Data screening method and device, electronic equipment and storage medium

Abstract

The application relates to a data screening method and device, electronic equipment and a storage medium, and is applied to the technical field of data processing. The method comprises the following steps: acquiring user scanning data corresponding to a two-dimensional code of each region, performing attribute feature identification on the user scanning data to obtain feature information of the user scanning data under one or more attribute features; determining a feature value corresponding to the user scanning data according to the feature information of the user scanning data under one or more attribute features; determining a data category corresponding to the user scanning data according to the feature value; for the user scanning data in each data category, classifying the user scanning data in the data category according to the feature information of the user scanning data in the data category, and performing data screening according to the obtained classification result. The application can improve the efficiency and accuracy of data screening.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a data screening method and device, electronic equipment and a storage medium. BACKGROUND

[0002] In a big data environment, with the rapid accumulation of data, it is very important to analyze the value contained in the massive data and screen out valuable data. Data screening plays a crucial role in the entire data processing process.

[0003] In the related art, valuable data can be screened out from data by using a data screening algorithm. However, due to the increase in data volume and the increase in data complexity, the efficiency and accuracy of data screening are low. SUMMARY

[0004] To solve the above technical problems, the present application provides a data screening method and device, electronic equipment and a storage medium.

[0005] According to a first aspect of the present application, a data screening method is provided, comprising:

[0006] Obtaining user scanning data corresponding to a two-dimensional code in each region, performing attribute feature recognition on the user scanning data, and obtaining feature information of the user scanning data under one or more attribute features;

[0007] According to the feature information of the user scanning data under one or more attribute features, determining a feature value corresponding to the user scanning data;

[0008] According to the feature value, determining a data category corresponding to the user scanning data;

[0009] For user scanning data in each data category, according to the feature information of the user scanning data in the data category, classifying the user scanning data in the data category, and performing data screening according to the obtained classification result.

[0010] Optionally, the method further comprises:

[0011] Obtaining face feature information corresponding to the user scanning data;

[0012] Establishing a corresponding relationship between the face feature information and the user scanning data.

[0013] Optionally, the method further comprises:

[0014] If the feature information under one or more attribute features does not contain the feature information under the target attribute feature, the feature information under the target attribute feature is filled according to the feature information under one or more attribute features of the user scanning data and other user scanning data.

[0015] Optionally, the filling of the feature information under the target attribute feature according to the feature information under one or more attribute features of the user scanning data and other user scanning data comprises:

[0016] The filling of the feature information under the target attribute feature according to the feature information under one or more attribute features of the user scanning data and other user scanning data comprises using a Bayesian formula or a decision tree.

[0017] Optionally, the determining of the data category corresponding to the user scanning data according to the feature value comprises:

[0018] The data range to which the feature value belongs is determined according to the data ranges respectively corresponding to a plurality of data categories.

[0019] The data category corresponding to the data range to which the feature value belongs is determined as the data category corresponding to the user scanning data.

[0020] Optionally, the determining of the feature value corresponding to the user scanning data according to the feature information under one or more attribute features of the user scanning data comprises:

[0021] The feature value corresponding to the feature information under a single attribute feature of the user scanning data is determined according to the feature information under the single attribute feature of the user scanning data.

[0022] The sum of the feature values corresponding to the feature information under one or more attribute features of the user scanning data is determined as the feature value corresponding to the user scanning data.

[0023] Optionally, after the data category corresponding to the user scanning data is determined, the method further comprises:

[0024] The user scanning data is stored into an object storage bucket corresponding to the data category.

[0025] According to a second aspect of the present application, a data screening device is provided, comprising:

[0026] A feature information determining module is configured to obtain user scanning data corresponding to two-dimensional codes of each region, perform attribute feature recognition on the user scanning data, and obtain feature information of the user scanning data under one or more attribute features.

[0027] The feature value determination module is configured to determine a feature value corresponding to the user scan data according to feature information of the user scan data under one or more attribute features.

[0028] The data category determination module is configured to determine a data category corresponding to the user scan data according to the feature value.

[0029] The data screening module is configured to, for user scan data in each data category, classify the user scan data in the data category according to feature information of the user scan data in the data category, and perform data screening according to a classification result obtained.

[0030] Optionally, the data screening apparatus further includes:

[0031] The face feature information acquisition module is configured to acquire face feature information corresponding to the user scan data.

[0032] The corresponding relationship establishment module is configured to establish a corresponding relationship between the face feature information and the user scan data.

[0033] Optionally, the data screening apparatus further includes:

[0034] The information filling module is configured to, if feature information under a target attribute feature is not included in the feature information under one or more attribute features, fill the feature information under the target attribute feature according to feature information of the user scan data and other user scan data under one or more attribute features.

[0035] Optionally, the information filling module is specifically configured to, if feature information under a target attribute feature is not included in the feature information under one or more attribute features, fill the feature information under the target attribute feature according to feature information of the user scan data and other user scan data under one or more attribute features by using a Bayesian formula or a decision tree.

[0036] Optionally, the data category determination module is specifically configured to determine a data range to which the feature value belongs according to data ranges respectively corresponding to a plurality of data categories, and determine, as a data category corresponding to the user scan data, a data category corresponding to the data range to which the feature value belongs.

[0037] Optionally, the feature value determination module is specifically configured to determine a feature value corresponding to feature information of the user scan data under a single attribute feature according to the feature information of the user scan data under the single attribute feature, and determine, as the feature value corresponding to the user scan data, a sum of feature values corresponding to the feature information of the user scan data under one or more attribute features.

[0038] Optionally, the data screening device further comprises:

[0039] The storage module is configured to store the user scan data into the object storage bucket corresponding to the data category.

[0040] According to a third aspect of the present application, an electronic device is provided, comprising: a processor configured to execute a computer program stored in a memory, the computer program, when executed by the processor, implements the method of the first aspect.

[0041] According to a fourth aspect of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, the computer program, when executed by a processor, implements the method of the first aspect.

[0042] According to a fifth aspect of the present application, a computer program product is provided, and when the computer program product is executed on a computer, the computer program product causes the computer to execute the method of the first aspect.

[0043] The technical solution provided by the embodiments of the present application has the following advantages compared with the prior art:

[0044] Since the user scan data corresponding to the two-dimensional code of each area contains the relevant information of the user entering each area, the data volume of the user scan data is constantly increasing. Moreover, with the change of the scene, the complexity of the user scan data will also constantly increase. Therefore, the data category corresponding to the user scan data can be determined according to the feature information of the user scan data under one or more attribute features. Then, the user scan data in each data category is classified according to the feature information of the user scan data in the data category, and data screening is performed according to the classification result. Through the classification and then screening manner, the efficiency of data screening can be improved, and since the feature information represents the information of the user scan data under each attribute feature, the user scan data can be accurately classified based on the feature information of the user scan data, so that the accuracy of data screening can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced here. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0047] Figure 1A flow chart of a data screening method in an embodiment of the present application;

[0048] Figure 2 Another flow chart of a data screening method in an embodiment of the present application;

[0049] Figure 3 A structural schematic diagram of a data screening device in an embodiment of the present application;

[0050] Figure 4 A structural schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the solutions of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein; obviously, the embodiments in the description are only some of the embodiments of the present application, not all the embodiments.

[0053] At present, in order to effectively count the personnel entering and leaving each area, different two-dimensional codes can be set for each area, and when a user enters a certain area (such as an office building, a shopping mall, a community, etc.), the user scans the two-dimensional code of the area. In this way, in some cases (for example, in the case of infectious diseases), the trajectory of the users in a certain area can be effectively tracked.

[0054] As the amount of data generated after the user scans the two-dimensional codes of different areas increases, and the amount of data also becomes increasingly complex, for example, the detection of infectious diseases and the vaccination of users can be added. Although effective data can be screened out by a screening algorithm, the efficiency and accuracy of data screening are low.

[0055] In order to solve the above-mentioned problems, the embodiments of the present application provide a data screening method, device, electronic device and storage medium to improve the efficiency and accuracy of data screening.

[0056] Reference Figure 1 , Figure 1 A flow chart of a data screening method in an embodiment of the present application, which can include the following steps:

[0057] In step S110, the user scanning data corresponding to the two-dimensional codes of each area is obtained, the attribute feature of the user scanning data is identified, and the feature information of the user scanning data under one or more attribute features is obtained.

[0058] Step S120: Determine the feature value corresponding to the user scan data based on the feature information of the user scan data under one or more attribute features.

[0059] Step S130: Determine the data category corresponding to the user's scanned data based on the feature value.

[0060] Step S140: For the user scan data in each data category, classify the user scan data in the data category according to the feature information of the user scan data in the data category, and filter the data according to the obtained classification results.

[0061] In the data filtering method of this application embodiment, since the user scan data corresponding to the QR codes in each area contains relevant information about users entering each area, the amount of user scan data is constantly increasing. Furthermore, as the scenario changes, the complexity of the user scan data also increases. Therefore, the data category corresponding to the user scan data can be determined first based on the feature information of the user scan data under one or more attribute features. Then, based on the feature information of the user scan data in each data category, the user scan data in each data category is classified, and data filtering is performed based on the classification results. This method of classifying before filtering improves the efficiency of data filtering. Moreover, since the feature information represents the information of the user scan data under various attribute features, the user scan data can be accurately classified based on the feature information, thus improving the accuracy of data filtering.

[0062] See Figure 2 , Figure 2 This is another flowchart of the data filtering method in the embodiments of this application, which may include the following steps:

[0063] Step S210: Obtain user scanning data corresponding to the QR codes in each area, perform attribute feature recognition on the user scanning data, and obtain feature information of the user scanning data under one or more attribute features.

[0064] Each area refers to a pre-defined region based on geographical location. These areas can be large or small. One area can correspond to one QR code or multiple QR codes. For example, if an office building has multiple entrances, different QR codes can be assigned to each entrance. Furthermore, a large area can contain smaller areas. For instance, shops within a shopping mall can also have corresponding QR codes. Users can scan the mall's QR code upon entering, and then scan the QR code of a specific shop within the mall.

[0065] After a user scans a two-dimensional code of a region each time, corresponding user scanning data can be generated. The user scanning data can include the time of scanning, information of the region, and information of the user. For example, the name of the region, the name of the user, the household information of the user, the virus detection situation of the user, and the vaccination situation of the user can be included. In the embodiments of the present application, a plurality of attribute features can be set in advance, which can include the name, the gender, the age range, the province, the district, the county, and the town to which the household belongs, the virus detection situation, and the vaccination situation.

[0066] It can be understood that the user scanning data corresponding to the two-dimensional codes of different regions can be different. For example, the scanning data corresponding to the two-dimensional code of region A includes the household information, and the scanning data corresponding to the two-dimensional code of region B does not include the household information. Therefore, attribute feature identification can be performed on the user scanning data, and the feature information of the user scanning data under one attribute feature can be obtained, or the feature information of the user scanning data under a plurality of attribute features can be obtained. The attribute features corresponding to different user scanning data can be the same or different. For example, for a certain user scanning data, the obtained feature information can include Beijing, Chaoyang District, 20 years old, and the like. For another user scanning data, the corresponding feature information can include Beijing, Haidian District, 30 years old, and the like. In the embodiments of the present application, a feature extraction model can be trained in advance, and the user scanning data can be input into the feature extraction model, so that the corresponding feature information can be output.

[0067] In step S220, if the feature information under the target attribute feature is not included in the feature information under one or more attribute features, the feature information under the target attribute feature can be filled according to the feature information under one or more attribute features of the user scanning data and other user scanning data.

[0068] As described above, since the plurality of attribute features are set in advance, not every user scanning data has corresponding feature information under every attribute feature. For example, the user scanning data does not have corresponding feature information under the target attribute feature, and at this time, there is a problem of information missing. The present application can fill the feature information under the target attribute feature according to the obtained and known user scanning data.

[0069] Optionally, the feature information under the target attribute feature can be filled by using the Bayes formula or the decision tree according to the feature information under one or more attribute features of the user scanning data and other user scanning data. For example, the feature information under the target attribute feature, i.e., the target feature information, can be extracted from other user scanning data, and according to each target feature information, the conditional probability of the target feature information under the target attribute feature can be calculated by combining the corresponding feature information of the user scanning data. The target feature information with the maximum corresponding conditional probability value is taken as the feature information of the user scanning data under the target attribute feature.

[0070] The decision tree is a commonly used classification method, which is a decision analysis method for evaluating project risks and determining the feasibility by constructing a decision tree to obtain the probability that the expected value of net present value is greater than or equal to zero on the basis of the probability of various situations, and is a graphical method for intuitive use of probability analysis. Therefore, the feature information of the user scanning data under the target attribute characteristics can also be inferred by the decision tree.

[0071] In step S230, the feature value corresponding to the user scanning data is determined according to the feature information of the user scanning data under one or more attribute characteristics.

[0072] In the embodiment of the present application, the feature information refers to string information, and the feature information can be converted into a feature value by data conversion, so as to classify the user scanning data according to the feature value. In some embodiments, the feature value corresponding to the feature information of the user scanning data under a single attribute characteristic is determined according to the feature information of the user scanning data under the single attribute characteristic. For example, different numerical values can be set in advance for the feature information under different attribute characteristics, and the size of the numerical value can be positively correlated with the importance of the attribute characteristic. That is, a larger numerical value can be corresponded to the feature information under an important attribute characteristic, and a smaller numerical value can be corresponded to the feature information under an unimportant attribute characteristic. The sum of the feature values corresponding to the feature information of the user scanning data under one or more attribute characteristics is determined as the feature value corresponding to the user scanning data. In some embodiments, the feature value corresponding to the feature information under a certain attribute characteristic can also be determined as the feature value corresponding to the user scanning data.

[0073] In step S240, the data category corresponding to the user scanning data is determined according to the feature value.

[0074] In some embodiments, the data range to which the feature value belongs can be determined according to the data ranges respectively corresponding to a plurality of data categories, and the data category corresponding to the data range to which the feature value belongs is determined as the data category corresponding to the user scanning data.

[0075] For example, three data categories are included in total, the data range corresponding to the data category A is 0-10 (including 10), the data range corresponding to the data category B is 10-20 (including 20), and the data range corresponding to the data category C is 20-30 (including 30). If the feature value corresponding to the user scanning data is 15, then the data category corresponding to the user scanning data can be determined as the data category B.

[0076] In step S250, the user scanning data is stored into the object storage bucket corresponding to the data category.

[0077] Each data category can have a corresponding object storage bucket (OSS, Object Storage Service), which is a carrier of objects, i.e., a container for storing objects. Here, the object is the user scan data. After determining the data category, the user scan data can be stored in the object storage bucket corresponding to the data category.

[0078] In some embodiments, when the user scans a two-dimensional code in a certain area, the user can first perform a face scan, so the face feature information corresponding to the user scan data can also be obtained, and the correspondence between the face feature information and the user scan data can be established. In this way, the object storage bucket can store the correspondence between the face feature information and the user scan data, and the face feature information can be regarded as the identity of the user.

[0079] Step S260, for the user scan data in each data category, the user scan data in the data category is classified according to the feature information of the user scan data in the data category, and data screening is performed according to the obtained classification result.

[0080] Each object storage bucket stores data of the same data category, and the application can further classify the data in each object storage bucket according to the feature information of the user scan data. It should be noted that different object storage buckets can use different data classification methods, which can be set according to actual scenarios to meet different data processing needs. For example, the travel trajectory of a certain user can be screened from the classification result.

[0081] The data screening method of the embodiment of the application can perform attribute feature recognition on the obtained user scan data to obtain feature information of the user scan data under one or more attribute features. In the case of missing information, the missing information can also be filled according to the known information (the feature information of the user scan data and the feature information of other user scan data). According to the feature information of the user scan data under one or more attribute features, the data category corresponding to the user scan data can be determined, and the user scan data can be stored in the corresponding object storage bucket. Face feature information can also be obtained, and the correspondence between the face feature information and the user scan data can be established and stored. Further, the user scan data in each data category can be classified according to the feature information of the user scan data in the data category, and data screening can be performed according to the classification result. The first classification and then screening method can improve the efficiency of data screening, and since the feature information represents the information of the user scan data under each attribute feature, the user scan data can be accurately classified based on the feature information of the user scan data, so the accuracy of data screening can be improved.

[0082] The embodiment of the application also provides a data screening device, which is shown inFigure 3 The data screening device 300 comprises:

[0083] The feature information determination module 310 is configured to acquire user scanning data corresponding to the two-dimensional code of each region, perform attribute feature recognition on the user scanning data, and obtain feature information of the user scanning data under one or more attribute features.

[0084] The feature value determination module 320 is configured to determine a feature value corresponding to the user scanning data according to the feature information of the user scanning data under one or more attribute features.

[0085] The data category determination module 330 is configured to determine a data category corresponding to the user scanning data according to the feature value.

[0086] The data screening module 340 is configured to, for the user scanning data in each data category, perform classification on the user scanning data in the data category according to the feature information of the user scanning data in the data category, and perform data screening according to the obtained classification result.

[0087] Optionally, the data screening device 300 further comprises:

[0088] The face feature information acquisition module is configured to acquire face feature information corresponding to the user scanning data.

[0089] The corresponding relationship establishment module is configured to establish a corresponding relationship between the face feature information and the user scanning data.

[0090] Optionally, the data screening device 300 further comprises:

[0091] The information filling module is configured to, if the feature information under one or more attribute features does not contain feature information under a target attribute feature, fill the feature information under the target attribute feature according to the feature information of the user scanning data and other user scanning data under one or more attribute features.

[0092] Optionally, the information filling module is specifically configured to, if the feature information under one or more attribute features does not contain feature information under a target attribute feature, fill the feature information under the target attribute feature according to the feature information of the user scanning data and other user scanning data under one or more attribute features by using a Bayesian formula or a decision tree.

[0093] Optionally, the data category determination module 330 is specifically configured to determine a data range to which the feature value belongs according to a data range corresponding to each of a plurality of data categories, and determine, as the data category corresponding to the user scanning data, a data category corresponding to the data range to which the feature value belongs.

[0094] Optionally, the characteristic value determination module 320 is specifically configured to determine a characteristic value corresponding to the characteristic information of the user scanning data under a single attribute characteristic according to the characteristic information of the user scanning data under the single attribute characteristic; and determine a sum of the characteristic values corresponding to the characteristic information of the user scanning data under one or more attribute characteristics as the characteristic value corresponding to the user scanning data.

[0095] Optionally, the data screening apparatus 300 further comprises:

[0096] The storage module is configured to store the user scanning data into the object storage bucket corresponding to the data category.

[0097] The specific details of the modules or units in the above apparatus have been described in detail in the corresponding method, and thus will not be described here again.

[0098] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into embodied by multiple modules or units.

[0099] In the exemplary embodiments of the present application, an electronic device is also provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-mentioned data screening method in the exemplary embodiments.

[0100] Figure 4 A structural schematic diagram of an electronic device in the embodiments of the present application. It should be noted that, Figure 4 The electronic device 400 shown is only one example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0101] As Figure 4 shown, the electronic device 400 comprises a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage portion 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for system operation are also stored. The central processing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0102] The following components are connected to the I / O interface 405: an input part 406 including a keyboard, a mouse, etc.; an output part 407 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 408 including a hard disk, etc.; and a communication part 409 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication part 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as necessary. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 410 as necessary, so that a computer program read out therefrom is installed in the storage part 408 as necessary.

[0103] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit 401, various functions defined in the apparatus of the present application are executed.

[0104] In embodiments of the present application, there is also provided a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the data screening method described above.

[0105] It should be noted that the computer readable storage medium shown in the present application can be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any suitable combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, radio frequency, etc., or any suitable combination of the above.

[0106] In embodiments of the present application, there is also provided a computer program product, which, when run on a computer, causes the computer to perform the data screening method described above.

[0107] It has to be noted that, in the present document, relational terms are intended to encompass the various possible relationships between means or components or steps in a process. For example, the phrase "first means and second means" is intended to mean that a first means and a second means can be active together, alone, or in any combination, that one or more first means is / are an alternative to one or more second means, or in an "either / or" relationship, and / or that one or more first means and one or more second means are concurrent in some processes but not necessarily in others. In addition, the phrase "first means and second means" is not necessarily used in the same context, although it can be. The phrase "first means and second means" is also not necessarily used in the same sentence, although it can be. The phrase "first means and second means" is also not necessarily of the same category, although it can be. The phrase "first means and second means" is also not necessarily of the same meaning, although it can be.

[0108] The foregoing detailed description has set forth various embodiments of the application via the use of specific terminology. However, embodiments thereof can be practiced without the specific details ("every" embodiment) set forth above. In general, the terms used are intended to be illustrative and not restrictive, and it will be apparent to those skilled in the art that other variations and modifications of the application can be made without departing from the spirit or scope of the application. Accordingly, the scope of the application should be determined not with the foregoing description alone, but instead should be determined in accordance with the appended claims and their equivalents.

Claims

1. A method of data screening, characterized by, The method comprises: obtaining user scanning data corresponding to the two-dimensional code of each region, performing attribute feature recognition on the user scanning data to obtain feature information of the user scanning data under one or more attribute features; determining a feature value corresponding to the user scanning data according to the feature information of the user scanning data under one or more attribute features; determining a data category corresponding to the user scanning data according to the feature value; for user scanning data in each data category, classifying the user scanning data in the data category according to the feature information of the user scanning data in the data category, and performing data screening according to the obtained classification result; the determining of the feature value corresponding to the user scanning data according to the feature information of the user scanning data under one or more attribute features comprises: determining a feature value corresponding to the feature information of the user scanning data under a single attribute feature according to the feature information of the user scanning data under the single attribute feature; summing the feature values corresponding to the feature information of the user scanning data under one or more attribute features to determine the feature value corresponding to the user scanning data.

2. The method of claim 1, wherein, The method further comprises: obtaining face feature information corresponding to the user scanning data; establishing a correspondence between the face feature information and the user scanning data.

3. The method according to claim 1 or 2, characterized in that, The method further comprises: if the feature information under one or more attribute features does not contain feature information under a target attribute feature, filling the feature information under the target attribute feature according to the feature information of the user scanning data and other user scanning data under one or more attribute features.

4. The method of claim 3, wherein, filling the feature information under the target attribute feature according to the feature information of the user scanning data and other user scanning data under one or more attribute features comprises: filling the feature information under the target attribute feature according to the feature information of the user scanning data and other user scanning data under one or more attribute features by using a Bayesian formula or a decision tree.

5. The method of claim 1, wherein, The determining of the data category corresponding to the user scanning data according to the feature value comprises: determining a data range to which the feature value belongs according to data ranges respectively corresponding to a plurality of data categories; determining a data category corresponding to the data range to which the feature value belongs as the data category corresponding to the user scanning data.

6. The method of claim 1, wherein, After determining the data category corresponding to the user scanning data, the method further comprises: storing the user scanning data in an object storage bucket corresponding to the data category.

7. A data screening device characterized by comprising: The device comprises: a feature information determination module configured to obtain user scanning data corresponding to the two-dimensional code of each region, perform attribute feature recognition on the user scanning data, and obtain feature information of the user scanning data under one or more attribute features; a feature value determination module configured to determine a feature value corresponding to the user scanning data according to the feature information of the user scanning data under one or more attribute features; a data category determination module configured to determine a data category corresponding to the user scanning data according to the feature value. The data screening module is configured to scan the user scan data in each data category, classify the user scan data in the data category according to the feature information of the user scan data in the data category, and perform data screening according to the obtained classification result. The feature value determination module is specifically configured to determine a feature value corresponding to the feature information of the user scan data under a single attribute feature according to the feature information of the user scan data under the single attribute feature, and determine a sum of feature values corresponding to the feature information of the user scan data under one or more attribute features as a feature value corresponding to the user scan data.

8. An electronic device, comprising: The computer program is configured to implement the data screening method according to any one of claims 1-6 when executed by the processor. The computer program is configured to implement the data screening method according to any one of claims 1-6 when executed by the processor.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, ​

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