Monitoring method and friction power generation type intelligent cushion

Abstract

The invention relates to the technical field of monitoring, and discloses a monitoring method and a friction power generation type intelligent cushion, the method is applied to the friction power generation type intelligent cushion, the friction power generation type intelligent cushion comprises a plurality of arrays, each array comprises friction nano-generators, the friction nano-generators are connected in parallel with respective corresponding resistors and then connected with an ADC collector, and the ADC collector is connected with the friction nano-generators. The monitoring method comprises the following steps: acquiring a first voltage signal or a second voltage signal; determining a large-amplitude signal and a small-amplitude signal based on the first voltage signal and the second voltage signal; and performing action prediction based on the large-amplitude signal, the small-amplitude signal and a deep learning model. According to the invention, each friction nano-generator is connected in parallel with the corresponding resistor, the first voltage signal or the second voltage signal is utilized to analyze signals with different amplitudes, and the action corresponding to the signal is judged through the deep learning model, so that the monitoring effect of the friction nano-generator is improved.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to a monitoring method and a friction-generating intelligent seat cushion. Background Art

[0002] With the advent of the 5G era, the significant increase in data transmission rate has promoted the rapid development of Internet of Things technology. Nowadays, a large number of wearable sensors, processors and other electronic devices can be interconnected, and the massive data collected by human sensors can be transmitted to the digital world in real time through high-speed wireless networks. With the help of artificial intelligence technology, these data can be efficiently processed and analyzed to accurately judge the state of the human body, making it possible to achieve real-time sitting posture monitoring. At present, the commonly used sensors in seat cushions are capacitive and resistive sensors. These two sensors need to be powered by an external power supply to work, and there is a problem of high maintenance costs.

[0003] As a self-powered sensor, the triboelectric nanogenerator converts low-frequency pressure signals into electrical signals through electrostatic friction. Since the output signal of the triboelectric nanogenerator itself is limited, many improvement methods have been used to enhance the electrical output performance of the triboelectric nanogenerator and increase its sensitivity, which increases the interference of the external environment on the triboelectric nanogenerator and affects the commercialization process.

[0004] When the friction nanogenerator is used as a smart cushion, in addition to the large-amplitude signals caused by large body movements such as sitting down and standing up, there are also small movements such as breathing, heartbeat and center of gravity shift. When detecting small movements, interference from muscle movement, clothing friction, and external vibration will affect the detection of small movements. At present, there is no solution to eliminate external environmental interference when the friction nanogenerator monitors small or large movements. Summary of the invention

[0005] Based on this, it is necessary to address the technical problem that the friction nanogenerator monitoring effect in the existing technology is poor, and propose a monitoring method, a friction power generation smart cushion and a storage medium.

[0006] In a first aspect, a monitoring method is provided, which is applied to a friction power generation smart cushion, wherein the friction power generation smart cushion comprises a plurality of arrays, wherein the arrays comprise respective friction nanogenerators, wherein the friction nanogenerators are connected in parallel with respective corresponding resistors and then connected to an ADC collector, wherein the resistance values ​​of the resistors connected in parallel with the respective friction nanogenerators are different, and the monitoring method comprises:

[0007] Acquiring a voltage signal of each friction nanogenerator in the array;

[0008] Determine a first voltage signal or a second voltage signal according to whether the voltage signal of the resistor with the largest resistance connected in parallel with the friction nanogenerator is in a saturated state, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude among the voltage signals corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator;

[0009] Determine a large amplitude signal and a small amplitude signal based on a first voltage signal and a time period in which the first voltage signal appears; or determine a large amplitude signal and a small amplitude signal based on a second voltage signal and a time in which the second voltage signal appears;

[0010] Based on each of the large-amplitude signals, each of the small-amplitude signals and a trained deep learning model, action prediction is performed to obtain a prediction result.

[0011] In a second aspect, a friction-generating intelligent seat cushion is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned monitoring method when executing the computer program.

[0012] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned monitoring method are implemented.

[0013] The monitoring method proposed in the present invention is applied to a friction power generation smart cushion, wherein the friction power generation smart cushion includes multiple arrays, wherein the arrays include various friction nanogenerators, wherein the friction nanogenerators are connected in parallel with respective corresponding resistors and then connected to an ADC collector, wherein the resistance values ​​of the resistors connected in parallel with the friction nanogenerators are different, and the monitoring method obtains the voltage signals of the various friction nanogenerators in the array, and then determines the first voltage signal or the second voltage signal according to whether the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude in the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator, and then determines the large amplitude signal and the small amplitude signal based on the first voltage signal and the time period when the first voltage signal appears; or determines the large amplitude signal and the small amplitude signal based on the second voltage signal and the time when the second voltage signal appears, and finally performs action prediction based on each of the large amplitude signals, each of the small amplitude signals and the trained deep learning model to obtain a prediction result. The present invention utilizes multiple friction nanogenerators in parallel with multi-resistance resistors, utilizes the first voltage signal or the second voltage signal to analyze signals of different amplitudes, and determines the large-amplitude and small-amplitude actions corresponding to the signal through a deep learning model, thereby greatly improving the monitoring effect of the friction nanogenerator. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] in:

[0016] Figure 1 A structural block diagram of a friction nanogenerator of a monitoring device in an embodiment;

[0017] Figure 2 is another structural block diagram of a friction nanogenerator of a monitoring device in an embodiment;

[0018] Figure 3 FIG. 4 is a flow chart of a monitoring method in one embodiment. DETAILED DESCRIPTION

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The monitoring method provided in the embodiment of the present invention can be applied to a friction-generating smart cushion. Figure 1 , Figure 1The friction power generation smart cushion, ADC acquisition device, and host computer are connected in sequence, wherein the friction power generation smart cushion communicates with the host computer through the ADC acquisition device. The host computer can also be replaced by a server, which can receive and obtain the voltage signal of each friction nano generator in the array through the friction power generation smart cushion, and then the server determines the first voltage signal or the second voltage signal according to whether the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nano generator is in a saturated state, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nano generator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude in the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nano generator, and then the server determines the large amplitude signal and the small amplitude signal based on the first voltage signal and the time period when the first voltage signal appears; or, determines the large amplitude signal and the small amplitude signal based on the second voltage signal and the time when the second voltage signal appears, and finally the server predicts the action based on each of the large amplitude signals, each of the small amplitude signals, and the trained deep learning model to obtain the prediction result. The server may be implemented by an independent server or a server cluster consisting of multiple servers.

[0023] The friction-generating smart cushion comprises an array of multiple friction nanogenerators, and the preparation of the friction-generating smart cushion comprises the following steps:

[0024] Step A: First, an array of friction nanogenerators is prepared. In the present invention, the friction nanogenerator is stacked in sequence by a first electrode, a nanocomposite negative friction layer, a spacer layer, a positive friction layer material and a second electrode. Preferably, the first electrode, the nanocomposite negative friction layer, the spacer layer, the positive friction layer material and the second electrode are perpendicular to each other. Preferably, the first electrode and the second electrode materials of the dual-electrode structure friction nanogenerator include but are not limited to one of the conductive materials such as Cu, Al, ITO, Ag nanowires, etc. The preferred positive friction layer material of the dual-electrode structure friction nanogenerator includes but is not limited to nylon, PLA, PFA and other polymer films and metal materials. Usually, the spacer layer material is selected as one of the nanocomposite materials or insulating materials; further, the insulating material includes glass, nylon, etc.;

[0025] The method for preparing the above-mentioned friction nanogenerator comprises the following steps:

[0026] Step A1: combining the nanomaterial with the negative friction layer material to form a nanocomposite material.

[0027] Step A2: Introducing the nanocomposite material into a mold to prepare nanocomposite negative friction layers with spacer layer structures of various sizes

[0028] Step A3: stacking the nanocomposite negative friction layer, the first electrode array and the second electrode array to obtain dual-electrode friction nanogenerators of various sizes.

[0029] Step A4: The first electrodes and the second electrodes of the obtained multiple friction nanogenerators are respectively combined with the flexible PCB board to make a friction nanogenerator array.

[0030] Step A5: A plurality of electrodes are designed on the flexible PCB board, which are respectively connected to a plurality of friction nanogenerators and output to the data acquisition device through wiring.

[0031] Step A6: The flexible PCB board includes an electrode layer and a protective layer. The electrode layers of the two flexible PCB boards are respectively connected to the first electrode and the second electrode of the friction nanogenerator, forming a structure from top to bottom of a protective layer, a PCB electrode, a first electrode, a nanocomposite negative friction layer, a spacer layer, a positive friction layer material, a second electrode, a PCB electrode and a protective layer.

[0032] The connection methods of the above-mentioned PCB electrode and the first electrode and the second electrode of the friction nanogenerator include but are not limited to silver paste, vertical conductive glue or spin coating.

[0033] Preferably, the arrangement of the above-mentioned flexible PCB board includes but is not limited to combining 3-5 friction nanogenerators into an array, and then arranging 9 arrays into a 3*3 large array for use as a cushion. The number of large arrays can be expanded as needed, including 5*5 to n*n, refer to Figure 2 , the friction nanogenerator is represented by a square, and four friction nanogenerators form an array.

[0034] Step B: Connect the array of friction nanogenerators to the resistor array. Specifically, the friction nanogenerators in the array are connected to resistors with resistance values ​​ranging from 100MΩ to 10GΩ, respectively, for gradient detection of large pressure signals and small pressure signals. (Principle explanation: The principle and basis of connecting resistors of different sizes in parallel: According to the characteristics of the friction nanogenerator, the larger the resistance connected in parallel with the friction nanogenerator, the larger the voltage signal it generates. At the same time, since the friction nanogenerator itself can be regarded as a capacitor, according to the formula τ=RC, where R is the external resistor and C is the equivalent capacitance of the friction nanogenerator, it can be seen that the larger the resistance connected in parallel with the friction nanogenerator, the longer its discharge time. According to this principle, the source of the external signal can be distinguished by analyzing the signal size and discharge time at the same time in the array. According to the formula for collecting voltage in the system, V=Voc*R1 / R1+R2, where R1 is the external resistor, R2 is the internal resistance of the friction nanogenerator, and Voc is the equivalent capacitance of the friction nanogenerator. The open circuit voltage of the tribo-nanogenerator. It can be seen that after the friction nanogenerator is connected in parallel with different resistors, the voltage signal collected by it increases with the increase of the external resistance. Take the four acquisition channels of four small columns with external resistances of 100MΩ, 500MΩ, 1GΩ and 10GΩ as an example, corresponding to acquisition channels 1-4. For large movements, the voltage signal is very easy to saturate under the external resistance of 10G ohm (channel 4), or a signal with obvious peak value will appear. The large movement signals of other channels (channels 1-3) are extracted through the saturation time or the time when the peak value appears, and with the assistance of artificial intelligence. For small movement signals, the signal amplitude can be amplified and the discharge time can be extended through channels 3 and 4 with external large resistors, which is convenient for signal extraction)

[0035] Step C: Connecting the data acquisition system, specifically, connecting the triboelectric nanogenerator array connected in parallel with the resistor array to the acquisition system. Preferably, the acquisition chip used in the acquisition system includes but is not limited to ADI 1298, and the sampling frequency is 500Hz-1000Hz.

[0036] See also Figure 2 As shown, Figure 2 A flow chart of a monitoring method provided in one embodiment of the present invention is provided. The monitoring method is applied to the friction power generation type smart cushion. The friction power generation type smart cushion includes a plurality of arrays. The arrays include various friction nanogenerators. The friction nanogenerators are connected in parallel with respective corresponding resistors and then connected to an ADC collector. The resistance values ​​of the resistors connected in parallel with the various friction nanogenerators are different. The monitoring method includes the following steps:

[0037] Step S101: acquiring a voltage signal of each friction nanogenerator in the array;

[0038] In this embodiment, when pressure is applied to the triboelectric nanogenerators, voltage signals of the triboelectric nanogenerators are acquired.

[0039] As an example, an array of friction nanogenerators is placed on a chair to collect external interference signals. Preferably, the external interference signals include but are not limited to wind interference, floor vibration, passers-by walking, etc., and then the pressure change signals of people when sitting still and switching actions are collected respectively. The switching actions include but are not limited to common actions such as standing up, sitting down, raising legs, leaning forward and leaning back. (The test signal contains small signals such as center of gravity transfer and heartbeat and large signals during switching actions). The collected pressure change signal is filtered out by a notch filter to remove the 50Hz power frequency signal interference, and a low-pass filter is used to obtain the required voltage signal of each friction nanogenerator in the array.

[0040] Step S102: determining a first voltage signal or a second voltage signal according to whether the voltage signal of the resistor with the largest resistance connected in parallel with the friction nanogenerator is in a saturated state, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator when the resistor is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude among the voltage signals corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator;

[0041] Step S103: determining a large amplitude signal and a small amplitude signal based on the first voltage signal and the time period when the first voltage signal appears; or determining a large amplitude signal and a small amplitude signal based on the second voltage signal and the time when the second voltage signal appears;

[0042] Step S104: performing action prediction based on each of the large-amplitude signals, each of the small-amplitude signals and the trained deep learning model to obtain a prediction result.

[0043] As an example, the method for obtaining a trained deep learning model includes the following: preprocessing the collected voltage signal, and the specific preprocessing methods include but are not limited to filtering, wavelet transform, Z-Score standardization, sliding window segmentation, etc., and all preprocessed data are labeled according to the action category at the time of collection, and divided into training set, verification set and test set in the deep learning process at a ratio of 8:1:1. A deep learning network is constructed to perform multi-classification tasks, wherein the main network layer architecture includes but is not limited to convolutional neural network CNN and long short-term memory network LSTM, and functional layers such as batch normalization BatchNorm li ze and random inactivation Dropout are added therein to prevent overfitting of the deep learning network during training. The input data of the deep learning network is the preprocessed signal mentioned above, and the output result is the action category number. The deep learning network trains network parameters in a supervised learning manner, updates parameters through the back propagation algorithm, and optimizes the training process by setting strategies such as early stopping Ear l yStopping and learning rate decay. After training, the deep learning network can accept the preprocessed signal as input, calculate the category to which the signal belongs with the highest probability through forward reasoning, and return it as the classification result.

[0044] In this embodiment, each large-amplitude signal is input into a trained deep learning model for motion prediction to obtain the large-amplitude motion type corresponding to the large-amplitude signal, and each small-amplitude signal is input into a trained deep learning model for motion prediction to obtain the small-amplitude motion type corresponding to the small-amplitude signal.

[0045] In one embodiment, the step of determining the first voltage signal and the second voltage signal according to whether the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state comprises:

[0046] Step 201: when the voltage signal of the resistor with the largest resistance value connected in parallel with the triboelectric nanogenerator is in a saturated state, the voltage signal corresponding to the saturation state of the resistor with the largest resistance value connected in parallel with the triboelectric nanogenerator is determined as the first voltage signal, and the time period in which the first voltage signal appears is recorded;

[0047] Step 202: When the voltage signal of the resistor with the largest resistance connected in parallel with the friction nanogenerator is not in a saturated state, the voltage signal with the largest amplitude in the voltage signals corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator is recorded as the second voltage signal.

[0048] As an example, for an array, when the force applied to the array (100N) is large, each channel signal in the array may be saturated, so it is necessary to query from high to low according to the size of the channel external resistance. When the resistance channel with the largest resistance value (10GΩ) is saturated, record the time when the saturation signal appears. According to this time, continue to query the signal of the next resistance channel in the same array (100MΩ). If it is not saturated, record this signal. If it is saturated, continue to query downward, and query the signals of four channels in an array by analogy, and record the unsaturated signals in the four channels. Record this group of signals as large-amplitude signals.

[0049] In one embodiment, the step of determining a large amplitude signal and a small amplitude signal based on the first voltage signal and a time period in which the first voltage signal appears includes:

[0050] Step 301: for each array, according to the time period when the first voltage signal appears, extract the voltage signals corresponding to the resistors in the array in the same time period as the first voltage signal set;

[0051] Step 302: extracting an unsaturated voltage signal from the first voltage signal set, and using the unsaturated voltage signal as the large-amplitude signal;

[0052] Step 303: Perform artificial intelligence decoupling and noise filtering based on the large-amplitude signal to obtain a small-amplitude signal.

[0053] In this embodiment, based on the peak value of the voltage signal in the resistor with the largest resistance connected in parallel with the friction nanogenerator and the discharge time of different channels, the signal corresponding to the recorded signal in channel 4 in other channels in the array is filtered out, and the filtered signal is filtered out with the required small amplitude signal through the characteristic value of the interference signal learned by artificial intelligence.

[0054] In one embodiment, the step of determining the large amplitude signal and the small amplitude signal based on the second voltage signal and the time when the second voltage signal appears includes:

[0055] Step 401: for each array, according to the time period when the second voltage signal appears, extract the voltage signal corresponding to each resistor in the array in the same time period as a large amplitude signal;

[0056] Step 402: Perform artificial intelligence decoupling and noise filtering based on the large-amplitude signal to obtain a small-amplitude signal.

[0057] In this embodiment, when the force (0.1N) is relatively small, each channel signal in the array will not be saturated, so according to the time period when the second voltage signal appears, the voltage signal corresponding to each resistor in the array in the same time period is extracted.

[0058] The present invention utilizes a plurality of friction nanogenerators connected in parallel with multi-resistance resistors, utilizes the difference in amplitude and discharge time to analyze signals of different amplitudes, and determines the large-amplitude and small-amplitude actions corresponding to the signals through the analyzed different signals.

[0059] It should be noted that in the current applications of friction nanogenerators, many friction nanogenerator wearable devices are only connected to one input impedance and collect signals. Although most noise signals can be removed by filtering, various physiological signals of different amplitudes, such as human body movements, heartbeats, and center of gravity shifts, are mixed, and it is impossible to distinguish the specific movements and physiological states corresponding to the signals. By connecting multiple friction nanogenerators in parallel with resistors of different sizes and collecting voltages and analyzing them, the collected signals can be distinguished.

[0060] In one embodiment, a friction power generation smart cushion is provided, which includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the friction power generation smart cushion is used to provide computing and control capabilities. The memory of the friction power generation smart cushion includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the friction power generation smart cushion is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a monitoring method.

[0061] In one embodiment, a friction power generation smart cushion is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0062] Acquiring a voltage signal of each friction nanogenerator in the array;

[0063] When the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state, determine the first voltage signal or the second voltage signal, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude among the voltage signals corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator;

[0064] Determine a large amplitude signal and a small amplitude signal based on a first voltage signal and a time period in which the first voltage signal appears; or determine a large amplitude signal and a small amplitude signal based on a second voltage signal and a time in which the second voltage signal appears;

[0065] Based on each of the large-amplitude signals, each of the small-amplitude signals and a trained deep learning model, action prediction is performed to obtain a prediction result.

[0066] The present invention utilizes multiple friction nanogenerators in parallel with multi-resistance resistors, utilizes the first voltage signal or the second voltage signal to analyze signals of different amplitudes, and determines the large-amplitude and small-amplitude actions corresponding to the signal through a deep learning model, thereby greatly improving the monitoring effect of the friction nanogenerator.

[0067] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0068] Acquiring a voltage signal of each friction nanogenerator in the array;

[0069] When the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state, determine the first voltage signal or the second voltage signal, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude among the voltage signals corresponding to the resistor with the largest resistance value connected in parallel with the friction nanogenerator;

[0070] Determine a large amplitude signal and a small amplitude signal based on a first voltage signal and a time period in which the first voltage signal appears; or determine a large amplitude signal and a small amplitude signal based on a second voltage signal and a time in which the second voltage signal appears;

[0071] Based on each of the large-amplitude signals, each of the small-amplitude signals and a trained deep learning model, action prediction is performed to obtain a prediction result.

[0072] The present invention utilizes multiple friction nanogenerators in parallel with multi-resistance resistors, utilizes the first voltage signal or the second voltage signal to analyze signals of different amplitudes, and determines the large-amplitude and small-amplitude actions corresponding to the signal through a deep learning model, thereby greatly improving the monitoring effect of the friction nanogenerator.

[0073] It should be noted that the functions or steps that can be achieved by the computer-readable storage medium or the friction-electric intelligent cushion can be found in the corresponding descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0075] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0076] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A monitoring method, characterized in that: The invention is applied to a friction power generation smart cushion, wherein the friction power generation smart cushion comprises a plurality of arrays, wherein the arrays comprise respective friction nanogenerators, wherein the friction nanogenerators are connected in parallel with respective corresponding resistors and then connected to an ADC collector, wherein the resistance values ​​of the resistors connected in parallel with the respective friction nanogenerators are different, and the monitoring method comprises: Acquiring a voltage signal of each friction nanogenerator in the array; Determine a first voltage signal or a second voltage signal according to whether the voltage signal of the resistor with the largest resistance connected in parallel with the friction nanogenerator is in a saturated state, wherein the first voltage signal refers to the voltage signal corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator when it is in a saturated state, and the second voltage signal refers to the voltage signal with the largest amplitude among the voltage signals corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator; Determine a large amplitude signal and a small amplitude signal based on a first voltage signal and a time period in which the first voltage signal appears; or determine a large amplitude signal and a small amplitude signal based on a second voltage signal and a time in which the second voltage signal appears; Based on each of the large-amplitude signals, each of the small-amplitude signals and a trained deep learning model, action prediction is performed to obtain a prediction result.

2. The monitoring method according to claim 1, characterized in that: The step of determining the first voltage signal and the second voltage signal according to whether the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state comprises: When the voltage signal of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is in a saturated state, the voltage signal corresponding to the saturation state of the resistor with the largest resistance value connected in parallel with the friction nanogenerator is determined as the first voltage signal, and the time period in which the first voltage signal appears is recorded; When the voltage signal of the resistor with the largest resistance connected in parallel with the friction nanogenerator is not in a saturated state, the voltage signal with the largest amplitude in the voltage signals corresponding to the resistor with the largest resistance connected in parallel with the friction nanogenerator is recorded as the second voltage signal.

3. The monitoring method according to claim 2, characterized in that: The step of determining a large amplitude signal and a small amplitude signal based on the first voltage signal and a time period in which the first voltage signal appears comprises: For each array, according to the time period in which the first voltage signal appears, extract the voltage signals corresponding to the resistors in the array in the same time period as the first voltage signal set; Extracting an unsaturated voltage signal from the first voltage signal set, and using the unsaturated voltage signal as the large-amplitude signal; Artificial intelligence decoupling and noise filtering are performed based on the large-amplitude signal to obtain a small-amplitude signal.

4. The monitoring method according to claim 2, characterized in that: The step of determining the large amplitude signal and the small amplitude signal based on the second voltage signal and the time when the second voltage signal appears includes: For each array, according to the time period in which the second voltage signal appears, extract the voltage signal corresponding to each resistor in the array in the same time period as the large amplitude signal; Artificial intelligence decoupling and noise filtering are performed based on the large-amplitude signal to obtain a small-amplitude signal.

5. A friction power generation smart cushion, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the monitoring method according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the monitoring method according to any one of claims 1 to 4 are implemented.

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