Method and device for determining output gain of electric field sensor, medium and electronic equipment

Abstract

The invention discloses an electric field sensor output gain determination method and device, a medium and electronic equipment, and relates to the field of electric field sensors. The method can accurately describe the output gain characteristics of the sensor, and reduces the increase of manpower and time costs caused by manual experiments. The method for determining the output gain of the electric field sensor comprises the following steps of: acquiring a polar plate distance of each electric field sensor and a cascade distance between adjacent electric field sensors, and determining an equivalent distance of a cascade capacitive electric field sensor according to the polar plate distance and the cascade distance; obtaining the self-capacitance of each electric field sensor and the parasitic capacitance between the adjacent electric field sensors, and determining the equivalent capacitance of the cascaded capacitive electric field sensor according to the self-capacitance and the parasitic capacitance; and determining an output gain model of the cascaded capacitive electric field sensor based on the equivalent spacing and the equivalent capacitance.

Description

Technical Field

[0001] This application relates to the field of electric field sensors, and particularly to a method, apparatus, medium, and electronic device for determining the output gain of an electric field sensor. Background Technology

[0002] Power cables and their terminals are exposed to high electric field strength for extended periods during operation. When the insulation layer ages due to continuous thermal, electrical, or mechanical stress, the electric field strength in the weakened areas may exceed the material's breakdown threshold, triggering partial discharge. Partial discharge, as a weak, incomplete breakdown arc phenomenon, causes electric field distortion around the discharge point, which may gradually damage the cable's insulation layer, leading to insulation material damage and deterioration, and ultimately affecting the cable's performance and lifespan.

[0003] To achieve accurate detection of partial discharge in cables, in addition to requiring sensors with high sensitivity and electric field acquisition capabilities, the sensors must also be able to measure extremely weak signals while maintaining miniaturization. Currently, the design of cascaded capacitive electric field sensors mainly relies on experience-driven methods centered on trial and error. That is, this approach typically estimates the output charge or voltage based on simplified empirical formulas or semi-quantitative models. These models are generally derived from historical experimental data or industry-standard assumptions. The sensor's performance indicators, such as gain, sensitivity, and output stability, are then gradually optimized through repeated physical testing and parameter adjustments, resulting in inefficient processes and poor accuracy. Summary of the Invention

[0004] This application provides a method, apparatus, medium, and electronic device for determining the output gain of an electric field sensor, which can accurately determine the output gain model and describe the output gain of the electric field sensor through the model.

[0005] Firstly, this application provides a method for determining the output gain of an electric field sensor. This method is applied to a cascaded capacitive electric field sensor composed of multiple stacked electric field sensors. The specific process includes: Obtain the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and determine the equivalent spacing of the cascaded capacitive electric field sensors based on the electrode spacing and the cascade spacing. Obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and parasitic capacitance. The output gain model of the cascaded capacitive electric field sensor is determined based on the equivalent spacing and equivalent capacitance.

[0006] Specifically, determining the equivalent spacing of the cascaded capacitive electric field sensor based on the electrode spacing and the cascade spacing includes: The effective spacing of the cascaded capacitive electric field sensor is obtained by superimposing the electrode spacing of the electric field sensor and subtracting the cascade spacing.

[0007] Specifically, determining the equivalent capacitance of the cascaded capacitive electric field sensor based on its own capacitance and parasitic capacitance includes: The equivalent capacitance of the cascaded capacitive electric field sensor is obtained by accumulating the self-capacitance of the electric field sensor and subtracting the parasitic capacitance.

[0008] The output gain model of the cascaded capacitive electric field sensor is as follows:

[0009] Among them, H( ) represents the output gain. Let V be the voltage induced by the cascaded capacitive electric field sensor, and E be the electric field strength. For equivalent spacing, This is the equivalent capacitance. Where j is the load resistance, and j is the imaginary unit. For frequency.

[0010] Based on the above, the method also includes: Based on the output gain, the low-frequency gain model of the cascaded capacitive electric field sensor is determined.

[0011] Based on the above, the method also includes: Using the electrode spacing, cascade spacing, and number of cascade layers as optimization variables, the optimal solution that satisfies the maximum value of the low-frequency gain model is obtained through an optimization algorithm. The structural parameters of the cascaded capacitive electric field sensor are adjusted using the optimal solution.

[0012] The method also includes: A simulation model of the cascaded capacitive electric field sensor is generated based on the low-frequency gain model. The output characteristics corresponding to different structural parameters are obtained through the simulation model. The structural parameters include the plate area and the plate spacing.

[0013] According to the electric field sensor output gain determination method provided in this embodiment, based on the cascaded structure of the electric field sensor, the overall equivalent spacing considering the cascade spacing and the equivalent capacitance considering parasitic capacitance are determined. The output gain model of the cascaded capacitive electric field sensor is then determined by combining the equivalent spacing and equivalent capacitance, achieving precise quantification of the relationship satisfied by the sensor output gain and improving the accuracy and efficiency of output gain calculation. The output gain model can directly describe the output gain of the cascaded capacitive electric field sensor, eliminating the need for manual experimental estimation and reducing manpower and time costs. Furthermore, the output gain model can determine the relationship between the sensor gain and the sensor's structural parameters, which is beneficial for exploring the sensor's optimization space and improving its performance.

[0014] Secondly, this application provides an electric field sensor output gain determination device, comprising: The spacing determination module is used to obtain the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and to determine the equivalent spacing of the cascaded capacitive electric field sensors based on the electrode spacing and the cascade spacing. The capacitance determination module is used to obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and to determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and parasitic capacitance. The output gain determination module is used to determine the output gain of the cascaded capacitive electric field sensor based on the equivalent spacing and equivalent capacitance.

[0015] Thirdly, this application provides an electronic device including a memory and one or more processors. The memory stores one or more computer programs, each including instructions that, when executed by the processor, cause the electronic device to perform the electric field sensor output gain determination method as described in the first aspect.

[0016] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the electric field sensor output gain determination method as described in the first aspect.

[0017] Fifthly, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the electric field sensor output gain determination method as described in the first aspect.

[0018] Understandably, the beneficial effects that the electric field sensor output gain determination device, electronic device, computer-readable storage medium, and computer program product provided above can be referred to the beneficial effects in the first aspect, and will not be repeated here. Attached Figure Description

[0019] Figure 1 A schematic diagram of a sensor structure in the method for determining the output gain of an electric field sensor provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of the cascaded capacitive electric field sensor in the electric field sensor output gain determination method provided in the embodiments of this application; Figure 3 A flowchart illustrating the method for determining the output gain of an electric field sensor provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the electric field sensor output gain determination device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. For example, "first chip" and "second chip" are only used to distinguish different chips and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0021] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or for a period of time after the occurrence of a certain situation. The embodiments of this application do not make specific limitations on this.

[0022] The implementation of this embodiment will now be described in detail with reference to the accompanying drawings.

[0023] This embodiment provides a method for determining the output gain of an electric field sensor. For example, this method can be applied to various electronic devices such as computers (PCs), tablets, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle-mounted systems; it can also be applied to servers, cloud computing, server clusters, etc. This embodiment does not impose any special limitations on it.

[0024] Specifically, the method for determining the output gain of an electric field sensor provided in this embodiment is applied to a cascaded capacitive electric field sensor composed of multiple stacked electric field sensors. The electric field sensor is a flexible sensor.

[0025] Taking a traditional parallel plate sensor as an example, such as Figure 1 As shown, the sensor structure includes inherent plates and a dielectric layer forming a capacitor, where d is the distance between two parallel plates. Let be the dielectric constant of the dielectric layer. This sensor can generate an induced current under the action of an electric field E.

[0026] The capacitance is:

[0027] Where A is the relative area between the two plates of the capacitive sensor. d is the dielectric constant of the intermediate medium, and d is the distance between the two plates.

[0028] In this embodiment, the cascaded capacitive electric field sensor structure formed by stacking multiple sensors is as follows: Figure 2 As shown. Reference Figure 2 , The total spacing formed by stacking multiple sensors. The distance between the plates of the first sensor is called the plate spacing. The distance between the dielectric layers of two adjacent sensors is called the cascade spacing, and so on. The dielectric constant of the dielectric layer of the first sensor. For each of the two cascaded sensors, there is a node constant for the dielectric layer between them, and so on.

[0029] Based on the above sensor structure, this embodiment provides a method for determining the output gain of an electric field sensor. Figure 3 A flowchart illustrating the method is shown. (For example...) Figure 3 As shown, the method for determining the output gain of this electric field sensor may include the following steps: Step 101: Obtain the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and determine the equivalent spacing of the cascaded capacitive electric field sensors based on the electrode spacing and the cascade spacing.

[0030] The equivalent spacing of the cascaded capacitive electric field sensor is obtained by superimposing the electrode spacings of the electric field sensors and subtracting the cascade spacing, as shown below: (1) Step 102: Obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and parasitic capacitance.

[0031] The equivalent capacitance of the cascaded capacitive electric field sensor is obtained by summing its own capacitance and subtracting the parasitic capacitance, as shown below: (2) Step 103: Determine the output gain model of the cascaded capacitive electric field sensor based on the equivalent spacing and equivalent capacitance.

[0032] Output gain refers to the amplification factor or sensitivity of a sensor under changing signals, expressed as the ratio of the change in output signal to the change in input signal. When the input signal frequency approaches zero, the ratio of the change in the sensor's output signal to the change in the input signal is called the low-frequency gain. Low-frequency gain reflects the sensor's output response to minute input changes and determines the sensor's sensing capability. Accurate low-frequency gain allows the sensor's readings to be converted into actual numerical values.

[0033] Based on the fundamental principles of electromagnetic field theory, it can be known that the output voltage... The analytical relationship between the applied electric field strength E and the external electric field strength E is as follows: (3) (4) Among them, H( ) represents the output gain. Let V be the voltage induced by the cascaded capacitive electric field sensor, and E be the electric field strength. For equivalent spacing, This is the equivalent capacitance. Where j is the load resistance, and j is the imaginary unit. For frequency.

[0034] Formula (4) is the output gain model, which describes the relationship between the output gain of the cascaded capacitive electric field sensor and the equivalent spacing and equivalent capacitance.

[0035] Furthermore, based on the output gain, a low-frequency gain model for the cascaded capacitive electric field sensor is determined.

[0036] The magnitude of the gain is expressed as follows: (5) At low frequencies, i.e., frequency If the value is relatively small, and the denominator in the above formula is approximately 1, then the low-frequency gain is expressed as: (6) In this embodiment, given the fixed plate spacing of the electric field sensor and the cascade spacing between adjacent electric field sensors, the low-frequency gain of the cascaded capacitive electric field sensor can be determined using the aforementioned low-frequency gain formula, thereby accurately quantifying the sensor's output characteristics. Furthermore, the sensor can be placed in a fixed position, and the applied electric field strength E can be precisely set by adjusting the inter-plate voltage. Simultaneously, a high-precision charge amplifier can be used to collect real-time measurement data of the output charge. This measurement data can then be substituted into the sensor's output gain model to solve for or calibrate the sensor's equivalent capacitance, equivalent spacing, etc. Statistical optimization techniques, such as the least squares method, can be applied to optimize key parameters in the mathematical model (including coupling terms). System calibration is performed. The least squares method achieves optimal parameter estimation by minimizing the sum of squared residuals between the model's predicted output and the experimental data, while also considering the effects of measurement errors and environmental disturbances (such as temperature fluctuations or electromagnetic noise) to improve calibration robustness. This process typically involves iterative calculations, such as dynamically adjusting parameter values ​​using gradient descent algorithms, until a goodness-of-fit index, such as the coefficient of determination R², exceeds 0.95. This indicates that the model has high statistical interpretability, accurately captures the sensor's response characteristics under different electric field conditions, and verifies its adaptability and scalability to complex multilayer structures, such as n-layer electrode configurations.

[0037] This embodiment also includes using the electrode spacing, cascade spacing, and number of cascade layers as optimization variables, and solving for the optimal solution that satisfies the maximum value of the low-frequency gain model through an optimization algorithm; the structural parameters of the cascaded capacitive electric field sensor are then adjusted based on the optimal solution. Structural parameters include electrode spacing, cascade spacing, and number of cascade layers, and may also include the relative area between the electrodes; this embodiment is not limited to these. By optimizing the sensor's structural parameters, the low-frequency gain is maximized.

[0038] A simulation model of the cascaded capacitive electric field sensor is generated based on the low-frequency gain model. The output characteristics corresponding to different structural parameters are obtained through the simulation model. The structural parameters include the plate area and the plate spacing. The output characteristics can be the sensor's gain curve and sensitivity characteristic graph. These graphs can intuitively show the relationship between the sensor's performance and parameter changes, thus facilitating designers to identify the optimal design point.

[0039] The method for determining the output gain of an electric field sensor provided in this embodiment is applicable to the rapid design of high-gain, miniaturized electric field sensors. Such sensors are widely used in power system monitoring, smart grids, and IoT devices. In cascaded capacitor structures (multi-layer plates), the output charge or voltage is significantly affected by electric field coupling, dielectric constant, and structural parameters. This solution directly derives the output characteristics by combining electromagnetic theory formulas and semi-empirical calibration, significantly improving design efficiency. The core lies in deriving the analytical formula for the output characteristics of a cascaded capacitor electric field sensor based on capacitance theory and electromagnetic field equations, and calibrating empirical parameters using a small amount of experimental data to achieve rapid and high-precision prediction of output characteristics. Specifically, firstly, a physical model of the sensor is established using electromagnetic field theory, deriving the theoretical relationship between the output charge or voltage and the electric field strength and structural parameters (i.e., the output gain model and low-frequency gain). Then, key data points are obtained experimentally to calibrate empirical parameters in the model (e.g., equivalent capacitance) to ensure prediction accuracy. Finally, the calibrated model is applied to optimize the sensor design, replacing the traditional trial-and-error process, accelerating sensor optimization, reducing the number of experiments and costs, and providing theoretical support for sensor miniaturization and high performance.

[0040] For example, the above method also includes the following: 1. Establish a mathematical model The core initial step is to establish a mathematical model, aiming to construct a framework that combines theoretical rigor with practical applicability to accurately describe the sensor's response behavior under complex electric field environments. Specifically, this implementation method takes a typical cascaded capacitive electric field sensor as an example. First, based on the fundamental principles of electromagnetic field theory, the analytical relationship between the output voltage V and the applied electric field intensity E is systematically derived:

[0041] in, This is the equivalent spacing between the plates, and its optimized design directly affects the sensor's sensitivity and linear range.

[0042]

[0043] The equivalent capacitance of the entire circuit is composed of multiple capacitors connected in parallel.

[0044] .

[0045] Based on the formula above, the gain formula can be derived as follows:

[0046] This gain formula is the mathematical model for the output gain. The magnitude of the gain is expressed as:

[0047] The modulus at low frequencies is:

[0048] in, This represents the relative area between the two plates of a capacitive sensor. For external resistors, in addition, As a coupling term, it is specifically used to quantify the electric field interaction effects in multilayer structures, such as the uneven charge distribution caused by edge fields and the signal crosstalk caused by parasitic capacitance. These effects are often ignored in traditional single-layer models, but they significantly affect prediction accuracy in cascaded configurations. The initial value of the coupling term is determined through theoretical calculations, such as analytical solutions using Maxwell's equations.

[0049] In addition to the results derived theoretically, numerical simulations using finite element analysis software can be employed to estimate the electromagnetic coupling strength between multiple plates and verify the model's scalability in complex structures (such as n-layer plate configurations). This process not only strengthens the model's physical foundation but also enhances repeatability by introducing standardized parameters and assumptions (such as uniform medium distribution and ideal boundary conditions), ensuring the model remains stable under different operating conditions (such as temperature variations or frequency fluctuations). Furthermore, the model's establishment lays the theoretical foundation for subsequent calibration and optimization steps, such as correcting coupling term parameters through experimental data inversion or adjusting plate geometry parameters using iterative algorithms, thereby achieving high-precision prediction of sensor output characteristics.

[0050] 2. Experimental Calibration During the experimental calibration phase, the previously established mathematical model was configured to verify and optimize it, ensuring high accuracy and practicality in predicting the output characteristics of the cascaded capacitive electric field sensor. Specifically, a standard electric field source system, such as a parallel-plate electrode device, was employed. This device generates a highly controllable and spatially uniform electric field environment. The applied electric field strength E was precisely set within a range (e.g., from low to high field strength, covering the sensor's typical operating range) by adjusting the voltage between the plates, thus simulating the electric field distribution in real-world applications. In this environment, the sensor was placed in a fixed position, and a high-precision charge amplifier (e.g., a model with low noise and wide bandwidth) was used to acquire real-time measurement data of the output charge. This data was recorded using digital storage devices to ensure the repeatability and accuracy of the measurements. Subsequently, statistical optimization techniques, such as the least squares method, were applied to optimize key parameters in the mathematical model (including coupling terms). System calibration is performed using the least squares method, which minimizes the sum of squared residuals between the model's predicted output and the experimental data to achieve optimal parameter estimation. This method also considers the effects of measurement errors and environmental interference (such as temperature fluctuations or electromagnetic noise) to improve calibration robustness. This process typically involves iterative calculations, such as using gradient descent to dynamically adjust parameter values ​​until a goodness-of-fit index, such as the coefficient of determination R², exceeds 0.95. This indicates that the model has high statistical interpretability, accurately captures the sensor's response characteristics under different electric field conditions, and verifies its adaptability and scalability to complex multilayer structures (such as n-layer electrode configurations). Notably, experimental calibration requires only a small number of representative data points (e.g., 3-5), covering only the linear and nonlinear operating regions of the sensor. This avoids the resource waste and time costs associated with relying on numerous trial-and-error experiments in traditional methods. This efficiency stems from the model's robust theoretical foundation, enabling rapid convergence of the calibration process and significantly improving the overall efficiency of the sensor design phase. It also provides reliable support for real-time prediction and fault diagnosis in subsequent practical applications (such as power system monitoring or environmental electric field sensing). This phase not only strengthens the empirical foundation of the model but also ensures its portability and cost-effectiveness in industrial scenarios.

[0051] 3. Application Optimization During the application optimization phase, the calibrated mathematical model is systematically integrated into an advanced simulation environment, such as using COMSOL Multiphysics simulation software, to achieve efficient prediction and design optimization of the output characteristics of cascaded capacitive electric field sensors. Specifically, the integration process involves integrating model parameters (such as calibrated coupling terms) into the model. and structural parameters (e.g., ...) are encoded into simulation scripts or user-defined functions to simulate the sensor's response behavior under different operating conditions in a virtual environment. For example, key structural parameters such as electrode area and electrode spacing are systematically adjusted through parameter scanning methods to generate gain curves and sensitivity characteristic maps. These graphs visually demonstrate the relationship between sensor performance and parameter changes and help identify the optimal design point. In the simulation, the optimal number of layers n is determined through iterative calculations and sensitivity analysis. This selection is based on a multi-objective optimization criterion: maximizing charge output gain while minimizing the sensor's physical size, thereby achieving a synergistic improvement in miniaturization and high sensitivity. The optimization process may employ genetic algorithms or gradient descent to automatically explore the parameter space and avoid local optima, ensuring the design remains robust under constraints such as manufacturing tolerances and material costs. This method can replace the traditional design process that relies on trial and error experiments. Simulation-driven prediction significantly reduces the number of physical prototype iterations, greatly reducing sensor design time and cost. This is mainly due to the efficient data generation of simulation tools and the repeatability verification of the model. Meanwhile, the model's scalability is enhanced through generalized parameter settings, making it suitable for customized applications of various cascaded sensor structures (such as multilayer flexible electrodes or heterogeneous material configurations) in power system monitoring (e.g., high-voltage line electric field distribution analysis) and IoT devices (e.g., wearable sensor networks). This promotes the rapid implementation and innovation of the technology in real-world scenarios, such as achieving real-time fault detection in smart grids or providing high-precision electric field mapping in environmental monitoring. This stage not only validates the practical value of semi-empirical prediction methods but also ensures design consistency and portability through standardized simulation processes.

[0052] Furthermore, this embodiment also provides an electric field sensor output gain determination device, which can be used to execute the above-described electric field sensor output gain determination method. For example... Figure 4 As shown, the electric field sensor output gain determination device 200 specifically includes: a spacing determination module 201, used to acquire the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and to determine the equivalent spacing of the cascaded capacitive electric field sensor based on the electrode spacing and the cascade spacing; a capacitance determination module 202, used to acquire the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and to determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and the parasitic capacitance; and an output gain determination module 203, used to determine the output gain of the cascaded capacitive electric field sensor based on the equivalent spacing and the equivalent capacitance.

[0053] In one exemplary embodiment, the spacing determination module 201 is specifically used to superimpose the electrode spacing of the electric field sensor and subtract the cascade spacing to obtain the equivalent spacing of the cascaded capacitive electric field sensor.

[0054] In one exemplary embodiment, the capacitance determination module 202 is specifically used to accumulate the self-capacitance of the electric field sensor and subtract the parasitic capacitance to obtain the equivalent capacitance of the cascaded capacitive electric field sensor.

[0055] In one exemplary embodiment, the output gain model of the cascaded capacitive electric field sensor is:

[0056] Among them, H( ) represents the output gain. Let V be the voltage induced by the cascaded capacitive electric field sensor, and E be the electric field strength. For equivalent spacing, This is the equivalent capacitance. Where j is the load resistance, and j is the imaginary unit. For frequency.

[0057] In one exemplary embodiment, the device further includes a low-frequency gain determination module for determining a low-frequency gain model of the cascaded capacitive electric field sensor based on the output gain.

[0058] In one exemplary embodiment, the device further includes a sensor optimization module, which uses the electrode spacing, cascade spacing, and number of cascade layers as optimization variables, and uses an optimization algorithm to find the optimal solution that satisfies the maximum value of the low-frequency gain model; and adjusts the structural parameters of the cascaded capacitive electric field sensor using the optimal solution.

[0059] In one exemplary embodiment, the device further includes a simulation module for generating a simulation model of the cascaded capacitive electric field sensor based on the low-frequency gain model, and obtaining output characteristics corresponding to different structural parameters through the simulation model, wherein the structural parameters include plate area and plate spacing.

[0060] The specific details of each module or unit in the above-mentioned electric field sensor output gain determination device have been described in detail in the corresponding electric field sensor output gain determination method, so they will not be repeated here.

[0061] This application also provides an electronic device. Figure 5 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0062] like Figure 5As shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0063] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0064] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined in the embodiments of this application.

[0065] For example, when the computer program is executed by the central processing unit (CPU) 601, it can perform the following: obtain the plate spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and determine the equivalent spacing of the cascaded capacitive electric field sensor based on the plate spacing and the cascade spacing; obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and the parasitic capacitance; and determine the output gain model of the cascaded capacitive electric field sensor based on the equivalent spacing and the equivalent capacitance.

[0066] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0068] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0069] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0070] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the output gain of an electric field sensor, characterized in that, A cascaded capacitive electric field sensor, applicable to multiple stacked electric field sensors, includes: Obtain the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and determine the equivalent spacing of the cascaded capacitive electric field sensors based on the electrode spacing and the cascade spacing. Obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and parasitic capacitance. The output gain model of the cascaded capacitive electric field sensor is determined based on the equivalent spacing and equivalent capacitance.

2. The method for determining the output gain of an electric field sensor according to claim 1, characterized in that, Determining the equivalent spacing of the cascaded capacitive electric field sensor based on the electrode spacing and the cascade spacing includes: The effective spacing of the cascaded capacitive electric field sensor is obtained by superimposing the electrode spacing of the electric field sensor and subtracting the cascade spacing.

3. The method for determining the output gain of an electric field sensor according to claim 1, characterized in that, Determining the equivalent capacitance of the cascaded capacitive electric field sensor based on its own capacitance and parasitic capacitance includes: The equivalent capacitance of the cascaded capacitive electric field sensor is obtained by accumulating the self-capacitance of the electric field sensor and subtracting the parasitic capacitance.

4. The method for determining the output gain of an electric field sensor according to claim 1, characterized in that, The output gain model of the cascaded capacitive electric field sensor is as follows: Among them, H( () represents the output gain. Let V be the voltage induced by the cascaded capacitive electric field sensor, and E be the electric field strength. For equivalent spacing, This is the equivalent capacitance. Where j is the load resistance, and j is the imaginary unit. For frequency.

5. The method for determining the output gain of an electric field sensor according to claim 1, characterized in that, Also includes: Based on the output gain, the low-frequency gain model of the cascaded capacitive electric field sensor is determined.

6. The method for determining the output gain of an electric field sensor according to claim 5, characterized in that, Also includes: Using the electrode spacing, cascade spacing, and number of cascade layers as optimization variables, the optimal solution that satisfies the maximum value of the low-frequency gain model is obtained through an optimization algorithm. The structural parameters of the cascaded capacitive electric field sensor are adjusted using the optimal solution.

7. The method for determining the output gain of an electric field sensor according to claim 5, characterized in that, Also includes: A simulation model of the cascaded capacitive electric field sensor is generated based on the low-frequency gain model. The output characteristics corresponding to different structural parameters are obtained through the simulation model. The structural parameters include the plate area and the plate spacing.

8. A device for determining the output gain of an electric field sensor, characterized in that, include: The spacing determination module is used to obtain the electrode spacing of each electric field sensor and the cascade spacing between adjacent electric field sensors, and to determine the equivalent spacing of the cascaded capacitive electric field sensors based on the electrode spacing and the cascade spacing. The capacitance determination module is used to obtain the self-capacitance of each electric field sensor and the parasitic capacitance between adjacent electric field sensors, and to determine the equivalent capacitance of the cascaded capacitive electric field sensor based on the self-capacitance and parasitic capacitance. The output gain determination module is used to determine the output gain of the cascaded capacitive electric field sensor based on the equivalent spacing and equivalent capacitance.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the method for determining the output gain of an electric field sensor as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the electric field sensor output gain determination method according to any one of claims 1 to 7.

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