Dynamic Power Matching Method, Energy Prediction and Allocation Method and System for Passive Intelligent Lock

Abstract

A dynamic power matching method, an energy prediction and distribution method, and a system for a passive intelligent lock, which relate to the technical field of passive intelligent locks, and include: a dynamic power distribution module for executing the dynamic power matching method of the passive intelligent lock; an energy prediction and distribution module for executing the energy prediction and distribution method of the passive intelligent lock; a high-precision power sensor and an adjustable matching network. The dynamic power matching method can dynamically adjust the energy reception strategy of the passive intelligent lock according to the real-time change 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, improve the energy transmission efficiency under the condition of limited electric energy, and avoid energy waste. The energy prediction and distribution algorithm combines an advanced machine learning model, can accurately predict the energy demand of the passive intelligent lock, and realizes the advance planning and reasonable distribution of energy, ensuring that the intelligent lock can work stably in various complex scenarios.

Description

Technical Field

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

[0002] At present, with the rapid development of smart home and industrial automation, as a key device to ensure security and convenience, the NFC unlocking function of smart locks is very popular among users due to its convenience. However, for passive NFC smart locks, they are not self-powered and only when a handheld terminal (such as a mobile phone, a smart bracelet, a smart lock watch, a dedicated smart key, etc.) approaches, through the principle of electromagnetic induction, the NFC module of the handheld terminal reversely powers the NFC smart lock to achieve a short-term operation. This feature avoids the trouble of built-in battery maintenance and replacement in applications, and is especially suitable for complex and harsh environments in industrial scenarios.

[0003] However, at present, the energy exchange efficiency between the passive NFC module and the handheld terminal is low, resulting in the passive NFC smart lock often being unable to be opened due to insufficient energy supply. Since the NFC output power of the handheld terminal has dynamic changes, and the prior art fails to fully consider the energy demand characteristics of the passive NFC smart lock in different scenarios (especially in industrial scenarios with irregular use), a large amount of energy is lost during the short-distance transmission between the smart lock and the handheld terminal, and the charging speed is slow, seriously affecting the normal use of the device and the user experience. For example, in an industrial environment, the passive NFC smart lock may not be able to obtain enough energy and cannot respond to the unlocking instruction in a timely manner at a critical moment, bringing a major obstacle to the inspection and production process. At the same time, the passive NFC smart lock only obtains electric energy at the moment of operation and cannot assist in energy management through the battery level change information like a traditional battery-powered smart lock, which further increases the difficulty of efficient energy utilization. Summary of the Invention

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

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A dynamic power matching method for a passive intelligent lock, which includes the following steps:

[0007] S1: Real-time monitor the radio frequency signal power output by the NFC of the handheld terminal through a high-precision power sensor integrated in the passive intelligent lock ;

[0008] S2: Based on the current task and load power consumption of the passive intelligent lock , evaluate its own energy requirements through a preset energy requirement evaluation function;

[0009] S3 According to the maximum power transfer theorem, dynamically adjust the equivalent impedance of the receiving end through an adjustable matching network to make it conjugate match with the equivalent impedance of the transmitting end. The adjustable matching network consists of multiple inductors and capacitors and its total impedance , where is the number of matching elements, is the resistance of the matching element, is the inductance of the matching element, is the capacitance of the matching element, f is the operating frequency specified by the NFC standard;

[0010] S4 Use the least mean square algorithm to filter the radio frequency signal, and adjust the inductance and capacitance values 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, adjust the resonant frequency to match the radio frequency signal frequency; when the output power is lower than the threshold, enter the low-power mode, reduce the operating voltage and current , and use a low-power signal demodulation algorithm.

[0011] The high-precision power sensor is realized based on the thermocouple effect or the diode detection principle.

[0012] The adjustable matching network dynamically adjusts the inductance and capacitance values through a digital potentiometer.

[0013] The low-power signal demodulation algorithm is realized based on the least mean square error criterion.

[0014] An energy prediction and distribution method for a passive intelligent lock, which includes the following steps:

[0015] A1 Use the time stamp as an index to record historical energy exchange data, including different time periods , different usage scenarios under the energy transfer amount , transfer time , the output power of the handheld terminal NFC and the operation task currently executed by the intelligent lock information;

[0016] A2 Establish a model, including the following steps:

[0017] A21 Preprocess the recorded historical energy exchange data;

[0018] A22 Construct an LSTM-ARIMA energy requirement prediction model, where the LSTM model consists of an input gate 、 Forgetting gate 、 Output gate and memory cell The ARIMA model takes the output of the LSTM model and the time series data as inputs together to capture the changing pattern of energy demand;

[0019] A23 uses the stochastic gradient descent algorithm to train the LSTM-ARIMA energy demand prediction model, and uses the mean squared error as the loss function;

[0020] A3 Energy interaction and distribution:

[0021] The passive intelligent 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 intelligent lock 、 Energy demand value 、 Desired transmission time and priority identifier ;

[0022] After the handheld terminal NFC receives the request, it calculates the actual energy that can be allocated to the intelligent lock according to its own energy output ability 、 Current battery level and the energy distribution strategy preset by the user for response, and adjusts the output rate and transmission time to meet , and at the same time sends confirmation transmission information to the passive intelligent lock through a feedback instruction;

[0023] During the energy transmission process, the passive intelligent lock dynamically adjusts the energy request parameters to match the deviation between the actual reception rate and the expected rate.

[0024] If the actual received energy rate deviates from the expected received energy rate by more than the set threshold, the passive intelligent lock adjusts the and values in the request instruction packet and sends a request to the handheld terminal NFC again.

[0025] The format of the energy request instruction packet is

[0026]

[0027] A passive intelligent lock system, which includes:

[0028] A dynamic power distribution module for performing the dynamic power matching method of the passive intelligent lock as described above;

[0028] An energy prediction and distribution module for performing the energy prediction and distribution method of the above passive intelligent lock;

[0029] A high-precision power sensor and an adjustable matching network integrated in the passive intelligent lock.

[0030] It further includes a non-volatile memory for storing historical energy exchange data.

[0031] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the dynamic power matching method of the above passive intelligent lock or the energy prediction and distribution method of the above passive intelligent lock.

[0032] Advantages of the present invention:

[0033] 1. The dynamic power matching algorithm of the present invention can dynamically adjust the energy reception strategy of the passive intelligent lock according to the real-time change of the output power of the handheld terminal NFC through real-time monitoring, impedance matching based on the maximum power transfer theorem, and adaptive filtering and power adjustment, improving the energy transmission efficiency under limited power conditions and avoiding energy waste.

[0034] 2. The energy prediction and distribution algorithm combined with an advanced machine learning model can accurately predict the energy demand of the passive intelligent lock and achieve early planning and reasonable distribution of energy, ensuring that the intelligent lock can work stably in various complex scenarios, significantly improving the user experience, especially suitable for the requirements of irregular use in industrial scenarios. At the same time, the innovative energy interaction and distribution mechanism further improves the efficiency and stability of energy exchange by expanding the communication protocol and optimizing the energy distribution strategy. Description of the Drawings

[0035] Figure 1 is the schematic circuit diagram of the dynamic power matching of the present invention.

[0036] Figure 2 is the adjustable matching network diagram of the present invention.

[0037] Figure 3 is the flow chart of the energy prediction and distribution algorithm of the present invention.

[0038] Figure 4 is the flow chart of the energy interaction communication protocol of the present invention.

[0039] Figure 5 is the schematic diagram of the principle that the control unit adjusts the inductance and capacitance in the receiving circuit through a digital potentiometer in the present invention. Detailed Embodiments

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0042] As Figure 1 shown, the present invention provides a dynamic power matching method for a passive intelligent lock, which includes the following steps:

[0043] S1: Through a high-precision power sensor integrated in the passive intelligent lock, the radio frequency signal power output by the handheld terminal NFC is monitored in real time ;

[0044] S2: Based on the current task and load power consumption of the passive intelligent lock , its energy demand is evaluated through a preset energy demand evaluation function;

[0045] S3: According to the maximum power transfer theorem, the equivalent impedance of the receiving end is dynamically adjusted through an adjustable matching network to make it conjugate match with the equivalent impedance of the transmitting end. The adjustable matching network is composed of multiple inductors and capacitors . As Figure 2 shown, its total impedance , where is the number of matching elements, is the resistance of the matching element, is the inductance of the matching element, is the capacitance of the matching element, f is the operating frequency specified by the NFC standard, generally 13.56 MHz.

[0046] S4: The least mean square algorithm is used to filter the radio frequency signal, and the inductance and capacitance values of the adjustable matching network are adjusted 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 radio frequency signal frequency; when the output power is lower than the threshold, the low-power mode is entered to reduce the operating voltage and current of the amplifier, and a low-power signal demodulation algorithm is adopted.

[0047] Assume the received radio frequency signal is the superposition of a modulated signal containing useful information and noise, that is , where is the useful signal, is the additive noise. The goal of demodulation is to recover the original useful signal from . Then, the adaptive filter outputs

[0048]

[0049] where is the filter output, is the n th i filter coefficient at time N is the filter order.

[0050] The error is calculated according to the following formula:

[0051]

[0052] where is the error signal, is the desired signal.

[0053] Then, the filter weight coefficients are updated:

[0054]

[0055] Here is the step size factor.

[0056] Continuously iterate and update according to the above formula to minimize the mean square error to achieve signal demodulation.

[0057] Integrate a high-precision power sensor based on the thermocouple effect or diode detection principle in the NFC module of the passive intelligent 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, set an energy demand assessment module, which evaluates its own energy demand based on the current operation tasks of the intelligent lock (such as authentication, unlocking, etc.) and load power consumption and other factors through a preset energy demand assessment function . Specifically, can be expressed as

[0058] (1)

[0059] where and is a coefficient calibrated according to the actual situation, is the estimated time required to execute the current operation task, is the basic energy required for the intelligent lock to maintain its basic operating state. Since the intelligent lock is passive, the energy demand assessment needs to ensure that the energy demand for critical operations is preferentially met within the limited power supply time.

[0060] According to the maximum power transfer theorem, when the equivalent impedance of the receiving end of the NFC module of the passive intelligent lock is conjugate to the equivalent impedance of the NFC transmitting end of the handheld terminal the power transfer is maximized. An adjustable matching network composed of multiple inductors and capacitors is set in the receiving circuit of the intelligent lock, and its total impedance can be expressed as

[0061] (2)(2)

[0062] where is the number of matching elements, is the resistance of the matching element. The control unit dynamically adjusts the values of the inductor and capacitor through components such as digital potentiometers, so that , thereby achieving dynamic impedance matching and improving the energy transfer efficiency. The received power and the transfer efficiency , the output power are related as , and the transfer efficiency is related to the impedance matching degree. The higher the matching degree, is closer to 1.

[0063] The least mean square (LMS) adaptive filtering algorithm is used to process the NFC output power signal of the handheld terminal in real time. Let the input signal be , the desired response be , and the filter weight vector be , then the LMS algorithm updates the weight vector

[0064] (3)

[0065] where is the step size factor to minimize the output error .

[0066] Through adaptive filtering, the noise interference in the power signal is removed to accurately obtain the power change trend. When the power monitoring unit detects that the NFC output power of the handheld terminal is higher than the set threshold When, according to the result of the energy demand evaluation module, the control unit uses the adaptively filtered power signal to change the inductance by adjusting the digital potentiometer in the circuit and capacitance values. According to the resonance frequency formula , the resonance frequency of the receiving circuit is made to match the radio frequency signal frequency at high power output , thereby increasing the received power . When it is detected that the NFC output power of the handheld terminal is lower than the set threshold , the control unit adjusts the NFC module of the smart lock to enter the low-power receiving mode. The working voltage and current of the amplifier in the receiving circuit are reduced. According to the power formula

[0067] , such as Figure 3 shown, the present invention also provides an energy prediction and distribution method for a passive smart lock, which includes the following steps:

[0068] A1 records historical energy exchange data indexed by time stamps, including the energy transfer amount , transmission time under different time periods , different usage scenarios , the output power of the handheld terminal NFC, and the operation task information currently executed by the smart lock;

[0069] A2 builds a model, including the following steps:

[0070] A21 preprocesses the recorded historical energy exchange data;

[0071] A22 constructs an LSTM-ARIMA energy demand prediction model, where the LSTM model consists of an input gate , a forget gate , an output gate , and a memory unit . The ARIMA model takes the output of the LSTM model and the time series data as inputs together to capture the change law of energy demand;

[0072] A23 trains the LSTM-ARIMA energy demand prediction model using the stochastic gradient descent algorithm and uses the mean square error as the loss function;

[0073] A3 Energy interaction and distribution:

[0074] The A31 passive intelligent 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 intelligent lock , energy demand value , expected transmission time and priority identifier ;

[0075] After the A32 handheld terminal NFC receives the request, it calculates the actual energy that can be allocated to the intelligent lock according to its own energy output capacity , current battery level and the energy distribution strategy preset by the user , and adjusts the output rate and transmission time to meet , and at the same time sends confirmation transmission information to the passive intelligent lock through a feedback instruction;

[0076] During the energy transmission process, the A33 passive intelligent lock dynamically adjusts the energy request parameters to match the deviation between the actual reception rate and the expected rate.

[0077] In the passive NFC intelligent lock electronic solution, a data storage module is built. A non-volatile memory such as flash memory is used, and the historical energy exchange data is recorded with a timestamp as the index, including different time periods , different usage scenarios (such as weekdays, holidays, peak industrial operation periods, etc.) of the energy transmission volume , transmission time , the output power of the handheld terminal NFC and the operation tasks currently executed by the intelligent lock and other information. Since the intelligent lock is passive and used irregularly, the data collection focuses on the key parameters during each energy transmission process to reduce unnecessary data storage overhead.

[0078] (1) Data preprocessing: First, preprocess the collected historical data. Let the original data set be , where contains time , usage scenario , energy transmission volume , transmission time , the output power of the handheld terminal NFC and operation tasks and other information. For the energy transmission volume and transmission time Normalization is performed, and the normalization formula is

[0079] , (4)

[0080] where 、 are the minimum and maximum values of the energy transfer amount respectively, 、 are the minimum and maximum values of the transfer time respectively.

[0081] For the operation task , it is converted into a vector form using one-hot encoding for subsequent model processing.

[0082] (2)Model selection and construction: The long short-term memory network (LSTM) combined with the autoregressive integrated moving average model (ARIMA( p , d , q )) in the time series analysis algorithm is used to construct the energy demand prediction model. The LSTM model consists of an input gate , a forget gate , an output gate and a memory cell . The input gate determines how much information in the input data will be written into the memory cell, and the calculation formula is

[0083] (5)

[0084] where is the Sigmoid function, 、 are weight matrices, 、 are bias vectors, is the input data at the current moment, is the hidden state at the previous moment.

[0085] The forget gate controls which important information in the memory cell will be retained

[0086] (6)

[0087] The update formula of the memory cell is

[0088] (7)

[0089] where represents element-wise multiplication. The output gate Determines which information in the memory unit will be output, , the hidden state .

[0090] The prediction formula of the ARIMA( p , d , q ) model is

[0091] (8)

[0092] Where is the autoregressive part, is the backshift operator, , are the autoregressive coefficients; is the order of differencing, used to make the time series stationary; is the moving average part, are the moving average coefficients, is the white noise sequence.

[0093] Take the output of the LSTM model as one of the inputs of the ARIMA model and perform joint modeling with the time series data. Let the output of the LSTM model be , which after processing is jointly input into the ARIMA model with the time series data . By adjusting the model parameters , , as well as the weights and biases in the LSTM model, enable the model to accurately capture the changing pattern of energy demand.

[0094] (3) Model training: Divide the preprocessed data into a training set and a test set according to a certain ratio, for example, 70% as the training set and 30% as the test set. Use the Stochastic Gradient Descent (SGD) algorithm to train the model. The loss function is selected as the Mean Squared Error (MSE), and the calculation formula of MSE is

[0095] (9)

[0096] Where is the number of training samples, is the model prediction value, is the true value. During the training process, continuously adjust the weights and biases of the LSTM model and the parameters , , of the ARIMA model to minimize the loss function, thereby obtaining the optimal energy demand prediction model.

[0097] Such as Figure 4As 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 mark ( The value range is 1~5, the larger the value, the higher the priority). The instruction packet format can be expressed as .

[0098] 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.).

[0099] 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 by the energy monitoring module on the smart lock side.

[0100] The present invention also provides a passive smart lock system, which includes:

[0101] A dynamic power distribution module, which is used to execute the dynamic power matching method of the above-mentioned passive smart lock;

[0102] An energy prediction and distribution module, which is used to execute the energy prediction and distribution method of the above-mentioned passive smart lock;

[0103] A high-precision power sensor and an adjustable matching network integrated in the passive smart lock.

[0104] 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 radio frequency signal into a corresponding voltage signal, and converts the analog voltage signal into a digital signal through an A / D conversion chip, so as to collect the radio frequency signal power output by the handheld terminal NFC in real time . The energy demand evaluation module calculates the current energy demand through a preset algorithm according to the operation tasks currently executed by the smart lock and the load power consumption and other factors. During the calculation process, a simplified calculation model is adopted to reduce the occupation of limited computing resources. For example, for the unlocking task, it can be set as an empirical value according to experimental tests, and then combined with the actual load power consumption to calculate the energy demand through . Calculate the energy demand.

[0105] During the impedance matching process, the control unit adjusts the inductance in the receiving circuit through components such as a digital potentiometer and capacitance values, as shown in Figure 5 . The digital potentiometer receives the instruction of the control unit through the SPI communication interface, changes its resistance value, and thus indirectly changes the inductance and capacitance values to achieve dynamic impedance matching. When the power monitoring unit detects that the output power of the handheld terminal NFC is higher than the set threshold , the control unit adjusts the receiving circuit parameters to make the resonant frequency of the receiving circuit more matched with the radio frequency signal frequency at high power output, and improves the receiving power.

[0106] (3) During the adaptive filtering and power adjustment process, the LMS algorithm is used to process the power signal in real time. In terms of hardware implementation, a digital signal processor (DSP) or a field programmable gate array (FPGA) is used to efficiently execute the LMS algorithm. When the output power of the handheld terminal NFC Below the set threshold When, the control unit reduces the operating voltage of the amplifier in the receiving circuit through PWM (Pulse Width Modulation) technology and simultaneously adjusts the bias current of the amplifier to reduce energy consumption. A demodulation algorithm based on the Minimum Mean Square Error (MMSE) criterion is adopted to ensure stable energy reception under low-power input.

[0107] The data storage module uses non-volatile memory, such as flash memory, to record historical energy exchange data for at least three months. Optionally, a programming language like Python can be used to build an energy demand prediction model that combines a Long Short-Term Memory network (LSTM) with an ARIMA model, using deep learning frameworks such as TensorFlow or PyTorch. The historical data is preprocessed, including operations such as data cleaning and normalization, and then the model is trained and optimized. The model parameters are updated regularly every day to adapt to changes in energy demand under different scenarios. During the data processing, considering the passive and irregular usage characteristics of the smart lock, the data storage structure is optimized to improve data reading and processing efficiency.

[0108] During the model training and operation, the smart lock and the handheld terminal NFC interact and negotiate through control instructions in the NFC communication protocol. When the smart lock predicts a high energy demand, it sends a request instruction containing the energy demand value and the expected transmission time to the handheld terminal NFC. The handheld terminal NFC adjusts the energy output parameters according to its own energy output capacity and user settings to achieve reasonable energy distribution. During the communication process, a simple and efficient communication protocol is adopted to reduce the data transmission volume and lower the consumption of limited energy. Specifically, the smart lock side uses a microcontroller (MCU) to generate an instruction packet that conforms to the extended communication protocol, and during the communication process, a simple and efficient communication protocol is adopted to reduce the data transmission volume and lower the consumption of limited energy. Specifically, the smart lock side uses a microcontroller (MCU) to generate an instruction packet that conforms to the extended communication protocol, and through the modulation and demodulation circuit of the NFC physical layer, the instruction packet is modulated into a radio frequency signal with a specific frequency (such as 13.56 MHz). The modulation method uses Binary Phase Shift Keying (BPSK), and its modulation formula is where is the carrier amplitude, is the carrier frequency, corresponds to and respectively when in logical "0" and "1".

[0109] The modulated signal is amplified by a power amplifier to ensure that the signal strength is sufficient to be stably received by the NFC module at the handheld end. Before sending the instruction packet, the MCU at the smart lock end performs CRC (Cyclic Redundancy Check) encoding on the instruction packet, generating a check code and attaching it to the end of the instruction packet to ensure the accuracy of data transmission. The CRC encoding formula is , where is the original data polynomial, is the generating polynomial, is the number of bits of the check code.

[0110] After the NFC module at the handheld end receives the signal, it first demodulates the radio frequency signal into a digital signal through a demodulation circuit and then performs CRC verification. If the verification passes, it parses the content of the instruction packet; if the verification fails, it sends a retransmission request to the smart lock end. The NFC module at the handheld end generates a feedback instruction packet according to the parsed content of the instruction packet and the established energy distribution strategy. Similarly, the feedback instruction packet also needs to be modulated and amplified at the handheld end before being sent back to the smart lock. After receiving the feedback instruction packet, the smart lock end 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-consuming.

[0111] 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 for a passive smart lock or the above-mentioned energy prediction and distribution method for a passive smart lock.

[0112] Embodiment 1

[0113] Industrial inspection scenario

[0114] ‌Scenario description‌: During industrial inspections, inspectors need to frequently unlock passive NFC smart locks to access critical equipment.

[0115] ‌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 unlocking instructions at critical moments. At the same time, the dynamic power matching algorithm improves the energy transmission efficiency and reduces energy waste.

[0116] Embodiment 2

[0117] Smart home scenario

[0118] ‌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 for NFC unlocking.

[0119] Implementation effect: The method of the present invention enables the smart lock to intelligently adjust the energy reception strategy according to the usage habits and needs of family members. When a family member approaches, the smart lock can quickly respond and unlock, improving the user experience.

[0120] Embodiment III

[0121] Logistics and warehousing scenario

[0122] Scenario description: In a logistics and warehousing center, a passive NFC smart lock is used to manage the access of goods. Warehouse administrators use special smart keys for NFC unlocking.

[0123] Implementation effect: Through the method of the present invention, the smart lock can accurately predict the unlocking needs of warehouse administrators and request sufficient energy. At the same time, the energy prediction and allocation algorithm ensures the stable operation of the smart lock during peak periods, improving the efficiency of warehousing management.

[0124] The embodiments should not be regarded as limitations of 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 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.

6. The energy prediction and allocation method of 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 is 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.

Images