CN111339894A - Data processing and risk identification method, device, equipment and medium - Google Patents
Data processing and risk identification method, device, equipment and medium Download PDFInfo
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Abstract
The embodiment of the specification discloses a data processing method, a risk identification method, a device, equipment and a medium, wherein the data processing method comprises the following steps: acquiring video data; judging scene data corresponding to the video data; and judging risk data corresponding to the video data according to the scene data, wherein the risk data is used for judging whether risks exist in handling business and/or operation of a target object corresponding to the video data.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a medium for data processing and risk identification.
Background
In many practical application scenarios, in order to ensure the rights and interests of users and service providers, risk identification and/or user qualification are required when users handle certain services or certain operations.
Disclosure of Invention
The embodiment of the specification provides a data processing method, a risk identification method, a device, equipment and a medium, which are used for solving the technical problem of how to better perform data processing and/or risk identification.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
an embodiment of the present specification provides a data processing method, including:
acquiring video data;
judging scene data corresponding to the video data;
and judging risk data corresponding to the video data according to the scene data, wherein the risk data is used for judging whether risks exist in handling business and/or operation of a target object corresponding to the video data.
In an embodiment of the present specification, the scene data comprises scene type data and/or scene position data and/or status data of the target object.
In an embodiment of the present specification, the scene data includes scene type data, and the scene type data includes a family scene and/or a substitute shooting scene.
In an embodiment of the present specification, the scene data includes a home location and/or a non-home location.
In an embodiment of the present specification, the determining, according to the scene data, risk data corresponding to the video data includes:
if the scene data comprise a family scene, judging the tidiness data corresponding to the scene data;
and judging risk data corresponding to the video data according to the tidiness data.
In an embodiment of the present specification, the risk data includes a scene risk, and determining the risk data corresponding to the video data according to the smoothness data includes:
judging whether scene risks exist in the video data according to the tidiness degree;
and/or the presence of a gas in the gas,
the risk data comprises life state risks, and the step of judging the risk data corresponding to the video data according to the tidiness data comprises the following steps:
and judging whether the video data has life state risk according to the finishment.
In an embodiment of this specification, the risk data includes a scene risk, and determining the risk data corresponding to the video data according to the scene data includes:
and if the scene data comprise the alternate shooting scene, judging that the video data have scene risks.
In an embodiment of this specification, if it is determined that there is a risk in transacting a service with a target object corresponding to the video data, risk prompt information is output.
In an embodiment of the present specification, the determining, according to the scene data, risk data corresponding to the video data includes:
and judging matching data of the target object corresponding to the video data and the service handling registration object according to the scene data, wherein the matching data belongs to risk data.
In an embodiment of the present specification, determining, according to the scene data, matching data between a target object corresponding to the video data and a service transaction registration object includes:
comparing the scene data corresponding to the video object with the scene data corresponding to the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
In an embodiment of the present specification, the scene data includes a target object corresponding to the video data;
the judging of the matching data of the target object corresponding to the video data and the service transaction registration object according to the scene data comprises the following steps:
acquiring a characteristic image of the target object, and comparing the characteristic image of the target object with a characteristic image of a service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
In an embodiment of the present specification, the scene data includes state data of a target object corresponding to the video;
the judging of the matching data of the target object corresponding to the video data and the service transaction registration object according to the scene data comprises the following steps:
comparing the state data of the target object with the state data of the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
In an embodiment of the present specification, the risk data includes whether a target object corresponding to the video belongs to a risk group, and the scene data includes state data of the target object;
determining the risk data corresponding to the video data according to the scene data includes:
judging the crowd attribute of a target object corresponding to the video data according to the state data;
and determining whether the target object belongs to a risk group according to the crowd attribute.
In an embodiment of the present specification, the determining scene data corresponding to the video data includes:
and judging scene data corresponding to the video data according to the video background of the video data.
In an embodiment of the present specification, acquiring video data includes:
the target object is photographed using an image capturing apparatus to acquire video data.
In an embodiment of the present specification, the risk data is used to determine whether a target object corresponding to the video data has a business transaction qualification.
An embodiment of the present specification provides a risk identification method, including:
judging whether the operation corresponding to the target object needs risk identification;
if yes, acquiring video data aiming at the target object;
judging scene data corresponding to the video data;
and judging risk data corresponding to the video data according to the scene data, and judging whether the operation has a risk and/or whether the target object has the handling qualification of the operation according to the risk data.
An embodiment of the present specification provides a data processing apparatus, including:
the video module is used for acquiring video data;
the scene module is used for judging scene data corresponding to the video data;
and the risk module is used for judging risk data corresponding to the video data according to the scene data, and the risk data is used for judging whether a risk exists in business handling and/or operation of a target object corresponding to the video data.
An embodiment of the present specification provides a risk identification device, including:
the operation identification module is used for judging whether the operation corresponding to the target object needs risk identification;
the video module is used for acquiring video data aiming at a target object if the operation corresponding to the target object needs risk identification;
the scene module is used for judging scene data corresponding to the video data;
and the risk module is used for judging risk data corresponding to the video data according to the scene data and judging whether the operation has risk and/or whether the target object has the handling qualification of the operation according to the risk data.
An embodiment of the present specification provides a data processing apparatus, including:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the data processing method described above.
An embodiment of the present specification provides a risk identification device, including:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the risk identification method described above.
The present specification provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the data processing method is implemented.
Embodiments of the present specification provide a computer-readable storage medium storing computer-executable instructions, which when executed by a processor implement the risk identification method described above.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
corresponding scene data are obtained through analysis of the video data, risk data corresponding to the video data are determined according to the scene data, whether risks exist in operation and/or whether a target object is qualified or not is judged according to the risk data, the accuracy of the obtained risk data can be effectively ensured, and therefore the accuracy of data processing and risk identification results is effectively improved; the video data is analyzed from the perspective of the external environment, so that risk data is obtained, effective data support is provided for risk identification and analysis of user qualification, and accuracy of risk identification and user qualification confirmation is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments of the present specification or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a flowchart illustrating a data processing method in the first embodiment of the present specification.
Fig. 2 is a schematic flow chart of a risk identification method in a second embodiment of the present specification.
Fig. 3 is a schematic structural diagram of a data processing apparatus in a third embodiment of the present specification.
Fig. 4 is a schematic structural diagram of a risk identification device in a fourth embodiment of the present specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present disclosure, shall fall within the scope of protection of the present application.
In many practical application scenarios, in order to ensure the rights and interests of users and service providers, risk identification and/or user qualification are required when users handle certain services or certain operations. The user qualification information is used for determining whether the user has the qualification for transacting certain services, and the more comprehensive the user qualification information contains, the more accurate the user qualification determined according to the user qualification information.
In the prior art, multiple kinds of description information of a current user (for example, an identity certificate, a family condition, a working condition, a property condition, and the like of the current user) are generally collected manually, user qualification information of the current user is obtained through authentication and analysis of the user description information, and finally, qualification of the current user is confirmed through analysis of the user qualification information. The comprehensiveness and authenticity of the user qualification information directly influence the accuracy of the user qualification confirmation result.
However, since the user profile is generally data of a private nature and covers aspects of personal life, obtaining sufficiently comprehensive and effective user qualification information is difficult to perform (e.g., lack of channels for obtaining or excessive effort for obtaining information). In the process of acquiring the user qualification information, not only many kinds of user qualification information cannot be acquired smoothly, but also authenticity of the user qualification information which can be acquired is questioned, which seriously affects the accuracy of the confirmation result of the user qualification.
In a practical application scenario, the current state of the user can be used as an important reference factor for qualification confirmation of some users. For example, the handling of some services requires that the handling person completely know all the details of the services and carefully consider the details of the services to decide to handle, so as to ensure that the handling person can seriously fulfill the obligations related to the services after the services are handled; or, some services need a transactor to decide to transact the services by himself without the interference of external factors, so that the legality of the service transaction can be ensured. Therefore, in some procedures for determining user qualification, determining the current status of the user (or obtaining user qualification information that can prove the current status of the user) is a very important link.
Further, in an actual application scenario, the current state of the user is laterally reflected by the current environment where the user is located. For example, when the user is in a noisy street, the user can be approximately considered that the current state of the user cannot be seriously considered; when the user is in a remote and inhospitable environment, the safety degree of the user is questioned, and external factor interference can exist greatly.
Based on the above analysis, in an embodiment of the present specification, a risk state description of a scene in which the target object is located is used to describe a risk that may exist when the user performs a business operation in the environment, and the risk state description of the scene in which the target object is located is used as part of risk identification and/or user qualification information. The risk state description of the scene where the target object is located is obtained to serve as powerful data support for risk identification and/or user qualification. Because the risk state description of the scene where the target object is located can effectively reflect the current state of the user, and the scene where the target object is located is relatively easy to confirm, the accuracy of the user qualification confirmation result can be effectively improved by using the risk state description of the scene where the target object is located as a part of the user qualification information.
According to the method of the embodiment of the specification, the video data is analyzed from the perspective of the external environment, so that the user qualification information aiming at the scene risk degree is obtained, effective data support is provided for the analysis of the user qualification, and the accuracy of the user qualification confirmation result is effectively improved.
Further, with the development of network technology, the user qualification information is usually acquired in an online manner and transmitted in the form of electronic photos and electronic documents. Often, a service provider does not directly face a user, and cannot directly confirm a current scene of the user, so that scene information of the user needs to be submitted by the user, and authenticity exists.
In view of the above problems, in an embodiment of the present specification, video data is used as a source of user qualification information, and a risk state description of an environment where a user is currently located is obtained by analyzing the video data submitted by the user. Because the counterfeiting difficulty of the video data is far higher than that of the electronic photos and the electronic documents, the credibility of the user qualification information obtained through the video data is far higher than that of the user qualification information obtained through the electronic photos and the electronic documents. According to the method provided by the embodiment of the specification, the user qualification information is acquired through the video data, and the credibility of the acquired user qualification information can be effectively ensured.
According to the method provided by the embodiment of the specification, the user qualification information is obtained through analysis of the video data, so that the reliability of the obtained user qualification information can be effectively ensured, and the accuracy of the user qualification confirmation result is effectively improved.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a first embodiment of the present description provides a data processing method including:
s101: acquiring video data;
in this embodiment, the video data may correspond to a target object (i.e., a user transacting a certain specified service or performing a certain specified operation or operations), i.e., the target object exists in the video data. The video data can be generated by the image acquisition equipment, and the image acquisition equipment can be used for shooting the target object to generate the video data so as to meet the requirement that the video data corresponds to the target object, meanwhile, the obtained video data can be ensured to correctly reflect the state of the target object, and the inaccuracy possibly caused by the use of the pre-recorded video is avoided.
In this embodiment, the generated video data may be acquired through data call, which is not limited in this embodiment.
S103: judging scene data corresponding to the video data;
by analyzing the video data, scene data corresponding to the video data, that is, scene data of a scene in which the target object shown in the video data is located, can be obtained. Specifically, the scene data corresponding to the video data may be determined according to a video background of the video data.
In this embodiment, the scene data corresponding to the video data may include scene type data and/or scene position data and/or state data of the target object.
The scene type data comprises family scenes and/or alternate shooting scenes, wherein the family scenes can refer to families of which scene arrangements in the video data are target objects; the alternate shooting scene here may refer to video data non-target object shooting or non-specified device shooting or non-specified person shooting.
The scene location data includes a home location and/or a non-home location (e.g., business), where a home location may refer to a home of the target object.
The status data of the target object may refer to the age and/or clothing of the target object.
S105: and judging risk data corresponding to the video data according to the scene data, wherein the risk data is used for judging whether risks exist in handling business and/or operation of a target object corresponding to the video data.
In this embodiment, the scene data may be analyzed, and risk data corresponding to the video data may be determined according to the scene data. The risk data may be regarded as a risk state description (of the target object), and the risk state description may be used to describe a risk that may exist when the user performs a business operation in an environment shown in the video data, and may be output as user qualification information.
In an embodiment, in the process of parsing the context information, the context information may be parsed based on a preset rule. The preset rules can be formulated according to the environmental requirements of the user for executing the business operation. For example, in some financial business operation environment requirements, a user performing business operation is required to be in a safe, quiet and non-interference environment, and accordingly, corresponding rules can be formulated, such as parsing scene data to determine whether the scene data conforms to the safe, quiet and non-interference environment. The preset rule can be formulated according to the relevance of the target object and the scene.
In the embodiment, the video data is analyzed from the perspective of the external environment, so that the user qualification information aiming at the scene risk degree is obtained, and effective data support is provided for the analysis of the user qualification, so that the accuracy of the user qualification confirmation result is effectively improved; furthermore, according to the method of the embodiment of the present specification, the user qualification information is obtained by analyzing the video data, so that the reliability of the obtained user qualification information can be effectively ensured, and the accuracy of the user qualification confirmation result is effectively improved.
In this embodiment, the risk state description of the scene where the target object is located is a description of a risk degree of the scene where the target object is located. The risk state description is a reference, and if the risk state description expresses that the scene risk exists, the risk state description is not equal to that the target object does not have the qualification for handling the business, but only indicates that the target object may not have the qualification for handling, and further qualification analysis and determination are needed. In the determination of the qualification of the user, the risk state description can be used as a main factor or an auxiliary factor for the qualification determination of the user. The user qualification can be judged by integrating the risk state description and other user qualification information instead of only judging the user qualification by using the risk state description.
Specifically, in the process of acquiring scene information, the scene type and/or the scene position of the scene where the target object is located are/is acquired; and in the process of obtaining the risk state description, judging whether the scene of the target object is a predefined risk abnormal scene or not according to the scene type and/or the scene position of the scene of the target object.
For example, in an application scenario, transacting a business requires the transactor to decide to transact after having full knowledge of all business details and careful attention. Then, if the transactor is in an environment that cannot be considered seriously, the business transaction decisions that he makes under that environment are risky. That is, a transactor in an environment that cannot be considered seriously will not have sufficient user qualifications to transact current business. Therefore, in the application scenario, a scenario that cannot be considered seriously is defined as a risk abnormal scenario, and whether the target object is in the risk abnormal scenario or not is determined, so that whether the target object currently has the qualification of handling the business or not can be further determined.
For example, in an application scenario of opening a payment account or a fund account, in some special environment scenarios (for example, some sales promotion places), a sales promoter invites a new mobile phone number to register the payment account or the fund account by sending a gift, and guides the sales promoter to open the payment account or the fund account. In this case there is a risk that the account number of the payment account or the fund account is mastered by the promoter. Thus, in an application scenario, a predefined sales promotion location (a specific location may be defined as a sales promotion location, e.g. a business overload) is a risk scenario. When the target object expects to open the payment account or the fund account, video data corresponding to the target object is obtained, scene data corresponding to the video data is judged, and whether the target object is in a risk scene or not is judged according to the scene data. If so, the target object may be under qualified and may require further analysis and validation.
In a practical application scenario, the state of cleanliness of a living environment of a person can reflect the living state (quality of life), and users with poor living state (low quality of life) are prone to qualification risks. As mentioned above, it can be determined that the scene data includes a family scene and/or a substitute shooting scene (non-family scene), and in this embodiment, determining the risk data corresponding to the video data according to the scene data includes:
if the scene data comprises a family scene, judging the finishing degree data (including but not limited to scene finishing degree and target object personal finishing degree) corresponding to the scene data; and judging risk data corresponding to the video data according to the tidiness data.
In this embodiment, the scene data includes a scene risk, and the scene risk, that is, a risk exists in a scene (for executing a specific operation or handling a specific service) displayed in the video data, is the same as below. The step of judging the risk data corresponding to the video data according to the tidiness data comprises the following steps: according to the tidiness, the living state of the target object can be judged, and therefore whether scene risks exist in the video data or not can be judged.
And/or the presence of a gas in the gas,
the risk data includes a life state risk, and a scene risk exists, namely a risk exists for executing a specified operation or handling a specified business according to the life state reflected by the scene displayed in the video data. The step of judging the risk data corresponding to the video data according to the tidiness data comprises the following steps:
and judging whether the video data has life state risk according to the finishment.
In an actual application scene, a plurality of services require independent handling by a handling person, and if a third party assists the handling person to handle, potential safety hazards exist. As mentioned above, it may be determined that the scene data includes a family scene and/or a substitute shooting scene (non-family scene), in this embodiment, the risk data includes a scene risk, and determining the risk data corresponding to the video data according to the scene data includes:
and if the scene data comprises a (third-party) generation shooting scene, judging that the video data has scene risks.
In a practical application scenario, when a specified operation is executed, a specified service is transacted, or user qualification is confirmed, it is often necessary to confirm whether or not a user (target object) who has transacted the service is the user himself (hereinafter referred to as a service transaction registration object) on a registration document for transacting the service. In this embodiment, matching data (matching data may belong to risk data) between the target object corresponding to the video data and the service transaction registration object may be determined according to the scene data, so as to provide powerful data support for determining whether the target object is the service transaction registration object, and improve accuracy of determining whether the target object is the service transaction registration object.
In this embodiment, the determining, according to the scene data, matching data between the target object corresponding to the video data and a service transaction registration object may include:
comparing the scene data corresponding to the video object (namely the scene data corresponding to the target object) with the scene data corresponding to the service handling registration object; and judging the matching data of the target object and the registered object according to the comparison result.
For example, when it can be determined from the video data of a certain person handling the business that the place (belonging to the scene data corresponding to the video data) where the certain person is located is not matched with the place (belonging to the scene data corresponding to the business handling registration object) where the certain person is personally registered (for example, a person whose living and working area is city a suddenly appears in city B to handle the business), certain potential safety hazard exists in handling the business (possibly, other people handle the business by impersonation). Therefore, the scene data corresponding to the video object is compared with the scene data corresponding to the service handling registration object; matching data of the target object and the registered object can be determined based on the comparison result.
The video data can not only show the environment where the user is located, but also show the user himself. In this embodiment, personal information or status data (including but not limited to age and clothing) of the target object may be obtained through analysis of the video data, and a risk status description of an environment in which the target object is located and/or matching data of the target object and a business transaction registration object may be obtained through parsing of the personal information or status data of the target object.
In this embodiment, the scene data includes state data of a target object corresponding to the video;
the judging of the matching data of the target object corresponding to the video data and the service transaction registration object according to the scene data comprises the following steps:
comparing the state data of the target object with the state data of the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
For example, it is determined that the target object exhibits age data (belonging to scene data) in the video data, and the age data exhibited by the target object in the video data is compared with age data registered by the business transaction registration object; as another example, it is determined that the target object exhibits clothing (belongs to the scene data) in the video data; the clothing data exhibited by the target object in the video data is compared with the clothing data registered by the business transaction registration object (for example, the clothing data in the business transaction registration object reservation image).
In an actual application scenario, some people have security risks when handling a specific business. For example, people of older ages are not used to a mobile phone, so that the people are prone to have insufficient knowledge of business details and wrong business process when handling mobile phone related business, and are also prone to have business process errors guided by a third party. For example, children do not have independent operation capability, so that the situations of insufficient understanding of business details and wrong handling process are easy to occur, and the situation of wrong handling of business guided by a third party is easy to occur.
In this embodiment, the risk data may include whether a target object corresponding to the video belongs to a risk group, and the scene data may include status data (including but not limited to age) of the target object;
determining the risk data corresponding to the video data according to the scene data includes:
according to the state data, judging the crowd attribute (such as whether the crowd attribute belongs to the category of old people or children) of the target object corresponding to the video data;
and determining whether the target object belongs to a risk group according to the crowd attribute.
In this embodiment, determining, according to the scene data, matching data between a target object corresponding to the video data and a service transaction registration object includes:
acquiring a characteristic image of the target object from the video data, and comparing the characteristic image of the target object with a characteristic image of a service transaction registration object; and judging the matching data of the target object and the registered object according to the comparison result. The characteristic images of the target object are the face images of the target object at different time of the video data, namely, the face images of a plurality of target objects are obtained by capturing the images of different time points of the video data; the feature image of the business transaction registration object may be a reserved face image of the business transaction registration object. And comparing the face images of the plurality of target objects with the reserved face image of the service transaction registration object, and judging matching data according to the comparison result. Because the counterfeiting difficulty of the video data is generally higher than that of the image, the reality of the characteristic image of the target object obtained through the video data is higher; the accuracy of comparison through the face images of the multiple target objects can also improve the accuracy of the comparison result. By comparing the characteristic images, the accuracy of judging whether the target object is a business registration object can be effectively improved.
In this embodiment, the risk data may be used to determine whether a risk exists in a transaction and/or an operation of a target object corresponding to the video data, that is, to perform risk identification. For example, if there is a scene risk and/or there is a risk life state and/or the matching degree of the target object and the service transaction registration object is low and/or the target object belongs to a risk group, it may be determined that there is a risk when the target object corresponding to the video data handles the designated service or designated operation, or the target object does not have the handling qualification of the designated service or designated operation. The "service and/or operation" herein may be a designated service and/or operation (e.g., provisioning a payment service or performing a payment operation), and may also generally refer to all services and/or operations that a service provider may provide for a target object.
In this embodiment, if it is determined that there is a risk in transacting the service with the target object corresponding to the video data, risk prompt information may be output.
In the embodiment, corresponding scene data is obtained through analysis of video data, risk data corresponding to the video data is determined according to the scene data, whether risks exist in operation and/or whether a target object is qualified is judged according to the risk data, and judgment of the risk data through the video data is achieved; the scene data is obtained from the video data, and the risk data is obtained from the scene data, so that the accuracy of the obtained risk data can be effectively ensured, and the accuracy of data processing and risk identification results is effectively improved; the video data is analyzed from the external environment angle (scene angle) so as to obtain the risk data, and effective data support is provided for risk identification and analysis of user qualification, so that the accuracy of risk identification and user qualification confirmation is effectively improved.
As shown in fig. 2, a second embodiment of the present disclosure provides a risk identification method, including:
s201: judging whether the operation corresponding to the target object needs risk identification;
in this embodiment, it may be set that the designated operation requires risk identification, for example, opening a financial account and transacting a financial service. If the target object (e.g., a business handling user) is to handle or execute the operations, the operations may be referred to as operations corresponding to the target object.
S203: if the risk identification is needed, acquiring video data aiming at the target object;
as described above, if it is determined that risk identification is required, video data for the target object can be acquired to participate in the first embodiment.
S205: judging scene data corresponding to the video data;
see the first embodiment.
S207: and judging risk data corresponding to the video data according to the scene data, and judging whether the operation has a risk and/or whether the target object has the handling qualification of the operation according to the risk data.
See the first embodiment.
In the embodiment, corresponding scene data is obtained through analysis of video data, risk data corresponding to the video data is determined according to the scene data, whether risks exist in operation and/or whether a target object is qualified is judged according to the risk data, and judgment of the risk data through the video data is achieved; the scene data is obtained from the video data, and the risk data is obtained from the scene data, so that the accuracy of the obtained risk data can be effectively ensured, and the accuracy of data processing and risk identification results is effectively improved; the video data is analyzed from the external environment angle (scene angle) so as to obtain the risk data, and effective data support is provided for risk identification and analysis of user qualification, so that the accuracy of risk identification and user qualification confirmation is effectively improved.
As shown in fig. 3, a third embodiment of the present specification provides a data processing apparatus including:
a video module 301, configured to obtain video data;
a scene module 303, configured to determine scene data corresponding to the video data;
and a risk module 305, configured to determine, according to the scene data, risk data corresponding to the video data, where the risk data is used to determine whether a risk exists in handling a business and/or an operation of a target object corresponding to the video data.
Optionally, the scene data includes scene type data and/or scene position data and/or state data of the target object.
Optionally, the scene type data includes a family scene and/or a substitute shooting scene.
Optionally, the scene data includes a home location and/or a non-home location.
Optionally, determining, according to the scene data, risk data corresponding to the video data includes:
if the scene data comprise a family scene, judging the tidiness data corresponding to the scene data;
and judging risk data corresponding to the video data according to the tidiness data.
Optionally, the risk data includes a scene risk, and determining the risk data corresponding to the video data according to the smoothness data includes:
judging whether scene risks exist in the video data according to the tidiness degree;
and/or the presence of a gas in the gas,
the risk data comprises life state risks, and the step of judging the risk data corresponding to the video data according to the tidiness data comprises the following steps:
and judging whether the video data has life state risk according to the finishment.
Optionally, the risk data includes a scene risk, and determining, according to the scene data, the risk data corresponding to the video data includes:
and if the scene data comprise the alternate shooting scene, judging that the video data have scene risks.
Optionally, determining, according to the scene data, risk data corresponding to the video data includes:
and judging matching data of the target object corresponding to the video data and the service handling registration object according to the scene data, wherein the matching data belongs to risk data.
Optionally, determining, according to the scene data, matching data between a target object corresponding to the video data and a service transaction registration object includes:
comparing the scene data corresponding to the video object with the scene data corresponding to the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
Optionally, determining, according to the scene data, matching data between a target object corresponding to the video data and a service transaction registration object includes:
acquiring a characteristic image of the target object from the video data, and comparing the characteristic image of the target object with a characteristic image of a service transaction registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
Optionally, the feature image of the target object is that the target object is a face image at different times of the video data.
Optionally, the scene data includes state data of a target object corresponding to the video;
the judging of the matching data of the target object corresponding to the video data and the service transaction registration object according to the scene data comprises the following steps:
comparing the state data of the target object with the state data of the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
Optionally, the risk data includes whether a target object corresponding to the video belongs to a risk group, and the scene data includes state data of the target object;
determining the risk data corresponding to the video data according to the scene data includes:
judging the crowd attribute of a target object corresponding to the video data according to the state data;
and determining whether the target object belongs to a risk group according to the crowd attribute.
Optionally, the determining the scene data corresponding to the video data includes:
and judging scene data corresponding to the video data according to the video background of the video data.
Optionally, the risk module 305 is further configured to: and if the target object corresponding to the video data is judged to have risk in handling the business, outputting risk prompt information.
Optionally, the acquiring the video data includes:
the target object is photographed using an image capturing apparatus to acquire video data.
Optionally, the risk data is used to determine whether a target object corresponding to the video data has a business handling qualification.
As shown in fig. 4, a fourth embodiment of the present specification provides a risk identification device, including:
an operation identification module 401, configured to determine whether an operation corresponding to a target object requires risk identification;
a video module 403, configured to obtain video data for a target object if it is determined that an operation corresponding to the target object requires risk identification;
a scene module 405, configured to determine scene data corresponding to the video data;
and the risk module 407 is configured to determine risk data corresponding to the video data according to the scene data, and determine whether the operation has a risk and/or whether the target object has the qualification of the operation according to the risk data.
A fifth embodiment of the present specification provides a data processing apparatus including:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the data processing method of the first embodiment.
A sixth embodiment of the present specification provides a risk identification device, including:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the risk identification method of the second embodiment.
A seventh embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the data processing method of the first embodiment.
An eighth embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the risk identification method of the second embodiment.
The above embodiments may be combined, and the modules with the same name may be the same or different between different embodiments.
While certain embodiments of the present disclosure have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily have to be in the particular order shown or in sequential order to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device, and non-volatile computer-readable storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and in relation to the description, reference may be made to some portions of the description of the method embodiments.
The apparatus, the device, the nonvolatile computer readable storage medium, and the method provided in the embodiments of the present specification correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), AHDL (advanced Hardware description ip address) Language, traffic, CUPL (core University Programming Language), HDCal, JHDL (Java Hardware description ip address Language), Lava, Lola, HDL, PALASM, palms, rhyd (Hardware runtime Language), and Hardware Language (Hardware Language-Language) which is currently used by native Language. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, MicrochIP address PIC18F26K20, and Silicone LabsC8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, the present specification embodiments may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. 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 (24)
1. A method of data processing, comprising:
acquiring video data;
judging scene data corresponding to the video data;
and judging risk data corresponding to the video data according to the scene data, wherein the risk data is used for judging whether risks exist in handling business and/or operation of a target object corresponding to the video data.
2. The method of claim 1, the scene data comprising scene type data and/or scene position data and/or state data of the target object.
3. The method of claim 1, the scene type data comprising a family scene and/or a proxy scene.
4. The method of claim 1, the context data comprising a home location and/or a non-home location.
5. The method of claim 1, wherein determining risk data corresponding to the video data based on the scene data comprises:
if the scene data comprise a family scene, judging the tidiness data corresponding to the scene data;
and judging risk data corresponding to the video data according to the tidiness data.
6. The method of claim 5, the risk data comprising a scene risk, determining the corresponding risk data for the video data from the smoothness data comprising:
judging whether scene risks exist in the video data according to the tidiness degree;
and/or the presence of a gas in the gas,
the risk data comprises life state risks, and the step of judging the risk data corresponding to the video data according to the tidiness data comprises the following steps:
and judging whether the video data has life state risk according to the finishment.
7. The method of claim 1, the risk data comprising scene risk, determining the risk data corresponding to the video data from the scene data comprising:
and if the scene data comprise the alternate shooting scene, judging that the video data have scene risks.
8. The method of claim 1, wherein determining risk data corresponding to the video data based on the scene data comprises:
and judging matching data of the target object corresponding to the video data and the service handling registration object according to the scene data, wherein the matching data belongs to risk data.
9. The method of claim 8, determining from the scene data that the target object corresponding to the video data matches a business transaction registration object comprises:
comparing the scene data corresponding to the video object with the scene data corresponding to the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
10. The method of claim 8, determining from the scene data that the target object corresponding to the video data matches a business transaction registration object comprises:
acquiring a characteristic image of the target object from the video data, and comparing the characteristic image of the target object with a characteristic image of a service transaction registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
11. The method of claim 10, wherein the characteristic image of the target object is that the target object is a facial image at different times in the video data.
12. The method of claim 8, the scene data comprising status data of a target object to which the video corresponds;
the judging of the matching data of the target object corresponding to the video data and the service transaction registration object according to the scene data comprises the following steps:
comparing the state data of the target object with the state data of the service handling registration object;
and judging the matching data of the target object and the registered object according to the comparison result.
13. The method of claim 1, wherein the risk data includes whether a target object corresponding to the video belongs to a risk group, and the scene data includes state data of the target object;
determining the risk data corresponding to the video data according to the scene data includes:
judging the crowd attribute of a target object corresponding to the video data according to the state data;
and determining whether the target object belongs to a risk group according to the crowd attribute.
14. The method of claim 1, wherein determining scene data corresponding to the video data comprises:
and judging scene data corresponding to the video data according to the video background of the video data.
15. The method of claim 1, further comprising:
and if the target object corresponding to the video data is judged to have risk in handling the business, outputting risk prompt information.
16. The method of claim 1, acquiring video data comprising:
the target object is photographed using an image capturing apparatus to acquire video data.
17. The method of claim 1, further comprising:
and the risk data is used for judging whether the target object corresponding to the video data has business handling qualification.
18. A risk identification method, comprising:
judging whether the operation corresponding to the target object needs risk identification;
if yes, acquiring video data aiming at the target object;
judging scene data corresponding to the video data;
and judging risk data corresponding to the video data according to the scene data, and judging whether the operation has a risk and/or whether the target object has the handling qualification of the operation according to the risk data.
19. A data processing apparatus comprising:
the video module is used for acquiring video data;
the scene module is used for judging scene data corresponding to the video data;
and the risk module is used for judging risk data corresponding to the video data according to the scene data, and the risk data is used for judging whether a risk exists in business handling and/or operation of a target object corresponding to the video data.
20. A risk identification device comprising:
the operation identification module is used for judging whether the operation corresponding to the target object needs risk identification;
the video module is used for acquiring video data aiming at a target object if the operation corresponding to the target object needs risk identification;
the scene module is used for judging scene data corresponding to the video data;
and the risk module is used for judging risk data corresponding to the video data according to the scene data and judging whether the operation has risk and/or whether the target object has the handling qualification of the operation according to the risk data.
21. A data processing apparatus comprising:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 17.
22. A risk identification device comprising:
at least one processor;
and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of claim 18.
23. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 17.
24. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement the method of claim 18.
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