Verification processing method, processing device and computer readable storage medium

Abstract

The present invention provides a verification processing method, a processing device and a computer-readable storage medium, the method comprising: receiving verification information of a target user, wherein the verification information comprises a current recognition result for a target object; verifying the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a custom answer made by the target user for the target object. The embodiment of the present invention increases the difficulty of maliciously cracking the verified target object, thereby improving the security of the Internet of Things.

Description

Technical Field

[0001] The present invention relates to the field of Internet security technology, and in particular to a verification processing method, a processing device and a computer-readable storage medium. Background Art

[0002] With the development of mobile Internet, smart electronic devices (such as mobile phones and PADs) have been rapidly popularized. The traditional verification of target objects (i.e. verification codes) is based on the recognition results of pictures or numbers, for example, the verification of the selection of pictures containing specified objects, or the verification of the addition and subtraction results of two numerical values. In this verification method, the machine can crack the verification code through deep learning. Therefore, in the prior art, it is relatively difficult for the verification code to be maliciously cracked, resulting in low security of the Internet of Things. Summary of the invention

[0003] The embodiments of the present invention provide a verification processing method, a processing device and a computer-readable storage medium to solve the problem in the prior art that the difficulty of a verification code being maliciously cracked is low, resulting in low security of the Internet of Things.

[0004] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0005] In a first aspect, an embodiment of the present invention provides a verification processing method, including:

[0006] Receiving verification information of a target user, wherein the verification information includes a current recognition result for the target object;

[0007] The verification information is verified according to a preset matching relationship between a historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0008] In a second aspect, an embodiment of the present invention provides a verification processing method, including:

[0009] Send verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object; the verification information is used by the service to verify the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0010] In a third aspect, an embodiment of the present invention provides a server, including:

[0011] A first receiving module is used to receive verification information of a target user, wherein the verification information includes a current recognition result for a target object;

[0012] A verification module is used to verify the verification information according to a matching relationship between a preset historical recognition result of a target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0013] In a fourth aspect, an embodiment of the present invention provides a client, including:

[0014] The first sending module is used to send verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object; the verification information is used by the service to verify the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0015] In a fifth aspect, an embodiment of the present invention provides a processing device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned verification processing method when executed by the processor.

[0016] In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned verification processing method are implemented.

[0017] The embodiment of the present invention receives verification information from a target user, wherein the verification information includes a current recognition result for a target object; the verification information is verified based on a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a custom answer made by the target user for the target object. Since different users may have different custom answers for the target object, the recognition results for the same verification data for different users may be different. Therefore, the embodiment of the present invention increases the difficulty of malicious hacking of the verified target object, thereby increasing the security of the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0019] Figure 1 It is one of the flow charts of the verification processing method provided by the embodiment of the present invention;

[0020] Figure 2 This is the second flowchart of the verification processing method provided by the embodiment of the present invention;

[0021] Figure 3 This is the third flowchart of the verification processing method provided by the embodiment of the present invention;

[0022] Figure 4 is a structural diagram of a server provided by an embodiment of the present invention;

[0023] Figure 5 is a structural diagram of a client provided by an embodiment of the present invention;

[0024] Figure 6 is a structural diagram of a server provided by another embodiment of the present invention;

[0025] Figure 7 It is a structural diagram of a server provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 , Figure 1 is a flow chart of a verification processing method provided by an embodiment of the present invention, such as Figure 1 As shown, the following steps are included:

[0028] Step 101: receiving verification information of a target user, wherein the verification information includes a current recognition result for a target object.

[0029] Step 102, verifying the verification information according to a preset matching relationship between a historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0030] The verification processing method provided in the embodiment of the present invention is mainly applied to the server, and is used to verify the content selected by the target user for the target object on the client. The client can display the target object for the target user to make a cognitive selection of the target object, and the selected content represents the current recognition result of the target user for the target object. After the user makes a selection, verification information carrying the current recognition result can be sent to the server to verify the user's verification behavior.

[0031] Optionally, the target object can be understood as a verification code, such as a verification code composed of four picture elements, or can be understood as a specific element in the verification code, for example, it can be understood as one or more elements contained in the verification code. The following embodiments are described by taking the target object as a verification code as an example.

[0032] The preset historical recognition result of the target user corresponding to the target object can be understood as the historical recognition result of the target user for the target object pre-stored by the server, or can be understood as the data information used to determine the historical recognition result corresponding to the target user for the target object pre-stored. Specifically, the historical recognition result of the target user for the target object can be determined when the server generates the target object.

[0033] It should be noted that the recognition results (ie, the results of personalized cognition) of different users for the same target object may be the same or different. In this embodiment, different target objects may be set for different users, thereby improving the accuracy of the verification operation.

[0034] Furthermore, the historical recognition result of the target user corresponding to the target object is unique, but the historical recognition results of different users may not be unique. For example, different users may have different subjective cognitions of colors, graphics or objects, such as the following cognitive questions: "the brightest color", "the most favorite color", "the most expensive object", "the most missed age group", "the most disliked thing", "the highest dream" and "the most admired person", etc. In other words, the historical recognition result of the target user for the target object is pre-stored as a non-standardized result, or according to the pre-stored data information of the target user, the historical recognition result of the target user corresponding to the target object can be generated as a non-standardized and non-unique result.

[0035] In addition, the target user's historical recognition results for the target object do not include "similar" methods, such as tablet computer and Ipad, money and Money, etc.; the target user's historical recognition results for the target object in this application refer to "relative" relationships, such as "most favorite", "most valuable", etc.

[0036] When the current recognition result of the target user matches the preset historical recognition result of the target user corresponding to the target object, it can be understood that this verification has passed; when the current recognition result of the target user does not match the preset historical recognition result of the target user corresponding to the target object, it can be understood that this verification has failed.

[0037] It should be understood that the analysis and calculation method for determining whether the current recognition result of the target user matches the preset historical recognition result of the target user corresponding to the target object can refer to the relevant technology and is not further limited here.

[0038] The embodiment of the present invention receives verification information from a target user, wherein the verification information includes a current recognition result for a target object; the verification information is verified based on a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a custom answer made by the target user for the target object. Since different users may have different custom answers for the target object, the recognition results for the same verification data for different users may be different. Therefore, the embodiment of the present invention increases the difficulty of malicious hacking of the verified target object, thereby increasing the security of the Internet of Things.

[0039] It should be noted that, when the above app is used, relevant device information and geographic location information can be obtained to assist in improving the pass rate. For example, if the currently obtained information is the same as the previous record, the scoring standard can be lowered to increase the chance of passing the verification. That is, in one embodiment, the method also includes:

[0040] Acquire target information, the target information including at least one of current time information and geographic location information of a terminal corresponding to the target user;

[0041] Determining the preset historical recognition result of the target user corresponding to the target object according to the target information;

[0042] Among them, when the target information matches the historical operation information, the preset historical recognition result of the target user corresponding to the target object is the first historical recognition result; when the target information does not match the historical operation information, the preset historical recognition result of the target user corresponding to the target object is the second historical recognition result, and the custom answer in the second historical recognition result is a subset of the custom answer in the first historical recognition result.

[0043] The above historical operation information can be understood as: the usage time corresponding to the historical verification and the geographical location information of the terminal corresponding to the historical verification. The matching of the target information and the historical operation information can be understood as that the location and / or time of this verification is the same as the location and / or time of the historical verification.

[0044] For example, the custom answer in the second historical recognition result is a subset of the custom answer in the first historical recognition result. It can be understood that when the target object includes four options A, B, C and D, if the user-defined answer is A, the similarity between B and A is 80%, the similarity between C and A is 10%, and the similarity between D and A is 5%. At this time, when the target information matches the historical operation information, the first historical recognition result can be expressed as either A or B; when the target information does not match the historical operation information, the first historical recognition result is expressed as A.

[0045] In this embodiment, since different historical recognition results are set for verification according to the matching conditions between the target information and the historical operation information, the flexibility of verification is improved.

[0046] Furthermore, when the target information does not match the historical operation information, and the number of times the target user refreshes the verification code is greater than a preset value, the target user can be authenticated by biometric identification or by using a digital password, thereby improving the security of the verification operation.

[0047] Optionally, in one embodiment, before the step of verifying the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the step further includes:

[0048] The historical recognition results of the target user corresponding to the target object are determined according to a preset user portrait data dictionary, wherein the user portrait data dictionary is used to determine the historical recognition results of at least one target object and the target user corresponding to the at least one target object.

[0049] It should be noted that the target object can be a verification code temporarily generated by the server or a verification code pre-stored by the server. For example, in this embodiment, the target object is a verification code generated by the server. At this time, the target object can include a first verification picture generated according to the target verification rule and the user portrait data dictionary corresponding to the target user and at least one second verification picture generated based on a preset database.

[0050] The above-mentioned user portrait data dictionary may include first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, and the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognitive verification (ie, cognitive rules).

[0051] Optionally, the color data dictionary may include one or more color features, such as red, white, yellow, and black; the graphic data dictionary may include one or more graphics, such as photos, stones, houses, plants, and animals. The semantic rule data dictionary may be associated with color features and / or graphics to represent the user's perception of color and / or graphics, such as "the brightest color", "the most favorite color", and "the most expensive item". Each rule may be managed with one or more first user data, such as the brightest color may include one or more.

[0052] In other embodiments, the target object is a verification code pre-stored by the server. For example, the user portrait data dictionary can be used to store historical recognition results of at least one target object and a target user corresponding to the at least one target object.

[0053] In the embodiment of the present invention, a user portrait data dictionary based on historical recognition results of at least one target object and a target user corresponding to the at least one target object is preset in the server, so that the target object can be dynamically generated, thereby improving the flexibility of verification.

[0054] Furthermore, the verification information also includes identity information; the historical recognition result of the target user corresponding to the target object is determined according to the preset user portrait data dictionary, specifically including:

[0055] Confirming a corresponding user portrait data dictionary according to the identity information;

[0056] The historical recognition result corresponding to the target object is determined according to the corresponding user portrait data dictionary.

[0057] In an embodiment of the present invention, specifically, a user can use a registered account to log in through a client installed on an electronic device to access or execute a corresponding service. The above-mentioned target user can be understood as an account, and can also be understood as a user who uses an account to operate. In one embodiment, when the above-mentioned target user is understood as a user, the above-mentioned identity information can be understood as the account number of the target user, and can also be understood as a device identifier for logging in to the account number, and can also be understood as a device identifier bound to the account number. In another embodiment, when the above-mentioned target user is understood as an account, the identity information can be understood as the account number of the target account, and can also be understood as a device identifier bound to the target account. In other words, in an embodiment of the present invention, the above-mentioned identity information is used to identify the target user.

[0058] Optionally, if the target user is a natural person, the identity information may be facial features, voice / voiceprint features, iris features and / or fingerprint features, etc. Correspondingly, in this embodiment, a neural network or the like may be used for machine recognition of facial features, voice / voiceprint features, iris features and / or fingerprint features, etc.

[0059] Optionally, since the verification information carries identity information, a corresponding user portrait data dictionary is determined based on the identity information, and the historical recognition result corresponding to the target object is determined based on the corresponding user portrait data dictionary. In this way, different identity information can be set to correspond to different user portrait data dictionaries, or a group of related identity information can be set to correspond to the same user portrait data dictionary. For example, a family corresponds to the same user portrait data dictionary, or a department of a company corresponds to the same user portrait data dictionary. Since different user groups are concerned about different content, a user portrait data dictionary is set based on identity information. The user portrait data dictionary can represent the behavioral habit characteristics of the target user, thereby improving the pertinence of generating the target object for personalized cognition, and thus improving the accuracy of the authentication result.

[0060] Furthermore, in one embodiment, after the step of verifying the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the method further includes:

[0061] When the historical recognition result matches the current recognition result, confirming that the verification is successful;

[0062] When the verification is successful, the user portrait data dictionary is updated according to the matching relationship between the current recognition result and the target object.

[0063] In the embodiment of the present invention, the matching relationship between the current recognition result and the target object can be understood as the verification behavior of this user verification. After each verification is passed, the corresponding verification behavior will be recorded, and based on the verification behavior, AI (artificial intelligence) training is used to determine whether to change the user portrait data dictionary. Optionally, the preset parameter attributes in each target verification picture can be recorded and scored, and the overall score value of the preset parameter attributes can be determined based on the verification behaviors recorded multiple times. Based on the overall score value, it is determined whether to add it to the user portrait data dictionary to enrich the user portrait data dictionary, so that each user has a unique user portrait data dictionary. In this way, the difficulty of user verification is increased, and the difficulty of cracking user behavior verification is increased. For example, when selecting the object that the user likes, a red car is selected 5 times and a white car is selected once; when selecting the most expensive electronic product, a red mobile phone is selected 4 times and a black mobile phone is selected 1 time. At this time, it can be understood that the user's favorite color is red, so the semantic rule "user favorite color" and the color data dictionary "red" are added to the user portrait data dictionary, and the two are associated. Since the embodiment of the present invention updates the user portrait data dictionary according to the matching relationship between the current recognition result and the target object after verification, the user portrait data dictionary is closer to the behavioral habits of the target user, thereby improving the accuracy of the current recognition result judgment.

[0064] It should be noted that, when the user portrait data dictionary stores historical recognition results of at least one target object and a target user corresponding to the at least one target object, the current recognition result of the target user and the matching relationship of the target object can be stored in the user portrait data dictionary.

[0065] It should be understood that in this embodiment, the number of verification failures within a preset time period before the current verification is passed can also be counted. When the number of verification failures is greater than the preset value, the user portrait data dictionary is updated according to the matching relationship between the current recognition result corresponding to the verification and the target object. In this way, the historical authentication results can be updated as the user's cognition of things changes in different time periods, thereby improving the accuracy of authentication.

[0066] Furthermore, the server may also generate the target object for the target user to verify. Specifically, before the step 101, the method may further include:

[0067] Obtain the target user's identity information;

[0068] Determine a user portrait data dictionary corresponding to the target user according to the identity information, where the user portrait data dictionary corresponding to the target user is used to represent the behavioral habit characteristics of the target user;

[0069] Generate a target object matching the target user, wherein the target object includes a first verification picture generated according to a target verification rule and a user portrait data dictionary corresponding to the target user and at least one second verification picture generated based on a preset database;

[0070] Sending the target object to the client corresponding to the target user;

[0071] Among them, the current recognition result for the target object includes picture information of the target verification pictures selected by the target user based on the target verification rule, and the target verification pictures are a preset number of verification pictures among the first verification pictures and the second verification pictures.

[0072] In an embodiment of the present invention, when a user logs in or performs a corresponding operation after logging in, the identification information of the target user will be obtained in the case where user behavior verification can be triggered. For example, in the user's login interface, after the user enters the user name (i.e., account number) (for example, clicks the password input control or the acquisition control of the target object to be verified, etc.), the identification information of the target user will be obtained. It is also possible to obtain the identification information of the target user (such as the currently logged-in account) after logging in, when performing corresponding operations such as payment or information saving, so as to trigger the generation of the target object.

[0073] Optionally, in one embodiment, the user portrait data dictionary corresponding to the target user includes first user data and second user data, wherein the first user data includes a color data dictionary and / or a graphic data dictionary, and the second user data includes a semantic rule data dictionary, wherein the semantic rule data dictionary includes rules for user cognition verification (i.e., cognitive rules). Optionally, the color data dictionary may include one or more color features, such as red, white, yellow, and black; the graphic data dictionary may include one or more graphics, such as photos, stones, houses, plants, and animals. The semantic rule data dictionary may be associated with color features and / or graphics to represent the user's cognition of colors and / or graphics, such as "the brightest color", "the most favorite color", and "the most expensive item". Each rule may be managed with one or more first user data, such as the brightest color may include one or more.

[0074] It should be understood that in the embodiment of the present invention, since different users have different cognitions of different things, the user portrait data dictionaries formed are different.

[0075] Optionally, in another embodiment, the user portrait data dictionary may also only include the first user data. In this case, the above-mentioned semantic rule data dictionary may be expressed as a unified rule, that is, each user adopts a unified rule for verification.

[0076] In an embodiment of the present invention, one or more first verification pictures can be generated according to the target verification rules and the user portrait data dictionary, and one or more second verification pictures can be generated based on a preset database. At this time, the first verification picture can be understood as a picture associated with the user portrait data dictionary (i.e., a higher degree of match or similarity), and the second verification picture can be understood as a picture not associated with the user portrait data dictionary (i.e., a lower degree of match or similarity).

[0077] It should be understood that in the first embodiment, the first verification picture can be generated only based on the color data dictionary and the target verification rule; for example, the above target verification rule is to select the brightest color, and the brightest color in the user portrait data dictionary is associated with red, which can be understood as the user thinks that the brightest color is red. In this case, the first verification picture can be a red block (such as a red square), and the second verification picture can include blocks of colors other than red, for example, black blocks and yellow blocks. In addition, each verification picture is an object of a different color, for example, the first verification picture is a red leaf, and the second verification picture includes black leaves and yellow leaves. In this case, the above target verification rule can also be understood as selecting the leaves with the brightest color.

[0078] In the second embodiment, the first verification image may be generated based only on the graphic data dictionary and the target verification rule. For example, the target verification rule is to select the most valuable item, which may be associated with a house. In this case, the first verification image may be a house, and the second verification image may include items such as cash, stones, and flowers.

[0079] In the third embodiment, the first verification image may also be generated based on the color data dictionary, the graphic data dictionary and the target verification rule. For example, the target verification rule is to select the most favorite item, and the most favorite item is associated with red and a car. In this case, the first verification image generated may be a red car, and the second verification image may include a white car, a black stone and a red flower.

[0080] In the embodiment of the present invention, due to the different behavioral characteristics of different users, the corresponding user portrait data dictionary is different, so the verification results of the same target object for different users may be different. Therefore, the embodiment of the present invention increases the difficulty of cracking the verified target object, thereby improving the security of the Internet of Things.

[0081] Furthermore, when the user profile data dictionary includes a semantic rule data dictionary, the target verification rule may be a rule randomly selected from the semantic rule data dictionary. In other words, in this embodiment, before generating verification data matching the target user, the method further includes:

[0082] The target verification rule is determined by randomly selecting a rule in the semantic rule data dictionary.

[0083] In an embodiment of the present invention, since a semantic rule data dictionary is set in the user portrait data dictionary, a first verification picture is generated based on the rules in the semantic rule data dictionary as target verification rules. Different users have different preferences, thereby improving the targetedness of verification picture generation and ensuring the accuracy of user behavior verification.

[0084] Optionally, the target verification rule may be a rule in the semantic rule data dictionary, or a combination of multiple rules in the semantic data dictionary. For example, the two randomly selected rules include "brightest color" and "favorite animal". In this case, the target verification rule may be the favorite animal and the brightest color; or it may be understood as: the favorite animal and the animal's fur color is the brightest. In this case, the first verification image may be obtained based on the graphic associated with the favorite animal (e.g., dog) and the brightest color (e.g., yellow). In this case, the first verification image may be a yellow dog, and the second verification image may be a black cat, a yellow cat, a white pig, etc.

[0085] It should be understood that the above-mentioned verification of the verification information based on the matching relationship between the preset historical recognition results of the target user corresponding to the target object and the current recognition results of the target user can be understood as: verifying the target verification picture based on the picture information and the user portrait data dictionary, wherein, when the target verification picture matches the user portrait data dictionary, the verification passes; when the target verification picture does not match the user portrait data dictionary, the verification fails.

[0086] In an embodiment of the present invention, the way in which a user selects a target verification picture can be set according to actual needs. For example, a user can select a target verification picture by clicking on a verification picture; or slide a control or a picture onto a verification picture to select a target verification picture; or drag a verification picture to a certain area to select a target verification picture.

[0087] Furthermore, in an embodiment of the present invention, the user portrait data dictionary may also include third user data, which is used to represent the user's sliding habits. For example, it may include sliding speed and sliding direction. When the client responds to the verification request, the verification request also includes sliding data, and the sliding data is compared with the third user data to determine the similarity between the two, thereby increasing the difficulty of malicious cracking of the verification.

[0088] After the user selects the target verification picture, the image information of the target verification picture can be fed back to the server, and the server verifies the target verification picture selected by the user. The image information can specifically be a verification picture, or it can be information such as an image identifier of the target verification picture, as long as it can inform the user of the target verification picture selected. For example, in one embodiment, the above-mentioned image information may include coordinate information. Optionally, the above-mentioned verification data may carry the coordinate information of each verification picture, and the client can control the verification picture to be displayed at a specified location based on the coordinate information. At this time, after the user selects the corresponding target verification picture, the coordinate information of the verification picture can be fed back to the server.

[0089] It should be understood that verifying the target verification picture based on the picture information and the user portrait data dictionary can be understood as comparing the target verification picture with the user portrait data dictionary to determine the degree of match between the target verification picture and the user portrait data dictionary. That is to say, the similarity between this verification behavior and the user's habitual behavior. For example, extract key information from the target verification picture, such as the color, object and other basic attributes of the picture, and score the target verification picture based on the basic attributes and the user portrait data dictionary. When the score is greater than or equal to the preset value, it is determined that the target verification picture matches the user portrait data dictionary. For example, a scoring value can be set for each first user data, that is, each color data dictionary and each graphic data dictionary are set with a corresponding scoring value, and the final score of the target verification picture is obtained by weighted average calculation, and the degree of match between the target verification picture and the user portrait data dictionary is determined based on the score.

[0090] In an optional embodiment, the image information of the first verification image may also be pre-stored, and the target verification image may be verified based on the image information of the first verification image. In this embodiment, the image information of the first verification image may be understood as a preset historical recognition result of the target user corresponding to the target object. For example, when the target verification image contains all the first verification images and does not contain the second verification image, it is considered that the verification is passed, otherwise the verification fails. Alternatively, when the target verification image is contained in the second verification image, it is considered that the verification fails, otherwise the verification passes.

[0091] Further, in the case of passing the verification, the verification behavior of this time is recorded, and the verification behavior is used to represent the target verification picture selected based on the first verification picture, the second verification picture and the target verification rule;

[0092] The user portrait data dictionary is updated according to the verification behavior.

[0093] In the embodiment of the present invention, after each verification is passed, the corresponding verification behavior will be recorded, and based on the verification behavior, AI training is used to determine whether to change the user portrait data dictionary. Optionally, the preset parameter attributes in each target verification picture can be recorded and scored, and the overall score value of the preset parameter attributes can be determined based on the verification behaviors recorded multiple times. Based on the overall score value, it is determined whether to add it to the user portrait data dictionary to enrich the user portrait data, so that each user has a unique user portrait data dictionary. In this way, the difficulty of user verification can be increased, and then the difficulty of cracking user behavior verification can be increased. For example, when selecting the user's favorite object, a red car was selected 5 times and a white car was selected once; when selecting the most expensive electronic product, a red mobile phone was selected 4 times and a black mobile phone was selected once. At this time, it can be understood that the user's favorite color is red, so the semantic rule "user favorite color" and the color data dictionary "red" are added to the user portrait data dictionary, and the two are associated.

[0094] Further, based on the above embodiment, after the target user initially registers, test data can be output to test the user's behavior habits, which is convenient for later verification. For example, in an embodiment of the present invention, before obtaining the identification information of the target user, the method further includes:

[0095] After the target user initially registers, sending test data to a client corresponding to the target user, wherein the test data at least includes the target object;

[0096] receiving a test result fed back by the target user through the client, wherein the test result at least includes the historical recognition result;

[0097] The user portrait data dictionary corresponding to the target user is determined according to the test result.

[0098] In the embodiment of the present invention, one or more combinations can be selected from the graphic library, color library and semantic rule library to form fixed test options and sent to the client to perform user habit testing. For example:

[0099] For the first test, please select "Red" from the following. The page selection area displays multiple graphics of different colors. When the target user clicks the trigger button or slides the screen, the "selection box" in the selection area moves to the corresponding position. At the same time, the XY axis coordinate information of the "selection box" is obtained and compared with the XY axis coordinate information of the "graphic", so as to confirm which graphic the user has selected.

[0100] For the second assessment, please select "Plants" from the following. The selection area on the page displays: graphics such as plants, animals, and man-made objects.

[0101] The third assessment is a combination of two dimensions. Please select "yellow car" from the following. The page selection area displays "orange car", "green plant", "red flag", "brown stone" and "yellow car" and other graphics. Users need to identify both "color" and "object" dimensions at the same time.

[0102] Similarly, the difficulty of subsequent evaluations can be increased by combining three dimensions. For example, please select "the most expensive electronic product", "the most memorable thing", "the spring landscape" or "the brightest object" from the following.

[0103] Through the above evaluation results, a unique "user portrait" information of each user can be constructed to form a user portrait data dictionary. In one embodiment, the following user portrait information can be included:

[0104] 1. "Sliding track information": Do you usually slide along the X axis first, or along the Y axis first; or slide along the X and Y axes simultaneously; or the sliding speed and frequency.

[0105] 2. "Perception of color": can distinguish between normal and color-blind people, as well as the perception of colors such as "bright, dark, warm, and cold".

[0106] 3. “Perception of objects”: It can distinguish whether users prefer to choose “natural objects” or “artificial objects” or other information expressed by graphics.

[0107] It should be noted that the various optional implementation modes introduced in the embodiments of the present invention may be implemented in combination with each other or may be implemented separately, and the embodiments of the present invention are not limited to this.

[0108] Furthermore, in one embodiment, after the step of verifying the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the method further includes:

[0109] When the historical recognition result matches the current recognition result, confirming that the verification is successful;

[0110] If the verification is passed and the preset parameter value is less than the first preset value, sending the training data to the client corresponding to the target user;

[0111] Re-determining the historical recognition result of the target user based on the recognition result of the target user on the training data;

[0112] Wherein, the parameter value is reduced by a first preset value each time the verification fails.

[0113] In this embodiment, the above parameter value is used to determine whether retraining is needed. At each verification, it can be determined whether to adjust the parameter value based on the verification result. For example, in one embodiment, when the target information matches the historical operation information, each time the verification fails, the preset parameter value will be reduced by a first preset value. In the case where the reduced parameter value is less than the second preset value, after the verification is passed, it can be prompted that the current verification pass rate is low. At this time, multiple sets of new training data can be provided for training. In this way, the accuracy of subsequent verification can be improved.

[0114] It should be understood that if the above parameter values ​​pass the current verification, they can be restored to the initial values ​​during the next verification or before the next verification.

[0115] For example, the initial value of the preset parameter value is 90%, which represents the matching relationship between the historical recognition results and the current recognition results; then, in the case of each verification failure, the parameter value is reduced by 1% (that is, the first preset value is 1%) to 89%; if the verification fails again, the parameter value is reduced successively until the parameter value is reduced to 80% (that is, the second preset value is 80%), reminding that the verification threshold of the current parameter value is too low; at this time, the user can be prompted to retrain.

[0116] Furthermore, in one embodiment, the pass rate of each target object verification can be unified. When the pass rate of a target object is low, the user can be reminded to re-determine or update the historical recognition result corresponding to the target object if the verification is passed. In other words, the number of wrong answers to a certain question is counted, and then when the unlocking is successful, the answer to the repeatedly wrong question is updated according to certain rules.

[0117] In order to better understand the specific implementation of the present invention, the following Figure 2 The specific implementation process of the present invention is described in detail. Figure 2 , including the following processes:

[0118] Step 1: Build a basic data dictionary on the server and generate a data dictionary based on user portraits through the test process.

[0119] Step 2: The server randomly generates multiple colored graphics with coordinate information and cognitive rules (also known as verification rules, such as the brightest color, the highest value, the fastest runner, the one that swims in the water, etc.) based on the user portrait data dictionary and the preset database (graphic library, color library, and cognitive vocabulary library).

[0120] Step 3, using the coordinate information as the key point on the client side, displaying the graphics with colors and the recognition rules in a fixed area.

[0121] Step 4: The user selects a graphic that the user believes meets the cognitive rule by sliding a gesture or by using a mouse button on the client according to the cognitive rule.

[0122] Step 5, record the user data corresponding to the current user operation (sliding time, speed, selected coordinate information, etc.) and send it to the server. The server compares the user data with the data in the user portrait data dictionary to determine whether the sliding time and speed meet the human selection operation habits, and whether the object corresponding to the selected coordinate information meets the habit characteristics of the current user, which includes the personalized cognitive habits of the current user.

[0123] See also Figure 3 , Figure 3 is a flow chart of another verification processing method provided by an embodiment of the present invention. Figure 3 As shown, the verification processing method includes:

[0124] Step 301, sending verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object; the verification information is used by the service to verify the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0125] Optionally, the verification information also includes identity information, and the identity information is used by the server to confirm the corresponding user portrait data dictionary, and determine the historical recognition result of the target user corresponding to the target object based on the corresponding user portrait data dictionary.

[0126] Optionally, before sending the verification information of the target user to the server, the method further includes:

[0127] Receive the target object sent by the server; wherein, the target object is generated by the server based on a user portrait data dictionary corresponding to the target user, and the user portrait data dictionary corresponding to the target user is determined according to the identity information of the target user; the target object includes a first verification picture generated according to a target verification rule and the user portrait data dictionary corresponding to the target user, and at least one second verification picture generated based on a preset database.

[0128] Optionally, the user portrait data dictionary corresponding to the target user includes first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognition verification.

[0129] It should be noted that this embodiment is Figure 2 The implementation method of the client corresponding to the embodiment shown in the figure can be found in the specific implementation method. Figure 2 The related descriptions of the illustrated embodiments and the achievement of the same beneficial effects will not be repeated here to avoid duplication.

[0130] See also Figure 4 , Figure 4 is a structural diagram of a server provided by an embodiment of the present invention, such as Figure 4 As shown, the server 400 includes:

[0131] The first receiving module 401 is used to receive verification information of a target user, wherein the verification information includes a current recognition result for a target object;

[0132] The verification module 402 is used to verify the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0133] Optionally, the server 400 further includes:

[0134] The first determination module is used to determine the historical recognition results of the target user corresponding to the target object according to a preset user portrait data dictionary, wherein the user portrait data dictionary is used to determine the historical recognition results of at least one target object and the target user corresponding to the at least one target object.

[0135] Optionally, the verification information further includes identity information; and the first determination module is specifically configured to:

[0136] Confirming a corresponding user portrait data dictionary according to the identity information;

[0137] The historical recognition result corresponding to the target object is determined according to the corresponding user portrait data dictionary.

[0138] Optionally, the server 400 further includes:

[0139] A second determination module, configured to confirm that the verification is successful when the historical recognition result matches the current recognition result;

[0140] The updating module is used to update the user portrait data dictionary according to the matching relationship between the current recognition result and the target object when the verification is passed.

[0141] Optionally, the server 400 further includes:

[0142] A first acquisition module is used to acquire the identity information of the target user;

[0143] A third determination module is used to determine a user portrait data dictionary corresponding to the target user according to the identity information, where the user portrait data dictionary corresponding to the target user is used to represent the behavioral habit characteristics of the target user;

[0144] A generation module, configured to generate a target object matching the target user, wherein the target object includes a first verification picture generated according to a target verification rule and a user portrait data dictionary corresponding to the target user and at least one second verification picture generated based on a preset database;

[0145] A second sending module, used to send the target object to the client corresponding to the target user;

[0146] Among them, the current recognition result for the target object includes picture information of the target verification pictures selected by the target user based on the target verification rule, and the target verification pictures are a preset number of verification pictures among the first verification pictures and the second verification pictures.

[0147] Optionally, the user portrait data dictionary corresponding to the target user includes first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognition verification.

[0148] Optionally, the server 400 further includes:

[0149] The fourth determination module is used to randomly select a rule from the semantic rule data dictionary to determine the target verification rule.

[0150] Optionally, the server 400 further includes:

[0151] A second sending module, configured to send test data to a client corresponding to the target user after the target user initially registers, wherein the test data at least includes the target object;

[0152] The first receiving module is used to receive the test result fed back by the target user through the client, wherein the test result at least includes the historical recognition result;

[0153] The first determination module is further used to determine the user portrait data dictionary corresponding to the target user according to the test result.

[0154] Optionally, the server 400 further includes:

[0155] A fourth determination module, configured to confirm that the verification is successful when the historical recognition result matches the current recognition result;

[0156] A third sending module is used to send the training data to the client corresponding to the target user when the verification is passed and the preset parameter value is less than the first preset value;

[0157] A fifth determination module, configured to re-determine the historical recognition result of the target user based on the recognition result of the target user on the training data;

[0158] Wherein, the parameter value is reduced by a first preset value each time the verification fails.

[0159] The server provided by the embodiment of the present invention can realize Figure 1 To avoid repetition, the various processes implemented by the server in the method embodiment are not described here.

[0160] See also Figure 5 , Figure 5 is a structural diagram of a client provided by an embodiment of the present invention, such as Figure 5 As shown, the client 500 includes:

[0161] The first sending module is used to send verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object; the verification information is used by the service to verify the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0162] Optionally, the verification information also includes identity information, and the identity information is used by the server to confirm the corresponding user portrait data dictionary, and determine the historical recognition result of the target user corresponding to the target object based on the corresponding user portrait data dictionary.

[0163] Optionally, the client 500 further includes:

[0164] A second receiving module is used to receive the target object sent by the server; wherein, the target object is generated by the server based on a user portrait data dictionary corresponding to the target user, and the user portrait data dictionary corresponding to the target user is determined according to the identity information of the target user; the target object includes a first verification picture generated according to a target verification rule and the user portrait data dictionary corresponding to the target user, and at least one second verification picture generated based on a preset database.

[0165] Optionally, the user portrait data dictionary corresponding to the target user includes first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognition verification.

[0166] The client provided by the embodiment of the present invention can realize Figure 3 In order to avoid repetition, the various processes implemented by the client in the method embodiment are not described here.

[0167] See also Figure 6 , Figure 6 is a structural diagram of another server provided by an embodiment of the present invention, such as Figure 6 As shown, the server 600 includes: a processor 601, a transceiver 602, a memory 603 and a bus interface, wherein:

[0168] The transceiver 602 is used to receive the verification information of the target user, wherein the verification information includes the current recognition result for the target object;

[0169] Processor 601 is used to verify the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0170] It should be understood that in this embodiment, the processor 601 and the transceiver 602 can implement Figure 1 To avoid repetition, the various processes implemented by the server in the method embodiment are not described here.

[0171] exist Figure 6In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 601 and memory represented by memory 603. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 602 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 604 may also be an interface capable of external / internal connection of devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0172] The processor 601 is responsible for managing the bus architecture and general processing, and the memory 603 can store data used by the processor 601 when performing operations.

[0173] Figure 7 A schematic diagram of the hardware structure of a client to implement various embodiments of the present invention is shown below.

[0174] The client 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, a processor 710, and a power supply 711. Those skilled in the art will appreciate that Figure 7 The client structure shown in the figure does not constitute a limitation on the client, and the client may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiment of the present invention, the client includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a car client, a wearable device, and a pedometer.

[0175] The radio frequency unit 701 is used to send verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object; the verification information is used by the service to verify the verification information according to a matching relationship between a preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

[0176] It should be understood that in this embodiment, the processor 710 and the radio frequency unit 701 can implement Figure 3 In order to avoid repetition, the various processes implemented by the client in the method embodiment are not described here.

[0177] It should be understood that in the embodiment of the present invention, the radio frequency unit 701 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 710 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 701 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 701 can also communicate with the network and other devices through a wireless communication system.

[0178] The client provides users with wireless broadband Internet access through the network module 702, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0179] The audio output unit 703 can convert the audio data received by the RF unit 701 or the network module 702 or stored in the memory 709 into an audio signal and output it as sound. In addition, the audio output unit 703 can also provide audio output related to a specific function performed by the client 700 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 703 includes a speaker, a buzzer, a receiver, etc.

[0180] The input unit 704 is used to receive audio or video signals. The input unit 704 may include a graphics processor (GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 706. The image frame processed by the graphics processor 7041 can be stored in the memory 709 (or other storage medium) or sent via the radio frequency unit 701 or the network module 702. The microphone 7042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 701 in the case of a telephone call mode.

[0181] The client 700 also includes at least one sensor 705, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 7061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 7061 and / or the backlight when the client 700 moves to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the client posture (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 705 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0182] The display unit 706 is used to display information input by the user or information provided to the user. The display unit 706 may include a display panel 7061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0183] The user input unit 707 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the client. Specifically, the user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 7071 or near the touch panel 7071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 7071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 710, receives the command sent by the processor 710 and executes it. In addition, the touch panel 7071 can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 7071, the user input unit 707 may also include other input devices 7072. Specifically, other input devices 7072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0184] Furthermore, the touch panel 7071 may be overlaid on the display panel 7061. When the touch panel 7071 detects a touch operation on or near it, it transmits the information to the processor 710 to determine the type of the touch event. Then, the processor 710 provides a corresponding visual output on the display panel 7061 according to the type of the touch event. Figure 7 In the embodiment, the touch panel 7071 and the display panel 7061 are used as two independent components to implement the input and output functions of the client. However, in some embodiments, the touch panel 7071 and the display panel 7061 can be integrated to implement the input and output functions of the client, which is not limited here.

[0185] The interface unit 708 is an interface for connecting an external device to the client 700. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 708 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements in the client 700 or may be used to transmit data between the client 700 and an external device.

[0186] The memory 709 can be used to store software programs and various data. The memory 709 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 709 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0187] The processor 710 is the control center of the client, and uses various interfaces and lines to connect various parts of the entire client. By running or executing software programs and / or modules stored in the memory 709, and calling data stored in the memory 709, the processor 710 performs various functions of the client and processes data, thereby monitoring the client as a whole. The processor 710 may include one or more processing units; preferably, the processor 710 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 710.

[0188] The client 700 may also include a power supply 711 (such as a battery) for supplying power to various components. Preferably, the power supply 711 may be logically connected to the processor 710 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0189] In addition, the client 700 includes some functional modules not shown, which will not be described here.

[0190] Optionally, an embodiment of the present invention provides a processing device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned verification processing method when executed by the processor. In this embodiment, the processing device can be the above-mentioned server or client. When the processing device is a server, each process of the verification processing method embodiment in the server can be implemented; when the processing device is a client, each process of the verification processing method embodiment in the client can be implemented.

[0191] like Figure 6 When the processing device is a server, the server includes a processor 601, a memory 603, and a computer program stored in the memory 603 and executable on the processor 601. When the computer program is executed by the processor 601, each process of the above-mentioned verification processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0192] like Figure 7 When the processing device is a client, the client includes a processor 710, a memory 709, and a computer program stored in the memory 709 and executable on the processor 710. When the computer program is executed by the processor 710, each process of the above-mentioned verification processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0193] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned verification processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0194] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0195] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0196] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A verification processing method, characterized in that: include: Receive verification information of a target user, wherein the verification information includes a current recognition result for a target object, the current recognition result is used to represent the target user's cognitive selection of the target object, the target object includes a first verification picture generated according to a target verification rule and a user portrait data dictionary corresponding to the target user and at least one second verification picture generated based on a preset database, the user portrait data dictionary corresponding to the target user is used to represent the behavioral habit characteristics of the target user, the current recognition result for the target object includes picture information of a target verification picture selected by the target user based on the target verification rule, and the target verification picture is a preset number of verification pictures among the first verification picture and the second verification picture; The verification information is verified according to a preset matching relationship between a historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, wherein the historical recognition result is a customized answer made by the target user for the target object.

2. The method according to claim 1, characterized in that Before the step of verifying the verification information according to the matching relationship between the preset historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the method further includes: The historical recognition results of the target user corresponding to the target object are determined according to a preset user portrait data dictionary, wherein the user portrait data dictionary is used to determine the historical recognition results of at least one target object and the target user corresponding to the at least one target object.

3. The method according to claim 2, characterized in that After the step of verifying the verification information according to the preset matching relationship between the historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the method further includes: When the historical recognition result matches the current recognition result, confirming that the verification is successful; When the verification is successful, the user portrait data dictionary is updated according to the matching relationship between the current recognition result and the target object.

4. The method according to claim 1, characterized in that: Before receiving the verification information of the target user, the method further includes: Obtain the target user's identity information; Determine a user portrait data dictionary corresponding to the target user according to the identity information; Generating a target object matching the target user; The target object is sent to the client corresponding to the target user.

5. The method according to claim 4, characterized in that The user portrait data dictionary corresponding to the target user includes first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognition verification; Before generating a target object matching the target user, the method further includes: The target verification rule is determined by randomly selecting a rule in the semantic rule data dictionary.

6. The method according to claim 1, characterized in that After the step of verifying the verification information according to the preset matching relationship between the historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, the method further includes: When the historical recognition result matches the current recognition result, confirming that the verification is successful; If the verification is passed and the preset parameter value is less than the first preset value, sending the training data to the client corresponding to the target user; re-determining the historical recognition result of the target user based on the recognition result of the target user on the training data; Wherein, the parameter value is reduced by a first preset value each time the verification fails.

7. A verification processing method, characterized in that: include: Sending verification information of the target user to the server; wherein the verification information includes a current recognition result for the target object, and the current recognition result is used to indicate the target user's cognitive selection of the target object; the verification information is used by the service to verify the verification information according to a preset matching relationship between a historical recognition result of the target user corresponding to the target object and the current recognition result of the target user, and the historical recognition result is a customized answer made by the target user for the target object; Among them, the target object includes a first verification picture generated according to the target verification rules and the user portrait data dictionary corresponding to the target user, and at least one second verification picture generated based on a preset database. The user portrait data dictionary corresponding to the target user is used to represent the behavioral habit characteristics of the target user. The current recognition result for the target object includes the picture information of the target verification picture selected by the target user based on the target verification rules. The target verification picture is a preset number of verification pictures among the first verification picture and the second verification picture.

8. The method according to claim 7, characterized in that The verification information also includes identity information, and the identity information is used by the server to confirm the corresponding user portrait data dictionary and determine the historical recognition result of the target user corresponding to the target object based on the corresponding user portrait data dictionary; Before sending the verification information of the target user to the server, the method further includes: Receiving a target object sent by the server; wherein the target object is generated by the server based on a user portrait data dictionary corresponding to the target user, and the user portrait data dictionary corresponding to the target user is determined according to the identity information of the target user; Among them, the user portrait data dictionary corresponding to the target user includes first user data and second user data, the first user data includes a color data dictionary and / or a graphic data dictionary, and the second user data includes a semantic rule data dictionary, and the semantic rule data dictionary includes rules for user cognition verification.

9. A processing device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the verification processing method as claimed in any one of claims 1 to 6 or claims 7 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the verification processing method according to any one of claims 1 to 6 or claims 7 to 8 are implemented.

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