A fault detection method and system for intelligent door lock

Abstract

The present invention relates to the field of fault detection technology, and specifically discloses a fault detection method and system for a smart door lock, comprising the following steps: step one: detecting a lock core of a smart door lock to obtain detection data of the lock core; step two: calculating an abnormality coefficient of the lock core based on the detection data; step three: determining whether the smart door lock has a fault based on the abnormality coefficient, and generating a fault determination signal; step four: evaluating the fault conditions of the smart door locks in a detection partition based on past data of all smart door locks, determining whether the detection partition is a serious area, and generating a partition evaluation signal; step five: regulating the inspection cycle of the detection partition based on the serious fault signal; the present invention detects the lock core through multiple dimensions, which not only improves the accuracy of fault detection of the smart door lock, but also realizes timely early warning of faults, thereby providing a strong guarantee for the safe operation of the smart door lock.

Description

Technical Field

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

[0002] Smart door locks are improved upon traditional mechanical locks and are more intelligent and simple in terms of user security, identification, and management. Smart door locks are the executive components of door locking in access control systems.

[0003] Different from traditional mechanical locks, smart door locks are composite locks that combine security, convenience, and advanced technology.

[0004] However, in the prior art, the fault detection of smart door locks is not comprehensive enough, and the door lock core cannot be detected through multiple dimensions, resulting in low accuracy of smart door lock fault detection and failure to provide strong protection for the safe operation of smart door locks. Summary of the invention

[0005] The purpose of the present invention is to provide a fault detection method and 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:

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

[0008] Step 1: Detect the lock core of the smart door lock and obtain the detection data of the lock core;

[0009] Among them, the detection data include: offset coefficient and wear coefficient;

[0010] Step 2: Calculate the abnormal coefficient of the lock core based on the detection data;

[0011] Step 3: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal;

[0012] Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal;

[0013] Step 4: Based on the past data of all smart door locks, evaluate the fault conditions of the smart door locks in the detection zone, determine whether the detection zone is a serious area, and generate a zone evaluation signal;

[0014] Among them, the partition assessment signal includes: serious fault signal and minor fault signal;

[0015] Step 5: Based on the serious fault signal, the inspection cycle of the detection partition is adjusted.

[0016] As a further solution of the present invention: the process of acquiring the detection data is as follows:

[0017] Detect the current position of the smart door lock cylinder and obtain the offset coefficient based on the current position and the preset position;

[0018] At the same time, the lock core of the smart door lock is tested to obtain the wear coefficient.

[0019] As a further solution of the present invention: the process of obtaining the offset coefficient is:

[0020] Take photos of the current position of the lock cylinder of the smart door lock at multiple angles to obtain the current position of the lock cylinder at multiple angles, and compare the current position of each angle with the corresponding preset position to obtain the offset representation value of each angle position, and then sum the offset representation values ​​of each angle to obtain the offset coefficient;

[0021] The process of obtaining the offset characterization value is as follows:

[0022] Based on the current position of a single angle, the current position is compared with its corresponding preset position for overlap, the area of ​​the non-overlapping part between the current position and the preset position is extracted to obtain the offset area, and the offset area is ratio-calculated with the area threshold to obtain the offset characterization value.

[0023] As a further solution of the present invention: the process of obtaining the wear coefficient is:

[0024] Based on the current position of the lock core at multiple angles, the area representation value and the perimeter representation value of the current position at each angle are obtained respectively, and the area representation value is multiplied by the perimeter representation value to obtain the wear representation value;

[0025] Then the wear characterization values ​​of the current positions of all angles are summed up and calculated to obtain the wear coefficient;

[0026] The process of obtaining the area characterization value is as follows:

[0027] Based on the current position of a single angle, the area of ​​the current position is extracted to obtain the lock core area, and the difference between the lock core area and the preset area is calculated, and the absolute value is taken to obtain the area difference, and then the area difference is calculated to the preset area to obtain the area representation value;

[0028] The process of obtaining the perimeter representation value is as follows:

[0029] Based on the current position of a single angle, the circumference of the current position is extracted to obtain the circumference of the lock core, and the difference between the circumference of the lock core and the preset circumference is calculated, and the absolute value is taken to obtain the circumference difference, and then the circumference difference is calculated as a ratio with the preset circumference to obtain the circumference representation value.

[0030] As a further solution of the present invention: the process of obtaining the abnormal coefficient is:

[0031] By formula: , calculate the abnormal coefficient YC, where, , are preset proportional factors, all of which are greater than 0, PY is the offset coefficient, and MS is the wear coefficient.

[0032] As a further solution of the present invention: the process of generating the fault determination signal is:

[0033] Preset an abnormal coefficient threshold, and compare and analyze the abnormal coefficient with the abnormal coefficient threshold;

[0034] If the abnormal coefficient is less than or equal to the abnormal coefficient threshold, a door lock normal signal is generated;

[0035] If the abnormal coefficient is greater than the abnormal coefficient threshold, a door lock fault signal is generated.

[0036] As a further solution of the present invention: the process of generating the partition evaluation signal is:

[0037] Based on the total detection area, the total detection area is divided into several detection partitions, and based on the past data of all smart door locks in each detection partition, the evaluation coefficient of each detection partition is obtained;

[0038] Among them, the past data of the smart door lock includes: the fault interval of each fault in the target period, the abnormal coefficient of each fault in the target period, and the number of faults in the target period;

[0039] Preset an evaluation coefficient threshold, and compare and analyze the evaluation coefficient with the evaluation coefficient threshold;

[0040] If the evaluation coefficient is ≤ the evaluation coefficient threshold, a minor fault signal is generated;

[0041] If the evaluation coefficient>the evaluation coefficient threshold, a serious fault signal is generated.

[0042] As a further solution of the present invention: the process of obtaining the evaluation coefficient is:

[0043] Calculate the evaluation characterization value of each smart door lock in the detection partition respectively, and sum the evaluation characterization values ​​of all smart door locks in the detection partition to obtain the evaluation coefficient;

[0044] The process of obtaining the evaluation characterization value is as follows:

[0045] Based on the single smart door lock in the detection partition, the formula is: , calculate the evaluation characterization value PG, where i represents the i-th failure of the smart door lock within the target period, i takes values ​​of 1, 2, 3, ..., N, N is the number of failures, JG is the failure interval, and YC is the abnormality coefficient.

[0046] As a further solution of the present invention: the process of regulating the inspection cycle of the detection partition is:

[0047] Based on the serious fault signal, the evaluation coefficient of the detection partition is extracted, thereby calculating the inspection cycle control value of the detection partition, and adjusting the inspection cycle of the detection partition based on the inspection cycle control value;

[0048] The process of obtaining the inspection cycle control value is as follows:

[0049] By formula: , calculate the inspection cycle control value TK, where PX is the evaluation coefficient, PXY is the evaluation coefficient threshold, and ZQ is the preset inspection cycle.

[0050] In a second aspect, the present invention provides a fault detection system for an intelligent door lock, the system comprising:

[0051] Data acquisition module: detect the lock core of the smart door lock and obtain the detection data of the lock core;

[0052] Among them, the detection data include: offset coefficient and wear coefficient;

[0053] Data processing module: calculates the abnormal coefficient of the lock core based on the detection data;

[0054] Fault determination signal: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal;

[0055] Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal;

[0056] Partition evaluation module: Based on the past data of all smart door locks, the fault conditions of the smart door locks in the detection partition are evaluated, whether the detection partition is a serious area, and a partition evaluation signal is generated;

[0057] Among them, the partition assessment signal includes: serious fault signal and minor fault signal;

[0058] Inspection and control module: Based on serious fault signals, the inspection cycle of the detection partition is adjusted.

[0059] Beneficial effects of the present invention:

[0060] (1) The present invention takes multi-angle photos of the lock core of the smart door lock to accurately capture the current position of the lock core. In the spatial dimension, the image processing technology is used to compare the current position of the lock core with the preset ideal position to calculate the offset coefficient, which reflects the offset degree of the lock core position. At the same time, in the physical attribute dimension, the wear coefficient is obtained by measuring the area and circumference of the current position of the lock core and comparing them with the preset value, and the wear state of the lock core is quantitatively evaluated. Based on the offset coefficient and the wear coefficient, the abnormality coefficient is calculated. The coefficient combines the offset and wear of the lock core and is an important indicator for evaluating the overall state of the lock core. Finally, according to the preset abnormality coefficient threshold, the calculated abnormality coefficient is judged. If the abnormality coefficient is lower than or equal to the threshold, it is judged that the smart door lock is in normal state and has no fault. If the abnormality coefficient is higher than the threshold, a fault warning signal is issued to prompt the user or management personnel to check and maintain. The present invention detects the lock core in multiple dimensions, which not only improves the accuracy of fault detection of the smart door lock, but also realizes timely warning of faults, providing a strong guarantee for the safe operation of the smart door lock.

[0061] (2) The present invention first divides the detection area into multiple partitions. The past data of the smart door locks in each partition is fully collected, including the fault interval, abnormal coefficient and number of faults. Through in-depth analysis of the past data, an evaluation coefficient is calculated for each partition. The coefficient comprehensively reflects the degree and frequency of faults of the smart door locks in the partition. Then, the evaluation coefficient threshold is set as a dividing line to divide the partition into a minor fault area and a major fault area. When the partition evaluation coefficient is lower than the threshold, the system sends a minor fault signal, indicating that the state of the smart door lock in the area is relatively stable. When the evaluation coefficient exceeds the threshold, a major fault signal is triggered, indicating that the smart door lock in the area is relatively stable. This area requires special attention; to further improve maintenance efficiency, the present invention also introduces an intelligent control mechanism for the inspection cycle. For partitions that send out serious fault signals, the inspection cycle control value will be automatically calculated according to the evaluation coefficient, and the value will be adjusted based on the preset inspection cycle; when the evaluation coefficient is high, the control value will shorten the inspection cycle to ensure that the fault can be discovered and handled in time; otherwise, the inspection cycle may be appropriately extended to optimize resource allocation; the present invention not only improves the stability and safety of the smart door lock system through data-driven fault evaluation and intelligent inspection cycle control, but also realizes the precision and efficiency of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] Figure 1 It is a flowchart of the present invention;

[0064] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Embodiment 1:

[0067] See also Figure 1 As shown, a fault detection method for a smart door lock according to an embodiment of the present invention comprises the following steps:

[0068] Step 1: Detect the lock core of the smart door lock and obtain the detection data of the lock core;

[0069] Among them, the detection data include: offset coefficient and wear coefficient;

[0070] In some implementation schemes, the current position of the lock cylinder of the smart door lock is detected, and the offset coefficient is obtained based on the current position and the preset position;

[0071] It should be explained that the preset position is the current position preset by a person skilled in the art based on the operation of the smart door lock cylinder, and the preset position is an empirical value;

[0072] At the same time, the lock core of the smart door lock is tested to obtain the wear coefficient;

[0073] First, specifically, the process of obtaining the offset coefficient is:

[0074] Use multiple cameras to take photos of the current position of the lock cylinder of the smart door lock at multiple angles to obtain the current position of the lock cylinder at multiple angles, and compare the current position of each angle with the corresponding preset position to obtain the offset representation value of each angle position, and then sum the offset representation values ​​of each angle to obtain the offset coefficient;

[0075] It should be explained that the current position of the lock cylinder of the smart door lock is photographed from multiple angles, where the selection of multiple angles should be able to cover all key parts of the lock cylinder (key parts include the front, side (left and right sides), top and bottom), there should be no obvious blind spots between the multiple angles, and it should not affect the normal operation of the lock cylinder;

[0076] It needs to be further explained that the offset coefficient indicates the degree of deviation between the current position of the lock core and its preset position. The larger the offset coefficient, the more serious the lock core offset is, and the greater the possibility that the lock core is worn, loose or has other problems. The smaller the offset coefficient, the better the state of the lock core.

[0077] Furthermore, the process of obtaining the offset characterization value is as follows:

[0078] Based on the current position of a single angle, the current position is compared with the corresponding preset position, the area of ​​the non-overlapping part between the current position and the preset position is extracted to obtain the offset area, and the ratio of the offset area to the area threshold is calculated to obtain the offset characterization value;

[0079] It should be explained that the offset area is calculated by ratio with the area threshold. The area threshold here is to de-dimension the offset area, thereby obtaining a dimensionless value, namely, the offset characterization value;

[0080] Second, specifically, the process of obtaining the wear coefficient is:

[0081] Based on the current position of the lock core at multiple angles, the area representation value and the perimeter representation value of the current position at each angle are obtained respectively, and the area representation value is multiplied by the perimeter representation value to obtain the wear representation value;

[0082] Then the wear characterization values ​​of the current positions of all angles are summed up and calculated to obtain the wear coefficient;

[0083] It should be explained that the wear coefficient represents a quantitative indicator of the degree of wear of the lock core due to use, time, environment and other factors. The wear coefficient combines the wear of the lock core from multiple angles. The higher the wear coefficient, the more serious the wear of the lock core, which may affect the functionality, safety and service life of the lock core. On the contrary, a lower wear coefficient indicates that the lock core is less worn and is in a better working condition.

[0084] First, the process of obtaining the area characterization value is as follows:

[0085] Based on the current position of a single angle, the area of ​​the current position is extracted to obtain the lock core area, and the difference between the lock core area and the preset area is calculated, and the absolute value is taken to obtain the area difference, and then the area difference is calculated to the preset area to obtain the area representation value;

[0086] It should be explained that the preset area is a lock cylinder area preset by a person skilled in the art based on the current position of the lock cylinder of the smart door lock, and the preset area is an empirical value;

[0087] It needs to be further explained that the area representation value provides a quantitative indicator to evaluate the area change caused by wear, deformation or other factors of the lock core at that angle. By comparing the area representation values ​​at different angles, the overall wear condition of the lock core can be fully understood. The larger the area representation value, the greater the difference between the current area of ​​the lock core at that angle and the preset area.

[0088] Secondly, the process of obtaining the perimeter characterization value is as follows:

[0089] Based on the current position of a single angle, extract the perimeter of the current position (the perimeter is the side length of the plane of the current angle of the lock core in the current position image), obtain the perimeter of the lock core, and calculate the difference between the perimeter of the lock core and the preset perimeter, and take the absolute value to obtain the perimeter difference, and then calculate the ratio of the perimeter difference to the preset perimeter to obtain the perimeter representation value;

[0090] It should be explained that the preset circumference is the circumference of the lock cylinder preset by a person skilled in the art based on the current position of the lock cylinder of the smart door lock, and the preset circumference is an empirical value;

[0091] It needs to be further explained that the circumference representation value is expressed as the difference between the circumference of the current position of the smart door lock cylinder and the preset circumference, which reflects the size change of the lock cylinder due to wear or deformation. The larger the value of the circumference representation value, the more obvious the change of the circumference of the lock cylinder at this angle, which may be caused by wear, deformation, etc.

[0092] Step 2: Calculate the abnormal coefficient of the lock core based on the detection data;

[0093] In some embodiments, by the formula: , calculate the abnormal coefficient YC, where, , is the preset proportional factor, and both are greater than 0, PY is the offset coefficient, and MS is the wear coefficient;

[0094] Step 3: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal;

[0095] Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal;

[0096] In some implementation schemes, an abnormal coefficient threshold is preset, and the abnormal coefficient is compared and analyzed with the abnormal coefficient threshold;

[0097] If the abnormal coefficient is less than or equal to the abnormal coefficient threshold, it means that the current position offset of the lock core is small and the wear of the lock core is small, that is, it is determined that the smart door lock has no fault and a normal door lock signal is generated;

[0098] If the abnormal coefficient is greater than the abnormal coefficient threshold, it means that the current position of the lock core is greatly offset or the lock core is greatly worn, that is, the smart door lock is judged to be faulty and a door lock fault signal is generated;

[0099] The technical solution of the embodiment of the present invention is mainly as follows: by shooting the lock cylinder of the smart door lock at multiple angles to accurately capture the current position of the lock cylinder, in the spatial dimension, using image processing technology to compare the current position of the lock cylinder with the preset ideal position, and calculating the offset coefficient, which reflects the degree of offset of the lock cylinder position; at the same time, in the physical property dimension, by measuring the area and circumference of the current position of the lock cylinder and comparing them with the preset value, the wear coefficient is obtained to quantitatively evaluate the wear state of the lock cylinder; based on the offset coefficient and the wear coefficient, the abnormality coefficient is calculated, which combines the offset and wear of the lock cylinder and is an important indicator for evaluating the overall state of the lock cylinder; finally, according to the preset abnormality coefficient threshold, the calculated abnormality coefficient is judged; if the abnormality coefficient is lower than or equal to the threshold, it is judged that the smart door lock is in normal state and there is no fault; if the abnormality coefficient is higher than the threshold, a fault warning signal is issued to prompt the user or management personnel to check and maintain; the embodiment of the present invention detects the lock cylinder through multiple dimensions, which not only improves the accuracy of fault detection of the smart door lock, but also realizes timely warning of faults, providing a strong guarantee for the safe operation of the smart door lock.

[0100] Embodiment 2:

[0101] Based on Example 1, please refer to Figure 1 As shown, the fault detection method of a smart door lock according to an embodiment of the present invention further includes the following steps:

[0102] Step 4: Based on the past data of all smart door locks, evaluate the fault conditions of the smart door locks in the detection zone, determine whether the detection zone is a serious area, and generate a zone evaluation signal;

[0103] Among them, the partition assessment signal includes: serious fault signal and minor fault signal;

[0104] In some implementation schemes, based on the total detection area, the total detection area is divided into a number of detection partitions, and based on the past data of all smart door locks in each detection partition, the evaluation coefficient of each detection partition is obtained;

[0105] It should be explained that the total inspection area can be divided into several inspection zones according to geographical location. For example, according to the building layout, floor distribution, room location, etc., similar or adjacent areas can be divided into one inspection zone, so as to facilitate the inspection personnel to quickly familiarize themselves with and cover the entire zone, thereby improving the inspection efficiency;

[0106] The past data of the smart door lock include: the fault interval of each fault in the target period (the fault interval is the interval between each time the smart door lock generates a door lock fault signal), the abnormal coefficient of each fault in the target period, and the number of faults in the target period (the number of faults is the number of times the smart door lock generates a door lock fault signal in the target period, where the target period includes but is not limited to: 6 months, 1 year);

[0107] Preset an evaluation coefficient threshold, and compare and analyze the evaluation coefficient with the evaluation coefficient threshold;

[0108] If the evaluation coefficient is less than or equal to the evaluation coefficient threshold, it means that the smart door locks in the detection zone have few faults or the faults are relatively minor, that is, a minor fault signal is generated;

[0109] If the evaluation coefficient is greater than the evaluation coefficient threshold, it means that the smart door locks in the detection zone have many faults or the faults are serious, that is, a serious fault signal is generated;

[0110] Specifically, the process of obtaining the evaluation coefficient is:

[0111] Calculate the evaluation characterization value of each smart door lock in the detection partition respectively, and sum the evaluation characterization values ​​of all smart door locks in the detection partition to obtain the evaluation coefficient;

[0112] Furthermore, the process of obtaining the evaluation characterization value is as follows:

[0113] Based on the single smart door lock in the detection partition, the formula is: , calculate the evaluation characterization value PG, where i represents the i-th failure of the smart door lock within the target period, i takes values ​​of 1, 2, 3, ..., N, N is the number of failures, JG is the failure interval, and YC is the abnormality coefficient;

[0114] Step 5: Based on the serious fault signal, adjust the inspection cycle of the detection partition;

[0115] In some embodiments, based on the severe fault signal, the evaluation coefficient of the detection partition is extracted to calculate the inspection cycle control value of the detection partition, and the inspection cycle of the detection partition is controlled based on the inspection cycle control value;

[0116] Specifically, the process of obtaining the inspection cycle control value is as follows:

[0117] By formula: , calculate the inspection cycle control value TK, where PX is the evaluation coefficient, PXY is the evaluation coefficient threshold, and ZQ is the preset inspection cycle;

[0118] It should be explained that the preset inspection cycle is an inspection cycle pre-set for the detection partition by a person skilled in the art based on a minor fault signal, and the preset inspection cycle is an empirical value;

[0119] The technical solution of the embodiment of the present invention is mainly as follows: first, the detection area is subdivided into multiple partitions, and the past data of the smart door locks in each partition is comprehensively collected, including the fault interval, abnormality coefficient and number of faults. Through in-depth analysis of the past data, an evaluation coefficient is calculated for each partition, and the coefficient comprehensively reflects the degree and frequency of faults of the smart door locks in the partition; then, the evaluation coefficient threshold is set as a dividing line to divide the partition into a minor fault area and a major fault area; when the partition evaluation coefficient is lower than the threshold, the system sends a minor fault signal, indicating that the state of the smart door lock in the area is relatively stable; and when the evaluation coefficient exceeds the threshold, a major fault signal is triggered. , prompting that this area needs special attention; to further improve maintenance efficiency, the present invention also introduces an intelligent control mechanism for the inspection cycle. For the partition that sends out a serious fault signal, the inspection cycle control value will be automatically calculated according to the evaluation coefficient, and the value will be adjusted based on the preset inspection cycle; when the evaluation coefficient is high, the control value will shorten the inspection cycle to ensure that the fault can be discovered and handled in time; otherwise, the inspection cycle may be appropriately extended to optimize resource allocation; the embodiment of the present invention not only improves the stability and safety of the smart door lock system through data-driven fault evaluation and intelligent inspection cycle control, but also realizes the precision and efficiency of maintenance work.

[0120] Embodiment 3:

[0121] Based on Example 1 and Example 2, please refer to Figure 2 As shown, a fault detection system for a smart door lock according to an embodiment of the present invention includes:

[0122] Data acquisition module: detect the lock core of the smart door lock and obtain the detection data of the lock core;

[0123] Among them, the detection data include: offset coefficient and wear coefficient;

[0124] Data processing module: calculates the abnormal coefficient of the lock core based on the detection data;

[0125] Fault determination signal: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal;

[0126] Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal;

[0127] Partition evaluation module: Based on the past data of all smart door locks, the fault conditions of the smart door locks in the detection partition are evaluated, whether the detection partition is a serious area, and a partition evaluation signal is generated;

[0128] Among them, the partition assessment signal includes: serious fault signal and minor fault signal;

[0129] Inspection and control module: Based on serious fault signals, the inspection cycle of the detection partition is adjusted.

[0130] The threshold value of the present invention is set for the convenience of comparison. The threshold value depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data.

[0131] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The factors in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of test data and sets a corresponding abnormal coefficient for each group of test data; substitutes the set abnormal coefficient and the collected test data into the formula, any two formulas constitute a binary linear equation system, and the calculated factors are screened and averaged to obtain a 1 、a 2 The values ​​of are 1.42 and 2.13 respectively;

[0132] The size of the factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the factor depends on the amount of test data and the initial setting of the corresponding abnormal coefficient for each set of test data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the abnormal coefficient is proportional to the value of the offset coefficient.

[0133] 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 fault detection method for a smart door lock, characterized in that: The following steps are involved: Step 1: Detect the lock core of the smart door lock and obtain the detection data of the lock core; Among them, the detection data include: offset coefficient and wear coefficient; The process of obtaining the offset coefficient is: Take photos of the current position of the lock cylinder of the smart door lock at multiple angles to obtain the current position of the lock cylinder at multiple angles, and compare the current position of each angle with the corresponding preset position to obtain the offset representation value of each angle position, and then sum the offset representation values ​​of each angle to obtain the offset coefficient; The process of obtaining the offset characterization value is as follows: Based on the current position of a single angle, the current position is compared with the corresponding preset position, the area of ​​the non-overlapping part between the current position and the preset position is extracted to obtain the offset area, and the ratio of the offset area to the area threshold is calculated to obtain the offset characterization value; The process of obtaining the wear coefficient is: Based on the current position of the lock core at multiple angles, the area representation value and the perimeter representation value of the current position at each angle are obtained respectively, and the area representation value is multiplied by the perimeter representation value to obtain the wear representation value; Then the wear characterization values ​​of the current positions of all angles are summed up and calculated to obtain the wear coefficient; The process of obtaining the area characterization value is as follows: Based on the current position of a single angle, the area of ​​the current position is extracted to obtain the lock core area, and the difference between the lock core area and the preset area is calculated, and the absolute value is taken to obtain the area difference, and then the area difference is calculated to the preset area to obtain the area representation value; The process of obtaining the perimeter representation value is as follows: Based on the current position of a single angle, the circumference of the current position is extracted to obtain the circumference of the lock core, and the difference between the circumference of the lock core and the preset circumference is calculated, and the absolute value is taken to obtain the circumference difference, and then the circumference difference is calculated to the preset circumference to obtain the circumference representation value; Step 2: Calculate the abnormal coefficient of the lock core based on the detection data; The process of obtaining the anomaly coefficient is: By formula: , calculate the abnormal coefficient YC, where, , is the preset proportional factor, and both are greater than 0, PY is the offset coefficient, and MS is the wear coefficient; Step 3: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal; Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal; Step 4: Based on the past data of all smart door locks, evaluate the fault conditions of the smart door locks in the detection zone, determine whether the detection zone is a serious area, and generate a zone evaluation signal; Among them, the partition assessment signal includes: serious fault signal and minor fault signal; Step 5: Based on the serious fault signal, the inspection cycle of the detection partition is adjusted.

2. A fault detection method for a smart door lock according to claim 1, characterized in that: The process of generating a fault judgment signal is: Preset an abnormal coefficient threshold, and compare and analyze the abnormal coefficient with the abnormal coefficient threshold; If the abnormal coefficient is less than or equal to the abnormal coefficient threshold, a door lock normal signal is generated; If the abnormal coefficient is greater than the abnormal coefficient threshold, a door lock fault signal is generated.

3. A fault detection method for a smart door lock according to claim 1, characterized in that: The process of generating partition evaluation signals is: Based on the total detection area, the total detection area is divided into several detection partitions, and based on the past data of all smart door locks in each detection partition, the evaluation coefficient of each detection partition is obtained; Among them, the past data of the smart door lock includes: the fault interval of each fault in the target period, the abnormal coefficient of each fault in the target period, and the number of faults in the target period; Preset an evaluation coefficient threshold, and compare and analyze the evaluation coefficient with the evaluation coefficient threshold; If the evaluation coefficient is ≤ the evaluation coefficient threshold, a minor fault signal is generated; If the evaluation coefficient>the evaluation coefficient threshold, a serious fault signal is generated.

4. A fault detection 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 3, and the system comprises: Data acquisition module: detect the lock core of the smart door lock and obtain the detection data of the lock core; Among them, the detection data include: offset coefficient and wear coefficient; Data processing module: calculates the abnormal coefficient of the lock core based on the detection data; Fault determination signal: Based on the abnormality coefficient, determine whether the smart door lock has a fault and generate a fault determination signal; Wherein, the fault determination signal includes: a door lock fault signal and a door lock normal signal; Partition evaluation module: Based on the past data of all smart door locks, the fault conditions of the smart door locks in the detection partition are evaluated, whether the detection partition is a serious area, and a partition evaluation signal is generated; Among them, the partition assessment signal includes: serious fault signal and minor fault signal; Inspection and control module: Based on serious fault signals, the inspection cycle of the detection partition is adjusted.

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