Data processing method and electronic device

Abstract

This application provides a data processing method and an electronic device. The method includes: responding to a user's access request to a personal knowledge base, determining at least one behavioral data point of the user within a first preset time period; determining the user's behavioral ability level based on the at least one behavioral data point and the corresponding weight coefficient, wherein the behavioral ability level characterizes the degree of security of the user's use of the personal knowledge base; and determining a target decision based on the behavioral ability level and a preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

Description

Technical Field

[0001] This application relates to the field of data processing, and more particularly to a data processing method and an electronic device. Background Technology

[0002] In artificial intelligence, a Personal Knowledge Base (PKB) refers to a system or framework designed to help individuals organize, manage, and retrieve their personal information and knowledge. It leverages AI technology to enhance the functionality and user experience of traditional knowledge management systems. The PKB extracts key data from prompts and logs on electronic devices after a user issues a command or periodically. If users have low security awareness when using electronic devices, their PKBs can be compromised, leading to the extraction and leakage of private data from prompts and logs. Summary of the Invention

[0003] This application provides a data processing method and an electronic device.

[0004] One embodiment of this application provides a data processing method, the method comprising:

[0005] In response to a user's request to access a personal knowledge base, at least one piece of behavioral data of the user within a first preset time period is determined; The user's behavioral ability level is determined based on the at least one behavioral data point and the corresponding weight coefficient of the behavioral data point, and the behavioral ability level characterizes the degree of security of the user in using the personal knowledge base; Target decisions are determined based on the behavioral ability level and a preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

[0006] The method further includes: The user's first operation on the personal knowledge base is detected, and the parameters corresponding to the first operation are obtained. The first operation indicates the setting of the usage strategy or preset threshold of the personal knowledge base. The preset threshold of the personal knowledge base is determined based on the parameters.

[0007] The step of determining the preset threshold of the personal knowledge base based on the parameters includes: If the first operation instruction is determined to set a preset threshold for the personal knowledge base, then the preset threshold is determined based on the parameter; If the first operation instruction is determined to set the usage strategy of the personal knowledge base, then the usage strategy of the personal knowledge base is determined based on the parameters, and a preset threshold of the personal knowledge base is determined based on the usage strategy and mapping information, wherein the mapping information includes the mapping relationship between the usage strategy and the preset threshold.

[0008] The step of determining the target decision based on the behavioral ability level and the preset threshold of the personal knowledge base includes: If the behavioral ability level is determined to be less than a preset threshold of the personal knowledge base, the access request to the personal knowledge base is rejected. Alternatively, if the behavioral ability level is determined to be greater than a preset threshold of the personal knowledge base, the access request to the personal knowledge base is allowed. The access request includes a synchronization request, which instructs the extraction of data related to the usage strategy of the personal knowledge base from the electronic device's prompts and / or log information.

[0009] The method further includes: Obtain at least one historical behavioral ability level of a user within a second preset time period and a historical comparison result of the historical behavioral ability level, wherein the historical comparison result is determined based on the historical behavioral ability level and the corresponding historical preset threshold. The preset threshold is evaluated based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level to obtain the evaluation result; The preset threshold is adjusted based on the evaluation results.

[0010] The evaluation of the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level includes: The mean of effective behavioral ability levels is determined based on the effective behavioral ability level, wherein the effective behavioral ability level is the behavioral ability level that is greater than a preset threshold and / or the historical behavioral ability level that is greater than the corresponding historical preset threshold, as indicated by the comparison result. If the difference between the preset threshold and the average effective behavioral ability level is greater than the preset difference, then the evaluation result is determined to be that the preset threshold needs to be adjusted.

[0011] The step of adjusting the preset threshold based on the evaluation result includes: If the evaluation results indicate that the preset threshold needs to be adjusted, the preset threshold is adjusted based on the average effective behavioral ability level and the preset threshold.

[0012] The method further includes: Obtain at least one historical behavioral ability level of the user within a third preset time period and at least one behavioral data for determining the historical behavioral ability level; Based on the at least one historical behavioral ability level and at least one behavioral data used to determine the historical behavioral ability level, the initial weight coefficients corresponding to the behavioral data are iteratively optimized to obtain the weight coefficients corresponding to the behavioral data.

[0013] The method further includes: Obtain the current status information of the electronic device, wherein the status information includes at least the device information, location information, and network information connected to the electronic device; An authentication policy is determined based on the status information. The authentication policy is used to authenticate the user to obtain a corresponding authentication result. The authentication result is used to determine whether to allow or deny the user's access request.

[0014] In another aspect, this application provides an electronic device, including: a processor and a storage device, wherein the processor and the storage device are electrically connected; In response to a user's access request to a personal knowledge base, the processor determines at least one piece of user behavior data from the storage device within a first preset time period; determines the user's behavioral ability level based on the at least one piece of behavioral data and the corresponding weight coefficient, the behavioral ability level representing the user's level of security in using the personal knowledge base; and determines a target decision based on the behavioral ability level and a preset threshold of the personal knowledge base, the preset threshold being determined based on the usage strategy of the personal knowledge base.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0017] Figure 1 A flowchart of a data processing method according to an embodiment of this application is shown; Figure 2 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 3 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 4A flowchart of a data processing method according to another embodiment of this application is shown; Figure 5 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 6 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 7 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 8 A flowchart of a data processing method according to another embodiment of this application is shown; Figure 9 A schematic diagram of the composition structure of an electronic device according to an embodiment of this application is shown; Figure 10 A schematic diagram of the structure of a data processing apparatus according to an embodiment of this application is shown. Detailed Implementation

[0018] To make the objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To improve the security of personal knowledge bases and prevent data leakage, one embodiment of this application provides a data processing method, such as... Figure 1 As shown, the method includes: Step 101: In response to the user's request to access the personal knowledge base, determine at least one piece of behavioral data of the user within a first preset time period.

[0020] In this embodiment, user access requests to the personal knowledge base include synchronization requests (user requests to extract necessary data from device prompts and / or log information and synchronize it to the personal knowledge base), sharing requests (user requests to share data in the personal knowledge base with other AI models or services), and usage requests (user requests to use the personal knowledge base, including querying its content, generating required information using it, and extracting factual data from it). In other embodiments, user access requests to the personal knowledge base can be any request initiated specifically for the personal knowledge base.

[0021] As shown in Table 1, in this embodiment, based on security factors such as password, multi-factor authentication, biometrics, keeping software updated, automatic updates, default security settings, application permission management, screen lock, device lock, device tracking, phishing, malware, suspicious activity monitoring, and public network risks, the corresponding user behavior data is determined. This includes whether a strong and unique password is created for each account, whether multi-factor authentication is enabled for sensitive accounts or devices, whether biometric options are enabled, whether applications and antivirus software are the latest versions, whether automatic updates for applications and system software are enabled, whether default security features are always enabled, whether application permissions are frequently revoked or restricted, whether the device is protected by a screen lock, whether the device is locked after the screen is locked, whether "Find My Device" is enabled, whether suspicious links are selected, awareness of spyware and adware, unauthorized login or transaction behavior, and the frequency of connection to public networks. In other embodiments, the behavior data may also include any other behavior data related to device security.

[0022] Table 1

[0023] Step 102: Determine the user's behavioral ability level based on the at least one behavioral data and the weight coefficient corresponding to the behavioral data. The behavioral ability level represents the degree of security of the user's use of the personal knowledge base.

[0024] Each behavioral data point can be converted into a corresponding quantifiable value. Then, each quantifiable value is multiplied by the weight coefficient corresponding to that behavioral data point, and the sum of all the product results is used to obtain the user's behavioral ability level.

[0025] The behavioral capability level characterizes the level of security a user has when using a personal knowledge base. A higher behavioral capability level indicates that the user has strong security awareness, follows proper security practices, and faces a lower risk of data leakage and other security breaches when using the personal knowledge base. A lower behavioral capability level indicates that the user has weak security awareness, engages in multiple security behaviors that do not conform to best practices, and faces a higher security risk when using the personal knowledge base.

[0026] In this embodiment, the corresponding weight coefficients can be determined based on behavioral data using methods such as the analytic hierarchy process, entropy weighting, empirical scoring, logistic regression, or random forest model. In other embodiments, the corresponding weight coefficients can also be determined based on behavioral data using any other method. This is only an example and not a specific limitation.

[0027] Step 103: Determine the target decision based on the behavioral ability level and the preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

[0028] The system compares the user's behavioral ability level with a preset threshold. If the behavioral ability level is greater than or equal to the preset threshold, the target decision is to allow the user's access request to the personal knowledge base. If the behavioral ability level is less than the preset threshold, the target decision is to deny the user's access request to the personal knowledge base, or to enable multi-factor authentication and allow the access request only after the user has successfully authenticated.

[0029] The preset threshold can be determined based on the historical behavioral ability level and the historical comparison results corresponding to the historical behavioral ability level, or it can be determined based on the user group profile, that is, users are classified according to the user's age, occupation, frequency of use of personal knowledge base, technical proficiency and other dimensions, and preset thresholds are set for each user group to match their security awareness level and usage needs.

[0030] In the above scheme, when a user's access request to the personal knowledge base is detected, the user's behavioral data within a first preset time period is determined. Then, by combining this behavioral data with its corresponding weight coefficients, the user's behavioral ability level is determined, accurately representing the security level of the user's use of the personal knowledge base. The behavioral ability level is then compared with a preset threshold. Based on the comparison result, a target decision is made. For users whose security level meets the requirements, their access request is allowed, ensuring a normal user experience. For users whose security level does not meet the requirements, their access request is denied or multi-factor authentication is enabled before allowing access. This approach effectively reduces the risk of the personal knowledge base being stolen, reduces the possibility of privacy data leakage, and thus improves the overall security of the personal knowledge base, protecting user privacy and data security.

[0031] This application also provides a data processing method in one example, such as Figure 2 As shown, the method further includes: Step 201: Detect the user's first operation on the personal knowledge base, obtain the parameters corresponding to the first operation, and the first operation indicates the setting of the usage strategy or preset threshold of the personal knowledge base.

[0032] In this embodiment, the first operation can be either setting a preset threshold for a personal knowledge base or setting a usage strategy for a personal knowledge base. If the first operation is setting a preset threshold for a personal knowledge base, the parameters corresponding to the first operation include the specific value of the preset threshold input through the first operation. If the first operation is setting a usage strategy for a personal knowledge base, the parameters corresponding to the first operation include the usage strategy selected through the first operation.

[0033] Step 202: Determine the preset threshold of the personal knowledge base based on the parameters.

[0034] If the first operation is to set a preset threshold for a personal knowledge base, then the preset threshold for the personal knowledge base is set according to the specific value of the preset threshold entered in the first operation. If the first operation is to set a usage strategy for a personal knowledge base, then the preset threshold for the personal knowledge base is determined according to the usage strategy selected in the first operation.

[0035] In the above solution, the system detects the user's first action of directly setting a preset threshold or setting a usage policy for the personal knowledge base, thereby obtaining the parameters corresponding to that first action. Then, by setting the preset threshold based on the obtained parameters or determining the corresponding preset threshold based on the set usage policy, the setting of the preset threshold becomes more flexible and can fully adapt to the user's actual usage needs and security requirements. Furthermore, determining the corresponding preset threshold based on the personal knowledge base's usage policy ensures that the set preset threshold accurately matches the personal knowledge base's usage policy, making the target decisions made based on the preset threshold and behavioral ability level more targeted. This not only ensures the security of using the personal knowledge base but also meets the personalized usage needs of different users, improving the user experience of the personal knowledge base.

[0036] This application also provides a data processing method in one example, such as Figure 3 As shown, determining the preset threshold of the personal knowledge base based on the parameters includes: Step 301: If the first operation instruction sets a preset threshold for the personal knowledge base, then the preset threshold is determined based on the parameters.

[0037] For example, when a user initiates a first action to set a preset threshold, after detecting this first action, the corresponding parameter obtained is the preset threshold value of 75 set through the first action. Therefore, the preset threshold of the personal knowledge base is set to 75. The preset threshold is the value input by the user through the first action or other actions. The specific preset threshold can be set according to the user's needs; this is only an example and not a specific limitation.

[0038] Step 302: If the first operation instruction is determined to set the usage strategy of the personal knowledge base, then the usage strategy of the personal knowledge base is determined based on the parameters, and a preset threshold of the personal knowledge base is determined based on the usage strategy and mapping information, wherein the mapping information includes the mapping relationship between the usage strategy and the preset threshold.

[0039] For example, when a user initiates the first operation of setting usage policies, selecting the usage policy of "disable synchronization when the device is in an untrusted location, allow normal synchronization when the device is in a trusted location and force two-factor authentication when the device unlock verification fails more than 2 times", after detecting this first operation, the corresponding parameter is obtained as the identifier of the selected usage policy. Based on the identifier and mapping information of the usage policy, the corresponding preset threshold is determined to be 80. Therefore, the preset threshold of the personal knowledge base is set to 80.

[0040] In the above solution, the preset threshold of the personal knowledge base is set according to the user's different first operation, which satisfies the user's different setting preferences, while ensuring the rationality and adaptability of the preset threshold. This lays a reliable foundation for making accurate target decisions based on the preset threshold and behavioral ability level, thereby better ensuring the security of the personal knowledge base and improving the user experience.

[0041] This application also provides a data processing method in one example, such as Figure 4 As shown, the step of determining the target decision based on the behavioral ability level and the preset threshold of the personal knowledge base includes: Step 401: If the behavioral ability level is determined to be less than the preset threshold of the personal knowledge base, the access request to the personal knowledge base is rejected.

[0042] For example, the preset threshold for the personal knowledge base is set to 70. Based on the user's behavioral data over the past 30 days, the user's behavioral ability level is determined to be 62, which is lower than the preset threshold of 70. Therefore, it is determined that the user's security level in using the personal knowledge base does not meet the preset standard, and thus, the user's access request to the personal knowledge base is rejected.

[0043] Alternatively, in step 402, if the behavioral ability level is determined to be greater than a preset threshold of the personal knowledge base, the access request to the personal knowledge base is allowed. The access request includes a synchronization request, which instructs the extraction of data related to the usage strategy of the personal knowledge base from the prompt information and / or log information of the electronic device.

[0044] For example, the preset threshold for the personal knowledge base is set to 70. Based on the user's behavioral data over the past 30 days, the user's behavioral ability level is determined to be 88, which is greater than the preset threshold of 70. Therefore, the user's security level in using the personal knowledge base is determined to meet the preset standard, and the user's access request to the personal knowledge base is allowed.

[0045] Once the synchronization request is granted, various types of data related to the usage policy will be extracted from the electronic device's prompts and / or log information. This includes key information that can be processed by the model or personal knowledge base, such as medical, financial, commercial, personal identity information, social information, historical location, and search history. After the data is extracted, fact extraction processing is performed on this raw data, and the valid fact data is synchronized to the personal knowledge base to update the local data in the personal knowledge base. At the same time, the extracted and organized compliant data can also be synchronized to other related models or server endpoints to achieve cross-data sharing between different endpoints.

[0046] In the above solution, by comparing a user's behavioral ability level with a preset threshold for the personal knowledge base, access requests are denied when the behavioral ability level is below the preset threshold. This effectively prevents data leakage from the personal knowledge base due to the user's insufficient security level, thus ensuring the security of the personal knowledge base. Conversely, when the behavioral ability level is above the preset threshold, access requests are granted, satisfying the user's need to use the personal knowledge base normally while ensuring its security.

[0047] This application also provides a data processing method in one example, such as Figure 5 As shown, the method further includes: Step 501: Obtain at least one historical behavioral ability level of the user within a second preset time period and a historical comparison result of the historical behavioral ability level, wherein the historical comparison result is determined based on the historical behavioral ability level and the corresponding historical preset threshold.

[0048] In this embodiment, the historical behavioral capability level is determined based on the user's behavioral data, triggered by the user's access request to the personal knowledge base within a second preset time period in the past. The historical comparison result of the historical behavioral capability level is obtained by comparing the determined historical behavioral capability level with the historical preset threshold at that time. For example, on January 5th, the behavioral capability level was 78, which is greater than the preset threshold of 70, so the access request was allowed.

[0049] Step 502: Evaluate the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level, to obtain the evaluation result.

[0050] Based on the currently determined behavioral capability level, the comparison results of this behavioral capability level, historical behavioral capability levels, and historical comparison results of historical behavioral capability levels, this analysis examines whether the current preset threshold accurately matches the user's level of safe behavior, yielding an evaluation result. The evaluation result includes whether the current preset threshold needs adjustment, and by how much.

[0051] For example, historical behavioral ability levels that are greater than a preset historical threshold are selected. If the current comparison result indicates that the currently determined behavioral ability level is greater than the current preset threshold, the mean of the selected historical behavioral ability levels is subtracted from the currently determined behavioral ability level. If this difference is greater than a preset difference, the assessment result is determined to require adjustment of the preset threshold, and the adjustment value is determined based on this difference.

[0052] Step 503: Adjust the preset threshold based on the evaluation results.

[0053] For example, if the evaluation result is "the preset threshold needs to be adjusted by +5", and the current preset threshold of the personal knowledge base is 70, then add 5 to the preset threshold to get the adjusted preset threshold of 75.

[0054] In the above solution, by obtaining the user's historical behavioral ability level and corresponding historical comparison results within the second preset time period, and then analyzing the current behavioral ability level, the comparison results of the behavioral ability level, the historical behavioral ability level, and the historical comparison results of the historical behavioral ability level, it is possible to accurately determine whether the current preset threshold matches the user's security behavior level. This allows for the determination of whether the preset threshold needs to be adjusted and the specific adjustment value, avoiding the problem of insufficient security protection or excessive restriction of user use caused by a fixed preset threshold. This ensures that the security protection of the personal knowledge base is always at an appropriate level, and also better matches the user's actual usage situation, further improving the user's experience of using the personal knowledge base.

[0055] This application also provides a data processing method in one example, such as Figure 6 As shown, the evaluation of the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level includes: Step 601: Determine the average effective behavioral ability level based on the effective behavioral ability level. The effective behavioral ability level is the behavioral ability level that is greater than a preset threshold and / or the historical behavioral ability level that is greater than the corresponding historical preset threshold, as indicated by the comparison result.

[0056] For example, the current preset threshold for the personal knowledge base is 70. The user's current behavioral ability level is determined to be 85, which is greater than the current preset threshold of 70, thus belonging to a valid behavioral ability level. The historical preset thresholds for the second preset time period are all 70, and the three historical behavioral ability levels obtained are 78, 66, and 83. The historical comparison results indicate that the historical behavioral ability levels 78 and 83 are greater than the historical preset threshold of 70, while 66 is excluded because it is less than 70. Therefore, the valid behavioral ability level includes the current behavioral ability level of 85 and the historical behavioral ability levels of 78 and 83. Summing these valid behavioral ability levels and then averaging them yields an average valid behavioral ability level of 82.

[0057] Step 602: If the difference between the preset threshold and the average effective behavioral ability level is greater than the preset difference, then the evaluation result is determined to be that the preset threshold needs to be adjusted.

[0058] Continuing with the previous example, the average effective behavioral ability level is 82, and the current preset threshold for the personal knowledge base is 70. The difference between the preset threshold and the average effective behavioral ability level is determined to be 12. This difference is greater than the preset difference of 10. Therefore, the assessment result indicates that the preset threshold needs adjustment. The adjustment value is determined by subtracting the preset difference from the difference between the average effective behavioral ability level and the preset threshold, which equals 2. The required adjustment value is +2. Therefore, the assessment result is "The preset threshold needs adjustment, and the adjustment value is +2".

[0059] In the above scheme, behavioral ability levels exceeding the current preset threshold and historical behavioral ability levels exceeding the corresponding historical preset thresholds are selected as effective behavioral ability levels. The average of these effective behavioral ability levels is then calculated, representing the user's actual security level when their safe behaviors meet the standards. The preset threshold of the personal knowledge base is then compared with this average of effective behavioral ability levels. When the difference between the two exceeds a preset threshold, it is determined that the preset threshold needs adjustment, and the specific adjustment value is determined based on this difference. This approach accurately identifies situations where the preset threshold does not match the user's actual security level. Furthermore, by determining the adjustment value in a reasonable manner, the preset threshold dynamically adapts to the user's actual security behavior level. This effectively ensures the security protection of the personal knowledge base without excessively restricting normal user access, thus better balancing the security and user experience of the personal knowledge base.

[0060] In one example of this application, a data processing method is also provided, wherein adjusting the preset threshold based on the evaluation result includes: If the evaluation results indicate that the preset threshold needs to be adjusted, the preset threshold is adjusted based on the average effective behavioral ability level and the preset threshold.

[0061] In this embodiment, the preset threshold is adjusted based on the average effective behavioral ability level and the preset threshold. The following four methods can be used: The first method involves calculating the difference between the average effective behavioral ability level and a preset threshold, and then extracting a portion of this difference as an adjustment value according to a preset ratio.

[0062] For example, if the preset threshold for a personal knowledge base is 70 and the average effective behavioral ability level is 82, the difference between the two is 12. If the preset ratio is set to 50%, then the value of the difference determined according to the preset ratio is 6. Using 6 as the adjustment value, the preset threshold is adjusted to 76.

[0063] The second method involves pre-setting different difference ranges and corresponding adjustment values, and matching the corresponding adjustment values ​​according to the range to which the difference between the average effective behavioral ability level and the preset threshold belongs.

[0064] For example, the correspondence between the preset difference range and the adjustment value is: "difference 0-5 corresponds to adjustment value 1, difference 5-10 corresponds to adjustment value 3, difference 10-15 corresponds to adjustment value 5, and difference above 15 corresponds to adjustment value 7". The preset threshold of the personal knowledge base is 70, the average effective behavioral ability level is 82, and the difference between the two is 12. This difference belongs to the 10-15 range, and the corresponding adjustment value is 5. Therefore, the preset threshold is adjusted to 75.

[0065] The third method involves calculating the difference between the average effective behavioral ability level and a preset threshold, introducing a smoothing coefficient to correct this difference, and limiting the maximum magnitude of a single adjustment to determine the final adjustment value. The smoothing coefficient can be preset based on experience.

[0066] For example, the preset threshold of the personal knowledge base is 70, and the average effective behavioral ability level is 82. The difference between the two is 12. A smoothing coefficient of 0.7 is introduced to correct the difference. Calculate 12 × 0.7 = 8.4, which is rounded to 9. At the same time, the maximum adjustment range in a single adjustment is limited to 8. Therefore, the maximum adjustment range of 8 is taken as the final adjustment value, and the preset threshold is adjusted to 78.

[0067] The fourth method involves calculating the ratio of the average effective behavioral ability level to a preset threshold, setting a preset ratio range, and determining the corresponding adjustment value based on the proportion or value exceeding the preset ratio range.

[0068] For example, if the preset ratio range is set to 1-1.1, the preset threshold is 70, and the average effective behavioral ability level is 82, the calculated ratio between the two is approximately 1.17. The portion of this ratio that exceeds the preset ratio range is 0.07. If a rule is set that for every 0.01 that exceeds the preset ratio range, the corresponding adjustment value is 1, and the adjustment value is determined to be 7, the preset threshold is adjusted to 77.

[0069] In the above solution, when the assessment results indicate that the preset threshold needs adjustment, various methods are used to determine the adjustment value based on the average effective behavioral ability level and the preset threshold. This adapts to different usage scenarios and security needs, making the adjustment of the preset threshold more flexible. Through these methods, the adjustment value can be reasonably derived based on the user's actual security behavior data, ensuring that the adjusted preset threshold accurately matches the user's security behavior capabilities and avoiding a disconnect between the preset threshold and the user's actual situation. This further improves the user experience.

[0070] This application also provides a data processing method in one example, such as Figure 7 As shown, the method further includes: Step 701: Obtain at least one historical behavioral ability level of the user within a third preset time period and at least one behavioral data for determining the historical behavioral ability level.

[0071] In this embodiment, the third preset time period is preset data. The historical behavioral ability level is the behavioral ability level determined based on the user's behavioral data, triggered by the user's access request to the personal knowledge base within the past third preset time period. At least one piece of behavioral data for the historical behavioral ability level is at least one piece of user behavioral data used to determine the historical behavioral ability level when it is determined.

[0072] Step 702: Based on the at least one historical behavioral ability level and at least one behavioral data used to determine the historical behavioral ability level, iteratively optimize the initial weight coefficient corresponding to the behavioral data to obtain the weight coefficient corresponding to the behavioral data.

[0073] In this embodiment, initial weight coefficients for each behavioral data point are set using linear regression. Behavioral data from the past third preset time period are converted into quantifiable values ​​and substituted into the linear regression formula to calculate the predicted behavioral ability level. The predicted behavioral ability level is then compared with the corresponding actual historical behavioral ability level, and the error value is calculated. The initial weight coefficients are adjusted based on the error value. If the error value is greater than a preset error threshold, the weight coefficients for each behavioral data point are fine-tuned according to a preset adjustment rule, such as gradient descent. The adjusted weight coefficients are then substituted into the formula to recalculate the predicted behavioral ability level and compared with the actual value. This iterative process of comparison and adjustment is repeated until the error value between the calculated predicted behavioral ability level and the actual historical behavioral ability level is less than or equal to the preset error threshold. The iteration then stops, and the optimized weight coefficients for each behavioral data point are finally obtained.

[0074] In the above scheme, by acquiring the user's historical behavioral ability level and corresponding behavioral data within a third preset time period, and then setting initial weight coefficients through linear regression, the behavioral data is transformed into quantifiable values ​​to determine the predicted behavioral ability level. The predicted behavioral ability level is then compared with the actual historical behavioral ability level, and the weight coefficients are iteratively adjusted based on the error value until the error meets the preset requirements. Through continuous comparison and adjustment, the weight coefficients are continuously corrected, enabling the optimized weight coefficients to more accurately represent the impact of various behavioral data on the user's behavioral ability level. The optimized weight coefficients make the determined behavioral ability level more consistent with the user's actual safety behavior level, thus making the target decisions based on the behavioral ability level and preset thresholds more reasonable, better ensuring the security of the personal knowledge base, and improving the user experience.

[0075] This application also provides a data processing method in one example, such as Figure 8 As shown, the method further includes: Step 801: Obtain the current status information of the electronic device. The status information includes at least the device information, location information, and network information of the electronic device.

[0076] When responding to a user's request to access the personal knowledge base, if it is determined that the user's behavioral ability level is greater than the preset threshold of the personal knowledge base, further authentication of the user can be performed before allowing the access request. The authentication policy for the user can be determined based on the current status information of the electronic device.

[0077] In this embodiment, the current status information of the electronic device includes device information, location information, and network information. Device information includes at least the unique identifier of the electronic device. Location information includes the latitude and longitude coordinates of the electronic device's current geographical location, the type of the current area determined by location services, and whether it belongs to a user-preset list of trusted locations. Network information includes the type of network the electronic device is currently connected to, network identifier, network security attributes, etc.

[0078] Step 802: Determine an authentication policy based on the status information. The authentication policy is used to authenticate the user to obtain a corresponding authentication result. The authentication result is used to determine whether to allow or deny the user's access request.

[0079] Based on device information, determine whether the current device is a trusted device; based on location information, determine whether the current device is in a trusted location; based on network information, determine whether the current device is connected to a trusted network.

[0080] Then, the authentication policy is determined based on the status information, including: The first approach, if the electronic device is a trusted device and connected to a trusted network, sets the authentication policy to single-factor authentication. Users only need to complete verification using a password, SMS verification code, biometric authentication, or dynamic token to grant access. If the electronic device is an untrusted device or connected to an untrusted network, the authentication policy is multi-factor authentication, requiring users to use two or more verification methods before granting access.

[0081] The second approach is as follows: If the electronic device is a trusted device, located in a trusted location, and connected to a trusted network, then the authentication policy is determined to be single-factor authentication. The user's access request is granted only if they complete verification using any one of the following methods: password, SMS verification code, biometric authentication, or dynamic token. If the electronic device is an untrusted device, located in an untrusted location, or connected to an untrusted network, then the authentication policy is determined to be multi-factor authentication. The user's access request is granted only if they complete verification using two or more methods.

[0082] The third method: Determine whether the current network is high-risk based on network information, such as an unencrypted public network. If the connected network is high-risk, the authentication policy is set to multi-factor authentication, requiring the user to pass two or more authentication methods before granting access. If the connected network is low-risk, determine whether the electronic device is a trusted device and located in a trusted location. If the electronic device is a trusted device and located in a trusted location, the authentication policy is set to single-factor authentication, requiring the user to complete verification using any one of the following methods: password, SMS verification code, biometrics, or dynamic token. If the electronic device is untrusted or connected to an untrusted network, the authentication policy is also set to multi-factor authentication, requiring the user to pass two or more authentication methods before granting access.

[0083] In the above solution, by acquiring the current device information, location information, and network information of the electronic device, the system obtains the usage environment and security-related status information of the electronic device. Then, through different combinations of this status information, it provides multiple methods to determine authentication strategies, flexibly adapting to different security scenarios. When the device, location, and network are all in a secure and trustworthy state, single-factor authentication allows users to easily complete verification, ensuring smooth normal use. When there are security risks such as untrusted devices, untrusted locations, or high-risk networks, multi-factor authentication is used to strengthen verification requirements and improve access protection security. This approach avoids the security deficiencies or inconveniences of single authentication methods, accurately matches the security needs of different scenarios, makes the authentication process more targeted, effectively reduces the risk of unauthorized access, further protects the security of personal knowledge bases, and also considers the user experience.

[0084] This application provides an electronic device, Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0085] like Figure 9 As shown, the electronic device 900 includes a processor 901 and a storage device 902, which are electrically connected. In response to a user's request to access a personal knowledge base, the processor 901 determines at least one piece of user behavior data within a first preset time period from the storage device 902; determines the user's behavioral ability level based on the at least one piece of behavioral data and the corresponding weight coefficient, the behavioral ability level representing the user's level of security in using the personal knowledge base; and determines a target decision based on the behavioral ability level and a preset threshold of the personal knowledge base, the preset threshold being determined based on the usage strategy of the personal knowledge base.

[0086] The processor 901 detects the user's first operation on the personal knowledge base, obtains parameters corresponding to the first operation, the first operation instructs the setting of a usage strategy or preset threshold for the personal knowledge base; and determines the preset threshold for the personal knowledge base based on the parameters.

[0087] Wherein, the processor 901 determines that the first operation instruction sets a preset threshold for the personal knowledge base, and then determines the preset threshold based on the parameters; and determines that the first operation instruction sets a usage strategy for the personal knowledge base, and then determines the usage strategy for the personal knowledge base based on the parameters, and determines the preset threshold for the personal knowledge base based on the usage strategy and mapping information, wherein the mapping information includes the mapping relationship between the usage strategy and the preset threshold.

[0088] Wherein, the processor 901 determines that the behavioral ability level is less than a preset threshold of the personal knowledge base and rejects the access request to the personal knowledge base; or, determines that the behavioral ability level is greater than the preset threshold of the personal knowledge base and allows the access request to the personal knowledge base, wherein the access request includes a synchronization request, the synchronization request indicating the extraction of data related to the usage strategy of the personal knowledge base from the prompt information and / or log information of the electronic device.

[0089] Specifically, the processor 901 obtains from the storage device 902 at least one historical behavioral ability level of the user within a second preset time period and a historical comparison result of the historical behavioral ability level, the historical comparison result being determined based on the historical behavioral ability level and the corresponding historical preset threshold; evaluates the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level and the historical comparison result of the historical behavioral ability level, to obtain an evaluation result; and adjusts the preset threshold based on the evaluation result.

[0090] The processor 901 determines the average effective behavioral ability level based on the effective behavioral ability level, wherein the effective behavioral ability level is the behavioral ability level that is greater than a preset threshold and / or the historical behavioral ability level that is greater than the corresponding historical preset threshold; and if the difference between the preset threshold and the average effective behavioral ability level is greater than a preset difference, then the evaluation result is determined to be that the preset threshold needs to be adjusted.

[0091] If the evaluation result indicates that the preset threshold needs to be adjusted, the processor 901 adjusts the preset threshold based on the average effective behavioral ability level and the preset threshold.

[0092] The processor 901 obtains from the storage device 902 at least one historical behavioral ability level of the user within a third preset time period and at least one behavioral data for determining the historical behavioral ability level; and iteratively optimizes the initial weight coefficient corresponding to the behavioral data based on the at least one historical behavioral ability level and the at least one behavioral data for determining the historical behavioral ability level to obtain the weight coefficient corresponding to the behavioral data.

[0093] The processor 901 obtains the current status information of the electronic device, which includes at least the device information, location information, and network information of the electronic device; and determines an authentication policy based on the status information. The authentication policy is used to authenticate the user to obtain a corresponding authentication result, and the authentication result is used to determine whether to allow or deny the user's access request.

[0094] To implement the above data processing methods, such as Figure 10 As shown, an example of this application provides a data processing apparatus, including: The data collection module 1001 is used to respond to a user's request to access a personal knowledge base and determine at least one piece of user behavior data within a first preset time period. Calculation module 1002 is used to determine the user's behavioral ability level based on the at least one behavioral data and the weight coefficient corresponding to the behavioral data, wherein the behavioral ability level characterizes the user's security level in using the personal knowledge base; The processing module 1003 is used to determine a target decision based on the behavioral ability level and a preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

[0095] The processing module 1003 is further configured to detect the user's first operation on the personal knowledge base, obtain the parameters corresponding to the first operation, and the first operation indicates the setting of the usage strategy or preset threshold of the personal knowledge base. The calculation module 1002 is also used to determine a preset threshold for the personal knowledge base based on the parameters.

[0096] The calculation module 1002 is further configured to determine the preset threshold of the personal knowledge base by the first operation instruction, and then determine the preset threshold based on the parameter. The processing module 1003 is further configured to determine the usage strategy of the personal knowledge base based on the first operation instruction, and then determine the usage strategy of the personal knowledge base based on the parameters, and determine the preset threshold of the personal knowledge base based on the usage strategy and mapping information, wherein the mapping information includes the mapping relationship between the usage strategy and the preset threshold.

[0097] The processing module 1003 is further configured to determine that the behavioral ability level is less than a preset threshold of the personal knowledge base and reject the access request to the personal knowledge base. The processing module 1003 is further configured to determine that the behavioral ability level is greater than a preset threshold of the personal knowledge base, and to allow access to the personal knowledge base. The access request includes a synchronization request, which instructs the extraction of data related to the usage strategy of the personal knowledge base from the prompt information and / or log information of the electronic device.

[0098] The acquisition module 1001 is further configured to obtain at least one historical behavioral ability level of a user within a second preset time period and a historical comparison result of the historical behavioral ability level, wherein the historical comparison result is determined based on the historical behavioral ability level and the corresponding historical preset threshold. The calculation module 1002 is further configured to evaluate the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level, to obtain an evaluation result; The processing module 1003 is also used to adjust the preset threshold based on the evaluation result.

[0099] The calculation module 1002 is further configured to determine the average effective behavioral ability level based on the effective behavioral ability level, wherein the effective behavioral ability level is the behavioral ability level that is greater than a preset threshold and / or the historical behavioral ability level that is greater than the corresponding historical preset threshold, as indicated by the comparison result. The calculation module 1002 is further configured to determine that the evaluation result requires adjustment of the preset threshold if the difference between the preset threshold and the average effective behavioral ability level is greater than a preset difference.

[0100] The calculation module 1002 is further configured to adjust the preset threshold based on the average effective behavioral ability level and the preset threshold if the evaluation result indicates that the preset threshold needs to be adjusted.

[0101] The acquisition module 1001 is further configured to obtain at least one historical behavioral ability level of the user within a third preset time period and at least one behavioral data for determining the historical behavioral ability level. The calculation module 1002 is further configured to iteratively optimize the initial weight coefficient corresponding to the behavior data based on the at least one historical behavior ability level and at least one type of behavior data used to determine the historical behavior ability level, so as to obtain the weight coefficient corresponding to the behavior data.

[0102] The acquisition module 1001 is also used to obtain the current status information of the electronic device, the status information including at least the device information, the location information and the network information connected to the electronic device; The processing module 1003 is further configured to determine an authentication policy based on the status information. The authentication policy is used to authenticate the user to obtain a corresponding authentication result. The authentication result is used to determine whether to allow or deny the user's access request.

[0103] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0107] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0108] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0109] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0111] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A data processing method, the method comprising: In response to a user's request to access a personal knowledge base, at least one piece of behavioral data of the user within a first preset time period is determined; The user's behavioral ability level is determined based on the at least one behavioral data point and the corresponding weight coefficient of the behavioral data point, and the behavioral ability level characterizes the degree of security of the user in using the personal knowledge base; Target decisions are determined based on the behavioral ability level and a preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

2. The method according to claim 1, further comprising: The user's first operation on the personal knowledge base is detected, and the parameters corresponding to the first operation are obtained. The first operation indicates the setting of the usage strategy or preset threshold of the personal knowledge base. The preset threshold of the personal knowledge base is determined based on the parameters.

3. The method according to claim 2, wherein determining the preset threshold of the personal knowledge base based on the parameters includes: If the first operation instruction is determined to set a preset threshold for the personal knowledge base, then the preset threshold is determined based on the parameter; If the first operation instruction is determined to set the usage strategy of the personal knowledge base, then the usage strategy of the personal knowledge base is determined based on the parameters, and a preset threshold of the personal knowledge base is determined based on the usage strategy and mapping information, wherein the mapping information includes the mapping relationship between the usage strategy and the preset threshold.

4. The method according to claim 1, wherein determining the target decision based on the behavioral ability level and the preset threshold of the personal knowledge base includes: If the behavioral ability level is determined to be less than a preset threshold of the personal knowledge base, the access request to the personal knowledge base is rejected. Alternatively, if the behavioral ability level is determined to be greater than a preset threshold of the personal knowledge base, the access request to the personal knowledge base is allowed. The access request includes a synchronization request, which instructs the extraction of data related to the usage strategy of the personal knowledge base from the electronic device's prompts and / or log information.

5. The method according to claim 1, further comprising: Obtain at least one historical behavioral ability level of a user within a second preset time period and a historical comparison result of the historical behavioral ability level, wherein the historical comparison result is determined based on the historical behavioral ability level and the corresponding historical preset threshold. The preset threshold is evaluated based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level to obtain the evaluation result; The preset threshold is adjusted based on the evaluation results.

6. The method according to claim 5, wherein evaluating the preset threshold based on the behavioral ability level, the comparison result of the behavioral ability level, the at least one historical behavioral ability level, and the historical comparison result of the historical behavioral ability level comprises: The mean of effective behavioral ability levels is determined based on the effective behavioral ability level, wherein the effective behavioral ability level is the behavioral ability level that is greater than a preset threshold and / or the historical behavioral ability level that is greater than the corresponding historical preset threshold, as indicated by the comparison result. If the difference between the preset threshold and the average effective behavioral ability level is greater than the preset difference, then the evaluation result indicates that the preset threshold needs to be adjusted.

7. The method according to claim 6, wherein adjusting the preset threshold based on the evaluation result includes: If the evaluation results indicate that the preset threshold needs to be adjusted, the preset threshold is adjusted based on the average effective behavioral ability level and the preset threshold.

8. The method according to claim 1, further comprising: Obtain at least one historical behavioral ability level of the user within a third preset time period and at least one behavioral data for determining the historical behavioral ability level; Based on the at least one historical behavioral ability level and at least one behavioral data used to determine the historical behavioral ability level, the initial weight coefficients corresponding to the behavioral data are iteratively optimized to obtain the weight coefficients corresponding to the behavioral data.

9. The method according to claim 1, further comprising: Obtain the current status information of the electronic device, wherein the status information includes at least the device information, location information, and network information connected to the electronic device; An authentication policy is determined based on the status information. The authentication policy is used to authenticate the user to obtain a corresponding authentication result. The authentication result is used to determine whether to allow or deny the user's access request.

10. An electronic device comprising: A processor and a storage device, wherein the processor and the storage device are electrically connected; In response to a user's request to access a personal knowledge base, the processor determines at least one piece of user behavior data within a first preset time period from the storage device; and determines the user's behavioral ability level based on the at least one piece of behavioral data and the weight coefficient corresponding to the behavioral data, wherein the behavioral ability level characterizes the degree of security of the user's use of the personal knowledge base. And determine target decisions based on the behavioral ability level and a preset threshold of the personal knowledge base, wherein the preset threshold is determined based on the usage strategy of the personal knowledge base.

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