Power equipment intelligent lock remote automatic control method and system based on internet of things

Abstract

The application discloses a power equipment intelligent lock remote automatic control method and system based on Internet of Things, relates to the technical field of control analysis, and comprises the following steps: generating high-frequency alternating pulse current driving data when a slope feature does not reach a mutation condition, generating reverse backtracking release stroke data and forward impact pulse data with current integral limitation when the slope feature reaches the mutation condition, and executing corresponding correction actions according to the generated data; intercepting lock buckle pressure resistance sensor time sequence data after each correction action to identify a pressure instantaneous drop pulse, outputting a real unlocking success identifier if the identification is successful, outputting a mechanical fault locking identifier if the identification is not successful after a preset threshold number of cycles, and collecting the current output identifier into an unlocking identifier state; collecting current integral total energy consumption data in the adaptive correction cycle, and uploading the current integral total energy consumption data and the unlocking identifier state to a cloud server. The application has the effect of improving the remote control efficiency of the intelligent lock.

Description

Technical Field

[0001] This application relates to the field of control analysis technology, and in particular to a remote automatic control method and system for smart locks of power equipment based on the Internet of Things. Background Technology

[0002] As a critical component of power transmission and distribution systems, the operational safety of power equipment directly impacts power supply continuity and on-site work safety. Smart locks, serving as access control entry points for power equipment cabinets, enclosures, and isolated areas, not only perform safety management functions such as preventing accidental opening and unauthorized operations, but also affect the efficiency of remote inspections, emergency repairs, and equipment maintenance. Therefore, the ability of smart locks to reliably unlock under complex on-site conditions has become a crucial aspect of power Internet of Things (IoT) management.

[0003] In practical applications, smart locks for electrical equipment are typically exposed to outdoor, high-humidity, low-temperature, dusty, and corrosive environments for extended periods. Changes in ambient temperature and humidity affect the lock body's lubrication, the clearance between metal components, and the resistance to bolt movement. Furthermore, there is an inherent correlation between the motor drive current, bolt displacement, and latch pressure during the unlocking process: current changes reflect the motor load, displacement changes reflect the bolt's movement progress, and latch pressure changes reflect the mechanical release state between the bolt and latch. Relying solely on whether the unlocking command was successfully issued is insufficient to accurately reflect the actual internal execution of the lock body.

[0004] However, existing remote control methods for smart locks on power equipment are mostly based on single-drive and result feedback, lacking synchronous analysis of environmental impacts, mechanical jamming, and changes in lock force during the unlocking process. When the lock tongue fails to reach its designated position due to corrosion, misalignment, freezing, or other reasons, the system often has to repeatedly send fixed unlocking actions, which may cause motor overload and increased energy consumption, or misjudge mechanical failure as unlocking failure, lacking process-oriented identification of jamming types and release states. This results in insufficient adaptability of remote unlocking control, with fault location relying on manual verification, making it difficult to meet the management needs of unattended and refined operation and maintenance of power equipment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a remote automatic control method and system for smart locks of power equipment based on the Internet of Things.

[0006] In a first aspect, this application provides a remote automatic control method for smart locks of power equipment based on the Internet of Things, comprising the following steps: Receive the unlocking command and trigger the data sampling program to obtain the temperature and humidity data of the smart lock environment, and simultaneously collect the timing data of the motor drive current, the timing data of the bolt displacement and the timing data of the latch piezoresistive sensor during the unlocking process; Based on the ambient temperature and humidity data of the smart lock, the reference unlocking current timing envelope is corrected to generate environmentally adapted current envelope data; When the timing data of the bolt displacement does not reach the target unlocking displacement and a stagnant section appears, extract the timing data of the latch piezoresistive sensor in the corresponding section. If the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition, the timing data of the motor drive current and the timing data of the latch displacement in the same stagnant section are mapped into a two-dimensional phase trajectory curve. Based on the slope characteristics of the two-dimensional phase trajectory curve, an adaptive correction loop is entered. When the slope characteristics do not reach the abrupt change condition, high-frequency alternating pulse current drive data is generated. When the slope characteristics reach the abrupt change condition, reverse step-back release stroke data and positive impact pulse data with current integral limitation are generated, and corresponding correction actions are performed based on the generated data. After each correction action, the timing data of the latch piezoresistive sensor is captured to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the number of cycles reaches the preset threshold and the identification is still unsuccessful, a mechanical fault lockout indicator is output, and the currently output indicator is collected as the unlocking indicator status. The total energy consumption data of the current integral within the adaptive correction loop is summarized, and the total energy consumption data of the current integral along with the unlocking status is uploaded to the cloud server.

[0007] Secondly, this application provides a remote automatic control system for smart locks of power equipment based on the Internet of Things, including: The data acquisition module is used to receive unlocking commands and trigger the data sampling program to obtain the ambient temperature and humidity data of the smart lock, and simultaneously collect the timing data of motor drive current, bolt displacement and latch piezoresistive sensor during the unlocking process. The reference correction module is used to correct the reference unlocking current timing envelope based on the temperature and humidity data of the smart lock environment, and generate environmentally adapted current envelope data. The segment extraction module is used to extract the timing data of the latch piezoresistive sensor in the corresponding segment when the timing data of the latch displacement has not reached the target unlocking displacement and a stagnant segment occurs. The curve mapping module is used to map the motor drive current timing data and the latch displacement timing data into a two-dimensional phase trajectory curve when the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition. The correction loop module is used to enter an adaptive correction loop based on the slope characteristics of the two-dimensional phase trajectory curve. When the slope characteristics do not reach the abrupt change condition, it generates high-frequency alternating pulse current drive data. When the slope characteristics reach the abrupt change condition, it generates reverse step-back release stroke data and positive impact pulse data with current integral limitation, and executes corresponding correction actions based on the generated data. The identification output module is used to intercept the timing data of the latch piezoresistive sensor after each correction action to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the identification is still unsuccessful after the number of cycles reaches a preset threshold, a mechanical fault lockout indicator is output, and the currently output indicators are collected as the unlocking indicator status. The summary upload module is used to summarize the total energy consumption data of the current integral within the adaptive correction loop, and upload the total energy consumption data of the current integral along with the unlocking status to the cloud server.

[0008] In summary, this application includes at least one of the following beneficial technical effects: 1. This application provides a remote automatic control method for smart locks of power equipment based on the Internet of Things. Upon receiving an unlocking command, a data sampling program is synchronously triggered to collect ambient temperature and humidity data, motor drive current timing data, latch displacement timing data, and latch piezoresistive sensor timing data during the unlocking process. Furthermore, the baseline unlocking current timing envelope is corrected based on the ambient temperature and humidity data to generate environmentally adapted current envelope data. This allows subsequent jamming identification to no longer rely solely on a fixed current threshold or a single displacement result, but rather to adaptively determine based on actual environmental factors such as increased low-temperature lubrication damping, changes in high-humidity friction, and motor load fluctuations. When the latch displacement fails to reach the target unlocking displacement and a stagnant section appears, this application does not directly classify it as unlocking failure. Instead, it extracts the latch piezoresistive sensor timing data within the same section to determine if a step drop in latch pressure occurs. If the step drop condition is not met, the motor drive current timing data and latch displacement timing data are mapped into a two-dimensional phase trajectory curve. The phase trajectory slope characteristics are used to further distinguish between high-friction slow jamming, latch squeeze jamming, and the critical release state of the mechanism. Thus, a progressive collaborative relationship is formed between the environmental adaptation current envelope, the stagnant section of the latch displacement, the change of the latch piezoresistive resistance, and the current-displacement two-dimensional phase trajectory: environmental data is used to eliminate the interference of external operating conditions on current judgment, displacement data is used to locate the time period of abnormality, piezoresistive data is used to verify whether the latch is actually released, and the phase trajectory slope is used to determine the type of correction strategy, so that the unlocking fault identification is upgraded from single threshold judgment to multi-source time-series coupling judgment. In comparative tests of the same model of smart lock, a total of 1200 unlocking samples were selected under five working conditions: normal temperature, -10℃ low temperature, 85%RH high humidity, 2mm door body bias, and high friction on the lock tongue guide surface. Compared with the traditional solution that uses a fixed current threshold and a single displacement endpoint for judgment, this application improved the accuracy of identifying real mechanical jamming from approximately 86.4% to 96.8%, reduced the false judgment rate of normal current increase caused by low ambient temperature from 11.7% to 2.9%, and improved the detection rate of false unlocking state where the lock tongue has been displaced but the latch has not been released from 78.2% to 95.1%, effectively improving the reliability and precision of unlocking state judgment under complex working conditions. 2. After completing the identification of the stagnant section and the slope analysis of the two-dimensional phase trajectory, an adaptive correction loop is further entered based on the slope characteristics: when the slope characteristics do not reach the abrupt change condition, high-frequency alternating pulse current drive data is generated to break the high friction static lag and slight adhesion with a small impact; when the slope characteristics reach the abrupt change condition, reverse step-back release stroke data and positive impact pulse data with current integral limitation are generated. The severe bias jamming is released by first unloading the latch clamping force in reverse and then by controlled positive impact, and the current integral limitation is used to suppress motor overheating and gear impact damage. After each correction action, this application does not only judge the unlocking result based on the final displacement of the latch tongue, but also intercepts the timing data of the latch piezoresistive sensor to identify the instantaneous pressure drop pulse. Only when the rapid release characteristic of the latch pressure is identified is a true unlocking success mark output; if the number of cycles reaches the preset threshold and the identification is still unsuccessful, a mechanical fault lockout mark is output, and the total energy consumption data of current integral and the unlocking mark status are uploaded to the cloud server for subsequent fault tracking, life assessment and operation and maintenance strategy optimization. The aforementioned technical features exhibit a significant synergistic effect: the two-dimensional phase trajectory slope is responsible for selecting the type of correction action; the high-frequency alternating pulse is responsible for low-loss micro-perturbation dehysteresis; the reverse backstep release is responsible for relieving structural compression; the forward impact pulse is responsible for completing the final release; the current integral limit is responsible for ensuring energy consumption and thermal safety; the instantaneous drop in piezoresistive resistance is responsible for confirming the actual unlocking result; and cloud uploading provides a traceable data loop for long-term operation and maintenance. Through comparative verification with 300 prototypes and a cumulative 30,000 unlocking cycles, under abnormal operating conditions where the door bias is 1.5mm to 3mm and the latch clamping force is increased to 1.8 times the normal value, the automatic recovery success rate of the traditional repetitive forward drive scheme is approximately 72.6%, while this application improves it to 93.4%; the average single abnormal unlocking recovery time is reduced from 4.8s to 2.9s; the peak motor temperature rise is reduced from 18.5℃ to 11.2℃; the peak gearbox impact noise is reduced by approximately 21%; and the number of motor overcurrent protection triggers after continuous abnormal correction is reduced by approximately 67%. Therefore, this invention not only improves the unlocking success rate of smart locks in complex scenarios such as door deformation, latch bias, and low temperature and high friction, but also achieves real unlocking confirmation and fault locking reporting while avoiding blind high current impact, significantly enhancing the security, durability and remote operation and maintenance capabilities of the smart lock system. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1This is a flowchart of a method for remote automatic control of smart locks for power equipment based on the Internet of Things, according to an embodiment of this application.

[0011] Figure 2 This is a flowchart of a method for adapting current envelope data to the environment according to an embodiment of this application.

[0012] Figure 3 This is a flowchart of a method for driving data with high-frequency alternating pulse current according to an embodiment of this application.

[0013] Figure 4 This is a flowchart illustrating the method for generating reverse backstep release stroke data and forward impact pulse data according to an embodiment of this application.

[0014] Figure 5 This is a schematic diagram of a system for remote automatic control of smart locks for power equipment based on the Internet of Things, according to an embodiment of this application. Detailed Implementation

[0015] The following description, in conjunction with the implementation of the present invention, is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention, and all such modifications and additions should fall within the protection scope of the present invention.

[0016] Example 1 This application addresses remote unlocking control of smart locks in scenarios such as power equipment cabinets, ring main units, distribution boxes, and outdoor terminal boxes in substations. These smart locks typically employ a local controller to handle unlocking command parsing, data sampling program scheduling, motor drive control, and local data caching. A cloud server is responsible for receiving unlocking process recordings, merging historical unlocking process records, and generating smart lock health trend data. This application does not simply consider the successful issuance of the unlocking command as the unlocking result. Instead, it simultaneously observes the timing data of the motor drive current, bolt displacement, and latch piezoresistive sensor during unlocking execution. Furthermore, it incorporates ambient temperature and humidity data from the smart lock environment to correct the baseline unlocking current timing envelope, ensuring that subsequent judgments are based on the data correlation within the same unlocking process. In this way, when abnormalities such as rust and jamming of the lock tongue, lock buckle bias, and low temperature resistance occur, the local controller of the smart lock can complete the identification of the stagnant section, the construction of the two-dimensional phase trajectory curve, the adaptive correction cycle, and the identification of the instantaneous pressure drop pulse locally. Then, it uploads the total energy consumption data of current integration and the unlocking status to the cloud server, reducing the misjudgment and additional losses caused by repeated blind driving in remote control.

[0017] This application discloses a remote automatic control method for smart locks on power equipment based on the Internet of Things.

[0018] Reference Figure 1-4A remote automatic control method for smart locks on power equipment based on the Internet of Things includes the following steps: Receive the unlocking command and trigger the data sampling program to obtain the temperature and humidity data of the smart lock environment, and simultaneously collect the timing data of the motor drive current, the timing data of the bolt displacement and the timing data of the latch piezoresistive sensor during the unlocking process; Furthermore, upon receiving the unlocking command and triggering the data sampling program, the system acquires ambient temperature and humidity data of the smart lock, and simultaneously collects timing data of the motor drive current, bolt displacement, and latch piezoresistive sensor during the unlocking process, including: In response to the unlock command, extract the initial temperature and humidity simulation signal; The initial temperature and humidity analog signal is filtered, denoised, and converted from analog to digital to generate environmental temperature and humidity data for the smart lock. The unified sampling clock is started by taking the response time of the unlock command as the sampling starting point; At each sampling cycle of the unified sampling clock, the motor feedback current data, the latch position data, and the latch resistance data are read respectively. Write the motor feedback current data, latch position data and latch piezoresistive data under the same sampling cycle into the same timestamp data frame; Based on the order of the timestamp data frames, generate the timing data of the motor drive current, the timing data of the latch displacement, and the timing data of the latch piezoresistive sensor.

[0019] In one specific implementation, the cloud server sends an unlocking command to the local controller of the smart lock. The data format of the unlocking command can be a data frame containing a smart lock identifier, command sequence number, authorization timestamp, and action type field, wherein the action type field contains an unlocking action flag. After verifying the data integrity and authorization timestamp of the unlocking command, the local controller of the smart lock receives the unlocking command and triggers a data sampling program. After the data sampling program starts, the local controller of the smart lock first acquires the ambient temperature and humidity data of the smart lock. The ambient temperature and humidity data of the smart lock can include temperature data and humidity data, for example, temperature data is a digital value within the range of -20℃ to 70℃, and humidity data is a digital value within the range of 0%RH to 100%RH. At the same time, the local controller of the smart lock synchronously collects the timing data of the motor drive current, the timing data of the bolt displacement, and the timing data of the latch piezoresistive sensor during the unlocking execution. The timing data of the motor drive current comes from the current sampling resistor or Hall current detection channel in the motor drive circuit, the timing data of the bolt displacement comes from the bolt position detection channel, and the timing data of the latch piezoresistive sensor comes from the piezoresistive detection channel set in the force area of ​​the latch. All three types of timing data have timestamps from the same sampling clock, enabling subsequent time-aligned analysis of the correspondence between current, displacement, and piezoresistive data.

[0020] Specifically, the triggering of the data sampling procedure and the generation of timing data can be implemented using the following relatively stable method. After the smart lock local controller responds to the unlocking command, it extracts the initial temperature and humidity analog signal. The initial temperature and humidity analog signal is the voltage output of the temperature and humidity detection circuit. For example, the temperature channel outputs a voltage signal from 0.5V to 2.5V, and the humidity channel outputs a voltage signal from 0.8V to 3.0V. The smart lock local controller performs filtering, noise reduction, and analog-to-digital conversion on the initial temperature and humidity analog signal to generate the smart lock's environmental temperature and humidity data. Filtering and noise reduction can use a first-order low-pass filter. The filtered data... satisfy ,in, This represents the initial simulated temperature and humidity signal currently being sampled. This represents the filtering result of the previous sampling cycle. This represents the filter coefficient, which can range from 0.2 to 0.5. The analog-to-digital conversion can use 12-bit or 16-bit conversion precision. The converted digital value is then converted into temperature and humidity data according to the sensor calibration curve.

[0021] in, Confirmation can be completed during the smart lock factory calibration stage. The temperature and humidity detection circuit is placed in a constant temperature and humidity environment and a step-change environment to collect initial temperature and humidity simulation signals. First, the random fluctuation amplitude of the initial temperature and humidity simulation signal under the constant temperature and humidity environment is statistically analyzed. Then, the response time required for the filtered output to reach a stable value after the actual temperature and humidity change under the step-change environment is recorded. Under the premise of ensuring that the random fluctuation of the filtered smart lock environment temperature and humidity data does not exceed the preset allowable error, the maximum response time that meets the environmental recognition requirements before unlocking is selected. The value is used as the filtering coefficient. In actual settings, if the initial temperature and humidity analog signal has significant noise, then decrease the value. To enhance the filtering and noise reduction effect; if the ambient temperature and humidity change rapidly or faster generation of smart lock ambient temperature and humidity data is required, then increase the [processing speed / capacity]. To improve response speed, ultimately enabling It consistently falls within the value range of 0.2 to 0.5.

[0022] After generating the ambient temperature and humidity data for the smart lock, the local controller of the smart lock starts a unified sampling clock, using the response time of the unlock command as the sampling starting point. The sampling period of the unified sampling clock can be set from 2ms to 10ms; in this embodiment, 5ms is used to balance the transient current changes of the motor and the local processing load. At each sampling cycle of the unified sampling clock, the local controller of the smart lock reads the motor feedback current data, the latch position data, and the latch resistance data, respectively. The motor feedback current data is in milliamperes (mA), the latch position data is in millimeters (m), and the latch resistance data can be the amplified and converted equivalent digital value of the pressure from the piezoresistive sensor. The local controller of the smart lock writes the motor feedback current data, latch position data, and latch resistance data from the same sampling cycle into the same timestamp data frame. A timestamp data frame may contain a timestamp. Motor feedback current data Locking tongue position data and latching resistance data Where k represents the sampling cycle number. As timestamp data frames are continuously written to the buffer, the smart lock's local controller generates timing data for the motor drive current, bolt displacement, and latch piezoresistive sensor, according to the order of the timestamp data frames. Since the three types of data come from the same timestamp data frame, no additional complex resampling is required when extracting data within the same stagnant segment, resulting in a relatively stable data correlation.

[0023] Based on the ambient temperature and humidity data of the smart lock, the reference unlocking current timing envelope is corrected to generate environmentally adapted current envelope data; Furthermore, based on the ambient temperature and humidity data of the smart lock, the reference unlocking current timing envelope is corrected to generate environmentally adapted current envelope data, including: Extract temperature and humidity data from the ambient temperature and humidity data of the smart lock; Map temperature data to lubrication resistance compensation data, and map humidity data to corrosion resistance compensation data. The lubrication resistance compensation data and corrosion resistance compensation data are weighted and synthesized to generate environmental resistance correction data; Read the upper limit and lower limit data of the reference current at each time point in the reference unlocking current timing envelope; The upper limit data and lower limit data of the reference current are adjusted using environmental resistance correction data to generate upper limit data and lower limit data of environmentally adapted current. The upper limit data and lower limit data of the environmental adaptation current are combined in chronological order to generate the environmental adaptation current envelope data.

[0024] In one specific implementation, the local controller of the smart lock pre-stores a reference unlocking current timing envelope. This reference unlocking current timing envelope is a data boundary formed through multiple unlocking tests conducted when the smart lock is under standard temperature and humidity conditions, with normal lock body lubrication and no off-center load on the bolt. It includes upper and lower limit data for the reference current at multiple time points. Because smart locks are affected by increased lubrication resistance at low temperatures and increased corrosion resistance at high humidity in outdoor environments, directly using a fixed reference unlocking current timing envelope can easily misinterpret normal load changes caused by the environment as mechanical abnormalities. Therefore, the local controller of the smart lock corrects the reference unlocking current timing envelope based on the smart lock's environmental temperature and humidity data, generating environmentally adapted current envelope data. This environmentally adapted current envelope data still maintains a time-series format, containing upper and lower limit data for the environmentally adapted current at each time point. This data is used for subsequent calculations of the deviation of the motor drive current timing data from the environmentally adapted current envelope data, as well as for generating duty cycle data for positive impulse pulses.

[0025] Specifically, the generation of environmental adaptation current envelope data is completed in the local controller. The smart lock's local controller extracts temperature and humidity data from the smart lock's ambient temperature and humidity data, then maps the temperature data to lubrication resistance compensation data and the humidity data to corrosion resistance compensation data. The lubrication resistance compensation data can be calculated by gradually increasing the value as the temperature falls below a reference temperature, which can be set to 25°C. For example, the lubrication resistance compensation data... satisfy ,in, This represents temperature data, in degrees Celsius (°C). This is a dimensionless compensation coefficient; when the temperature data is -20℃, Approximately 1. Corrosion resistance compensation data can be calculated by gradually increasing the humidity as it exceeds the reference humidity. The reference humidity can be taken as 60% RH. For example, corrosion resistance compensation data... ,in, This represents humidity data, in %RH. This is a dimensionless compensation coefficient; when the humidity data is 100%RH, The value is 1. The amplitude limiting function in the above calculation is used to prevent the compensation data from having negative values.

[0026] Subsequently, the smart lock's local controller performs weighted synthesis of lubrication resistance compensation data and corrosion resistance compensation data to generate environmental resistance correction data. Satisfy ,in, This indicates the weight of the lubrication resistance compensation data. The weights of the corrosion resistance compensation data are indicated by the values ​​of the two, which can be determined through unlocking tests of the same model of smart lock in low-temperature and high-humidity chambers. Take 0.6, The value is set to 0.4. The local controller of the smart lock reads the upper limit and lower limit data of the reference current at each time point in the reference unlocking current timing envelope, and adjusts the upper limit and lower limit data of the reference current respectively using environmental resistance correction data to generate environmental adaptation current upper limit and lower limit data. For example, at a certain time point... Environmental adaptation current upper limit data Press Calculation of environmental adaptation current lower limit data Press Calculate, where, This represents the upper limit of the reference current at that point in time. This represents the lower limit of the reference current at that point in time. and These represent correction coefficients for the upper and lower boundaries, respectively, and can be set to 0.15 to 0.35 and 0.05 to 0.20. The smart lock's local controller combines the upper and lower environmental adaptation current data in chronological order to generate environmental adaptation current envelope data. This data is stored in a local cache during unlocking and is retrieved for subsequent deviation calculations and current integral limit processing.

[0027] in, and Confirmation can be completed during the environmental stress calibration stage of the same model of smart lock. Multiple smart locks of the same model were placed in low-temperature chambers and humidity chambers for repeated unlocking tests. The low-temperature chamber test mainly changed the temperature data while keeping the humidity data relatively stable, and the humidity chamber test mainly changed the humidity data while keeping the temperature data relatively stable. The unlocking current increment, bolt displacement hysteresis, and correction action trigger frequency were recorded relative to the normal temperature and humidity baseline under different environmental conditions. Subsequently, the proportion of load increment caused by temperature changes and the proportion of load increment caused by humidity changes were calculated. The average or median of multiple test results was used as the weighting basis for lubrication resistance compensation data and corrosion resistance compensation data in the environmental resistance correction data. If the unlocking current increment and displacement hysteresis are more significant under low-temperature conditions, the unlocking current increment and displacement hysteresis are increased. If the long-term load increase caused by corrosion resistance is more significant under high humidity conditions, then increase... Ultimately, it led to and The value of satisfies the normalization requirement under the same environmental resistance correction model, and can usually be set to . This is to ensure that the environmental resistance correction data is not repeatedly amplified.

[0028] It is indeed accepted that the upper boundary safety margin of the reference unlocking current timing envelope should be considered. During calibration, multiple normal unlocking actions are performed under different combinations of temperature and humidity data. First, based on the established... and Calculate the environmental resistance correction data corresponding to each test condition, and then calculate the maximum excess ratio of the actual motor drive current timing data to the upper limit of the reference current data under each condition. The value should be chosen such that the corrected environmental current limit covers most normal load increases caused by environmental factors, while still retaining the ability to identify abnormal blocking currents. In practice, the ratio of the normal unlocking current exceeding the limit under various operating conditions to the environmental resistance correction data can be statistically analyzed, and the 95th percentile or the average plus one standard deviation can be used as the baseline. The candidate values ​​are then combined with the motor's rated current and the driver's protection current for limiting, ultimately resulting in... It is determined to be within the range of 0.15 to 0.35.

[0029] The confirmation primarily considers the impact of environmental changes on the low-load boundary of normal unlocking, avoiding the misjudgment of the light-load normal unlocking process due to excessively high environmental adaptation current lower limit data. During calibration, actual motor drive current timing data during the normal unlocking process are collected under different combinations of temperature and humidity data, comparing its overall rise ratio relative to the reference current lower limit data, with a focus on observing the low-current range in the initial unlocking stage and the near-release stage of the bolt. Since the reference current lower limit data is used to describe the minimum load level required for normal operation, its influence from environmental resistance is generally less than that of the reference current upper limit data. Should be smaller The ratio of the normal unlocking current low boundary rise to the environmental resistance correction data under various operating conditions can be calculated in practice. The median or 75th percentile under stable operating conditions is taken as the... The candidate values ​​were selected, and historical unlocking data was played back to verify that they would not cause normal low-current unlocking samples to be misjudged. It is determined to be within the range of 0.05 to 0.20.

[0030] When the timing data of the bolt displacement does not reach the target unlocking displacement and a stagnant section appears, extract the timing data of the latch piezoresistive sensor in the corresponding section. Furthermore, when the bolt displacement timing data fails to reach the target unlocking displacement and a stall occurs, the timing data of the latch piezoresistive sensor within the corresponding segment is extracted, including: Read the latch displacement data corresponding to adjacent timestamps in the latch displacement timing data; Calculate the displacement difference between adjacent latch displacement data, and mark the continuous time period in which the displacement difference is lower than the displacement change threshold as a candidate stagnation section; Extract the bolt displacement data corresponding to the end of the candidate stagnation section and compare it with the target unlocking displacement; When the bolt displacement data at the end of the candidate stall section is less than the target unlocking displacement, the candidate stall section is confirmed as a stall section. Based on the start and end timestamps of the stall section, piezoresistive data within the same time range is extracted from the timing data of the latching piezoresistive sensor to generate the stall section piezoresistive data.

[0031] In one specific implementation, after the unlocking process begins, the local controller of the smart lock controls the motor to drive the bolt movement according to the initial unlocking stroke, while continuously reading the bolt displacement timing data. The target unlocking displacement can be pre-written into the local storage by the lock body structural parameters. For example, for a smart lock with an effective bolt retraction stroke of 8.0 mm, the target unlocking displacement can be set to a calibration value within the range of 7.6 mm to 8.2 mm. When the bolt displacement timing data has reached the target unlocking displacement, and the timing data of the latch piezoresistive sensor shows a significant release change, the local controller of the smart lock can complete the unlocking process as usual. When the bolt displacement timing data has not reached the target unlocking displacement and a stagnant section appears, the local controller of the smart lock does not immediately and simply determine that the unlocking has failed, but instead extracts the timing data of the latch piezoresistive sensor in the corresponding section. The stagnation zone here reflects insufficient displacement change of the latch within a continuous period of time. If the timing data of the latch piezoresistive sensor in the corresponding zone shows a step drop, it indicates that the latch pressure has been released, and there may be hysteresis or structural gap influence in the latch displacement detection. If the timing data of the latch piezoresistive sensor in the corresponding zone does not meet the step drop condition, the coupling state between the motor load and the latch displacement should be further analyzed.

[0032] Specifically, the identification of bolt stoppage relies on the bolt displacement timing data generated under the aforementioned unified sampling clock. The smart lock's local controller reads the bolt displacement data corresponding to adjacent timestamps in the bolt displacement timing data and calculates the displacement difference between adjacent bolt displacement data. If the... The latch displacement data for each timestamp is , No. The latch displacement data for each timestamp is Then the displacement difference ,in, This indicates taking the absolute value. The displacement change threshold can be determined based on the resolution of the bolt displacement detection channel and the normal unlocking speed of the motor. For example, when the displacement detection resolution is 0.02mm, the displacement change threshold can be set to 0.03mm to 0.08mm. The local controller of the smart lock marks continuous time periods where the displacement difference is lower than the displacement change threshold as candidate stagnation segments. The minimum duration of the continuous time period can be set to 50ms to 150ms to avoid mismarking caused by jitter of a single sampling point.

[0033] The determination of the displacement change threshold can be completed during the normal unlocking calibration of the smart lock. During calibration, a prototype with good lock body lubrication, no door bias voltage, and stable motor power supply is selected. Timing data of the bolt displacement during multiple normal unlocking processes are continuously collected using a unified sampling clock, and analyzed in conjunction with the static noise of the bolt displacement detection channel. First, bolt position data is collected when the motor is not moving and the bolt is stationary to obtain the peak-to-peak value or standard deviation of the noise in the bolt displacement detection channel. Then, the displacement difference distribution under adjacent sampling cycles is calculated during normal unlocking. The displacement change threshold should be higher than the spurious displacement change caused by static noise, while being lower than the typical adjacent displacement difference during continuous bolt movement during normal unlocking. In practice, the overlap between 1.5 to 3 times the peak value of static noise and 50% to 80% of the quintile of the displacement difference between adjacent normal unlocking can be taken as the candidate range. If the displacement detection resolution is 0.02 mm and the displacement difference between adjacent normal unlocking is significantly higher than 0.08 mm, the displacement change threshold can be set within 0.03 mm to 0.08 mm. The ability to stably mark candidate stall sections without mismarking normal deceleration sections can be verified by replaying slightly stuck samples.

[0034] After obtaining candidate stagnation sections, the smart lock's local controller extracts the bolt displacement data corresponding to the end of the candidate stagnation section and compares it with the target unlocking displacement. When the bolt displacement data corresponding to the end of the candidate stagnation section is less than the target unlocking displacement, it indicates that the bolt has not yet completed its retraction action, and the candidate stagnation section is confirmed as a stagnation section. If the bolt displacement data corresponding to the end of the candidate stagnation section has reached the target unlocking displacement, then this section can be used as a natural deceleration section before unlocking is completed and will not enter the subsequent adaptive correction loop. After the stagnation section is confirmed, the smart lock's local controller extracts piezoresistive data within the same time range from the timing data of the latch piezoresistive sensor according to the start and end timestamps of the stagnation section, generating stagnation section piezoresistive data. Since the start and end timestamps of the stagnation section come directly from the timestamp data frame, the stagnation section piezoresistive data maintains a sampling point-level correspondence with the stagnation section current data and stagnation section displacement data, providing a unified time basis for subsequent step descent condition judgment and two-dimensional phase trajectory curve generation.

[0035] If the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition, the timing data of the motor drive current and the timing data of the latch displacement in the same stagnant section are mapped into a two-dimensional phase trajectory curve. Furthermore, when the timing data of the latch piezoresistive sensor does not meet the step descent condition, the timing data of the motor drive current and the timing data of the latch displacement are mapped into a two-dimensional phase trajectory curve, including: Adjacent differential processing is performed on the piezoresistive data in the stagnant section to generate piezoresistive differential data; When the decrease in the differential pressure data is lower than the pressure decrease threshold, it is determined that the pressure resistance data in the stagnant section does not meet the step decrease condition. According to the start and end timestamps of the stall section, extract the stall section current data from the motor drive current timing data, and extract the stall section displacement data from the lock tongue displacement timing data. The current data of the stagnant section under the same time stamp is used as the vertical data, and the displacement data of the stagnant section under the same time stamp is used as the horizontal data to form phase trajectory points; Connect the phase trajectory points in the order of timestamps to generate a two-dimensional phase trajectory curve.

[0036] In one specific implementation, when the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition, the local controller of the smart lock maps the timing data of the motor drive current and the timing data of the latch displacement in the same stagnant section into a two-dimensional phase trajectory curve. This two-dimensional phase trajectory curve uses the displacement data of the stagnant section as the horizontal data and the current data of the stagnant section as the vertical data. Data points at the same timestamp form phase trajectory points, which are then connected in time-stamp order. If the current continuously increases while the displacement remains almost still, the two-dimensional phase trajectory curve will exhibit a steep slope; if the current change is accompanied by a small displacement creep, the slope of the two-dimensional phase trajectory curve will be relatively gentle but may remain at a high level for a long time; if a rigid blockage is suddenly encountered, the local slope data will show abrupt changes. Based on this, the local controller of the smart lock extracts the slope characteristics of the two-dimensional phase trajectory curve and enters an adaptive correction loop according to these characteristics.

[0037] Specifically, after the piezoresistive data of the stalled section is generated, the local controller of the smart lock performs adjacent differential processing on the piezoresistive data of the stalled section to generate piezoresistive differential data. If the first... The piezoresistive data is as follows: , No. The piezoresistive data is as follows: Then the piezoresistive differential data Since this implementation focuses on pressure reduction, the magnitude of the reduction can be expressed as... When the decrease in the differential pressure resistance data is lower than the pressure drop threshold, the local controller of the smart lock determines that the pressure resistance data in the stagnant section does not meet the step drop condition. The pressure drop threshold can be determined by statistically analyzing the timing data of the latch pressure resistance sensor at the moment of normal unlocking and release. For example, it can be set to 40% to 70% of the average decrease in normal release, or it can be set to 8% to 15% of the full scale of the latch pressure resistance data. This setting can distinguish between the sudden drop caused by the actual release of the latch and the small fluctuations caused by motor vibration.

[0038] The pressure drop threshold can be determined by comparing normal release samples with non-release disturbance samples. During calibration, multiple sets of timing data from the latch piezoresistive sensor are collected at the moment of normal unlocking and release. The drop amplitude between adjacent sampling cycles is calculated, and the maximum drop amplitude corresponding to each actual release is extracted to obtain the normal release drop amplitude distribution. Simultaneously, timing data from the latch piezoresistive sensor are collected under conditions where no actual release occurs, such as motor start-up, stop, reverse step-back, and slight door vibration, to obtain the non-release disturbance drop amplitude distribution. The pressure drop threshold should be lower than the main distribution range of the normal release drop amplitude, but higher than the upper limit or high percentile of the non-release disturbance drop amplitude. In practice, 40% to 70% of the average normal release drop amplitude can be used as the initial candidate value, and then compared with the 95th percentile of the drop amplitude without release disturbance. The higher of the two is taken as the pressure drop threshold. If there is a large difference between the sensor full scale and the installation preload, the amplitude can also be limited to 8% to 15% of the full scale of the latch pressure resistance data to ensure that the threshold can both identify the step drop caused by the actual release of the latch and filter out the small fluctuations caused by motor vibration.

[0039] When the piezoresistive data of the stalled section does not meet the step descent condition, the local controller of the smart lock extracts the stalled section current data from the motor drive current timing data and the stalled section displacement data from the latch displacement timing data according to the start and end timestamps of the stalled section. Then, the local controller uses the stalled section current data at the same timestamp as the vertical data and the stalled section displacement data at the same timestamp as the horizontal data to form phase trajectory points. If a certain timestamp is... The corresponding displacement data for the stagnation section is The corresponding current data for the stagnant section is Then the phase trajectory points can be represented as The local controller of the smart lock connects the phase trajectory points in timestamp order to generate a two-dimensional phase trajectory curve. This curve is not used for graphical display, but rather serves as a data structure for the local controller to calculate the slope characteristics. Each segment of adjacent phase trajectory points simultaneously contains displacement and current changes, reflecting the synchronization relationship between the motor load and the bolt movement.

[0040] Based on the slope characteristics of the two-dimensional phase trajectory curve, an adaptive correction loop is entered. When the slope characteristics do not reach the abrupt change condition, high-frequency alternating pulse current drive data is generated. When the slope characteristics reach the abrupt change condition, reverse step-back release stroke data and positive impact pulse data with current integral limitation are generated, and corresponding correction actions are performed based on the generated data. Furthermore, based on the slope characteristics of the two-dimensional phase trajectory curve, an adaptive correction loop is entered, including: Calculate the local slope data between adjacent phase trajectory points based on the current and displacement changes of adjacent phase trajectory points in the two-dimensional phase trajectory curve. Segmented statistical analysis is performed on local slope data to generate mean slope data and slope jump data; When the slope jump data is lower than the mutation threshold and the slope mean data is higher than the friction threshold, high friction stuck state data is generated. When the slope jump data reaches the mutation threshold, rigid jamming state data is generated; The high-friction jamming state data is mapped to high-frequency alternating pulse current drive data, and the rigid jamming state data is mapped to reverse step-back release stroke data and forward impact pulse data.

[0041] In one specific implementation, the adaptive correction cycle is constrained by the number of cycles, the timing data of the latch piezoresistive sensor after each correction action, and the current integral limit. When the slope characteristic does not reach the abrupt change condition, the smart lock local controller generates high-frequency alternating pulse current drive data. This high-frequency alternating pulse current drive data is suitable for scenarios with slight sticking or corrosion resistance of the latch corresponding to high-friction jamming conditions. Its pulse amplitude and frequency data are formed based on the current deviation and displacement lag, used to allow the latch to move away from the static friction peak under a small alternating force. When the slope characteristic reaches the abrupt change condition, the smart lock local controller generates reverse backstep release stroke data and positive impact pulse data with current integral limit. The reverse backstep release stroke data is used to allow the latch to retract a small stroke in the opposite direction to release the latch bias, while the positive impact pulse data with current integral limit applies a positive impact within the controlled energy range. The smart lock local controller executes the corresponding correction action based on the generated data. After each correction action, it does not only read the motor stop state but also intercepts the timing data of the latch piezoresistive sensor to identify the instantaneous pressure drop pulse.

[0042] Specifically, after the two-dimensional phase trajectory curve is generated, the local controller of the smart lock calculates the local slope data between adjacent phase trajectory points based on the current and displacement changes of adjacent phase trajectory points in the two-dimensional phase trajectory curve. For adjacent phase trajectory points... and Local slope data Press Calculate, where, This represents the change in current. It represents the amount of displacement change. To prevent minute displacements with a denominator of zero, a displacement detection resolution of 10% to 50% can be used. The unit of local slope data can be understood as the ratio of current change to displacement change. The local controller of the smart lock performs segmented statistics on the local slope data, generating average slope data and slope jump data. For example, the stagnant section can be divided into statistical segments every 20ms to 50ms. The average slope data is the arithmetic mean of the local slope data within this statistical segment, and the slope jump data is the absolute value of the difference between the average slope data of adjacent statistical segments. Alternatively, the difference between the maximum and minimum local slope data within the same statistical segment can be used.

[0043] Both the mutation threshold and the friction threshold can be determined during factory calibration or maintenance calibration. The friction threshold can be determined from test data of insufficient normal lubrication but no rigid obstruction, while the mutation threshold can be determined from test data of rigid jamming caused by the bolt pressing against the edge of the latch or door body bias. In this embodiment, when the slope jump data is lower than the mutation threshold and the average slope data is higher than the friction threshold, the smart lock local controller generates high friction jamming state data. This state indicates that the bolt displacement change is obstructed but the current response does not show a drastic jump, which is more consistent with the crawling difficulties caused by static friction or slight corrosion. When the slope jump data reaches the mutation threshold, the smart lock local controller generates rigid jamming state data. This state indicates that the bolt encounters a significant mechanical boundary or latch bias point during movement. Subsequently, the smart lock local controller maps the high friction jamming state data to high-frequency alternating pulse current drive data and the rigid jamming state data to reverse step-back release stroke data and forward impact pulse data. Since the two types of state data come from the same two-dimensional phase trajectory curve, their correction actions can change with the current jamming state and are no longer fixed, repetitive unlocking actions.

[0044] Furthermore, the high-friction jamming state data is mapped to high-frequency alternating pulse current drive data, including: Extract the timing data of motor drive current and lock tongue displacement within the corresponding section of the high-friction jamming state data; Based on the deviation of the motor drive current timing data from the environmental adaptation current envelope data, pulse amplitude data is generated. Based on the displacement difference in the stagnant section of the latch displacement timing data, pulse frequency data is generated; Combine pulse amplitude data and pulse frequency data into high-frequency alternating pulse current drive data; After executing the correction action corresponding to the high-frequency alternating pulse current drive data, the timing data of the latching piezoresistive sensor after the action ends is captured; When the captured timing data of the latching piezoresistive sensor shows a sudden drop in pressure pulse, the high friction jamming state data is updated to the released state data.

[0045] Specifically, for high-friction jamming state data, the smart lock local controller extracts the motor drive current timing data and latch displacement timing data within the corresponding segment of the high-friction jamming state data. The segment corresponding to the high-friction jamming state data can be the statistical segment that generated the state data plus one statistical segment before and after it, to avoid generating pulse parameters based solely on extremely short data fragments. The smart lock local controller generates pulse amplitude data based on the deviation of the motor drive current timing data from the environmental adaptation current envelope data. The deviation can be calculated according to... Calculate, where, Indicates the timestamp within the corresponding segment Motor drive current timing data, This represents the upper limit of the environmental adaptation current envelope data at the same point in time. This indicates taking the larger value. This indicates that the average value is calculated over all sampling points within the corresponding segment. (Pulse amplitude data) Press Formation, among which, Based on the basic pulse amplitude, This is the amplitude adjustment coefficient. It should also be limited by the rated current of the motor, for example, not exceeding 80% to 95% of the rated current.

[0046] in, and This can be determined through a graded correction test of high-friction jamming samples. During calibration, select a smart lock of the same model, apply controllable frictional resistance to the bolt guide surface, or use different lubrication conditions to generate high-friction jamming data. First, test drive with low-amplitude pulses that do not exceed the motor's continuous operating safe current, gradually increase the pulse amplitude, and record the minimum pulse amplitude that can produce a stable, small displacement of the bolt without triggering the overcurrent protection of the actuator. Determine this value or a slightly higher safety margin value as the minimum acceptable value. Subsequently, the deviation of the motor drive current timing data from the environmental adaptation current envelope data was collected under different environmental adaptation current envelope data, and the additional pulse amplitude required for effective dehysteresis corresponding to each deviation was recorded. The relationship was then analyzed through linear fitting. Sure ,in, Indicates in The pulse amplitude is increased based on the existing value. This indicates the deviation of the motor drive current timing data from the environmental adaptation current envelope data. After fitting, it is also necessary to... The maximum value under all operating conditions is limited to the allowable range of the motor's rated current. Playback of normal unlocking and slight jamming samples verifies that it will not cause excessive impact, ultimately achieving a balance between disengagement capability and motor protection requirements. and .

[0047] The smart lock's local controller also generates pulse frequency data based on the displacement difference of the bolt displacement timing data within the stationary section. If the first and last bolt displacement data within the stationary section are respectively... and Then the displacement difference .when A smaller value indicates that the latch is almost held in place by static friction, and the pulse frequency data can be taken as a higher value, such as 80Hz to 150Hz; when Since there is already slight crawling, the pulse frequency data can be set to a lower value, such as 30Hz to 80Hz, to avoid excessive disturbance to the lock body.

[0048] Subsequently, the smart lock's local controller combines the pulse amplitude and frequency data into high-frequency alternating pulse current drive data. This high-frequency alternating pulse current drive data may also include pulse duration and a positive-to-negative alternation ratio. The pulse duration can range from 100ms to 500ms, and the positive-to-negative alternation ratio can be 1:1 or slightly offset towards the unlocking direction based on the current position of the latch. After executing the correction action corresponding to the high-frequency alternating pulse current drive data, the smart lock's local controller intercepts the timing data of the latch piezoresistive sensor after the action ends. When the intercepted timing data of the latch piezoresistive sensor shows a momentary pressure drop pulse, the smart lock's local controller updates the high-friction jamming state data to a released state, and uses this update result to terminate the high-friction jamming branch in this adaptive correction loop.

[0049] Furthermore, the rigid jamming state data is mapped to reverse recoil release stroke data and forward impact pulse data, including: Extract the abrupt change starting point of the two-dimensional phase trajectory curve within the corresponding section of the rigid jamming state data; Read the latch displacement data and motor drive current data corresponding to the mutation start point; Based on the difference between the latch displacement data corresponding to the mutation start point and the target unlocking displacement, reverse backstep release stroke data is generated; Based on the difference between the motor drive current data corresponding to the mutation start point and the upper boundary current data at the corresponding time point of the environmental adaptation current envelope data, the duty cycle data of the positive impact pulse data is generated. The duty cycle data is subjected to current integral limiting to generate positive impulse pulse data with current integral limiting. After executing the release action corresponding to the reverse step-back release stroke data, the impact action corresponding to the forward impact pulse data is then executed.

[0050] Specifically, for rigid jamming state data, the smart lock local controller extracts the abrupt change starting point of the two-dimensional phase trajectory curve within the corresponding segment of the rigid jamming state data. The abrupt change starting point can be determined as the initial phase trajectory point of the statistical segment where the slope jump data first reaches the abrupt change threshold, or it can be the phase trajectory point before the maximum jump of the local slope data within that statistical segment. The smart lock local controller reads the latch displacement data and motor drive current data corresponding to the abrupt change starting point. For example, assuming the latch displacement data corresponding to the abrupt change starting point is... The motor drive current data corresponding to the mutation start point is The target unlocking displacement is The local controller of the smart lock then generates reverse backstep release travel data based on the difference between the bolt displacement data corresponding to the abrupt change starting point and the target unlocking displacement. Can be with Size-related, for example ,in, This represents the minimum reverse backstep release travel data. This indicates the maximum reverse backstep release travel data. Indicates the regression coefficient. A thickness of 0.1mm to 0.3mm is acceptable. A margin of 0.8mm to 1.5mm is acceptable. This way, the farther the bolt is from the target unlocking displacement, the greater the retraction release stroke will be, but it will not exceed the allowable reverse movement range of the lock body.

[0051] in, , and The determination can be made through a reverse backstep release calibration test: Select a smart lock of the same model, and repeatedly perform the unlocking action under conditions such as normal assembly, slight bias, high friction jamming, and latch compression. When the bolt stops or the latch fails to release, apply different reverse backstep distances and record the bolt regaining movement clearance, the success rate of forward unlocking again, the peak motor current, and the total unlocking time. The minimum stroke should be selected to eliminate gear backlash, release slight preload, and produce detectable reverse displacement of the locking tongue. It should not be too small to have no release effect. It can usually be set to 0.1mm to 0.3mm depending on the displacement detection resolution and transmission clearance. The maximum safe back travel distance should be taken to form an effective unloading space under severe bias or high friction conditions. At the same time, it should not cause the bolt to re-enter the locked position, affect the user's ability to open the door, or cause impact on the mechanism. It can usually be determined by extreme jamming sample test within the range of 0.8mm to 1.5mm. The required reverse travel distance is determined based on the correlation between the severity of the jamming and the required reverse travel distance. The deviation of the motor drive current from the environmental adaptation current envelope, the duration of the latch stoppage, or the degree to which the latch pressure does not decrease can be used as indicators of jamming strength. The minimum reverse travel distance required to achieve successful release under different jamming strengths is statistically analyzed, and obtained through linear fitting or piecewise fitting. Ultimately, the calculated backstep release path is achieved through and After limiting the amplitude, it can cover most of the pent-up demand for release without causing excessive reverse action.

[0052] The duty cycle data for the positive impact pulse is determined by the current deviation. The smart lock's local controller generates the duty cycle data for the positive impact pulse based on the difference between the motor drive current data corresponding to the abrupt change in the starting point and the upper boundary current data at the corresponding time point of the environmental adaptation current envelope data. If the upper boundary current data at the corresponding time point of the environmental adaptation current envelope data is... Then the current difference Duty cycle data Press Calculate, where, Based on duty cycle, This is the duty cycle adjustment coefficient. It is usually limited to the range of 20% to 70%.

[0053] in, and The accuracy of the calibration can be determined by comprehensively considering the temperature rise of the pulse correction drive, the success rate of disengagement, and the impact test of the mechanism. During calibration, the same model of smart lock is selected, and different degrees of bolt jamming samples are constructed under various conditions, including normal temperature and humidity, low temperature, high humidity, high friction, and door body bias. The pulse amplitude and pulse period are fixed initially, and the duty cycle is gradually increased from a lower duty cycle. At each duty cycle, the effective displacement of the bolt, the success rate of disengagement, the peak motor current, the coil temperature rise, the driver temperature rise, and the lock body impact noise are recorded. The lowest or lower duty cycle that can stably produce a correction effect under slight jamming conditions, while keeping the motor temperature rise and impact within a safe range, is determined as the base duty cycle. Normally should be made It falls within the lower half of the 20% to 70% limit range to preserve room for subsequent adjustments. The required duty cycle increment is determined based on the relationship between the jamming strength index and the required jamming strength index. The deviation of the motor drive current from the environmental adaptation current envelope, the duration of latching tongue stagnation, the insufficient displacement per unit time, or the unreleased latching pressure can be used as the jamming strength index. The minimum effective duty cycle required to achieve the target disengagement success rate under different jamming strengths is statistically analyzed, and then calculated according to the relationship... Fitting ,in, Indicates in The duty cycle increment based on the base This represents the normalized hysteresis strength index; after fitting, it is still necessary to... The calculation results were limited to the range of 20% to 70%, and the motor temperature rise was verified to be within the limit, the driver to not overcurrent, and the locking tongue to not experience obvious repeated impacts through continuous correction actions. This determined a balance between disengagement capability, power consumption, and safety. and .

[0054] Subsequently, the smart lock's local controller performs current integral limiting processing on the duty cycle data, generating positive impulse pulse data with current integral limiting. Current integral limiting processing can be achieved using... In the form of, This represents the real-time motor current during the duration of the positive impact pulse. Represents the integral time element. This represents the upper limit of the allowable current integral for a single impact. In a discrete implementation, it can be calculated. ,in, For the first Motor current per sampling cycle, The sampling period is [percentage]. If the integral of the predicted current exceeds [percentage], [the sampling period will be determined]. The smart lock's local controller reduces the duty cycle data or shortens the duration of the forward impact pulse data. After executing the release action corresponding to the reverse backstep release stroke data, it executes the impact action corresponding to the forward impact pulse data, causing the bolt to first retract from the bias point and then impact in the target unlocking displacement direction under limited energy.

[0055] After each correction action, the timing data of the latch piezoresistive sensor is captured to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the number of cycles reaches the preset threshold and the identification is still unsuccessful, a mechanical fault lockout indicator is output, and the currently output indicator is collected as the unlocking indicator status. The total energy consumption data of the current integral within the adaptive correction loop is summarized, and the total energy consumption data of the current integral along with the unlocking status is uploaded to the cloud server.

[0056] Furthermore, after each correction action, timing data from the latch piezoresistive sensor is captured to identify the instantaneous pressure drop pulse, including: Record the timestamp of the end of each correction action; Starting from the action end timestamp, extract the pressure recognition time window data from the timing data of the latch piezoresistive sensor. The pressure identification time window data is subjected to adjacent difference processing to generate a pressure drop amplitude sequence; Extract the maximum decrease value from the pressure decrease value sequence and read the timestamp corresponding to the maximum decrease value; When the maximum drop value reaches the pressure drop threshold and the timestamp corresponding to the maximum drop value is within the pressure recognition time window, instantaneous pressure drop pulse data is generated. The instantaneous pressure drop pulse data is correlated with the timing data of the bolt displacement within the same time window. When the bolt displacement data within the same time window reaches the target unlocking displacement, a true unlocking success indicator is output.

[0057] Furthermore, the total energy consumption data of the current integral within the adaptive correction loop is summarized, and the total energy consumption data of the current integral, along with the unlocking status, is uploaded to the cloud server, including: In each cycle of the adaptive correction loop, the timing data of the motor drive current corresponding to the cycle is extracted; The timing data of the motor drive current corresponding to the cycle period is processed by time integration to generate single-cycle current integral energy consumption data. The single-cycle current integral energy consumption data is accumulated sequentially according to the cycle period to generate the total current integral energy consumption data. Extract the smart lock identifier from the local storage, and combine the total energy consumption data of current integration, cycle count data, smart lock ambient temperature and humidity data, unlock identifier status, and smart lock identifier into unlock process record data; The unlocking process data is uploaded to the cloud server; The cloud server retrieves historical unlocking process records based on the smart lock identifier, and merges the current unlocking process record data with the historical unlocking process record data in chronological order to generate smart lock health trend data.

[0058] In one specific implementation, the instantaneous pressure drop pulse recognition is used to distinguish between a genuine mechanical release and the end of a simple drive action. After each correction action, the smart lock's local controller intercepts the timing data from the latch resistance sensor. If the recognition is successful, it outputs a genuine unlocking success indicator. Successful recognition depends not only on the pressure drop but also on whether the bolt displacement timing data within the same time window reaches the target unlocking displacement, preventing pressure fluctuations caused by external vibrations or motor backlash from being mistakenly interpreted as unlocking success. If the adaptive correction cycle reaches a preset threshold number of iterations without successful recognition, the smart lock's local controller outputs a mechanical fault locking indicator and aggregates the currently output indicator into an unlocking status. The preset threshold can be calibrated based on the smart lock motor's rated power, lock body structural strength, and on-site maintenance strategies; for example, it can be set to 3 to 6 iterations. Finally, the smart lock's local controller summarizes the total energy consumption data of the current integral within the adaptive correction cycle and uploads this data along with the unlocking status to the cloud server, enabling the cloud server to generate subsequent operational status records based on the actual unlocking process.

[0059] The preset threshold can be determined through a comprehensive calibration of correction success rate, mechanism safety, and user experience. Continuous correction tests are conducted on the same model of smart lock under various conditions, including normal door gap, slight bias, severe bias, high friction, low temperature, and low battery. The success rate improvement, motor peak current, cumulative power-on time, coil temperature rise, driver temperature rise, impact marks on the latch and buckle, gear wear, and user waiting time are recorded after each pulse correction or reverse step-back release. Statistical analysis shows that successful releases typically occur in the first few correction actions, while the success rate gain in subsequent actions significantly decreases. The inflection point of the success rate gain can be used as a candidate threshold. Simultaneously, the upper limit of single correction energy and total continuous correction energy is calculated based on the motor's rated power and allowable temperature rise. The upper limit of the allowable number of impacts is determined based on the lock body structural strength and gear life. This, combined with on-site maintenance strategies, determines whether to report the fault in advance or prompt manual intervention. The final preset threshold should not exceed the minimum of the thermal safety upper limit, the mechanical impact upper limit, and the number of acceptable waiting times for the user, and should be slightly higher than the number of corrections required for most recoverable stuck samples.

[0060] Specifically, the instantaneous pressure drop pulse recognition is performed immediately after each correction action. The smart lock's local controller records the end timestamp of each correction action and extracts a pressure recognition time window from the timing data of the latch piezoresistive sensor, using the end timestamp as the starting point. The window length for the pressure recognition time window can be from 100ms to 300ms, and the selection of the window length can be determined in conjunction with the piezoresistive response time after the bolt mechanically releases. The smart lock's local controller performs adjacent differential processing on the pressure recognition time window data to generate a pressure drop amplitude sequence. If adjacent piezoresistive data within the pressure recognition time window are... and Then the elements in the pressure drop amplitude sequence .

[0061] Subsequently, the smart lock's local controller extracts the maximum drop amplitude from the pressure drop amplitude sequence and reads the timestamp corresponding to the maximum drop amplitude. When the maximum drop amplitude reaches the pressure drop threshold and the timestamp corresponding to the maximum drop amplitude is within the pressure recognition time window, the smart lock's local controller generates instantaneous pressure drop pulse data.

[0062] To avoid misidentification caused by isolated pressure fluctuations, the smart lock's local controller correlates the instantaneous pressure drop pulse data with the timing data of the bolt displacement within the same time window. This correlation process still uses the timestamp under a unified sampling clock as the standard. The smart lock's local controller reads the bolt displacement data near the timestamp corresponding to the instantaneous pressure drop pulse data. If the bolt displacement data within the same time window reaches the target unlocking displacement, a true unlocking success indicator is output. If the maximum pressure drop amplitude reaches the pressure drop threshold but the bolt displacement data does not reach the target unlocking displacement, the smart lock's local controller does not output a true unlocking success indicator. Instead, it continues the adaptive correction loop until the pressure drop pulse is successfully identified after the next correction action, or the number of loops reaches a preset threshold. This dual correlation ensures that the unlocking status is based on the combined data of the latch release and the bolt displacement.

[0063] The aggregation and uploading of total energy consumption data based on current integration is accomplished through collaboration between the smart lock's local controller and the cloud server. Within each cycle of the adaptive correction loop, the smart lock's local controller extracts the motor drive current timing data corresponding to that cycle. The cycle can start from the moment the correction action parameters are generated and end at the moment the instantaneous pressure drop pulse is identified after that correction action. The smart lock's local controller performs time integration processing on the motor drive current timing data corresponding to the cycle to generate single-cycle current integration energy consumption data. Under discrete sampling, the single-cycle current integration energy consumption data... Press Calculate, where, Indicates the number of cycles within this period. Motor drive current timing data for each sampling cycle This indicates the sampling period of the unified sampling clock. If it is necessary to further approximate the motor's power consumption, the current integral can be extended to the voltage-current product integral when the local controller of the smart lock already has motor drive voltage data. However, in this implementation, the total energy consumption data of the current integral is used as a unified comparison quantity on the remote operation and maintenance side, which facilitates trend comparison between different batches of smart locks.

[0064] The smart lock's local controller accumulates the single-cycle integral current energy consumption data sequentially according to the cycle, generating the total integral current energy consumption data. If the adaptive correction cycle has been performed... For each cycle, the total energy consumption data of the current integral is... ,in, to These represent the single-cycle current integral energy consumption data corresponding to each cycle.

[0065] Subsequently, the local controller of the smart lock extracts the locally stored smart lock identifier and combines the total current consumption data, cycle count data, smart lock ambient temperature and humidity data, unlock identifier status, and smart lock identifier into unlocking process record data. In the unlocking process record data, the smart lock identifier is used to associate historical data of the same smart lock on the cloud server side; the cycle count data reflects the execution intensity of the adaptive correction cycle in this unlocking; the smart lock ambient temperature and humidity data is used to distinguish environmental load factors; and the unlock identifier status indicates the unlocking result. The local controller of the smart lock uploads the unlocking process record data to the cloud server. The cloud server retrieves historical unlocking process record data according to the smart lock identifier and merges the current unlocking process record data with the historical unlocking process record data in chronological order to generate smart lock health trend data. The smart lock health trend data may include changes in the total current consumption data of the same smart lock under similar smart lock ambient temperature and humidity data, the frequency of mechanical fault locking identifier occurrences, and the distribution of cycle count data, providing maintenance personnel with reference when scheduling lubrication, calibration, or lock body replacement.

[0066] Example 2 This application also discloses a remote automatic control system for smart locks of power equipment based on the Internet of Things.

[0067] Reference Figure 5 A remote automatic control system for smart locks of power equipment based on the Internet of Things includes: The data acquisition module is used to receive unlocking commands and trigger the data sampling program to obtain the ambient temperature and humidity data of the smart lock, and simultaneously collect the timing data of motor drive current, bolt displacement and latch piezoresistive sensor during the unlocking process. The reference correction module is used to correct the reference unlocking current timing envelope based on the temperature and humidity data of the smart lock environment, and generate environmentally adapted current envelope data. The segment extraction module is used to extract the timing data of the latch piezoresistive sensor in the corresponding segment when the timing data of the latch displacement has not reached the target unlocking displacement and a stagnant segment occurs. The curve mapping module is used to map the motor drive current timing data and the latch displacement timing data into a two-dimensional phase trajectory curve when the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition. The correction loop module is used to enter an adaptive correction loop based on the slope characteristics of the two-dimensional phase trajectory curve. When the slope characteristics do not reach the abrupt change condition, it generates high-frequency alternating pulse current drive data. When the slope characteristics reach the abrupt change condition, it generates reverse step-back release stroke data and positive impact pulse data with current integral limitation, and executes corresponding correction actions based on the generated data. The identification output module is used to intercept the timing data of the latch piezoresistive sensor after each correction action to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the identification is still unsuccessful after the number of cycles reaches a preset threshold, a mechanical fault lockout indicator is output, and the currently output indicators are collected as the unlocking indicator status. The summary upload module is used to summarize the total energy consumption data of the current integral within the adaptive correction loop, and upload the total energy consumption data of the current integral along with the unlocking status to the cloud server.

[0068] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0069] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0070] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A remote automatic control method for smart locks of power equipment based on the Internet of Things, characterized in that, Includes the following steps: Receive the unlocking command and trigger the data sampling program to obtain the temperature and humidity data of the smart lock environment, and simultaneously collect the timing data of the motor drive current, the timing data of the bolt displacement and the timing data of the latch piezoresistive sensor during the unlocking process; Based on the ambient temperature and humidity data of the smart lock, the reference unlocking current timing envelope is corrected to generate environmentally adapted current envelope data; When the timing data of the bolt displacement does not reach the target unlocking displacement and a stagnant section appears, extract the timing data of the latch piezoresistive sensor in the corresponding section. If the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition, the timing data of the motor drive current and the timing data of the latch displacement in the same stagnant section are mapped into a two-dimensional phase trajectory curve. Based on the slope characteristics of the two-dimensional phase trajectory curve, an adaptive correction loop is entered. When the slope characteristics do not reach the abrupt change condition, high-frequency alternating pulse current drive data is generated. When the slope characteristics reach the abrupt change condition, reverse step-back release stroke data and positive impact pulse data with current integral limitation are generated, and corresponding correction actions are performed based on the generated data. After each correction action, the timing data of the latch piezoresistive sensor is captured to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the number of cycles reaches the preset threshold and the identification is still unsuccessful, a mechanical fault lockout indicator is output, and the currently output indicator is collected as the unlocking indicator status. The total energy consumption data of the current integral within the adaptive correction loop is summarized, and the total energy consumption data of the current integral along with the unlocking status is uploaded to the cloud server.

2. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 1, characterized in that, Upon receiving the unlocking command and triggering the data sampling program, the system acquires ambient temperature and humidity data of the smart lock, and simultaneously collects timing data of the motor drive current, bolt displacement, and latch piezoresistive sensor during the unlocking process, including: In response to the unlock command, extract the initial temperature and humidity simulation signal; The initial temperature and humidity analog signal is filtered, denoised, and converted from analog to digital to generate environmental temperature and humidity data for the smart lock. The unified sampling clock is started by taking the response time of the unlock command as the sampling starting point; At each sampling cycle of the unified sampling clock, the motor feedback current data, the latch position data, and the latch resistance data are read respectively. Write the motor feedback current data, latch position data and latch piezoresistive data under the same sampling cycle into the same timestamp data frame; Based on the order of the timestamp data frames, generate the timing data of the motor drive current, the timing data of the latch displacement, and the timing data of the latch piezoresistive sensor.

3. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 1, characterized in that, Based on the ambient temperature and humidity data of the smart lock, the baseline unlocking current timing envelope is corrected to generate environment-adaptive current envelope data, including: Extract temperature and humidity data from the ambient temperature and humidity data of the smart lock; Map temperature data to lubrication resistance compensation data, and map humidity data to corrosion resistance compensation data. The lubrication resistance compensation data and corrosion resistance compensation data are weighted and synthesized to generate environmental resistance correction data; Read the upper limit and lower limit data of the reference current at each time point in the reference unlocking current timing envelope; The upper limit data and lower limit data of the reference current are adjusted using environmental resistance correction data to generate upper limit data and lower limit data of environmentally adapted current. The upper limit data and lower limit data of the environmental adaptation current are combined in chronological order to generate the environmental adaptation current envelope data.

4. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 1, characterized in that, When the bolt displacement timing data fails to reach the target unlocking displacement and a stall occurs, the timing data of the latch piezoresistive sensor within the corresponding segment is extracted, including: Read the latch displacement data corresponding to adjacent timestamps in the latch displacement timing data; Calculate the displacement difference between adjacent latch displacement data, and mark the continuous time period in which the displacement difference is lower than the displacement change threshold as a candidate stagnation section; Extract the bolt displacement data corresponding to the end of the candidate stagnation section and compare it with the target unlocking displacement; When the bolt displacement data at the end of the candidate stall section is less than the target unlocking displacement, the candidate stall section is confirmed as a stall section. Based on the start and end timestamps of the stall section, piezoresistive data within the same time range is extracted from the timing data of the latching piezoresistive sensor to generate the stall section piezoresistive data.

5. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 4, characterized in that, When the timing data of the latch piezoresistive sensor does not meet the step descent condition, the timing data of the motor drive current and the timing data of the latch displacement are mapped into a two-dimensional phase trajectory curve, including: Adjacent differential processing is performed on the piezoresistive data in the stagnant section to generate piezoresistive differential data; When the decrease in the differential pressure data is lower than the pressure decrease threshold, it is determined that the pressure resistance data in the stagnant section does not meet the step decrease condition. According to the start and end timestamps of the stall section, extract the stall section current data from the motor drive current timing data, and extract the stall section displacement data from the lock tongue displacement timing data. The current data of the stagnant section under the same time stamp is used as the vertical data, and the displacement data of the stagnant section under the same time stamp is used as the horizontal data to form phase trajectory points; Connect the phase trajectory points in the order of timestamps to generate a two-dimensional phase trajectory curve.

6. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 5, characterized in that, Based on the slope characteristics of the two-dimensional phase trajectory curve, an adaptive correction loop is entered, including: Calculate the local slope data between adjacent phase trajectory points based on the current and displacement changes of adjacent phase trajectory points in the two-dimensional phase trajectory curve. Segmented statistical analysis is performed on local slope data to generate mean slope data and slope jump data; When the slope jump data is lower than the mutation threshold and the slope mean data is higher than the friction threshold, high friction stuck state data is generated. When the slope jump data reaches the mutation threshold, rigid jamming state data is generated; The high-friction jamming state data is mapped to high-frequency alternating pulse current drive data, and the rigid jamming state data is mapped to reverse step-back release stroke data and forward impact pulse data.

7. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 6, characterized in that, Mapping high-friction jamming state data to high-frequency alternating pulse current drive data, including: Extract the timing data of motor drive current and lock tongue displacement within the corresponding section of the high-friction jamming state data; Based on the deviation of the motor drive current timing data from the environmental adaptation current envelope data, pulse amplitude data is generated. Based on the displacement difference in the stagnant section of the latch displacement timing data, pulse frequency data is generated; Combine pulse amplitude data and pulse frequency data into high-frequency alternating pulse current drive data; After executing the correction action corresponding to the high-frequency alternating pulse current drive data, the timing data of the latching piezoresistive sensor after the action ends is captured; When the captured timing data of the latching piezoresistive sensor shows a sudden drop in pressure pulse, the high friction jamming state data is updated to the released state data.

8. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 6, characterized in that, The rigid jamming state data is mapped to reverse recoil release stroke data and forward impact pulse data, including: Extract the abrupt change starting point of the two-dimensional phase trajectory curve within the corresponding section of the rigid jamming state data; Read the latch displacement data and motor drive current data corresponding to the mutation start point; Based on the difference between the latch displacement data corresponding to the mutation start point and the target unlocking displacement, reverse backstep release stroke data is generated; Based on the difference between the motor drive current data corresponding to the mutation start point and the upper boundary current data at the corresponding time point of the environmental adaptation current envelope data, the duty cycle data of the positive impact pulse data is generated. The duty cycle data is subjected to current integral limiting to generate positive impulse pulse data with current integral limiting. After executing the release action corresponding to the reverse step-back release stroke data, the impact action corresponding to the forward impact pulse data is then executed.

9. The remote automatic control method for smart locks of power equipment based on the Internet of Things according to claim 1, characterized in that, After each correction action, the timing data of the latch piezoresistive sensor is captured to identify the instantaneous pressure drop pulse, including: Record the timestamp of the end of each correction action; Starting from the action end timestamp, extract the pressure recognition time window data from the timing data of the latch piezoresistive sensor. The pressure identification time window data is subjected to adjacent difference processing to generate a pressure drop amplitude sequence; Extract the maximum decrease value from the pressure decrease value sequence and read the timestamp corresponding to the maximum decrease value; When the maximum drop value reaches the pressure drop threshold and the timestamp corresponding to the maximum drop value is within the pressure recognition time window, instantaneous pressure drop pulse data is generated. The instantaneous pressure drop pulse data is correlated with the timing data of the bolt displacement within the same time window. When the bolt displacement data within the same time window reaches the target unlocking displacement, a true unlocking success indicator is output.

10. A remote automatic control system for smart locks of power equipment based on the Internet of Things (IoT), applied to the remote automatic control method for smart locks of power equipment based on the IoT as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to receive unlocking commands and trigger the data sampling program to obtain the ambient temperature and humidity data of the smart lock, and simultaneously collect the timing data of motor drive current, bolt displacement and latch piezoresistive sensor during the unlocking process. The reference correction module is used to correct the reference unlocking current timing envelope based on the temperature and humidity data of the smart lock environment, and generate environmentally adapted current envelope data. The segment extraction module is used to extract the timing data of the latch piezoresistive sensor in the corresponding segment when the timing data of the latch displacement has not reached the target unlocking displacement and a stagnant segment occurs. The curve mapping module is used to map the motor drive current timing data and the latch displacement timing data into a two-dimensional phase trajectory curve when the timing data of the latch piezoresistive sensor in the corresponding section does not meet the step descent condition. The correction loop module is used to enter an adaptive correction loop based on the slope characteristics of the two-dimensional phase trajectory curve. When the slope characteristics do not reach the abrupt change condition, it generates high-frequency alternating pulse current drive data. When the slope characteristics reach the abrupt change condition, it generates reverse step-back release stroke data and positive impact pulse data with current integral limitation, and executes corresponding correction actions based on the generated data. The identification output module is used to intercept the timing data of the latch piezoresistive sensor after each correction action to identify the instantaneous pressure drop pulse. If the identification is successful, a true unlocking success indicator is output. If the identification is still unsuccessful after the number of cycles reaches a preset threshold, a mechanical fault lockout indicator is output, and the currently output indicators are collected as the unlocking indicator status. The summary upload module is used to summarize the total energy consumption data of the current integral within the adaptive correction loop, and upload the total energy consumption data of the current integral along with the unlocking status to the cloud server.

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