A method, device and electronic equipment for adjusting a cash withdrawal limit

Abstract

The application discloses a cash withdrawal quota adjustment method and device and electronic equipment, which can be applied to the financial field or other fields. In the application, when a target user performs a cash withdrawal operation in an ATM, a cash withdrawal quota matched with the target user is determined based on the expression of the target user, the environment where the target user is located and historical cash withdrawal information, so that the target user performs the cash withdrawal operation according to the cash withdrawal quota. That is, through the application, the cash withdrawal quota matched with the user can be personalized for the user, compared with the fixed limit mode of the ATM, the appropriate cash withdrawal quota can be accurately set for the user based on the actual situation of the user, and the accuracy of the cash withdrawal quota setting is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more particularly to a method, device and electronic device for adjusting a withdrawal limit. Background Art

[0002] In order to improve the convenience of withdrawing money, ATMs are usually set up at the entrance of banks. Users can withdraw money at ATMs, which is convenient and fast, and there are no time restrictions.

[0003] Currently, ATMs have a certain withdrawal limit when users withdraw money. If the withdrawal limit is exceeded, further withdrawals are not allowed. For example, the daily withdrawal amount cannot exceed 50,000 yuan.

[0004] ATM withdrawal limits are fixed, and all users use the same withdrawal limit. This fixed limit method has low limit accuracy and poor user experience. Summary of the Invention

[0005] In view of this, the present invention provides a withdrawal limit adjustment method, device and electronic device to solve the problems of low limit accuracy and poor user experience in the fixed limit method.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for adjusting a withdrawal limit, comprising:

[0008] Obtain the target user's facial image and environmental information inside the ATM;

[0009] Performing a feature point extraction operation on the facial image to obtain facial feature point information, and performing an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information;

[0010] Performing an environmental safety level analysis on the environmental information to obtain a second safety factor corresponding to the environmental information;

[0011] Obtaining historical withdrawal information of the target user, and invoking a preset data processing model to process the historical withdrawal information to obtain a third safety factor corresponding to the historical withdrawal information; the preset data processing model is trained based on training data; the training data includes withdrawal information samples and safety factor samples corresponding to the withdrawal information samples;

[0012] Performing a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain a withdrawal safety factor for the target user;

[0013] According to the withdrawal safety factor, the target user's withdrawal limit setting operation is performed, so that the target user can perform a withdrawal operation according to the withdrawal limit.

[0014] Optionally, performing a withdrawal limit setting operation for the target user according to the withdrawal safety factor includes:

[0015] When the withdrawal safety factor is greater than a preset safety factor threshold, obtaining the target user's bank card balance;

[0016] Controlling the display interface of the ATM to display credit limit adjustment information, wherein the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card;

[0017] Upon receiving the confirmation instruction selected by the target user, displaying a password input box and obtaining the withdrawal password entered by the target user in the password input box;

[0018] If the withdrawal password is a legal withdrawal password, the withdrawal limit of the target user is adjusted to the bank card balance.

[0019] Optionally, performing a withdrawal limit setting operation for the target user according to the withdrawal safety factor includes:

[0020] If the withdrawal safety factor is not greater than a preset safety factor threshold, determining a withdrawal limit corresponding to a withdrawal safety factor interval in which the withdrawal safety factor is located, and using the withdrawal limit as a target withdrawal limit corresponding to the withdrawal safety factor; the withdrawal limit is not greater than a fixed withdrawal limit;

[0021] The withdrawal limit of the target user is adjusted to the target withdrawal limit.

[0022] Optionally, performing an environmental safety level analysis on the environmental information to obtain a second safety factor corresponding to the environmental information includes:

[0023] Detecting people in the environmental information to determine the number of people around the target user in the ATM;

[0024] Performing an occlusion detection on the ATM in the environmental information to determine whether the ATM is blocked, and obtaining a first occlusion judgment result;

[0025] detecting a target user behavior in the environmental information to determine whether a password input keyboard is obstructed when the target user enters a password, and obtaining a second obstruction determination result;

[0026] A second safety factor corresponding to the number of surrounding persons, the first occlusion judgment result, and the second occlusion judgment result is determined.

[0027] Optionally, performing an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information includes:

[0028] Calling an expression recognition model to perform an expression analysis operation on the facial feature point information to obtain a facial expression corresponding to the facial feature point information, and determining a first safety factor corresponding to the facial expression;

[0029] The expression recognition model is obtained by training based on training samples, and the training samples include facial feature point samples and safety factor samples corresponding to the facial feature point samples.

[0030] A withdrawal limit adjustment device, comprising:

[0031] A data acquisition module is used to obtain the target user's facial image and environmental information in the ATM;

[0032] a first coefficient determination module, configured to perform a feature point extraction operation on the facial image to obtain facial feature point information, and perform an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information;

[0033] a second coefficient determination module, configured to perform an environmental safety level analysis on the environmental information to obtain a second safety coefficient corresponding to the environmental information;

[0034] a third coefficient determination module, configured to obtain historical withdrawal information of the target user and process the historical withdrawal information using a preset data processing model to obtain a third safety coefficient corresponding to the historical withdrawal information; the preset data processing model is trained based on training data; the training data includes withdrawal information samples and safety coefficient samples corresponding to the withdrawal information samples;

[0035] a withdrawal safety factor determination module, configured to perform a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain a withdrawal safety factor for the target user;

[0036] The limit setting module is used to set the withdrawal limit of the target user according to the withdrawal safety factor, so that the target user can perform the withdrawal operation according to the withdrawal limit.

[0037] Optionally, the credit limit setting module includes:

[0038] A balance acquisition submodule, configured to acquire the target user's bank card balance when the withdrawal safety factor is greater than a preset safety factor threshold;

[0039] A display submodule, configured to control the display interface of the ATM to display credit limit adjustment information, wherein the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card;

[0040] A password acquisition submodule is used to display a password input box and acquire the withdrawal password entered by the target user in the password input box when receiving a confirmation instruction selected by the target user;

[0041] The first limit adjustment submodule is used to adjust the withdrawal limit of the target user to the bank card balance if the withdrawal password is a valid withdrawal password.

[0042] Optionally, the credit limit setting module includes:

[0043] The second limit adjustment submodule is used to determine the withdrawal limit corresponding to the withdrawal safety factor interval in which the withdrawal safety factor is located when the withdrawal safety factor is not greater than the preset safety factor threshold, and use it as the target withdrawal limit corresponding to the withdrawal safety factor, and adjust the withdrawal limit of the target user to the target withdrawal limit; the withdrawal limit is not greater than the fixed withdrawal limit.

[0044] Optionally, the second coefficient determination module includes:

[0045] A first detection submodule is configured to detect people in the environmental information to determine the number of people around the target user in the ATM;

[0046] A second detection submodule is configured to perform an occlusion detection on the ATM in the environmental information to determine whether the ATM is blocked and obtain a first occlusion judgment result;

[0047] a third detection submodule, configured to detect the target user behavior in the environmental information to determine whether a password input keyboard is blocked when the target user enters a password, and obtain a second blockage judgment result;

[0048] The coefficient determination submodule is used to determine a second safety coefficient corresponding to the number of people in the surrounding area, the first occlusion judgment result, and the second occlusion judgment result.

[0049] An electronic device comprising: a memory and a processor;

[0050] Wherein, the memory is used to store programs;

[0051] The processor calls the program and is used to execute the above-mentioned withdrawal limit adjustment method.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention provides a withdrawal limit adjustment method, device, and electronic device. In this invention, when a target user makes a withdrawal at an ATM, a withdrawal limit matching the target user is determined based on the target user's facial expression, surroundings, and historical withdrawal information, allowing the target user to make a withdrawal according to the withdrawal limit. Specifically, the present invention can personalize and match a withdrawal limit tailored to the user. Compared to fixed limits on ATMs, this method can accurately set a suitable withdrawal limit for the user based on their actual situation, improving the accuracy of withdrawal limit settings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0055] Figure 1 A method flow chart of a method for adjusting a withdrawal limit provided by an embodiment of the present invention;

[0056] Figure 2 A flow chart of another method for adjusting a withdrawal limit provided by an embodiment of the present invention;

[0057] Figure 3 A method flow chart of another withdrawal limit adjustment method provided by an embodiment of the present invention;

[0058] Figure 4 A schematic structural diagram of a withdrawal limit adjustment device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0060] Currently, ATMs have a certain withdrawal limit when users withdraw money. If the withdrawal limit is exceeded, further withdrawals are not allowed. For example, the daily withdrawal amount cannot exceed 50,000 yuan.

[0061] ATM withdrawal limits are fixed, and all users use the same withdrawal limit. This fixed limit method has low limit accuracy and poor user experience.

[0062] In order to solve this problem, the present invention sets a personalized withdrawal limit for the user based on the user's actual situation.

[0063] Specifically, the present invention provides a withdrawal limit adjustment method, device, and electronic device. In this invention, when a target user makes a withdrawal at an ATM, a withdrawal limit matching the target user is determined based on the target user's facial expression, surroundings, and historical withdrawal information, allowing the target user to make a withdrawal according to the withdrawal limit. This allows the present invention to personalize and match a withdrawal limit tailored to the user. Compared to the fixed limit approach of ATMs, this method accurately sets a suitable withdrawal limit for the user based on their actual situation, improving the accuracy of withdrawal limit settings.

[0064] It should be noted that the withdrawal limit adjustment method, device, and electronic device provided by the present invention can be used in the financial sector or other fields, for example, in ATM applications within the financial sector. Other fields are any fields other than the financial sector. The above description is merely illustrative and does not limit the application areas of the withdrawal limit adjustment method, device, and electronic device provided by the present invention.

[0065] Based on the above content, an embodiment of the present invention provides a method for adjusting a withdrawal limit, which can be applied to electronic devices that communicate with an ATM, such as a controller, a server, etc.

[0066] Reference Figure 1 , the withdrawal limit adjustment methods may include:

[0067] S11. Obtaining a target user's facial image and environmental information in an ATM.

[0068] Specifically, an ATM is equipped with an image acquisition device, such as a camera, to perform image acquisition operations. In actual applications, the number of cameras can be configured based on actual conditions. Generally, one camera can be provided for capturing facial images, and at least one camera can be provided to comprehensively capture environmental information within the ATM.

[0069] When a user enters an ATM to withdraw cash, a camera can be set up to capture images when someone is inside the ATM. The camera captures the facial image of the user withdrawing cash (referred to as the target user in this embodiment), as well as information about the target user's surroundings. This environmental information is also displayed in image form. After the camera captures the data, it is transmitted to the electronic device that executes the withdrawal limit adjustment method in this embodiment.

[0070] S12. Perform a feature point extraction operation on the facial image to obtain facial feature point information, and perform an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information.

[0071] In practical applications, if a user experiences unusual emotions such as anxiety, nervousness, or uneasiness when withdrawing money, they may be forced to withdraw money. In this case, allowing the user to withdraw large amounts could result in the user being defrauded. Therefore, in this embodiment, user emotions can be used as a basis for adjusting the withdrawal limit. Based on the user's emotions, the security procedures for this withdrawal are analyzed.

[0072] Specifically, after acquiring the face image, a feature point extraction operation is performed on the face image to obtain face feature point information, which is generally the pixel value of the feature point.

[0073] When extracting feature points, the main features are the mouth, left and right eyelashes, left and right eyes, nose, chin and other parts.

[0074] All facial feature point information is combined into a vector, and then an expression recognition model is called to perform expression analysis on the facial feature point information to obtain the facial expression corresponding to the facial feature point information, and determine a first safety factor corresponding to the facial expression.

[0075] In this embodiment, the first safety factor may be a point value, such as 8 points.

[0076] In practice, the main facial expressions are sadness, happiness, surprise, fear, and so on. Each expression has corresponding characteristics. For example, when someone is afraid, their mouth and eyes are open, their eyebrows are raised, and their nostrils are widened. Users also have different expressions in different situations. For example, when they are under duress, they may have a fearful expression, while when they are happy, they may have a happy expression. Different situations also correspond to different safety factors. For example, when they are under duress, they may have a fearful expression, which indicates a low safety level, while when they are happy, they may have a happy expression, which indicates a high safety level. Therefore, facial expressions can reflect a user's current safety level.

[0077] The expression recognition model in this embodiment is obtained through training based on training samples, and the training samples include facial feature point samples and safety factor samples corresponding to the facial feature point samples.

[0078] Among them, the expression recognition model can be a CNN model. In practical applications, the safety factor samples of the facial feature point samples are pre-labeled, and then the facial feature point samples and the safety factor samples corresponding to the facial feature point samples are used to train the CNN model. After the training is completed, the model can be used to perform safety factor scoring.

[0079] The CNN model first performs an expression analysis operation on the facial feature point information to obtain the facial expression corresponding to the facial feature point information, and then obtains a first safety factor corresponding to the facial expression based on the correspondence between the facial expression and the safety factor.

[0080] S13: Perform an environmental safety level analysis on the environmental information to obtain a second safety factor corresponding to the environmental information.

[0081] In practice, if a user is under duress, perhaps accompanied by multiple companions, withdrawing cash from an ATM, the security level is low. Alternatively, if a user is accompanied by a companion and does not cover their PIN with their hand, this could also indicate duress, resulting in a low security level. Alternatively, if the ATM is unobstructed, the user's security level is also low. Furthermore, if the ATM is accompanied by a companion and there is no obstruction around the user, the user's security level is also low.

[0082] Therefore, the user's surrounding environment information can also reflect the user's security level, thereby affecting the user's withdrawal limit. Therefore, environmental information can also be used as a basis for adjusting the withdrawal limit.

[0083] In practical applications, step S13 may include:

[0084] S21. Detecting people in the environmental information to determine the number of people around the target user in the ATM.

[0085] Specifically, a target detection operation may be performed on the environmental information to detect the number of people in the environmental information image. The number of people other than the target user is the number of people around the target user.

[0086] S22: Perform an obstruction detection on the ATM in the environmental information to determine whether the ATM is obstructed, and obtain a first obstruction judgment result.

[0087] Specifically, the ATM in the environmental information image may be detected to determine whether there is an obstruction on the ATM, thereby obtaining a first obstruction determination result. The first obstruction determination result is that there is no obstruction or there is obstruction.

[0088] S23: Detect the target user's behavior in the environmental information to determine whether a password input keyboard is blocked when the target user enters a password, and obtain a second blocking judgment result.

[0089] Specifically, when the target user inputs the password, it is detected whether the user blocks the password input keyboard of the ATM, for example, by using hands, clothes, etc.

[0090] S24: Determine a second safety factor corresponding to the number of surrounding people, the first occlusion judgment result, and the second occlusion judgment result.

[0091] Specifically, the second safety factor under different circumstances can be determined in advance.

[0092] For example, if a user enters their password with a companion and does not cover their hand, they may be under duress, and the security level is 0.2. If the user is alone, the security level is 0.8, and so on. The second security factor can be determined based on the actual situation.

[0093] S14. Obtain historical withdrawal information of the target user, and call a preset data processing model to process the historical withdrawal information to obtain a third safety factor corresponding to the historical withdrawal information.

[0094] After obtaining the target user's facial image through the above steps, the backend database is searched for historical withdrawal information corresponding to the face. Alternatively, when withdrawing money, the user inserts a bank card and searches for historical withdrawal information using the bank card number or user identity information.

[0095] The historical withdrawal information may include withdrawal frequency, withdrawal amount, withdrawal time, and withdrawal location, etc. After obtaining the historical withdrawal information, data cleaning and data processing operations may be performed on the information to ensure the accuracy of the obtained data.

[0096] In actual applications, a user's historical withdrawal information can also reflect the user's credit limit requirements. For example, if the user's withdrawal amount exceeds 30,000 in most of the time, the user's credit limit requirements will be relatively large.

[0097] Therefore, historical withdrawal information can be used as a parameter for credit limit adjustment. In this embodiment, if the user's historical withdrawal amount is large, the safety factor of increasing the user's credit limit will be higher. If the user's historical withdrawal amount is small, the safety factor of increasing the user's credit limit will be lower.

[0098] In this embodiment, a neural network model is pre-trained, which is called a preset data processing model in this embodiment.

[0099] The preset data processing model is trained based on training data; the training data includes withdrawal information samples and safety factor samples corresponding to the withdrawal information samples.

[0100] After the preset data processing model is trained, the preset data processing model can be used to process the historical withdrawal information to obtain a third safety factor corresponding to the historical withdrawal information.

[0101] S15. Perform a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain the withdrawal safety factor of the target user.

[0102] Specifically, different weight values ​​of safety factors can be pre-set, and the weight values ​​can be set according to the degree of impact on the withdrawal limit. For example, the weight value of the first safety factor is 0.5, the weight value of the second safety factor is 0.3, and the weight value of the third safety factor is 0.2.

[0103] After obtaining the weight value, a weighted sum operation is performed on the first safety factor, the second safety factor and the third safety factor to obtain the withdrawal safety factor of the target user during this withdrawal operation.

[0104] S16. According to the withdrawal safety factor, the target user's withdrawal limit is set, so that the target user can withdraw money according to the withdrawal limit.

[0105] In actual applications, when the withdrawal safety factor is high, the withdrawal limit can be increased so that the target user's withdrawal limit is greater than the fixed limit.

[0106] When the withdrawal safety factor is low, the withdrawal limit can be lowered so that the target user's withdrawal limit is no greater than the fixed limit.

[0107] Reference Figure 3 , step S16 may include:

[0108] S31. Determine whether the withdrawal safety factor is greater than a preset safety factor threshold; if so, execute step S32; if not, execute step S36.

[0109] Specifically, if the withdrawal security factor is greater than the preset security factor threshold, it means that the withdrawal security level is high and the withdrawal limit can be increased. If the withdrawal security factor is not greater than the preset security factor threshold, it means that the withdrawal security level is low and the withdrawal limit can be reduced or not adjusted.

[0110] S32: Obtain the target user's bank card balance.

[0111] In actual application, if the user's withdrawal safety factor is greater than the preset safety factor threshold (such as 0.7), it means that the user's withdrawal safety factor is high and the withdrawal limit can be increased. At this time, the withdrawal limit can be adjusted to the target user's bank card balance.

[0112] The target user's bank card balance can be obtained by communicating with the ATM or querying the backend.

[0113] S33: Control the display interface of the ATM to display credit limit adjustment information, where the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card.

[0114] Specifically, when the credit limit is increased, an ATM will give a prompt that the credit limit is adjusted to the bank card balance, and provide confirmation and cancellation buttons.

[0115] If the user clicks the Confirm button, he / she agrees to increase the withdrawal limit to the bank card balance. If the user clicks the Cancel button, he / she does not agree to increase the withdrawal limit to the bank card balance, and the limit is still fixed at this time.

[0116] S34. Upon receiving the confirmation instruction selected by the target user, displaying a password input box and obtaining the withdrawal password entered by the target user in the password input box;

[0117] After the user clicks the confirmation button, the confirmation instruction selected by the target user will be received, and then a password input box will be displayed, which is used for the user to enter the withdrawal password. After the user enters the withdrawal password and clicks confirm, the withdrawal password entered by the user in the password input box will be obtained.

[0118] S35. If the withdrawal password is a valid withdrawal password, the withdrawal limit of the target user is adjusted to the bank card balance.

[0119] If the withdrawal password entered by the user is the withdrawal password pre-set by the user, i.e., a valid withdrawal password, the withdrawal limit of the target user is adjusted to the bank card balance. The user can then make a withdrawal, and the maximum withdrawal amount is the bank card balance.

[0120] S36. Determine a withdrawal limit corresponding to the withdrawal safety factor interval in which the withdrawal safety factor is located, and use the result as a target withdrawal limit corresponding to the withdrawal safety factor.

[0121] Wherein, the withdrawal limit is not greater than the fixed withdrawal limit.

[0122] Specifically, if the withdrawal safety factor is not greater than the preset safety factor threshold, it means that the withdrawal safety level is low, and the withdrawal limit can be lowered or not adjusted to impose certain restrictions on the withdrawal amount.

[0123] In this embodiment, different withdrawal safety factor intervals can be pre-set, and each interval corresponds to a withdrawal limit. The smaller the interval value, the lower the corresponding withdrawal limit value.

[0124] For example, the withdrawal safety factor range is [0.1, 0.2], corresponding to the limit one, the withdrawal safety factor range is [0.2, 0.3], corresponding to the limit two, and so on.

[0125] In this embodiment, the withdrawal limit corresponding to the withdrawal safety factor interval in which the withdrawal safety factor is located can be queried, and then used as the target withdrawal limit corresponding to the withdrawal safety factor.

[0126] S37. Adjust the withdrawal limit of the target user to the target withdrawal limit.

[0127] In this embodiment, after the target withdrawal limit is determined, the withdrawal limit of the target user is adjusted to the target withdrawal limit. The user can perform a withdrawal operation according to the target withdrawal limit, thereby improving the user's safety.

[0128] It should be noted that if the user believes that the target withdrawal limit does not meet the withdrawal needs, he or she can contact the manual customer service by phone, or click the manual button on the ATM. The manual customer service can verify whether the user is currently safe through outbound calls or direct communication with the user. If safe, the manual customer service can adjust the withdrawal limit of the target user to the amount required by the user or a fixed amount.

[0129] In this embodiment, when a target user withdraws money from an ATM, a withdrawal limit matching the target user is determined based on the target user's facial expression, surroundings, and historical withdrawal information, allowing the target user to withdraw money according to the withdrawal limit. Specifically, the present invention can match a withdrawal limit tailored to the user. Compared to fixed limits on ATMs, this method can accurately set a suitable withdrawal limit for the user based on their actual situation, improving the accuracy of withdrawal limit settings.

[0130] In addition, the present invention can adjust the withdrawal limit according to the user's security level to ensure the user's financial security.

[0131] Optionally, based on the above-mentioned embodiment of a method for adjusting a withdrawal limit, another embodiment of the present invention provides a device for adjusting a withdrawal limit, referring to Figure 4 , which may include:

[0132] The data acquisition module 11 is used to obtain the face image and environmental information of the target user in the ATM;

[0133] a first coefficient determination module 12, configured to perform a feature point extraction operation on the facial image to obtain facial feature point information, and perform an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information;

[0134] A second coefficient determination module 13 is configured to perform an environmental safety level analysis on the environmental information to obtain a second safety coefficient corresponding to the environmental information;

[0135] A third coefficient determination module 14 is configured to obtain historical withdrawal information of the target user and process the historical withdrawal information using a preset data processing model to obtain a third safety coefficient corresponding to the historical withdrawal information; the preset data processing model is trained based on training data; the training data includes withdrawal information samples and safety coefficient samples corresponding to the withdrawal information samples;

[0136] A withdrawal safety factor determination module 15 is configured to perform a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain a withdrawal safety factor for the target user;

[0137] The limit setting module 16 is used to set the withdrawal limit of the target user according to the withdrawal safety factor, so that the target user can perform a withdrawal operation according to the withdrawal limit.

[0138] Furthermore, the quota setting module includes:

[0139] A balance acquisition submodule, configured to acquire the target user's bank card balance when the withdrawal safety factor is greater than a preset safety factor threshold;

[0140] A display submodule, configured to control the display interface of the ATM to display credit limit adjustment information, wherein the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card;

[0141] A password acquisition submodule is used to display a password input box and acquire the withdrawal password entered by the target user in the password input box when receiving a confirmation instruction selected by the target user;

[0142] The first limit adjustment submodule is used to adjust the withdrawal limit of the target user to the bank card balance if the withdrawal password is a valid withdrawal password.

[0143] Furthermore, the quota setting module includes:

[0144] The second limit adjustment submodule is used to determine the withdrawal limit corresponding to the withdrawal safety factor interval in which the withdrawal safety factor is located when the withdrawal safety factor is not greater than the preset safety factor threshold, and use it as the target withdrawal limit corresponding to the withdrawal safety factor, and adjust the withdrawal limit of the target user to the target withdrawal limit; the withdrawal limit is not greater than the fixed withdrawal limit.

[0145] Furthermore, the second coefficient determination module includes:

[0146] A first detection submodule is configured to detect people in the environmental information to determine the number of people around the target user in the ATM;

[0147] A second detection submodule is configured to perform an occlusion detection on the ATM in the environmental information to determine whether the ATM is blocked and obtain a first occlusion judgment result;

[0148] a third detection submodule, configured to detect the target user behavior in the environmental information to determine whether a password input keyboard is blocked when the target user enters a password, and obtain a second blockage judgment result;

[0149] The coefficient determination submodule is used to determine a second safety coefficient corresponding to the number of people in the surrounding area, the first occlusion judgment result, and the second occlusion judgment result.

[0150] Furthermore, the first coefficient determination module 12 is configured to perform an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information, specifically for:

[0151] Calling an expression recognition model to perform an expression analysis operation on the facial feature point information to obtain a facial expression corresponding to the facial feature point information, and determining a first safety factor corresponding to the facial expression;

[0152] The expression recognition model is obtained by training based on training samples, and the training samples include facial feature point samples and safety factor samples corresponding to the facial feature point samples.

[0153] In this embodiment, when a target user withdraws money from an ATM, a withdrawal limit matching the target user is determined based on the target user's facial expression, surroundings, and historical withdrawal information, allowing the target user to withdraw money according to the withdrawal limit. Specifically, the present invention can match a withdrawal limit tailored to the user. Compared to fixed limits on ATMs, this method can accurately set a suitable withdrawal limit for the user based on their actual situation, improving the accuracy of withdrawal limit settings.

[0154] In addition, the present invention can adjust the withdrawal limit according to the user's security level to ensure the user's financial security.

[0155] It should be noted that, for the working process of each module and sub-module in this embodiment, please refer to the corresponding description in the above embodiment, which will not be repeated here.

[0156] Optionally, based on the above-mentioned embodiment of a method and apparatus for adjusting a withdrawal limit, another embodiment of the present invention provides an electronic device, comprising: a memory and a processor;

[0157] Wherein, the memory is used to store programs;

[0158] The processor calls the program and is used to execute the above-mentioned withdrawal limit adjustment method.

[0159] In this embodiment, when a target user withdraws money from an ATM, a withdrawal limit matching the target user is determined based on the target user's facial expression, surroundings, and historical withdrawal information, allowing the target user to withdraw money according to the withdrawal limit. Specifically, the present invention can match a withdrawal limit tailored to the user. Compared to fixed limits on ATMs, this method can accurately set a suitable withdrawal limit for the user based on their actual situation, improving the accuracy of withdrawal limit settings.

[0160] In addition, the present invention can adjust the withdrawal limit according to the user's security level to ensure the user's financial security.

[0161] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adjusting a withdrawal limit, characterized in that: include: Obtain the target user's facial image and environmental information inside the ATM; Performing a feature point extraction operation on the facial image to obtain facial feature point information, and performing an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information; Performing an environmental safety level analysis on the environmental information to obtain a second safety factor corresponding to the environmental information; Obtaining historical withdrawal information of the target user, and invoking a preset data processing model to process the historical withdrawal information to obtain a third safety factor corresponding to the historical withdrawal information; the preset data processing model is trained based on training data; The training data includes a withdrawal information sample and a safety factor sample corresponding to the withdrawal information sample; Performing a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain a withdrawal safety factor for the target user; performing a withdrawal limit setting operation for the target user according to the withdrawal safety factor, so that the target user can withdraw money according to the withdrawal limit; wherein, if the withdrawal safety factor is greater than a preset safety factor threshold, the withdrawal limit is increased; if the withdrawal safety factor is not greater than the preset safety factor threshold, the withdrawal limit is decreased; The step of performing an environmental safety level analysis on the environmental information to obtain a second safety factor corresponding to the environmental information includes: Detecting people in the environmental information to determine the number of people around the target user in the ATM; Performing an occlusion detection on the ATM in the environmental information to determine whether the ATM is blocked, and obtaining a first occlusion judgment result; detecting a target user behavior in the environmental information to determine whether a password input keyboard is obstructed when the target user enters a password, and obtaining a second obstruction determination result; A second safety factor corresponding to the number of surrounding persons, the first occlusion judgment result, and the second occlusion judgment result is determined.

2. The withdrawal limit adjustment method according to claim 1, characterized in that: According to the withdrawal safety factor, the target user's withdrawal limit setting operation is performed, including: When the withdrawal safety factor is greater than a preset safety factor threshold, obtaining the target user's bank card balance; Controlling the display interface of the ATM to display credit limit adjustment information, wherein the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card; Upon receiving the confirmation instruction selected by the target user, displaying a password input box and obtaining the withdrawal password entered by the target user in the password input box; If the withdrawal password is a legal withdrawal password, the withdrawal limit of the target user is adjusted to the bank card balance.

3. The withdrawal limit adjustment method according to claim 2, characterized in that: According to the withdrawal safety factor, the target user's withdrawal limit setting operation is performed, including: If the withdrawal safety factor is not greater than a preset safety factor threshold, determining a withdrawal limit corresponding to a withdrawal safety factor interval in which the withdrawal safety factor is located, and using the withdrawal limit as a target withdrawal limit corresponding to the withdrawal safety factor; the withdrawal limit is not greater than a fixed withdrawal limit; The withdrawal limit of the target user is adjusted to the target withdrawal limit.

4. The withdrawal limit adjustment method according to claim 1, wherein: Performing an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information includes: Calling an expression recognition model to perform an expression analysis operation on the facial feature point information to obtain a facial expression corresponding to the facial feature point information, and determining a first safety factor corresponding to the facial expression; The expression recognition model is obtained by training based on training samples, and the training samples include facial feature point samples and safety factor samples corresponding to the facial feature point samples.

5. A withdrawal limit adjustment device, characterized in that: include: A data acquisition module is used to obtain the target user's facial image and environmental information in the ATM; a first coefficient determination module, configured to perform a feature point extraction operation on the facial image to obtain facial feature point information, and perform an expression analysis operation on the facial feature point information to obtain a first safety factor corresponding to the facial feature point information; a second coefficient determination module, configured to perform an environmental safety level analysis on the environmental information to obtain a second safety coefficient corresponding to the environmental information; a third coefficient determination module, configured to obtain historical withdrawal information of the target user and process the historical withdrawal information using a preset data processing model to obtain a third safety coefficient corresponding to the historical withdrawal information; the preset data processing model is trained based on training data; the training data includes withdrawal information samples and safety coefficient samples corresponding to the withdrawal information samples; a withdrawal safety factor determination module, configured to perform a weighted sum operation on the first safety factor, the second safety factor, and the third safety factor to obtain a withdrawal safety factor for the target user; a limit setting module, configured to set a withdrawal limit for the target user according to the withdrawal safety factor, so that the target user can withdraw money according to the withdrawal limit; wherein, if the withdrawal safety factor is greater than a preset safety factor threshold, the withdrawal limit is increased; if the withdrawal safety factor is not greater than the preset safety factor threshold, the withdrawal limit is decreased; The second coefficient determination module includes: A first detection submodule is configured to detect people in the environmental information to determine the number of people around the target user in the ATM; A second detection submodule is configured to perform an occlusion detection on the ATM in the environmental information to determine whether the ATM is blocked and obtain a first occlusion judgment result; a third detection submodule, configured to detect the target user behavior in the environmental information to determine whether a password input keyboard is blocked when the target user enters a password, and obtain a second blockage judgment result; The coefficient determination submodule is used to determine a second safety coefficient corresponding to the number of people in the surrounding area, the first occlusion judgment result, and the second occlusion judgment result.

6. The withdrawal limit adjustment device according to claim 5, characterized in that: The quota setting module includes: A balance acquisition submodule, configured to acquire the target user's bank card balance when the withdrawal safety factor is greater than a preset safety factor threshold; A display submodule, configured to control the display interface of the ATM to display credit limit adjustment information, wherein the content of the credit limit adjustment information is that the credit limit is increased to the balance of the bank card; A password acquisition submodule is used to display a password input box and acquire the withdrawal password entered by the target user in the password input box when receiving a confirmation instruction selected by the target user; The first limit adjustment submodule is used to adjust the withdrawal limit of the target user to the bank card balance if the withdrawal password is a valid withdrawal password.

7. The withdrawal limit adjustment device according to claim 6, characterized in that: The quota setting module includes: The second limit adjustment submodule is used to determine the withdrawal limit corresponding to the withdrawal safety factor interval in which the withdrawal safety factor is located when the withdrawal safety factor is not greater than the preset safety factor threshold, and use it as the target withdrawal limit corresponding to the withdrawal safety factor, and adjust the withdrawal limit of the target user to the target withdrawal limit; the withdrawal limit is not greater than the fixed withdrawal limit.

8. An electronic device, characterized in that: include: memory and processor; Wherein, the memory is used to store programs; The processor calls the program and is used to execute the withdrawal limit adjustment method as described in any one of claims 1-4.

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