Data processing method, device and storage medium

Abstract

The embodiment of the present application provides a data processing method, device and storage medium. In the embodiment of the present application, the client device obtains human-machine identification data, and provides the human-machine identification data and the service identification code to the server device, so that the server device can verify the validity of the human-machine identification data based on the service identification code, thereby obtaining the human-machine identification result, realizing the integration between human-machine identification and service, so that human-machine identification can be achieved and the adverse effects of human-machine identification on service can be reduced.

Description

[0001] This case is a divisional application of the patent application with application number 2018101230564, application date February 7, 2018, and patent name "Risk Control, Human-Machine Identification and Data Processing Methods, Equipment and Systems". Technical Field

[0002] The present application relates to the field of Internet technology, and in particular to a risk control, human-machine identification and data processing method, device and system. Background Art

[0003] In some business scenarios, cheating behaviors using machine scripts or programs often occur. For example, in e-commerce promotion activities, behaviors such as using machine scripts or programs to swipe red envelopes and grab coupons often occur. Therefore, it is necessary to distinguish between normal human operations and machine script or program operations in a certain business scenario, that is, human-machine identification.

[0004] In the existing human-machine identification solution, the verification server usually generates a verification code and provides it to the user, requiring the user to enter the verification code in a way that only humans can do. If the user enters the verification code correctly, the current operation is determined to be a normal human operation, and the corresponding business request will be released; otherwise, the current operation is determined to be a cheating behavior performed by a machine script or program operation, and the corresponding business request will be blocked.

[0005] Existing human-machine identification solutions can eliminate cheating behaviors using machine scripts or programs to a certain extent, but sometimes misjudgments may occur, which will have an adverse impact on the business. Summary of the invention

[0006] Multiple aspects of the present application provide a risk control, human-machine identification and data processing method, device and system to minimize the adverse effects of human-machine identification on the business while realizing human-machine identification.

[0007] The present application provides a risk control method applicable to a client device, the method comprising:

[0008] Collect human-machine identification parameters when the operation object initiates a service request;

[0009] In response to a business request initiated by the operation object, the business request and the human-machine identification parameters are sent to a server-side device, so that the server-side device determines a processing method for the business request based on a business risk control policy corresponding to the business request and a human-machine identification result for the operation object obtained by the human-machine identification device based on the human-machine identification parameters.

[0010] The present application also provides a risk control method, which is applicable to a server device, and the method includes:

[0011] Receiving a service request and a human-machine identification parameter sent by a client device, wherein the human-machine identification parameter is collected by the client device during the process of the operation object initiating the service request;

[0012] Forwarding the human-machine identification parameter to a human-machine identification device, and receiving a human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameter; and

[0013] Determine how to handle the business request based on the human-machine recognition result and the business risk control strategy corresponding to the business request.

[0014] The present application also provides a human-machine identification method, which is applicable to a human-machine identification device, and the method includes:

[0015] Receiving a human-machine identification parameter sent by a server device, wherein the human-machine identification parameter is collected by a client device and provided to the server device during a process in which an operation object initiates a service request;

[0016] Performing human-machine identification on the operation object according to the human-machine identification parameter to obtain a human-machine identification result;

[0017] The human-machine recognition result is sent to the server device, so that the server device can determine the processing method of the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0018] The embodiment of the present application also provides a client device, including: a memory, a processor, and a communication component;

[0019] The memory is used to store computer programs;

[0020] The processor, coupled to the memory and the communication component, is configured to execute the computer program to:

[0021] Collect human-machine identification parameters when the operation object initiates a service request;

[0022] In response to a service request initiated by the operation object, the service request and the human-machine identification parameter are sent to the server device through the communication component, so that the server device determines a processing method for the service request according to the service risk control policy corresponding to the service request and the human-machine identification result for the operation object obtained by the human-machine identification device according to the human-machine identification parameter;

[0023] The communication component is used to send the service request and the human-machine identification parameter to the server device.

[0024] The embodiment of the present application also provides a server device, including: a memory, a processor, and a communication component;

[0025] The memory is used to store computer programs;

[0026] The processor, coupled to the memory and the communication component, is configured to execute the computer program to:

[0027] Receiving, through the communication component, a service request and a human-machine identification parameter sent by a client device, wherein the human-machine identification parameter is collected by the client device during the process of the operation object initiating the service request;

[0028] Forwarding the human-machine identification parameter to the human-machine identification device through the communication component, and receiving the human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameter; and

[0029] Determine how to handle the business request based on the human-machine recognition result and the business risk control strategy corresponding to the business request.

[0030] The embodiment of the present application also provides a human-machine identification device, including: a memory, a processor, and a communication component;

[0031] The memory is used to store computer programs;

[0032] The processor, coupled to the memory and the communication component, is configured to execute the computer program to:

[0033] Receiving, through the communication component, a human-machine identification parameter sent by a server device, wherein the human-machine identification parameter is collected by the client device and provided to the server device during the process of the operation object initiating a service request;

[0034] Performing human-machine identification on the operation object according to the human-machine identification parameter to obtain a human-machine identification result;

[0035] The human-machine recognition result is sent to the server device through the communication component, so that the server device can determine the processing method of the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0036] The embodiment of the present application also provides a risk control system, including: a client device, a server device and a human-machine identification device;

[0037] The client device is used to collect human-machine identification parameters during the process of the operation object initiating a service request, and in response to the service request initiated by the operation object, send the service request and the human-machine identification parameters to the server device;

[0038] The server device is used to receive the service request and the human-machine identification parameter sent by the client device, forward the human-machine identification parameter to the human-machine identification device and receive the human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameter, and determine the processing method for the service request in combination with the human-machine identification result and the service risk control policy corresponding to the service request;

[0039] The human-machine identification device is used to receive the human-machine identification parameters sent by the server device, perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result and send the human-machine identification result to the server device.

[0040] The embodiment of the present application also provides another risk control system, including: a client device, a server device and a human-machine identification device;

[0041] The client device is used to collect human-machine identification parameters during the process of the operation object initiating a service request, and in response to the service request initiated by the operation object, send the service request and the human-machine identification parameters to the server device and the human-machine identification device respectively;

[0042] The human-machine identification device is used to receive the human-machine identification parameter sent by the client device, perform human-machine identification on the operation object according to the human-machine identification parameter to obtain the human-machine identification result and send the human-machine identification result to the server device;

[0043] The server device is used to receive the service request sent by the client device, receive the human-machine recognition result sent by the human-machine recognition device, and determine the processing method for the service request based on the human-machine recognition result and the service risk control policy corresponding to the service request.

[0044] The present application also provides a data processing method, including:

[0045] Receiving client data, wherein the client data includes human-machine identification data and a service identification code;

[0046] According to the service identification code, select a human-machine identification rule or adjust a human-machine identification threshold;

[0047] Based on the human-machine identification rule or the human-machine identification threshold, determining whether the human-machine identification data is valid;

[0048] If valid, the feedback is notified through human-machine recognition; if invalid, the feedback is not notified through human-machine recognition.

[0049] The present application also provides another data processing method, including:

[0050] Obtain human and machine identification data;

[0051] The client data is sent to the server device, wherein the client data includes the human-machine identification data and a service identification code, wherein the service identification code is used to indicate a human-machine identification rule or a human-machine identification threshold.

[0052] In the embodiment of the present application, the server device cooperates with the human-machine identification device, and the human-machine identification device provides the human-machine identification result to the server device, and the server device determines the processing method of the corresponding business request based on the human-machine identification result of the human-machine identification device and the business risk control strategy corresponding to the business request; or, the server device verifies the validity of the human-machine identification data based on the business identification code, and feeds back the human-machine identification result based on the validity of the human-machine identification data. It can be seen that the embodiment of the present application combines human-machine identification with business, so it can not only achieve human-machine identification, but also reduce the adverse effects of human-machine identification on business. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0054] Figure 1 A schematic diagram of the structure of a wind control system provided by an exemplary embodiment of the present application;

[0055] Figure 2 A flow chart of a risk control method provided for another exemplary embodiment of the present application;

[0056] Figure 3 A flowchart of another risk control method provided for yet another exemplary embodiment of the present application;

[0057] Figure 4 A flowchart of a human-machine identification method provided for another exemplary embodiment of the present application;

[0058] Figure 5 A flowchart of another risk control method provided for another exemplary embodiment of the present application;

[0059] Figure 6 A schematic diagram of the structure of another wind control system provided as another exemplary embodiment of the present application;

[0060] Figure 7a Another exemplary embodiment of the present application is based on Figure 6 A flow chart of a risk control method implemented by the risk control system shown;

[0061] Figure 7b A schematic diagram of the structure of a business system provided for another exemplary embodiment of the present application;

[0062] Figure 7c A flowchart of a data processing method provided for another exemplary embodiment of the present application;

[0063] Figure 7d A flowchart of another data processing method provided for yet another exemplary embodiment of the present application;

[0064] Figure 8a A schematic structural diagram of a wind control device provided as another exemplary embodiment of the present application;

[0065] Figure 8b A schematic diagram of the structure of a client device provided for another exemplary embodiment of the present application;

[0066] Figure 9a A schematic structural diagram of another wind control device provided as another exemplary embodiment of the present application;

[0067] Figure 9b A schematic diagram of the structure of a server device provided for another exemplary embodiment of the present application;

[0068] Fig.10a A schematic diagram of the structure of a human-machine identification device provided for another exemplary embodiment of the present application;

[0069] Fig.10b A schematic diagram of the structure of a human-machine identification device provided for another exemplary embodiment of the present application;

[0070] Fig.11a A schematic diagram of the structure of another client device provided for yet another exemplary embodiment of the present application;

[0071] Fig.11b A schematic diagram of the structure of another server device provided as yet another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0073] In view of the technical problem that the existing human-machine identification scheme may have an adverse impact on the business, in some embodiments of the present application: human-machine identification is combined with the business, and the service-side device providing the business combines the human-machine identification result with the business risk control strategy to determine the processing method for the corresponding business request, or verifies the validity of the human-machine identification data based on the business identification code, and feeds back the human-machine identification result based on the validity of the human-machine identification data. Since human-machine identification and business are combined, human-machine identification can be achieved, and the adverse impact of human-machine identification on the business can be reduced.

[0074] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0075] Figure 1 The following is a schematic diagram of a wind control system provided by an exemplary embodiment of the present application. Figure 1 As shown, the risk control system includes: a client device 10, a server device 20 and a human-machine identification device 30. The client device 10 is connected to the server device 20 for communication; the server device 20 is connected to the human-machine identification device 30 for communication.

[0076] Among them, the communication connection between the client device 10 and the server device 20 and between the server device 20 and the human-machine identification device 30 can be wireless or wired. In this embodiment, if the client device 10 is connected to the server device 20 through a mobile network, or the server device 20 is connected to the human-machine identification device 30 through a mobile network, the network standard of the mobile network can be any one of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G+ (LTE+), WiMax and new network standards that will appear in the future.

[0077] In this embodiment, the client device 10 mainly refers to a device that carries a business client and mainly provides local services for users. In terms of physical implementation, the client device 10 can be a terminal device such as a smart phone, a tablet computer, a personal computer, a wearable device, etc. The client device 10 generally includes at least one processing unit and at least one memory. The number of processing units and memories depends on the configuration and type of the client device 10. The memory may include volatile, such as RAM, or non-volatile, such as read-only memory (ROM), flash memory, etc., or may include both types at the same time. The memory generally stores an operating system (OS), one or more application programs (app), such as a business client corresponding to the business service end, and may also store program data, etc. In addition to the processing unit and memory, the client device 10 also includes basic configurations such as a network card chip, an IO bus, and audio and video components. Optionally, according to the implementation form of the client device 10, the client device 10 may also include some peripheral devices, such as a keyboard, a mouse, an input pen, a printer, etc. These peripheral devices are well known in the art and will not be described in detail here.

[0078] In this embodiment, the server device 20 mainly refers to the device that carries the service service end, and mainly serves the client device 10. The service content includes responding to the service request sent by the client device 10, providing resources to the client device 10, and saving the data of the client device 10. In physical implementation, the server device 20 can be any device that can provide computing services, respond to the service request of the client device 10, and perform corresponding business processing, such as a conventional server, cloud server, cloud host, virtual center, etc. The composition of the server mainly includes a processor, a hard disk, a memory, a system bus, etc., which is similar to the general computer architecture.

[0079] Depending on the business scenario, the implementation form of the client device 10 and the server device 20 and the business functions provided may vary. For example, the client device 10 and the server device 20 may be an email client device and an email server that provide email sending and receiving services, or an instant messaging client device and an instant messaging server that provide instant messaging services, or an online shopping client device and an online shopping server that provide online shopping services in the field of e-commerce, etc. Among them, the online shopping server mainly refers to a server that processes transactions in a network virtual environment, usually refers to a server that uses the network to conduct online commodity transactions, such as a transaction server of each e-commerce platform or online transaction website, or a third-party server.

[0080] In the embodiment of the present application, the user of the client (hereinafter referred to as the user) can request the service processing logic of the server device 20 through the client device 10, thereby realizing the corresponding service functions, such as instant messaging, sending and receiving emails, or online shopping, etc. In some business scenarios, some users may use machine scripts / programs to frequently request the service processing logic of the server device 20 to gain benefits for themselves. In addition, this cheating behavior using machine scripts / programs will also increase the processing burden of the server device 20, which is not conducive to the normal development of business.

[0081] For example, in the field of e-commerce, in order to attract new users, there are often activities where newly registered users can receive a certain amount of red envelopes for free, which are referred to as new user red envelope activities. In the new user red envelope activities, some users will use account automatic registration software tools (a machine script / program) to continuously register new accounts, thereby receiving a large number of red envelopes, but in fact the user receiving the red envelopes is the same. The emergence of this behavior is not conducive to the purpose of the new user red envelope activity and cannot attract new users in the true sense.

[0082] In order to prevent cheating using machine scripts / programs, a human-machine identification device 30 is added to the risk control system of this embodiment. The human-machine identification device 30 mainly performs human-machine identification on relevant operations, and tries to identify and block cheating using machine scripts / programs. In physical implementation, the human-machine identification device 30 can be any device that can realize the human-machine identification function, such as a server device such as a conventional server, a cloud server, a cloud host, a virtual center, or a terminal device such as a smart phone, a tablet computer, a personal computer, a wearable device, etc.

[0083] In this embodiment, in addition to using the human-machine identification device 30 to perform human-machine identification on relevant operations, the server device 20 will also cooperate with the human-machine identification device 30, and at the same time combine human-machine identification and business risk control strategies to determine the processing method for business requests, so that the business risk control strategy and the human-machine identification results can influence and correct each other to produce more favorable risk control results, which can not only achieve human-machine identification, but also reduce the adverse effects of human-machine identification on the business.

[0084] The following describes in detail the process of the client device 10 initiating a service request to the server device 20 and the process of the server device 20 and the human-machine recognition device 30 cooperating with each other to perform risk control during this process.

[0085] Since it is impossible to determine at the beginning whether the service request is initiated by a normal user or a cheating machine script / program, the service request initiator is called an operation object. The operation object may be a normal user or a cheating machine script / program. The operation object can interact with the client device 10 to initiate a service request to the server device 20. Taking the login process as an example, the operation object can perform the following interactive operations with the client device 10: click on the login page, enter the user name and password, move the mouse, slide the scroll bar, and click the login button, etc., so as to initiate a login request to the server device 20.

[0086] For the client device 10, human-machine identification parameters can be collected during the process of the operation object initiating a service request, and in response to the service request initiated by the operation object (such as clicking a login button), the service request and the collected human-machine identification parameters can be sent to the server device 20.

[0087] For the server device 20, it can receive the service request and human-machine identification parameters sent by the client device 10. After receiving the service request and human-machine identification parameters, the server device 20 forwards the human-machine identification parameters to the human-machine identification device 30, so that the human-machine identification device 30 can perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result, and receive the human-machine identification result for the operation object returned by the human-machine identification device 30 according to the human-machine identification parameters; further, the processing method for the service request can be determined in combination with the human-machine identification result and the service risk control policy corresponding to the service request.

[0088] For the human-machine identification device 30, it can receive the human-machine identification parameters sent by the server device 20, perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result and send the human-machine identification result to the server device 20, so that the server device 20 can determine the processing method of the business request based on the human-machine identification result and the business risk control policy corresponding to the business request.

[0089] In this embodiment, the server device cooperates with the human-machine recognition device, and human-machine recognition is no longer regarded as a closed system. Instead, human-machine recognition is coupled to the business risk control strategy. The human-machine recognition device provides the human-machine recognition result to the server device, and then the server device determines the processing method for the corresponding business request based on the human-machine recognition result of the human-machine recognition device and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine recognition result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine recognition and reducing the adverse effects of human-machine recognition on the business.

[0090] In each embodiment of the present application, when the operation object initiates a service request, the client device 10 will send the service request and the collected human-machine identification parameters to the server device 20. The embodiment of the present application does not limit the method in which the client device 10 sends the service request and the human-machine identification parameters to the server device 20. Any sending method that can successfully send the service request and the human-machine identification parameters to the server device 20 is applicable to the embodiment of the present application. The following examples are given:

[0091] In some exemplary embodiments, the client device 10 may collect human-machine identification parameters during the process of the operation object initiating a service request, and when the operation object initiates the service request, the client device 10 may send the service request and the human-machine identification parameters to the server device 20 through different communication processes. For example, when the operation object initiates the service request, the client device 10 may first send the service request to the server device 20, and then send the human-machine identification parameters to the server device 20. Alternatively, when the operation object initiates the service request, the client device 10 may first send the human-machine identification parameters to the server device 20, and then send the service request to the server device 20. In this way, the amount of data sent by the client device 10 to the server device 20 in each communication process is relatively small, and the burden of each communication is relatively light.

[0092] In other exemplary embodiments, the client device 10 may collect human-machine identification parameters during the process of the operation object initiating a service request, and when the operation object initiates the service request, the service request and the human-machine identification parameters are sent to the server device 20 in the same communication process. For example, when the operation object initiates the service request, the client device 10 may carry the collected human-machine identification parameters in the service request and send it to the server device 20. This method is conducive to reducing the number of communications between the client device 10 and the server device 20, and is conducive to reducing the communication burden between the client device 10 and the server device 20.

[0093] In some other exemplary embodiments, the client device 10 can collect human-machine identification parameters and send them to the server device 20 in real time when the operation object initiates a service request, and when the operation object initiates a service request, send the service request to the server device 20. In this way, the client device 10 sends the collected human-machine identification parameters to the server device 20 in real time, which is conducive to improving the real-time performance of human-machine identification.

[0094] In each embodiment of the present application, in order to facilitate human-machine identification, the client device 10 can collect human-machine identification parameters during the process of the operation object initiating a service request. Human-machine identification parameters mainly refer to the parameters required for human-machine identification of the operation object. The embodiments of the present application do not limit the human-machine identification parameters, and all parameters that can be used for human-machine identification are applicable to each embodiment of the present application. The following examples are given:

[0095] In some exemplary embodiments, the human-machine identification parameters may include some behavior data generated when the operation object interacts with the client device 10 in the process of initiating a service request. For the convenience of distinction in description, the behavior data here is referred to as first behavior data. The first behavior data may be various behavior data generated by the operation object in the process of initiating a service request, and to a certain extent, it can reflect whether the operation object is a normal user or a cheating machine script / program.

[0096] For example, taking the login process in a certain application scenario as an example, the client device 10 displays the application homepage, which is provided with a login button; the operation object clicks the login button to enter the login page, which displays a user name and password input box; the operation object moves the mouse to position the cursor in the user name input box, enters the user name in the user name input box, and then moves the mouse to position the cursor in the password input box and enters the password in the password input box; the operation object can slide the scroll bar to the login button and click the login button to issue a login request. In the above example where the operation object initiates a login request, the first behavior data collected by the client device 10 may include but is not limited to: the operation of clicking the login button, the operation of entering the user name and password, the operation of sliding the scroll bar, and the operation of clicking the login button. If these behavior data are successfully collected, it can be reflected to a certain extent that the operation object initiating the login request is a normal user.

[0097] Based on the above, the client device 10 can collect the first behavior data generated by the interaction between the operation object and the client device 10 as the human-machine identification parameter during the process of the operation object initiating a service request.

[0098] In other exemplary embodiments, the human-machine identification parameters may include the soft / hard environment parameters of the client device 10. The soft / hard environment parameters mainly refer to some parameters related to the software and / hardware of the client device 10, such as the IP address, MAC address, device model, CPU model, operating system version, memory size, disk type and size of the client device 10. By judging whether the soft / hard environment parameters of the client device 10 are adapted to the operation of some cheating machine scripts / programs, it is judged to a certain extent whether the operation object is a cheating machine script / program. Based on this, the client device 10 can collect the soft / hard environment parameters of the client device 10 as the human-machine identification parameters during the process of the operation object initiating a service request.

[0099] In some further exemplary embodiments, the human-machine identification parameters may include both the first behavior data generated when the operation object interacts with the client device 10 and the soft / hard environment parameters of the client device 10. Based on this, the client device 10 may collect the first behavior data generated when the operation object interacts with the client device 10 and the soft / hard environment parameters of the client device 10 as the human-machine identification parameters during the process of the operation object initiating a service request.

[0100] Correspondingly, the human-machine identification parameters are different, and the process of the human-machine identification device 30 performing human-machine identification on the operation object according to the human-machine identification parameters will also be different.

[0101] Taking the human-machine identification parameters including the first behavior data as an example, the human-machine identification device 30 can mainly determine whether the operation object is a cheating machine script / program or determine the probability that the operation object is a cheating machine script / program based on the first behavior data as the human-machine identification result.

[0102] Taking the example that the human-machine identification parameters include the soft / hard environment parameters of the client device 10, the human-machine identification device 30 can mainly determine whether the operation object is a cheating machine script / program or determine the probability that the operation object is a cheating machine script / program based on the soft / hard environment parameters of the client device 10 and the operating environment parameters of the known cheating machine script / program as the human-machine identification result.

[0103] Taking the example that the human-machine identification parameters include both the first behavior data and the soft / hard environment parameters of the client device 10, the human-machine identification device 30 can determine the first probability that the operation object is a cheating machine script / program according to the first behavior data; and determine the second probability that the operation object is a cheating machine script / program according to the soft / hard environment parameters of the client device 10 and the operating environment parameters of the known cheating machine script / program; and then combine the first probability and the second probability to obtain the final human-machine identification result. For example, the human-machine identification device 30 can multiply the first probability and the second probability, use the multiplication result as the final probability that the operation object is a cheating machine script / program, and return the final probability as the human-machine identification result to the server device 20. Alternatively, the human-machine identification device 30 can multiply the first probability and the second probability, determine the result of whether the operation object is a cheating machine script / program according to the multiplication result, and return the final result of whether it is a cheating machine script / program to the server device 20 as the human-machine identification result. Alternatively, the human-machine identification device 30 may also weighted-multiply the first probability and the second probability according to the weight coefficients corresponding to the first behavior data and the soft / hard environment parameters of the client device 10, use the multiplication result as the final probability that the operation object is a cheating machine script / program, and return the final probability as the human-machine identification result to the server device 20. Alternatively, the human-machine identification device 30 may weighted-multiply the first probability and the second probability according to the weight coefficients corresponding to the first behavior data and the soft / hard environment parameters of the client device 10, determine whether the operation object is a cheating machine script / program or not according to the multiplication result, and return the final result of whether it is a cheating machine script / program or not as the human-machine identification result to the server device 20.

[0104] Further optionally, after obtaining the human-machine identification parameters, the client device 10 can also encrypt and protect the human-machine identification parameters, that is, during the transmission process, the human-machine identification parameters can be encrypted to improve security and confidentiality. In the case where the client device 10 encrypts the human-machine identification parameters, the operation of decrypting the human-machine identification parameters can be the responsibility of the server device 20, that is, the server device 20 first decrypts the human-machine identification parameters, and then forwards the decrypted human-machine identification parameters to the human-machine identification device 30; or, the operation of decrypting the human-machine identification parameters can also be the responsibility of the human-machine identification device 30, that is, the server device 20 directly forwards the encrypted human-machine identification parameters to the human-machine identification device 30, and the human-machine identification device 30 first decrypts the human-machine identification parameters, and then performs human-machine identification on the operation object according to the decrypted human-machine identification parameters. The embodiment of the present application does not limit the method of encrypting and decrypting the human-machine identification parameters, and various known encryption and decryption methods can be used.

[0105] In the above exemplary embodiments, the client device 10 collects the first behavior data generated by the operation object interacting with the client device 10 in the process of initiating a service request and / or the soft / hard environment parameters of the client device 10. In the process of collecting these human-machine identification parameters, the user does not need to participate in the interactive operations related to human-machine identification, so that the user can focus more on the operations related to the service. This mode greatly reduces the interference caused to the user by the human-machine identification process.

[0106] It is worth noting that in the process of collecting human-machine identification parameters, users may not participate in interactive operations related to human-machine identification, but this is not limited to this. For example, in the process of a user initiating a service request, in addition to participating in interactive operations related to the service, the user may also participate in some interactive operations related to human-machine identification, such as entering a verification code or moving the mouse and clicking a verification button, etc., to further improve the accuracy of the human-machine identification results.

[0107] In some exemplary embodiments, after determining the processing method for the service request, the server device 20 may send a response message to the client device 10, and the response message is used to reflect the processing method of the service request by the server device 20. For the client device 10, the response message sent by the server device 20 may be received, and a result page matching the response message may be output, so that the operation object can understand the processing method of the service request by the server device 20.

[0108] Optionally, the response message indicates that the server device 20 accepts the service request or rejects the service request. When the response message indicates that the server device 20 accepts the service request, the client device 10 may output to the user a page indicating that the request is accepted or that the service is being processed or the service processing result. When the response message indicates that the server device 20 rejects the service request, the client device 10 may output to the user a page indicating that the request is rejected or that the request fails.

[0109] For example, taking the example of the server device 20 providing cloud storage services to enterprises, a normal user can initiate a registration request to the server device 20 through the client device 10 to apply for cloud storage space. During the registration process, in order to prevent cheating registration behavior using machine scripts / programs, the client device 10 can collect the behavior data generated by the operation object during the registration request and / or the soft / hard environment parameters of the client device 10 as human-machine identification parameters, and provide the human-machine identification parameters to the server device 20, which is then forwarded by the server device 20 to the human-machine identification device 30, and the human-machine identification device 30 determines whether the operation object is a normal user based on these human-machine identification parameters.

[0110] In the above registration process, in addition to identifying whether the operation object initiating the registration request is a normal user, the server device 20 can also use the business risk control strategy to further review the registration request from a business perspective. In the example where the server device 20 provides cloud storage services to enterprises, the business risk control strategy may include but is not limited to: user name and password setting rules, such as requiring the user name to be the enterprise's email account, the password must be composed of numbers, characters and special characters, and the password length cannot be less than 8 characters, and the cloud storage space cannot exceed the upper limit, such as 15G, etc. Based on this, the server device 20 can further determine whether the registration request complies with the business risk control strategy based on some business-related information carried in the registration request, such as the user name and password entered by the operation object and the size of the cloud storage space required to be applied for. For example, if the user name entered by the operation object is the enterprise's email account, and the password is composed of numbers, characters and special characters, the password length cannot be less than 8 characters, and the size of the cloud storage space required to be applied for is 10G (<15G), then it is determined that the registration request complies with the business risk control strategy.

[0111] When the operation object initiating the registration request is a normal user and the registration request also complies with the business risk control policy, the server device 20 can return a registration success response message to the client device 10. The client device 10 can display the registration success message to the operation object (the operation object at this time is actually a normal user) on the interface, so that the operation object can use the cloud storage space for data storage.

[0112] Furthermore, in the process of using the cloud storage space, the operation object may initiate business requests such as storage requests, query requests, and download requests to the server device 20. In the process of the operation object initiating these business requests, the server device 20 is combined with the human-machine identification device 30, and human-machine identification and business risk control may also be performed on business requests such as storage requests, query requests, and download requests, so as to reduce the risks brought to the server device 20 by some malicious attacks, improve the security of the server device 20, and ensure the business performance of the server device 20.

[0113] In each embodiment of the present application, the server device 20 can combine the human-machine recognition result and the business risk control strategy corresponding to the business request to determine the processing method for the business request. There are many ways for the server device 20 to combine the human-machine recognition result and the business risk control strategy corresponding to the business request. The following examples are given:

[0114] In some exemplary embodiments, after receiving the business request and human-machine identification parameters, the server-side device 20 will, on the one hand, send the human-machine identification parameters to the human-machine identification device 30 and wait to receive the human-machine identification result based on the human-machine identification parameters for the operation object returned by the human-machine identification device 30; on the other hand, it can determine the business risk control strategy corresponding to the business request, and determine the business risk level of the business request based on the business risk control strategy; after determining the business risk level of the business request and receiving the human-machine identification result returned by the human-machine identification device 30, determine the processing method of the business request in combination with the human-machine identification result and the business risk level of the business request.

[0115] For example, the server device 20 can combine human-machine identification with business risk control strategies, such as whitelists, blacklists or business thresholds, and then determine the business risk level of the business request based on these business risk control strategies. For example, when a business request comes from a device or IP address in the whitelist, it can be determined that the business request belongs to a low risk level; when a business request comes from a device or IP address in the blacklist, it can be determined that the business request belongs to a high risk level. In this example, only high and low risk levels are used as examples for illustration. In fact, the business risk level is not limited to high and low, and can be flexibly set according to actual business needs.

[0116] In other exemplary embodiments, after receiving the business request and human-machine identification parameters, the server-side device 20 can send the human-machine identification parameters to the human-machine identification device 30 and wait to receive the human-machine identification result based on the human-machine identification parameters for the operation object returned by the human-machine identification device 30; after receiving the human-machine identification result returned by the human-machine identification device 30, the credibility of the human-machine identification result can be judged in combination with some factors related to the business needs; when it is determined that the credibility of the human-machine identification result is not very high (for example, less than the set credibility threshold), the business risk level of the business request can be further determined according to the business risk control strategy corresponding to the business request, and then the processing method of the business request can be determined in combination with the human-machine identification result and the business risk level of the business request.

[0117] For example, when the business needs do not need to consider the human-machine recognition results, it can be determined that the credibility of the human-machine recognition results is not high. For another example, the number or percentage of business requests that should be blocked according to the human-machine recognition results within a certain period of time can be counted. When the number or percentage of business requests that should be blocked according to the human-machine recognition results exceeds the corresponding maximum threshold, it means that most of the business requests are cheating machine scripts / programs, which is not reasonable. Therefore, it can be determined that the credibility of the human-machine recognition results is not high.

[0118] In the above-mentioned embodiments, in the process of determining the processing method of the service request by combining the human-machine recognition result and the service risk level of the service request:

[0119] When the human-machine recognition result indicates that the operation object is a cheating machine script / program or the probability that the operation object is a cheating machine script / program is greater than or equal to the set probability threshold, and the business risk level of the business request is greater than or equal to the preset business risk level threshold, it is determined to reject the business request.

[0120] When the human-machine recognition result indicates that the operation object is not a cheating machine script / program or the probability that the operation object is a cheating machine script / program is less than the set probability threshold, and the business risk level of the business request is less than the preset business risk level threshold, it is determined to accept the business request.

[0121] When the human-machine recognition result indicates that the operation object is a cheating machine script / program or the probability that the operation object is a cheating machine script / program is greater than or equal to the set probability threshold, but the business risk level of the business request is less than the preset business risk level threshold (for example, the business request comes from a device or IP address in the whitelist), determine to accept the business request.

[0122] When the human-machine recognition result indicates that the operation object is not a cheating machine script / program or the probability that the operation object is a cheating machine script / program is less than the set probability threshold, but the business risk level of the business request is greater than or equal to the preset business risk level threshold (for example, the business request comes from a device or IP address in the blacklist), it is determined to reject the business request.

[0123] From the above, it can be seen that the human-machine recognition result of the human-machine recognition device 30 can actually be regarded as a risk control dimension when the server device 20 performs business risk control. Based on this, the server device 20 can choose to trust the human-machine recognition result, or it can correct the human-machine recognition result according to the business risk level of the business request. For example, some business requests that will be blocked by the human-machine recognition result but will not have an adverse impact on the business can be released, or some business requests that will be released by the human-machine recognition result but will have an adverse impact on the business can be blocked, thereby achieving a more reasonable risk control effect.

[0124] In some exemplary embodiments, in addition to combining human-machine identification with business risk control strategies, a verification code solution may be further deployed on the server device 20 and implemented as a downstream solution of the business risk control strategy.

[0125] In these exemplary embodiments, the server device 20 preferentially determines the processing method of the service request by combining the human-machine recognition result and the service risk level of the service request; when the processing method of the service request is to reject the service request, the server device 20 can generate a verification code and send the verification code to the client device 10, so that the operation object can enter the verification code through the client device 10. For the client device 10, it can also receive the verification code sent by the server device 20, output the verification code in one or several ways, and provide it to the operation object, such as broadcasting the verification code in voice, or displaying the verification code in SMS, system message, application message or notification message; if the operation object is a normal user, the verification code can be recognized and entered into the client device 10. Based on this, the client device 10 can respond to the operation of the operation object entering the verification code, obtain the second behavior data generated by the operation object during the process of entering the verification code and the verification code entered by the operation object, and send the obtained second behavior data and the verification code entered by the operation object to the server device 20, so that the server device 20 can redetermine the processing method for the business request based on the second behavior data and the verification code entered by the operation object. For the server device 20, it will also receive the second behavior data returned by the client device 10 and the verification code entered by the operation object, and will redetermine the processing method for the business request based on the second behavior data and the verification code entered by the operation object. Among them, the second behavior data mainly refers to some behavior data generated by the operation object during the process of entering the verification code, such as the input operation that can be included in the verification code box, the order of input characters, etc. Among them, depending on the verification code scheme, the behavior data generated by the user will also be different. There are corresponding exemplary descriptions in the example of the verification code scheme below.

[0126] Optionally, when the processing method for the service request is to accept the service request, the server device 20 may execute the processing flow for the service request instead of entering the verification code flow.

[0127] In terms of implementation, the above verification code scheme needs to include some operations that can only be performed by humans or features that only humans have, so as to distinguish whether the operation object is a normal user or a machine script / program. Any verification code scheme that can distinguish whether the operation object is a normal user or a machine script / program is applicable to the embodiments of the present application. The following examples are given:

[0128] In a verification code scheme, the server device 20 can generate a verification code image that can be recognized by normal humans but cannot be recognized by machines by confusing and distorting the image based on human visual recognition, and display it to the operating object through the client device 10, so that the operating object can identify the correct verification code from these verification code images and enter it, and then distinguish whether the operating object is a normal user (i.e., a human) or a machine script / program in combination with the verification code entered by the operating object. This verification code scheme can be referred to as an image verification code scheme. In this example, the second behavior data may include: the order and position of clicking characters in the image containing the verification code, and the number of click operations, etc.

[0129] In another verification code scheme, the server device 20 can embed a user interface (UI) that supports user interaction on the client device 10 to guide the operation object to complete a certain interactive operation, and then distinguish whether the operation object is a normal user (i.e., a human) or a machine script / program based on the interactive operation of the operation object on the user interface collected by the client device 10 (such as clicking a specific button, sliding a specific button, etc.). This verification code scheme can be referred to as a behavioral verification code scheme. In this example, the second behavioral data can include: operations such as clicking a specific button on the UI and sliding a specific button.

[0130] In another verification code scheme, the server device 20 generates questions that normal humans can understand and answer, but machines cannot answer, through knowledge graphs and other means based on human understanding of natural language and social common sense, and displays them to the operating object through the client device 10, so that the operating object can answer these questions, and then distinguish whether the operating object is a normal user (i.e., a human) or a machine script / program based on the operating object's answers to these questions. This verification code scheme can be referred to as a semantic verification code scheme. In this example, the second behavior data may include: operations for answering questions, etc.

[0131] In another verification code scheme, the server device 20 can collect human voiceprints, fingerprints, irises, body movements and other biometric features for the operation object through the sensors on the client device 10, and then distinguish whether the operation object is a normal user (i.e., a human) or a machine script / program based on whether the client device 10 collects these biometric features and the collected biometric features. This verification code scheme can be referred to as a biometric verification code scheme. In this example, the second behavior data may include: the sound signal emitted, the placement of fingers to provide fingerprints, the adjustment of eyeballs to provide irises or corresponding body movements and other data.

[0132] In the above-mentioned solution combined with the verification code, when the server device 10 determines that the business request needs to be rejected based on the human-machine recognition result and the business risk control strategy, the advantages of the verification code solution can be further utilized to perform further identification through the verification code solution to further reduce the misjudgment rate.

[0133] Furthermore, in the above-mentioned solution combined with the verification code, in order to meet the flexibility of use, the user can be allowed to flexibly configure whether to use the verification code solution and which verification code solution to use according to their own needs. For example, the user can configure whether to use the verification code solution and which verification code solution to use through the setting function provided by the client device 10. In addition, the user can also change the verification code solution at any time according to the needs. In this way, the access cost of various verification code solutions can be greatly reduced, and the flexibility of the entire system in implementation can be increased.

[0134] based on Figure 1 The system shown, the following embodiments of the present application also provide some risk control methods and human-machine identification methods.

[0135] Figure 2 The following is a flow chart of a risk control method provided by another exemplary embodiment of the present application. The method is mainly described from the perspective of the client device. Figure 2 As shown, the method includes:

[0136] 201. Collect human-machine identification parameters during the process of the operation object initiating a service request.

[0137] 202. In response to a business request initiated by the operation object, the business request and human-machine identification parameters are sent to the server device, so that the server device can determine the processing method for the business request based on the business risk control policy corresponding to the business request and the human-machine identification result for the operation object obtained by the human-machine identification device based on the human-machine identification parameters.

[0138] In this embodiment, the user of the client (referred to as user for short) can request the service processing logic of the server device through the client device, thereby realizing corresponding service functions, such as instant messaging, sending and receiving emails, or online shopping.

[0139] In some business scenarios, some users may use machine scripts / programs to frequently request the business processing logic of the server device to gain benefits for themselves. In addition, this cheating behavior using machine scripts / programs will also increase the processing burden of the server device, which is not conducive to the normal development of business.

[0140] Since it is impossible to determine at the beginning whether the service request is initiated by a normal user or a cheating machine script / program, the service request initiator is called the operation object. The operation object may be a normal user or a cheating machine script / program. The operation object can interact with the client device to initiate a service request to the server device. Taking the login process as an example, the operation object can perform the following interactive operations with the client device: click on the login page, enter the user name and password, move the mouse, slide the scroll bar, and click the login button, etc., to initiate a login request to the server device.

[0141] For the client device, human-machine identification parameters are collected during the process of the operation object initiating a service request, and the service request and the collected human-machine identification parameters can be sent to the server device in response to the service request initiated by the operation object (for example, the operation of clicking the login button). In this embodiment, the client device provides the service request and human-machine identification parameters to the server device, so that the server device can simultaneously combine the human-machine identification obtained by the human-machine identification device based on the human-machine identification parameters with the business risk control strategy corresponding to the business request to perform risk control on the business request, so that the business risk control strategy and the human-machine identification result can influence and correct each other to produce a more favorable risk control result, so that both human-machine identification and the adverse impact of human-machine identification on the business can be achieved.

[0142] In an exemplary embodiment, after sending the service request and human-machine identification parameters to the server device, the method further includes: receiving a response message sent by the server device, and outputting a result page matching the response message, wherein the response message reflects the way the server device handles the service request.

[0143] In an exemplary embodiment, the above step 202 may be: in response to a service request initiated by the operation object, the human-machine identification parameter is carried in the service request and sent to the server device.

[0144] In an exemplary embodiment, the above step 201 may be: in the process of the operation object initiating a service request, collecting first behavior data generated by the interaction between the operation object and the client device and / or soft / hard environment parameters of the client device as human-machine identification parameters.

[0145] In an exemplary embodiment, the method also includes: when the server-side device processes the service request by rejecting the service request, receiving a verification code sent by the server-side device and outputting the verification code; and in response to the operation object entering the verification code, obtaining second behavior data generated by the operation object during the process of entering the verification code and the verification code entered by the operation object and sending them to the server-side device, so that the server-side device can redetermine the processing method for the service request based on the second behavior data and the verification code entered by the operation object.

[0146] Figure 3 A flow chart of another risk control method provided as another exemplary embodiment of the present application. The method is mainly described from the perspective of the server device. Figure 3 As shown, the method includes:

[0147] 301. Receive a service request and a human-machine identification parameter sent by a client device. The human-machine identification parameter is collected by the client device when the operation object initiates the service request.

[0148] 302. Forward the human-machine identification parameters to the human-machine identification device, and receive the human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameters.

[0149] 303. Determine how to handle the business request based on the human-machine recognition result and the business risk control strategy corresponding to the business request.

[0150] In this embodiment, the server device receives the service request and human-machine identification parameters sent by the client device. After receiving the service request and human-machine identification parameters, the server device forwards the human-machine identification parameters to the human-machine identification device, so that the human-machine identification device can perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result, and receives the human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameters; further, the processing method for the service request can be determined in combination with the human-machine identification result and the service risk control policy corresponding to the service request.

[0151] In this embodiment, the server device cooperates with the human-machine recognition device, and human-machine recognition is no longer regarded as a closed system. Instead, human-machine recognition is coupled to the business risk control strategy. The human-machine recognition device provides the human-machine recognition result to the server device, and then the server device determines the processing method for the corresponding business request based on the human-machine recognition result of the human-machine recognition device and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine recognition result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine recognition and reducing the adverse effects of human-machine recognition on the business.

[0152] In an exemplary embodiment, after determining the processing method for the business request, the method also includes: sending a response message to the client device so that the client device outputs a result page matching the response message; wherein the response message reflects the processing method of the service request by the server device.

[0153] In an exemplary embodiment, the above step 303 may be: determining the business risk level of the business request according to the business risk control strategy; and determining the processing method of the business request in combination with the human-machine recognition result and the business risk level of the business request.

[0154] Further optionally, the above-mentioned determination of a processing method for the service request based on the human-machine recognition result and the service risk level of the service request includes:

[0155] When the human-machine recognition result indicates that the operation object is a cheating machine script / program or the probability of indicating that the operation object is a cheating machine script / program is greater than or equal to the set probability threshold, but the business risk level of the business request is less than the preset business risk level threshold, determine to accept the business request; or

[0156] When the human-machine recognition result indicates that the operation object is not a cheating machine script / program or the probability that the operation object is a cheating machine script / program is less than the set probability threshold, but the business risk level of the business request is greater than or equal to the preset business risk level threshold, it is determined to reject the business request.

[0157] In an exemplary embodiment, the method also includes: when the processing method for the business request is to reject the business request, sending a verification code to the client device for the operation object to enter the verification code; receiving second behavior data generated by the operation object in the process of entering the verification code and the verification code entered by the operation object returned by the client device; and re-determining the processing method for the business request based on the second behavior data and the verification code entered by the operation object.

[0158] Figure 4 A flowchart of a human-machine identification method provided for another exemplary embodiment of the present application is shown in FIG. The method is mainly described from the perspective of a human-machine identification device. Figure 4 As shown, the method includes:

[0159] 401. Receive a human-machine identification parameter sent by a server device. The human-machine identification parameter is collected by a client device and provided to the server device during a process in which an operation object initiates a service request.

[0160] 402. Perform human-machine identification on the operation object according to the human-machine identification parameters to obtain a human-machine identification result.

[0161] 403. Send the human-machine recognition result to the server device, so that the server device can determine the processing method of the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0162] In this embodiment, the human-machine identification device receives the human-machine identification parameters sent by the server device, performs human-machine identification on the operation object according to the human-machine identification parameters to obtain a human-machine identification result and sends the human-machine identification result to the server device, so that the server device can determine the processing method for the business request in combination with the human-machine identification result and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine identification result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine identification and reducing the adverse effects of human-machine identification on the business.

[0163] In an exemplary embodiment, the human-machine identification parameters include: the first behavior data generated by the operation object interacting with the client device in the process of initiating a service request and the soft / hard environment parameters of the client device. Based on this, the above step 402 can be: according to the first behavior data, determine the first probability that the operation object is a cheating machine script / program; according to the soft / hard environment parameters of the client device and the operating environment parameters of the known cheating machine script / program, determine the second probability that the operation object is a cheating machine script / program; combine the first probability and the second probability to obtain the human-machine identification result.

[0164] Figure 5 A flow chart of another risk control method provided as another exemplary embodiment of the present application. Figure 5 As shown, the method includes:

[0165] 501. The operation object initiates a service request through a client device, and the client device collects human-machine identification parameters during the process of the operation object initiating the service request.

[0166] For example, when the operation object initiates a business request, it may be necessary to fill in the user name and password, click the corresponding button on the page, scroll the scroll bar, etc. The client device can monitor and collect the behavior data generated by the operation object in this process, and collect the soft / hard environment parameters of the client device as human-machine identification parameters.

[0167] 502. The client device responds to the service request initiated by the operation object and sends the human-machine identification parameter to the server device together with the service request.

[0168] It is worth noting that in addition to human-machine identification parameters, the service request also carries some other parameters, such as the IP address and MAC address of the client device and some necessary service-related parameters, such as user name, password, service identification code and other information.

[0169] 503. The server device parses the human-machine identification parameters from the service request and forwards the human-machine identification parameters to the human-machine identification device.

[0170] 504. The human-machine identification device performs human-machine identification on the operation object according to the human-machine identification parameters sent by the server device to obtain a human-machine identification result.

[0171] 505. The human-machine identification device sends the human-machine identification result to the server device.

[0172] 506. The server device determines how to handle the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0173] The server device processes the service request in two ways: accepting the service request or rejecting the service request.

[0174] 507. When the server device rejects the service request, the server device automatically generates a verification code and sends the verification code to the client device.

[0175] 508. The client device outputs the verification code sent by the server device in one or more ways.

[0176] For example, the client device may output the verification code by voice broadcast, or may output the verification code by displaying it on a corresponding user interface, or may control a corresponding indicator light to flash in a control mode matching the verification code to output the verification code. Of course, the client device may output the verification code in any of the methods listed here, or may combine several methods to output the verification code.

[0177] 509. In response to the operation of the operation object inputting the verification code, the client device obtains the second behavior data generated by the operation object during the process of inputting the verification code and the verification code input by the operation object.

[0178] 510. The client device sends the second behavior data and the verification code input by the operation object to the server device.

[0179] 511. The server device re-determines a processing method for the service request according to the second behavior data and the verification code input by the operation object.

[0180] 512. The server device sends a response message to the client device, where the response message reflects the processing method of the server device for the service request.

[0181] 513. The client device receives the response message sent by the server device, and outputs a result page matching the response message.

[0182] In this embodiment, the server device cooperates with the human-machine recognition device, and human-machine recognition is no longer regarded as a closed system. Instead, human-machine recognition is coupled to the business risk control strategy. The human-machine recognition device provides the human-machine recognition result to the server device, and then the server device determines the processing method for the corresponding business request based on the human-machine recognition result of the human-machine recognition device and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine recognition result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine recognition and reducing the adverse effects of human-machine recognition on the business.

[0183] Figure 6 The following is a schematic diagram of the structure of another wind control system provided by another exemplary embodiment of the present application. Figure 6 As shown, the system includes: a client device 61, a server device 62 and a human-machine identification device 63. The client device 61 is connected to the server device 62 and the human-machine identification device 63 in communication. Optionally, the server device 62 can be connected to the human-machine identification device 63 in communication.

[0184] Regarding the communication connection mode and physical implementation form between the client device 61, the server device 62 and the human-machine recognition device 63, Figure 1 The client device 10, the server device 20 and the human-machine identification device 30 in the embodiment shown are similar, and can be seen in detail. Figure 1 The illustrated embodiments will not be described in detail here.

[0185] In the risk control system of this embodiment, a human-machine identification device 63 is also added, and human-machine identification is performed on relevant operations through the human-machine identification device 63. In addition, the server device 62 will also cooperate with the human-machine identification device 63, and at the same time, combine human-machine identification with the business risk control strategy to determine the processing method for the business request, so that the business risk control strategy and the human-machine identification results can influence and correct each other to produce a more favorable risk control result. In this way, human-machine identification can be achieved and the adverse effects of human-machine identification on the business can be reduced.

[0186] In the risk control system of this embodiment, the client device 61, the server device 62 and the human-machine identification device 63 cooperate with each other to work on the same principle. Figure 1 The working principle of the wind control system shown is different and is described in detail below.

[0187] Similarly, in the present embodiment, since it is not possible to determine whether the service request is initiated by a normal user or a cheating machine script / program at the beginning, the service request initiator is referred to as an operation object. The operation object may be a normal user or a cheating machine script / program. The operation object can interact with the client device 61 to initiate a service request to the server device 62. Taking the login process as an example, the operation object can perform the following interactive operations with the client device 61: click on the login page, enter the user name and password, move the mouse, slide the scroll bar, and click the login button, etc., so as to initiate a login request to the server device 62.

[0188] For the client device 61, human-machine identification parameters are collected during the process of the operation object initiating a business request, and in response to the business request initiated by the operation object (such as clicking the login button), the business request and the collected human-machine identification parameters are sent to the server device 62 and the human-machine identification device 63 respectively.

[0189] For the human-machine identification device 63, it can receive the human-machine identification parameters sent by the client device 61, perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result and send the human-machine identification result to the server device 62, so that the server device 62 can determine the processing method of the business request based on the human-machine identification result and the business risk control policy corresponding to the business request.

[0190] For the server device 62, it can receive the business request sent by the client device 61 and receive the human-machine recognition result sent by the human-machine recognition device 63. Then, it can determine the processing method for the business request based on the business risk control strategy corresponding to the human-machine recognition result and the business request.

[0191] Optionally, when there is no communication connection between the human-machine recognition device 63 and the server device 62, the human-machine recognition device 63 may forward the human-machine recognition result to the server device 62 through the client device 61. When there is a communication connection between the human-machine recognition device 63 and the server device 62, the human-machine recognition device 63 may directly send the human-machine recognition result to the server device 62 through the communication connection between the human-machine recognition device 63 and the server device 62.

[0192] It is worth noting that for some other operations that can be performed by the client device 61, the server device 62 and the human-machine recognition device 63 and the detailed implementation methods of related operations, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0193] In this embodiment, the server device cooperates with the human-machine recognition device, and human-machine recognition is no longer regarded as a closed system. Instead, human-machine recognition is coupled to the business risk control strategy. The human-machine recognition device provides the human-machine recognition result to the server device, and then the server device determines the processing method for the corresponding business request based on the human-machine recognition result of the human-machine recognition device and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine recognition result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine recognition and reducing the adverse effects of human-machine recognition on the business.

[0194] Figure 7a Another exemplary embodiment of the present application is based on Figure 6 The flow chart of the risk control method implemented by the risk control system is shown in FIG. Figure 7a As shown, the method includes:

[0195] 701. The operation object initiates a service request through a client device, and the client device collects human-machine identification parameters during the process of the operation object initiating the service request.

[0196] For example, when the operation object initiates a business request, it may be necessary to fill in the user name and password, click the corresponding button on the page, scroll the scroll bar, etc. The client device can monitor and collect the behavior data generated by the operation object in this process, and collect the soft / hard environment parameters of the client device as human-machine identification parameters.

[0197] 702. The client device responds to the service request initiated by the operation object and sends the service request and human-machine identification parameters to the server device and the human-machine identification device respectively.

[0198] It is worth noting that the service request carries the IP address and MAC address of the client device and some necessary service-related parameters, such as user name, password and other information.

[0199] 703. The human-machine identification device performs human-machine identification on the operation object according to the human-machine identification parameters sent by the client device to obtain a human-machine identification result.

[0200] 704. The human-machine identification device directly sends the human-machine identification result to the server device based on the communication connection between the human-machine identification device and the server device.

[0201] Optionally, when there is no communication connection between the human-machine recognition device and the server device, the human-machine recognition device may forward the human-machine recognition result to the server device through the client device.

[0202] 705. The server device determines how to handle the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0203] The server device processes the service request in two ways: accepting the service request or rejecting the service request.

[0204] 706. When the server device rejects the service request, the server device automatically generates a verification code and sends the verification code to the client device.

[0205] 707. The client device outputs the verification code sent by the server device in one or more ways.

[0206] For example, the client device may output the verification code by voice broadcast, or may output the verification code by displaying it on a corresponding user interface, or may control a corresponding indicator light to flash in a control mode matching the verification code to output the verification code. Of course, the client device may output the verification code in any of the methods listed here, or may combine several methods to output the verification code.

[0207] 708. In response to the operation of the operation object inputting the verification code, the client device obtains the second behavior data generated by the operation object during the process of inputting the verification code and the verification code input by the operation object.

[0208] 709. The client device sends the second behavior data and the verification code input by the operation object to the server device.

[0209] 710. The server device re-determines a processing method for the service request according to the second behavior data and the verification code input by the operation object.

[0210] 711. The server device sends a response message to the client device, where the response message reflects the processing method of the server device for the service request.

[0211] 712. The client device receives the response message sent by the server device, and outputs a result page matching the response message.

[0212] In this embodiment, the server device cooperates with the human-machine recognition device, and human-machine recognition is no longer regarded as a closed system. Instead, human-machine recognition is coupled to the business risk control strategy. The human-machine recognition device provides the human-machine recognition result to the server device, and then the server device determines the processing method for the corresponding business request based on the human-machine recognition result of the human-machine recognition device and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine recognition result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine recognition and reducing the adverse effects of human-machine recognition on the business.

[0213] Figure 7bA schematic diagram of the structure of a business system provided by another exemplary embodiment of the present application. Figure 7b As shown, the business system includes: a client device 71 and a server device 72. The client device 71 and the server device 72 are connected in communication. The client device 71 and the server device 72 may be connected in communication by wireless or wired. The client device 71 is connected in communication with the server device 72 via a mobile network, and the network standard of the mobile network may be any one of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G+ (LTE+), WiMax, and new network standards that will appear in the future.

[0214] In this embodiment, the client device 71 mainly refers to a device that carries a service client and mainly provides local services to users. In terms of physical implementation, the client device 71 can be a terminal device such as a smart phone, a tablet computer, a personal computer, a wearable device, etc.

[0215] In this embodiment, the server device 72 mainly refers to the device that carries the service server, and mainly serves the client device 71. The service content includes responding to the service request sent by the client device 71, providing resources to the client device 71, and saving the data of the client device 71. In physical implementation, the server device 72 can be any device that can provide computing services, respond to the service request of the client device 71, and perform corresponding business processing, such as a conventional server, a cloud server, a cloud host, a virtual center, etc.

[0216] In the embodiment of the present application, the user of the client (referred to as the user) can request the service processing logic of the server device 72 through the client device 71, thereby realizing the corresponding service functions, such as instant messaging, sending and receiving emails, or online shopping, etc. In some business scenarios, some users may use machine scripts / programs to frequently request the service processing logic of the server device 72 to gain benefits for themselves. In addition, this cheating behavior using machine scripts / programs will also increase the processing burden of the server device 72, which is not conducive to the normal development of business.

[0217] In order to prevent cheating by machine scripts / programs, in the business system of this embodiment, the server device 72 adds a human-machine recognition function and combines human-machine recognition with a business identification code, which can achieve both human-machine recognition and reduce the adverse effects of human-machine recognition on the business.

[0218] The following describes in detail the process of the client device 71 initiating a service request to the server device 72 and the process of the server device 72 performing human-machine identification in combination with the service identification code during the process.

[0219] For the client device 71, it can obtain human-machine identification data and send the client data to the server device 72, wherein the client data includes human-machine identification data and a service identification code, and the service identification code is used to indicate the human-machine identification rules or human-machine identification thresholds, so that the server device 72 can judge the validity of the human-machine identification data based on the human-machine identification rules or human-machine identification thresholds indicated by the service identification code to obtain the human-machine identification results.

[0220] Optionally, the client device 71 may obtain human-machine identification data in response to a service request initiated by an operation object. The operation object here refers to the initiator of the service request. The operation object may be a normal user or a cheating machine script / program. Regarding the detailed implementation process of the client device 71 in response to the operation of initiating a service request by the operation object and obtaining human-machine identification data, please refer to the process implementation of the client device 10 collecting human-machine identification parameters in the aforementioned embodiment, which will not be repeated here.

[0221] For the server device 72, it can receive client data, select human-machine identification rules or adjust human-machine identification thresholds according to the business identification code in the client data; and then determine whether the human-machine identification data is valid based on the selected human-machine identification rules or the adjusted human-machine identification thresholds.

[0222] Optionally, if it is determined that the human-machine identification data is valid, a notification of passing the human-machine identification is fed back; if it is determined that the human-machine identification data is invalid, a notification of failing the human-machine identification is fed back.

[0223] In some exemplary embodiments, a mapping relationship between each service identification code and a human-machine identification rule or a human-machine identification threshold can be pre-established. Based on this, after receiving the client data, the server device 72 can query the mapping relationship to determine the human-machine identification rule or human-machine identification threshold corresponding to the service identification code in the client data.

[0224] In some exemplary embodiments, the above-mentioned human-machine identification rules or human-machine identification thresholds may be rules or thresholds that integrate business risk control strategies and have human-machine identification functions, or rules or thresholds that have human-machine identification functions determined according to business risk control strategies. The detailed implementation method of the server device 72 judging whether the human-machine identification data is valid according to these human-machine identification rules or human-machine identification thresholds may refer to or be analogous to the implementation of the server device 20 in the aforementioned embodiment combining the human-machine identification results and the business risk control strategy corresponding to the business request to determine the processing method for the business request, which may not be described in detail here.

[0225] In this embodiment, the server-side device verifies the validity of the human-machine identification data based on the business identification code to obtain the human-machine identification result, thereby realizing the integration of human-machine identification and business. Therefore, it can not only achieve human-machine identification, but also reduce the adverse effects of human-machine identification on the business.

[0226] based on Figure 7b The system shown, the following embodiments of the present application also provide some data processing methods.

[0227] Figure 7c A flowchart of a data processing method provided for another exemplary embodiment of the present application is provided. The method is mainly described from the perspective of a client device. Figure 7c As shown, the method includes:

[0228] 71c. Obtain human-machine identification data.

[0229] 72c. Send the client data to the server device, wherein the client data includes human-machine identification data and a service identification code, and the service identification code is used to indicate a human-machine identification rule or a human-machine identification threshold.

[0230] Optionally, in step 71c, human-machine identification data may be obtained in response to a service request initiated by an operation object. The operation object here refers to the initiator of the service request. The operation object may be a normal user or a cheating machine script / program.

[0231] The human-machine identification rule or human-machine identification threshold indicated by the service identification code is used by the server device 72 to make a validity judgment on the human-machine identification data to obtain a human-machine identification result.

[0232] In this embodiment, the client device obtains human-machine identification data, and provides the human-machine identification data and the business identification code to the server device, so that the server device can verify the validity of the human-machine identification data based on the business identification code, thereby obtaining the human-machine identification result, realizing the integration of human-machine identification and business, so that human-machine identification can be achieved and the adverse effects of human-machine identification on the business can be reduced.

[0233] Figure 7d A flowchart of another data processing method provided for another exemplary embodiment of the present application is provided. The method is mainly described from the perspective of the server device. Figure 7d As shown, the method includes:

[0234] 71d. Receive client data, where the client data includes human-machine identification data and a service identification code.

[0235] 72d. Select human-machine identification rules or adjust human-machine identification thresholds based on the business identification code.

[0236] 73d. Based on the human-machine identification rule or human-machine identification threshold, determine whether the human-machine identification data is valid. If yes, that is, the human-machine identification data is valid, optionally, step 74d can be continued; if no, that is, the human-machine identification data is invalid, optionally, step 75d can be continued.

[0237] 74d. Feedback is notified through human-machine recognition.

[0238] 75d. Feedback notification of failure to pass human-machine recognition.

[0239] In this embodiment, the server-side device verifies the validity of the human-machine identification data based on the business identification code to obtain the human-machine identification result, thereby realizing the integration of human-machine identification and business. Therefore, it can not only achieve human-machine identification, but also reduce the adverse effects of human-machine identification on the business.

[0240] Hereby explain, Figure 7c and Figure 7d The method shown can also be applied to Figure 1 In the risk control system shown in the figure, Figure 7c and Figure 7d The method shown is applied to Figure 1 When the risk control system is used, the operation of "determining whether the human-machine identification data is valid based on the human-machine identification rules or human-machine identification thresholds" in the method can be deployed on the human-machine identification device for implementation. Figure 2-Figure 5 as well as Figure 7a The method shown can also be applied to Figure 7b In the business system shown in Figure 2-Figure 5 as well as Figure 7a The method shown is applied to Figure 7b When the business system shown in the figure is used, the human-machine recognition operation implemented by the human-machine recognition device can be integrated into the server device, and the relevant communication process can be omitted. Based on the above embodiments, those skilled in the art can easily think of the implementation process of these expansion solutions, which will not be described in detail.

[0241] It should be noted that in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel, and the sequence numbers of the operations, such as 201, 202, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0242] Figure 8a The following is a schematic diagram of the structure of a wind control device provided by another exemplary embodiment of the present application. Figure 8a As shown, the device includes: a collection module 81 and a sending module 82.

[0243] The collection module 81 is used to collect human-machine identification parameters when the operation object initiates a service request.

[0244] The sending module 82 is used to respond to the business request initiated by the operation object and send the business request and human-machine identification parameters to the server device, so that the server device can determine the processing method of the business request based on the business risk control policy corresponding to the business request and the human-machine identification result for the operation object obtained by the human-machine identification device based on the human-machine identification parameters.

[0245] In an exemplary embodiment, the wind control device further includes: a receiving module 83 and an output module 84 .

[0246] The receiving module 83 is used to receive the response message sent by the server device. The output module 84 is connected to the receiving module 83 and is used to output a result page matching the response message received by the receiving module 83, wherein the response message reflects the processing mode of the server device for the service request.

[0247] In an exemplary embodiment, the sending module 82 is specifically used to: in response to a service request initiated by the operation object, send the human-machine identification parameter to the server device by carrying it in the service request.

[0248] In an exemplary embodiment, the collection module 81 is specifically used to collect first behavior data generated by the interaction between the operation object and the client device and / or soft / hard environment parameters of the client device as human-machine identification parameters during the process of the operation object initiating a service request.

[0249] In an exemplary embodiment, the receiving module 83 is also used to: when the processing mode of the service request of the service end device is to reject the service request, receive the verification code sent by the service end device, and the output module 84 is also used to output the verification code received by the receiving module 83. Accordingly, the acquisition module 81 is also used to respond to the operation of the operation object inputting the verification code, and obtain the second behavior data generated by the operation object in the process of inputting the verification code and the verification code input by the operation object. Accordingly, the sending module 82 is also used to send the second behavior data obtained by the acquisition module 81 and the verification code input by the operation object to the service end device, so that the service end device can redetermine the processing mode of the service request according to the second behavior data and the verification code input by the operation object.

[0250] The above describes the internal functions and structures of the wind control device, such as Figure 8bAs shown, in practice, the risk control device can be implemented as a client device, including: a memory 801, a processor 802 and a communication component 803.

[0251] The memory 801 is used to store computer programs and can be configured to store various other data to support operations on the client device. Examples of such data include instructions for any application or method operating on the client device, contact data, phone book data, messages, pictures, videos, etc.

[0252] The memory 801 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0253] The processor 802 is coupled to the memory 801 and is configured to execute the computer program in the memory 801 to:

[0254] Collect human-machine identification parameters when the operation object initiates a service request;

[0255] In response to the business request initiated by the operation object, the business request and human-machine identification parameters are sent to the server device through the communication component 803, so that the server device can determine the processing method of the business request based on the business risk control policy corresponding to the business request and the human-machine identification result for the operation object obtained by the human-machine identification device based on the human-machine identification parameters.

[0256] Correspondingly, the communication component 83 is used to send the service request and human-machine identification parameters to the server device.

[0257] In an exemplary embodiment, the processor 802 is further used to: receive a response message sent by the server device through the communication component 803, and output a result page matching the response message, wherein the response message reflects the processing method of the server device for the service request. Accordingly, the communication component 803 is also used to receive a response message sent by the server device.

[0258] In an exemplary embodiment, the processor 802 is specifically configured to: in response to a service request initiated by the operation object, send the human-machine identification parameter to the service request through the communication component 803 to the server device.

[0259] In an exemplary embodiment, the processor 802 is specifically configured to: during the process of the operation object initiating a service request, collect first behavior data generated by the interaction between the operation object and the client device and / or soft / hard environment parameters of the client device as human-machine identification parameters.

[0260] In an exemplary embodiment, the processor 802 is also used to: when the server-side device processes the service request by rejecting the service request, receive the verification code sent by the server-side device through the communication component 803, and output the verification code; in response to the operation of the operation object entering the verification code, obtain the second behavior data generated by the operation object in the process of entering the verification code and the verification code entered by the operation object, and send the second behavior data and the verification code entered by the operation object to the server-side device through the communication component 803, so that the server-side device can redetermine the processing method for the service request based on the second behavior data and the verification code entered by the operation object.

[0261] Further, if Figure 8b As shown, the client device also includes: a display 804, a power component 805, an audio component 806 and other components. Figure 8b Only some components are shown schematically, which does not mean that the client device only includes Figure 8b Components shown.

[0262] The above-mentioned risk control device or client device can provide business requests and human-machine identification parameters to the server-side device, so that the server-side device can simultaneously combine the human-machine identification obtained by the human-machine identification device based on the human-machine identification parameters with the business risk control strategy corresponding to the business request to perform risk control on the business request, so that the business risk control strategy and the human-machine identification results can influence and correct each other to produce a more favorable risk control result, which can not only achieve human-machine identification, but also reduce the adverse effects of human-machine identification on the business.

[0263] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step or operation that can be performed by a client device in the above method embodiment.

[0264] Figure 9a The following is a schematic diagram of the structure of another wind control device provided by another exemplary embodiment of the present application. Figure 9a As shown, the device includes: a receiving module 91, a sending module 92 and a determining module 93.

[0265] The receiving module 91 is used to receive a service request and a human-machine identification parameter sent by a client device. The human-machine identification parameter is collected by the client device during the process of the operation object initiating the service request.

[0266] The sending module 92 is used to forward the human-machine identification parameters to the human-machine identification device.

[0267] The receiving module 91 is further used to receive the human-machine recognition result for the operation object returned by the human-machine recognition device according to the human-machine recognition parameters.

[0268] The determination module 93 is used to determine the processing method for the business request based on the human-machine recognition result received by the receiving module 91 and the business risk control policy corresponding to the business request.

[0269] In an exemplary embodiment, the sending module 92 is also used to: after the determination module 93 determines the processing method for the business request, send a response message to the client device, so that the client device outputs a result page matching the response message; wherein the response message reflects the processing method of the service request by the server device.

[0270] In an exemplary embodiment, the determination module 93 is specifically used to: determine the business risk level of the business request according to the business risk control strategy; and determine the processing method of the business request in combination with the human-machine recognition result and the business risk level of the business request.

[0271] Further, the determination module 93 is specifically used to: when the human-machine recognition result indicates that the operation object is a cheating machine script / program or the probability of indicating that the operation object is a cheating machine script / program is greater than or equal to a set probability threshold, but the business risk level of the business request is less than the preset business risk level threshold, determine to accept the business request; or, when the human-machine recognition result indicates that the operation object is not a cheating machine script / program or the probability of indicating that the operation object is a cheating machine script / program is less than the set probability threshold, but the business risk level of the business request is greater than or equal to the preset business risk level threshold, determine to reject the business request.

[0272] In an exemplary embodiment, the sending module 92 is further used to: when the processing method for the service request is to reject the service request, send a verification code to the client device for the operation object to enter the verification code. Correspondingly, the receiving module 91 is also used to: receive the second behavior data generated by the operation object in the process of entering the verification code and the verification code entered by the operation object returned by the client device. The determining module 93 is also used to: re-determine the processing method for the service request according to the second behavior data and the verification code entered by the operation object.

[0273] The above describes the internal functions and structures of the wind control device, such as Figure 9b As shown, in practice, the risk control device can be implemented as a server-side device, including: a memory 901, a processor 902 and a communication component 903.

[0274] The memory 901 is used to store computer programs and can be configured to store various other data to support operations on the server device. Examples of such data include instructions for any application or method operating on the server device, contact data, phone book data, messages, pictures, videos, etc.

[0275] Memory 901 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0276] The processor 902 is coupled to the memory 901 and is configured to execute the computer program in the memory 901 to:

[0277] Receiving, through the communication component 903, a service request and a human-machine identification parameter sent by a client device, wherein the human-machine identification parameter is collected by the client device during the process of the operation object initiating the service request;

[0278] Forwarding the human-machine identification parameters to the human-machine identification device through the communication component 903, and receiving the human-machine identification result for the operation object returned by the human-machine identification device according to the human-machine identification parameters; and

[0279] Combine the human-machine recognition results with the business risk control strategy corresponding to the business request to determine how to handle the business request.

[0280] Correspondingly, the communication component 903 is used to receive the service request and human-machine identification parameters sent by the client device, forward the human-machine identification parameters to the human-machine identification device, and receive the human-machine identification results returned by the human-machine identification device.

[0281] In an exemplary embodiment, the processor 902 is further configured to: after determining the processing method for the service request, send a response message to the client device through the communication component 903, so that the client device outputs a result page matching the response message; wherein the response message reflects the processing method of the service request by the server device. Accordingly, the communication component 903 is further configured to: send a response message to the client device.

[0282] In an exemplary embodiment, the processor 902 is specifically used to: determine the business risk level of the business request according to the business risk control strategy; and determine the processing method of the business request in combination with the human-machine recognition result and the business risk level of the business request.

[0283] Further, the processor 902 is specifically used to: when the human-machine recognition result indicates that the operation object is a cheating machine script / program or the probability of indicating that the operation object is a cheating machine script / program is greater than or equal to a set probability threshold, but the business risk level of the business request is less than the preset business risk level threshold, determine to accept the business request; or, when the human-machine recognition result indicates that the operation object is not a cheating machine script / program or the probability of indicating that the operation object is a cheating machine script / program is less than the set probability threshold, but the business risk level of the business request is greater than or equal to the preset business risk level threshold, determine to reject the business request.

[0284] In an exemplary embodiment, the processor 902 is further used to: when the processing method for the service request is to reject the service request, send a verification code to the client device through the communication component 903 for the operation object to enter the verification code, receive the second behavior data generated by the operation object in the process of entering the verification code and the verification code entered by the operation object returned by the client device through the communication component 903; and re-determine the processing method for the service request according to the second behavior data and the verification code entered by the operation object. Correspondingly, the communication component 903 is also used to: send the verification code to the client device, and receive the second behavior data returned by the client device and the verification code entered by the operation object.

[0285] Further, if Figure 9b As shown, the server device also includes: a display 904, a power component 905, an audio component 906 and other components. Figure 9b Only some components are shown schematically, which does not mean that the server device only includes Figure 9b Components shown.

[0286] The above-mentioned risk control device or server-side equipment can cooperate with the human-machine identification device. Human-machine identification is no longer regarded as a closed system. Instead, human-machine identification is coupled to the business risk control strategy. Then, the human-machine identification result of the human-machine identification device and the business risk control strategy corresponding to the business request can be combined to determine the processing method of the corresponding business request, so that the business risk control strategy and the human-machine identification result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine identification and reducing the adverse effects of human-machine identification on the business.

[0287] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step or operation that can be performed by the server-side device in the above method embodiment.

[0288] Fig.10a The following is a schematic diagram of a human-machine identification device provided by another exemplary embodiment of the present application. Fig.10aAs shown, the device includes: a receiving module 101, an identification module 102 and a sending module 103.

[0289] The receiving module 101 is used to receive the human-machine identification parameters sent by the server device. The human-machine identification parameters are collected by the client device and provided to the server device during the process of the operation object initiating a service request.

[0290] The identification module 102 is used to perform human-machine identification on the operation object according to the human-machine identification parameters to obtain a human-machine identification result.

[0291] The sending module 103 is used to send the human-machine recognition result to the server device, so that the server device can determine the processing method of the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0292] In an exemplary embodiment, the human-machine identification parameters include: the first behavior data generated by the operation object interacting with the client device in the process of initiating a service request and the soft / hard environment parameters of the client device. Based on this, the identification module 102 is specifically used to: determine the first probability that the operation object is a cheating machine script / program based on the first behavior data; determine the second probability that the operation object is a cheating machine script / program based on the soft / hard environment parameters of the client device and the operating environment parameters of the known cheating machine script / program; and obtain the human-machine identification result by combining the first probability and the second probability.

[0293] The above describes the internal functions and structure of the human-machine recognition device. Fig.10b As shown, in practice, the human-machine identification apparatus can be implemented as a human-machine identification device, including: a memory 1001 , a processor 1002 and a communication component 1003 .

[0294] The memory 1001 is used to store computer programs and can be configured to store various other data to support operations on the captcha device. Examples of such data include instructions for any application or method used to operate on the captcha device, contact data, phone book data, messages, pictures, videos, etc.

[0295] Memory 1001 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0296] The processor 1002 is coupled to the memory 1001 and is configured to execute the computer program in the memory 1001 to:

[0297] Receive the human-machine identification parameters sent by the server device through the communication component 1003, where the human-machine identification parameters are collected by the client device and provided to the server device during the process of the operation object initiating a service request;

[0298] Performing human-machine identification on the operation object according to the human-machine identification parameters to obtain a human-machine identification result;

[0299] The human-machine recognition result is sent to the server device through the communication component 1003, so that the server device can determine the processing method of the business request based on the human-machine recognition result and the business risk control policy corresponding to the business request.

[0300] In an exemplary embodiment, the human-machine identification parameters include: the first behavior data generated by the operation object interacting with the client device in the process of initiating a service request and the soft / hard environment parameters of the client device. Based on this, the processor 1002 is specifically used to: determine the first probability that the operation object is a cheating machine script / program according to the first behavior data; determine the second probability that the operation object is a cheating machine script / program according to the soft / hard environment parameters of the client device and the operating environment parameters of the known cheating machine script / program; and obtain the human-machine identification result by combining the first probability and the second probability.

[0301] Further, if Fig.10b As shown, the human-machine identification device also includes: a display 1004, a power supply component 1005, an audio component 1006 and other components. Fig.10b Only some components are shown schematically, which does not mean that the human-machine recognition device only includes Fig.10b Components shown.

[0302] The above-mentioned human-machine identification device or equipment can perform human-machine identification on the operation object according to the human-machine identification parameters to obtain the human-machine identification result and send the human-machine identification result to the server device, so that the server device can determine the processing method of the business request in combination with the human-machine identification result and the business risk control strategy corresponding to the business request, so that the business risk control strategy and the human-machine identification result can influence and correct each other to produce a more favorable risk control result, thereby achieving the beneficial effect of both achieving human-machine identification and reducing the adverse effects of human-machine identification on the business.

[0303] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step or operation that can be performed by the human-machine recognition device in the above method embodiment.

[0304] Fig.11a A schematic diagram of the structure of another client device provided by another exemplary embodiment of the present application. Fig.11aAs shown, the client device includes: a memory 111 , a processor 112 , and a communication component 113 .

[0305] The memory 111 is used to store computer programs and can be configured to store various other data to support operations on the client device. Examples of such data include instructions for any application or method operating on the client device, contact data, phone book data, messages, pictures, videos, etc.

[0306] The processor 112 is coupled to the memory 111 and is used to execute the computer program in the memory 111 to: obtain human-machine identification data, and send client data to the server device through the communication component 113, wherein the client data includes human-machine identification data and a service identification code, and the service identification code is used to indicate a human-machine identification rule or a human-machine identification threshold.

[0307] In addition, Fig.11a As shown, the human-machine identification device also includes: a display 114, a power component 115, an audio component 116 and other components. Fig.11a Only some components are shown schematically, which does not mean that the client device only includes Fig.11a Components shown.

[0308] Accordingly, the present application also provides a computer-readable storage medium storing a computer program, which can achieve the above-mentioned Figure 7b-7c The various steps or operations in the illustrated embodiments may be performed by a client device.

[0309] Fig.11b A schematic diagram of the structure of another server device provided by another exemplary embodiment of the present application. Fig.11b As shown, the client device includes: a memory 121 , a processor 122 and a communication component 123 .

[0310] The memory 121 is used to store computer programs and can be configured to store various other data to support operations on the client device. Examples of such data include instructions for any application or method operating on the client device, contact data, phone book data, messages, pictures, videos, etc.

[0311] The processor 122 is coupled to the memory 121 and is used to execute the computer program in the memory 121, so as to: receive client data through the communication component 123, select human-machine identification rules or adjust the human-machine identification threshold according to the service identification code in the client data; and then determine whether the human-machine identification data is valid based on the selected human-machine identification rules or the adjusted human-machine identification threshold.

[0312] Further optionally, the processor 122 is also used to: if it is determined that the human-machine identification data is valid, feedback a human-machine identification passing notification through the communication component 123; if it is determined that the human-machine identification data is invalid, feedback a human-machine identification failing notification through the communication component 123.

[0313] In addition, Fig.11b As shown, the human-machine identification device also includes: a display 124, a power supply component 125, an audio component 126 and other components. Fig.11b Only some components are shown schematically, which does not mean that the server device only includes Fig.11b Components shown.

[0314] Accordingly, the present application also provides a computer-readable storage medium storing a computer program, which can achieve the above-mentioned Figure 7b and Figure 7d The illustrated embodiments include steps or operations that can be performed by a server device.

[0315] exist Figure 8b , Figure 9b , Fig.10b , Fig.11a and Fig.11b In the embodiment, the communication component is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0316] exist Figure 8b , Figure 9b , Fig.10b , Fig.11a and Fig.11b In the embodiment, the display includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0317] exist Figure 8b , Figure 9b , Fig.10b , Fig.11a and Fig.11b In the present invention, a power supply component provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0318] exist Figure 8b , Figure 9b , Fig.10b , Fig.11a and Fig.11b In some embodiments, the audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0319] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0320] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0321] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0322] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0323] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0324] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0325] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0326] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0327] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A data processing method, applicable to a server device, characterized in that: The method comprises: Receiving client data, wherein the client data includes human-machine identification data and a service identification code; According to the service identification code and the pre-established mapping relationship between each service identification code and the human-machine identification rule or the human-machine identification threshold, select the human-machine identification rule or adjust the human-machine identification threshold; The human-machine identification rule or human-machine identification threshold is a rule or threshold that integrates the business risk control strategy and has the human-machine identification function, or a rule or threshold that has the human-machine identification function determined according to the business risk control strategy; Based on the human-machine identification rule or the human-machine identification threshold, it is determined whether the human-machine identification data is valid.

2. The method according to claim 1, characterized in that: Also includes: If the human-machine identification data is valid, feedback is given through human-machine identification notification; If the human-machine identification data is invalid, a notification indicating that human-machine identification failed is fed back.

3. The method according to claim 1, characterized in that The human-machine identification rule or human-machine identification threshold is a rule or threshold that integrates the business risk control strategy and has the human-machine identification function, or a rule or threshold that has the human-machine identification function determined according to the business risk control strategy; Based on the human-machine identification rule or the human-machine identification threshold, judging whether the human-machine identification data is valid includes: Determining, according to the business risk control strategy, a business risk level of a business request corresponding to the human-machine identification data, where the business request is initiated by the operation object; Whether the human-machine identification data is valid is determined according to the business risk level of the business request.

4. The method according to claim 3, characterized in that Judging whether the human-machine identification data is valid according to the business risk level of the business request includes: In the case where the human-machine identification data indicates that the operation object is a cheating machine script / program or the probability that the operation object is a cheating machine script / program is greater than or equal to a set probability threshold, if the business risk level of the business request is less than the preset business risk level threshold, then the human-machine identification data is determined to be invalid; or When the human-machine identification result indicates that the operation object is not a cheating machine script / program or indicates that the probability of the operation object being a cheating machine script / program is less than a set probability threshold, if the business risk level of the business request is greater than or equal to the preset business risk level threshold, the human-machine identification data is determined to be invalid.

5. A data processing method, applicable to a client device, characterized in that: The method comprises: Obtain human and machine identification data; Sending client data to the server device, wherein the client data includes the human-machine identification data and a service identification code, wherein the service identification code is used to indicate a human-machine identification rule or a human-machine identification threshold; Among them, the human-machine identification rule or human-machine identification threshold is a rule or threshold that integrates the business risk control strategy and has the human-machine identification function, or is a rule or threshold with the human-machine identification function determined according to the business risk control strategy.

6. The method according to claim 5, characterized in that Obtain human and machine identification data, including: In response to a service request initiated by an operation object, human-machine identification data is obtained.

7. The method according to claim 6, characterized in that In response to a service request initiated by an operation object, human-machine identification data is obtained, including: In the process of the operation object initiating a service request, first behavior data generated by the interaction between the operation object and the client device and / or soft / hard environment parameters of the client device are collected as the human-machine identification data.

8. A server device, characterized in that: include: Memory and processor; The memory is used to store a computer program, and the processor is coupled to the memory and is used to execute the computer program in the memory to implement the steps in the method according to any one of claims 1 to 4.

9. A client device, characterized in that: include: Memory and processor; The memory is used to store a computer program, and the processor is coupled to the memory and is used to execute the computer program in the memory to implement the steps in the method according to any one of claims 5 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps in the method according to any one of claims 1 to 7 can be implemented.

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