A radar static suppression method for a security door lock and an intelligent security door lock

Abstract

The present application relates to the technical field of intelligent door lock sensing control, and discloses a radar static suppression method for a security door lock and a smart security door lock. The radar static suppression method first confirms that the door lock is in a closed and locked state and meets the triggering condition through a lock tongue state sensor, starts radar environment baseline collection, and constructs an environment baseline matrix representing fixed reflection characteristics of the installation environment. Then, the distance gate corresponding to the distance dimension spectrum is divided into a static suppression area and a normal detection area, and adaptive detection thresholds are calculated for each distance gate based on the baseline matrix. During dynamic detection, a valid human body approach event is determined through differential calculation, continuous frame verification and human body micro-motion feature matching, the biometric identification module is triggered, and the switch door state switching detection mode is linked. The present application does not need to modify the door lock hardware, can suppress the reflection interference of the metal back plate, reduce the false triggering probability and standby power consumption, and improve the detection stability of the door lock.

Description

Technical Field

[0001] This invention relates to the field of intelligent door lock sensing and control technology, and in particular discloses a radar static suppression method for anti-theft door locks and an intelligent anti-theft door lock. Background Technology

[0002] Currently, smart security door locks equipped with biometric modules generally detect user approach by integrating human body sensing radar within the lock panel, triggering the biometric module to start. However, the metal backplate and mounting plate of the door lock will generate stable and strong reflections of the radar signal. Existing technology cannot simultaneously achieve effective suppression of the metal fixed reflection signal and accurate detection of stationary human targets in front of the door without changing the door lock hardware structure. This easily leads to problems such as continuous false triggering of the radar, excessive standby power consumption of the door lock, and poor stability of human body proximity detection, which seriously affects the user experience and service life of smart door locks. Summary of the Invention

[0003] In order to overcome the shortcomings and deficiencies of the existing technology, the purpose of this invention is to provide a radar static suppression method for anti-theft door locks and an intelligent anti-theft door lock. The aim is to solve the problems that the existing technology cannot simultaneously achieve effective suppression of fixed metal reflection signals and accurate detection of stationary human targets in front of the door without changing the door lock hardware structure, which easily leads to continuous false triggering of radar, excessive standby power consumption of door locks, and poor stability of human proximity detection.

[0004] To achieve the above objectives, the present invention provides a static radar suppression method for an anti-theft door lock. The anti-theft door lock includes a lock body, a main control module, a radar module, a bolt status sensor, and a biometric module. The radar module, bolt status sensor, and biometric module are all communicatively connected to the main control module. The method includes the following steps: S1, Precondition Judgment and Initialization Trigger: The main control module obtains the locking status of the door lock body in real time through the latch status sensor. When the door lock body is in the closed locking state and the preset initialization trigger conditions are met, the main control module sends a command to the radar module to start the radar environment baseline acquisition process. S2, Environmental baseline acquisition and static echo feature extraction: After receiving the command, the radar module enters the baseline acquisition mode, transmits the FMCW frequency modulated continuous wave signal and receives the echo signal. After preprocessing the echo signal, the range dimension spectrum data is obtained. Through multi-frame data coherent averaging, an environmental baseline matrix characterizing the fixed reflection characteristics of the installation environment is constructed. S3, Partition adaptive detection threshold setting based on static echo characteristics: The main control module divides all distance gates corresponding to the distance dimension spectrum into a static suppression zone corresponding to the close range of the metal back plate and a normal detection zone corresponding to the effective detection range of the human body, according to the installation position of the radar in the door lock body. Based on the environmental baseline matrix obtained in step S2, the corresponding adaptive detection threshold is calculated for each distance gate in the static suppression zone and the normal detection zone, and the environmental baseline matrix and the adaptive detection threshold are stored in the memory of the main control module. S4, Dynamic Detection and Trigger Control: When the door lock body is in normal standby mode, the radar module enters intermittent dynamic detection mode, collects echo signals, completes preprocessing, and uploads them to the main control module. The main control module processes the data in real time to obtain the distance dimension spectrum data of the current frame, performs differential calculation between the distance dimension spectrum data of the current frame and the pre-stored environmental baseline matrix, and obtains the echo amplitude change corresponding to each distance gate. When the echo amplitude change of at least one distance gate exceeds its corresponding adaptive detection threshold, and the detection distance corresponding to the distance gate is within the preset effective human detection range, the main control module determines it as a pending confirmation trigger event. If the determination conditions of the pending confirmation trigger event are met for a consecutive preset number of frames, and the human micro-motion features of the corresponding echo signal are extracted, the main control module determines it as a valid human proximity event and triggers the biometric recognition module to start. When the door lock body is detected to be in an open state, the main control module controls the radar module to pause the static suppression detection mode, and resumes it after the door lock body closes and locks again.

[0005] Furthermore, the preset initialization triggering conditions mentioned in step S1 include any one or more combinations of the following: the door lock is installed for the first time upon power-on; the user manually triggers the calibration command; the door lock is restarted; the preset periodic calibration time period is reached; the door lock remains in a closed state for a preset duration and there are no dynamic targets in the environment.

[0006] Furthermore, the biometric module is one of a face recognition module, a fingerprint recognition module, an iris recognition module, or a palm print recognition module.

[0007] Furthermore, step S2 specifically includes the following steps: S21, the radar module transmits an FMCW frequency-modulated continuous wave signal according to the preset frequency sweep parameters. After receiving the echo signal, it performs mixing and low-pass filtering to obtain an intermediate frequency signal. The intermediate frequency signal is then subjected to a range-dimensional fast Fourier transform to obtain the range-dimensional spectrum data corresponding to each frame of echo. The range-dimensional spectrum data contains N range gates, each range gate corresponding to a detection distance and the amplitude value of the echo signal at that distance. S22, the radar module continuously acquires range-dimensional spectrum data for a preset number of frames M. After removing abnormal frames whose amplitude changes exceed a preset threshold, the amplitude values ​​of each range gate in the remaining valid frames are arithmetically averaged to obtain the environmental baseline matrix. ,in, The baseline amplitude value corresponding to the i-th distance gate. ; S23. During the data acquisition process, the main control module detects dynamic targets in the environment in real time. If a dynamic target is detected, the data acquisition is immediately paused and restarted when there are no dynamic targets in the environment.

[0008] Furthermore, in step S3, the static suppression zone corresponds to a distance range of 1 to K, where K < N, which corresponds to the radar's close-range range and covers the fixed reflection distance range of the door lock's metal backplate and mounting plate; the normal detection zone corresponds to a distance range of K+1 to N, which corresponds to the radar's medium-to-long-range range and covers the effective detection range for human approach.

[0009] Furthermore, in step S3, the adaptive detection threshold is calculated as follows: for the i-th distance gate in the static suppression region, where 1≤i≤K, its adaptive detection threshold... Where α is a preset suppression coefficient, with a value ranging from 1.2 to 1.8; for the i-th distance gate in the normal detection region, where Its adaptive detection threshold is calculated using the cell-average constant false alarm rate (CFAR) detection algorithm. , where β is the preset false alarm coefficient, with a value range of 3 to 5, and σ is the standard deviation of the background noise corresponding to the distance gate.

[0010] Furthermore, in step S4, the intermittent dynamic detection mode is a low-power intermittent detection mode, in which the radar module transmits one frame of FMCW signal according to a preset intermittent period of 300ms to 1s and completes the acquisition and processing of echo signals.

[0011] Further, in step S4, the method for extracting the human body micro-motion features is as follows: perform Doppler fast Fourier transform on the echo signal corresponding to the distance gate that meets the amplitude change threshold condition, extract micro-Doppler features, determine whether the feature matches the Doppler frequency shift features corresponding to human breathing and limb micro-motion, and eliminate static interference from fixed objects.

[0012] Furthermore, in step S4, when the door lock is detected to be in an open state, the main control module controls the radar module to pause the static suppression detection mode and switch to the door opening temporary detection mode, pausing the calling of the original environmental baseline matrix and the adaptive detection threshold; after the door lock is closed again and locking is completed, it switches back to the static suppression detection mode and performs a fast incremental update of the environmental baseline. Further, the incremental update of the environmental baseline comprises the following steps: a periodic update period is preset, when the door lock main body is in a closed and locked state and no moving target is triggered for a continuous preset duration, the radar module collects echo signals of a preset number of frames P, processes the echo signals to obtain a temporary baseline matrix B', and updates the original environmental baseline matrix by using a weighted average algorithm, wherein the update formula is , wherein γ is a preset weight coefficient, with a value ranging from 0.7 to 0.9, and P<M; after the baseline update is completed, the adaptive detection threshold of each range gate is recalculated synchronously.

[0013] An intelligent anti-theft door lock comprises a door lock main body, a main control module, a radar module, a lock tongue state sensor and a biometric identification module, wherein the radar module, the lock tongue state sensor and the biometric identification module are all in communication connection with the main control module, and the main control module is configured to execute the steps of the radar static suppression method for the anti-theft door lock.

[0014] Principle of the technical solution of the present invention: the present invention takes the closed and locked state of the door lock as the pre-check basis, firstly starts the radar environment baseline collection process when the closed and locked state meets the preset condition, constructs an environmental baseline matrix representing the fixed reflection characteristics of the installation environment through coherent average processing of multi-frame echo signals, then divides all range gates corresponding to the range-dimensional frequency spectrum into a static suppression area corresponding to the short-range range of the metal back plate and a normal detection area for effective human body detection according to the installation position and detection distance interval of the radar, calculates an adaptive detection threshold for each range gate of the two areas based on the environmental baseline matrix, and finally completes accurate determination of an effective human body approaching event through differential calculation of real-time echo data and the environmental baseline, continuous frame check and human body micro-motion feature matching in the dynamic detection stage, and simultaneously switches detection modes in linkage with the opening and closing state of the door lock.

[0015] Beneficial effects of the present invention: compared with the prior art, the present invention, without any modification to the hardware structure of the door lock, can fundamentally suppress the fixed reflection interference caused by the metal back plate through accurate environmental baseline collection and zoned adaptive threshold setting, greatly reduce the false trigger probability of the radar and the standby power consumption of the door lock, meanwhile abandon the technical defects of traditional moving target cancellation, realize accurate detection of stationary human targets in front of the door through human body micro-motion feature matching, perfectly adapt to the use requirements of biometric door locks, can also adapt to different installation environments and working condition changes, and significantly improve the detection stability and practicability of the intelligent door lock in complex installation environments. Description of Drawings

[0016] Figure 1 is a schematic step diagram of the radar static suppression method of the present invention; Figure 2 is a schematic sub-step diagram of step S2 in the radar static suppression method of the present invention; Figure 3This is a flowchart of the overall algorithm for the radar static suppression method of the present invention; Figure 4 This is a schematic diagram of the baseline acquisition sub-process of the present invention; Figure 5 This is a schematic diagram of the dynamic detection and judgment process of the present invention; Figure 6 This is a schematic diagram of the mode switching process of the present invention; Figure 7 This is a schematic diagram of the baseline incremental update process of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0018] Please see Figures 1 to 7 As shown in this embodiment, the anti-theft door lock to which this invention is applicable has a hardware architecture including a door lock body, a main control module, a radar module, a bolt status sensor, and a biometric module. The main control module uses a low-power MCU, specifically an STM32L4 series or ESP32 series microcontroller with signal processing and peripheral control capabilities. The radar module uses a 24GHz FMCW millimeter-wave radar chip with a built-in 1-transmit 2-receive antenna array, a distance resolution of 0.06m, and a maximum detection distance of 3m. The latch status sensor is a through-beam photoelectric sensor, fixed inside the latch groove of the lock body, and works in conjunction with the latch to realize real-time detection of the door opening / closing and locking status; the biometric module adopts a 3D structured light face recognition module, which communicates with the main control module through a UART serial port; the output terminals of the radar module and the latch status sensor are electrically connected to the corresponding I / O ports or peripheral interfaces of the main control module, forming a complete hardware execution system, providing hardware support for the implementation of the method of this invention.

[0019] The specific implementation process of the precondition determination and initialization triggering step S1 is as follows: After the main control module is powered on, it continuously reads the level signal output by the latch status sensor with a fixed polling period of 100ms. When the latch is fully extended and the door lock body (hereinafter referred to as the door lock) is in the closed and locked state, the latch blocks the light path of the sensor, and the sensor outputs a stable low level. When the latch retracts and the door lock is in the open state, the sensor outputs a high level; the main control module will only start the initialization trigger condition judgment logic after confirming that the door lock is in a stable closed locking state after three consecutive low level readings; otherwise, it will continue to poll the locking state. When the door lock is in the closed and locked state and any preset initialization trigger condition is met, the main control module immediately sends a baseline acquisition command to the radar module via the SPI bus to formally start the radar environment baseline acquisition process.

[0020] Compared to existing technologies that lack pre-locking status verification and directly start data acquisition upon power-on, this solution ensures that the baseline acquisition environment is completely consistent with the normal standby operating environment of the door lock, avoiding distortion of baseline data acquired when the door is not closed or the locking is not completed, thus guaranteeing the effectiveness of static suppression from the root.

[0021] The initialization triggering conditions preset in step S1 are implemented in the following specific logic: Triggering conditions are divided into two categories: immediate triggering and periodic triggering. The immediate triggering conditions include three types: the door lock is installed for the first time after power-on, the user manually triggers the calibration command through the door lock touch panel or the accompanying APP, and the door lock is restarted after power failure. When any of the immediate triggering conditions is met, the main control module immediately starts the baseline acquisition process. The periodic triggering conditions include the arrival of the preset periodic calibration period from 2:00 to 4:00 a.m. every day, and the door lock being closed for 30 minutes with no dynamic targets in the environment. When either periodic triggering condition is met, the main control module first checks the door lock status and starts the baseline acquisition process after confirming that it meets the requirements.

[0022] In this embodiment, the environmental baseline acquisition and static echo feature extraction step S2 is implemented as follows: After receiving the baseline acquisition command from the main control module, the radar module immediately switches from low-power sleep mode to baseline acquisition mode, transmits FMCW frequency-modulated continuous wave signals according to the preset frequency sweep parameters, and simultaneously acquires the echo signals reflected by obstacles through the receiving antenna. After the radar module completes basic preprocessing of the echo signals, it uploads the intermediate frequency data to the main control module. The main control module completes subsequent range-dimensional spectrum calculations and feature extraction, and finally constructs an environmental baseline matrix that characterizes the fixed reflection characteristics of the installation environment.

[0023] Specifically, the implementation process of step S21 is as follows: The radar module transmits a linear frequency modulated continuous wave with a frame period of 100ms according to the preset frequency sweep parameters. The transmission signal bandwidth is 250MHz and the frequency sweep time is 64us. The radio frequency front end of the radar module mixes the received echo signal with the transmitted local oscillator signal, filters out the high-frequency carrier to obtain the intermediate frequency signal, and then passes it through a low-pass filter with a cutoff frequency of 1MHz to filter out high-frequency noise in the intermediate frequency signal. The radar module then samples the preprocessed intermediate frequency signal into a digital signal using a 12-bit ADC and uploads it to the main control module. The main control module performs a 128-point 1D-FFT operation on the sampled intermediate frequency signal to obtain the distance-dimensional spectrum data corresponding to each frame of echo. This distance-dimensional spectrum data contains 128 distance gates, each corresponding to a fixed physical detection distance interval. The distance width of a single distance gate is 0.06m. Each distance gate stores the amplitude value of the echo signal within that interval, realizing a one-to-one mapping between the echo signal intensity and the detection distance.

[0024] In this embodiment, step S22 is implemented as follows: The main control module controls the radar module to continuously acquire 32 frames of range-dimensional spectrum data, and performs an abnormal frame removal operation on all acquired frames.

[0025] The specific logic for abnormal frame removal is as follows: Calculate the arithmetic mean of all distance threshold values ​​in a single frame. When the difference between the average amplitude value of a frame and the average amplitude value of the two adjacent frames exceeds 30%, the frame is identified as an abnormal frame affected by transient interference and removed from the acquisition sequence.

[0026] For the remaining valid frames after removing abnormal frames, the main control module performs an arithmetic average operation according to the distance gate number. That is, for all valid frames, the amplitude values ​​of the distance gate with the same number are added together and then divided by the total number of valid frames to obtain the baseline amplitude value corresponding to that distance gate.

[0027] The baseline amplitude values ​​of all distance gates are arranged in ascending order of their serial numbers, ultimately yielding the environmental baseline matrix. Where N=128 is the total number of distance gates. Let be the baseline amplitude value corresponding to the i-th distance gate, where i is a positive integer and This matrix fully records the stable echo characteristics of all fixed obstacles in the current installation environment, including the door lock metal backplate, mounting plate, and surrounding fixed walls / cabinets.

[0028] Compared to existing technologies that use single-frame data to construct baselines, this approach effectively counteracts the interference of random noise on baseline data through multi-frame coherent averaging and outlier frame removal, accurately purifies fixed reflection features, and ensures the stability and accuracy of baseline data.

[0029] In this embodiment, step S23 is implemented as follows: Throughout the baseline acquisition process, the main control module performs real-time dynamic target detection on each frame of range-dimensional spectrum data uploaded by the radar module.

[0030] The specific logic of dynamic object detection is as follows: The distance dimension spectrum data of the current frame is differentially calculated with the spectrum data of the previous frame. When there are two or more consecutive frames in which the amplitude value of at least one distance gate changes more than the preset dynamic threshold, the main control module determines that there is a moving target in the current acquisition environment. At this moment, the main control module immediately sends a pause acquisition command to the radar module to terminate the current acquisition process, and at the same time issues a prompt "calibrating, please stay away from the door lock" through the door lock buzzer and touch panel; After the main control module detects five consecutive frames of data and confirms that there are no dynamic targets in the environment, it restarts the complete baseline acquisition process to ensure that the echo characteristics of dynamic targets are not mixed into the final constructed baseline matrix.

[0031] In this embodiment, the specific implementation process of the partition adaptive detection threshold setting step S3 is as follows: Based on the installation position of the radar module in the front panel of the door lock and the distance mapping relationship of the radar, the main control module divides the 128 distance gates into two independent detection areas. The static suppression area corresponds to distance gate numbers 1 to 32, which corresponds to a close range of detection distance from 0 to 0.6m. This range completely covers the physical installation position of the door lock's metal back plate and mounting plate, and is the core interference range of strong reflection from the metal fixture.

[0032] The normal detection zone corresponds to door numbers 33 to 128, which corresponds to a medium-to-long distance range of 0.6m to 3m. This range covers the effective detection range of a human approaching the door lock, without fixed strong metal reflection interference. After the region division is completed, the main control module calculates the corresponding adaptive detection threshold for each distance gate in the two regions based on the environmental baseline matrix obtained in step S2. After the calculation is completed, the main control module stores the environmental baseline matrix and the adaptive detection thresholds of all distance gates into its own Flash non-volatile memory for retrieval at any time in the subsequent dynamic detection stage.

[0033] The specific calculation method for the adaptive detection threshold in step S3 is as follows: For the i-th distance gate within the static suppression region, where The formula for calculating its adaptive detection threshold is: , where α is a preset suppression coefficient, with a value range of 1.2 to 1.8; For scenarios with strong metal reflection, such as steel security doors, the value of α is 1.6; for scenarios with weak metal reflection, such as wooden doors, the value of α is 1.3. The threshold setting logic is such that the trigger condition is met only when the echo amplitude within the distance gate is significantly raised relative to the baseline, thus filtering out stable echo signals from the metal backplate at the source.

[0034] For the i-th distance gate within the normal detection zone, where 33≤i≤128, its adaptive detection threshold is calculated using the unit average constant false alarm rate (CFAR) detection algorithm, and the calculation formula is as follows: ; Where β is the preset false alarm coefficient, with a value range of 3 to 5, and σ is the standard deviation of the background noise corresponding to the distance gate.

[0035] This threshold setting logic can effectively suppress random environmental noise and reduce the probability of false triggering while ensuring the sensitivity of human body detection.

[0036] Specifically, the dynamic detection and trigger control implementation process in step S4 is as follows: After the door lock completes baseline acquisition and threshold setting, it enters normal standby mode. The main control module controls the radar module to enter low-power intermittent dynamic detection mode. The radar module periodically wakes up and completes the transmission, echo acquisition and preprocessing of one frame of FMCW signal according to a preset interval of 500ms. The rest of the time, it is in low-power sleep mode. After receiving the current frame range-dimensional spectrum data uploaded by the radar module, the main control module first performs a difference calculation between the spectrum data of the current frame and the pre-stored environmental baseline matrix in Flash to obtain the echo amplitude change corresponding to each range gate. , in This represents the amplitude value of the i-th distance gate in the current frame; The main control module then iterates through all distance gates, determining whether there exists at least one distance gate that satisfies the condition. Furthermore, the detection distance corresponding to this distance gate is within the preset effective human body detection range of 0.3m to 1.5m; If the above conditions are met, the main control module will determine the event as a pending confirmation trigger event; otherwise, it will continue to the next round of intermittent detection.

[0037] In this embodiment, the secondary verification and triggering logic of the event to be confirmed in step S4 is as follows: Once the main control module determines the event to be confirmed, it immediately verifies the detection data for a preset number of consecutive frames. Only when two consecutive frames of data meet the determination conditions for the event to be confirmed will it enter the human body micro-motion feature verification stage.

[0038] Specifically, the verification of human body micro-motion features is achieved in the following ways: The main control module retrieves the original echo data corresponding to the distance gate that meets the threshold condition, performs Doppler 2D-FFT operation on the data, and extracts the micro-Doppler features of the echo signal; The main control module has a pre-stored standard micro-Doppler frequency shift feature library corresponding to human breathing and limb micro-movements. The extracted real-time features are matched with the standard feature library. When the matching degree exceeds the preset 80% threshold, it is determined that the echo signal comes from the human target. When all verification conditions are met, the main control module determines it as a valid human proximity event and immediately sends a trigger signal to the face recognition module via the UART serial port to start the face recognition module and the fill light, thus completing the user identification and unlocking process.

[0039] Compared to existing technologies that rely solely on amplitude changes to determine human proximity, this solution uses continuous frame verification and matching of human micro-motion features to effectively distinguish human targets from newly added fixed obstacles at the doorway, completely resolving the issue of false triggering. It can also accurately identify users standing still in front of the door, meeting the core usage requirements of facial recognition door locks.

[0040] Preferably, during the entire dynamic detection process in step S4, the main control module continuously polls the level signal of the latch state sensor at a period of 100ms. When the sensor outputs a high level and the door lock is in the open state, the main control module immediately controls the radar module to pause the current static suppression detection mode and switch to the door opening temporary detection mode. In the door opening temporary detection mode, the original environmental baseline matrix and adaptive detection threshold are suspended, and the preset door opening temporary detection threshold is used for detection to avoid false triggering and detection failure caused by drastic changes in the environment after the door is opened. Once the main control module detects that the door has closed again, the latch has fully extended and locked, it immediately controls the radar module to switch back to static suppression detection mode and performs a rapid incremental update of the environmental baseline to ensure that the baseline data matches the current environment.

[0041] In this embodiment, the incremental update of the environmental baseline is divided into two modes: periodic incremental update and rapid incremental update after the door is opened.

[0042] The specific execution method of the periodic incremental update is as follows: During the preset update period at 3:00 AM every day, if the door lock is in the closed and locked state and there is no dynamic target triggering event for 5 consecutive minutes, the main control module controls the radar module to collect 8 frames of echo signals and obtains the temporary baseline matrix B' according to the same processing procedure as the initial acquisition. The original environmental baseline matrix was then updated using a weighted average algorithm, with the update formula being: , where γ is a preset weighting coefficient with a value of 0.8.

[0043] The execution method for rapid incremental updates after the door is opened is as follows: after the door is locked, the main control module controls the radar module to collect 4 frames of echo signals and uses the same weighted average algorithm to complete the baseline update, with the weight coefficient γ set to 0.9; after the baseline update is completed, the main control module synchronously recalculates the adaptive detection thresholds for all distance doors, overwriting the original stored threshold data.

[0044] Compared to existing technologies where the baseline can only be initialized for acquisition, this solution uses an incremental update mechanism to adapt to slow changes such as temperature drift, door deformation, and fine-tuning of the surrounding environment, ensuring the long-term validity of the baseline data and avoiding the decline of static suppression effect over time.

[0045] Specifically, the biometric module can be any one of a face recognition module, a fingerprint recognition module, an iris recognition module, or a palm print recognition module.

[0046] When using modules that do not require prior power-on, such as fingerprint recognition modules, the main control module triggers the door lock touch panel backlight to light up and the touch buttons to wake up after determining a valid human approach event, adapting to the usage needs of different types of door locks.

[0047] The present invention also provides an intelligent anti-theft door lock, which includes a door lock body, a main control module, a radar module, a bolt status sensor and a biometric identification module.

[0048] The main body of the intelligent anti-theft door lock includes an outer front panel, an inner rear panel, a mechanical lock body, and a metal mounting back plate; The outer front panel and the inner rear panel are fixed to the inner and outer sides of the door body by through bolts. The mechanical lock body is embedded in the door body and is connected to the handle of the outer front panel and the deadbolt mechanism of the inner rear panel respectively. The metal mounting back plate is made of cold-rolled steel plate and is fixed to the outer surface of the door body by expansion screws. It is located between the outer front panel and the door body and is used for the overall installation and fixation of the door lock. It is also the core source of the radar fixed reflection signal that this invention aims to suppress. The surface of the metal mounting back plate is parallel to the plane of the door body and the distance between it and the inner cavity of the outer front panel is 2mm~5mm.

[0049] In this embodiment, the specific components and connections used in the main control module, radar module, latch status sensor, and biometric module are consistent with those described above, and will not be elaborated further here.

[0050] In this embodiment, the smart anti-theft door lock is also equipped with a power module. The power module is fixedly installed in the battery cavity of the rear panel inside the door. It is powered by two 18650 lithium batteries connected in series, with a rated output voltage of 7.4V. The power module has a built-in multi-channel LDO low dropout linear regulator chip, which can output stable voltages of 3.3V and 5V respectively. It supplies power to the main control module, radar module, lock tongue status sensor and face recognition module through PCB traces and power lines.

[0051] The power detection pin of the power module is electrically connected to the ADC sampling channel of the main control module. The main control module can monitor the remaining power of the door lock in real time and adjust the intermittent detection cycle of the radar module when the power is low, thereby further optimizing the standby power consumption.

[0052] The hardware structure of this smart anti-theft door lock, together with the radar static suppression method of this invention, forms a complete technical solution. Compared with conventional smart door locks with radar sensors in the prior art, this solution does not require adding absorbing materials around the radar or modifying the structure and material of the metal mounting backplate. It can effectively suppress the fixed reflection signal of the metal backplate through the hardware layout of this embodiment and the algorithm program built into the main control module. At the same time, it can accurately identify human targets standing still in front of the door, greatly reduce the probability of false triggering of the door lock and standby power consumption, and improve the detection stability and practicality of the smart door lock in different installation environments.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for static radar suppression of a burglarproof door lock, the burglarproof door lock comprising a lock body, a main control module, a radar module, a bolt state sensor, and a biometric module, wherein the radar module, the bolt state sensor, and the biometric module are all communicatively connected to the main control module; characterized in that, Includes the following steps: S1, Precondition Judgment and Initialization Trigger: The main control module obtains the locking status of the door lock body in real time through the latch status sensor. When the door lock body is in the closed locking state and the preset initialization trigger conditions are met, the main control module sends a command to the radar module to start the radar environment baseline acquisition process. S2, Environmental baseline acquisition and static echo feature extraction: After receiving the command, the radar module enters the baseline acquisition mode, transmits the FMCW frequency modulated continuous wave signal and receives the echo signal. After preprocessing the echo signal, the range dimension spectrum data is obtained. Through multi-frame data coherent averaging, an environmental baseline matrix characterizing the fixed reflection characteristics of the installation environment is constructed. S3, Partition adaptive detection threshold setting based on static echo characteristics: The main control module divides all distance gates corresponding to the distance dimension spectrum into a static suppression zone corresponding to the close range of the metal back plate and a normal detection zone corresponding to the effective detection range of the human body, according to the installation position of the radar in the door lock body. Based on the environmental baseline matrix obtained in step S2, the corresponding adaptive detection threshold is calculated for each distance gate in the static suppression zone and the normal detection zone, and the environmental baseline matrix and the adaptive detection threshold are stored in the memory of the main control module. S4, Dynamic Detection and Trigger Control: When the door lock body is in normal standby mode, the radar module enters intermittent dynamic detection mode, collects echo signals, completes preprocessing, and uploads them to the main control module. The main control module processes the data in real time to obtain the distance dimension spectrum data of the current frame, performs differential calculation between the distance dimension spectrum data of the current frame and the pre-stored environmental baseline matrix, and obtains the echo amplitude change corresponding to each distance gate. When the echo amplitude change of at least one distance gate exceeds its corresponding adaptive detection threshold, and the detection distance corresponding to the distance gate is within the preset effective human detection range, the main control module determines it as a trigger event to be confirmed. If the preset number of consecutive frames meets the judgment conditions for the event to be confirmed and the human micro-motion features of the corresponding echo signal are extracted, the main control module determines it as a valid human proximity event and triggers the biometric module to start. When the door lock body is detected to be in an open state, the main control module controls the radar module to pause the static suppression detection mode and resumes it after the door lock body closes and locks again.

2. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: The preset initialization triggering conditions mentioned in step S1 include any one or more combinations of the following: the door lock is installed for the first time, the user manually triggers the calibration command, the door lock is restarted, the preset periodic calibration period is reached, the door lock remains in the closed state for a preset duration and there are no dynamic targets in the environment.

3. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: The biometric module is one of the following: face recognition module, fingerprint recognition module, iris recognition module, and palmprint recognition module.

4. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21, the radar module transmits an FMCW frequency-modulated continuous wave signal according to the preset frequency sweep parameters. After receiving the echo signal, it performs mixing and low-pass filtering to obtain an intermediate frequency signal. The intermediate frequency signal is then subjected to a range-dimensional fast Fourier transform to obtain the range-dimensional spectrum data corresponding to each frame of echo. The range-dimensional spectrum data contains N range gates, each range gate corresponding to a detection distance and the amplitude value of the echo signal at that distance. S22, the radar module continuously acquires range-dimensional spectrum data for a preset number of frames M. After removing abnormal frames whose amplitude changes exceed a preset threshold, the amplitude values ​​of each range gate in the remaining valid frames are arithmetically averaged to obtain the environmental baseline matrix. ,in, The baseline amplitude value corresponding to the i-th distance gate. ; S23. During the data acquisition process, the main control module detects dynamic targets in the environment in real time. If a dynamic target is detected, the data acquisition is immediately paused and restarted when there are no dynamic targets in the environment.

5. The radar static suppression method for anti-theft door locks according to claim 4, characterized in that: In step S3, the static suppression zone corresponds to a distance range of 1 to K, where K < N, which corresponds to the radar's close-range range and covers the fixed reflection distance range of the door lock's metal backplate and mounting plate; the normal detection zone corresponds to a distance range of K+1 to N, which corresponds to the radar's medium-to-long-range range and covers the effective detection range for human approach.

6. The radar static suppression method for anti-theft door locks according to claim 5, characterized in that: In step S3, the adaptive detection threshold is calculated as follows: for the i-th distance gate in the static suppression region, where... Its adaptive detection threshold , where α is a preset suppression coefficient, ranging from 1.2 to 1.8; for the i-th distance gate in the normal detection region, where Its adaptive detection threshold is calculated using the cell-average constant false alarm rate (CFAR) detection algorithm. , where β is the preset false alarm coefficient, with a value range of 3 to 5, and σ is the standard deviation of the background noise corresponding to the distance gate.

7. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: In step S4, the intermittent dynamic detection mode is a low-power intermittent detection mode. The radar module transmits one frame of FMCW signal according to a preset intermittent period of 300ms to 1s and completes the acquisition and processing of echo signals.

8. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: In step S4, the method for extracting the human micro-motion features is as follows: perform Doppler fast Fourier transform on the echo signal corresponding to the distance gate that meets the amplitude change threshold condition, extract micro-Doppler features, determine whether the feature matches the Doppler frequency shift features corresponding to human breathing and limb micro-motion, and eliminate static interference from fixed objects.

9. The radar static suppression method for anti-theft door locks according to claim 1, characterized in that: In step S4, when the door lock is detected to be in an open state, the main control module controls the radar module to pause the static suppression detection mode and switch to the temporary door opening detection mode, pausing the calling of the original environmental baseline matrix and the adaptive detection threshold. After the door lock closes and completes locking, switch back to static suppression detection mode and perform a quick incremental update of the environmental baseline.

10. An intelligent anti-theft door lock, characterized in that: The lock includes a door lock body, a main control module, a radar module, a bolt status sensor, and a biometric module. The radar module, bolt status sensor, and biometric module are all communicatively connected to the main control module. The main control module is configured to execute the steps of the radar static suppression method for the anti-theft door lock according to any one of claims 1-9.

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