Data processing method and device, equipment and storage medium

Abstract

The present disclosure provides a data processing method, device and equipment and storage medium, which can be applied to the field of image recognition and the field of finance. The method comprises: performing feature extraction on business image data of a target object to obtain target feature information of the target object, wherein the business image data is data generated by the target object when handling a business; performing similarity matching on the target feature information and preset information to obtain a feature evaluation result of the target object, wherein the preset information comprises different kinds of preset feature information and preset target information corresponding to the target object; and in a case where the feature evaluation result represents that the target feature information of the target object has a business purpose, determining an object processing scheme corresponding to the target object to perform an operation in the object processing scheme on the target object, wherein the object processing scheme is associated with the business handled by the target object.

Description

Technical Field

[0001] This disclosure relates to the fields of image recognition and financial technology, and in particular to a data processing method, apparatus, device, medium, and program product. Background Technology

[0002] Currently, more and more companies are focusing on analyzing the structured data generated by customers when they conduct business, in order to improve their competitiveness. They then execute business-related operations based on the analysis results to achieve business control.

[0003] In realizing the present invention, the inventors discovered at least the following problems in the related technology: because the business processing environment may change, the accuracy of the analysis results obtained by analyzing structured data is low, resulting in poor business control and a low user experience. Summary of the Invention

[0004] In view of the above problems, this disclosure provides data processing methods, apparatus, devices, media and program products.

[0005] According to a first aspect of this disclosure, a data processing method is provided, comprising:

[0006] Feature extraction is performed on the business image data of the target object to obtain the target feature information of the target object, wherein the business image data is the data generated by the target object when handling business.

[0007] The target feature information is matched with the preset information to obtain the feature evaluation result of the target object. The preset information includes different types of preset feature information and preset target information corresponding to the target object.

[0008] If the above feature evaluation results indicate that the target feature information of the target object has a business purpose, an object processing scheme corresponding to the target object is determined, and the operation in the object processing scheme is executed on the target object, wherein the object processing scheme is related to the business handled by the target object.

[0009] According to embodiments of this disclosure, the above-described feature extraction of the business image data of the target object to obtain the target feature information of the target object includes:

[0010] The feature map of the person in the above business image data is divided into points to obtain the feature information of the person;

[0011] Feature extraction is performed on the business interaction graph in the above business image data to obtain interaction feature information, which includes signature information.

[0012] According to embodiments of this disclosure, the above-mentioned similarity matching between the target feature information and preset information to obtain the feature evaluation result of the target object includes:

[0013] The aforementioned character characteristics are matched with different types of the aforementioned preset characteristics to obtain emotion assessment results, age assessment results, and clothing assessment results.

[0014] The aforementioned character feature information is matched with the preset identity information in the preset target information to obtain the identity assessment result;

[0015] The interaction feature information is matched with the preset interaction information in the preset target information to obtain the interaction evaluation result.

[0016] The above-mentioned emotion assessment results, age assessment results, clothing assessment results, identity assessment results, and interaction assessment results are combined to obtain the above-mentioned feature assessment results.

[0017] According to embodiments of this disclosure, before determining the object processing scheme corresponding to the target object, the method further includes:

[0018] If the emotion assessment results in the above feature assessment results determine that the target object's emotion is abnormal, it is determined that the target object's target feature information has a first business purpose, wherein the above first business purpose can be used to make suggestions for business.

[0019] If the identity of the target object is determined to be abnormal based on the identity assessment results and interaction assessment results in the above feature assessment results, it is determined that the target feature information of the target object has a second business purpose, wherein the above second business purpose can be used to provide early warning for business.

[0020] If the target object is determined to be a potential target based on the age assessment results and clothing assessment results in the above feature assessment results, it is determined that the target feature information of the target object has a third business use, wherein the above third business use can be used to recommend services.

[0021] According to embodiments of this disclosure, the above-mentioned determination of the object processing scheme corresponding to the target object includes:

[0022] If the above feature evaluation results indicate that the target feature information of the target object has the above-mentioned first business purpose, the above emotion evaluation results are matched with the preset suggestion table to obtain the suggestion results, wherein the above preset suggestion table includes the mapping relationship between the emotion evaluation results and the suggestion results;

[0023] The above-mentioned suggested results are determined as the handling plan for the above-mentioned target, wherein the above-mentioned suggested results include operations for eliminating the abnormal emotions of the above-mentioned target, and the above-mentioned operations are related to the business handled by the above-mentioned target.

[0024] According to embodiments of this disclosure, the above-mentioned determination of the object processing scheme corresponding to the target object further includes:

[0025] If the above feature evaluation results indicate that the target feature information of the target object has the second business purpose, the object processing solution is determined to be to output early warning information related to the business handled by the target object.

[0026] According to embodiments of this disclosure, the above-mentioned determination of the object processing scheme corresponding to the target object further includes:

[0027] If the above feature evaluation results indicate that the target feature information of the target object has the above-mentioned third business purpose, the resource storage value and historical business processing information of the target object shall be determined.

[0028] Based on the above-mentioned resource storage values ​​and the above-mentioned historical business processing information, the target recommended business is determined, wherein the similarity between the business information of the above-mentioned target recommended business and the business information of the business processed by the above-mentioned target object is greater than a threshold.

[0029] The proposed solution for handling the above objects is to recommend the aforementioned target recommendation service to the aforementioned target objects.

[0030] According to embodiments of this disclosure, the above data processing method further includes:

[0031] If the above feature evaluation results meet the preset conditions, the above target objects are marked as objects of interest, and the above business image data and the above feature evaluation results are visualized.

[0032] A second aspect of this disclosure provides a data processing apparatus, comprising:

[0033] The feature extraction module is used to extract features from the business image data of the target object to obtain the target feature information of the target object, wherein the business image data is the data generated by the target object when handling business.

[0034] The feature matching module is used to perform similarity matching between the target feature information and preset information to obtain the feature evaluation result of the target object. The preset information includes different types of preset feature information and preset target information corresponding to the target object.

[0035] The scheme determination module is used to determine the object processing scheme corresponding to the target object when the above feature evaluation results indicate that the target feature information of the target object has business applications, so as to perform the operation in the above object processing scheme on the target object, wherein the above object processing scheme is related to the business handled by the target object.

[0036] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the methods described above.

[0037] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods described above.

[0038] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0039] According to the data processing methods, apparatus, equipment, media, and program products provided in this disclosure, target feature information of the target object is obtained by extracting features from business image data generated during the business processing of the target object. The target feature information is then matched with preset information to obtain the feature evaluation result of the target object, facilitating the screening of target objects with business applications. If it is determined that the target feature information of the target object has business applications, an object processing plan is determined. Since corresponding operations can be performed on the target object based on the feature evaluation result, the user experience is effectively improved. By monitoring and analyzing the business image data generated during the business processing of the target object, the ability to control the business is further enhanced. Attached Figure Description

[0040] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0041] Figure 1 The illustrations depict application scenarios of data processing methods, apparatuses, devices, media, and program products according to embodiments of the present disclosure.

[0042] Figure 2 A flowchart illustrating a data processing method according to an embodiment of the present disclosure is shown schematically.

[0043] Figure 3 A data flow diagram of a data processing method according to an embodiment of the present disclosure is illustrated schematically;

[0044] Figure 4 A schematic block diagram of a data processing apparatus according to embodiments of the present disclosure is shown; and

[0045] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure. Detailed Implementation

[0046] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0047] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0048] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0049] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0050] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0051] Currently, more and more companies are focusing on analyzing the structured data generated by customers when they conduct business, in order to improve their competitiveness. They then execute business-related operations based on the analysis results to achieve business control.

[0052] In realizing the present invention, the inventors discovered that the related technology has at least the following problems: because the business processing environment may change, the accuracy of the analysis results obtained by analyzing structured data is low, the ability to control the business is poor, and the user experience is low.

[0053] In view of the above, embodiments of this disclosure provide a data processing method, a data processing apparatus, an electronic device, a readable storage medium, and a computer program product. The data processing method includes: extracting features from business image data of a target object to obtain target feature information of the target object, wherein the business image data is data generated by the target object when handling business; performing similarity matching between the target feature information and preset information to obtain a feature evaluation result of the target object, wherein the preset information includes different types of preset feature information and preset target information corresponding to the target object; and, if the feature evaluation result indicates that the target feature information of the target object has a business purpose, determining an object processing scheme corresponding to the target object to execute the operation in the object processing scheme on the target object, wherein the object processing scheme is associated with the business handled by the target object.

[0054] Figure 1 The diagram illustrates an application scenario of the data processing method according to an embodiment of the present disclosure.

[0055] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0056] Users can interact with server 105 via network 104 using at least one of the first terminal device 101, second terminal device 102, and third terminal device 103 to receive or send data information, etc. Various communication client applications can be installed on the first terminal device 101, second terminal device 102, and third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0057] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0058] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0059] It should be noted that the data processing method provided in this embodiment can generally be executed by server 105. Correspondingly, the data processing device provided in this embodiment can generally be located in server 105. The data processing method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the data processing device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0060] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0061] The following will be based on Figure 1 The described scene, through Figures 2-5 The data processing method of the disclosed embodiments will be described in detail.

[0062] Figure 2 A flowchart illustrating a data processing method according to an embodiment of the present disclosure is shown schematically.

[0063] like Figure 2 As shown, the data processing method 200 of this embodiment includes operations S210 to S230.

[0064] In operation S210, feature extraction is performed on the business image data of the target object to obtain the target feature information of the target object. The business image data is the data generated by the target object when handling business.

[0065] In operation S220, the target feature information is matched with the preset information to obtain the feature evaluation result of the target object. The preset information includes different types of preset feature information and preset target information corresponding to the target object.

[0066] In operation S230, if the feature evaluation results indicate that the target feature information of the target object has business applications, an object processing plan corresponding to the target object is determined, and the operation in the object processing plan is executed on the target object. The object processing plan is related to the business handled by the target object.

[0067] According to embodiments of this disclosure, feature extraction is performed on the business image data of the target object, wherein the target object can be a person handling business, i.e., the object for feature evaluation. The business image data may include video, images, and other data involved in the business handling process. Specifically, it may include video generated by the target object operating a self-service business handling machine, or image information generated during the operation. The extracted target feature information may include facial feature information, fingerprint information, clothing feature information, accessory feature information, signature feature information, etc., of the target object during the business handling process.

[0068] According to embodiments of this disclosure, a method for feature extraction from business image data of a target object may include at least one of the following methods: Convolutional Neural Networks (CNN), Principal Component Analysis (PCA), Scale Invariant Feature Transform (Scale Space Theory), Histogram of Oriented Gradients (HOG), and Scale Space Theory. Feature extraction from video in business image data can be performed directly on the video, or each frame of the video can be extracted separately and its features extracted individually.

[0069] According to embodiments of this disclosure, the preset information includes different types of preset feature information and preset target information. The different types of preset feature information may include characteristics such as the target object's emotions, clothing, and age. The preset target information may include facial feature information, fingerprint information, signature information, etc., pre-recorded in the business processing agency's system.

[0070] According to embodiments of this disclosure, target feature information is matched with preset information based on similarity to obtain feature evaluation results for the target object. During the matching process, the target feature information is matched one by one with different types of preset feature information and preset target information. Specifically, the target feature information is matched with preset feature information such as the target object's emotion, clothing, and age; and the target feature information is matched with preset target information such as facial features, fingerprints, and signatures pre-recorded in the system. After matching, different types of feature evaluation results are obtained accordingly.

[0071] According to embodiments of this disclosure, target feature information is matched with preset information to obtain multiple similarities. These multiple similarities correspond to different types of preset feature information and preset target information within the preset information. A feature evaluation result for the target feature information is determined based on these multiple similarities. The feature evaluation result may include feature information corresponding to each target similarity. The target similarity is selected from the multiple similarities and can be either the highest or the lowest similarity among the multiple similarities.

[0072] According to embodiments of this disclosure, the presence of business applications for target feature information of a target object is determined based on feature evaluation results. If the feature evaluation results show anomalies or indicate that the target object can be used for business recommendations, then the target feature information of the target object can be considered to have business applications. Business applications can include various types. For example, obtaining suggestions or opinions through the target object, recommending businesses to the target object, or providing risk warnings to institutions or enterprises, etc. When the feature evaluation results indicate that the target feature information of the target object has business applications, an object processing scheme corresponding to the target object is determined. Here, the object processing scheme refers to the specific implementation method adopted for different business applications. Different business applications correspond to different object processing schemes.

[0073] For example, retrieve image data from user A's business transaction process, and extract the clothing and age characteristics of the target object from the image data. Match the clothing characteristics with corresponding preset clothing features to obtain a clothing feature evaluation result. Match the age characteristics with corresponding preset age features to obtain an age feature evaluation result. The preset clothing features may include information on luxury clothing, valuable jewelry, and the quantity of valuable jewelry. The clothing feature evaluation result represents the target object's clothing characteristics. The age feature evaluation result represents the target object's age characteristics. If it is determined that the target object's clothing characteristics indicate the wearer is wearing valuable jewelry, and the age characteristics indicate the target object is older, then the target object's feature information is deemed useful for business purposes, and other services can be recommended to user A via SMS or telephone.

[0074] According to embodiments of this disclosure, a feature evaluation result of the target object is obtained by performing similarity matching between target feature information and preset information. Based on the evaluation result, it is determined whether the target feature information has business applications. Since the target feature information is obtained by feature extraction from business image data, if the feature evaluation result indicates that the target feature information of the target object has business applications, a business processing scheme is executed on the target object. Therefore, this at least partially solves the problem of ineffective utilization of digital data within enterprises or industries, further accelerating the development speed of enterprises and industries.

[0075] According to embodiments of this disclosure, feature extraction is performed on the business image data of the target object to obtain target feature information of the target object, including: dividing the human feature map in the business image data into points to obtain human feature information; and extracting features from the business interaction map in the business image data to obtain interaction feature information, wherein the interaction feature information includes signature information.

[0076] According to embodiments of this disclosure, the person feature information may include facial feature information and body feature information. Facial feature information may include feature information of parts such as the eyes, nose, mouth, and eyebrows. Body feature information may include clothing feature information and accessory feature information. Both facial feature information and body feature information can be obtained by dividing the person feature map in the business image data into points.

[0077] According to embodiments of this disclosure, a facial landmark detection algorithm is used to segment a person's feature map, thereby performing face detection. The segmented landmarks may include the positions of features such as eyes, nose, mouth, and eyebrows. By extracting features from the segmented landmarks, the facial features of the target object during the business transaction process, as well as the positional changes of these features, are obtained. This facial feature information can be used to identify the user or analyze the target object's emotions.

[0078] According to embodiments of this disclosure, human body feature information can be extracted by dividing key points, wherein the key points can include the head, neck, and wrists. The acquisition of human body feature information also includes identifying user clothing. By dividing human body features into key points, for example, by extracting features from key points such as the neck and wrists in a human body image, image data of key parts of the target object can be obtained. Furthermore, by extracting clothing image data from a person's image, clothing information can be obtained.

[0079] According to embodiments of this disclosure, feature extraction is performed on the business interaction graph in the business image data to obtain interaction feature information. The interaction feature information may include signature information, fingerprint information, etc. The business interaction graph feature extraction method may include at least one of the following: normalization, detail feature extraction, local feature extraction, ridge feature extraction, image processing, point extraction, trajectory analysis, and feature vector extraction. Specifically, features such as the speed of the signature, the trajectory change trend of the signature, the range of the pen stroke angle, and the change trend of the pen stroke angle in the business interaction graph can be extracted using one or more of the above methods. For example, fingerprint features can be extracted using one or more of the above methods to obtain fingerprint information such as the shape of the fingerprint ridges and the spacing between the ridges.

[0080] According to embodiments of this disclosure, by extracting the character feature information and interaction feature information from the business data image, target feature information of some target objects is obtained, which enriches the dimensions of subsequent feature evaluation and increases the accuracy of feature evaluation results.

[0081] According to embodiments of this disclosure, similarity matching is performed between target feature information and preset information to obtain feature evaluation results for the target object. This includes: performing similarity matching between person feature information and different types of preset feature information to obtain emotion evaluation results, age evaluation results, and clothing evaluation results; performing similarity matching between person feature information and preset identity information in preset target information to obtain identity evaluation results; performing similarity matching between interaction feature information and preset interaction information in preset target information to obtain interaction evaluation results; and combining the emotion evaluation results, age evaluation results, clothing evaluation results, identity evaluation results, and interaction evaluation results to obtain feature evaluation results.

[0082] According to embodiments of this disclosure, similarity matching is performed between person feature information and preset emotional features in preset target information. Specifically, facial feature information extracted from a person feature map can be matched with preset emotional features to obtain an emotion assessment result. The preset emotional features may include features such as anger, happiness, disappointment, and anxiety.

[0083] According to embodiments of this disclosure, during the similarity matching process, features such as the position of the mouth, the degree of mouth opening, the angle of the mouth, the trend of change of the corners of the mouth, the position of the corners of the eyes, and the trend of change of the corners of the eyes can be extracted from facial feature information. These features are then input into an emotion classification model. The emotion classification model outputs the similarity score corresponding to each preset emotion feature, and the emotion feature with the highest similarity score is determined as the emotion assessment result.

[0084] According to embodiments of this disclosure, a person's feature information is matched with a preset age feature in preset target information based on similarity. Specifically, facial feature information extracted from a person's feature map can be matched with preset age features to obtain an age assessment result. The preset age feature may include multiple age ranges, such as: 18–28, 23–36, 28–38, 32–42, 37–47, 42–52, 47–57, 52–60, 56–65, and 65–100.

[0085] According to embodiments of this disclosure, during the similarity matching process, features of facial features such as the eyes, mouth, and forehead can be extracted and input into an age prediction model. The age prediction model outputs the similarity score for each age range, and the age range with the highest similarity score is determined as the age assessment result.

[0086] According to embodiments of this disclosure, similarity matching is performed between person feature information and preset clothing features in preset target information. Specifically, clothing feature information extracted from a person feature map can be matched with preset clothing features to obtain a clothing evaluation result. The preset clothing features may include characteristics such as wearing expensive jewelry, wearing ordinary jewelry, or not wearing jewelry.

[0087] According to embodiments of this disclosure, during the similarity matching process, clothing features can be extracted from the person's feature map and input into the jewelry recognition model. The jewelry recognition model outputs the similarity score corresponding to each preset clothing feature, and the clothing feature with the highest similarity score is determined as the clothing evaluation result.

[0088] According to embodiments of this disclosure, a similarity match is performed between person feature information and preset identity feature information in preset target information. Specifically, facial features can be extracted from facial feature information and compared with preset identity features in preset identity feature information to obtain a similarity score. If the similarity score is greater than or equal to a preset threshold, the identity assessment result is determined to indicate an identity match. If the similarity score is less than the preset threshold, the identity assessment result is determined to indicate an identity mismatch. The preset threshold can be set to 72%, and this preset threshold is limited by a similarity threshold for facial recognition within the reference domain. The setting of the preset threshold can be based on the actual situation during business processing.

[0089] For example, the facial feature information of the target object is compared with the preset identity feature information. If the comparison result indicates that the similarity is higher than 72%, the identity assessment result is determined to represent identity matching, and it is determined that the person currently handling the business is the person himself, and there is no abnormality in his identity.

[0090] According to embodiments of this disclosure, interaction feature information is matched with preset interaction information in preset target information to obtain an identity assessment result. The interaction feature information may include signature information, fingerprint information, etc., left by the target object during historical business transactions. The interaction feature information is compared with the preset interaction information to obtain a similarity score. If the similarity score is greater than or equal to a preset threshold, the identity assessment result is determined to indicate an identity match. If the similarity score is less than the preset threshold, the identity assessment result is determined to indicate an identity mismatch. The preset threshold can be set to 60%, and its setting can be based on the actual situation during business transactions.

[0091] According to embodiments of this disclosure, the feature evaluation results can be combined from different dimensions, including emotion evaluation results, age evaluation results, clothing evaluation results, identity evaluation results, and interaction evaluation results, based on different needs. For example, emotion evaluation results can be selected as the feature evaluation results to assess the target's satisfaction during the business processing; and the feature evaluation results can be obtained by comprehensively judging age evaluation results and clothing evaluation results to determine whether the current target has the potential to handle other business.

[0092] According to embodiments of this disclosure, character feature information and interaction feature information are divided into several different dimensions for evaluation, including emotion, age, clothing, identity, and interaction. Since the evaluation needs of the target object vary, the dimensions selected for the corresponding evaluation results also differ. Corresponding to different needs, the obtained emotion evaluation results, age evaluation results, clothing evaluation results, identity evaluation results, and interaction evaluation results are combined to obtain the final feature evaluation result, further improving the utilization value of the target feature information and enhancing its flexibility in application.

[0093] According to embodiments of this disclosure, before determining the object processing scheme corresponding to the target object, the method further includes: if it is determined that the target object's emotions are abnormal based on the emotion assessment results in the feature assessment results, determining that the target object's target feature information has a first business use, wherein the first business use can be used to make suggestions for the business; if it is determined that the target object's identity is abnormal based on the identity assessment results and interaction assessment results in the feature assessment results, determining that the target object's target feature information has a second business use, wherein the second business use can be used to issue early warnings for the business; if it is determined that the target object is a potential object based on the age assessment results and clothing assessment results in the feature assessment results, determining that the target object's target feature information has a third business use, wherein the third business use can be used to recommend the business.

[0094] According to embodiments of this disclosure, if the target object's emotions are negative, or if the target object's emotions turn negative during the business processing process, it can be determined that the target object's emotions are abnormal. Negative emotions can include anger, frustration, etc. When the target object's emotions are abnormal during the business processing process, it can be determined that the target object's characteristic information has a first business purpose. This first business purpose can include advisory purposes, such as eliminating potential dissatisfaction from the target object through telephone follow-ups, and understanding the target object's opinions and suggestions regarding the business processing process.

[0095] For example, feature extraction is performed on the image information generated by target A during the business process to obtain the corresponding facial feature information. An emotion classification model is used to obtain A's emotions at different time points during the business process. Analysis reveals changes in A's emotions, indicating abnormalities and thus determining the significance of a follow-up phone call. This follow-up call allows for understanding A's opinions and suggestions regarding the business process.

[0096] According to embodiments of this disclosure, there can be various situations in which the identity of the target object is determined to be abnormal, and specific considerations can be made based on the importance of the identity assessment results and the interaction assessment results.

[0097] For example, if the identity assessment result indicates that the character feature information does not match the preset identity information in the preset target information, and the interaction assessment result indicates that the interaction feature information does not match the preset interaction information in the preset target information, then the identity of the target object is deemed abnormal.

[0098] For example, in the feature evaluation results corresponding to the target object, the identity evaluation result indicates that the character feature information does not match the preset identity information in the preset target information, while the interaction evaluation result indicates that the interaction feature information matches the preset interaction information in the preset target information. In this case, the target object's identity is also considered abnormal.

[0099] For example, in the feature evaluation results corresponding to the target object, the identity evaluation result indicates that the character feature information matches the preset identity information in the preset target information, while the interaction evaluation result indicates that the interaction feature information does not match the preset interaction information in the preset target information. In this case, the target object's identity is also considered abnormal.

[0100] According to embodiments of this disclosure, if it is determined that the identity of the target object is abnormal, then it is determined that the target object has a second business purpose. The second business purpose may include issuing an early warning to the current target object. For example, if the target object's identity is determined to be abnormal, and the target object is conducting business at a self-service machine, the current business can be terminated, and a notification can be displayed to the target object on the self-service machine's screen. Alternatively, a message notification can be sent to the system of the institution to which the self-service machine belongs.

[0101] According to embodiments of this disclosure, the existence of a third business use for target object information is determined based on age assessment results and clothing assessment results. If the age assessment results determine that the target object is within a preset time period and the target object is wearing valuable jewelry, then the target object is identified as a potential target. The target object's characteristic information currently has a third business use. Here, a potential target indicates that the target object has the potential to handle other business, and business recommendations can be made to the target object.

[0102] For example, if image recognition predicts that the target's age is between 18 and 28, and the clothing assessment indicates that the target is wearing valuable jewelry, then it can be determined that the target's characteristic information has a third business purpose.

[0103] According to embodiments of this disclosure, when judging the utilization value of target feature information for a third business purpose, if the target object wears valuable jewelry, the utilization value of the target feature information can be ranked by the age assessment results. Specifically, it can be considered that the higher the age group to which the target object belongs, the more valuable the corresponding target feature information is.

[0104] Figure 3 A data flow diagram of a data processing method according to an embodiment of the present disclosure is illustrated schematically.

[0105] like Figure 3As shown, the data flow 300 of the data processing method in this embodiment mainly describes the process from data image data 301 to deriving business applications.

[0106] According to embodiments of this disclosure, such as Figure 3 As shown, feature extraction is performed on business image data 301 to obtain character feature information 302 and interaction feature information 311. The character feature image is matched with different types of preset feature information 303 to obtain emotion evaluation result 304. Based on the emotion evaluation result 304, it is determined whether the target feature information of the target object has a first business purpose 305.

[0107] Based on the age assessment result 306 and clothing assessment result 307 obtained by similarity matching between the person's characteristic information 302 and different types of preset characteristic information 303, it is determined whether the target characteristic information of the target object has a primary business purpose 305.

[0108] Based on the identity evaluation result 310 obtained by matching the similarity between the character feature information 302 and the preset identity information 309 of different types, the interaction evaluation result 313 is obtained by matching the similarity between the interaction feature information 311 and the designed interaction information 312, and based on the identity evaluation result 310 and the interaction evaluation result 313, it is determined whether the target feature information of the target object has a second business purpose 314.

[0109] According to embodiments of this disclosure, before determining the object processing scheme corresponding to the target object, the business purpose of the target feature information of the target object is determined based on different types of evaluation results. Since the object processing scheme corresponding to the target object is determined by the business purpose, the object processing scheme is more intuitive, and the accuracy of object processing scheme selection is improved.

[0110] According to embodiments of this disclosure, determining an object processing scheme corresponding to a target object includes: when the feature evaluation result indicates that the target feature information of the target object has a first business purpose, matching the emotion evaluation result with a preset suggestion table to obtain a suggestion result, wherein the preset suggestion table includes a mapping relationship between the emotion evaluation result and the suggestion result; determining the suggestion result as an object processing scheme, wherein the suggestion result includes an operation for eliminating the abnormal emotion of the target object, and the operation is related to the business handled by the target object.

[0111] According to embodiments of this disclosure, when the feature evaluation result characterizes the target feature information of the target object and has a first business purpose, the emotion characterized by the emotion evaluation result is used as an index to match the emotions in a preset suggestion table, thereby obtaining the corresponding suggestion result in the preset suggestion table. Each emotion in the preset suggestion table has a corresponding suggestion result. For example, the suggestion result corresponding to emotions such as disappointment and frustration could be a suggested telephone follow-up, while the suggestion result corresponding to emotions such as ease and joy could be a suggested SMS follow-up.

[0112] For example, if the emotion assessment result indicates that the current target's emotion is disappointment, and the suggestion result corresponding to the emotion of disappointment in the preset suggestion table is to make a follow-up phone call, then the follow-up phone call will be determined as the target's handling plan, and the abnormal emotion of the target towards the business will be eliminated through the follow-up phone call.

[0113] According to embodiments of this disclosure, the suggested outcome can also be a specific object handling solution. For example, the suggested outcome corresponding to emotions such as disappointment and frustration can be telephone follow-up feedback. This feedback may include the following: first, inquiring whether the target object is satisfied with the service provided; if the target object answers no, further inquiring about the reasons for dissatisfaction, and recording the call.

[0114] According to the embodiments of this disclosure, by establishing a mapping relationship between emotion assessment results and establishment results, an object processing solution is obtained. Since different emotion assessment results have corresponding suggested results, the process of manually judging emotion assessment results to obtain the final object processing solution is eliminated, thereby improving the efficiency of business follow-up.

[0115] According to embodiments of this disclosure, determining an object processing scheme corresponding to a target object further includes: if the feature evaluation results indicate that the target feature information of the target object has a second business use, determining the object processing scheme as outputting early warning information related to the business handled by the target object.

[0116] According to embodiments of this disclosure, when the feature evaluation results indicate that the target feature information of the target object has a second business application, a reminder can be sent automatically to relevant personnel via SMS. These relevant personnel can be individuals corresponding to the preset target information during the current business transaction. The reminder method may also include making a phone call via human customer service or automatically initiating a voice call via intelligent customer service.

[0117] According to embodiments of this disclosure, by timely acquiring the person characteristic information and interaction characteristic information contained in the image data during the business processing, and comprehensively analyzing the result to obtain the feature evaluation result, security issues in the business processing process can be detected in a timely manner, thereby improving the security of the business processing process.

[0118] According to embodiments of this disclosure, determining an object processing scheme corresponding to a target object further includes: determining the resource storage value and historical business processing information of the target object when the feature evaluation results indicate that the target feature information of the target object has a third business use; determining a target recommended business based on the resource storage value and historical business processing information, wherein the similarity between the business information of the target recommended business and the business information of the business processed by the target object is greater than a threshold; and determining the object processing scheme as recommending the target recommended business to the target object.

[0119] According to embodiments of this disclosure, when the feature evaluation results indicate that the target feature information of the target object has a third business application, the resource storage value and historical business processing information of the target object are determined. The resource storage value may be the amount of deposits of the target object, and the historical business processing information may be the business transactions previously processed by the target object.

[0120] According to embodiments of this disclosure, a target recommended service is determined based on the resource storage value and historical business processing information. If the resource storage is greater than the preset storage value and the historical business processing information includes services similar to those processed by the target object, then services with a similarity greater than a threshold between the business information of the services processed by the target object are searched in the database, and services with a similarity greater than the threshold are determined as target recommended services.

[0121] For example, if the resource storage value of a target object is greater than the preset storage value, services that are highly similar to the current services can be recommended to the target object based on the services it has previously handled.

[0122] According to embodiments of this disclosure, by analyzing the current target object's resource storage value and historical business processing information, the business that needs to be recommended to the target object is determined. The business processing capability and business processing tendency of the target object are reasonably analyzed, which increases the probability that the target object will accept the recommended business and improves the discovery efficiency of potential target objects.

[0123] According to embodiments of this disclosure, the data processing method further includes: marking the target object as an object of interest when it is determined that the feature evaluation result meets preset conditions, and visually displaying the business image data and the feature evaluation result.

[0124] According to embodiments of this disclosure, the preset conditions may vary depending on the specific business application.

[0125] According to embodiments of this disclosure, corresponding to the first business application, the preset conditions may include the target object's emotion assessment result indicating negative emotions such as anger or frustration. That is, if the target object's emotion assessment result indicates a negative type of emotion, the emotion assessment result can be considered to meet the preset conditions, and the target object can be marked as a subject of attention.

[0126] According to embodiments of this disclosure, corresponding to the second business application, the preset condition can use the anomaly in the identity assessment result as an important indicator. That is, if the identity assessment result representation does not match, the target object is directly marked as an object of interest. The preset condition also includes a mismatch in the interaction assessment result representation. If the identity assessment result representation information matches, but the interaction assessment result guarantee information does not match, the target object is marked as an object of interest.

[0127] According to embodiments of this disclosure, corresponding to the third business application, the preset conditions can use the clothing assessment result of the target object as an important indicator. That is, if the clothing assessment result of the target object indicates that it is wearing expensive jewelry, then the target object is marked as a target of interest. The age assessment result can be used as a reference indicator for the preset conditions. The higher the age indicated by the age assessment result, the higher the level of the target of interest is considered.

[0128] According to embodiments of this disclosure, the image data and feature evaluation results of the target object set as the object of interest can be organized and visualized using line charts, bar charts, tables, or tables.

[0129] Specifically, objects of interest corresponding to different business purposes can be recorded in different tables. That is, information about objects of interest with the first business purpose is recorded in the first business purpose table, information about objects of interest with the second business purpose is recorded in the second business purpose table, and information about objects of interest with the third business purpose is recorded in the third business purpose table.

[0130] According to embodiments of this disclosure, the target object corresponding to the feature evaluation result that meets the preset conditions is set as the object of interest, and the business image data and feature evaluation results of the object of interest are visualized to facilitate data analysis and utilization.

[0131] Based on the above data processing method, this disclosure also provides a data processing apparatus. The following will be combined with... Figure 4 The device is described in detail.

[0132] Figure 4 A schematic block diagram of a data processing apparatus according to an embodiment of the present disclosure is shown.

[0133] like Figure 4 As shown, the data processing device 400 of this embodiment includes a feature acquisition module 410, a feature matching module 420, and a scheme determination module 430.

[0134] The feature extraction module 410 is used to extract features from the business image data of the target object to obtain the target feature information of the target object. The business image data is data generated when the target object is processing business. In one embodiment, the feature extraction module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0135] The feature matching module 420 is used to perform similarity matching between target feature information and preset information to obtain the feature evaluation result of the target object. The preset information includes different types of preset feature information and preset target information corresponding to the target object. In one embodiment, the feature matching module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0136] The scheme determination module 430 is used to determine an object processing scheme corresponding to the target object when the feature evaluation results indicate that the target feature information of the target object has a business purpose, so as to execute the operation in the object processing scheme on the target object. The object processing scheme is associated with the business handled by the target object. In one embodiment, the scheme determination module 430 can be used to execute the operation S230 described above, which will not be repeated here.

[0137] According to embodiments of this disclosure, the data processing device extracts features from business image data generated by a target object during business processing to obtain target feature information of the target object. The target feature information is then matched with preset information for similarity to obtain a feature evaluation result of the target object, facilitating the screening of target objects with business applications. If it is determined that the target feature information of the target object has business applications, an object processing plan is determined. Since corresponding operations can be performed on the target object based on its feature evaluation result, the user experience is effectively improved. By monitoring and analyzing the business image data generated by the target object during business processing, the ability to control business operations is enhanced.

[0138] According to embodiments of this disclosure, the feature extraction module 410 includes: a first acquisition submodule and a second acquisition submodule.

[0139] The first acquisition submodule is used to divide the feature map of people in the business image data into points to obtain the feature information of people.

[0140] The second acquisition submodule extracts features from the business interaction graph in the business image data to obtain interaction feature information, which includes signature information.

[0141] According to embodiments of this disclosure, the feature matching module 420 includes: a third acquisition submodule, a fourth acquisition submodule, a fifth acquisition submodule, and a sixth acquisition submodule.

[0142] The third acquisition submodule is used to perform similarity matching between the character feature information and different types of preset feature information to obtain emotion assessment results, age assessment results, and clothing assessment results.

[0143] The fourth acquisition submodule is used to perform similarity matching between the person's feature information and the preset identity information in the preset target information to obtain the identity assessment result.

[0144] The fifth acquisition submodule is used to perform similarity matching between the interaction feature information and the preset interaction information in the preset target information to obtain the interaction evaluation result.

[0145] The sixth acquisition submodule is used to combine the emotion assessment results, age assessment results, clothing assessment results, identity assessment results, and interaction assessment results to obtain the feature assessment results.

[0146] According to the embodiments disclosed herein, the solution determination module 430 further includes: a suggestion submodule, an early warning submodule, and a recommendation submodule.

[0147] The suggestion submodule is used to determine that the target object's target feature information has a first business purpose when the emotion assessment result in the feature assessment result determines that the target object's emotion is abnormal. The first business purpose can be used to make suggestions for business.

[0148] The early warning submodule is used to determine that the target object's target feature information has a second business purpose when the identity assessment result and interaction assessment result of the target object are determined to be abnormal based on the feature assessment result. The second business purpose can be used to issue an early warning for the business.

[0149] The recommendation submodule is used to determine whether the target object's target feature information has a third business use when the target object is identified as a potential object based on the age assessment results and clothing assessment results in the feature assessment results. The third business use can be used to make recommendations for the business.

[0150] According to an embodiment of this disclosure, the scheme determination module 430 includes: a first determination submodule and a second determination submodule.

[0151] The first determining submodule is used to match the emotion assessment result with a preset suggestion table to obtain a suggestion result when the feature assessment result represents the target feature information of the target object and has a first business purpose. The preset suggestion table includes the mapping relationship between the emotion assessment result and the suggestion result.

[0152] The second determining submodule is used to determine the suggested results as a solution for the target object. The suggested results include operations to eliminate the abnormal emotions of the target object, and the operations are related to the business handled by the target object.

[0153] According to embodiments of this disclosure, the scheme determination module 430 further includes: a third determination

[0154] The third determination submodule is used to determine the object processing solution when the target feature information of the target object, as characterized by the feature evaluation results, has a second business purpose, and output early warning information related to the business handled by the target object.

[0155] According to embodiments of this disclosure, the scheme determination module 430 further includes: a fourth determination submodule, a fifth determination submodule, and a sixth determination submodule.

[0156] The fourth determination submodule is used to determine the resource storage value and historical business processing information of the target object when the target feature information of the target object characterized by the feature evaluation results has a third business purpose.

[0157] The fifth determination submodule is used to determine the target recommended business based on resource storage values ​​and historical business processing information, wherein the similarity between the business information of the target recommended business and the business information of the business processed by the target object is greater than a threshold.

[0158] The sixth submodule is used to determine the object processing scheme as recommending target recommendation services to the target object.

[0159] According to embodiments of this disclosure, the data processing apparatus further includes a display module.

[0160] The display module, when the feature evaluation results meet the preset conditions, marks the target object as an object of interest and visualizes the business image data and feature evaluation results.

[0161] According to embodiments of this disclosure, any plurality of modules among the feature acquisition module 410, feature matching module 420, and scheme determination module 430 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the feature acquisition module 410, feature matching module 420, and scheme determination module 430 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the feature acquisition module 410, feature matching module 420, and scheme determination module 430 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0162] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure.

[0163] like Figure 5 As shown, an electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0164] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0165] According to embodiments of this disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0166] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0167] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0168] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to enable the computer system to implement the data processing methods provided in the embodiments of this disclosure.

[0169] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0170] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0171] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0172] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, p5thon, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0173] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0174] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0175] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A data processing method, comprising: Feature extraction is performed on the business image data of the target object to obtain the target feature information of the target object, wherein the business image data is the data generated by the target object when handling business; The target feature information is matched with preset information to obtain the feature evaluation result of the target object. The preset information includes different types of preset feature information and preset target information corresponding to the target object. If the feature evaluation result indicates that the target feature information of the target object has a business purpose, an object processing scheme corresponding to the target object is determined, and the operation in the object processing scheme is executed on the target object, wherein the object processing scheme is associated with the business handled by the target object; The method further includes: If the target object's emotion is determined to be abnormal based on the emotion assessment result in the feature assessment result, the target object's target feature information is determined to have a first business purpose, wherein the first business purpose can be used to make suggestions for business. If the identity of the target object is determined to be abnormal based on the identity evaluation result and interaction evaluation result in the feature evaluation result, it is determined that the target feature information of the target object has a second business purpose, wherein the second business purpose can be used to provide early warning for the business. If the target object is determined to be a potential object based on the age assessment result and the clothing assessment result in the feature assessment result, it is determined that the target object's target feature information has a third business use, wherein the third business use characterizes the ability to make business recommendations.

2. The method according to claim 1, wherein, The step of extracting features from the business image data of the target object to obtain the target feature information of the target object includes: The feature map of the person in the business image data is divided into points to obtain the person feature information; Feature extraction is performed on the business interaction graph in the business image data to obtain interaction feature information, wherein the interaction feature information includes signature information.

3. The method according to claim 2, wherein, The step of performing similarity matching between the target feature information and preset information to obtain the feature evaluation result of the target object includes: The person's characteristic information is matched with different types of preset characteristic information to obtain emotion assessment results, age assessment results, and clothing assessment results. The similarity of the person's characteristic information with the preset identity information in the preset target information is matched to obtain the identity assessment result; The interaction feature information is matched with the preset interaction information in the preset target information to obtain the interaction evaluation result; The emotion assessment results, age assessment results, clothing assessment results, identity assessment results, and interaction assessment results are combined to obtain the feature assessment results.

4. The method according to claim 1, wherein, The determination of the object processing scheme corresponding to the target object includes: When the feature evaluation result indicates that the target feature information of the target object has the first business purpose, the emotion evaluation result is matched with a preset suggestion table to obtain a suggestion result, wherein the preset suggestion table includes a mapping relationship between the emotion evaluation result and the suggestion result; The suggested results are determined as the object handling plan, wherein the suggested results include operations for eliminating the abnormal emotions of the target object, and the operations are related to the business handled by the target object.

5. The method according to claim 1, wherein, The step of determining the object processing scheme corresponding to the target object further includes: If the feature evaluation result indicates that the target feature information of the target object has the second business purpose, the object processing plan is determined to be to output early warning information related to the business handled by the target object.

6. The method according to claim 1, wherein, The step of determining the object processing scheme corresponding to the target object further includes: If the feature evaluation results indicate that the target feature information of the target object has the third business purpose, the resource storage value and historical business processing information of the target object are determined. The target recommended business is determined based on the resource storage value and the historical business processing information, wherein the similarity between the business information of the target recommended business and the business information of the business processed by the target object is greater than a threshold. The object processing solution is determined to be recommending the target recommendation service to the target object.

7. The method according to claim 1, further comprising: If the feature evaluation result meets the preset conditions, the target object is marked as an object of interest, and the business image data and the feature evaluation result are visualized.

8. A data processing apparatus, comprising: The feature extraction module is used to extract features from the business image data of the target object to obtain the target feature information of the target object, wherein the business image data is the data generated by the target object when handling business; A feature matching module is used to perform similarity matching between the target feature information and preset information to obtain the feature evaluation result of the target object, wherein the preset information includes preset feature information of different types and preset target information corresponding to the target object; and The scheme determination module is used to determine an object processing scheme corresponding to the target object when the feature evaluation result indicates that the target feature information of the target object has a business purpose, so as to perform the operation in the object processing scheme on the target object, wherein the object processing scheme is associated with the business handled by the target object; The suggestion submodule is used to determine that the target feature information of the target object has a first business purpose when the target object's emotion is determined to be abnormal based on the emotion assessment result in the feature assessment result. The first business purpose can be used to make suggestions for business. The early warning submodule is used to determine that the target feature information of the target object has a second business purpose when the identity of the target object is determined to be abnormal based on the identity assessment result and interaction assessment result in the feature assessment result. The second business purpose can be used to provide early warning for the business. The recommendation submodule is used to determine that the target object's target feature information has a third business use when the target object is determined to be a potential object based on the age assessment result and clothing assessment result in the feature assessment result. The third business use can be used to recommend services.

9. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 8.

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