Object material and pressure cooperative detection device based on capacitance sensing and use method thereof

Abstract

The invention relates to the technical field of sensors, in particular to an object material and pressure cooperative detection device based on capacitive sensing and a use method thereof. The common electrode layer is arranged above the flexible substrate; the pressure sensitive layer is stacked below the common electrode layer and is used for detecting the change of pressure applied to the pressure sensitive layer; the material detection layer is stacked above the common electrode layer and is used for acquiring a capacitance signal generated when an object is in contact so as to obtain an initial dielectric constant; the flexible base, the common electrode layer, the pressure sensitive layer and the material detection layer adopt a multi-layer stacking framework; the common electrode layer comprises a signal processing circuit, a connection pressure sensitive layer and a material detection layer, material identification and pressure identification of an object can be realized at the same time by adopting the laminated flexible capacitance sensor, the volume is small, the cost is low, the sensor is more suitable for light and thin products, and meanwhile, the measured dielectric constant is calibrated in real time; and the accuracy of material detection is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensors, and specifically to an object material and pressure collaborative detection device based on capacitance sensing and its usage method. Background Art

[0002] In recent years, due to advantages such as high sensitivity and low power consumption, capacitance sensing technology has been widely used in industrial detection (such as material sorting), medical devices (such as tactile feedback prosthetics), and consumer electronics (such as intelligent touch control), etc.

[0003] However, existing technologies mostly focus on single-parameter detection (such as only identifying materials or only measuring pressure), and face significant bottlenecks in achieving multi-parameter collaborative perception: Technical Defect 1: Traditional solutions usually optimize and design sensors only for a single physical quantity. For example, material detection relies on dielectric constant differences, while pressure detection is based on the change in plate spacing. The independent operation of the two leads to the need to use multiple sensors in the device, resulting in a bulky volume and high cost. Technical Defect 2: Currently, the material detection of capacitance sensors mainly relies on the difference in the dielectric constant of the object to be measured for identification. However, different materials may have similar dielectric constants, and existing technologies lack a real-time calibration mechanism, resulting in a high material identification error and difficulty in meeting the requirements of high-precision scenarios. Therefore, in view of the above problems, an object material and pressure collaborative detection device based on capacitance sensing and its usage method are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an object material and pressure collaborative detection device based on capacitance sensing and its usage method to solve the problems that existing technologies mostly focus on single-parameter detection (such as only identifying materials or only measuring pressure), and face significant bottlenecks in achieving multi-parameter collaborative perception: Technical Defect 1: Traditional solutions usually optimize and design sensors only for a single physical quantity. For example, material detection relies on dielectric constant differences, while pressure detection is based on the change in plate spacing. The independent operation of the two leads to the need to use multiple sensors in the device, resulting in a bulky volume and high cost. Technical Defect 2: Currently, the material detection of capacitance sensors mainly relies on the difference in the dielectric constant of the object to be measured for identification. However, different materials may have similar dielectric constants, and existing technologies lack a real-time calibration mechanism, resulting in a high material identification error and difficulty in meeting the requirements of high-precision scenarios.

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

[0006] Device and method for collaborative detection of object material and pressure based on capacitance sensing, flexible substrate; common electrode layer, disposed above the flexible substrate; pressure-sensitive layer, stacked below the common electrode layer for detecting pressure changes applied thereto; material detection layer, stacked above the common electrode layer for collecting capacitance signals generated when an object contacts to obtain a preliminary dielectric constant;

[0007] The flexible base, common electrode layer, pressure-sensitive layer and material detection layer adopt a multi-layer stacked structure; the common electrode layer includes a signal processing circuit, which is connected to the pressure-sensitive layer and the material detection layer for simultaneously collecting and processing the capacitance signals output by the two layers, and dynamically calibrating the preliminary dielectric constant according to the real-time pressure, so as to output the calibrated material information and pressure value.

[0008] As a further optimized content of the present invention, wherein: the device for collaborative detection of object material and pressure based on capacitance sensing further includes a Bluetooth main control chip and a display module, the Bluetooth main control chip is used for processing and sending data, and the display module is used for displaying material and weight data.

[0009] As a further optimized content of the present invention, wherein: the signal processing circuit includes a capacitance acquisition chip, which simultaneously samples the capacitance signals of the pressure-sensitive layer and the material detection layer, and performs data fusion processing on the sampled data.

[0010] As a further optimized content of the present invention, wherein: it includes the following steps: S1: Simultaneously collect the capacitance signals of the pressure-sensitive layer and the material detection layer through the signal processing circuit provided inside the flexible capacitance sensor, the capacitance signal of the pressure-sensitive layer is , the capacitance signal of the material detection layer is ; S2: Calculate the preliminary dielectric constant according to the capacitance signal collected by the material detection layer, the formula for calculating the preliminary dielectric constant is: Wherein, is the capacitance value of the material detection layer under the condition of no pressure; S3: Calculate the real-time pressure value according to the capacitance signal collected by the pressure-sensitive layer, the formula for calculating the real-time pressure value is: Wherein: is the capacitance value of the pressure-sensitive layer under the condition of no pressure, is the maximum range pressure; S4: Dynamically calibrate the preliminary dielectric constant by using the real-time pressure value to generate a calibrated dielectric constant , the calibrated dielectric constant The calculation formula is as follows: In the formula, is a constant, is the maximum range pressure; S5: Based on the calibrated dielectric constant, identify the material of the object to be measured, and output the object material and the corresponding pressure value.

[0011] As a further optimized content of the present invention, wherein: the output of the object material and the corresponding pressure value by the S5 further includes: According to the calibrated dielectric constant and the dielectric constant database of known materials, identify the object material; Output the identification result and the corresponding pressure value .

[0012] As a further optimized content of the present invention, wherein: the constant and the maximum range pressure The determination process is as follows: Step 1: In a controlled experimental environment, apply a series of known pressure values to the capacitive flexible sensor, synchronously record the capacitance signals of the pressure-sensitive layer and the material detection layer, and construct a capacitance data set including various pressure conditions; Step 2: According to the sensor design specifications and application scenarios, initially set and Value range; Step 3: Construct a mathematical model describing the behavior of the sensor, substitute the experimental data into the model, and form a basic framework for parameter optimization; Step 4: Select a genetic algorithm to define an error function that measures the difference between the model prediction and the experimental data, and automatically adjust and Value; Step 5: Use the optimized parameters to recalculate the model prediction value, and compare it with the reserved experimental data to verify the accuracy of the model; Step 6: Conduct a sensitivity analysis to quantify and The impact on the model output, and identify the key parameters; Step 7: According to the verification results, adjust the optimization algorithm or model structure if necessary, and repeat the iteration until the model prediction is highly consistent with the experimental data, and finally determine and Optimal value; Step 8: Write a software program to implement the above optimization process and automate the parameter determination; Step 9: Comprehensively record the key data and final parameter values in the optimization process, and provide detailed technical documents for sensor design, calibration and future maintenance.

[0013] As a further optimized content of the present invention, wherein: the genetic algorithm includes the following steps: A1: Initialize the population, where each individual represents a set of possible and values; A2: Calculate the fitness of each individual, and the fitness function is defined as the error between the model prediction value and the experimental data; A3: Select individuals with higher fitness to enter the next generation population; A4: Perform crossover and mutation operations on the selected individuals to generate new individuals; A5: Repeat the above steps until a preset number of iterations is reached or the fitness converges; A6: Output the and values corresponding to the optimal individual as the finally determined constant values.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In the present invention, the stacked flexible capacitive sensor can simultaneously realize the material identification and pressure identification of an object, is small in size and low in cost, is more suitable for thin and light products, and performs real-time calibration on the measured dielectric constant, effectively improving the accuracy of material detection; 2. In the present invention, the same capacitance acquisition chip circuit is adopted, the power consumption is effectively reduced, and there is no need to set other shielding effects, effectively improving the signal-to-noise ratio and reducing the power consumption and cost. Description of the Drawings

[0015] Figure 1 is a multi-layer stacked architecture diagram of the object material and pressure collaborative detection device based on capacitance sensing of the present invention; Figure 2 is a flowchart of the object material and pressure collaborative detection device based on capacitance sensing of the present invention and its usage method. Detailed Embodiments

[0016] Please refer to Figure 1-2 for a technical solution provided by the present invention: Device and method for collaborative detection of object material and pressure based on capacitance sensing, flexible substrate; common electrode layer, disposed above the flexible substrate; pressure-sensitive layer, stacked below the common electrode layer, for detecting pressure changes applied thereto; material detection layer, stacked above the common electrode layer, for collecting capacitance signals generated when an object contacts to obtain a preliminary dielectric constant; the flexible base, common electrode layer, pressure-sensitive layer and material detection layer adopt a multi-layer stacked architecture; the common electrode layer includes a signal processing circuit, connected to the pressure-sensitive layer and the material detection layer, for simultaneously collecting and processing capacitance signals output by the two layers, and dynamically calibrating the preliminary dielectric constant according to the real-time pressure, so as to output calibrated material information and pressure value. The device for collaborative detection of object material and pressure based on capacitance sensing further includes a Bluetooth main control chip and a display module. The Bluetooth main control chip is used for processing and sending data, and the display module is used for displaying material and weight data. The signal processing circuit includes a capacitance acquisition chip, which samples the capacitance signals of the pressure-sensitive layer and the material detection layer at the same time, and performs data fusion processing on the sampled data; The multi-layer stacked architecture of the common electrode layer, pressure-sensitive layer and material detection layer involved in the above content is as Figure 1 shown. A stacked flexible capacitive sensor is adopted. Through the hardware structure innovation and data fusion algorithm of the flexible capacitive sensor, the functional linkage of material identification and pressure detection is realized. The sensor structure adopts a stacked structure design of a flexible capacitive sensor, including a common electrode layer, a pressure-sensitive layer and a material detection layer; the pressure-sensitive layer detects pressure through micro-capacitance changes, and the material detection layer identifies materials through dielectric constants. By integrating the electrode layers of the pressure detection layer and the material identification layer into a common electrode layer, this design significantly optimizes the structure and performance: on the one hand, it simplifies the multi-layer stacked architecture, reduces the thickness and production cost of the sensor, is more suitable for miniaturized devices, and at the same time reduces the interlayer impedance and signal interference, improving the detection accuracy and response speed; on the other hand, the synergistic effect of the common electrode realizes the synchronous acquisition of pressure distribution and material dielectric characteristics, providing a more reliable, lightweight and cost-effective solution for highly integrated applications such as flexible electronics and robot touch;

[0017] As Figure 2 shown, specifically, it includes the following steps: S1: Simultaneously collect the capacitance signals of the pressure-sensitive layer and the material detection layer through the signal processing circuit provided inside the flexible capacitive sensor. The capacitance signal of the pressure-sensitive layer is , and the capacitance signal of the material detection layer is ; S2: Calculate the preliminary dielectric constant according to the capacitance signal collected by the material detection layer. The calculation formula of the preliminary dielectric constant is: In the formula, is the capacitance value of the material detection layer under no-pressure condition; S3: Calculate the real-time pressure value according to the capacitance signal collected by the pressure-sensitive layer Calculate the real-time pressure value , the real-time pressure value The calculation formula is: In the formula: is the capacitance value of the pressure-sensitive layer under no-pressure condition, is the maximum range pressure; S4: Dynamically calibrate the preliminary dielectric constant using the real-time pressure value to generate the calibrated dielectric constant , the calibrated dielectric constant The calculation formula is: In the formula, is a constant, is the maximum range pressure; S5: Identify the material of the object to be measured based on the calibrated dielectric constant and output the object material and the corresponding pressure value. S5 outputting the object material and the corresponding pressure value further includes: According to the calibrated dielectric constant and the dielectric constant database of known materials, identify the object material; Output the identification result and the corresponding pressure value ; By adopting the dynamic calibration method, the extraction accuracy of the material dielectric parameters is corrected in real time when the object contacts the sensor and causes pressure changes, and a capacitance acquisition chip is used to collect the corresponding capacitance. The capacitance chip acquisition circuit samples the capacitance values of the pressure-sensitive layer and the material detection layer simultaneously, calculates the dielectric constant and the pressure value according to the adopted capacitance values. By using the same capacitance acquisition chip circuit, the power consumption is effectively reduced, no additional driving circuit is required, and no signal isolation measure is required, which has a positive effect on signal recognition; As a further implementation technical solution of this scheme, the determination process of the constant and the maximum range pressure is as follows: Step 1: Under a controlled experimental environment, apply a series of known pressure values to the capacitive flexible sensor, synchronously record the capacitance signals of the pressure-sensitive layer and the material detection layer, and construct a capacitance data set containing various pressure conditions; Step 2: According to the sensor design specifications and application scenarios, initially set the value ranges of and ; Step 3: Construct a mathematical model describing the sensor behavior, substitute the experimental data into the model to form a basic framework for parameter optimization; Step 4: Select a genetic algorithm to define an error function that measures the difference between the model prediction and the experimental data, and automatically adjust the values of and by iteratively searching to minimize the error; Step 5: Recalculate the model prediction values using the optimized parameters and compare with the reserved experimental data to verify the accuracy of the model; Step 6: Conduct a sensitivity analysis to quantify and the impact on the model output and identify the key parameters; Step 7: According to the verification results, adjust the optimization algorithm or model structure if necessary, and repeat the iteration until the model prediction highly coincides with the experimental data. Finally, determine and the optimal values; Step 8: Write a software program to implement the above optimization process and automate the parameter determination; Step 9: Comprehensively record the key data and final parameter values during the optimization process to provide detailed technical documentation for sensor design, calibration, and future maintenance. The genetic algorithm includes the following steps: A1: Initialize the population, where each individual represents a set of possible and values; A2: Calculate the fitness of each individual, and the fitness function is defined as the error between the model prediction value and the experimental data; A3: Select the individuals with higher fitness to enter the next generation population; A4: Perform crossover and mutation operations on the selected individuals to generate new individuals; A5: Repeat the above steps until the preset number of iterations is reached or the fitness converges; A6: Output the and values corresponding to the optimal individual as the finally determined constant values. The method accurately determines the constants and the maximum range pressure in the capacitive flexible sensor through systematic experimental data collection, mathematical model construction, and genetic algorithm optimization. This process automates the parameter determination, significantly improves the measurement accuracy and reliability, adapts to various application scenarios. The introduction of the genetic algorithm realizes efficient global optimization, avoids local optima, and ensures the optimal matching of parameters with experimental data. In addition, the sensitivity analysis identifies the key parameters, enhances the robustness and adaptability of the model, provides detailed technical support for sensor design and maintenance, and expands the application potential of the sensor in complex environments.

[0018] Example 1:

[0019] Application scenario: In the grasping system of an industrial robot, a device for collaborative detection of object material and pressure based on capacitive sensing is installed on the robot finger to detect the material of the grasped object and the applied pressure in real time. Such a device can help the robot automatically adjust the clamping force according to different materials and pressures, improving the stability and success rate of grasping;

[0020] Installation method: The detection device is tightly integrated into the internal structure of the robot finger to ensure full contact with the surface of the grasped object. The flexible substrate adapts to the curved shape of the finger. The common electrode layer, pressure-sensitive layer, and material detection layer adopt a multi-layer stacked architecture to ensure accurate signal acquisition.

[0021] Data processing and feedback: The signal processing circuit continuously collects the capacitance signals of the pressure-sensitive layer and the material detection layer, and performs data fusion processing through a capacitance acquisition chip. The calculated preliminary dielectric constant and real-time pressure value are adjusted through a dynamic calibration algorithm to generate calibrated material information and pressure value. These data are sent to the central control system of the robot through a Bluetooth master chip, and the central control system automatically adjusts the clamping force according to the material and pressure information.

[0022] Automatic adjustment mechanism: According to the characteristics of different materials and the real-time pressure value, the central control system of the robot adjusts the clamping force. For example, for fragile items (such as glass), the system reduces the clamping force to avoid damage; for hard items (such as metal), the system increases the clamping force to ensure stable grasping.

[0023] Embodiment 2:

[0024] Application scenario: In a medical prosthesis, the capacitance-based object material and pressure collaborative detection device is installed in the finger part of the prosthesis to simulate real tactile feedback. This device can identify the material of the contacted object (such as skin, cloth, etc.) and detect the applied pressure, generating corresponding tactile simulation signals to provide a more natural user experience. Installation method: The detection device is embedded in the internal structure of the prosthesis finger and is closely attached to the outer material of the prosthesis. The common electrode layer, pressure-sensitive layer, and material detection layer adopt a multi-layer stacked architecture to ensure good contact with the surface of the contacted object. The display module can be integrated on the control unit of the prosthesis for debugging and monitoring. Data processing and feedback: The signal processing circuit continuously collects the capacitance signals of the pressure-sensitive layer and the material detection layer, and performs data fusion processing through a capacitance acquisition chip. The calculated preliminary dielectric constant and real-time pressure value are adjusted through a dynamic calibration algorithm to generate calibrated material information and pressure value. These data are sent to the control system of the prosthesis through a Bluetooth master chip, and the control system generates tactile simulation signals according to the material and pressure information. Tactile simulation signal generation: According to the calibrated dielectric constant and pressure value, the control system generates corresponding tactile simulation signals. For example, when it is recognized that the contact is skin, the system enables the micro-pressure perception mode to provide more sensitive tactile feedback; when the contact is cloth, the system adjusts the pressure sensitivity to simulate a real touch feeling. Wherein: Material identification (skin / cloth) + pressure detection → generation of tactile simulation signals Adjust the pressure sensitivity according to the material type (e.g., enable the micro-pressure sensing mode when in contact with the skin); Principle: The structural principle of the tactile sensor used in this patent is an electrostatic pressure sensor based on the double electric layer principle. Due to its extremely high sensitivity, it is considered the core approach for developing highly sensitive pressure sensor devices. The electrostatic flexible pressure sensor consists of two layers of flexible electrodes and an intermediate ionic functional material layer. Due to the attraction between charges, a double electric layer is formed at the ion-electron interface, thus generating a relatively large capacitance per unit area. When pressure is applied, the ionic functional material layer is compressed, resulting in more and more capacitors being connected in parallel at the interface, significantly increasing the capacitance value.

[0025] In this article, specific examples are used to elaborate on the principle and implementation mode of the present invention. The description of the above examples is only for helping to understand the method of the present invention and its core idea. The above is only the preferred implementation mode of the present invention. It should be noted that due to the limitation of literal expression and objectively infinite specific structures, for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements, refinements or changes can be made, or the above technical features can be combined in an appropriate manner; these improvements, refinements, changes or combinations, or directly applying the concept and technical solution of the invention to other occasions without improvement, should all be regarded as the protection scope of the present invention.

Claims

1. An object material and pressure collaborative detection device based on capacitance sensing, characterized in that Comprising:    A flexible substrate;    A common electrode layer disposed above the flexible substrate;    A pressure sensitive layer stacked below the common electrode layer for detecting pressure changes applied thereto;    A material detection layer stacked above the common electrode layer for collecting capacitance signals generated when an object comes into contact to obtain a preliminary dielectric constant; The flexible base, the common electrode layer, the pressure sensitive layer, and the material detection layer adopt a multi-layer stacked architecture; The common electrode layer includes a signal processing circuit connected to the pressure sensitive layer and the material detection layer for simultaneously collecting and processing the capacitance signals output by the two layers, and dynamically calibrating the preliminary dielectric constant according to the real-time pressure, thereby outputting calibrated material information and pressure values.

2. The object material and pressure collaborative detection device based on capacitance sensing according to claim 1, characterized in that: The object material and pressure collaborative detection device based on capacitance sensing further includes a Bluetooth main control chip and a display module. The Bluetooth main control chip is used for processing and sending data, and the display module is used for displaying material and weight data.

3. The capacitance-sensing based object material and pressure collaborative detection device according to claim 1, characterized in that: The signal processing circuit includes a capacitance acquisition chip that simultaneously samples the capacitance signals of the pressure sensitive layer and the material detection layer and performs data fusion processing on the sampled data.

4. A method for using the capacitance-sensing based object material and pressure collaborative detection device according to any one of claims 1-3, characterized in that: Including the following steps: S1: Simultaneously collect the capacitance signals of the pressure-sensitive layer and the material detection layer through the signal processing circuit provided inside the flexible capacitance sensor. The capacitance signal of the pressure-sensitive layer is , and the capacitance signal of the material detection layer is ; S2: According to the capacitance signal collected by the material detection layer calculate the preliminary dielectric constant , the preliminary dielectric constant has the following calculation formula: In the formula, is the capacitance value of the material detection layer under the condition of no pressure; S3: Calculate the real-time pressure value based on the capacitance signal collected by the pressure-sensitive layer , the real-time pressure value The calculation formula is as follows: Where: is the capacitance value of the pressure-sensitive layer under the condition of no pressure, and is the maximum range pressure;​ S4: Dynamically calibrate the preliminary dielectric constant using the real-time pressure value to generate the calibrated dielectric constant. The calculation formula for the calibrated dielectric constant is: Wherein, is a constant, is the maximum range pressure; S5: Identify the material of the object to be measured based on the calibrated dielectric constant, and output the object material and the corresponding pressure value.

5. The method for using the device for collaborative detection of object material and pressure based on capacitance sensing according to claim 4, characterized in that: The S5 outputting the object material and the corresponding pressure value further includes: Based on the calibrated dielectric constant and the dielectric constant database of known materials, identify the material of the object; output the identification result and the corresponding pressure value .

6. The method of using the object material and pressure collaborative detection device based on capacitance sensing according to claim 4, characterized in that: The constant and the maximum range pressure are determined as follows: Step 1: Apply a series of known pressure values to the capacitive flexible sensor in a controlled experimental environment, synchronously record the capacitance signals of the pressure sensitive layer and the material detection layer, and construct a capacitance data set containing various pressure conditions; Step 2: Based on the sensor design specifications and application scenarios, preliminarily set and value ranges; Step 3: Construct a mathematical model describing the behavior of the sensor, and substitute the experimental data into the model to form a basic framework for parameter optimization; Step 4: Select a genetic algorithm to define an error function that measures the difference between the model prediction and the experimental data, and automatically adjust the values of and by iteratively searching for the minimum error. and by iteratively searching for the minimum error. Step 5: Use the optimized parameters to recalculate the model prediction value, and compare it with the reserved experimental data to verify the accuracy of the model; Step 6: Conduct sensitivity analysis to quantify and the impact on the model output and identify key parameters; Step 7: According to the verification results, adjust and optimize the algorithm or model structure when necessary, and repeat the iteration until the model prediction highly coincides with the experimental data, and finally determine and the optimal values; Step 8: Write a software program to implement the above optimization process and automate parameter determination; Step 9: Comprehensively record the key data and final parameter values in the optimization process to provide detailed technical documentation for sensor design, calibration, and future maintenance.

7. The method of using the device for collaborative detection of object material and pressure based on capacitance sensing according to claim 6, characterized in that: The genetic algorithm includes the following steps: A1: Initialize the population, where each individual represents a set of possible and values; A2: Calculate the fitness of each individual. The fitness function is defined as the error between the model prediction value and the experimental data therebetween; A3: Select individuals with higher fitness to enter the next generation population; A4: Perform crossover and mutation operations on the selected individuals to generate new individuals; A5: Repeat the above steps until a preset number of iterations is reached or the fitness converges; A6: Output the and values corresponding to the optimal individual as the finally determined constant values.

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