful, a grade maintenance process is performed on the first trust index.

[0165] In one possible implementation, the processor 901 performs a grade adjustment process on the first trustworthiness index according to the recognition result to obtain the second trustworthiness index, which can be performed as follows: If the safety verification grade of the business scenario does not satisfy the grade adjustment condition, obtain the past business data of the specified object, and use the past business data to train the grade recognition model; Invoke the trained grade recognition model to perform grade recognition processing on the given object to obtain the safety trust grade of the given object; and According to the safety trust grade of the predetermined object and the recognition result, a grade adjustment process is performed on the first trust index to obtain a second trust index.

[0166] In one possible implementation, the processor 901 is further used to perform the following operations after performing ID recognition processing on the predetermined object based on the biometric recognition data to obtain the recognition result of the predetermined object: If the recognition result is successful, output a presentation interface, the presentation interface including a presentation area and an adjustment control, the presentation area is used to present whether the predetermined object authorizes a grade adjustment process for the first trust index, and the adjustment control is used to receive a confirmation operation for the grade adjustment process for the first trust index; and According to the authorization confirmation operation triggered in the presentation area, a first trust index corresponding to a specified object in the business scenario is obtained, and a grade adjustment process is performed on the first trust index based on the recognition result, thereby triggering the execution of a step of obtaining a second trust index.

[0167] In one possible implementation, the grade adjustment process includes any one or more of a grade downgrade process, a grade upgrade process, and a grade maintenance process, and the adjustment control includes a first control, a second control, and a third control, of which the first control is used to receive a confirmation operation for the grade upgrade process, the second control is used to receive a confirmation operation for the grade downgrade process, and the third control is used to receive a confirmation operation for the grade maintenance process.

[0168] The processor 901 is further adapted to receive any one of the following operations: When the recognition result is successful, perform a grade adjustment on the first trust index to adjust it to the second trust index according to the authorization confirmation operation triggered on the first control in the presentation area; When the recognition result is a recognition failure, downgrade the first trust index to a third trust index according to an authorization confirmation operation triggered for the second control in the presentation area; and When the recognition result is success or failure, a grade maintenance process is performed on the first trust index according to an authorization confirmation operation triggered on the third control in the presentation area.

[0169] In one possible implementation, the processor 901 performs ID recognition processing on a predetermined object based on biometric recognition data to obtain a recognition result of the predetermined object, and performs the following operations: Performing feature extraction processing on the biometric recognition data to obtain biometric features of a predetermined object; Obtaining registration data of a predetermined object, the registration data being generated after the predetermined object has successfully registered its ID in a business scenario, the registration data including registration characteristics; and An ID recognition process is performed on the predetermined object based on the biometric features of the predetermined object and the registered features of the predetermined object, and a recognition result of the predetermined object is obtained.

[0170] In one possible implementation, the biometric data includes palm data, and the processor 901 performs feature extraction processing on the biometric data to obtain the biometric features of a predetermined object, which includes the following operations: performing a palm vein feature extraction process on the palm data to obtain palm vein features of a predetermined object; and Performing palm print feature extraction processing on the palm data to obtain palm print features of a predetermined object; and performing a feature fusion process on the palm print feature and the palm vein feature to obtain a biometric feature of a predetermined object; Among them, the feature fusion process includes at least one of the following processes: feature weighting process, feature alignment process, and feature calculation process.

[0171] In one possible implementation, the processor 901 acquires the biometric data collected for a predetermined object in a business scenario by performing the following operations: Invoking the acquisition device to acquire biological streaming data of the predetermined object, the biological streaming data including a plurality of biological images acquired from the acquisition of the predetermined object; and performing a selection process on a plurality of biometric images in accordance with predetermined selection conditions to obtain biometric recognition data of a predetermined object; The predetermined selection conditions include any one or more of image size, shooting angle, image contrast, image brightness, and clarity.

[0172] In one possible implementation, the business scenario is a payment scenario, and in the payment scenario, the risk management strategy is used to indicate that one credit indicator corresponds to one payment amount.

[0173] The processor 901 is used to perform a grade adjustment process from the first trustworthiness index to obtain a second trustworthiness index for a predetermined object based on the recognition result, and then further performs the following operations: Detect the asset amount requested for processing in payment scenarios; If the payment amount corresponding to the second credit indicator is equal to or greater than the asset amount requested for processing, calling the payment service to transfer the asset corresponding to the asset amount; and When the payment amount corresponding to the second credit indicator is smaller than the asset amount requested for processing, presentation information is output, and the presentation information is used to trigger a grade adjustment process of the second credit indicator for the specified object so that the payment amount corresponding to the second credit indicator after the grade adjustment process is equal to or greater than the asset amount requested for processing.

[0174] In one possible implementation manner, the first trust indicator refers to a trust indicator assigned to a predetermined object after the predetermined object completes ID registration in a business scenario, the business scenario is a payment scenario, and before the processor 901 acquires the biometric recognition data collected for the predetermined object in the business scenario, the following is further included: In response to an ID registration request for a payment scenario of a predetermined object, obtain scenario setting information of the payment scenario, where the scenario setting information includes a security verification grade of the payment scenario; When the safety verification grade of the payment scenario satisfies the grade adjustment condition, the payment scenario assigns a trust index to the given object according to the safety verification grade of the payment scenario; and If the safety verification grade of the payment scenario does not satisfy the grade adjustment condition, the payment scenario determines the reference trust index as the trust index assigned to the predetermined object.

[0175] In the embodiments of the present application, for the specific implementation of each operation performed by the processor of the computer device and the corresponding effects that can be achieved, reference can be made to the relevant descriptions of the above-mentioned embodiments, and detailed descriptions thereof will be omitted here.

[0176] In addition, according to the embodiments of the present application, a computer storage medium is further provided, in which a computer program is stored, the computer program including program instructions, and when a processor executes the program instructions, the method in the corresponding embodiment described above can be performed, and therefore detailed descriptions thereof will be omitted here. Furthermore, for technical details of the computer storage medium according to the present application that are not described in the embodiments, reference can be made to the description of the method embodiments of the present application. For example, the program instructions can be located in one computer device, or can be executed by multiple computer devices located in one location, or can be executed by multiple computer devices distributed in multiple locations and connected to each other via a communication network.

[0177] According to another aspect of the present application, the embodiments of the present application further provide a computer program product or a computer program, the computer program product or the computer program includes computer instructions stored in a computer-readable storage medium, and a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the method in the corresponding embodiment described above, and therefore detailed description thereof will be omitted here.

[0178] Although the preferred embodiment of the present application has been described above, the present application is not limited to this embodiment, and any modification to the present application falls within the technical scope of the present application as long as it does not depart from the spirit of the present application.

Claims

1. 1. A computer-implemented method for processing data, comprising: acquiring biometric recognition data collected for a predetermined object in a business scenario; performing ID recognition processing on the predetermined object based on the biometric recognition data to obtain a recognition result of the predetermined object; and obtaining a first trustworthiness index corresponding to the predetermined object in the business scenario; and performing a grade adjustment process on the first trustworthiness index based on the recognition result to obtain a second trustworthiness index; The method, wherein the second credit metric and the first credit metric are different, and in the business scenario, a risk management strategy associated with the second credit metric is different from a risk management strategy associated with the first credit metric.

2. 2. The method of claim 1, The step of performing a grade adjustment process on the first trustworthiness index based on the recognition result to obtain a second trustworthiness index includes: acquiring scenario setting information of the business scenario, the scenario setting information including a safety verification grade of the business scenario; and The method includes a step of performing a grade upgrade process on the first trust index according to a first adjustment method to obtain a second trust index when the safety verification grade of the business scenario satisfies a grade adjustment condition and the recognition result is successful recognition.

3. 3. The method of claim 2, The recognition result includes a recognition success or a recognition failure, and the method further includes: When the safety verification grade of the business scenario does not satisfy the grade adjustment condition or the recognition result is a recognition failure, performing a grade downgrade process on the first trust index according to a second adjustment method to obtain a third trust index; When the safety verification grade of the business scenario satisfies a grade adjustment condition and the recognition result is a recognition failure, performing a grade maintenance process on the first trust index; and performing a grade maintenance process on the first trust index when the safety verification grade of the business scenario does not satisfy a grade adjustment condition and the recognition result is a recognition success; and The method, wherein the first adjustment method is different from the second adjustment method.

4. 2. The method of claim 1, The step of performing a grade adjustment process on the first trustworthiness index based on the recognition result to obtain a second trustworthiness index includes: If the safety verification grade of the business scenario does not satisfy the grade adjustment condition, obtaining past business data of the predetermined object, and adopting the past business data to train a grade recognition model; Calling the trained rating recognition model to perform rating recognition processing on the predetermined object to obtain a safety trust rating of the predetermined object; and The method includes performing a grade adjustment process on the first trust index according to the safety trust grade of the predetermined object and the recognition result to obtain a second trust index.

5. 2. The method of claim 1, After the step of performing ID recognition processing on the predetermined object based on the biometric recognition data and obtaining a recognition result of the predetermined object, the method further includes: outputting a presentation interface when the recognition result is successful, the presentation interface including a presentation area and an adjustment control, the presentation area being used to present whether the predetermined object has authorized a grade adjustment process for the first trust index, and the adjustment control being used to receive a confirmation operation for the grade adjustment process for the first trust index; and The method includes a step of triggering the execution of a step of obtaining a first trust indicator corresponding to the specified object in the business scenario and performing a grade adjustment process on the first trust indicator based on the recognition result to obtain a second trust indicator in response to an authorization confirmation operation triggered in the presentation area.

6. 6. The method of claim 5, The grade adjustment process includes any one or more of a grade downgrade process, a grade upgrade process, and a grade maintenance process; the adjustment controls include a first control, a second control, and a third control; the first control is used to receive a confirmation operation for a grade upgrade process, the second control is used to receive a confirmation operation for a grade downgrade process, and the third control is used to receive a confirmation operation for a grade maintenance process; The method further comprises: upgrading the first trust index to a second trust index according to an authorization confirmation operation triggered for the first control in the presentation area when the recognition result is successful recognition; downgrading the first trust index to a third trust index according to an authorization confirmation operation triggered on the second control in the presentation area when the recognition result is a recognition failure; and performing a grade maintenance process on the first credit index according to an authorization confirmation operation triggered on the third control in the presentation area when the recognition result is a recognition success or a recognition failure; A method comprising any one of

7. 2. The method of claim 1, The step of performing ID recognition processing on the predetermined object based on the biometric recognition data and obtaining a recognition result of the predetermined object includes: performing a feature extraction process on the biometric recognition data to obtain biometric features of the predetermined object; acquiring registration data of the predetermined object, the registration data being generated after the predetermined object has successfully registered its ID in the business scenario, and the registration data including registration characteristics; and The method includes performing an ID recognition process on the predetermined object based on a biometric feature of the predetermined object and an enrollment feature of the predetermined object to obtain a recognition result of the predetermined object.

8. 2. The method of claim 1, the biometric data includes palm data; The step of performing a feature extraction process on the biometric recognition data to acquire biometric features of the predetermined object includes: performing a palm vein feature extraction process on the palm data to obtain palm vein features of the predetermined object; performing a palm print feature extraction process on the palm data to obtain palm print features of the predetermined object; and performing a feature fusion process on the palm print feature and the palm vein feature to obtain a biometric feature of the predetermined object; The method, wherein the feature fusion process includes at least one of a feature weighting process, a feature alignment process, and a feature computation process.

9. 2. The method of claim 1, The step of acquiring biometric recognition data collected about a predetermined object in the business scenario includes: calling a collection device to collect biological streaming data of the predetermined object, the biological streaming data including a plurality of biological images collected about the predetermined object; and a step of performing a selection process on the plurality of biometric images in accordance with a predetermined selection condition to obtain biometric recognition data of the predetermined object; The method, wherein the predetermined selection conditions include any one or more of image size, shooting angle, image contrast, image brightness, and sharpness.

10. 2. The method of claim 1, The business scenario is a payment scenario, and the risk management strategy in the payment scenario is used to indicate that one credit indicator corresponds to one payment amount; After the step of performing a grade adjustment process on the first trustworthiness index based on the recognition result to obtain a second trustworthiness index, the method further comprises: detecting an asset amount requested for processing in said payment scenario; If the payment amount corresponding to the second credit indicator is equal to or greater than the asset amount for which the transaction is requested, calling a payment service to perform a transfer transaction for assets corresponding to the asset amount; and A method comprising a step of outputting presentation information when the payment amount corresponding to the second credit indicator is smaller than the asset amount for which the processing is requested, wherein the presentation information is used to trigger a grade adjustment process on the second credit indicator of the specified object so that the payment amount corresponding to the second credit indicator after the grade adjustment process becomes equal to or greater than the asset amount for which the processing is requested.

11. 2. The method of claim 1, The first trust index refers to a trust index assigned to the specified object after the specified object completes ID registration in the business scenario; The business scenario is a payment scenario, Before the step of acquiring biometric recognition data collected about a predetermined object in the business scenario, the method further includes: acquiring scenario setting information of the payment scenario in response to an ID registration request of the predetermined object in the payment scenario, the scenario setting information including a security verification level of the payment scenario; When the safety verification grade of the payment scenario satisfies a grade adjustment condition, assigning a trust index to the predetermined object in the payment scenario according to the safety verification grade of the payment scenario; and The method includes a step of determining a reference trust index in the payment scenario as a trust index to be assigned to the predetermined object if the safety verification grade of the payment scenario does not satisfy a grade adjustment condition.

12. An apparatus for processing data, an acquisition unit for acquiring biometric data collected about a predetermined object in a business scenario; and a processing unit for performing ID recognition processing on the predetermined object based on the biometric recognition data and obtaining a recognition result of the predetermined object; The processing unit is further used for obtaining a first trust index corresponding to the predetermined object in the business scenario, and performing a grade adjustment process on the first trust index based on the recognition result to obtain a second trust index; The second credit metric and the first credit metric are different, and in the business scenario, a risk management strategy associated with the second credit metric is different from a risk management strategy associated with the first credit metric.

13. A computer device comprising: a processor; and a memory coupled to the processor; The storage device stores a computer program, A computing device, wherein the processor is configured to execute a computer program to implement the method of any one of claims 1 to 11.

14. A program for causing a computer to execute the method according to any one of claims 1 to 11.

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