Self-driven intelligent handwriting recognition handwriting board and method based on friction nano-generator

Abstract

The embodiment of the invention discloses a self-driven intelligent handwriting recognition handwriting board based on a friction nano-generator. The self-driven intelligent handwriting recognition handwriting board comprises a handwriting board upper bottom, a handwriting board lower bottom, a handwriting board writing surface and a 2 * 2 friction nano-generator array, the handwriting board writing surface is arranged above the handwriting board upper bottom and is bonded with the handwriting board upper bottom; the 2 * 2 friction nanometer generator array is arranged between the handwriting board upper bottom and the handwriting board lower bottom, and is used for extruding the handwriting board upper bottom and driving the 2 * 2 friction nanometer generator to generate induced current through friction power generation when a user is in contact with the handwriting board writing surface in the writing process. Contact separation is generated in the writing process through the 2 * 2 array friction nanometer generator arranged in the handwriting board, external circuit energy supply is not needed, an electrostatic induction phenomenon is generated spontaneously, induction current is output, and handwriting characteristics of different people are recognized by analyzing the induction current.

Description

Technical Field

[0001] The present invention relates to the field of micro-nano manufacturing self-driven intelligent sensing technology, and in particular to a self-driven intelligent handwriting recognition handwriting board and method based on a friction nanogenerator. Background Art

[0002] In today's era of rapid development of informatization, information security technology has received widespread attention and rapid development. As a core component of information security technology, biometric recognition technology plays an important role in the field of identity authentication with its unique advantages. Biometric recognition technology achieves efficient and difficult-to-forge identity authentication by identifying an individual's unique physiological or behavioral characteristics, such as fingerprints, pupils, gait, face and handwriting.

[0003] Among many biometric recognition technologies, handwriting recognition is highly favored due to its non-contact, low-cost and high stability. Handwriting recognition technology can effectively identify and verify the identity of the writer by analyzing personal writing habits, speed, strength and stroke order. However, current handwriting recognition methods mainly rely on manual recognition or handwriting image recognition technology based on machine learning. Manual recognition methods have problems of inconsistent recognition levels and low efficiency. Although handwriting image recognition technology based on machine learning has improved recognition efficiency to a certain extent, its accuracy rate is still insufficient when faced with a high level of forgery.

[0004] In order to overcome the limitations of existing handwriting recognition technology, the present application proposes a self-driven intelligent handwriting recognition handwriting tablet and method based on a friction nanogenerator. Summary of the invention

[0005] Based on this, it is necessary to propose a self-driven intelligent handwriting recognition handwriting tablet based on friction nanogenerator to address the above problems.

[0006] A self-driven intelligent handwriting recognition handwriting tablet based on a friction nanogenerator, comprising:

[0007] The upper bottom of the tablet, the lower bottom of the tablet, the writing surface of the tablet and the 2*2 friction nanogenerator array;

[0008] The writing surface of the handwriting board is arranged above the bottom of the handwriting board and is bonded to the bottom of the handwriting board;

[0009] The 2*2 friction nanogenerator array is arranged between the upper bottom of the handwriting board and the lower bottom of the handwriting board, and is used to generate an induced current by squeezing the bottom of the handwriting board and driving the 2*2 friction nanogenerators when the user contacts the writing surface of the handwriting board during writing.

[0010] In the above scheme, the 2*2 friction nanogenerator array includes: four friction nanogenerator self-driven sensors, which are arranged according to the 2*2 array rule and are arranged between the upper bottom of the handwriting board and the lower bottom of the handwriting board;

[0011] The four friction nano-power generation self-driven sensors each include: a sensor bottom, a spring, a sensor bottom support layer, a sensor bottom electrode, a sensor top friction electric layer, a sensor top electrode, a sensor top support layer and a sensor top bottom;

[0012] The lower support layer of the sensor is in close contact with the lower electrode of the sensor and is attached to the top of the lower bottom of the sensor. The upper friction electric layer of the sensor, the upper electrode of the sensor and the upper support layer of the sensor are in close contact with each other and are attached to the bottom of the upper bottom of the sensor. The upper bottom of the sensor is connected to the lower bottom of the sensor by springs arranged at the four corners.

[0013] In the above scheme, a processing circuit is also included;

[0014] The processing circuit is connected to the four friction nano-power generation self-driven sensors respectively, and is used for filtering, sampling, and transmitting the induced currents correspondingly generated by the four friction nano-power generation self-driven sensors.

[0015] In the above scheme, the writing surface of the handwriting board is in shape with the bottom of the handwriting board, the upper bottom of the handwriting board, the lower bottom of the handwriting board, the lower bottom of the sensor, and the upper bottom of the sensor are all square transparent flat plates made of PMMA material, and the material of the writing surface of the handwriting board is determined according to the specific environment.

[0016] In the above solution, the sensor lower support layer and the sensor upper support layer are both elastic foams, and are in a planar shape that fits the sensor upper bottom and the sensor lower bottom.

[0017] In the above scheme, the upper electrode of the sensor and the lower electrode of the sensor are both metal films, and their shapes are in conformity with the plane shapes of the upper supporting layer of the sensor and the lower supporting layer of the sensor, and the upper electrode of the sensor and the lower electrode of the sensor both lead out signal lines to be connected to the processing circuit.

[0018] In the above solution, the triboelectric layer on the sensor is a polarized FEP material.

[0019] The present application also proposes a writing behavior analysis method applied to the self-driven intelligent handwriting recognition handwriting board based on the friction nanogenerator, the method comprising:

[0020] Acquire four types of induced currents generated by frictional power generation when the four frictional nano-power generation self-driven sensors are in contact with the writing surface of the handwriting board during the writing process of the user;

[0021] Filtering and sampling the four induced currents;

[0022] The user's writing behavior is determined based on the electrical signal after filtering and sampling.

[0023] In the above scheme, the acquisition of four types of induced currents generated by frictional power generation when the four frictional nano-power generation self-driven sensors are in contact with the writing surface of the handwriting board during the writing process of the user specifically includes:

[0024] When the user writes on the writing surface of the handwriting board, the corresponding upper supporting layer of the sensor in the four friction nanopower generation self-driven sensors bears external force and drives the upper electrode of the sensor, the friction electric layer on the sensor and the lower electrode of the sensor to move relative to each other in the vertical direction. The distance between the upper electrode of the sensor and the lower electrode of the sensor in the four friction nanopower generation self-driven sensors changes, and an electric potential difference is generated between the upper electrode of the sensor and the lower electrode of the sensor, thereby generating a corresponding induced current.

[0025] The embodiment of the present invention has the following beneficial effects: comprising: a handwriting tablet upper bottom, a handwriting tablet lower bottom, a handwriting tablet writing surface and a 2*2 friction nanogenerator array; the handwriting tablet writing surface is arranged above the handwriting tablet lower bottom and bonded to the handwriting tablet lower bottom; the 2*2 friction nanogenerator array is arranged between the handwriting tablet lower bottom and the handwriting tablet lower bottom, and is used to generate an induced current by friction power generation by squeezing the handwriting tablet lower bottom and driving the 2*2 friction nanogenerator when the user contacts the handwriting tablet writing surface during writing. The present invention generates contact separation during writing by setting the 2*2 array friction nanogenerator inside the handwriting tablet, and can spontaneously generate signals through the energy generated by the user writing on the handwriting tablet, and can spontaneously generate electrostatic induction phenomenon without external additional energy supply. The friction nanogenerator has high sensitivity and can sense the slight differences in writing characteristics of different people. The user can be accurately identified for identity recognition by analyzing the generated signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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.

[0027] in:

[0028] Figure 1A schematic diagram of the structure of a self-driven intelligent handwriting recognition tablet based on a friction nanogenerator in one embodiment;

[0029] Figure 2 The handwriting images of user 1, user 2 and user 3 when they write the same text respectively;

[0030] Figure 3 For Figure 2 Schematic diagram of the output voltage signal corresponding to the user's handwriting diagram.

[0031] Reference numerals

[0032] 1. The upper bottom of the tablet; 2. The lower bottom of the tablet; 3. The lower bottom of the sensor; 4. The spring; 5. The lower supporting layer of the sensor; 6. The lower electrode of the sensor; 7. The upper triboelectric layer of the sensor; 8. The upper electrode of the sensor; 9. The upper supporting layer of the sensor; 10. The upper bottom of the sensor; 11-13. The friction nano-electricity self-driven sensor; 14. The writing surface of the tablet. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present invention; however, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details; in other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described, and it should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments presented here; on the contrary, providing these embodiments will make the disclosure thorough and complete and fully convey the scope of the present invention to those skilled in the art.

[0035] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "a", "an" and "said / the" are also intended to include the plural forms, unless the context clearly indicates otherwise, and it should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0036] As an emerging energy harvesting and self-driven sensing technology, the friction nanogenerator (TENG) has shown significant advantages in the field of energy harvesting and self-driven sensing due to its coupling mechanism based on electrostatic induction and triboelectric charging. TENG is based on Maxwell displacement current and can spontaneously convert mechanical energy into electrical energy through the principles of triboelectric charging and electrostatic induction without the need for external power supply. At present, TENG has been widely used in self-driven sensing fields such as acceleration, pressure, and wind speed, showing its broad application prospects.

[0037] According to the friction nanogenerator theory, TENG has four working modes, including vertical contact separation mode, lateral sliding mode, single electrode mode and independent layer mode. Considering that users mainly apply downward force and produce vertical displacement when writing on the handwriting tablet, the present invention provides a self-driven intelligent handwriting recognition handwriting tablet and method based on friction nanogenerator, which adopts the vertical contact separation mode as the working mode of the friction nanogenerator. This mode can effectively capture the mechanical changes of the user during the writing process and convert them into electrical signal output, providing strong support for achieving high-accuracy handwriting recognition.

[0038] In order to thoroughly understand the present invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by the present invention; optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementation methods.

[0039] The embodiment of the present invention is a self-driven intelligent handwriting recognition handwriting tablet based on a friction nanogenerator, such as Figure 1 As shown, it includes: a handwriting tablet upper bottom 1, a handwriting tablet lower bottom 2, a handwriting tablet writing surface 14 and a 2*2 friction nanogenerator array;

[0040] The writing surface 14 of the handwriting board is arranged above the bottom 1 of the handwriting board and is bonded to the bottom 1 of the handwriting board. The 2*2 friction nanogenerator array is arranged between the bottom 1 of the handwriting board and the lower bottom 2 of the handwriting board, and is used to generate an induced current by squeezing the bottom 1 of the handwriting board and driving the 2*2 friction nanogenerators to generate electricity through friction when the user contacts the writing surface 14 of the handwriting board during writing.

[0041] During the writing process, the user's contact and compression of the writing surface 14 of the handwriting board will cause relative movement between the bottom 1 of the handwriting board and the friction nanogenerator array, thereby generating electrostatic induction through friction power generation and spontaneously generating electrical signals. This feature eliminates dependence on external power sources and improves the portability of the device and the flexibility of usage scenarios.

[0042] Specifically, the 2*2 friction nanogenerator array includes: four friction nanogenerator self-driven sensors, which are arranged according to a 2*2 array rule and are arranged between the upper bottom 1 of the handwriting board and the lower bottom 2 of the handwriting board;

[0043] The four friction nano-power generation self-driven sensors all include: a sensor bottom 3, a spring 4, a sensor bottom support layer 5, a sensor bottom electrode 6, a sensor upper friction electric layer 7, a sensor upper electrode 8, a sensor upper support layer 9 and a sensor top bottom 10;

[0044] The use of a 2*2 friction nanogenerator array means that during the writing process, the handwriting tablet can output four signals at the same time. These four signals correspond to the writing behavior characteristics captured by sensors at different positions in the array, and can obtain more characteristic information about the writing behavior, thereby improving the accuracy and reliability of identity recognition.

[0045] The sensor lower support layer 5 is tightly attached to the sensor lower electrode 6 and attached above the sensor lower bottom 3. The sensor upper triboelectric layer 7, the sensor upper electrode 8 and the sensor upper support layer 9 are tightly attached and attached below the sensor upper bottom 10. The sensor upper bottom 10 and the sensor lower bottom 3 are connected by springs 4 set at the four corners.

[0046] Since the friction nanogenerator is highly sensitive to tiny physical changes (such as speed, force, frequency, etc. when writing), the handwriting tablet can sense the tiny differences when different people write. These differences are reflected in the electrical signals. By analyzing the electrical signals, high-precision identification of the writer's identity can be achieved, which helps solve the problems of low accuracy and easy imitation of handwriting in traditional handwriting recognition methods.

[0047] Preferably, the above-mentioned self-driven intelligent handwriting recognition handwriting tablet based on friction nanogenerator also includes a processing circuit;

[0048] The processing circuit is connected to the four friction nano-power generation self-driven sensors respectively, and is used for filtering, sampling and transmitting the induced currents correspondingly generated by the four friction nano-power generation self-driven sensors.

[0049] After the processing circuit is connected to four friction nano-power generation self-driven sensors, it can filter the induced current generated by each sensor, remove noise and clutter in the current signal, and make the signal clearer and smoother. In addition, the induced current generated by the four friction nano-power generation self-driven sensors is sampled regularly or continuously, and the characteristic value of the signal can be obtained at a certain time interval or frequency, and the continuous analog signal can be converted into a discrete digital signal to facilitate subsequent storage, transmission and processing. Finally, the processed signal is sent to a computer, mobile phone or other terminal device via wired or wireless means for further analysis and processing, ensuring that the handwriting board system can accurately identify and transmit the user's writing behavior information in the self-driven working state, thereby realizing the identification and verification of the user's identity.

[0050] The above four friction nano-power generation self-driven sensors are arranged in a regular 2*2 array. When writing, they simultaneously output four signals and connect them to the processing circuit for handwriting recognition. The processing circuit consists of an ADS1115 sampling chip, an ESP32 core board and peripheral circuits. The output voltage signal is collected and digitally filtered, and then transmitted to the terminal (mobile phone, computer, cloud) via Bluetooth.

[0051] In some embodiments, the writing surface 14 of the writing board is in shape with the bottom 1 of the writing board. The upper bottom 1 of the writing board, the lower bottom 2 of the writing board, the lower bottom 3 of the sensor, and the upper bottom 10 of the sensor are all square transparent flat plates made of PMMA. The material of the writing surface 14 of the writing board is determined according to the specific environment.

[0052] The above components are all made of square transparent flat panels made of PMMA (polymethyl methacrylate), which have excellent light transmittance, weather resistance and processing performance, so that the handwriting tablet not only maintains its beauty but also has good durability and stability. The transparent design makes the internal structure and sensor layout of the handwriting tablet clearly visible, which is convenient for maintenance and troubleshooting.

[0053] The material of the handwriting board writing surface 14 is determined according to the specific environment. In different writing environments or when using different writing tools, writing surfaces of different materials can be replaced to dynamically adjust the friction interface, thereby optimizing the writing experience and signal acquisition effect. For example, in an environment that requires a high friction coefficient, a rough writing surface material can be selected; in an environment that requires a low friction coefficient, a smooth writing surface material can be selected.

[0054] Preferably, the sensor lower support layer 5 and the sensor upper support layer 9 are both elastic foams, and are shaped in a planar shape that fits the sensor upper base 10 and the sensor lower base 3 .

[0055] The elastic foam serves as a supporting layer and can provide sufficient mechanical support to maintain the stability of the upper and lower structures of the sensor. This stable support is the basis for ensuring that the sensor is not deformed by external forces during operation. When the user writes on the handwriting board, the writing force will cause relative movement in the vertical direction between the upper and lower bottoms of the sensor. The resilience of the elastic foam also ensures that the sensor can quickly return to its initial state to prepare for the next signal sensing. The shape of the supporting layer fits the planar shape of the upper bottom 10 of the sensor and the lower bottom 3 of the sensor, which improves the fit between the supporting layer and other parts of the sensor, avoids unnecessary gaps and misalignments, and further optimizes the working effect of the sensor.

[0056] Preferably, the sensor upper electrode 8 and the sensor lower electrode 6 are both metal films, and their shapes fit the plane shapes of the sensor upper support layer 9 and the sensor lower support layer 5 , and the sensor upper electrode 8 and the sensor lower electrode 6 both lead out signal lines to connect with the processing circuit.

[0057] As an electrode material, metal film can ensure that the charges generated by the sensor during operation are transmitted quickly and effectively between electrodes due to its excellent conductive properties, thereby reducing energy loss and improving the sensitivity and response speed of the sensor. The shape of the electrode fits the plane shape of the supporting layer, ensuring close contact between the electrode and the triboelectric layer. During the writing process, the charges generated by friction can be accurately and timely captured by the electrode and converted into electrical signals. Finally, the electrode is connected to the processing circuit through a signal line, realizing fast and stable transmission of the electrical signal. The sensor can transmit the generated electrical signal to the processing circuit in real time, and then perform subsequent processing such as filtering, sampling, and transmission to a computer, providing strong data support for handwriting recognition.

[0058] Preferably, the triboelectric layer 7 on the sensor is a polarized FEP material, and the surface of the polarized FEP material carries a stable polarized charge, which helps to more effectively induce and accumulate charges during the operation of the triboelectric nanogenerator, that is, when the upper electrode and the lower electrode of the sensor produce relative motion due to the change in distance, thereby enhancing the output intensity of the electrical signal. At the same time, the polarized FEP material can reduce the interference of unnecessary charges, making the output of the electrical signal purer and improving the signal-to-noise ratio. This is crucial for subsequent signal processing and recognition, and helps to more accurately analyze the characteristics of writing behavior.

[0059] The above-mentioned self-driven intelligent handwriting recognition handwriting tablet based on friction nanogenerator has a simple structure, low cost, easy manufacturing, small size and light weight. It also provides a dual-mode storage strategy for storing personal handwriting data, that is, storing the picture of the user's handwriting plus the handwriting tablet sensor data at the same time, realizing dual recognition and improving recognition accuracy and security.

[0060] In some embodiments, the preparation process of the triboelectric nanoelectric self-driven sensor is as follows:

[0061] C1: Design size. Design the size of the upper bottom 1 and the lower bottom 2 of the tablet to be XXX. Design the size of the upper bottom 10 and the lower bottom 3 of the sensor to be XXX. Design the length of the spring 4 to ensure that the frictional electric layer 7 on the sensor and the lower electrode 6 of the sensor do not contact each other and leave an appropriate gap.

[0062] C2: Prepare materials. The electrodes are made of copper foil. The upper support layer 9 and the lower support layer 5 of the sensor are made of elastic foam. The friction electric layer 7 on the sensor is made of polarized FEP film. Prepare eight thinner signal lines to avoid affecting the operation of the handwriting board.

[0063] C3: Fabrication of a friction nanogenerator: attach the friction electric layer 7 on the sensor to the upper electrode 8 of the sensor, and then attach the two to the upper support layer 9 of the sensor and fit them to the upper bottom 10 of the sensor, with the electrode facing downward. The signal line is connected to the upper electrode 8 of the sensor, and the writing surface of the sensor writing board is attached to the upper bottom 10 of the sensor; attach the lower electrode 6 of the sensor to the lower support layer 5 of the sensor, and then attach the two to the lower bottom 3 of the sensor, with the electrodes facing upward. The signal line is connected to the lower electrode 6 of the sensor, and the upper bottom 10 of the sensor is connected to the lower bottom 3 of the sensor at four corners through four springs 4 to form a complete sensor unit (i.e., a friction nanogenerator self-driven sensor). A total of four sensor units are manufactured.

[0064] C4: Finally, assemble the smart handwriting recognition tablet with a 2*2 friction nanogenerator array and conduct the experiment:

[0065] like Figure 2 , 3 As shown, the handwriting images of user 01, user 02 and user 03 when writing the same text and the corresponding handwriting board output voltage signal images (01a, 02a, 03a) are obtained. It can be seen that the writing behavior characteristics of different people are obviously different, so the users can be identified by analyzing the characteristic differences.

[0066] The present application also proposes a writing behavior analysis method for a self-driven intelligent handwriting recognition handwriting tablet based on a friction nanogenerator, the method comprising:

[0067] Obtaining four types of induced currents generated by frictional power generation when four frictional nano-power generation self-driven sensors are in contact with the writing surface 14 of the handwriting board during the writing process of the user;

[0068] Filter and sample the four types of induced currents;

[0069] The user's writing behavior is determined based on the electrical signal after filtering and sampling.

[0070] With a 2*2 sensor array layout, the handwriting board can simultaneously collect electrical signals generated during writing from four different positions. This multi-channel signal collection method can more comprehensively reflect the user's writing behavior characteristics, including writing strength, speed, frequency, stroke order, etc. This solution uses the self-powered characteristics of the friction nanogenerator and the multi-channel signal collection method, combined with advanced signal processing technology, to achieve accurate recognition of the user's writing behavior characteristics. This physical behavior-based identity recognition method has a high degree of security and accuracy and can be widely used in information security, identity authentication and other fields.

[0071] In some embodiments, when four friction nano-power generation self-driven sensors are in contact with the writing surface 14 of the handwriting board during the writing process, four types of induced currents generated by friction power generation are obtained, specifically including:

[0072] When the user writes on the writing surface 14 of the handwriting board, the corresponding sensor upper support layer 9 in the four friction nanopower generation self-driven sensors bears external force and drives the sensor upper electrode 8, the sensor upper friction electric layer 7 and the sensor lower electrode 6 to move relative to each other in the vertical direction. The distance between the sensor upper electrode 8 and the sensor lower electrode 6 in the four friction nanopower generation self-driven sensors changes, and an electric potential difference is generated between the sensor upper electrode 8 and the sensor lower electrode 6, generating a corresponding induced current.

[0073] In summary, the present invention can sense the slight differences in writing characteristics of different people through the 2*2 array friction nanogenerators set inside the handwriting board, and spontaneously generate signals for the user's writing behavior without the need for additional external energy supply. Since each person has unique characteristics in speed, strength, frequency, etc. when writing on the handwriting board writing surface (14), the analysis of each person's writing signal characteristics can realize the identification of the operator's identity and status monitoring.

[0074] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the 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 the present 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. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0075] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present application. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. The above disclosure is only a preferred embodiment of the present invention, and of course it cannot be used to limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present invention.

Claims

1. A self-driven intelligent handwriting recognition tablet based on a friction nanogenerator, characterized in that: include: The upper bottom of the tablet, the lower bottom of the tablet, the writing surface of the tablet and the 2*2 friction nanogenerator array; The writing surface of the handwriting board is arranged above the bottom of the handwriting board and is bonded to the bottom of the handwriting board; The 2*2 friction nanogenerator array is arranged between the upper bottom of the handwriting board and the lower bottom of the handwriting board, and is used to generate an induced current by squeezing the bottom of the handwriting board and driving the 2*2 friction nanogenerators when the user contacts the writing surface of the handwriting board during writing.

2. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 1 is characterized in that: The 2*2 friction nanogenerator array includes: four friction nanogenerator self-driving sensors, which are arranged according to a 2*2 array rule and are arranged between the bottom of the handwriting board and the lower bottom of the handwriting board; The four friction nano-power generation self-driven sensors each include: a sensor bottom, a spring, a sensor bottom support layer, a sensor bottom electrode, a sensor top friction electric layer, a sensor top electrode, a sensor top support layer and a sensor top bottom; The lower support layer of the sensor is in close contact with the lower electrode of the sensor and is attached to the top of the lower bottom of the sensor. The upper friction electric layer of the sensor, the upper electrode of the sensor and the upper support layer of the sensor are in close contact with each other and are attached to the bottom of the upper bottom of the sensor. The upper bottom of the sensor is connected to the lower bottom of the sensor by springs arranged at the four corners.

3. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 2 is characterized in that: Also includes processing circuitry; The processing circuit is connected to the four friction nano-power generation self-driven sensors respectively, and is used for filtering, sampling, and transmitting the induced currents correspondingly generated by the four friction nano-power generation self-driven sensors.

4. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 3 is characterized in that: The writing surface of the handwriting board is in shape with the bottom of the handwriting board. The upper bottom of the handwriting board, the lower bottom of the handwriting board, the lower bottom of the sensor, and the upper bottom of the sensor are all square transparent flat plates made of PMMA material. The material of the writing surface of the handwriting board is determined according to the specific environment.

5. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 4 is characterized in that: The sensor lower support layer and the sensor upper support layer are both elastic foams, and are in a planar shape that fits the sensor upper bottom and the sensor lower bottom.

6. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 5 is characterized in that: The upper electrode of the sensor and the lower electrode of the sensor are both metal films, and their shapes fit the plane shapes of the upper supporting layer of the sensor and the lower supporting layer of the sensor. The upper electrode of the sensor and the lower electrode of the sensor both lead out signal lines to connect with the processing circuit.

7. The self-driven intelligent handwriting recognition tablet based on friction nanogenerator according to claim 6 is characterized in that: The triboelectric layer on the sensor is a polarized FEP material.

8. A writing behavior analysis method applied to the self-driven intelligent handwriting recognition handwriting tablet based on a friction nanogenerator as described in any one of claims 1 to 7, characterized in that: The method includes: Acquire four types of induced currents generated by frictional power generation when the four frictional nano-power generation self-driven sensors are in contact with the writing surface of the handwriting board during the writing process of the user; Filtering and sampling the four induced currents; The user's writing behavior is determined based on the electrical signal after filtering and sampling.

9. The mouse click behavior analysis method according to claim 8, characterized in that: The method of obtaining four types of induced currents generated by frictional power generation when the four frictional nano-power generation self-driven sensors are in contact with the writing surface of the handwriting board during the writing process of the user specifically includes: When the user writes on the writing surface of the handwriting board, the corresponding upper supporting layer of the sensor in the four friction nanopower generation self-driven sensors bears external force and drives the upper electrode of the sensor, the friction electric layer on the sensor and the lower electrode of the sensor to move relative to each other in the vertical direction. The distance between the upper electrode of the sensor and the lower electrode of the sensor in the four friction nanopower generation self-driven sensors changes, and an electric potential difference is generated between the upper electrode of the sensor and the lower electrode of the sensor, thereby generating a corresponding induced current.

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