Dynamic power matching method and energy prediction and distribution method and system of passive intelligent lock

Abstract

The invention discloses a dynamic power matching method and an energy prediction and distribution method and system of a passive intelligent lock, and relates to the technical field of passive intelligent locks, and the system comprises a dynamic power distribution module which is used for executing the dynamic power matching method of the passive intelligent lock; the energy prediction and distribution module is used for executing an energy prediction and distribution method of the passive intelligent lock; the invention discloses a high-precision power sensor and an adjustable matching network. According to the dynamic power matching method, through real-time monitoring, impedance matching based on the maximum power transmission theorem, self-adaptive filtering and power adjustment, the energy receiving strategy of the passive intelligent lock can be dynamically adjusted according to the real-time change of the NFC output power of the handheld terminal, the energy transmission efficiency is improved under the condition of limited electric energy, energy waste is avoided, and the service life of the passive intelligent lock is prolonged. An energy prediction and distribution algorithm is combined with an advanced machine learning model, the energy demand of the passive intelligent lock can be accurately predicted, advanced planning and reasonable distribution of energy are achieved, and it is ensured that the intelligent lock can stably work in various complex scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of passive intelligent locks, and in particular to a dynamic power matching method and an energy prediction and distribution method and system for a passive intelligent lock. Background Art

[0002] At a time when smart homes and industrial automation are developing rapidly, smart locks are key devices for ensuring safety and convenience, and their NFC unlocking function is popular among users due to its convenience. However, for passive NFC smart locks, they do not carry electricity themselves. Only when a handheld terminal (such as a mobile phone, bracelet, smart lock watch, dedicated smart key, etc.) is close to them, the NFC module of the handheld terminal reversely supplies power to the NFC smart lock through the principle of electromagnetic induction, achieving short-term operation. This feature avoids the trouble of maintaining and replacing the built-in battery in applications, and is particularly suitable for complex and harsh environments in industrial scenarios.

[0003] However, the energy exchange efficiency between the passive NFC module and the handheld terminal is currently low, which makes the passive NFC smart lock often unable to open due to insufficient energy supply. Since the NFC output power of the handheld terminal changes dynamically, and the existing technology fails to fully consider the energy demand characteristics of the passive NFC smart lock in different scenarios (especially irregular use in industrial scenarios), the energy loss is too large during the short-distance transmission process between the smart lock and the handheld terminal, and the charging speed is slow, which seriously affects the normal use of the device and user experience. For example, in an industrial environment, the passive NFC smart lock may not be able to respond to the unlocking command in time at a critical moment due to insufficient energy acquisition, which brings major obstacles to the inspection and production process. At the same time, the passive NFC smart lock only obtains electrical energy at the moment of operation, and cannot assist energy management through power change information like traditional battery-powered smart locks, which further increases the difficulty of efficient energy utilization. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a dynamic power matching method and an energy prediction and allocation method and system for a passive intelligent lock.

[0005] To achieve the above object, the present invention provides the following technical solutions: A dynamic power matching method for a passive smart lock comprises the following steps: S1 uses a high-precision power sensor integrated in a passive smart lock to monitor the RF signal power output by the handheld terminal NFC in real time. ; S2 is based on the current task and load power consumption of the passive smart lock , evaluate its own energy demand through a preset energy demand evaluation function; S3 dynamically adjusts the equivalent impedance of the receiving end through an adjustable matching network according to the maximum power transfer theorem, so that it is conjugate matched with the equivalent impedance of the transmitting end. The adjustable matching network consists of multiple inductors. and capacitor The total impedance ,in is the number of matching components, is the resistance of the matching element, For the inductance of the matching components, is the capacitance of the matching component, f The operating frequency specified by the NFC standard; S4 uses the least mean square algorithm to filter the RF signal and adjusts the inductance and capacitance of the adjustable matching network according to the filtered power change trend. When the output power of the handheld terminal is higher than the threshold, the resonant frequency is adjusted to match the RF signal frequency; when the output power is lower than the threshold, it enters the low power consumption mode and reduces the amplifier operating voltage. and current , and adopts low-power signal demodulation algorithm.

[0006] The high-precision power sensor is implemented based on the thermocouple effect or diode detection principle.

[0007] The adjustable matching network dynamically adjusts the inductance and capacitance values ​​through a digital potentiometer.

[0008] The low power consumption signal demodulation algorithm is implemented based on the minimum mean square error criterion.

[0009] A method for predicting and allocating energy for a passive smart lock comprises the following steps: A1 uses timestamp as index to record historical energy exchange data, including different time periods , Different usage scenarios Energy transfer under , Transmission time 、Output power of handheld terminal NFC And the operation tasks currently performed by the smart lock information; A2 builds the model, including the following steps: A21 pre-processes the recorded historical energy exchange data; A22 builds an LSTM-ARIMA energy demand forecasting model, where the LSTM model consists of an input gate , Forget Gate , output gate and memory unit Composition,the ARIMA model takes the output of the LSTM model and time series data as input to capture the changing pattern of energy demand; A23 uses the stochastic gradient descent algorithm to train the LSTM-ARIMA energy demand prediction model and uses the mean square error as the loss function; A3 Energy Interaction and Distribution: The A31 passive smart lock sends an energy request instruction packet through the NFC standard communication protocol. The energy request instruction packet contains the unique identification code of the smart lock. , Energy requirement , expected transmission time And priority indicator ; After receiving the request, the A32 handheld terminal NFC will output energy based on its own energy output capability. 、Current battery level And the energy allocation strategy preset by the user Respond and calculate the energy that can actually be allocated to the smart lock , and adjust the output rate and transmission time ,satisfy , and at the same time send confirmation transmission information to the passive smart lock through feedback instructions; During the energy transmission process, the A33 passive smart lock dynamically adjusts the energy request parameters to match the deviation between the actual receiving rate and the expected rate.

[0010] If the actual received energy rate Expected received energy rate If the deviation exceeds the set threshold, the passive smart lock adjusts the request instruction packet and value, and sends a request to the handheld NFC terminal again.

[0011] The format of the energy request instruction packet is .

[0012] A passive intelligent lock system, comprising: A dynamic power allocation module, used to execute the above-mentioned dynamic power matching method of the passive smart lock; An energy prediction and allocation module, used to execute the energy prediction and allocation method of the passive smart lock; High-precision power sensor and adjustable matching network integrated into passive smart lock.

[0013] It also includes a non-volatile memory for storing historical energy exchange data.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned dynamic power matching method for a passive smart lock or the above-mentioned energy prediction and allocation method for a passive smart lock.

[0015] Beneficial effects of the present invention: 1. The dynamic power matching algorithm of the present invention can dynamically adjust the energy receiving strategy of the passive smart lock according to the real-time changes of the NFC output power of the handheld terminal through real-time monitoring, impedance matching based on the maximum power transfer theorem, and adaptive filtering and power adjustment, thereby improving the energy transmission efficiency under limited power conditions and avoiding energy waste.

[0016] 2. The energy prediction and allocation algorithm combined with advanced machine learning models can accurately predict the energy requirements of passive smart locks, and realize advance planning and reasonable allocation of energy, ensuring that smart locks can work stably in various complex scenarios, significantly improving user experience, and is especially suitable for irregular use in industrial scenarios. At the same time, the innovative energy interaction and allocation mechanism further improves the efficiency and stability of energy exchange by expanding communication protocols and optimizing energy allocation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the dynamic power matching circuit of the present invention.

[0018] Figure 2 This is the adjustable matching network diagram of the present invention.

[0019] Figure 3 It is a flow chart of the energy prediction and allocation algorithm of the present invention.

[0020] Figure 4 It is the flow chart of the energy interactive communication protocol of the present invention.

[0021] Figure 5 The present invention is a schematic diagram of the principle that a control unit adjusts the inductance and capacitance in a receiving circuit through a digital potentiometer. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0024] like Figure 1As shown, the present invention provides a dynamic power matching method for a passive smart lock, which includes the following steps: S1 uses a high-precision power sensor integrated in a passive smart lock to monitor the RF signal power output by the handheld terminal NFC in real time. ; S2 is based on the current task and load power consumption of the passive smart lock , evaluate its own energy demand through a preset energy demand evaluation function; S3 dynamically adjusts the equivalent impedance of the receiving end through an adjustable matching network according to the maximum power transfer theorem, so that it is conjugate matched with the equivalent impedance of the transmitting end. The adjustable matching network consists of multiple inductors. and capacitor Composition, such as Figure 2 As shown, the total impedance ,in is the number of matching components, is the resistance of the matching element, For the inductance of the matching components, is the capacitance of the matching component, f The operating frequency specified by the NFC standard, typically 13.56 MHz.

[0025] S4 uses the least mean square algorithm to filter the RF signal and adjusts the inductance and capacitance of the adjustable matching network according to the filtered power change trend. When the output power of the handheld terminal is higher than the threshold, the resonant frequency is adjusted to match the RF signal frequency; when the output power is lower than the threshold, it enters the low power consumption mode and reduces the amplifier operating voltage. and current , and adopts low-power signal demodulation algorithm.

[0026] Assume that the received RF signal It is the superposition of the modulation signal containing useful information and noise, that is, ,in It is a useful signal. is additive noise. The goal of demodulation is to Recover the original useful signal Then the output is adaptively filtered.

[0027] in is the filter output, It is n Moment i filter coefficients, N is the filter order.

[0028] The error calculation is calculated as follows:

[0029] in is the error signal, It is an expected signal.

[0030] Then update the filter weight coefficients:

[0031] here is the step size factor.

[0032] Continue to iterate and update according to the above formula , so that the mean square error Minimize to achieve signal demodulation.

[0033] Integrate a high-precision power sensor based on the thermocouple effect or diode detection principle in the NFC module of the passive smart lock, such as the Vishay STHx series, Amphenol TC series, etc., to monitor the RF signal power output by the handheld terminal NFC in real time At the same time, an energy demand assessment module is set up, which is based on the current operation tasks of the smart lock (such as identity authentication, unlocking, etc.) and the load power consumption And other factors, through the preset energy demand evaluation function Assess your energy needs. Specifically, It can be expressed as (1) in and is the coefficient calibrated according to the actual situation, The estimated time required to perform the current operation task. The basic energy required to maintain the basic operating state of the smart lock. Since the smart lock is passive, the energy demand assessment needs to ensure that the energy needs of key operations are prioritized within the limited power supply time.

[0034] According to the maximum power transfer theorem, when the equivalent impedance of the passive smart lock NFC module receiving end is Equivalent impedance to the NFC transmitter of the handheld terminal The complex conjugate of When the power is equal, the energy transmission power is the maximum. In the smart lock receiving circuit, multiple inductors are set and capacitor The total impedance of the adjustable matching network is It can be expressed as (2) in is the number of matching components, The control unit dynamically adjusts the inductance through components such as digital potentiometers. and capacitor The value of , thereby achieving dynamic impedance matching and improving energy transmission efficiency. and transmission efficiency , output power The relationship is , transmission efficiency It is related to the impedance matching degree. The higher the matching degree, The closer to 1.

[0035] The least mean square (LMS) adaptive filtering algorithm is used to process the handheld NFC output power signal in real time. Assume that the input signal is , the expected response is , the filter weight vector is , then the LMS algorithm updates the weight vector by iteration (3) in is the step size factor to minimize the output error .

[0036] Through adaptive filtering, the noise interference in the power signal is removed to accurately obtain the power change trend. Above the set threshold When the control unit uses the power signal after adaptive filtering according to the result of the energy demand assessment module, it changes the inductance by adjusting the digital potentiometer in the circuit. and capacitor The value of the resonant frequency formula , so that the resonant frequency of the receiving circuit The RF signal frequency at high power output Matching, thereby increasing the received power When the handheld terminal NFC output power is detected Below the set threshold When the NFC module of the smart lock enters the low-power receiving mode, the control unit adjusts the NFC module of the smart lock to enter the low-power receiving mode. and current , according to the power formula , reduce energy consumption, and adopt a low-power signal demodulation algorithm based on the minimum mean square error (MMSE) criterion to ensure that energy can be effectively collected under low power input.

[0037] like Figure 3As shown, the present invention also provides an energy prediction and allocation method for a passive smart lock, which comprises the following steps: A1 uses timestamp as index to record historical energy exchange data, including different time periods , Different usage scenarios Energy transfer under , Transmission time 、Output power of handheld terminal NFC And the operation tasks currently performed by the smart lock information; A2 builds the model, including the following steps: A21 pre-processes the recorded historical energy exchange data; A22 builds an LSTM-ARIMA energy demand forecasting model, where the LSTM model consists of an input gate , Forget Gate , output gate and memory unit Composition,the ARIMA model takes the output of the LSTM model and time series data as input to capture the changing pattern of energy demand; A23 uses the stochastic gradient descent algorithm to train the LSTM-ARIMA energy demand prediction model and uses the mean square error as the loss function; A3 Energy Interaction and Distribution: The A31 passive smart lock sends an energy request instruction packet through the NFC standard communication protocol. The energy request instruction packet contains the unique identification code of the smart lock. , Energy requirement , expected transmission time And priority indicator ; After receiving the request, the A32 handheld terminal NFC will output energy based on its own energy output capability. , Current power And the energy allocation strategy preset by the user Respond and calculate the energy that can actually be allocated to the smart lock , and adjust the output rate and transmission time ,satisfy , and at the same time send confirmation transmission information to the passive smart lock through feedback instructions; During the energy transmission process, the A33 passive smart lock dynamically adjusts the energy request parameters to match the deviation between the actual receiving rate and the expected rate.

[0038] Build a data storage module in the passive NFC smart lock electronic solution, use non-volatile memory, such as flash memory, and use timestamps as indexes to record historical energy exchange data, including different time periods , Different usage scenarios (such as working days, holidays, peak industrial operation periods, etc.) , Transmission time 、Output power of handheld terminal NFC And the operation tasks currently performed by the smart lock Since the smart lock is passive and used irregularly, data collection focuses on the key parameters during each energy transmission process to reduce unnecessary data storage overhead.

[0039] (1) Data preprocessing: First, preprocess the collected historical data. Suppose the original data set is ,in Including time , Usage scenarios , Energy transfer , Transmission time 、Handheld terminal NFC output power And operational tasks Energy transfer and transmission time Normalization is performed, and the normalization formula is: , (4) in , are the minimum and maximum values ​​of energy transfer, respectively. , are the minimum and maximum transmission time respectively.

[0040] For operational tasks , One-Hot Encoding is used to convert it into vector form for subsequent model processing.

[0041] (2) Model selection and construction: Long short-term memory network (LSTM) is used in combination with the autoregressive integrated moving average model (ARIMA) in the time series analysis algorithm. p , d , q )) to build an energy demand prediction model. The LSTM model consists of an input gate , Forget Gate , output gate and memory unit Input Gate Determines how much information in the input data will be written into the memory unit. The calculation formula is (5) in is the Sigmoid function, , is the weight matrix, , is the bias vector, is the input data at the current moment, is the hidden state at the previous moment.

[0042] Forget Gate Controls which important information in the memory cell will be retained (6) Memory Unit The update formula is (7) in Represents element-wise multiplication. Output gate Determines which information in the memory cell will be output, , hidden state .

[0043] ARIMA p , d , q )The prediction formula of the model is (8) in is the autoregressive part, is the backshift operator, , is the autoregression coefficient; is the difference order, which is used to make the time series stationary; is the moving average part, is the moving average coefficient, is a white noise sequence.

[0044] The output of the LSTM model is used as one of the inputs of the ARIMA model and is jointly modeled with the time series data. Suppose the output of the LSTM model is , after processing and time series data Input into the ARIMA model together, by adjusting the model parameters , , As well as the weights and biases in the LSTM model, the model can accurately capture the changing patterns of energy demand.

[0045] (3) Model training: Divide the preprocessed data into a training set and a test set according to a certain ratio, for example, 70% as a training set and 30% as a test set. The model is trained using the stochastic gradient descent (SGD) algorithm, and the mean square error (MSE) is used as the loss function. The calculation formula for MSE is: (9) in is the number of training samples, is the model prediction value, During the training process, the weights and biases of the LSTM model and the parameters of the ARIMA model are continuously adjusted. , , , so that the loss function is minimized, thus obtaining the optimal energy demand prediction model.

[0046] like Figure 4 As shown, the NFC smart lock communicates with the handheld terminal based on the standard communication protocol established by the NFC Forum. On this basis, the present invention expands a set of energy interaction instruction sets, such as Figure 4 When the smart lock predicts energy demand, it sends a request to the handheld terminal NFC through a dedicated energy request instruction package. The instruction package contains the unique identification code of the smart lock. , Energy requirement , expected transmission time And priority indicator ( The value range is 1~5, the larger the value, the higher the priority). The instruction packet format can be expressed as .

[0047] After receiving the request, the NFC handheld terminal will 、Current battery level And the energy allocation strategy preset by the user The energy allocation strategy set by the user can be To describe, this function returns an energy distribution coefficient For example, if the user sets the priority to protect the battery of the handheld terminal, When it is below a certain threshold, The function may return a smaller According to the law of conservation of energy The handheld NFC terminal calculates the energy that can actually be allocated to the smart lock. , and adjust the output power and transmission time ,satisfy At the same time, the handheld terminal NFC sends a confirmation message to the smart lock through a feedback instruction package, which contains the actual distributed energy. , Estimated transmission time And energy transfer status code ( It is a discrete value, such as 1 for normal transmission, 2 for insufficient energy, etc.).

[0048] During the energy transmission process, the smart lock continuously monitors the energy reception status. Expected received energy rate The deviation exceeds a certain threshold (such as ), the smart lock adjusts the request instruction packet and value, and sends a request to the handheld terminal NFC again to ensure the stability and efficiency of energy transmission. Expected receiving energy rate , the actual received energy rate It can be measured and calculated through the energy monitoring module on the smart lock end.

[0049] The present invention also provides a passive intelligent lock system, which comprises: A dynamic power allocation module, used to execute the above-mentioned dynamic power matching method of the passive smart lock; An energy prediction and allocation module, used to execute the energy prediction and allocation method of the passive smart lock; High-precision power sensor and adjustable matching network integrated into passive smart lock.

[0050] The power monitoring unit in the passive smart lock NFC module uses a high-precision power sensor based on the thermocouple effect to convert the power of the RF signal into a corresponding voltage signal, and converts the analog voltage signal into a digital signal through an A / D conversion chip, thereby collecting the RF signal power output by the handheld terminal NFC in real time. The energy demand assessment module is based on the current operation tasks performed by the smart lock. And load power consumption The current energy demand is calculated by a preset algorithm based on factors such as In the calculation process, a simplified calculation model is used to reduce the occupation of limited computing resources. For example, for the lock opening task, It can be set as an empirical value based on experimental tests and then combined with the actual load power consumption. ,pass Calculate energy requirements.

[0051] During the impedance matching process, the control unit adjusts the inductance in the receiving circuit through components such as digital potentiometers. and capacitor The value of Figure 5 The digital potentiometer receives instructions from the control unit through the SPI communication interface, changes its resistance value, and thus indirectly changes the inductance. and capacitor The value of dynamic impedance matching is achieved. When the power monitoring unit detects that the handheld terminal NFC output power Above the set threshold When the receiving power is high, the control unit adjusts the receiving circuit parameters to make the resonant frequency of the receiving circuit more matched with the frequency of the RF signal during high power output, thereby improving the receiving power.

[0052] (3) In the process of adaptive filtering and power adjustment, the LMS algorithm is used to process the power signal in real time. In hardware implementation, a digital signal processor (DSP) or a field programmable gate array (FPGA) is used to efficiently execute the LMS algorithm. Below the set threshold When the control unit uses PWM (pulse width modulation) technology to reduce the operating voltage of the amplifier in the receiving circuit , while adjusting the amplifier bias current , reducing energy consumption. A demodulation algorithm based on the minimum mean square error (MMSE) criterion is used to ensure stable energy reception under low power input.

[0053] The data storage module uses non-volatile memory, such as flash memory, to record at least three months of historical energy exchange data. You can choose to use Python and other programming languages ​​to build a long short-term memory network (LSTM) combined with an ARIMA model to predict energy demand, using deep learning frameworks such as TensorFlow or PyTorch. Preprocess the historical data, including data cleaning, normalization and other operations, and then train and optimize the model. Update the model parameters regularly every day to adapt to changes in energy demand in different scenarios. During the data processing process, the data storage structure is optimized to improve data reading and processing efficiency in view of the characteristics of smart locks being passive and irregular in use.

[0054] During the model training and operation process, the smart lock and the handheld terminal NFC interact and negotiate through the control instructions in the NFC communication protocol. When the smart lock predicts that the energy demand is high, it sends a message containing the energy demand value to the handheld terminal NFC. and expected transmission time The NFC handheld terminal outputs energy based on its own energy capability. And user settings, adjust the energy output parameters to achieve reasonable distribution of energy. During the communication process, a simple and efficient communication protocol is adopted to reduce the amount of data transmission and reduce the consumption of limited energy. Specifically, the smart lock end uses a microcontroller (MCU) to generate an instruction packet that complies with the extended communication protocol, and during the communication process, a simple and efficient communication protocol is adopted to reduce the amount of data transmission and reduce the consumption of limited energy. Specifically, the smart lock end uses a microcontroller (MCU) to generate an instruction packet that complies with the extended communication protocol, and modulates the instruction packet with a radio frequency signal of a specific frequency (such as 13.56MHz) through the modulation and demodulation circuit of the NFC physical layer. The modulation method adopts binary phase shift keying (BPSK), and its modulation formula is ,in is the carrier amplitude, is the carrier frequency, At logic "0" and "1", they correspond to and .

[0055] The modulated signal is amplified by the power amplifier to ensure that the signal strength is sufficient to be stably received by the handheld NFC module. Before sending the instruction packet, the MCU of the smart lock will perform CRC (cyclic redundancy check) encoding on the instruction packet and generate a check code to attach to the end of the instruction packet to ensure the accuracy of data transmission. The CRC encoding formula is: ,in is the original data polynomial, To generate the polynomial, The number of check code digits.

[0056] After the handheld NFC module receives the signal, it first demodulates the RF signal to restore it to a digital signal through the demodulation circuit, and then performs a CRC check. If the check passes, the instruction packet content is parsed; if the check fails, a retransmission request is sent to the smart lock. The handheld NFC module generates a feedback instruction packet according to the parsed instruction packet content and the established energy allocation strategy. Similarly, the feedback instruction packet must also be modulated and amplified on the handheld terminal before being sent back to the smart lock. After receiving the feedback instruction packet, the smart lock further adjusts the energy interaction strategy based on the feedback information to ensure that the entire energy interaction process is efficient, stable and low-energy.

[0057] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned dynamic power matching method of the passive smart lock or the above-mentioned energy prediction and allocation method of the passive smart lock.

[0058] Example 1 Industrial inspection scene Scenario description: During industrial inspections, inspectors need to frequently open passive NFC smart locks to access key equipment.

[0059] ‌Implementation effect‌: Through the method of the present invention, the passive NFC smart lock can accurately predict and request sufficient energy to ensure timely response to the unlocking command at the critical moment. At the same time, the dynamic power matching algorithm improves the energy transmission efficiency and reduces energy waste.

[0060] Example 2 Smart home scene ‌Scenario description‌: In a smart home environment, a passive NFC smart lock is used to control the access control system. Family members use handheld terminals such as mobile phones to unlock the door with NFC.

[0061] ‌Implementation effect‌: The method of the present invention enables the smart lock to intelligently adjust the energy receiving strategy according to the usage habits and needs of family members. When family members approach, the smart lock can respond quickly and unlock, improving the user experience.

[0062] Embodiment 3 Logistics warehousing scene ‌Scenario description‌: In a logistics storage center, a passive NFC smart lock is used to manage the storage and access of goods. The warehouse manager uses a dedicated smart key to unlock the door with NFC.

[0063] ‌Implementation effect‌: Through the method of the present invention, the smart lock can accurately predict the warehouse manager's unlocking needs and request sufficient energy. At the same time, the energy prediction and allocation algorithm ensures the stable operation of the smart lock during peak hours and improves the efficiency of warehouse management.

[0064] The embodiments should not be regarded as limiting the present invention, but any improvements made based on the spirit of the present invention should be within the protection scope of the present invention.

Claims

1. A dynamic power matching method for a passive smart lock, characterized in that: It includes the following steps: S1 uses a high-precision power sensor integrated in a passive smart lock to monitor the RF signal power output by the handheld terminal NFC in real time. ; S2 is based on the current task and load power consumption of the passive smart lock , evaluate its own energy demand through a preset energy demand evaluation function; S3 dynamically adjusts the equivalent impedance of the receiving end through an adjustable matching network according to the maximum power transfer theorem, so that it is conjugate matched with the equivalent impedance of the transmitting end. The adjustable matching network consists of multiple inductors. and capacitor The total impedance ,in is the number of matching components, is the resistance of the matching element, For the inductance of the matching components, is the capacitance of the matching component, f The operating frequency specified by the NFC standard; S4 uses the least mean square algorithm to filter the RF signal and adjusts the inductance and capacitance of the adjustable matching network according to the filtered power change trend. When the output power of the handheld terminal is higher than the threshold, the resonant frequency is adjusted to match the RF signal frequency; when the output power is lower than the threshold, it enters the low power consumption mode and reduces the amplifier operating voltage. and current , and adopts low-power signal demodulation algorithm.

2. The dynamic power matching method of a passive smart lock according to claim 1, characterized in that: The high-precision power sensor is implemented based on the thermocouple effect or diode detection principle.

3. The dynamic power matching method of a passive smart lock according to claim 1, characterized in that: The adjustable matching network dynamically adjusts the inductance and capacitance values ​​through a digital potentiometer.

4. The dynamic power matching method of a passive smart lock according to claim 1, characterized in that: The low power consumption signal demodulation algorithm is implemented based on the minimum mean square error criterion.

5. A method for predicting and allocating energy for a passive smart lock, characterized in that: It includes the following steps: A1 uses timestamp as index to record historical energy exchange data, including different time periods , different usage scenarios Energy transfer under , Transmission time 、Output power of NFC handheld terminal And the operation tasks currently performed by the smart lock information; A2 builds the model, including the following steps: A21 pre-processes the recorded historical energy exchange data; A22 builds an LSTM-ARIMA energy demand forecasting model, where the LSTM model consists of an input gate , Forget Gate , output gate and memory unit Composition,the ARIMA model takes the output of the LSTM model and time series data as input to capture the changing pattern of energy demand; A23 uses the stochastic gradient descent algorithm to train the LSTM-ARIMA energy demand prediction model and uses the mean square error as the loss function; A3 Energy Interaction and Distribution: The A31 passive smart lock sends an energy request instruction packet through the NFC standard communication protocol. The energy request instruction packet contains the unique identification code of the smart lock. , Energy requirement , expected transmission time And priority mark ; After receiving the request, the A32 handheld terminal NFC will output energy based on its own energy output capability. , Current power And the energy allocation strategy preset by the user Respond and calculate the energy that can actually be allocated to the smart lock , and adjust the output rate and transmission time ,satisfy , and at the same time send confirmation transmission information to the passive smart lock through feedback instructions; During the energy transmission process, the A33 passive smart lock dynamically adjusts the energy request parameters to match the deviation between the actual receiving rate and the expected rate.

6. The energy prediction and allocation method for a passive smart lock according to claim 5, characterized in that: If the actual received energy rate Expected received energy rate If the deviation exceeds the set threshold, the passive smart lock adjusts the request instruction packet and value, and sends a request to the handheld NFC terminal again.

7. The energy prediction and allocation method for a passive smart lock according to claim 5, characterized in that: The format of the energy request instruction packet is .

8. A passive intelligent lock system, characterized in that: It includes: A dynamic power allocation module, used to execute the dynamic power matching method of the passive smart lock according to any one of claims 1 to 4; An energy prediction and allocation module, used to execute the energy prediction and allocation method for a passive smart lock as described in any one of claims 5 to 7; High-precision power sensor and adjustable matching network integrated into passive smart lock.

9. The passive intelligent lock system according to claim 8, characterized in that: It also includes a non-volatile memory for storing historical energy exchange data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the dynamic power matching method of the passive smart lock as described in any one of claims 1 to 4 or the energy prediction and allocation method of the passive smart lock as described in any one of claims 5-7.

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