Control method and control system of intelligent door lock

Abstract

The invention relates to the technical field of internet-of-things biological recognition, and particularly discloses a control method and a control system of an intelligent door lock, when a user uses the intelligent door lock to carry out fingerprint input, a fingerprint input value is obtained and compared with a fingerprint input threshold value, a signal whether input is qualified or not is generated, and based on a signal whether input is unqualified, the intelligent door lock is controlled. According to the method, the input influence value is obtained and compared with the input influence threshold value, the influence degree signal is generated, it is judged that fingerprint input is qualified, a user is prevented from conducting multiple times of inaccurate attempts when input fails, the unqualified input condition is analyzed, and corresponding processing can be adopted in the later maintenance process in a targeted mode.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things biometrics, and in particular to a control method and a control system for an intelligent door lock. Background Art

[0002] As an important part of smart home, the control method of smart door lock integrates a variety of advanced technologies to provide a safer, more convenient and intelligent user experience. With the continuous development of technologies such as the Internet of Things, artificial intelligence, and biometrics, the control method of smart door locks is also constantly innovating and improving. For example, when users are entering fingerprints, the control method can analyze the problems encountered during the user's entry process and make adjustments to the problems, thereby improving the accuracy and efficiency of the entered information.

[0003] The Chinese invention patent with publication number CN108661462B discloses a control method and control system for a smart door lock, which authenticates a wireless key to obtain a first verification result; obtains biometric information generated by a user, and authenticates the user based on the biometric information to obtain a second verification result; and executes an unlocking operation after both the first verification result and the second verification result are verified to be passed and an unlocking instruction is received.

[0004] However, there is no detection of whether the user's fingerprint is qualified when it is entered. For unqualified cases, there is no analysis of the influencing factors of the unqualified user fingerprint entry, and no corresponding treatment is taken in the later maintenance. There is also no analysis of inaccurate fingerprint entry to adjust the user's fingerprint entry to reduce the problem of fingerprint entry caused by entry errors. Summary of the invention

[0005] The purpose of the present invention is to provide a control method and control system for an intelligent door lock to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions: In a first aspect, the present invention provides a control method for an intelligent door lock, comprising the following steps: Step 1: When the user uses the smart door lock to enter the fingerprint, the finger placement value and the finger pressure value are obtained to determine whether the fingerprint entry is qualified, and a qualified entry signal is generated; Step 2: When the user uses the smart door lock to enter the fingerprint, the damage impact value is obtained and compared with the damage impact threshold to generate a damage impact degree signal; The damage impact degree signal includes a large damage impact degree signal and a small damage impact degree signal; When an input failure signal is generated, a damage impact value is obtained, and the damage impact value is compared with a damage impact threshold. If the damage impact value is greater than or equal to the damage impact threshold, a damage impact high degree signal is generated; Step 3: Based on the input inaccurate signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate an adjustment difficulty size signal; Wherein, adjusting the difficulty signal includes adjusting the difficulty high signal and adjusting the difficulty low signal; When an input failure signal is generated, an adjustment difficulty value is obtained, and the adjustment difficulty value is compared with an adjustment difficulty threshold value. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold value, a high adjustment difficulty signal is generated; Step 4: Based on the large signal of the impact degree, obtain the recognition area coefficient and the damage area coefficient, add the recognition area coefficient and the damage area coefficient to obtain the input adjustment coefficient, obtain the current pressing pressure and the input adjustment coefficient, multiply the current pressing pressure and the input adjustment coefficient to obtain the adjusted pressure value, and adjust the user fingerprint input.

[0007] As a further solution of the present invention: the finger placement value is obtained by: Get the area of ​​the finger placement area, mark it as the hand placement area, pair the hand placement area with the input area, and output the overlap area value; The way to obtain the finger press value is: The hand placement input area is divided into several sub-areas, and the pressing pressure value in each sub-area is obtained through a pressure transmitter, which is marked as a sub-area pressure value. The sub-area pressure values ​​are added and averaged to obtain the finger pressing value.

[0008] As a further solution of the present invention: the damage impact value is obtained in the following manner: The damage sub-region area value and the damage region distribution value are added together to obtain the damage impact value.

[0009] As a further solution of the present invention: the method for obtaining the area value of the damaged region is: Acquire a real-time input image, divide the real-time input image into several sub-regions, extract the damaged sub-region, obtain the area of ​​each damaged sub-region, add up the areas of each damaged sub-region to obtain the total area of ​​the damaged region, calculate the ratio of the total area of ​​the damaged region to the area of ​​the real-time input image, and obtain the area value of the damaged region.

[0010] As a further solution of the present invention: the damage area distribution value is obtained by: The central sub-region in the real-time input image is extracted to obtain the position of the center point of the central sub-region. Based on the damaged sub-region obtained above, the position of the center point of the damaged sub-region is obtained, and the center point position of the central sub-region is connected with the center point position of the damaged sub-region to obtain the center point length value. The standard deviation of the center point length value is calculated to obtain the damaged area distribution value.

[0011] As a further solution of the present invention: the method for obtaining the adjustment difficulty value is: The center angle deviation value and the recognition area deviation value are obtained, and the center deviation value and the recognition area deviation value are added together to obtain the adjustment difficulty value.

[0012] As a further solution of the present invention: the process of obtaining the center deviation value is as follows: Get the center position of the real-time recording image and mark it as the real-time recording center; Obtain a standard input image, extract the center position of the standard input image, and mark it as the standard input center; With the standard input center as the coordinate origin, establish an XY axis coordinate system, substitute the real-time input image into the XY axis coordinate system, connect the standard input center position with the real-time input center position with a straight line, obtain the straight line length value, and mark it as the center distance value; The center distance value is calculated by ratioing the center distance value with the standard image contour perimeter value to obtain the center distance deviation value; Draw a straight line connecting the standard input center position and the real-time input center position, mark it as the center deviation straight line, obtain the angle formed by the center deviation straight line and the X-axis, mark it as the deviation angle, obtain the tangent value of the deviation angle, mark it as the center angle deviation value; The center distance deviation value and the center angle deviation value are obtained, and the center distance deviation value and the center angle deviation value are multiplied to obtain the center deviation value.

[0013] As a further solution of the present invention: the process of obtaining the identification area deviation value is as follows: With the center of the real-time input image as the center point, draw a horizontal straight line and a vertical straight line respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, marked as the real-time input horizontal semi-axis value, extract the vertical length value, and mark it as the real-time input vertical semi-axis value; With the center of the standard input image as the center point, draw a straight line in the horizontal direction and a straight line in the vertical direction respectively, obtain the horizontal length value and the vertical length value in the ellipse, extract half of the horizontal length value, mark it as the standard input horizontal semi-axis value, extract the vertical length value, and mark it as the standard input vertical semi-axis value; The real-time input horizontal semi-axis value is subtracted from the standard input horizontal semi-axis value to obtain the absolute value, and the horizontal semi-axis deviation value is calculated by ratio between the horizontal semi-axis deviation value and the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation ratio; The vertical semi-axis value entered in real time is subtracted from the vertical semi-axis value entered in standard manner to obtain the absolute value, and the vertical semi-axis deviation value is obtained by calculating the ratio of the vertical semi-axis value entered in real time to the vertical semi-axis value entered in standard manner to obtain the vertical semi-axis deviation ratio; Based on the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio are added and summed to obtain the recognition area deviation value.

[0014] As a further solution of the present invention: the method of obtaining the identification area coefficient is: Get the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio through the formula: , the identification area coefficient α is calculated, where a is the horizontal semi-axis deviation ratio and b is the vertical semi-axis deviation ratio; The damage area coefficient is obtained as follows: The damage impact value is obtained, the damage impact value is subtracted from the damage impact threshold to obtain the damage impact deviation value, the damage impact deviation value is ratio-calculated with the damage impact threshold to obtain the damage area coefficient.

[0015] In a second aspect, the present invention provides a control system for an intelligent door lock, the system comprising the following modules: Entry monitoring module: When the user uses the smart door lock to enter the fingerprint, the finger placement value and the finger pressure value are obtained to determine whether the fingerprint entry is qualified and generate a qualified entry signal; Impact analysis module: When a user uses a smart door lock to enter a fingerprint, the damage impact value is obtained and compared with the damage impact threshold to generate a damage impact degree signal; The damage impact degree signal includes a large damage impact degree signal and a small damage impact degree signal; When an input failure signal is generated, a damage impact value is obtained, and the damage impact value is compared with a damage impact threshold. If the damage impact value is greater than or equal to the damage impact threshold, a damage impact high degree signal is generated; Difficulty assessment module: based on the input inaccurate signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate the adjustment difficulty size signal; Wherein, adjusting the difficulty signal includes adjusting the difficulty high signal and adjusting the difficulty low signal; When an input failure signal is generated, an adjustment difficulty value is obtained, and the adjustment difficulty value is compared with an adjustment difficulty threshold value. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold value, a high adjustment difficulty signal is generated; Input adjustment module: Based on the large signal of the impact degree, the recognition area coefficient and the damage area coefficient are obtained, the recognition area coefficient and the damage area coefficient are added together to obtain the input adjustment coefficient, the current pressing pressure and the input adjustment coefficient are obtained, the current pressing pressure and the input adjustment coefficient are multiplied to obtain the adjusted pressure value, and the user fingerprint input is adjusted.

[0016] Beneficial effects of the present invention: (1) When a user uses a smart door lock to enter a fingerprint, the present invention determines whether the fingerprint entry is qualified, generates a qualified entry signal, obtains a damage impact value based on the unqualified entry signal, and compares it with the damage impact threshold to generate a damage impact degree signal. During the fingerprint entry process, the system can immediately feedback whether the entry is qualified, avoid users repeatedly entering invalid fingerprints, save user time, and analyze whether the damage to the user's fingerprint affects normal entry; (2) The present invention obtains an adjustment difficulty value based on a large signal of the degree of damage impact, and compares it with an adjustment difficulty threshold to generate an adjustment difficulty magnitude signal. Based on the large signal of the degree of impact, an input adjustment coefficient is obtained to adjust the user's fingerprint input. Thus, when an abnormality occurs during fingerprint input by the user, the user's fingerprint input is adjusted based on the obtained input adjustment value, thereby reducing problems caused by input errors and improving input accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 is a flow chart of a method according to a first embodiment of the present invention; Figure 2 is a flow chart of the method of embodiment 2 of the present invention; Figure 3 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1: See also Figure 1 As shown, a control method of a smart door lock according to an embodiment of the present invention is specifically described as follows: Step 1: When the user uses the smart door lock to enter the fingerprint, determine whether the fingerprint entry is qualified and generate a qualified entry signal; Wherein, the inputting qualified or unqualified signal includes inputting qualified signal and inputting unqualified signal; In some embodiments, when a user uses a smart door lock to enter a fingerprint, a finger placement value and a finger pressure value are obtained; Furthermore, the finger placement value is obtained as follows: Get the area of ​​the finger placement area, mark it as the hand placement area, pair the hand placement area with the input area, and output the overlap area value; The overlapped area value is compared with the input area value to obtain the finger placement value; Compare the finger placement value with the finger placement range. The comparison process is as follows: If the finger placement value exists within the finger placement range, it means that the finger placement is qualified, and a finger placement qualified signal is generated; If the finger placement value does not exist in the finger placement range, it means that the finger placement is unqualified, and a finger placement unqualified signal is generated; Furthermore, based on the finger placement qualified signal, the finger pressing value is obtained, and the finger pressing value is compared with the finger pressing threshold. The comparison process is as follows: If the finger pressure value is less than or equal to the finger pressure threshold, it means that the pressure between the finger fingerprint and the input area is small, resulting in the input fingerprint not being clearly and completely recognized, and an input failure signal is generated; If the finger pressure value is greater than the finger pressure threshold, it means that the finger fingerprint and the recording area are pressed with great force, resulting in the recorded fingerprint being clearly and completely recognized, generating a qualified recording signal; Specifically, the finger pressure value is obtained as follows: The data is collected by setting a pressure sensor on the smart door lock; Step 2: When the user uses the smart door lock to enter the fingerprint, the damage impact value is obtained and compared with the damage impact threshold to generate a damage impact degree signal; The damage impact degree signal includes a large damage impact degree signal and a small damage impact degree signal; In some embodiments, when a user uses a smart door lock to enter a fingerprint, a damage impact value is obtained, and the damage impact value is compared with a damage impact threshold. The comparison process is as follows: If the damage impact value is greater than or equal to the damage impact threshold, it means that the finger fingerprint is severely damaged. During the fingerprint entry process, the recognition level is low, and a signal of a high damage impact level is generated; If the damage impact value is less than the damage impact threshold, it means that the damage degree of the fingerprint of the finger is small. During the fingerprint recording process, the recognition degree is high, and a small signal of the damage impact degree is generated; Exemplarily, the process of obtaining the damage impact value is as follows: Acquire a real-time input image, divide the real-time input image into a plurality of sub-regions, detect each sub-region, and if there is a blank in the sub-region, determine the sub-region to be a damaged sub-region; if there is no blank in the sub-region, determine the sub-region to be a non-damaged sub-region; Extract the damaged sub-regions, obtain the area of ​​each damaged sub-region, add up the areas of each damaged sub-region to obtain the total area of ​​the damaged region, calculate the ratio of the total area of ​​the damaged region to the area of ​​the real-time recorded image, and obtain the area value of the damaged region; Extract the central sub-region in the real-time input image to obtain the central point position of the central sub-region, obtain the central point position of the damaged sub-region based on the damaged sub-region obtained above, and connect the central point position of the central sub-region with the central point position of the damaged sub-region to obtain the central point length value, calculate the standard deviation of the central point length value, and obtain the damaged area distribution value; The damage sub-region area value and the damage region distribution value are added together to obtain the damage impact value; The specific implementation scheme of the embodiment of the present invention is: when a user uses a smart door lock to enter a fingerprint, it is determined whether the fingerprint entry is qualified, and a qualified entry signal is generated. Based on the unqualified entry signal, a damage impact value is obtained, and compared with the damage impact threshold, and a damage impact degree signal is generated. During the fingerprint entry process, the system can instantly feedback whether the entry is qualified, avoid users from repeatedly entering invalid fingerprints, save user time, and analyze whether the damage to the user's fingerprint affects normal entry.

[0021] Embodiment 2: Based on Example 1, please refer to Figure 2 As shown, a control method of a smart door lock according to an embodiment of the present invention further includes the following steps: Step 3: Based on the large signal of the damage impact degree, obtain the adjustment difficulty value, and compare it with the adjustment difficulty threshold to generate an adjustment difficulty size signal; Wherein, adjusting the difficulty signal includes adjusting the difficulty high signal and adjusting the difficulty low signal; In some embodiments, when an input failure signal is generated, an adjustment difficulty value is obtained, and the adjustment difficulty value is compared with an adjustment difficulty threshold. The comparison process is as follows: If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, it means that during the current fingerprint entry, there is a large deviation between the pressing state and the actual requirement, the adjustment difficulty coefficient is large, and a large adjustment difficulty signal is generated; If the input difficulty value is less than the adjustment difficulty threshold, it means that during the current fingerprint input, there is a large deviation between the pressing state and the actual requirement, the adjustment difficulty coefficient is small, and a small adjustment difficulty signal is generated; Exemplarily, the method for obtaining the adjustment difficulty value is: Obtaining a center angle deviation value and a recognition area deviation value, and adding the center deviation value and the recognition area deviation value to obtain an adjustment difficulty value; First, the process of obtaining the center deviation value is as follows: Based on the above real-time recording image, the center position of the real-time recording image is obtained and marked as the real-time recording center; Obtain a standard input image, extract the center position of the standard input image, and mark it as the standard input center; With the standard input center as the coordinate origin, establish an XY axis coordinate system, substitute the real-time input image into the XY axis coordinate system, connect the standard input center position with the real-time input center position with a straight line, obtain the straight line length value, and mark it as the center distance value; The center distance value is calculated by ratio with the standard image contour perimeter value to obtain the center deviation value; The center angle deviation value is obtained as follows: Based on the above XY coordinate system, a straight line connecting the standard input center position and the real-time input center position is obtained, marked as the center deviation straight line, the angle formed by the center deviation straight line and the X-axis is obtained, marked as the deviation angle, and the tangent value of the deviation angle is obtained, marked as the center angle deviation value; Obtain a center distance deviation value and a center angle deviation value, and calculate the product of the center distance deviation value and the center angle deviation value to obtain a center deviation value; Second, the process of obtaining the identification area deviation value is as follows: With the center of the real-time input image as the center point, draw a horizontal straight line and a vertical straight line respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, marked as the real-time input horizontal semi-axis value, extract the vertical length value, and mark it as the real-time input vertical semi-axis value; With the center of the standard input image as the center point, draw a straight line in the horizontal direction and a straight line in the vertical direction respectively, obtain the horizontal length value and the vertical length value in the ellipse, extract half of the horizontal length value, mark it as the standard input horizontal semi-axis value, extract the vertical length value, and mark it as the standard input vertical semi-axis value; The real-time input horizontal semi-axis value is subtracted from the standard input horizontal semi-axis value to obtain the absolute value, and the horizontal semi-axis deviation value is calculated by ratio between the horizontal semi-axis deviation value and the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation ratio; The vertical semi-axis value entered in real time is subtracted from the vertical semi-axis value entered in standard manner to obtain the absolute value, and the vertical semi-axis deviation value is obtained by calculating the ratio of the vertical semi-axis value entered in real time to the vertical semi-axis value entered in standard manner to obtain the vertical semi-axis deviation ratio; Based on the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio are added and summed to obtain a recognition area deviation value; Among them, it is necessary to further explain that: the meaning of the identification area deviation value is: when the user is recording the fingerprint, the center point position of the fingerprint area of ​​the finger is accurately matched with the center point position of the recording area, and the finger is pressed to make contact with the recording area. Since the fingerprint image of the finger is approximated as an ellipse, the real-time recording image and the standard recording image are characterized by the deviation of the length of the semi-axis of the ellipse to match the fingerprint in the longitudinal direction; Step 4: Based on the large impact signal, obtain the input adjustment coefficient and adjust the user fingerprint input; In some implementation schemes, based on the large impact degree signal, the current compression pressure and the input adjustment coefficient are obtained, and the current compression pressure and the input adjustment coefficient are multiplied to obtain the adjusted pressure value; Exemplary, the acquisition of the input adjustment coefficient: Obtaining an identification area coefficient and a damage area coefficient, adding the identification area coefficient and the damage area coefficient together to obtain an input adjustment coefficient; Among them, the method of obtaining the identification area coefficient is: Get the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio through the formula: , the identification area coefficient α is calculated, where a is the horizontal semi-axis deviation ratio and b is the vertical semi-axis deviation ratio; The damage area coefficient is obtained as follows: Obtain a damage impact value, subtract the damage impact value from the damage impact threshold to obtain a damage impact deviation value, calculate the ratio of the damage impact deviation value to the damage impact threshold to obtain a damage area coefficient; The specific implementation scheme of the embodiment of the present invention is: based on the large signal of the damage impact degree, an adjustment difficulty value is obtained, and compared with the adjustment difficulty threshold, an adjustment difficulty size signal is generated, and based on the large signal of the impact degree, an input adjustment coefficient is obtained to adjust the user fingerprint input, so that when an abnormality occurs when the user enters the fingerprint, the user fingerprint input is adjusted based on the obtained input adjustment value, thereby reducing problems caused by input errors and improving input accuracy.

[0022] Embodiment three: Based on the first and second embodiments, please refer to Figure 3, a control system of a smart door lock according to an embodiment of the present invention includes the following system modules: Entry monitoring module: when the user uses the smart door lock to enter the fingerprint, it determines whether the fingerprint entry is qualified and generates a signal whether the entry is qualified; Impact analysis module: based on the input unqualified signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate an adjustment difficulty size signal; If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, a high adjustment difficulty signal is generated; Accurate evaluation module: Based on the large impact signal, the input status value is obtained and compared with the input status threshold to generate a signal whether the input is accurate; If the input state value is greater than or equal to the input state threshold, an input inaccurate signal is generated; Entry adjustment module: based on the inaccurate entry signal, obtains the entry adjustment value and adjusts the user's fingerprint entry.

[0023] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A control method for a smart door lock, characterized in that: The following steps are involved: Step 1: When the user uses the smart door lock to enter the fingerprint, the finger placement value and the finger pressure value are obtained to determine whether the fingerprint entry is qualified, and a qualified entry signal is generated; Step 2: When the user uses the smart door lock to enter the fingerprint, the damage impact value is obtained and compared with the damage impact threshold to generate a damage impact degree signal; The damage impact degree signal includes a large damage impact degree signal and a small damage impact degree signal; When an input failure signal is generated, a damage impact value is obtained, and the damage impact value is compared with a damage impact threshold. If the damage impact value is greater than or equal to the damage impact threshold, a damage impact high degree signal is generated; Step 3: Based on the input inaccurate signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate an adjustment difficulty size signal; Wherein, adjusting the difficulty signal includes adjusting the difficulty high signal and adjusting the difficulty low signal; When an input failure signal is generated, an adjustment difficulty value is obtained, and the adjustment difficulty value is compared with an adjustment difficulty threshold value. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold value, a high adjustment difficulty signal is generated; Step 4: Based on the large signal of the impact degree, obtain the recognition area coefficient and the damage area coefficient, add the recognition area coefficient and the damage area coefficient to obtain the input adjustment coefficient, obtain the current pressing pressure and the input adjustment coefficient, multiply the current pressing pressure and the input adjustment coefficient to obtain the adjusted pressure value, and adjust the user fingerprint input.

2. The control method of a smart door lock according to claim 1, characterized in that: The finger placement value is obtained as follows: Get the area of ​​the finger placement area, mark it as the hand placement area, pair the hand placement area with the input area, and output the overlap area value; The way to obtain the finger press value is: The hand placement input area is divided into several sub-areas, and the pressing pressure value in each sub-area is obtained through a pressure transmitter, which is marked as a sub-area pressure value. The sub-area pressure values ​​are added and averaged to obtain the finger pressing value.

3. The control method of a smart door lock according to claim 1, characterized in that: The damage impact value is obtained in the following way: The damage sub-region area value and the damage region distribution value are added together to obtain the damage impact value.

4. The control method of a smart door lock according to claim 3, characterized in that: The method for obtaining the area value of the damaged region is: Acquire a real-time input image, divide the real-time input image into several sub-regions, extract the damaged sub-region, obtain the area of ​​each damaged sub-region, add up the areas of each damaged sub-region to obtain the total area of ​​the damaged region, calculate the ratio of the total area of ​​the damaged region to the area of ​​the real-time input image, and obtain the area value of the damaged region.

5. The control method of a smart door lock according to claim 3, characterized in that: The damage area distribution value is obtained in the following manner: The central sub-region in the real-time input image is extracted to obtain the position of the center point of the central sub-region. Based on the damaged sub-region obtained above, the position of the center point of the damaged sub-region is obtained, and the center point position of the central sub-region is connected with the center point position of the damaged sub-region to obtain the center point length value. The standard deviation of the center point length value is calculated to obtain the damaged area distribution value.

6. The control method of a smart door lock according to claim 4, characterized in that: The method for obtaining the adjustment difficulty value is as follows: The center deviation value and the recognition area deviation value are obtained, and the center deviation value and the recognition area deviation value are added together to obtain an adjustment difficulty value.

7. A control method for an intelligent door lock according to claim 6, characterized in that: The process of obtaining the center deviation value is as follows: Based on the above real-time recording image, the center position of the real-time recording image is obtained and marked as the real-time recording center; Obtain a standard input image, extract the center position of the standard input image, and mark it as the standard input center; With the standard input center as the coordinate origin, establish an XY axis coordinate system, substitute the real-time input image into the XY axis coordinate system, connect the standard input center position with the real-time input center position with a straight line, obtain the straight line length value, and mark it as the center distance value; The center distance value is calculated by ratioing the center distance value with the standard image contour perimeter value to obtain the center distance deviation value; Based on the XY axis coordinate system, a straight line connecting the standard input center position and the real-time input center position is obtained, marked as the center deviation straight line, the angle formed by the center deviation straight line and the X axis is obtained, marked as the deviation angle, and the tangent value of the deviation angle is obtained, marked as the center angle deviation value; The center distance deviation value and the center angle deviation value are obtained, and the center distance deviation value and the center angle deviation value are multiplied to obtain the center deviation value.

8. The control method of a smart door lock according to claim 6, characterized in that: The process of obtaining the identification area deviation value is as follows: With the center of the real-time input image as the center point, draw a horizontal straight line and a vertical straight line respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, marked as the real-time input horizontal semi-axis value, extract the vertical length value, and mark it as the real-time input vertical semi-axis value; With the center of the standard input image as the center point, draw a straight line in the horizontal direction and a straight line in the vertical direction respectively, obtain the horizontal length value and the vertical length value in the ellipse, extract half of the horizontal length value, mark it as the standard input horizontal semi-axis value, extract the vertical length value, and mark it as the standard input vertical semi-axis value; The real-time input horizontal semi-axis value is subtracted from the standard input horizontal semi-axis value to obtain the absolute value, and the horizontal semi-axis deviation value is calculated by ratio between the horizontal semi-axis deviation value and the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation ratio; The vertical semi-axis value entered in real time is subtracted from the vertical semi-axis value entered in standard manner to obtain the absolute value, and the vertical semi-axis deviation value is obtained by calculating the ratio of the vertical semi-axis value entered in real time to the vertical semi-axis value entered in standard manner to obtain the vertical semi-axis deviation ratio; Based on the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio are added and summed to obtain the recognition area deviation value.

9. The control method of a smart door lock according to claim 1, characterized in that: The method for obtaining the identification area coefficient is as follows: Get the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio through the formula: , the identification area coefficient α is calculated, where a is the horizontal semi-axis deviation ratio and b is the vertical semi-axis deviation ratio; The damage area coefficient is obtained as follows: The damage impact value is obtained, the damage impact value is subtracted from the damage impact threshold to obtain the damage impact deviation value, the damage impact deviation value is ratio-calculated with the damage impact threshold to obtain the damage area coefficient.

10. A control system for an intelligent door lock, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system includes the following modules: Entry monitoring module: When the user uses the smart door lock to enter the fingerprint, the finger placement value and the finger pressure value are obtained to determine whether the fingerprint entry is qualified and generate a qualified entry signal; Impact analysis module: When a user uses a smart door lock to enter a fingerprint, the damage impact value is obtained and compared with the damage impact threshold to generate a damage impact degree signal; The damage impact degree signal includes a large damage impact degree signal and a small damage impact degree signal; When an input failure signal is generated, a damage impact value is obtained, and the damage impact value is compared with a damage impact threshold. If the damage impact value is greater than or equal to the damage impact threshold, a damage impact high degree signal is generated; Difficulty assessment module: based on the input inaccurate signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate the adjustment difficulty size signal; Wherein, adjusting the difficulty signal includes adjusting the difficulty high signal and adjusting the difficulty low signal; When an input failure signal is generated, an adjustment difficulty value is obtained, and the adjustment difficulty value is compared with an adjustment difficulty threshold value. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold value, a high adjustment difficulty signal is generated; Input adjustment module: Based on the large signal of the impact degree, the recognition area coefficient and the damage area coefficient are obtained, the recognition area coefficient and the damage area coefficient are added together to obtain the input adjustment coefficient, the current pressing pressure and the input adjustment coefficient are obtained, the current pressing pressure and the input adjustment coefficient are multiplied to obtain the adjusted pressure value, and the user fingerprint input is adjusted.

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