Material distribution pattern detection method and device based on arrayed interdigital capacitor

Abstract

The application relates to a material distribution form detection method and device based on an array type interdigital capacitor, wherein the method comprises the following steps: determining an area coverage rate of target material and a capacitance mapping relationship coefficient of a capacitance change amount of at least one interdigital capacitor unit; measuring the capacitance of each point on the at least one interdigital capacitor unit, and determining an influence coefficient matrix of the material placed at each point; obtaining a capacitance matrix of each row and column coordinate in a target full array, and determining the material coverage rate on the at least one interdigital capacitor unit according to the capacitance matrix and a preset material distribution matrix; detecting the distribution form of the material, processing the material coverage rate, and generating a continuous material distribution atlas of the material. Therefore, the problems in the prior art that single-point measurement can only reflect local material position information, cannot capture the non-uniform distribution of the material surface, the fluctuation of the dielectric constant of the material can significantly affect the capacitance value, leads to measurement error, and a single-point sensor is difficult to track complex form changes in real time are solved.

Description

Technical Field

[0001] This application relates to the field of capacitive sensor measurement technology, and in particular to a method and apparatus for detecting material distribution patterns based on arrayed interdigital capacitors. Background Technology

[0002] Real-time monitoring of material levels (such as powder accumulation height and liquid level) and surface shape is a key technology for ensuring production safety, optimizing process flow, and controlling processes. Traditional material level measurement methods include mechanical (such as plumb bob detection), ultrasonic, radar, and capacitive methods. Among these, capacitive sensors are widely used due to their advantages of being non-contact, simple in structure, and low in cost. However, existing capacitive material level measurement technologies still have significant limitations, especially in terms of measurement accuracy and applicability under complex material conditions.

[0003] In related technologies, capacitive sensors often employ a single-point or single-plate structure, indirectly reflecting the concentration (or proportion) of the measured material by detecting changes in capacitance. For example, by combining equivalent dielectric constant models of different oil-water mixtures, a relationship between the water content of the oil and the dielectric constant of the mixed oil can be established. Using this relationship, a formula can be calculated between the water content of the oil and the total capacitance value of the detected interdigitated capacitors. The water content of the oil can then be obtained from the total capacitance value of the detected interdigitated capacitors. Another example is the determination of snow cover within a container based on capacitance information collected by the capacitive sensor, which can be used to determine snow cover on a solar panel. Furthermore, a capacitive measurement system has been constructed based on the capacitance changes corresponding to different material levels.

[0004] However, in related technologies, single-point measurement can only reflect local material level information and cannot capture non-uniform distribution on the material surface (such as slopes, depressions or arches formed by accumulation in non-fluid silos). Fluctuations in the dielectric constant of the material (such as humidity changes and composition differences) will significantly affect the capacitance value, leading to measurement errors. For rapidly changing material levels (such as high-speed flow or discharge scenarios), single-point sensors have difficulty tracking complex morphological changes in real time, which urgently needs to be improved. Summary of the Invention

[0005] This application provides a method and apparatus for detecting material distribution patterns based on arrayed interdigital capacitance, in order to solve the problems in related technologies, such as single-point measurement can only reflect local material level information and cannot capture non-uniform distribution on the material surface, fluctuations in the dielectric constant of the material will significantly affect the capacitance value, leading to measurement errors, and single-point sensors are difficult to track complex morphological changes in real time for rapidly changing material levels.

[0006] The first aspect of this application provides a method for detecting material distribution patterns based on arrayed interdigital capacitors. The method involves setting an array of interdigital capacitors on a target material, each array consisting of at least one interdigital capacitor unit. The method includes the following steps: determining a capacitance mapping coefficient between the area coverage of the target material and the capacitance change of the at least one interdigital capacitor unit based on the capacitance values ​​of the target material and the at least one interdigital capacitor unit; placing materials with the same area coverage sequentially on the at least one interdigital capacitor unit based on the capacitance coefficient, measuring the capacitance at each point on the at least one interdigital capacitor unit, calculating the influence coefficient of the capacitance at each point, and determining an influence coefficient matrix for the material placed at each point based on the influence coefficient and a preset influence matrix; obtaining the capacitance matrix of each row and column coordinate in the target full array through a full array scan based on the influence coefficient matrix, and determining the material coverage on the at least one interdigital capacitor unit according to the capacitance matrix and a preset material distribution matrix; detecting the distribution pattern of the material, and processing the material coverage according to the distribution pattern to generate a continuous material distribution map of the material.

[0007] Optionally, in one embodiment of this application, an orthogonal wiring architecture and a multiplexer are provided on the array of interdigitated capacitors. The orthogonal wiring architecture includes a row bus group and a column bus group. The input of the multiplexer is connected to a target controller, and the output of the multiplexer is connected to the row bus group and the column bus group. The method further includes: activating a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generating a column bus traversal result, and measuring the capacitance value of each point capacitor according to the column bus traversal result; or, activating a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generating a row bus traversal result, and measuring the capacitance value of each point capacitor according to the row bus traversal result.

[0008] Optionally, in one embodiment of this application, the preset influence matrix is:

[0009]

[0010] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio;

[0011] The influence coefficient matrix is ​​as follows:

[0012]

[0013] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of .

[0014] Optionally, in one embodiment of this application, the capacitor matrix is:

[0015]

[0016] Where, k C Let A be the capacitance coefficient, and let A be the material area coverage at position (i,j). i,j ∈[0%,100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e., k C =ΔC i,j / A i,j , When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of [x] 1,1 ,…,x M,N ] T Let x be the material distribution matrix to be determined. i,j Let (i,j) be the area coverage of the material to be determined, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through the full array scan, C i,j The actual measured capacitance value at position (i,j).

[0017] Optionally, in one embodiment of this application, determining the capacitance mapping relationship coefficient between the area coverage of the target material and the capacitance change of the at least one interdigitated capacitor unit includes: measuring at least one area coverage calibration point of the target material to generate measurement results, and calculating a weighted average and confidence interval of the capacitance coefficient based on the measurement results; establishing a target piecewise linear regression model based on the weighted average and the confidence interval, and determining the capacitance mapping relationship coefficient based on the target piecewise linear regression model.

[0018] Optionally, in one embodiment of this application, a capacitance sensor substrate is disposed on the array of interdigital capacitors, wherein the method further includes: measuring the capacitance change of the at least one interdigital capacitor unit using the conductive signal layer and the polymer insulating layer of the capacitance sensor substrate to generate measurement data results.

[0019] A second aspect of this application provides a material distribution pattern detection device based on an array of interdigital capacitors. The device involves setting an array of interdigital capacitors on a target material, each array consisting of at least one interdigital capacitor unit. The device includes: a determining module, configured to determine a capacitance mapping coefficient between the area coverage of the target material and the capacitance change of the at least one interdigital capacitor unit, based on the capacitance values ​​of the target material and the at least one interdigital capacitor unit; and a measuring module, configured to, based on the capacitance mapping coefficient, sequentially place materials with the same area coverage on the at least one interdigital capacitor unit and measure the capacitance change of the at least one interdigital capacitor unit. A module is used to calculate the influence coefficient of each point capacitance on an interdigitated capacitor unit, and to determine the influence coefficient matrix of the material placed at each point capacitance based on the influence coefficient and a preset influence matrix; an acquisition module is used to obtain the capacitance matrix of each row and column coordinate in the target full array by full array scanning based on the influence coefficient matrix, and to determine the material coverage on the at least one interdigitated capacitor unit according to the capacitance matrix and a preset material distribution matrix; a detection module is used to detect the distribution pattern of the material, and to process the material coverage according to the distribution pattern to generate a continuous material distribution map of the material.

[0020] Optionally, in one embodiment of this application, an orthogonal wiring architecture and a multiplexer are provided on the array of interdigitated capacitors. The orthogonal wiring architecture includes a row bus group and a column bus group. The input of the multiplexer is connected to a target controller, and the output of the multiplexer is connected to the row bus group and the column bus group. The device further includes: a first activation module, used to activate a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generate a column bus traversal result, and measure the capacitance value of each point capacitor according to the column bus traversal result; or, a second activation module, used to activate a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generate a row bus traversal result, and measure the capacitance value of each point capacitor according to the row bus traversal result.

[0021] Optionally, in one embodiment of this application, the preset influence matrix is:

[0022]

[0023] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio;

[0024] The influence coefficient matrix is ​​as follows:

[0025]

[0026] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of .

[0027] Optionally, in one embodiment of this application, the capacitor matrix is:

[0028]

[0029] Where, k C Let A be the capacitance coefficient, and let A be the material area coverage at position (i,j). i,j ∈[0%,100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e., k C =ΔC i,j / A i,j , When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of [x] 1,1 ,…,x M,N ] T Let x be the material distribution matrix to be determined. i,j Let (i,j) be the area coverage of the material to be determined, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through the full array scan, C i,j The actual measured capacitance value at position (i,j).

[0030] Optionally, in one embodiment of this application, the determining module includes: a calculation unit, configured to measure at least one area coverage calibration point of the target material to generate measurement results, and calculate a weighted average and confidence interval of the capacitance coefficient based on the measurement results; and a determining unit, configured to establish a target piecewise linear regression model based on the weighted average and the confidence interval, and determine the capacitance mapping relationship coefficient based on the target piecewise linear regression model.

[0031] Optionally, in one embodiment of this application, a capacitance sensor substrate is disposed on the array of interdigital capacitors, wherein the device further includes: a generation module, used to measure the capacitance change of the at least one interdigital capacitor unit using the conductive signal layer and the polymer insulating layer of the capacitance sensor substrate, so as to generate measurement data results.

[0032] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the material distribution pattern detection method based on arrayed interdigital capacitance as described in the above embodiments.

[0033] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described material distribution pattern detection method based on arrayed interdigital capacitors.

[0034] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described material distribution pattern detection method based on arrayed interdigital capacitors.

[0035] The multi-point measurement in this embodiment reflects the global information of the material level in the measured area better than single-point measurement. It can effectively capture the non-uniform distribution of the material surface (such as slopes, depressions, or arches formed by accumulation in non-fluid silos). Multi-point measurement can effectively reduce the sensitivity to local dielectric interference, that is, data mutations and anomalies at a single point have less impact on the overall measurement results, which can effectively reduce measurement errors. It can be applied to areas with rapidly changing material levels (such as high-speed filling or discharging scenarios), solving the problem that single-point sensors cannot track complex shape changes in real time. Thus, it solves the problems in related technologies, such as single-point measurement only reflecting local material level information, failing to capture the non-uniform distribution of the material surface, the significant impact of fluctuations in the dielectric constant of the material on the capacitance value leading to measurement errors, and the difficulty of single-point sensors in tracking complex shape changes in real time.

[0036] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0037] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0038] Figure 1 This is a flowchart of a material distribution pattern detection method based on arrayed interdigital capacitors according to an embodiment of this application;

[0039] Figure 2 The present application provides a schematic diagram of an array-type interdigitated capacitor method according to one embodiment of the present application.

[0040] Figure 3 This is a schematic diagram of an interdigitated capacitor cell according to an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the array interdigital capacitance detection arching phenomenon in Example 1;

[0042] Figure 5 This is a schematic diagram illustrating the principle of detecting arching phenomena using array interdigital capacitance in Example 1.

[0043] Figure 6 This is a schematic diagram of the array interdigitated capacitor installation in Example 1;

[0044] Figure 7 This is a schematic diagram of the array interdigitated capacitor installation in Example 3;

[0045] Figure 8 This is a schematic diagram of the array interdigitated capacitor installation in Example 4;

[0046] Figure 9 This is a schematic diagram of a material distribution pattern detection device based on an array of interdigital capacitors according to an embodiment of this application;

[0047] Figure 10 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0048] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0049] The following describes a material distribution pattern detection method and apparatus based on arrayed interdigital capacitance according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues mentioned in the background art, single-point measurement can only reflect local material level information and cannot capture the non-uniform distribution on the material surface. Fluctuations in the dielectric constant of the material significantly affect the capacitance value, leading to measurement errors. Single-point sensors struggle to track complex morphological changes in real time. This application provides a material distribution pattern detection method based on arrayed interdigital capacitance. In this method, multi-point measurement reflects the global information of the material level in the measured area better than single-point measurement. It can effectively capture the non-uniform distribution on the material surface (such as slopes, depressions, or arches formed by accumulation in non-fluid silos). Multi-point measurement can effectively reduce sensitivity to local dielectric interference; that is, data mutations and anomalies at a single point have less impact on the overall measurement result, effectively reducing measurement errors. It can be applied to areas with rapidly changing material levels (such as high-speed filling or discharging scenarios), solving the problem that single-point sensors struggle to track complex morphological changes in real time. This solves the problems in related technologies, such as single-point measurement only reflecting local material level information, failing to capture non-uniform distribution on the material surface, fluctuations in the material's dielectric constant significantly affecting the capacitance value, leading to measurement errors, and single-point sensors being unable to track complex shape changes in real time.

[0050] Specifically, Figure 1 This is a schematic flowchart of a material distribution pattern detection method based on arrayed interdigital capacitors provided in an embodiment of this application.

[0051] like Figure 1 As shown, this material distribution pattern detection method based on arrayed interdigital capacitors involves setting an arrayed interdigital capacitor on the target material. The arrayed interdigital capacitor consists of at least one interdigital capacitor unit, and includes the following steps:

[0052] In step S101, based on the target material and the capacitance value of at least one interdigitated capacitor unit, a capacitance mapping coefficient between the area coverage of the target material and the capacitance change of at least one interdigitated capacitor unit is determined.

[0053] It is understandable that, such as Figure 2 As shown, the array-type interdigital capacitor in this embodiment is an array-type interdigital capacitor sensor. The array-type interdigital capacitor can be a sensing array composed of m×n interdigital capacitor units, each unit C i,j The row and column coordinates are uniquely determined by i∈[1,m],j∈[1,n]. Figure 3 The diagram shows at least one interdigital capacitor unit, wherein the structural parameters of at least one interdigital capacitor unit satisfy the following: the ratio of the interdigital electrode width w to the spacing s is w / s∈[0.2,5], the unit envelope size D and the electrode length L satisfy D / L≥1.1, and the interval between adjacent units is d≥3s.

[0054] In actual implementation, the embodiments of this application can, during the calibration and measurement stage, control the MUX to select the target unit C. i,j The measurement material is covered in the interdigitated capacitor cell area and the capacitance value is collected to establish the area coverage rate A. i,j ∈[0%,100%] and capacitance change ΔC i,j Capacitance mapping coefficient k C =ΔC i,j / A i,j (abbreviated as k) C (where the capacitance coefficient is...)

[0055] The embodiments of this application use interdigital capacitance for measurement, which has the advantages of being non-contact, simple in structure, and low in cost.

[0056] Optionally, in one embodiment of this application, determining the capacitance mapping coefficient between the area coverage of the target material and the capacitance change of at least one interdigitated capacitor unit includes: measuring at least one area coverage calibration point of the target material to generate measurement results, and calculating the weighted average and confidence interval of the capacitance coefficient based on the measurement results; establishing a target piecewise linear regression model based on the weighted average and confidence interval, and determining the capacitance mapping coefficient based on the target piecewise linear regression model.

[0057] In this embodiment, at least five area coverage calibration points (0%, 20%, 40%, 60%, 80%, 100%) can be set. Each area coverage calibration point is measured at least three times to generate measurement results, and the capacitance coefficient k is calculated based on the measurement results. C The weighted average and confidence interval are used to establish a target piecewise linear regression model: k C =αA, where α is determined by the least squares method, and the capacitance mapping coefficient is determined according to the target piecewise linear regression model.

[0058] This application uses arrayed interdigitated capacitors. Multi-point measurement can better reflect the global information of the material level in the measured area than single-point measurement. It can effectively capture the non-uniform distribution on the material surface. Moreover, multi-point measurement can effectively reduce the sensitivity to local dielectric interference. That is, data mutations and anomalies at a single point have less impact on the overall measurement results, which can effectively reduce measurement errors. Using interdigitated capacitors for measurement has the advantages of being non-contact, simple in structure, and low in cost.

[0059] In step S102, based on the capacitance mapping relationship coefficient, materials that meet the same area coverage are placed sequentially on at least one interdigitated capacitor unit, and the capacitance at each point on at least one interdigitated capacitor unit is measured. The influence coefficient of each point capacitance is calculated, and the influence coefficient matrix of the material placed at each point capacitance is determined based on the influence coefficient and the preset influence matrix.

[0060] In actual implementation, this embodiment of the application can, based on the capacitance mapping relationship coefficient, place materials that meet the same area coverage rate sequentially on each interdigitated capacitor unit, measure the capacitance at each point on each interdigitated capacitor unit, and calculate the influence coefficient of the capacitance at each point:

[0061]

[0062] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio, i.e. This is the influence matrix for that position.

[0063] It should be noted that because different capacitors are connected to the same line, there is crosstalk between the capacitors, and changes in the capacitance value when not in operation will also affect the dielectric constant.

[0064] This application embodiment can determine the influence coefficient matrix of material placement at each point capacitance based on the influence coefficient and a preset influence matrix. In one embodiment of this application, the preset influence matrix... for:

[0065]

[0066] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio;

[0067] matrix Dimensionality reduction and expansion were performed, and the influence coefficient matrix M of the measured material placed at each location was obtained through full array scanning. β The influence coefficient matrix is ​​as follows:

[0068]

[0069] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of .

[0070] In step S103, based on the influence coefficient matrix, the capacitance matrix of each row and column coordinate in the target full array is obtained by full array scanning, and the material coverage rate on at least one interdigitated capacitor unit is determined according to the capacitance matrix and the preset material distribution matrix.

[0071] It is understood that the row and column coordinates in the target full array in this application embodiment can be actual row and column coordinate units.

[0072] In actual implementation, the embodiments of this application can obtain the capacitance matrix M of each row and column coordinate unit through full array scanning after calibration during the measurement and inverse problem solving stages. C M C =[C 1,1 ,…,C M,N ] T Construct matrix equation M C =k C M β X, where X is the material distribution matrix to be determined, X = [x 1,1 ,…,x M,N ] T .

[0073] In one embodiment of this application, the capacitance matrix is ​​expanded as follows:

[0074]

[0075] Where, k C Let A be the capacitance coefficient, and let A be the material area coverage at position (i,j). i,j ∈[0%,100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e., k C =ΔC i,j / A i,j , When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of [x] 1,1 ,…,x M,N ] T Let x be the material distribution matrix to be determined. i,j Let (i,j) be the area coverage of the material to be determined, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through full array scanning, C i,j The actual measured capacitance value at position (i,j).

[0076] Right now:

[0077]

[0078] By solving each x k,l The material coverage rate of each capacitor unit can then be obtained.

[0079] In step S104, the distribution pattern of the material is detected, and the material coverage is processed according to the distribution pattern to generate a continuous material distribution map.

[0080] Specifically, in the spatial interpolation stage, a linear interpolation algorithm is used to process the material coverage rate in the gap region between adjacent units to generate a continuous material distribution map.

[0081] In this embodiment, a controller is used. The controller includes a computer with functions such as capacitance measurement, input / output interface, and readable storage medium, and stores program instructions containing the above content. The instructions are configured as follows:

[0082] (1) Generate a three-dimensional material distribution grayscale map, where the grayscale mapping relationship satisfies G=255×(1-x) i,j / x max );

[0083] (2) Output dynamic change parameters, including: material change rate v = dx i,j / dt; Spatial non-uniformity index U=σ / μ, where σ is the distribution variance and μ is the mean; Characteristic parameter over-limit alarm signal.

[0084] The embodiments of this application can reflect the global information of the material level in the measured area through multi-point measurement, effectively capture the non-uniform distribution on the material surface, and can be applied to rapidly changing material level areas, solving the problem that single-point sensors are difficult to track complex shape changes in real time.

[0085] Optionally, in one embodiment of this application, an orthogonal wiring architecture and a multiplexer are provided on the array of interdigitated capacitors. The orthogonal wiring architecture includes a row bus group and a column bus group. The input of the multiplexer is connected to the target controller, and the output of the multiplexer is connected to the row bus group and the column bus group. The method further includes: activating a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generating a column bus traversal result, and measuring the capacitance value of each point capacitor according to the column bus traversal result; or, activating a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generating a row bus traversal result, and measuring the capacitance value of each point capacitor according to the row bus traversal result.

[0086] It is understood that the orthogonal wiring architecture in this application embodiment includes row bus groups and column bus groups. Each row bus group is connected to the first polarity terminal (cathode or anode) of the corresponding row interdigitated capacitor, and each column bus group is connected to the second polarity terminal (opposite to the first polarity) of the corresponding column interdigitated capacitor. The input terminal of the multiplexer in this application embodiment is connected to the target controller, and the output terminal of the multiplexer is connected to the row bus group and the column bus group.

[0087] In actual implementation, the embodiments of this application can, in row selection mode: activate a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generate column bus traversal results, and measure the capacitance value of each point capacitor according to the column bus traversal results; or, in column selection mode: activate a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generate row bus traversal results, and measure the capacitance value of each point capacitor according to the row bus traversal results.

[0088] The orthogonal scan period T satisfies T≤1 / (2f) max ), where f max This represents the highest frequency of dynamic changes in the material being measured.

[0089] The embodiments of this application can achieve robust real-time reconstruction of material morphology by optimizing electrode arrangement and designing data fusion algorithms, which has a positive effect in the fields of bulk material storage, conveying process monitoring and intelligent manufacturing.

[0090] Optionally, in one embodiment of this application, a capacitance sensor substrate is disposed on an array of interdigital capacitors, wherein the method further includes: measuring the capacitance change of at least one interdigital capacitor unit using the conductive signal layer and the polymer insulating layer of the capacitance sensor substrate to generate measurement data results.

[0091] It is understood that the sensor substrate in the embodiments of this application includes a conductive signal layer etched with an m×n interdigitated electrode array and a dielectric constant ε. r A polymer insulating layer ∈ [1.5, 20.5].

[0092] As one possible implementation, embodiments of this application can utilize the conductive layer signal layer and the polymer insulating layer of the capacitive sensor substrate to measure the capacitance change of at least one interdigital capacitor unit to generate measurement data results.

[0093] This application embodiment measures the object being measured using an array of interdigital capacitances, which can be applied to rapidly changing material level areas, solving the problem that single-point sensors have difficulty tracking complex shape changes in real time.

[0094] Specifically, it can be combined with Figures 4 to 8As shown, the working principle of the material distribution pattern detection method based on arrayed interdigital capacitors in this application is explained in detail with a specific embodiment.

[0095] This application can be implemented in the following embodiments:

[0096] Example 1:

[0097] This embodiment is applied in the field of aerospace exploration, specifically in the material monitoring scenario of a powder (such as lunar soil or Martian soil) storage and supply system during extraterrestrial exploration missions. For example... Figure 4 and Figure 5 As shown, inside the powder storage hopper, an innovative array-type interdigital capacitance sensing technology is used to monitor the material morphology in real time, addressing the bridging and arching phenomenon that may occur during the powder processing. When bridging and arching occur, the material distribution near the inner wall of the hopper will show obvious regional differences (such as the formation of localized empty "holes"). This application, based on the principle of array capacitance sensing, reconstructs the material morphology near the hopper through distributed array interdigital capacitance detection.

[0098] like Figure 6 As shown, a multi-layer PCB sensing module is integrated on the inner surface (230mm × 140mm) of the long inclined side of the trapezoidal feeding funnel. This module uses a four-layer FR-4 substrate structure (effective detection area 206mm × 126mm). The inner layer of the substrate contains 5×8 arrayed interdigital capacitors with a unit envelope size of 25mm × 25mm. The electrode width w and spacing s are both 0.3μm copper interdigital lines. The spacing l between adjacent unit capacitors is 5mm. The electrode lines converge on the outer layer to form a cathode / anode bus interface (e.g., ...). Figure 7 As shown), it is connected to the downstream multiplexer (MUX) via a shielded cable.

[0099] To implement the measurement method, it will be achieved by the following parts:

[0100] S1. First, the computer controls the MUX row-by-row gated detection unit to perform array capacitance measurement and material coverage area-capacitance characteristic calibration under controlled conditions. Taking the first row and first column of the bus as an example, the linear relationship (capacitance coefficient) between the area coverage rate (0-100%) and the relative capacitance change is obtained through a precision material coverage experiment:

[0101] k C =C / A

[0102] Where, k C Where C is the capacitance coefficient, C is the capacitance when there is no material, and A is the area covered by the measured powder.

[0103] The experimental data in this embodiment were obtained by averaging five repeated measurements, and the resulting capacitance coefficient is k. C=15.3pF / 100%, establish the unit reference sensitivity parameter.

[0104] S2. Under the experimental condition of full material coverage (100%), the material was placed on different electrode units, and the measured capacitance value of each array unit was obtained. Based on the capacitance value of its row and column, the normalized influence coefficient was calculated. For example, when the material was placed at position (1,1), the capacitance at position (1,2) was measured to be 3.1 pF. Then the influence coefficient was calculated as follows: By traversing the measurements in this manner, the influence coefficient matrix is ​​finally obtained.

[0105]

[0106] matrix Dimensionality reduction and expansion were performed, and the influence coefficient matrix Mβ of the measured material placed at each location was obtained through full array scanning.

[0107]

[0108] S3. Finally, based on each element in the influence matrix, the relationship between each actual measured relative capacitance and the coefficients in the influence matrix and the material coverage area can be obtained.

[0109] M C =k C M β X,

[0110] Where X is the material distribution matrix to be determined, X = [x 1,1 ,…,x M,N ] T The above equation expands to:

[0111]

[0112] Right now:

[0113]

[0114] A unique solution [x] is obtained through matrix inversion. 1,1 ,…,x M,N ] T This yields the material coverage rate on each capacitor unit. S4 then constructs a two-dimensional grayscale distribution map based on the calculation results.

[0115] G i,j =255×(1-x) i,j / x max C i,j

[0116] like Figure 5As shown, under normal operating conditions, the material will completely cover the surface of the capacitor, making the corresponding grayscale image appear black; however, when arching or bridging occurs, the lower capacitor unit is not covered by the material, so the grayscale image will appear white accordingly.

[0117] The adjacent regions of the capacitor are processed using a linear interpolation algorithm to generate a continuous material distribution grayscale image.

[0118] Ground simulation experiments have verified that this solution can detect abnormal material accumulation within the hopper in real time and rapidly, with a response time of <200ms. When bridging or arching occurs, the system can accurately identify regions of abrupt density gradient changes (eigenvalue variance Δσ² > 0.15). This embodiment successfully solves the technical challenge of non-contact material monitoring.

[0119] Example 2:

[0120] This embodiment focuses on the storage silo in the feed system of an industrial pulverized coal boiler combustion system. In thermal power generation systems, bridging and arching in the pulverized coal storage silo can lead to feed interruption, causing unstable boiler combustion or even shutdown risks. Traditional detection methods rely on silo vibration sensors or pressure transmitters, which suffer from low sensitivity, high false alarm rate (approximately 15%-20%), and cannot locate the blockage area in real time.

[0121] An 8×12 array of interdigitated capacitive sensor modules was integrated into the inner wall of the trapezoidal funnel section (3.2m×1.8m) of the central storage silo. Based on the same method as in Example 1, the relationship between the concentration and capacitance of pulverized coal at different surface locations was calibrated and a model was established to identify blockage patterns.

[0122] Actual measurements on a 660MW supercritical unit showed that the accuracy of arching warning was improved from 78% to 96.5% compared to the traditional method, the response time for arch breaking was shortened to 0.8 seconds (15 times better than manual operation), and the standard deviation of feed stability σ < 0.5%.

[0123] Example 3:

[0124] In solid-liquid two-phase flow separation processes in the chemical and energy fields, the surface vortex morphology and the distribution of the outer particle phase layer of the cyclone separator directly affect the separation efficiency. Traditional methods rely on pressure difference or flow meters to indirectly determine the separation state, making it difficult to capture the dynamic changes of the particle layer in real time, resulting in a lag in the split ratio adjustment and large fluctuations in separation efficiency. However, the present application can effectively detect changes in the solid phase distribution.

[0125] An 8×10 array of interdigitated capacitive sensor modules (with a corrosion-resistant ceramic substrate, 0.5mm electrode spacing, and a unit size of 20mm×20mm) is integrated on the outer wall of the cyclone separator (the area in contact with the particulate phase). Figure 7 (As shown). Dynamic control is achieved through the following steps:

[0126] 1. Under laboratory conditions, the nonlinear relationship between different particle layer thicknesses (0-15mm) and corresponding unit capacitance values ​​was calibrated, and a thickness-capacitance mapping model was established; the particle layer distribution matrix was obtained through full array scanning, and characteristic parameters were calculated:

[0127] Thickness gradient: A vortex core offset warning is triggered when the thickness difference Δh between adjacent elements exceeds 3mm;

[0128] Dynamic fluctuation index: Separation instability is determined based on the time-domain variance σ2 > 0.1 of the unit capacitance value;

[0129] 2. Closed-loop control: Input the above parameters into the PID controller to dynamically adjust the inlet flow split ratio (adjustment accuracy ±2%), so that the particle layer distribution tends to be uniform.

[0130] The results show that, based on actual measurements of a heavy oil separation system in a refinery, this method increases the separation efficiency from 82.4% to 89.7%, and shortens the response time for adjusting the split ratio from 5-8 seconds in the traditional method to less than 0.5 seconds, effectively suppressing the phenomenon of particle backmixing.

[0131] Example 4:

[0132] In the pharmaceutical and powder metallurgy industries, the density uniformity of compressed tablets is a key quality indicator. Current technologies rely on offline sampling inspection, which cannot detect localized porosity or over-compression defects in real time, resulting in a high scrap rate.

[0133] An array of interdigital capacitive sensors (electrode material is hardened tungsten steel, unit size 10mm×10mm, withstand voltage ≥1.5GPa) is embedded on the upper surface of the mold, as shown in the figure. Specific implementation includes:

[0134] The powder compressibility density (1.2-2.8 g / cm³) was established through experiments. 3 Piecewise functional relationship between dielectric constant and dielectric constant:

[0135] ε r =2.35 + 0.18ρ (ρ ≤ 2.0 g / cm³) 3 )

[0136] ε r＝ 2.71 + 0.05ρ (ρ > 2.0 g / cm³) 3 )

[0137] like Figure 8 As shown, based on the method of this application, the powder compression density distribution is statistically analyzed. During the pressing process, the array is scanned at a frequency of 100Hz to reconstruct the tablet density distribution map and calculate the uniformity coefficient: U=1-σ / μ (target U≥0.92).

[0138] Pressure compensation vector: The compensation pressure ΔP for each hydraulic cylinder is generated based on the density gradient distribution. i (Adjust the range ±5MPa).

[0139] Then, the tilt angle of the pressure head is adjusted in real time (accuracy ±0.05°) through a multi-axis servo system to eliminate density distribution deviation.

[0140] The results show that after application in a cemented carbide cutting tool production line, the non-uniformity of the pressing density decreased from 7.3% to 1.8%, while the equipment utilization rate increased by 22%, achieving a breakthrough of 48 hours of continuous production with zero defects.

[0141] In addition to the above embodiments, the present invention can also be used in the field of detecting the height of silos in industrial pulverized coal furnaces, slagging in pipelines, and other enclosed containers.

[0142] The material distribution pattern detection method based on arrayed interdigital capacitance proposed in this application provides a more comprehensive picture of the material level in the measured area compared to single-point measurement. It effectively captures non-uniform distributions on the material surface (such as slopes, depressions, or arches formed by accumulation in non-fluid silos). Multi-point measurement also effectively reduces sensitivity to local dielectric interference; that is, sudden changes and anomalies at a single point have less impact on the overall measurement result, thus reducing measurement errors. This method can be applied to areas with rapidly changing material levels (such as high-speed filling or discharging scenarios), solving the problem that single-point sensors struggle to track complex morphological changes in real time. Therefore, it addresses the issues in related technologies where single-point measurement only reflects local material level information, fails to capture non-uniform distributions on the material surface, and where fluctuations in the material's dielectric constant significantly affect capacitance values, leading to measurement errors and making it difficult for single-point sensors to track complex morphological changes in real time.

[0143] Next, referring to the accompanying drawings, a material distribution pattern detection device based on an array of interdigital capacitors is described according to an embodiment of this application.

[0144] Figure 9 This is a schematic diagram of the material distribution pattern detection device based on arrayed interdigital capacitors according to an embodiment of this application.

[0145] like Figure 9 As shown, the material distribution pattern detection device 10 based on arrayed interdigital capacitors includes: a determination module 100, a measurement module 200, an acquisition module 300, and a detection module 400.

[0146] Specifically, the determining module 100 is used to determine the capacitance mapping relationship coefficient between the area coverage of the target material and the capacitance change of the at least one interdigitated capacitor unit based on the target material and the capacitance value of at least one interdigitated capacitor unit.

[0147] The measurement module 200 is used to place materials with the same area coverage on at least one interdigitated capacitor unit in sequence based on the capacitance mapping relationship coefficient, measure the capacitance at each point on the at least one interdigitated capacitor unit, calculate the influence coefficient of each point capacitance, and determine the influence coefficient matrix of the material placed at each point capacitance based on the influence coefficient and the preset influence matrix.

[0148] The acquisition module 300 is used to obtain the capacitance matrix of each row and column coordinate in the target full array by scanning the entire array based on the influence coefficient matrix, and to determine the material coverage on at least one interdigitated capacitor cell according to the capacitance matrix and the preset material distribution matrix.

[0149] The detection module 400 is used to detect the distribution pattern of the material and process the material coverage based on the distribution pattern to generate a continuous material distribution map.

[0150] Optionally, in one embodiment of this application, an orthogonal wiring architecture and a multiplexer are provided on the array-type interdigitated capacitors. The orthogonal wiring architecture includes a row bus group and a column bus group. The input of the multiplexer is connected to the target controller, and the output of the multiplexer is connected to the row bus group and the column bus group. The material distribution pattern detection device 10 based on the array-type interdigitated capacitors further includes: a first activation module, used to activate a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generate a column bus traversal result, and measure the capacitance value of each point capacitor according to the column bus traversal result; or, a second activation module, used to activate a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generate a row bus traversal result, and measure the capacitance value of each point capacitor according to the row bus traversal result.

[0151] Optionally, in one embodiment of this application, the preset influence matrix is:

[0152]

[0153] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio;

[0154] The influence coefficient matrix is ​​as follows:

[0155]

[0156] in, When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of .

[0157] Optionally, in one embodiment of this application, the capacitance matrix is:

[0158]

[0159] Where, k C Let A be the capacitance coefficient, and let A be the material area coverage at position (i,j). i,j ∈[0%,100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e., k C =ΔC i,j / A i,j , When materials with the same area coverage are placed at position (m,n), the capacitance at position (i,j) is measured. Capacitance at position (m,n) The ratio of [x] 1,1 ,…,x M,N ] T Let x be the material distribution matrix to be determined. i,j Let (i,j) be the area coverage of the material to be determined, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through full array scanning, C i,j The actual measured capacitance value at position (i,j).

[0160] Optionally, in one embodiment of this application, the determining module 100 includes a calculation unit and a determining unit.

[0161] The calculation unit is used to measure at least one area coverage calibration point of the target material to generate measurement results, and calculate the weighted average value and confidence interval of the capacitance coefficient based on the measurement results.

[0162] The unit is defined to establish a target piecewise linear regression model based on the weighted average and confidence interval, and to determine the capacitance mapping coefficients based on the target piecewise linear regression model.

[0163] Optionally, in one embodiment of this application, a capacitive sensor substrate is disposed on an array of interdigital capacitors, wherein the material distribution pattern detection device 10 based on the array of interdigital capacitors further includes a generation module.

[0164] The generation module is used to measure the capacitance change of at least one interdigital capacitor unit using the conductive signal layer and the polymer insulating layer of the capacitor sensor substrate, so as to generate measurement data results.

[0165] It should be noted that the foregoing explanation of the embodiment of the material distribution pattern detection method based on arrayed interdigital capacitors also applies to the material distribution pattern detection device based on arrayed interdigital capacitors in this embodiment, and will not be repeated here.

[0166] The material distribution pattern detection device based on arrayed interdigital capacitance proposed in this application provides a more comprehensive picture of the material level in the measured area compared to single-point measurement. It effectively captures non-uniform distributions on the material surface (such as slopes, depressions, or arches formed by accumulation in non-fluid silos). Multi-point measurement effectively reduces sensitivity to local dielectric interference; that is, data mutations and anomalies at a single point have less impact on the overall measurement result, thus reducing measurement errors. It can be applied to areas with rapidly changing material levels (such as high-speed filling or discharging scenarios), solving the problem that single-point sensors struggle to track complex morphological changes in real time. Therefore, it addresses the issues in related technologies where single-point measurement only reflects local material level information, fails to capture non-uniform distributions on the material surface, and where fluctuations in the material's dielectric constant significantly affect capacitance values, leading to measurement errors and making it difficult for single-point sensors to track complex morphological changes in real time.

[0167] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0168] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0169] When the processor 1002 executes the program, it implements the material distribution pattern detection method based on arrayed interdigital capacitors provided in the above embodiments.

[0170] Furthermore, electronic devices also include:

[0171] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0172] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0173] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0174] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0175] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0176] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0177] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described material distribution pattern detection method based on arrayed interdigital capacitors.

[0178] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described material distribution pattern detection method based on arrayed interdigital capacitors.

[0179] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0180] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0181] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0182] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0183] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0184] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0185] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0186] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting material distribution patterns based on arrayed interdigital capacitance, characterized in that, An array of interdigitated capacitors is disposed on the target material, the array of interdigitated capacitors being composed of at least one interdigitated capacitor unit, wherein the method includes the following steps: Based on the target material and the capacitance value of the at least one interdigitated capacitor unit, determine the capacitance mapping relationship coefficient between the area coverage of the target material and the capacitance change of the at least one interdigitated capacitor unit; Based on the capacitance mapping relationship coefficient, materials with the same area coverage are placed sequentially on at least one interdigital capacitor unit, and the capacitance at each point on the at least one interdigital capacitor unit is measured. The influence coefficient of each point capacitance is calculated, and the influence coefficient matrix of the material placed at each point capacitance is determined based on the influence coefficient and the preset influence matrix. Based on the influence coefficient matrix, the capacitance matrix of each row and column coordinate in the target full array is obtained by full array scanning, and the material coverage on the at least one interdigitated capacitor unit is determined according to the capacitance matrix and the preset material distribution matrix. The distribution pattern of the material is detected, and the material coverage is processed according to the distribution pattern to generate a continuous material distribution map of the material; The preset influence matrix is ​​as follows: in, Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio; The influence coefficient matrix is ​​as follows: in, Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio; The capacitance matrix is: in, k C The capacitance coefficient is ( i , j Location material area coverage A i,j ∈[0%, 100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e. k C =ΔC i,j / A i,j , Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio, [ x 1,1 ,…, x M,N ] T The material distribution matrix to be determined is... x i, j for( i , j ) Location of the material to be determined, area coverage, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through full array scanning, C i, j for( i , j Actual measured capacitance value at the location.

2. The method according to claim 1, characterized in that, An orthogonal wiring architecture and a multiplexer are configured on the array of interdigitated capacitors. The orthogonal wiring architecture includes row bus groups and column bus groups. The input of the multiplexer is connected to a target controller, and the output of the multiplexer is connected to the row bus groups and the column bus groups. The method further includes: Activate a single row bus in the row bus group to traverse each column bus in the column bus group according to the single row bus, generate column bus traversal results, and measure the capacitance value of each point capacitor according to the column bus traversal results. Alternatively, activate a single column bus in the column bus group to traverse each row bus in the row bus group according to the single column bus, generate a row bus traversal result, and measure the capacitance value of each point capacitor according to the row bus traversal result.

3. The method according to claim 1, characterized in that, The determination of the capacitance mapping coefficient between the area coverage of the target material and the capacitance change of the at least one interdigitated capacitor unit includes: Measure at least one area coverage calibration point of the target material to generate measurement results, and calculate the weighted average and confidence interval of the capacitance coefficient based on the measurement results; A target piecewise linear regression model is established based on the weighted average and the confidence interval, and the capacitance mapping coefficient is determined based on the target piecewise linear regression model.

4. The method according to claim 1, characterized in that, A capacitive sensor substrate is disposed on the array of interdigital capacitors, wherein the method further includes: The capacitance change of at least one interdigital capacitor unit is measured using the conductive signal layer and the polymer insulating layer of the capacitor sensor substrate to generate measurement data results.

5. A material distribution pattern detection device based on arrayed interdigital capacitors, characterized in that, An array of interdigitated capacitors is disposed on the target material, the array of interdigitated capacitors being composed of at least one interdigitated capacitor unit, wherein the device includes: The determination module is used to determine the capacitance mapping relationship coefficient between the area coverage of the target material and the capacitance change of the at least one interdigitated capacitor unit based on the target material and the capacitance value of the at least one interdigitated capacitor unit. The measurement module is used to place materials with the same area coverage on at least one interdigitated capacitor unit in sequence based on the capacitance mapping relationship coefficient, measure the capacitance at each point on the at least one interdigitated capacitor unit, calculate the influence coefficient of the capacitance at each point, and determine the influence coefficient matrix of the material placed at each point capacitor based on the influence coefficient and the preset influence matrix. The acquisition module is used to obtain the capacitance matrix of each row and column coordinate in the target full array by full array scanning based on the influence coefficient matrix, and to determine the material coverage on the at least one interdigitated capacitor unit according to the capacitance matrix and the preset material distribution matrix. The detection module is used to detect the distribution pattern of the material and process the material coverage according to the distribution pattern to generate a continuous material distribution map of the material. The preset influence matrix is ​​as follows: in, Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio; The influence coefficient matrix is ​​as follows: in, Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio; The capacitance matrix is: in, k C The capacitance coefficient is ( i , j Location material area coverage A i,j ∈[0%, 100%] and capacitance change ΔC i,j The ratio of the capacitance mapping relationship, i.e. k C =ΔC i,j / A i,j , Materials with the same area coverage are placed in ( m , n ) position, measure ( i , j Capacitor at position ) and( m , n Capacitor at position ) The ratio, [ x 1,1 ,…, x M,N ] T The material distribution matrix to be determined is... x i, j for( i , j ) Location of the material to be determined, area coverage, [C 1,1 ,…,C M,N ] T To obtain the capacitance matrix of each actual row and column coordinate unit through full array scanning, C i, j for( i , j Actual measured capacitance value at the location.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the material distribution pattern detection method based on arrayed interdigital capacitance as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the material distribution pattern detection method based on arrayed interdigital capacitance as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the material distribution pattern detection method based on arrayed interdigital capacitors as described in any one of claims 1-4.

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