An intelligent user information management system based on data analysis

Abstract

This invention relates to the field of data analysis and processing technology, specifically to an intelligent user information management system based on data analysis. The system includes: a user historical information collection module, a management mode operation analysis module, a user operation prompt module, and a mode maintenance time adjustment module. The user historical information collection module collects historical operation data and smart lock operation data from users. The management mode operation analysis module analyzes abnormal user operation conditions when the smart lock is in management mode and sets warning values ​​for these abnormal conditions. The user operation prompt module monitors the smart lock in an inactive state during management mode and prompts the user to exit management mode when the monitored state duration reaches the warning value. The mode maintenance time adjustment module adjusts the automatic exit time of the smart lock management mode, reducing the probability of user unlocking failures in case of abnormal conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and processing, and in particular to an intelligent user information management system based on data analysis. Background Art

[0002] Smart door locks are different from traditional mechanical locks and are more intelligent in terms of user security, identification and management. They have higher security and bring great convenience to user operation. When using smart door locks, users need to set and enter passwords and other information. New user information can also be added in management mode. Good user information management can help users unlock the door smoothly and improve the security of using smart door locks.

[0003] When the smart door lock is in management mode, the user can perform data operations such as adding fingerprints and setting door lock passwords, but cannot perform unlocking operations in management mode and needs to exit the management mode first. There are generally two ways to exit the management mode: one is that the user manually presses a button to exit the management mode after the data operation, and the other is that the user automatically exits the management mode after a long period of inactivity. However, in management mode, there may be an abnormal situation where the user forgets to manually exit the management mode and performs an unlocking operation. If the smart door lock is still in management mode when performing an unlocking operation, it is easy to cause the unlocking operation to fail, which increases the inconvenience of users using smart door locks.

[0004] Therefore, people need an intelligent user information management system based on data analysis to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent user information management system based on data analysis to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent user information management system based on data analysis, the system comprising: a user history information collection module, a management mode operation analysis module, a user operation prompt module and a mode maintenance time adjustment module;

[0007] The user history information collection module collects the user's historical operation data on the smart door lock and the smart door lock operation data;

[0008] Analyze abnormal user operation conditions when the smart door lock is in the management mode through the management mode operation analysis module, and set an early warning value for the abnormal condition;

[0009] The user operation prompt module monitors the maintenance time of the smart door lock in the management mode without being operated, and prompts the user to exit the management mode when the monitoring maintenance time reaches the warning value;

[0010] The mode maintenance time adjustment module is used to adjust and manage the time for automatically exiting the smart door lock management mode;

[0011] The output end of the user history information collection module is connected to the input end of the management mode operation analysis module, the output end of the management mode operation analysis module is connected to the input end of the user operation prompt module, and the output end of the user operation prompt module is connected to the input end of the mode maintenance time adjustment module.

[0012] Furthermore, the user history information collection module includes an operation information collection unit and an abnormal data collection unit;

[0013] The operation information collection unit collects the user's previous operation information on the smart door lock. The operation information refers to the interval between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the door when the smart door lock was in the management mode. The start time when the user did not operate the smart door lock is earlier than the time when the user entered the password to unlock the door, and an interval time data set is generated. In the management mode, the user can set the smart door lock, such as setting the door lock password, entering fingerprints, adding new users, etc. The user did not operate the smart door lock means that the user did not perform any setting operation in the management mode;

[0014] The abnormal data collection unit collects the interval between the start time when the user did not operate the smart door lock in the management mode and the automatic exit time of the management mode as the benchmark interval time, and selects data less than the benchmark interval time from the interval time data set. After the user turns on the management mode of the smart door lock, if the smart door lock is not operated for a certain period of time, the smart door lock will automatically exit the management mode, and the above-mentioned elapsed time is the benchmark interval time;

[0015] The output end of the operation information collection unit is connected to the input end of the abnormal data collection unit, and all the collected data are collected after the user grants permission.

[0016] Furthermore, the management mode operation analysis module includes an abnormality judgment model establishment unit and an early warning value setting unit;

[0017] The abnormality judgment model establishment unit counts the number of occurrences of different interval durations selected, analyzes the abnormal probability of the user's historical operation based on the statistical results, and combines the interval duration data after the screening process and the historical operation abnormality probability data into data points, and establishes the user operation abnormality judgment model after fitting the data points;

[0018] Setting an operation abnormality probability threshold through the warning value setting unit, substituting the threshold into the user operation abnormality judgment model, and using the output value as the warning value;

[0019] The input end of the abnormality judgment model building unit is connected to the output end of the abnormality data acquisition unit, and the output end of the abnormality judgment model building unit is connected to the input end of the early warning value setting unit.

[0020] Furthermore, the user operation prompt module includes an operation status monitoring unit and a mode exit operation prompt unit;

[0021] When the current user does not operate the smart door lock and the smart door lock is in the management mode, the operation status monitoring unit monitors the maintenance time of the smart door lock in the non-operated state;

[0022] When the maintenance time of the mode exit operation prompt unit reaches the warning value, the management mode exit selection prompt information is sent to the user terminal to remind the user to exit the management mode of the smart door lock;

[0023] The input end of the operation status monitoring unit is connected to the output end of the warning value setting unit, and the output end of the operation status monitoring unit is connected to the input end of the mode exit operation prompt unit.

[0024] Further, the mode maintenance time adjustment module includes an operation time analysis unit and an automatic exit time adjustment unit;

[0025] The operation time analysis unit retrieves the interval between the start time when the smart door lock was not operated and the time when the user input the password to unlock the smart door lock when the smart door lock was in the management mode, and analyzes the best time for the smart door lock to automatically exit the management mode;

[0026] The automatic exit time adjustment unit adjusts the time for automatic exit of the smart door lock management mode to an optimal time;

[0027] The input end of the operation time analysis unit is connected to the output end of the mode exit operation prompt unit, and the output end of the operation time analysis unit is connected to the input end of the automatic exit time adjustment unit.

[0028] The following steps are involved:

[0029] S100: Collecting historical operation data of the user on the smart door lock and the operation data of the smart door lock;

[0030] S101: Analyze abnormal user operation when the smart door lock is in management mode, and set an early warning value for the abnormal condition;

[0031] S102: monitoring the duration of time that the smart door lock is in an unoperated state in the management mode, and prompting the user to exit the management mode when the duration reaches a warning value;

[0032] S103: Adjust and manage the time for automatically exiting the smart door lock management mode.

[0033] Further, in step S100: the interval time set between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode is collected as T={T1, T2, ..., T n}, collect the interval time between the start time when the user did not operate the smart door lock in the management mode and the automatic exit time of the management mode as the benchmark interval time, the benchmark interval time is L, in the management mode, if the user does not operate the smart door lock for L time, the smart door lock will automatically exit the management mode, compare the interval time in T with the benchmark interval time one by one, filter out the interval time set that is less than the benchmark interval time, and obtain the filtered different interval time set as T ’ ={T1 ’ , T2 ’ ,…T i ’ ,…T m ’}, where m represents the number of different interval durations that are filtered out, and the number of interval durations is counted to T ’ The number of times the interval duration in the selected interval duration set that is smaller than the benchmark interval duration appears is J={J1, J2, …J i , …J m}.

[0034] Further, in step S101: according to P i =(J i ) / ∑ m i=1 (J i ) Calculate the interval between the time when the user does not operate the smart door lock and the time when the user enters the password to unlock the smart door lock when the smart door lock is in management mode as T i ’ The user's historical operation abnormality probability P iIf the interval between the start time when the user does not operate the smart door lock and the time when the user enters the password to unlock the door is less than the reference interval, it means that the user enters the password to unlock the door when the smart door lock is in management mode, and the unlocking operation cannot be performed in management mode, which means that the user's corresponding operation is abnormal. The more times the same interval occurs, the more frequently the user performs the unlocking operation after the corresponding interval, and the higher the probability of abnormal operation. Among them, J i Indicates the interval duration T i ’ The number of times the user's historical operation abnormality probability set is obtained by looking at the number of times the user's historical operation abnormality probability set is smaller than the benchmark interval length. i , …P m}, the set T ’ And the data in the set P constitute the data point {(T1 ’ , P1), (T2 ’ , P2), … (T i ’ , P i ),…(T m ’ , P m )}, after fitting the data points, a user operation abnormality judgment model is established:

[0035] y=δ*x+η;

[0036] Among them, x represents the independent variable of the interval duration that is less than the benchmark interval duration in the model, y represents the dependent variable of the probability of abnormal operation in the model, δ represents the bias, and η represents the intercept. Solve δ and η respectively:

[0037] δ=[m∑ m i=1 (T i ’ ×P i )-∑ m i=1 (T i ’ )∑ m i=1 (P i )] / [m∑ m i=1 (T i ’ ) 2 -(∑ m i=1 (T i ’ )) 2 ];

[0038] η=[∑ mi=1 (P i )-δ∑ m i=1 (T i ’ )] / m;

[0039] Set the operation abnormality probability threshold to K, substitute K into the model: let y = K, output x = (K-η) / δ, and set (K-η) / δ as the warning value of the abnormal condition.

[0040] Further, in step S102: when the current user does not operate the smart door lock and the smart door lock is in the management mode, the maintenance time of the smart door lock in the non-operated state is monitored, and when the monitoring maintenance time reaches (K-η) / δ, a management mode exit selection prompt message is sent to the user terminal to remind the user to exit the management mode of the smart door lock;

[0041] The historical information of users using smart door locks is collected and analyzed through big data technology. The historical information is collected after the user grants permission. The abnormal data of users' previous unlocking operations in the management mode of the smart door lock are analyzed, the abnormal probability of users' historical operations is analyzed, and the interval time between the start time of the user's non-setting operation in the management mode and the abnormal unlocking operation time is analyzed. Then, the abnormal probability of users' historical operations under different time intervals is analyzed, and then a user operation abnormality judgment model is established. By setting the probability threshold and substituting it into the model, the value output by the model indicates how long the corresponding user has not manually exited the operation. The highest probability of forgetting to manually exit the management mode is determined, and the value output by the model is used as the warning value to monitor the status data of the current smart door lock: when the current user has not operated the smart door lock and the smart door lock is in the management mode, the smart door lock is monitored. The maintenance time of the smart door lock in the non-operated state is monitored. When the maintenance time reaches the warning value, it is predicted that the user may have forgotten to manually exit the management mode. If the unlocking operation is performed at this time, the user will fail, and the user will be reminded to exit the management mode, which effectively reduces the abnormal probability of the user forgetting to manually exit the management mode and performing the unlocking operation, thereby reducing the probability of the unlocking operation failure and improving the convenience of users using smart door locks.

[0042] Further, in step S103: the interval time set between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode is retrieved as T={T1, T2, ..., T n}, adjust the time for the smart door lock management mode to automatically exit according to the following formula:

[0043] W n+1 =μ*T n +(1-μ)*Z n ;

[0044] Among them, μ represents the smoothing coefficient, Z n Represents the exponential smoothing value of the nth interval length in set T. The exponential smoothing value Z1 of the first interval length in set T is calculated according to Z1=μ*T1+(1-μ)*[(T1+T2+T3) / 3]. The exponential smoothing value Z2 of the second interval length in set T is calculated according to Z2=μ*T1+(1-μ)*Z1. The exponential smoothing value Z3 of the third interval length in set T is calculated according to Z3=μ*T2+(1-μ)*Z2. Z is calculated by analogy. n , W n+1 Indicates the optimal interval between the time when the smart door lock automatically exits and the time when the user does not operate the smart door lock. The time when the smart door lock management mode automatically exits is adjusted to the optimal time. The optimal time is: the time interval W between the time when the user does not operate the smart door lock n+1 Automatically exit management mode when

[0045] By retrieving the interval time data between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode, the interval time data is analyzed to analyze the highest probability of how long the interval will be before abnormal operation when the user forgot to manually exit the management mode and performed the unlocking operation. Considering that the smart door lock will automatically exit the management mode when no operation is sensed for a long time, the best time for automatic exit from the management mode is set according to the analysis results, and the waiting time for automatic exit from the management mode of the smart door lock is adaptively adjusted. After the adjustment, even if the user performs the unlocking operation after a period of time without performing the setting operation, there is a certain probability that the smart door lock has automatically exited the management mode. The probability of failure of the unlocking operation when the user performs abnormal unlocking operation can also be reduced to a certain extent.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0047] The present invention collects and analyzes historical information of users using smart door locks through big data technology, analyzes abnormal data of users performing unlocking operations in the past when the smart door lock is in the management mode, analyzes the abnormal probability of users' historical operations, analyzes the interval time between the start time when the user does not perform any setting operation in the management mode and the time of performing abnormal unlocking operation, and then analyzes the abnormal probability of users' historical operations under different time intervals, and then establishes a user operation abnormality judgment model, and substitutes the probability threshold into the model by setting the probability threshold. The value output by the model indicates the highest probability that the corresponding user forgets to manually exit the management mode when the interval is too long and the manual operation is not performed to exit the management mode. The value output by the model is used as the warning value to monitor the status data of the current smart door lock: monitor the maintenance time of the smart door lock in the unoperated state when the current user does not operate the smart door lock and the smart door lock is in the management mode. When the maintenance time reaches the warning value, it is predicted that the user may have forgotten to manually exit the management mode. At this time, if the unlocking operation is performed, it will fail, and the user is reminded to exit the management mode, which effectively reduces the abnormal probability of the user forgetting to manually exit the management mode and performing the unlocking operation, thereby reducing the probability of the unlocking operation failure and improving the convenience of users using the smart door lock;

[0048] By retrieving the interval time data between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode, the interval time data is analyzed to analyze the highest probability of how long the interval will be before abnormal operation when the user forgot to manually exit the management mode and performed the unlocking operation. Considering that the smart door lock will automatically exit the management mode when no operation is sensed for a long time, the best time for automatic exit from the management mode is set according to the analysis results, and the waiting time for automatic exit from the management mode of the smart door lock is adaptively adjusted. After the adjustment, even if the user performs the unlocking operation after a period of time without performing the setting operation, there is a certain probability that the smart door lock has automatically exited the management mode. The probability of failure of the unlocking operation when the user performs abnormal unlocking operation can also be reduced to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 This is a structural diagram of an intelligent user information management system based on data analysis according to the present invention;

[0051] Figure 2 It is a flow chart of a user information intelligent management method based on data analysis of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] Combine the following Figure 1-Figure 2 The present invention is further described with reference to the accompanying drawings and specific embodiments.

[0054] Embodiment 1:

[0055] like Figure 1 As shown, this embodiment provides a user information intelligent management system based on data analysis, and the system includes: a user history information collection module, a management mode operation analysis module, a user operation prompt module and a mode maintenance time adjustment module, the user history information collection module collects the user's historical operation data on the smart door lock and the smart door lock operation data, the management mode operation analysis module analyzes the abnormal user operation when the smart door lock is in the management mode, sets the warning value of the abnormal condition, monitors the maintenance time of the smart door lock in the management mode when it is not operated, and prompts the user to exit the management mode when the monitoring maintenance time reaches the warning value, and adjusts and manages the time of automatic exit of the management mode of the smart door lock through the mode maintenance time adjustment module;

[0056] The output end of the user history information collection module is connected to the input end of the management mode operation analysis module, the output end of the management mode operation analysis module is connected to the input end of the user operation prompt module, and the output end of the user operation prompt module is connected to the input end of the mode maintenance time adjustment module.

[0057] The user history information collection module includes an operation information collection unit and an abnormal data collection unit. The operation information collection unit collects the user's previous operation information on the smart door lock. The operation information refers to the interval between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the door when the smart door lock was in the management mode. The start time when the user did not operate the smart door lock is earlier than the time when the user entered the password to unlock the door, and an interval time data set is generated. In the management mode, the user can set the smart door lock, such as setting the door lock password, entering fingerprints, adding new users, etc. The user did not operate the smart door lock means that the user did not perform any setting operation in the management mode. The abnormal data collection unit collects the interval between the start time when the user did not operate the smart door lock when the smart door lock was in the management mode and the automatic exit time of the management mode as the benchmark interval time, and selects data less than the benchmark interval time from the interval time data set. After the user turns on the management mode of the smart door lock, if the smart door lock is not operated for a certain period of time, the smart door lock will automatically exit the management mode. The above-mentioned elapsed time is the benchmark interval time;

[0058] The output end of the operation information collection unit is connected to the input end of the abnormal data collection unit, and all the collected data are collected after the user grants permission.

[0059] The management mode operation analysis module includes an abnormal judgment model building unit and an early warning value setting unit. The abnormal judgment model building unit counts the number of occurrences of different interval durations selected, analyzes the user's historical operation abnormality probability based on the statistical results, and combines the interval duration data after screening and the historical operation abnormality probability data into data points. After fitting the data points, a user operation abnormality judgment model is established. The early warning value setting unit sets an operation abnormality probability threshold, substitutes the threshold into the user operation abnormality judgment model, and uses the output value as the early warning value.

[0060] The input end of the abnormality judgment model establishment unit is connected to the output end of the abnormality data acquisition unit, and the output end of the abnormality judgment model establishment unit is connected to the input end of the warning value setting unit.

[0061] The user operation prompt module includes an operation status monitoring unit and a mode exit operation prompt unit. When the current user does not operate the smart door lock and the smart door lock is in the management mode, the operation status monitoring unit monitors the maintenance time of the smart door lock in the non-operated state. When the maintenance time reaches the warning value, the mode exit operation prompt unit sends a management mode exit selection prompt message to the user terminal to remind the user to exit the management mode of the smart door lock;

[0062] The input end of the operation status monitoring unit is connected to the output end of the warning value setting unit, and the output end of the operation status monitoring unit is connected to the input end of the mode exit operation prompt unit.

[0063] The mode maintenance time adjustment module includes an operation time analysis unit and an automatic exit time adjustment unit. The operation time analysis unit retrieves the interval between the start time when the smart door lock is not operated and the time when the user enters the password to unlock the smart door lock when the smart door lock is in the management mode in the past, analyzes the best time for the smart door lock to automatically exit the management mode, and adjusts the time for the smart door lock to automatically exit the management mode to the best time through the automatic exit time adjustment unit.

[0064] The input end of the operation time analysis unit is connected to the output end of the mode exit operation prompt unit, and the output end of the operation time analysis unit is connected to the input end of the automatic exit time adjustment unit.

[0065] Embodiment 2:

[0066] like Figure 2 As shown, this embodiment is implemented based on the management system in the embodiment, and specifically includes the following steps:

[0067] S100: Collect historical operation data of the user on the smart door lock and the operation data of the smart door lock, and collect the interval time set between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode, which is T={T1, T2, …, T n}, collect the interval time between the start time when the user did not operate the smart door lock in the management mode and the automatic exit time of the management mode as the benchmark interval time, the benchmark interval time is L, in the management mode, if the user does not operate the smart door lock for L time, the smart door lock will automatically exit the management mode, compare the interval time in T with the benchmark interval time one by one, filter out the interval time set that is less than the benchmark interval time, and obtain the filtered different interval time set as T ’ ={T1 ’ , T2 ’ ,…T i ’ ,…T m ’}, where m represents the number of different interval durations that are filtered out, and the number of interval durations is counted to T ’ The number of times the interval duration in the selected interval duration set that is smaller than the benchmark interval duration appears is J={J1, J2, …J i , …J m}, since there may be repeated intervals in the selected interval duration set that is smaller than the benchmark interval duration, and the more times the same interval duration appears in the initially selected interval duration set, the higher the probability that the user will enter the password to unlock the door at the corresponding interval in the management mode, because the password cannot be entered to unlock the door in the management mode;

[0068] S101: Analyze abnormal user operation when the smart door lock is in management mode, set an abnormal warning value, and i =(J i ) / ∑ m i=1 (J i ) Calculate the interval between the time when the user does not operate the smart door lock and the time when the user enters the password to unlock the smart door lock when the smart door lock is in management mode as T i ’ The user's historical operation abnormality probability P iIf the interval between the start time when the user does not operate the smart door lock and the time when the user enters the password to unlock the door is less than the reference interval, it means that the user enters the password to unlock the door when the smart door lock is in management mode, and the unlocking operation cannot be performed in management mode, which means that the user's corresponding operation is abnormal. The more times the same interval occurs, the more frequently the user performs the unlocking operation after the corresponding interval, and the higher the probability of abnormal operation. Among them, J i Indicates the interval duration T i ’ The number of times the user's historical operation abnormality probability set is obtained by looking at the number of times the user's historical operation abnormality probability set is smaller than the benchmark interval length. i , …P m}, the set T ’ And the data in the set P constitute the data point {(T1 ’ , P1), (T2 ’ , P2), … (T i ’ , P i ),…(T m ’ , P m )}, after fitting the data points, a user operation abnormality judgment model is established:

[0069] y=δ*x+η;

[0070] Where x represents the independent variable of the interval duration that is less than the benchmark interval duration in the model, y represents the dependent variable of the operation abnormality probability in the model, δ represents the bias, η represents the intercept, and the operation abnormality probability threshold is set to K. Substitute K into the model: let y=K, output x=(K-η) / δ, and set (K-η) / δ as the warning value of the abnormal condition;

[0071] S102: monitor the maintenance time of the smart door lock in the management mode without being operated, and prompt the user to exit the management mode when the monitoring maintenance time reaches the warning value. When the current user does not operate the smart door lock and the smart door lock is in the management mode, monitor the maintenance time of the smart door lock in the non-operated state, and send a management mode exit selection prompt message to the user terminal when the monitoring maintenance time reaches (K-η) / δ, to remind the user to exit the management mode of the smart door lock;

[0072] For example: the interval between the start time when the user did not operate the smart door lock in the management mode and the automatic exit time of the management mode is collected as the benchmark interval time: L=600, unit: s, and the interval time less than L is selected from the interval time set between the start time when the user did not operate the smart door lock in the management mode and the time when the user entered the password to unlock the smart door lock. The selected different interval time sets are T ’ ={T1 ’ , T2 ’ , T3 ’ , T4 ’ , T5 ’}={100, 420, 120, 60, 510}, unit: s, counted to T ’ The number of times the interval duration in the selected interval duration set that is smaller than the benchmark interval duration appears is J={J1, J2, J3, J4, J5}={4, 7, 2, 1, 10}. The calculated user historical operation abnormal probability set is P={P1, P2, P3, P4, P5}={0.17, 0.29, 0.08, 0.04, 0.42}. ’ And the data in the set P constitute the data point {(T1 ’ , P1), (T2 ’ , P2), (T3 ’ , P3), (T4 ’ , P4), (T5 ’ , P5)}={(100, 0.17),(420, 0.29),(120, 0.08),(60, 0.04),(510, 0.42)}, after fitting the data points, establish the user operation abnormality judgment model: y=0.0007x+0.03, set the operation abnormality probability threshold to K=0.4, substitute K into the model: let y=K=0.4, output x=(K-η) / δ=529, set the warning value of the abnormal situation to 529, when the current user does not operate the smart door lock and the smart door lock is in the management mode, monitor the maintenance time of the smart door lock in the non-operated state, and send the management mode exit selection prompt information to the user terminal when the monitoring maintenance time reaches 529 seconds to remind the user to exit the management mode of the smart door lock;

[0073] S103: Adjust and manage the time of automatic exit of the smart door lock management mode, and retrieve the interval time set between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the smart door lock when the smart door lock was in the management mode, which is T={T1, T2, ..., T n}, adjust the time for the smart door lock management mode to automatically exit according to the following formula:

[0074] W n+1 =μ*T n +(1-μ)*Z n ;

[0075] Among them, μ represents the smoothing coefficient, Z n Represents the exponential smoothing value of the nth interval length in set T. The exponential smoothing value Z1 of the first interval length in set T is calculated according to Z1=μ*T1+(1-μ)*[(T1+T2+T3) / 3]. The exponential smoothing value Z2 of the second interval length in set T is calculated according to Z2=μ*T1+(1-μ)*Z1. The exponential smoothing value Z3 of the third interval length in set T is calculated according to Z3=μ*T2+(1-μ)*Z2. Z is calculated by analogy. n , W n+1 Indicates the optimal interval between the time when the smart door lock automatically exits and the time when the user does not operate the smart door lock. The time when the smart door lock management mode automatically exits is adjusted to the optimal time. The optimal time is: the time interval W between the time when the user does not operate the smart door lock n+1 Automatically exit management mode.

[0076] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent user information management system based on data analysis, characterized in that: The system includes a user history information collection module, a management mode operation analysis module, a user operation prompt module and a mode maintenance time adjustment module; The user history information collection module collects the user's historical operation data on the smart door lock and the smart door lock operation data; Analyze abnormal user operation conditions when the smart door lock is in the management mode through the management mode operation analysis module, and set an early warning value for the abnormal condition; The user operation prompt module monitors the maintenance time of the smart door lock in the management mode without being operated, and prompts the user to exit the management mode when the monitoring maintenance time reaches the warning value; The mode maintenance time adjustment module is used to adjust and manage the time for automatically exiting the smart door lock management mode; The output end of the user history information collection module is connected to the input end of the management mode operation analysis module, the output end of the management mode operation analysis module is connected to the input end of the user operation prompt module, and the output end of the user operation prompt module is connected to the input end of the mode maintenance time adjustment module; The user history information collection module includes an operation information collection unit and an abnormal data collection unit; The operation information collection unit collects the user's previous operation information on the smart door lock, the operation information refers to the interval time between the start time when the user did not operate the smart door lock and the time when the user entered the password to unlock the door when the smart door lock was in the management mode in the past, and generates an interval time data set. The user did not operate the smart door lock means that the user did not perform any setting operation in the management mode; The abnormal data collection unit collects the interval between the start time when the user did not operate the smart door lock in the management mode and the automatic exit time of the management mode as the benchmark interval time, and filters out data less than the benchmark interval time from the interval time data set; The output end of the operation information acquisition unit is connected to the input end of the abnormal data acquisition unit; The user operation prompt module includes an operation status monitoring unit and a mode exit operation prompt unit; When the current user does not operate the smart door lock and the smart door lock is in the management mode, the operation status monitoring unit monitors the maintenance time of the smart door lock in the non-operated state; When the maintenance time of the mode exit operation prompt unit reaches the warning value, the management mode exit selection prompt information is sent to the user terminal to remind the user to exit the management mode of the smart door lock; The output end of the operation status monitoring unit is connected to the input end of the mode exit operation prompt unit; The mode maintenance time adjustment module includes an operation time analysis unit and an automatic exit time adjustment unit; The operation time analysis unit retrieves the interval between the start time when the smart door lock was not operated and the time when the user input the password to unlock the smart door lock when the smart door lock was in the management mode, and analyzes the best time for the smart door lock to automatically exit the management mode; The automatic exit time adjustment unit adjusts the time for automatic exit of the smart door lock management mode to an optimal time; The input end of the operation time analysis unit is connected to the output end of the mode exit operation prompt unit, and the output end of the operation time analysis unit is connected to the input end of the automatic exit time adjustment unit.

2. The user information intelligent management system based on data analysis according to claim 1 is characterized by: The management mode operation analysis module includes an abnormality judgment model establishment unit and an early warning value setting unit; The abnormality judgment model establishment unit counts the number of occurrences of different interval durations selected, analyzes the abnormal probability of the user's historical operation based on the statistical results, and combines the interval duration data after the screening process and the historical operation abnormality probability data into data points, and establishes the user operation abnormality judgment model after fitting the data points; Setting an operation abnormality probability threshold through the warning value setting unit, substituting the threshold into the user operation abnormality judgment model, and using the output value as the warning value; The input end of the abnormality judgment model building unit is connected to the output end of the abnormality data acquisition unit, and the output end of the abnormality judgment model building unit is connected to the input end of the early warning value setting unit.

3. The user information intelligent management system based on data analysis according to claim 2 is characterized by: The input end of the operation status monitoring unit is connected to the output end of the early warning value setting unit.

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