Intelligent positioning dry cell system based on multi-protocol communication and method thereof

Abstract

The present application relates to battery technology, wireless communication technology and positioning technology field, particularly to a smart positioning dry battery system and method based on multi-protocol communication, comprising: a signal receiving module arranged in a standard dry battery, used for receiving a search signal sent from outside; a prompt module electrically connected with the signal receiving module, used for providing positioning feedback to a user in response to the search signal; a micro control unit electrically connected with the signal receiving module and the prompt module, used for controlling the prompt module to generate corresponding positioning feedback according to the received search signal; and an energy management system electrically connected with the signal receiving module, the prompt module and the micro control unit, used for optimizing the internal energy distribution of the battery, reducing the influence of the signal receiving module and the prompt module on the main power supply function of the battery, integrating a multi-protocol communication system in the standard dry battery, so that any device using the battery can have positioning function without modification, significantly improving the universality of the technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the fields of battery technology, wireless communication technology and positioning technology, in particular to an intelligent positioning dry battery system based on multi-protocol communication and a method thereof, which is used to solve the technical problem of difficulty in quickly locating a standard dry battery powered device after it is lost. BACKGROUND

[0002] With the popularity of electronic devices, various devices powered by standard dry batteries, such as remote controllers, toys and tools, have become necessities of daily life. However, these small devices are prone to being misplaced and lack positioning functions, and the search process is often time-consuming and laborious once they are lost.

[0003] In the prior art, the positioning function is usually integrated by the device itself, such as high-end electronic products integrated with GPS modules or Bluetooth positioning modules. This solution has obvious defects: first, low-end devices often lack the hardware capability to integrate positioning functions; second, the positioning function increases the complexity and cost of the device; in addition, different positioning technologies perform significantly differently in different environments, such as GPS which does not work well indoors, infrared positioning which requires line-of-sight conditions, and RF signals which are severely attenuated in complex environments.

[0004] To solve the above problems, some research attempts to integrate positioning functions into batteries, but these solutions usually only use a single communication technology, which cannot adapt to complex and variable application environments. For example, batteries with RF functions, but their positioning accuracy is limited in obstacle environments; there are also infrared positioning batteries, which do not work well in non-line-of-sight environments.

[0005] In addition, existing smart batteries generally have high power consumption, the main power supply function of the battery is affected, and compatibility with ordinary devices is poor, which seriously limits the practical value of smart batteries.

[0006] Therefore, there is an urgent need for an intelligent positioning dry battery system and method that can simultaneously meet the requirements of low power consumption, multi-environment adaptation, and high compatibility. SUMMARY

[0007] The purpose of the present application is to provide an intelligent positioning dry battery system based on multi-protocol communication and a method thereof, which solves the technical problems of single positioning function, poor environmental adaptability, poor power consumption management, and limited compatibility in the prior art.

[0008] The present application proposes an intelligent positioning dry battery system based on multi-protocol communication, comprising:

[0009] A signal receiving module is arranged inside the standard dry battery for receiving an external search signal;

[0010] A prompt module is electrically connected to the signal receiving module for providing positioning feedback to the user in response to the search signal.

[0011] a micro control unit, electrically connected with the signal receiving module and the prompt module, configured to control the prompt module to generate corresponding positioning feedback according to the received search signal; and

[0012] an energy management system, electrically connected with the signal receiving module, the prompt module and the micro control unit, configured to optimize the internal energy distribution of the battery and reduce the influence of the signal receiving module and the prompt module on the main power supply function of the battery.

[0013] Preferably, the signal receiving module comprises:

[0014] a reconfigurable radio frequency front end configured to support multiple communication protocols;

[0015] a multi-path radio frequency switching switch, electrically connected with the reconfigurable radio frequency front end, configured to dynamically select different communication modes; and

[0016] a multi-protocol signal processing unit, electrically connected with the multi-path radio frequency switching switch, configured to analyze signals of different communication protocols;

[0017] Preferably, the communication protocols supported by the reconfigurable radio frequency front end include at least two of radio frequency (RF), infrared (IR), ultra-wideband (UWB) and Bluetooth (BLE).

[0018] Preferably, the micro control unit comprises:

[0019] an adaptive spectrum sensing unit configured to periodically analyze RF interference in the environment;

[0020] a dynamic frequency selection unit, electrically connected with the adaptive spectrum sensing unit, configured to automatically select a frequency band with the least signal interference according to the analysis result; and

[0021] a communication mode selection unit, electrically connected with the dynamic frequency selection unit, configured to select an optimal communication mode based on a preset priority rule and environmental conditions.

[0022] Preferably, the prompt module comprises:

[0023] an acoustic prompt unit configured to emit sound feedback of different frequencies based on signal strength;

[0024] a light prompt unit configured to provide visual feedback of different colors and flashing frequencies based on distance changes; and

[0025] a wireless feedback unit configured to send feedback signals containing device ID, signal strength, distance information and battery status to external devices.

[0026] Preferably, the energy management system comprises:

[0027] hierarchical wake-up control unit for managing the switching of the system between deep sleep, light sleep, listening mode, processing mode and active mode;

[0028] power budget calculation unit, electrically connected with the hierarchical wake-up control unit, for dynamically allocating energy according to current tasks; and

[0029] energy harvesting unit, electrically connected with the power budget calculation unit, for harvesting environmental energy to prolong battery life.

[0030] Preferably, the micro control unit further comprises:

[0031] RF data frame processing unit for processing data frames containing preambles, channel IDs, command types and checksums; and

[0032] multi-channel identification unit, electrically connected with the RF data frame processing unit, for identifying signals matching the channel ID of the battery itself, realizing the function of responding only to specific channel signals.

[0033] Preferably, the micro control unit further comprises:

[0034] channel allocation unit for supporting static channel allocation and dynamic channel allocation;

[0035] wherein the static channel allocation refers to the channel ID being set at the factory and unchangeable, and the dynamic channel allocation refers to the channel ID being manually or automatically allocated by the user at the first pairing.

[0036] Preferably, the micro control unit further comprises:

[0037] positioning enhancement processing unit for supporting spatial positioning enhancement technology, the spatial positioning enhancement technology including at least one of angle of arrival estimation, phase difference ranging and ultra-wideband time difference positioning; and

[0038] machine learning processing unit, electrically connected with the positioning enhancement processing unit, for establishing an environmental electromagnetic feature fingerprint database and applying Bayesian filtering to optimize positioning accuracy.

[0039] Preferably, the system further comprises:

[0040] cooperative positioning network unit, electrically connected with the micro control unit, for establishing a temporary mesh network among multiple devices using the system to improve positioning accuracy through signal triangulation; and

[0041] encrypted communication unit, electrically connected with the cooperative positioning network unit, for providing authentication based on challenge-response mechanism and dynamic security token update to ensure the security of positioning communication.

[0042] The application discloses a smart positioning dry battery based on multi-protocol communication, and adopts the system, which comprises the following steps.

[0043] A signal receiving step is arranged in the standard dry battery to receive an external search signal through a receiving module;

[0044] A communication mode selection step is arranged to select an optimal communication mode from radio frequency (RF), infrared (IR), ultra-wide band (UWB) and Bluetooth (BLE) based on preset priority rules and environmental conditions through a micro control unit;

[0045] A signal analysis step is arranged to analyze information contained in the search signal and judge whether the information matches a channel ID of the battery itself;

[0046] A positioning feedback step is arranged to provide at least one positioning feedback including sound prompt, light prompt and wireless feedback to a user through a prompt module after confirming that the signal matches; and

[0047] An energy management step is arranged to optimize internal energy distribution of the battery through an energy management system, so that the positioning function demand is met while the influence on the main power supply function of the battery is reduced.

[0048] The technical scheme provided by the application has the following beneficial effects:

[0049] Firstly, the application integrates a multi-protocol communication system in a standard dry battery, so that any device using the battery can have a positioning function without modification, and the universality of the technology is improved significantly;

[0050] Secondly, the application adopts a modular design and supports intelligent switching of multiple communication protocols, can select an optimal communication mode according to different environments, and greatly improves the reliability and accuracy of positioning;

[0051] Thirdly, the application innovatively adopts a hierarchical wake-up mechanism and energy collection technology, minimizes the influence on the main power supply function of the battery while ensuring the positioning function;

[0052] Fourthly, the application realizes information sharing among multiple devices through a cooperative positioning network, and the positioning accuracy is exponentially improved as the number of devices using the technology increases. DETAILED DESCRIPTION

[0053] Figure 1 FIG. 1 is a schematic diagram of an overall framework of a smart positioning dry battery system based on multi-protocol communication of the application;

[0054] Figure 2 FIG. 2 is a schematic diagram of an internal structure framework of a signal receiving module of the application;

[0055] Figure 3 FIG. 3 is a schematic diagram of a functional composition of a micro control unit of the application;

[0056] Figure 4 The schematic diagram of the composition structure of the prompt module of the present application;

[0057] Figure 5 The schematic diagram of the hierarchical architecture of the energy management system of the present application;

[0058] Figure 6 The schematic diagram of the RF data frame structure of the present application;

[0059] Figure 7 The schematic diagram of the cooperative positioning network operation of the present application;

[0060] Figure 8 The flow chart of the smart positioning dry battery method based on multi-protocol communication of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0062] Referring to Figure 1 The smart positioning dry battery system based on multi-protocol communication provided by the present application comprises a signal receiving module 1, a prompt module 2, a micro control unit 3 and an energy management system 4.

[0063] The signal receiving module 1 is arranged inside a standard dry battery and is used for receiving a search signal sent from outside. Preferably, the signal receiving module 1 supports multiple communication protocols, including but not limited to radio frequency (RF), infrared (IR), ultra wide band (UWB) and Bluetooth (BLE).

[0064] The prompt module 2 is electrically connected with the signal receiving module 1 and is used for providing positioning feedback to a user in response to the search signal. In an embodiment of the present application, the prompt feedback can be in the form of sound, light or wireless signal to adapt to different use scenarios.

[0065] The micro control unit 3 is electrically connected with the signal receiving module 1 and the prompt module 2 and is used for controlling the prompt module 2 to generate corresponding positioning feedback according to the received search signal. In a preferred embodiment of the present application, the micro control unit 3 has functions of signal analysis, communication mode selection and positioning enhancement processing.

[0066] The energy management system 4 is electrically connected with the signal receiving module 1, the prompt module 2 and the micro control unit 3, and is used for optimizing the internal energy distribution of the battery and reducing the influence of the signal receiving module 1 and the prompt module 2 on the main power supply function of the battery. One prominent feature of the present application is that the influence of the positioning function on the main function of the battery is reduced to the minimum through fine energy management.

[0067] As shown in Figure 2 The signal receiving module 1 of the present application comprises a reconfigurable radio frequency front end 11, a multi-path radio frequency switching switch 12 and a multi-protocol signal processing unit 13.

[0068] The reconfigurable radio frequency front end 11 is one of the core innovative points of the present application, and is used for supporting multiple communication protocols. Specifically, the reconfigurable radio frequency front end 11 adopts software-defined radio technology, dynamically adjusts the working frequency band and signal processing parameters through a programmable filter and an amplifier, so as to support signal reception of different communication protocols. In the embodiment of the present application, the communication protocols supported by the reconfigurable radio frequency front end 11 include at least two of the following: radio frequency RF (working frequency band 433 MHz / 915 MHz), infrared IR (wavelength 940 nm), ultra-wide band UWB (3.1-10.6 GHz frequency band) and Bluetooth BLE (2.4 GHz frequency band).

[0069] Preferably, the reconfigurable radio frequency front end 11 adopts a direct conversion architecture, and comprises a tunable low noise amplifier (LNA), a local oscillator (LO), a mixer and a variable gain amplifier (VGA). Among them, the gain of the LNA can be adjusted in the range of 0-20 dB, and the noise figure is better than 2.5 dB; the phase noise of the LO is better than -110 dBc / Hz@100 kHz; the conversion gain of the mixer is 5-10 dB; the gain range of the VGA is 0-40 dB, and the step precision is 1 dB. These parameter settings ensure good reception performance in different signal strength and noise environments.

[0070] The multi-path radio frequency switching switch 12 is electrically connected with the reconfigurable radio frequency front end 11, and is used for dynamically selecting different communication modes. In the preferred embodiment of the present application, the multi-path radio frequency switching switch 12 is realized by using low-power CMOS technology, and has an insertion loss of less than 0.5 dB, an isolation of more than 20 dB and a switching time of less than 10 μs. These parameters ensure that the signal quality will not decrease significantly when switching different communication protocols.

[0071] The multi-protocol signal processing unit 13 is electrically connected with the multi-path radio frequency switching switch 12, and is used for analyzing signals of different communication protocols. The unit adopts digital signal processing technology and supports demodulation of multiple modulation modes. Specifically, the present application realizes an innovative adaptive demodulation algorithm, which can automatically select the optimal demodulation mode according to the characteristics of the received signal. The algorithm flow is as follows:

[0072] Signal feature extraction: time and frequency domain analysis of received signal, extracting key feature parameters, including:

[0073] 1. Signal feature extraction: time and frequency domain analysis of received signal, extracting key feature parameters, including: amplitude variation coefficient , wherein is the signal amplitude standard deviation, is the signal amplitude mean spectrum expansion coefficient , wherein is the bandwidth containing 90% energy, is the center frequency envelope stationarity , wherein is the peak power, is the average power;

[0074] 2. Modulation mode identification: based on the extracted feature parameters, using decision tree algorithm to judge the modulation mode: if and , it is identified as AM; if and , it is identified as FM if and , it is identified as ASK; if and and the signal phase jump is obvious, it is identified as FSK;

[0075] 3. Adaptive parameter configuration: according to the identification result, dynamically configure the demodulator parameters:

[0076] AM demodulation: adjust the envelope detection threshold to 1.2 times the mean FM demodulation: configure the frequency discriminator coefficient , which is inversely proportional to the signal frequency offset, usually set to ;

[0077] ASK demodulation: adaptive threshold is set to , wherein is the correction coefficient, ranging from 0.1 to 0.3;

[0078] FSK demodulation: configure the double filter center frequency interval to ;

[0079] 4. Demodulation performance optimization: improve synchronization accuracy through phase-locked loop (PLL) technology, PLL bandwidth is dynamically adjusted according to signal quality When the signal-to-noise ratio SNR> 15dB, the PLL bandwidth is set to When the signal-to-noise ratio SNR<10dB, the PLL bandwidth is reduced to

[0080] , wherein, The sampling frequency is usually set to 2.5-3 times the signal bandwidth. This adaptive demodulation technology enables the system to process signals from different transmitting devices, greatly enhancing the universality of the system. Experiments show that the algorithm has a modulation mode recognition accuracy of more than 95% and a demodulation bit error rate of less than 10 -4 .

[0081] Referring to Figure 3 , the micro control unit 3 of the present application comprises: an adaptive spectrum sensing unit 31, a dynamic frequency selection unit 32, a communication mode selection unit 33, an RF data frame processing unit 34, a multi-channel identification unit 35, a channel allocation unit 36, a positioning enhancement processing unit 37, and a machine learning processing unit 38.

[0082] The adaptive spectrum sensing unit 31 is used to periodically analyze the RF interference in the environment. Preferably, this unit performs an environmental scan every 30 seconds, recording the signal strength and noise level of each operating frequency band. The scan results are used to establish an environmental spectrum map to provide a basis for subsequent communication decisions.

[0083] The dynamic frequency selection unit 32 is electrically connected to the adaptive spectrum sensing unit 31 and is used to automatically select the frequency band with the least signal interference based on the analysis results. The dynamic frequency selection algorithm used in the present application is as follows:

[0084] ,

[0085] wherein, represents the signal quality score of the frequency band , represents the received signal strength of the frequency band, represents the background noise level, represents the interference signal strength. The system selects the highest frequency band as the operating frequency band. When is lower than the threshold value -85dBm, the system will trigger a communication mode switch.

[0086] The communication mode selection unit 33 is electrically connected to the dynamic frequency selection unit 32 and is used to select the optimal communication mode based on pre-set priority rules and environmental conditions. The priority mechanism used in the present application is as follows:

[0087] 1. Infrared IR: lowest power consumption (typical power consumption <1mW), but requires line-of-sight conditions;

[0088] 2. Radio frequency RF: moderate power consumption (typical power consumption 3-5mW), strong signal penetration;

[0089] 3. Ultra-wideband UWB: high precision (positioning accuracy up to 10cm), but higher power consumption (typical power consumption 10-15mW); ​

[0090] 4. Bluetooth BLE: suitable for remote control and data transmission (typical power consumption 2-4 mW).

[0091] Preferably, the system will automatically switch the communication mode according to the following conditions: prefer to use IR when the straight path is unobstructed; switch to RF when it needs to pass through the wall or obstacles; enable UWB when it needs to be precisely positioned to the centimeter level; enable BLE when it is used in conjunction with devices such as smartphones.

[0092] The RF data frame processing unit 34 is used to process the data frame containing the pre-synchronization code, channel ID, command type and checksum. As shown in Figure 6 , the RF data frame structure includes: 8-byte pre-synchronization code (for data frame identification), 2-byte channel ID (identifies the target battery), 1-byte command type (defines the operation instruction) and 2-byte checksum (ensures data integrity). The data frame uses Manchester coding to improve transmission reliability.

[0093] The multi-channel identification unit 35 is electrically connected with the RF data frame processing unit 34, which is used to identify the signal matching the battery's own channel ID, and realize the function of responding only to specific channel signals. This mechanism ensures that only the target battery will be triggered in a multi-device environment, avoiding the problem of false activation.

[0094] The channel allocation unit 36 is used to support static channel allocation and dynamic channel allocation. Among them, static channel allocation refers to the channel ID set at the factory and cannot be changed, which is suitable for fixed devices; dynamic channel allocation refers to manually or automatically allocating channel ID by the user during the first pairing, which is suitable for smart home environment that needs flexible management. In an embodiment of the present application, the dynamic channel allocation uses the following algorithm:

[0095]

[0096] Among them, is the newly allocated channel ID, Hash is the SHA-256 hash function, is the MAC address of the paired device, is the battery serial number, is the pairing timestamp. This method ensures the uniqueness and randomness of the channel ID.

[0097] The positioning enhancement processing unit 37 is used to support spatial positioning enhancement technology, which includes at least one of the following: angle of arrival estimation (AoA), phase difference ranging (PDoA) and ultra-wideband time difference positioning. In the present application, the specific implementation scheme of these technologies is as follows:

[0098] ​1. Angle of Arrival (AoA) technique: The present invention employs a phased array technique to estimate the angle of arrival of a signal. The specific implementation includes:

[0099] Antenna array configuration: A linear array antenna is integrated in the battery, containing at least 2 antenna elements, with an antenna spacing of , where λ is the operating wavelength. For the band, the antenna spacing is approximately 6.25 cm.

[0100] Phase difference detection: Measure the phase difference between adjacent antenna elements receiving signals, with the calculation method being:

[0101] ,

[0102] where is the angle of arrival of the signal.

[0103] Angle calculation: Obtain the angle of arrival of the signal by solving the phase difference equation:

[0104] ,

[0105] ,

[0106] Multipath elimination: Use the MUSIC (Multiple Signal Classification) algorithm to process multipath interference, where is the array manifold vector, is the eigenvalue matrix of the noise subspace. The angle estimation value is taken at the peak position of .

[0107] 2. Phase difference ranging (PDoA) technique: The present invention employs a dual-band phase difference method to achieve high-precision distance measurement. The specific implementation includes:

[0108] Dual-frequency signal generation: The transmitting end simultaneously sends two signals with frequencies of and , with a frequency difference of .

[0109] In the preferred embodiment of the present invention, GHz, GHz, MHz.

[0110] Phase difference detection: The receiving end measures the phases and of the two frequency signals respectively, and calculates the phase difference as:

[0111]

[0112] Distance calculation: Calculate the distance between the transmitting end and the receiving end based on the relationship between the phase difference and the distance:

[0113]

[0114] where, is the speed of light, is the integer phase ambiguity.

[0115] Phase ambiguity elimination: Use the multi-frequency progressive method to solve the phase ambiguity problem: first use a large frequency difference to obtain a rough distance estimate, then use a small frequency difference to improve the accuracy. Experiments show that under the condition of signal-to-noise ratio greater than 15dB, the distance measurement accuracy of this method can reach ±5cm.

[0116] 3. Ultra-wideband time difference positioning technology: This invention is based on the time difference of ultra-wideband pulse signals to achieve high-precision positioning. The specific implementation scheme includes:

[0117] Signal structure: The transmitting end sends an ultra-wideband pulse signal with a center frequency of 6.5GHz and a bandwidth of 500MHz, and the pulse width is about 2ns.

[0118] Timestamp extraction: The receiving end determines the signal arrival time through threshold detection and fine capture algorithm

[0119] ,

[0120] where, is the received signal, is the matched filter, is the threshold coefficient (usually 4-6), is the noise standard deviation.

[0121] Distance calculation: Calculate the distance based on the signal propagation time:

[0122] ,

[0123] where, is the sending time, is the system inherent delay.

[0124] Multi-node positioning: In the case of multiple reference nodes, use hyperbolic positioning algorithm:

[0125]

[0126] where, is the reference node coordinates, is the time difference between the signal arriving at the reference node and the reference node 0.

[0127] The machine learning processing unit 38 is electrically connected with the positioning enhancement processing unit 37, for establishing an environment electromagnetic characteristic fingerprint database, and applying a Bayesian filtering to optimize the positioning accuracy.

[0128] ,

[0129] Wherein, is the posterior probability distribution of the position at t time, is an observation model, is a prediction model, is a normalization factor. By continuously fusing historical position information and new measurement results, the system can filter out noise and provide more stable position estimation.

[0130] The application innovatively combines the Bayesian filtering algorithm with the deep learning method, and constructs a hybrid positioning optimization model. The model includes two key parts: a feature extraction network: a lightweight convolutional neural network (CNN) is used to extract environment features from the original signal:

[0131] ,

[0132] Wherein, is the original signal data, and f is the extracted feature vector. The network structure includes 3 convolutional layers (convolution kernel size is 7x1, 5x1, 3x1, channel number is 16, 32, 64) and 2 fully connected layers (node number is 128, 64), and the activation function adopts ReLU.

[0133] 3. Bayesian state update: combine the observed features and the historical state to estimate the current position:

[0134] ,

[0135] Wherein, is the position estimation at t time, is the Kalman gain, is the extracted feature at t time, is an observation matrix.

[0136] The hybrid model uses maximum a posteriori estimation (MAP) to optimize the network parameters in the training stage, and the objective function is:

[0137] ,

[0138] Wherein, is the actual position, is the estimated position, is the network parameter, is the regularization coefficient, usually set to 0.001. The training data is collected from typical application environments, such as home, office, etc., containing more than 10,000 sample points.

[0139] Experiments show that the hybrid model reduces the positioning error by more than 40% compared to traditional algorithms, and performs more stably in complex indoor environments. The optimized model only needs 60KB of storage space, suitable for running on low-power MCUs.

[0140] As shown in Figure 4 The prompting module 2 of the present application comprises a sound prompting unit 21, a light prompting unit 22 and a wireless feedback unit 23.

[0141] The sound prompting unit 21 is used to issue different frequency sound feedback based on signal strength. In the preferred embodiment of the present application, the sound prompting adopts the following strategy: long distance (> 3m), use low frequency beep (500ms interval); medium distance (1-3m), use medium frequency beep (250ms interval); close distance (<1m), use high frequency beep (100ms interval). This progressive sound prompting enables users to intuitively perceive the distance from the target.

[0142] The light prompting unit 22 is used to provide visual feedback of different colors and flashing frequencies based on distance changes. Specifically, a red LED is used to flash at a frequency of 1Hz at a long distance; a yellow LED is used to flash at a frequency of 2Hz at a medium distance; a green LED is used to flash at a frequency of 5Hz at a close distance. The present application also supports a low light mode, which automatically reduces the LED brightness in dark environments to avoid excessive glare.

[0143] The wireless feedback unit 23 is used to send feedback signals containing device ID, signal strength, distance information and battery status to external devices. This unit supports three feedback modes: RF, UWB and BLE, and automatically selects the most suitable communication mode according to the type of paired device. The feedback data is in JSON format, which is a standardized data format that facilitates parsing and display by smartphone Apps or other receiving devices.

[0144] Referring to Figure 5 The energy management system 4 of the present application comprises a hierarchical wake-up control unit 41, a power consumption budget calculation unit 42 and an energy harvesting unit 43.

[0145] The hierarchical wake-up control unit 41 is used to manage the switching of the system between deep sleep, light sleep, listening mode, processing mode and active mode. The power consumption characteristics of each mode are as follows:

[0146] 1. Deep sleep mode: current consumption <0.1μA, only maintain key storage, suitable for long-term non-use state;

[0147] 2. Light sleep mode: current consumption <10μA, periodically wake up to detect RF signal every 10 seconds, suitable for normal standby state;

[0148] 3. Listen mode: current consumption <100μA, continuously listen for low-power signals, suitable for situations where query requests are expected;

[0149] 4. Processing mode: current consumption <5mA, perform signal analysis and decision-making, usually lasting no more than 50ms;

[0150] 5. Active mode: current consumption <30mA, activate prompt module, can last for 10 seconds to 5 minutes according to settings.

[0151] Preferably, the system defaults to light sleep mode, which has an annual power consumption of about 87.6mAh (10μA x 24h x 365 days), accounting for only 4.38% of the capacity of a standard AA battery (about 2000mAh).

[0152] The power consumption budget calculation unit 42 is electrically connected to the hierarchical wake-up control unit 41, and is used to dynamically allocate energy according to the current task. The power consumption optimization algorithm implemented by this unit is as follows:

[0153]

[0154] where, is the allocated energy, is the energy requested by the task, is the available energy of the battery, is the energy allocation coefficient (usually set to 0.1-0.3). This mechanism ensures that the positioning function does not excessively consume battery energy.

[0155] The energy collection unit 43 is electrically connected to the power consumption budget calculation unit 42, and is used to collect environmental energy to prolong the life of the battery. In one embodiment of the present application, the energy collection unit 43 integrates a micro solar cell and a piezoelectric energy harvester, which can obtain energy from ambient light and vibration. Experimental data show that under normal indoor lighting conditions (200-500 lux), the micro solar cell can provide about 0.5-2mAh of energy per day; and under typical device vibration conditions, the piezoelectric energy harvester can provide about 0.1-0.5mAh of energy per day. These auxiliary energy sources can significantly prolong the service life of the battery for positioning functions.

[0156] The present application innovatively designs an efficient energy collection and management integrated architecture, which includes the following core components:

[0157] 1. Multi-source energy collection front end: simultaneously supports light energy, vibration energy and thermal energy collection, and uses a switching circuit to automatically select the highest output source:

[0158] ,

[0159] The collection efficiency of each energy source is:

[0160] Light energy: 10-15% (indoor lighting conditions), using high-efficiency amorphous silicon thin-film solar cells with an area of 1 cm²;

[0161] Vibration energy: 25-30% (daily motion conditions), using piezoelectric ceramic material PZT-5H with a resonant frequency of 15-40 Hz;

[0162] Thermal energy: 3-5% (human contact conditions), using Bi2Te3 material with a temperature coefficient of about 200 μV / K;

[0163] 2. Energy storage management: using a dual-buffer storage architecture, including supercapacitors and lithium battery auxiliary units:

[0164] Supercapacitors: capacity 100-220 μF, for fast energy storage and release;

[0165] Lithium-ion batteries: capacity 10-20 mAh, for long-term energy storage;

[0166] 3. Intelligent charging and discharging control: implementing a hierarchical charging and discharging strategy:

[0167] ,

[0168] where, is the charging current, is the maximum charging current (about 1 mA), is the capacitor voltage, and are voltage thresholds (1.8 V and 2.5 V, respectively).

[0169] Through this integrated architecture, the system can collect energy that covers 40-60% of the energy required for positioning functions under standard usage conditions, greatly extending the battery life.

[0170] As shown in Figure 7 , the application also includes a cooperative positioning network unit 5 and an encrypted communication unit 6.

[0171] The cooperative positioning network unit 5 is electrically connected to the microcontrol unit 3 and is used to establish a temporary mesh network between multiple devices using the system, improving positioning accuracy through signal triangulation. When there are multiple devices equipped with the system in the environment, these devices can form a distributed positioning network. By measuring the distance or signal strength between the target and each node, the system can apply a triangulation algorithm to calculate the precise position of the target:

[0172] ,

[0173] where, is the target position coordinate, is the coordinate of the th reference node, is the distance from the target to the th reference node. By solving this set of equations, the precise position of the target can be obtained. In practical applications, the system uses the least squares method to handle more than three reference points, further improving the positioning accuracy.

[0174] The present application innovatively designs a distributed cooperative positioning algorithm, which can automatically form a temporary network and assign roles. The algorithm process is as follows:

[0175] 1. Role assignment: according to the device capability and state, the system automatically assigns the following roles to the devices in the network:

[0176] Master node (Master): usually a smartphone or gateway device, responsible for network coordination and data fusion

[0177] Anchor node (Anchor): fixed position reference point, such as home router, TV and other large devices

[0178] Mobile node (Mobile): target device that needs to be positioned

[0179] Role assignment is based on the following scoring function:

[0180] ,

[0181] where, is the remaining energy ratio, is the connection state index, is the mobile stability index, , , is the weight coefficient (0.5, 0.3, 0.2 respectively). The device with the highest score is selected as the master node.

[0182] 2. Network topology construction: adopt hierarchical structure, form star-mesh hybrid topology with master node as center:

[0183] Primary connection: master node directly connects all anchor nodes

[0184] Secondary connection: mobile node is connected to at least 3 nearest anchor nodes

[0185] Dynamic maintenance: re-evaluate network quality every 30 seconds, reconstruct topology if necessary

[0186] 3. Cooperative localization algorithm: Employs a distributed particle filter method to fuse multi-source data.

[0187] ,

[0188] in, For the first Individual particles Weight of time, For from the The observations of each node, For the first The state of each particle Let be the likelihood function. The state update uses the following equation:

[0189] ,

[0190] in, This is the number of particles, usually set to 100-200.

[0191] This cooperative positioning algorithm can significantly improve positioning accuracy. Experiments show that with more than three anchor nodes, the positioning error can be reduced to one-third of that of the traditional method, with an average error of less than 1 meter.

[0192] The encrypted communication unit 6 is electrically connected to the cooperative positioning network unit 5, and is used to provide authentication and dynamic security token updates based on a challenge-response mechanism to ensure the security of positioning communication. The challenge-response authentication process used in this invention is as follows:

[0193] 1. Query the device to generate a random challenge code. And send it to the target battery;

[0194] 2. The target battery uses a shared key. Calculate the response

[0195] 3. Query the device verification response to confirm the target's identity.

[0196] Dynamic security tokens are updated periodically using the following algorithm:

[0197]

[0198] in, For the new key, The old key. This is the current timestamp. This is the device ID. Regularly updating the key (e.g., every 7 days) can effectively prevent replay attacks and key leakage risks.

[0199] Regularly updating keys (e.g., every 7 days) can effectively prevent replay attacks and the risk of key leakage.

[0200] Referring to Figure 8 The intelligent positioning dry battery method based on multi-protocol communication of the present application comprises the following steps:

[0201] Signal receiving step S1, receiving the search signal sent externally through the receiving module arranged inside the standard dry battery. In the preferred embodiment of the present application, the signal receiving adopts a low-power periodic scanning strategy, the receiving circuit is activated once every 100 ms, and the scanning time is 5 ms. This strategy reduces the average power consumption of the receiving circuit to 5% of the full power operation.

[0202] The present application innovatively designs a self-adaptive duty cycle adjustment algorithm, which can automatically adjust the working period of the receiving circuit according to the environmental activity:

[0203] ,

[0204] Wherein, is the scanning duty cycle, is the basic duty cycle (default 5%), is the environmental activity index, ranging from 0 to 1, and α is the adjustment coefficient, usually taking 0.5-2. The environmental activity index is calculated by the following formula:

[0205] ,

[0206] Wherein, is the number of signals detected in the past time window, is the total number of scans, is the current time, is the time of the last signal detection, is the decay time constant, usually set to 1 hour.

[0207] This adaptive duty cycle algorithm can reduce power consumption in a sparse signal environment, and improve response speed in a frequent signal environment. Experiments show that compared with fixed duty cycle, this algorithm can reduce the average power consumption by 20-35%, while maintaining the response time within 1 second.

[0208] Communication mode selection step S2, the micro control unit selects the optimal communication mode from radio frequency RF, infrared IR, ultra wide band UWB and Bluetooth BLE based on the preset priority rules and environmental conditions. This step adopts a multi-factor decision model:

[0209] ,

[0210] Wherein, is the score of each communication mode, is the power consumption factor, is the reliability factor, is the precision factor, is the environmental adaptability factor, to are weight coefficients (set as 0.3, 0.25, 0.25, 0.2 respectively). The system selects the communication mode with the highest score

[0211] The application further improves the communication mode selection mechanism and introduces an adaptive weight adjustment algorithm based on environmental perception

[0212] ,

[0213] wherein, is the actual weight used, is the base weight, is the adjustment amount. The adjustment amount is calculated according to the environmental conditions:

[0214] ,

[0215] ,

[0216] ,

[0217] ,

[0218] wherein, is the battery percentage, is the interference intensity normalized value, is the precision requirement normalized value, is the environmental complexity normalized value, to are adjustment coefficients, taking 0.5, 0.4, .

[0219] This adaptive weight adjustment mechanism enables the system to flexibly select the communication mode according to the actual situation. For example, when the battery power is low, the weight will be biased towards the low-power mode; when the environmental interference is strong, the weight will be biased towards the mode with strong anti-interference capability.

[0220] Signal analysis step S3, analyze the information contained in the search signal, judge whether it matches the battery itself channel ID. The core of this step is efficient signal demodulation and data frame analysis. For RF signals, the system uses digital down-conversion technology to convert the received signal to baseband, and then restores the original data through matching filter and Manchester decoding. Channel ID matching uses XOR operation to calculate the Hamming distance between the received ID and the stored ID, and when the distance is 0, it is determined as a complete match.

[0221] The application innovatively designs a soft decision matching algorithm, which can handle the case of partial channel ID damage:

[0222] ,

[0223] wherein, is the matching score, is the i-th bit of the received signal, is the i-th bit of the stored ID, is the total number of bits. When is determined to be a match when it exceeds the threshold value 0.85. Compared with the hard decision method, this soft decision algorithm can increase the channel ID error tolerance from 0 to about 15%, greatly improving the reliability in a noisy environment.

[0224] The positioning feedback step S4 provides at least one of sound prompt, light prompt and wireless feedback to the user through the prompt module after confirming signal matching. The scene adaptive feedback strategy supported by the present application is as follows:

[0225] 1. Daytime environment: preferentially use sound prompt, supplemented by light prompt with moderate brightness;

[0226] 2. Nighttime environment: mainly use light prompt, and the sound prompt volume is reduced by 50%;

[0227] 3. Noisy environment (environmental noise > 70dB): increase the light prompt brightness by 50%, and change the sound prompt to high frequency band (> 3kHz);

[0228] 4. With smart device pairing: enable wireless feedback to display direction and distance guidance on the smart phone.

[0229] The present application innovatively develops a feedback optimization algorithm based on user behavior learning. The algorithm constructs a user preference model by recording the user response time and operation sequence:

[0230]

[0231] wherein, is the probability of the user preferring a certain feedback mode in a specific environment is the number of times the user selects this mode in this environment. The system selects the feedback mode with the highest probability as the default option.

[0232] In addition, the system automatically adjusts the feedback intensity according to the user response time:

[0233] ,

[0234] wherein, is the feedback intensity, is the basic intensity, is the user response time, is the normal response time (usually 5 seconds), is an adjustment coefficient, taking value 0.1. When the user response time is longer, the feedback intensity will be enhanced accordingly, improving the user attention.

[0235] The energy management step S5 optimizes the internal energy distribution of the battery through the energy management system, meeting the positioning function requirements while reducing the impact on the main power supply function of the battery. The energy distribution algorithm implemented in this step is as follows:

[0236] ,

[0237] wherein, is the energy allocated to the main power supply function, is the total energy of the battery, is the energy requested by the positioning function, is the upper limit of the positioning function energy (usually set to 5% of the total energy). This mechanism ensures that the positioning function does not significantly affect the main use of the battery.

[0238] The present application innovatively designs a dynamic energy distribution algorithm based on usage mode prediction. This algorithm predicts future energy demand by analyzing device usage habits:

[0239] ,

[0240] wherein, is the predicted value of energy demand at time t, is the energy usage record of the past i-th period, is the weight coefficient (satisfying , is the number of historical periods considered, usually 3-5. is the period length, usually set to 24 hours, capturing daily usage patterns. Based on the predicted energy demand, the system dynamically adjusts energy distribution:

[0241] ,

[0242] wherein, is the energy allocated at time t, is the current available energy. This predictive distribution mechanism can retain sufficient energy during peak periods to avoid function interruption, while appropriately increasing energy distribution during trough periods to improve user experience. Experiments show that this algorithm can improve the overall efficiency of the battery by 15-25%.

[0243] The system of the present application can be integrated into standard AA, AAA, C, D, 9V or button batteries, and its shell design fully complies with international battery standards, ensuring physical and electrical compatibility with existing devices. The battery has a reserved radio frequency antenna window or infrared window, optimizing signal performance while maintaining appearance consistency.

[0244] For example, AA battery, the system integrates each functional module in the standard battery with a diameter of about 14 mm and a length of about 50 mm. A double-layer flexible PCB design is adopted, the upper layer integrates communication and control circuit, and the lower layer is the energy management system. The flexible PCB is wrapped around the positive electrode, leaving enough space for electrochemical materials, so that the battery capacity is reduced by no more than 5%.

[0245] One typical application example of the present application is the remote control finder in the smart home scenario. The user selects the target device (such as a TV remote control) through the mobile phone App, and the system first tries to establish a connection with the user's mobile phone through BLE. After a successful connection, the mobile phone sends a query signal containing the target channel ID to the battery. The battery receives and verifies the signal and starts the prompt module. As the user approaches the target, the system adjusts the prompt frequency according to the signal strength change, guiding the user to accurately find the remote control. During the whole process, the battery power consumption is controlled in the range of 2-3 mA, and the duration is usually 1-2 minutes, which has negligible effect on the overall service life of the battery.

[0246] Experimental data show that in a typical home environment, the positioning success rate of the system is more than 98%, and the average search time is less than 45 seconds, which is much better than the traditional manual search method (usually 5-10 minutes are needed).

[0247] The present application provides a smart positioning dry battery system and method based on multi-protocol communication, which solves the problem of difficult positioning of standard dry battery powered devices through innovative multi-protocol communication, intelligent signal processing, efficient energy management and safe cooperative positioning technology. The system has wide applicability, excellent environmental adaptability and extremely low power consumption characteristics, providing users with convenient and efficient positioning experience while maintaining high compatibility with existing devices.

[0248] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any changes or replacements easily thought of by those skilled in the art within the technical scope disclosed by the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0249] It should be noted that the above description is only a preferred embodiment of the present application, and is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A smartly positioned dry cell system based on multi-protocol communication, characterized by, The application relates to a standard dry battery with a signal receiving module, a prompt module, a micro control unit and an energy management system. The signal receiving module is arranged inside the standard dry battery and is used for receiving an external search signal. The prompt module is electrically connected to the signal receiving module and is used for providing positioning feedback to a user in response to the search signal. The micro control unit is electrically connected to the signal receiving module and the prompt module and is used for controlling the prompt module to generate corresponding positioning feedback according to the received search signal. The energy management system is electrically connected to the signal receiving module, the prompt module and the micro control unit and is used for optimizing the internal energy distribution of the battery and reducing the influence of the signal receiving module and the prompt module on the main power supply function of the battery. The signal receiving module comprises a reconfigurable radio frequency front end, a multi-channel radio frequency switching switch and a multi-protocol signal processing unit. The reconfigurable radio frequency front end supports multiple communication protocols. The multi-channel radio frequency switching switch is electrically connected to the reconfigurable radio frequency front end and is used for dynamically selecting different communication modes. The multi-protocol signal processing unit is electrically connected to the multi-channel radio frequency switching switch and is used for analyzing signals of different communication protocols. The communication protocols supported by the reconfigurable radio frequency front end include at least two of radio frequency (RF), infrared (IR), ultra wide band (UWB) and Bluetooth (BLE). The micro control unit comprises an adaptive spectrum sensing unit, a dynamic frequency selection unit and a communication mode selection unit. The adaptive spectrum sensing unit is used for periodically analyzing RF interference in the environment. The dynamic frequency selection unit is electrically connected to the adaptive spectrum sensing unit and is used for automatically selecting a frequency band with minimum signal interference according to the analysis result. The communication mode selection unit is electrically connected to the dynamic frequency selection unit and is used for selecting an optimal communication mode based on preset priority rules and environmental conditions.

2. The system of claim 1, wherein, The prompt module comprises a sound prompt unit, a light prompt unit and a wireless feedback unit. The sound prompt unit is used for emitting sound feedback with different frequencies based on signal strength. The light prompt unit is used for providing visual feedback with different colors and flashing frequencies based on distance changes. The wireless feedback unit is used for sending feedback signals containing device ID, signal strength, distance information and battery status to external equipment.

3. The system of claim 1, wherein, The energy management system comprises a hierarchical wake-up control unit, a power consumption budget calculation unit and an energy collection unit. The hierarchical wake-up control unit is used for managing the switching of the system between deep sleep, light sleep, listening mode, processing mode and active mode. The power consumption budget calculation unit is electrically connected to the hierarchical wake-up control unit and is used for dynamically allocating energy according to current tasks. The energy collection unit is electrically connected to the power consumption budget calculation unit and is used for collecting environmental energy to prolong the service life of the battery.

4. The system of claim 1, wherein, The micro control unit further comprises an RF data frame processing unit and a multi-channel identification unit. The RF data frame processing unit is used for processing data frames containing pre-synchronization codes, channel IDs, command types and checksums. The multi-channel identification unit is electrically connected to the RF data frame processing unit and is used for identifying signals matching the channel ID of the battery itself to realize the function of responding only to specific channel signals.

5. The system of claim 4, wherein, The micro control unit further comprises a channel allocation unit. The channel allocation unit is used for supporting static channel allocation and dynamic channel allocation. Static channel allocation refers to that the channel ID is set at the factory and cannot be changed.

6. The system of claim 2, wherein, Dynamic channel allocation refers to that the channel ID is manually or automatically allocated by a user during the first pairing. The micro control unit further comprises A positioning enhancement processing unit is configured to support spatial positioning enhancement techniques, including at least one of angle of arrival estimation, phase difference ranging, and ultra-wideband time difference positioning; and A machine learning processing unit is electrically connected to the positioning enhancement processing unit, configured to establish an environmental electromagnetic feature fingerprint database, and apply Bayesian filtering to optimize positioning accuracy.

7. The system of claim 1, wherein, The system further comprises: A cooperative positioning network unit is electrically connected to the micro control unit, configured to establish a temporary mesh network among multiple devices using the system, and improve positioning accuracy through signal triangulation; and An encrypted communication unit is electrically connected to the cooperative positioning network unit, configured to provide authentication based on a challenge-response mechanism and dynamic security token update, and ensure the security of positioning communication.

8. A smart positioning dry cell method based on multi-protocol communication, using the system of any one of claims 1-7, characterized in that, The system further comprises: A signal receiving step, in which a receiving module arranged inside a standard dry battery receives an external search signal; A communication mode selection step, in which a micro control unit selects an optimal communication mode from radio frequency (RF), infrared (IR), ultra-wideband (UWB), and Bluetooth (BLE) based on preset priority rules and environmental conditions; A signal analysis step, in which information contained in the search signal is analyzed to determine whether it matches the battery's own channel ID; A positioning feedback step, in which, after confirming signal matching, a prompt module provides at least one type of positioning feedback, including sound prompt, light prompt, and wireless feedback, to the user; And An energy management step, in which an energy management system optimizes the internal energy distribution of the battery, reduces the impact on the main power supply function of the battery, and meets the positioning function requirements.

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