A control method and control system for an intelligent door lock

Abstract

The present invention relates to the field of Internet of Things biometric recognition technology, and specifically discloses a control method and a control system for an intelligent door lock. When a user uses the intelligent door lock to perform fingerprint entry, the fingerprint entry value is obtained and compared with a fingerprint entry threshold to generate a signal indicating whether the entry is qualified. Based on the signal indicating unqualified entry, an entry influence value is obtained and compared with an entry influence threshold to generate a signal indicating the degree of influence. By determining the qualification of fingerprint entry, it is possible to prevent the user from making multiple inaccurate attempts when the entry fails, and analyze the unqualified entry situations, so that corresponding measures can be taken during subsequent maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things biometric identification, and particularly 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 intelligent 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 biometric identification, the control method of intelligent door lock is also constantly innovating and improving. For example, during the fingerprint entry process, the problems that occur during the entry process can be analyzed, and corresponding adjustments can be made to improve the accuracy and efficiency of the entered information.

[0003] Chinese invention patent with publication number CN108661462B discloses a control method and a control system for an intelligent door lock, which perform identity verification on a wireless key to obtain a first verification result; obtain biometric information generated by a user, and perform identity verification on the user according to the biometric information to obtain a second verification result; and perform 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, it does not detect whether the fingerprint entry of the user is qualified during the fingerprint entry process. For unqualified situations, the influencing factors when the user's fingerprint entry is unqualified are analyzed, and corresponding processing is taken during later maintenance. Nor does it adjust the user's fingerprint entry by analyzing inaccurate fingerprint entry to reduce problems caused by incorrect entry. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method and a 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 by the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for an intelligent door lock, including the following steps:

[0008] Step 1: When a user uses the intelligent door lock for fingerprint entry, obtain the finger placement value and the finger pressing value, determine whether the fingerprint entry is qualified, and generate a signal indicating whether the entry is qualified;

[0009] Step 2: When a user uses the intelligent door lock for fingerprint entry, obtain the damage influence value, compare it with the damage influence threshold, and generate a signal indicating the degree of damage influence;

[0010] Among them, the signal indicating the degree of damage influence includes a signal indicating a large degree of damage influence and a signal indicating a small degree of damage influence;

[0011] When a signal indicating unqualified input is generated, obtain the damage influence value, compare the damage influence value with the damage influence threshold. If the damage influence value is greater than or equal to the damage influence threshold, generate a signal indicating a large damage influence degree;

[0012] Step 3: Based on the signal indicating inaccurate input, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate a signal indicating the size of the adjustment difficulty;

[0013] Among them, the signal indicating the size of the adjustment difficulty includes a signal indicating a large adjustment difficulty and a signal indicating a small adjustment difficulty;

[0014] When a signal indicating unqualified input is generated, obtain the adjustment difficulty value, compare the adjustment difficulty value with the adjustment difficulty threshold. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, generate a signal indicating a large adjustment difficulty;

[0015] Step 4: Based on the signal indicating a large influence degree, obtain the recognition area coefficient and the damage area coefficient, add the recognition area coefficient and the damage area coefficient together 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's fingerprint input.

[0016] As a further solution of the present invention: The method for obtaining the finger placement value is as follows:

[0017] Obtain the area of the finger placement area, mark it as the hand placement area area, perform pairing processing on the hand placement area area and the input area area, and output to obtain the overlapping area value;

[0018] The method for obtaining the finger pressing value is as follows:

[0019] Divide the hand placement input area into several sub-areas, obtain the pressing pressure value in each sub-area through a pressure transmitter, mark it as the sub-area pressure value, add up the sub-area pressure values and take the average value to obtain the finger pressing value.

[0020] As a further solution of the present invention: The method for obtaining the damage influence value is as follows:

[0021] Add up the damage sub-area area value and the damage area distribution value to obtain the damage influence value.

[0022] As a further solution of the present invention: The method for obtaining the damage area area value is as follows:

[0023] Obtain the real-time input image, divide the real-time input image into several sub-regions, extract the damaged sub-regions, obtain the area of each damaged sub-region, sum up the areas of each damaged sub-region to get the total area of the damaged region, and calculate the ratio of the total area of the damaged region to the area of the real-time input image to obtain the damaged region area value.

[0024] As a further solution of the present invention: The method for obtaining the damage region distribution value is as follows:

[0025] Extract the central sub-region in the real-time input image to obtain the position of the center point of the central sub-region. Based on the obtained damaged sub-regions, obtain the position of the center point of the damaged sub-region, and connect the position of the center point of the central sub-region and the position of the center point of the damaged sub-region to obtain the center point length value. Calculate the standard deviation of the center point length value to obtain the damage region distribution value.

[0026] As a further solution of the present invention: The method for obtaining the adjustment difficulty value is as follows:

[0027] Obtain the central angle deviation value and the recognition region deviation value, sum up the central deviation value and the recognition region deviation value to obtain the adjustment difficulty value.

[0028] As a further solution of the present invention: The process for obtaining the central deviation value is as follows:

[0029] Obtain the central position of the real-time input image and mark it as the real-time input center;

[0030] Obtain the standard input image, extract the central position of the standard input image and mark it as the standard input center;

[0031] Taking the standard input center as the coordinate origin, establish an X-Y axis coordinate system, substitute the real-time input image into the X-Y axis coordinate system, connect the position of the standard input center and the position of the real-time input center in a straight line, obtain the straight line length value and mark it as the center distance value;

[0032] Calculate the ratio of the center distance value to the perimeter value of the standard image contour to obtain the center distance deviation value;

[0033] Connect the straight line between the position of the standard input center and the position of the real-time input center, mark it as the central deviation straight line, obtain the angle formed by the central deviation straight line and the X axis, mark it as the deviation angle, obtain the tangent value of the deviation angle, and mark it as the central angle deviation value;

[0034] Obtain the center distance deviation value and the central angle deviation value, and calculate the product of the center distance deviation value and the central angle deviation value to obtain the central deviation value.

[0035] As a further solution of the present invention: The process for obtaining the recognition region deviation value is as follows:

[0036] Taking the center of the real-time input image as the center point, draw a horizontal line and a vertical line respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, which is marked as the real-time input horizontal semi-axis value, and extract the vertical length value, which is marked as the real-time input vertical semi-axis value;

[0037] Taking the center of the standard input image as the center point, draw a horizontal line and a vertical line respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, which is marked as the standard input horizontal semi-axis value, and extract the vertical length value, which is marked as the standard input vertical semi-axis value;

[0038] Take the absolute value of the difference between the real-time input horizontal semi-axis value and the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation value, and calculate the ratio of the horizontal semi-axis deviation value to the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation ratio;

[0039] Take the absolute value of the difference between the real-time input vertical semi-axis value and the standard input vertical semi-axis value to obtain the vertical semi-axis deviation value, and calculate the ratio of the real-time input vertical semi-axis value to the standard input vertical semi-axis value to obtain the vertical semi-axis deviation ratio;

[0040] Based on the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, add the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio to obtain the recognition area deviation value.

[0041] As a further solution of the present invention: the method for obtaining the recognition area coefficient is as follows:

[0042] Obtain the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, and through the formula: , calculate to obtain the recognition area coefficient α, a represents the horizontal semi-axis deviation ratio, and b represents the vertical semi-axis deviation ratio;

[0043] The method for obtaining the damage area coefficient is as follows:

[0044] Obtain the damage influence value, take the difference between the damage influence value and the damage influence threshold to obtain the damage influence deviation value, and calculate the ratio of the damage influence deviation value to the damage influence threshold to obtain the damage area coefficient.

[0045] In a second aspect, the present invention provides a control system for an intelligent door lock, and the system includes the following modules:

[0046] Input monitoring module: When the user uses the intelligent door lock to perform fingerprint input, obtain the finger placement value and the finger pressing value, determine whether the fingerprint input is qualified, and generate a signal indicating whether the input is qualified;

[0047] Impact analysis module: When the user uses the intelligent door lock for fingerprint entry, obtain the damage impact value, compare it with the damage impact threshold, and generate a signal indicating the degree of damage impact;

[0048] Among them, the signal indicating the degree of damage impact includes a signal indicating a large degree of damage impact and a signal indicating a small degree of damage impact;

[0049] When generating a signal indicating unqualified entry, obtain the damage impact value, compare the damage impact value with the damage impact threshold. If the damage impact value is greater than or equal to the damage impact threshold, generate a signal indicating a large degree of damage impact;

[0050] Difficulty assessment module: Based on the signal indicating inaccurate entry, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate a signal indicating the size of the adjustment difficulty;

[0051] Among them, the signal indicating the size of the adjustment difficulty includes a signal indicating a large adjustment difficulty and a signal indicating a small adjustment difficulty;

[0052] When generating a signal indicating unqualified entry, obtain the adjustment difficulty value, compare the adjustment difficulty value with the adjustment difficulty threshold. If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, generate a signal indicating a large adjustment difficulty;

[0053] Entry adjustment module: Based on the signal indicating a large degree of impact, obtain the recognition area coefficient and the damage area coefficient, add the recognition area coefficient and the damage area coefficient to obtain the entry adjustment coefficient, obtain the current pressing pressure and the entry adjustment coefficient, multiply the current pressing pressure and the entry adjustment coefficient to obtain the adjusted pressure value, and adjust the user's fingerprint entry.

[0054] Advantages of the present invention:

[0055] (1) When the user uses the intelligent door lock for fingerprint entry, the present invention determines whether the fingerprint entry is qualified, generates a signal indicating whether the entry is qualified. Based on the signal indicating unqualified entry, obtain the damage impact value, compare it with the damage impact threshold, and generate a signal indicating the degree of damage impact. During the fingerprint entry process, the system can immediately feedback whether the entry is qualified, avoid the user from repeatedly entering invalid fingerprints, save the user's time, and analyze whether the damage to the user's fingerprint during entry affects the normal entry;

[0056] (2) Based on the signal indicating a large degree of damage impact, the present invention obtains the adjustment difficulty value, compares it with the adjustment difficulty threshold, generates a signal indicating the size of the adjustment difficulty. Based on the signal indicating a large degree of impact, obtain the entry adjustment coefficient, and adjust the user's fingerprint entry. Thus, when the user's fingerprint entry is abnormal, based on the obtained entry adjustment value, the user's fingerprint entry is adjusted, reducing problems caused by entry errors and improving the entry accuracy. Description of the drawings

[0057] The present invention will be further described below with reference to the accompanying drawings.

[0058] Figure 1 is the flowchart of the method according to the first embodiment of the present invention;

[0059] Figure 2 is the flowchart of the method according to the second embodiment of the present invention;

[0060] Figure 3 is the schematic diagram of the system modules of the present invention. Specific embodiments

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1:

[0063] Please refer to Figure 1 As shown, a control method for an intelligent door lock according to an embodiment of the present invention specifically includes the following steps:

[0064] Step 1: When a user uses the intelligent door lock for fingerprint entry, determine whether the fingerprint entry is qualified and generate a signal indicating whether the entry is qualified;

[0065] Among them, the signal indicating whether the entry is qualified includes a qualified entry signal and an unqualified entry signal;

[0066] In some embodiments, when a user uses the intelligent door lock for fingerprint entry, a finger placement value and a finger pressing value are obtained;

[0067] Further, the method for obtaining the finger placement value is as follows:

[0068] Obtain the area of the finger placement area, mark it as the hand placement area, pair the hand placement area with the entry area, and output the overlapping area value;

[0069] Perform a ratio process on the overlapping area value and the entry area value to obtain the finger placement value;

[0070] Compare the finger placement value with the finger placement range interval. The comparison process is as follows:

[0071] If the finger placement value exists within the finger placement range interval, it indicates that the finger placement is qualified, and a finger placement qualified signal is generated;

[0072] If the finger placement value does not exist within the finger placement range interval, it indicates that the finger placement is unqualified, and a finger placement unqualified signal is generated;

[0073] Furthermore, based on the finger placement qualified signal, obtain the finger pressing value, and compare the finger pressing value with the finger pressing threshold. The comparison process is as follows:

[0074] If the finger pressing value is less than or equal to the finger pressing threshold, it indicates that the finger fingerprint has a relatively small pressing force on the input area, resulting in the fingerprint being inputted unable to be clearly and completely recognized, and an input unqualified signal is generated;

[0075] If the finger pressing value is greater than the finger pressing threshold, it indicates that the finger fingerprint has a relatively large pressing force on the input area, resulting in the fingerprint being inputted able to be clearly and completely recognized, and an input qualified signal is generated;

[0076] Specifically, the method for obtaining the finger pressing value is:

[0077] It is collected by setting a pressure sensor on the intelligent door lock;

[0078] Step 2: When the user uses the intelligent door lock for fingerprint input, obtain the damage influence value, compare it with the damage influence threshold, and generate a signal indicating the degree of damage influence;

[0079] Among them, the signal indicating the degree of damage influence includes a signal indicating a large degree of damage influence and a signal indicating a small degree of damage influence;

[0080] In some embodiments, when the user uses the intelligent door lock for fingerprint input, obtain the damage influence value, compare the damage influence value with the damage influence threshold. The comparison process is as follows:

[0081] If the damage influence value is greater than or equal to the damage influence threshold, it indicates that the finger fingerprint has a relatively large degree of damage, and during the fingerprint input process, the recognition degree is relatively low, and a signal indicating a large degree of damage influence is generated;

[0082] If the damage influence value is less than the damage influence threshold, it indicates that the finger fingerprint has a relatively small degree of damage, and during the fingerprint input process, the recognition degree is relatively high, and a signal indicating a small degree of damage influence is generated;

[0083] Exemplarily, the process for obtaining the damage influence value is as follows:

[0084] Obtain the real-time input image, divide the real-time input image into several sub-regions, detect each sub-region. If there is a blank in the sub-region, determine that the sub-region is a damaged sub-region; if there is no blank in the sub-region, determine that the sub-region is a non-damaged sub-region;

[0085] Extract the damaged sub-regions, obtain the area of each damaged sub-region, sum up the areas of each damaged sub-region to get 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 to obtain the damaged region area value;

[0086] Extract the central sub-region from the real-time input image to obtain the position of the center point of the central sub-region. Based on the damaged sub-regions obtained above, obtain the positions of the center points of the damaged sub-regions, and connect the position of the center point of the central sub-region with the positions of the center points of the damaged sub-regions to obtain the center point length value. Calculate the standard deviation of the center point length value to obtain the damaged region distribution value;

[0087] Sum up the damaged sub-region area value and the damaged region distribution value to obtain the damage influence value;

[0088] The specific implementation scheme of the embodiment of the present invention is as follows: When the user uses the intelligent door lock for fingerprint input, determine whether the fingerprint input is qualified, generate a signal indicating whether the input is qualified. Based on the signal indicating unqualified input, obtain the damage influence value, and compare it with the damage influence threshold to generate a signal indicating the degree of damage influence. During the fingerprint input process, the system can immediately feedback whether the input is qualified, avoid the user from repeatedly inputting invalid fingerprints, save the user's time, and analyze whether the damage to the input user's fingerprint affects the normal input.

[0089] Embodiment Two:

[0090] Based on Embodiment One, please refer to Figure 2 As shown, a control method for an intelligent door lock according to an embodiment of the present invention further includes the following specific steps:

[0091] Step Three: Based on the signal indicating a large degree of damage influence, obtain the adjustment difficulty value, and compare it with the adjustment difficulty threshold to generate a signal indicating the size of the adjustment difficulty;

[0092] Among them, the signal indicating the size of the adjustment difficulty includes a signal indicating a large adjustment difficulty and a signal indicating a small adjustment difficulty;

[0093] In some embodiments, when generating a signal indicating unqualified input, obtain the adjustment difficulty value, and compare the adjustment difficulty value with the adjustment difficulty threshold. The comparison process is as follows:

[0094] If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, it indicates that there is a large deviation between the pressing state and the actual requirements during the current fingerprint input, and the adjustment difficulty coefficient is large, generating a signal indicating a large adjustment difficulty;

[0095] If the input difficulty value is less than the adjustment difficulty threshold, it indicates that there is a large deviation between the pressing state and the actual requirements during the current fingerprint input, and the adjustment difficulty coefficient is small, generating a signal indicating a small adjustment difficulty;

[0096] Exemplarily, the way to obtain the adjustment difficulty value is as follows:

[0097] Obtain the central angle deviation value and the recognition area deviation value, add and sum the central deviation value and the recognition area deviation value to obtain the adjustment difficulty value;

[0098] First specifically, the process of obtaining the central deviation value is as follows:

[0099] Based on the above real-time input image, obtain the central position of the real-time input image and mark it as the real-time input center;

[0100] Obtain the standard input image, extract the central position of the standard input image and mark it as the standard input center;

[0101] Taking the standard input center as the coordinate origin, establish an X-Y axis coordinate system, substitute the real-time input image into the X-Y axis coordinate system, connect the standard input center position and the real-time input center position with a straight line, obtain the straight line length value and mark it as the central distance value;

[0102] Calculate the ratio of the central distance value to the perimeter value of the standard image contour to obtain the central deviation value;

[0103] The way to obtain the central angle deviation value is as follows:

[0104] Based on the above X-Y coordinate system, obtain the straight line connecting the standard input center position and the real-time input center position, mark it as the central deviation straight line, obtain the angle formed by the central deviation straight line and the X axis, mark it as the deviation angle, and obtain the tangent value of the deviation angle, which is marked as the central angle deviation value;

[0105] Obtain the central distance deviation value and the central angle deviation value, and calculate the product of the central distance deviation value and the central angle deviation value to obtain the central deviation value;

[0106] Second specifically, the process of obtaining the recognition area deviation value is as follows:

[0107] Taking the center of the real-time input image as the center point, draw horizontal and vertical straight lines respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, which is marked as the real-time input horizontal semi-axis value, and extract the vertical length value, which is marked as the real-time input vertical semi-axis value;

[0108] Taking the center of the standard input image as the center point, draw horizontal and vertical straight lines respectively, obtain the horizontal length value and the vertical length value in the ellipse, and extract half of the horizontal length value, which is marked as the standard input horizontal semi-axis value, and extract the vertical length value, which is marked as the standard input vertical semi-axis value;

[0109] Take the absolute value of the difference between the real-time input horizontal semi-axis value and the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation value, and calculate the ratio of the horizontal semi-axis deviation value to the standard input horizontal semi-axis value to obtain the horizontal semi-axis deviation ratio;

[0110] Take the absolute value of the difference between the real-time input vertical semi-axis value and the standard input vertical semi-axis value to obtain the vertical semi-axis deviation value, and calculate the ratio of the real-time input vertical semi-axis value to the standard input vertical semi-axis value to obtain the vertical semi-axis deviation ratio;

[0111] Based on the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, add the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio to obtain the recognition area deviation value;

[0112] Among them, it needs to be further explained that: the meaning represented by the recognition area deviation value is: when the user is performing fingerprint input, accurately pair the center point position of the finger fingerprint area with the center point position of the input area, and make contact between the finger and the input area by pressing the finger. Since the finger fingerprint image is approximated as an ellipse, therefore, the real-time input image and the standard input image are used to characterize the longitudinal matching of the finger fingerprint through the deviation of the ellipse semi-axis length;

[0113] Step Four: Based on the signal with a large influence degree, obtain the input adjustment coefficient and adjust the user's fingerprint input;

[0114] In some implementation schemes, based on the signal with a large influence degree, obtain the current pressing pressure and the input adjustment coefficient, and multiply the current pressing pressure and the input adjustment coefficient to obtain the adjusted pressure value;

[0115] Exemplarily, the acquisition of the input adjustment coefficient:

[0116] Obtain the recognition area coefficient and the damage area coefficient, and add the recognition area coefficient and the damage area coefficient to obtain the input adjustment coefficient;

[0117] Among them, the acquisition method of the recognition area coefficient is:

[0118] Obtain the horizontal semi-axis deviation ratio and the vertical semi-axis deviation ratio, and through the formula: , calculate to obtain the recognition area coefficient α, where a represents the horizontal semi-axis deviation ratio and b represents the vertical semi-axis deviation ratio;

[0119] The acquisition method of the damage area coefficient is:

[0120] Obtain the damage influence value, take the difference between the damage influence value and the damage influence threshold to obtain the damage influence deviation value, and calculate the ratio of the damage influence deviation value to the damage influence threshold to obtain the damage area coefficient;

[0121] The specific implementation scheme of the embodiment of the present invention is as follows: Based on the large-signal of the damage influence degree, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, generate the adjustment difficulty size signal, based on the large-signal of the influence degree, obtain the input adjustment coefficient, and adjust the user fingerprint input, so that when the user's fingerprint input is abnormal, based on the obtained input adjustment value, adjust the user fingerprint input, reduce the problems caused by input errors, and improve the input accuracy.

[0122] Embodiment 3:

[0123] Based on Embodiment 1 and Embodiment 2, as shown in Figure 3, the control system of an intelligent door lock described in the embodiment of the present invention includes the following system modules:

[0124] Input monitoring module: When the user uses the intelligent door lock to input fingerprints, determine whether the fingerprint input is qualified, and generate a signal indicating whether the input is qualified;

[0125] Influence analysis module: Based on the signal indicating unqualified input, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate the adjustment difficulty size signal;

[0126] If the adjustment difficulty value is greater than or equal to the adjustment difficulty threshold, generate a signal indicating a large adjustment difficulty;

[0127] Accurate evaluation module: Based on the large-signal of the influence degree, obtain the input status value, compare it with the input status threshold, and generate a signal indicating whether the input is accurate;

[0128] If the input status value is greater than or equal to the input status threshold, generate a signal indicating inaccurate input;

[0129] Input adjustment module: Based on the signal indicating inaccurate input, obtain the input adjustment value, and adjust the user fingerprint input.

[0130] The above has described a detailed description of an embodiment of the present invention, but the described content is only the preferred embodiment of the present invention, and cannot be considered as limiting the scope of implementation of the present invention. All equal changes and improvements made according to the scope of the present invention application shall still fall within the scope covered by the patent 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 damage impact degree large signal; The damage impact value is obtained as follows: The damage area value and the damage area distribution value are added together to obtain the damage impact value; The method for obtaining the area value of the damaged area is: Acquire a real-time input image, divide the real-time input image into a plurality of sub-regions, 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 input image, and obtain the area value of the damaged region; The damage area distribution value is obtained as follows: 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; 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 unqualified signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate an adjustment difficulty size signal; Wherein, adjusting the difficulty size signal includes adjusting the difficulty large 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 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.

4. The control method of a smart door lock according to claim 3, characterized in that: 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; 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.

5. The control method of a smart door lock according to claim 3, 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.

6. A control method for an intelligent door lock according to claim 5, 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.

7. 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 6, 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 damage impact degree large signal; The damage impact value is obtained as follows: The damage area value and the damage area distribution value are added together to obtain the damage impact value; The method for obtaining the area value of the damaged area is: Acquire a real-time input image, divide the real-time input image into a plurality of sub-regions, 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 input image, and obtain the area value of the damaged region; The damage area distribution value is obtained as follows: 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; 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 unqualified signal, obtain the adjustment difficulty value, compare it with the adjustment difficulty threshold, and generate the adjustment difficulty size signal; Wherein, adjusting the difficulty size signal includes adjusting the difficulty large 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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