Airflow field monitoring method based on hair structure flexible airflow sensor array

Abstract

The invention discloses an airflow field monitoring method based on a hair structure flexible airflow sensor array, and belongs to the technical field of airflow data processing, and the method comprises the following steps: S1, collecting sensing data of a monitoring position through the hair structure flexible airflow sensor array, and obtaining a sensing data array; s2, reconstructing an airflow field according to the sensing data array, extracting an airflow flowing speed from the reconstructed airflow field, calculating a speed field gradient through the reconstructed airflow field, and extracting a negative gradient direction from the speed field gradient as an airflow flowing direction; and S3, drawing a visual flow field cloud picture containing the airflow flowing speed information and the airflow flowing direction information, and displaying the visual flow field cloud picture in real time. The air flow field monitoring method based on the hair structure flexible air flow sensor array solves the problem that an existing air flow field monitoring mode is difficult to efficiently monitor air flow fields in the environment and on the surface of an object.

Description

Technical Field

[0001] The present invention relates to the technical field of airflow data processing, and in particular to an airflow field monitoring method based on a hair-structured flexible airflow sensor array. Background Art

[0002] Real-time monitoring of airflow fields and evaluating and analyzing their distribution has important scientific significance and engineering application value in fields such as environmental monitoring, aerospace, meteorological analysis, and aerodynamics. By monitoring and analyzing airflow fields in real time, we can effectively analyze the complex state of airflow fields, clarify the forms of airflow, and provide data support for practical analysis.

[0003] Due to the limitations of traditional airflow sensors, such as their large size, bulky structure, and high cost, the use of traditional airflow sensor arrays not only faces the challenges of high cost and difficult deployment, but also causes significant disturbances to the original airflow field during use, affecting the accuracy of monitoring results. Furthermore, airflow field monitoring using sensor arrays generates a large amount of sensor data, each of which only contains information about its placement and cannot reflect the overall airflow field, requiring further processing and analysis. Therefore, current airflow field monitoring methods are difficult to achieve efficient monitoring of airflow fields in the environment and on the surface of objects. Summary of the Invention

[0004] In order to overcome the defects of the prior art, the present invention provides an airflow field monitoring method based on a hair-structured flexible airflow sensor array to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for monitoring airflow field based on a hair-structured flexible airflow sensor array, comprising the following steps: S1: collecting sensor data of the monitoring position through the hair structure flexible airflow sensor array to obtain a sensor data array; S2: reconstructing the airflow field according to the sensor data array, extracting the airflow velocity from the reconstructed airflow field, calculating the velocity field gradient by reconstructing the airflow field, and extracting the negative gradient direction from the velocity field gradient as the airflow direction; S3: Draw a visual flow field cloud map containing airflow velocity information and airflow direction information and display it in real time.

[0006] Preferably, in step S1, the hair structure flexible airflow sensor array includes a plurality of sensor units, each of which includes a base layer, a conductive layer, an adhesive layer, and a fuzz layer. The base layer, the conductive layer, and the adhesive layer are sequentially arranged from bottom to top. The fuzz layer includes a plurality of conductive fibers, which are electrically connected to the conductive layer after passing through the adhesive layer. When the sensing unit is affected by the airflow at the monitoring position, the airflow acts on the conductive fibers of the villi layer, causing the adjacent conductive fibers to repeatedly contact and separate, causing the contact area between the conductive fibers to change, thereby changing the resistance of the villi layer. The resistance of the sensing unit ,in represents the resistance of the base layer and the conductive layer in series, represents the resistance of the villi layer; The resistance value of each sensor unit is converted into a voltage signal as the sensor data of the monitoring position, and the voltage signals of each sensor unit arranged in an array are acquired by the sensor data acquisition module to form a sensor data array.

[0007] Optionally, in step S2, a voltage signal obtained by the sensor data acquisition module in the absence of airflow is obtained as reference data, and a rate of change of the sensor data is calculated based on the reference data as preprocessed sensor data.

[0008] Specifically, in step S2, after obtaining the preprocessed sensor data, radial interpolation is performed based on the sensor data to obtain an interpolated sensor data array, and the airflow flow velocity in the airflow field is reconstructed using the numerical representation of the sensor data in the interpolated sensor data array, and then the velocity field gradient is calculated based on the interpolated sensor data.

[0009] Optionally, the sensing data is radially interpolated using a linear radial interpolation function, where the linear radial interpolation function is: ; in, represents the i-th interpolation point The corresponding sensor data value; Represents the coordinates of the i-th interpolation point; represents the coordinates of the jth known sensor data; Indicates The total number of sensing units in the evenly distributed hair structure flexible airflow sensor array is ; Represents the linear radial basis function, used to calculate the coordinates of the interpolation point Coordinates with sensor data The Euclidean distance of Represents the weight coefficient of the jth known sensor data; represents a first-order polynomial, which is used to eliminate singularities to ensure solvability, where represents the constant term coefficient of the first-order polynomial, represents the coefficient of the first-order polynomial, represents the set of real numbers, express dimensional real number set, given by It consists of real numbers.

[0010] It is worth noting that in step S2, the coordinate values of the interpolation point coordinates based on the radial interpolation of the sensor data are ; in and Represents the coordinate value of the jth known sensor data , i is and The i-th interpolation point to be inserted between , M represents the number of interpolation points that need to be inserted, and represents the grid step size, , .

[0011] Preferably, in step S2, after obtaining the interpolated sensor data array, the sensor data in the interpolated sensor data array are numbered by rows and columns according to the arrangement order of the interpolated sensor data array, so that each sensor data in the interpolated sensor data array has a unique row number and column number.

[0012] It is worth noting that, in step S2, the formula for calculating the velocity field gradient based on the interpolated sensor data array is: The formula for calculating the velocity field gradient based on the interpolated sensor data array is: ; ; in , represents the row number in the sensor data array, , respectively represent the column numbers in the sensor data array, Indicates the value of the sensor data with row number g and column number h, Indicates the coordinate value of the sensor data with row number g and column number h, It represents the velocity field gradient of the sensor data with row number g and column number h in the row direction. Represents the velocity field gradient in the column direction of the sensor data with row number g and column number h; is the value of the sensor data with column number h at the row boundary, is the row-direction coordinate of the sensor data with column number h at the row boundary, is the value of the sensor data with row number g at the column boundary, is the column-direction coordinate of the sensor data with row number g at the column boundary.

[0013] Specifically, in step S3, the visualized flow field cloud image includes a velocity field of the airflow field and a direction field of the airflow field; According to the air flow velocity, the velocity field of the air flow field is drawn in the form of a contour map, and the velocity magnitude is represented by color mapping; According to the direction of airflow, the direction field of the airflow field is drawn in the form of a streamline diagram, and the arrow points to the direction of airflow.

[0014] The beneficial effects of the present invention are: in response to the needs of real-time collection, evaluation and analysis of airflow velocity and airflow direction data at various positions in airflow field monitoring scenarios, in the airflow field monitoring method based on the hair-structured flexible airflow sensor array, the data is efficiently collected and analyzed to obtain a reconstructed airflow field, and the airflow field image is drawn in real time through the reconstructed airflow field and the airflow distribution is evaluated, thereby achieving efficient monitoring of the airflow field in the environment and on the surface of objects, and providing highly reliable actual monitoring data for structural design, performance optimization and model prediction in the fields of environmental monitoring, aerospace, meteorological monitoring and industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a framework diagram of an airflow field monitoring method based on a hair-structured flexible airflow sensor array in one embodiment of the present invention; Figure 2 Schematic diagram of the structure of a single sensing unit in a hair-structure-based flexible airflow sensor array according to one embodiment of the present invention; Figure 3 This is an equivalent circuit diagram of the electrical connection of a single sensing unit in a hair-structure-based flexible airflow sensor array according to one embodiment of the present invention; Figure 4 1 is a framework diagram of a data processing and analysis module in one embodiment of the present invention; Figure 5 is a schematic diagram of airflow field reconstruction in one embodiment of the present invention; Figure 6 is a schematic diagram of a velocity field of an airflow field in one embodiment of the present invention; Figure 7 is a schematic diagram of the direction field of the airflow field in one embodiment of the present invention; In the figure: 10 base layer; 20 conductive layer; 30 adhesive layer; 40 fleece layer. DETAILED DESCRIPTION

[0016] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0017] like Figure 1-7 As shown, a method for monitoring airflow field based on a hair-structured flexible airflow sensor array includes the following steps: S1: collecting sensor data of the monitoring position through the hair structure flexible airflow sensor array to obtain a sensor data array; S2: Airflow field reconstruction algorithm: reconstructs the airflow field based on the sensor data array, extracts the airflow velocity from the reconstructed airflow field, calculates the velocity field gradient through the reconstructed airflow field, and extracts the negative gradient direction from the velocity field gradient as the airflow direction; S3: The real-time drawing program draws a visual flow field cloud map containing airflow velocity information and airflow direction information and displays it in real time.

[0018] In response to the requirements of airflow field monitoring scenarios for real-time collection, evaluation and analysis of airflow velocity and airflow direction data at various locations, in the airflow field monitoring method based on the hair-structured flexible airflow sensor array, a reconstructed airflow field is obtained by efficiently collecting and analyzing the data. The airflow field image is drawn in real time through the reconstructed airflow field and the airflow distribution is evaluated, thereby realizing efficient monitoring of the airflow field in the environment and on the surface of objects, and providing highly reliable actual monitoring data for structural design, performance optimization and model prediction in the fields of environmental monitoring, aerospace, meteorological monitoring and industrial production.

[0019] Preferably, Figure 2 and 3 As shown, in step S1, the hair structure flexible airflow sensor array includes a plurality of sensor units, each of which includes a base layer 10, a conductive layer 20, an adhesive layer 30, and a fuzz layer 40. The base layer 10, the conductive layer 20, and the adhesive layer 30 are sequentially arranged from bottom to top. The fuzz layer 40 includes a plurality of conductive fibers, which pass through the adhesive layer 30 and are electrically connected to the conductive layer 20. When the sensing unit is affected by the airflow at the monitoring position, the airflow acts on the conductive fibers of the fluff layer 40, causing adjacent conductive fibers to repeatedly contact and separate, causing the contact area between the conductive fibers to change, thereby changing the resistance of the fluff layer 40; The resistance of the sensing unit ,in represents the resistance of the base layer 10 and the conductive layer 20 connected in series, represents the resistance of the pile layer 40; The resistance value of each sensor unit is converted into a voltage signal as the sensor data of the monitoring position, and the voltage signals of each sensor unit arranged in an array are acquired by the sensor data acquisition module to form a sensor data array.

[0020] Through the sensor data acquisition module, each sensor unit is independently connected through metal wires, and the sensor data of each sensor unit is collected and sent to the data processing and analysis module. In this embodiment, in order to solve the technical problems of the traditional airflow sensor array, such as the bulky structure, difficult preparation, and inaccurate monitoring results, a hair-structured flexible airflow sensor array is developed to improve the sensor's disturbance of the original airflow field, provide efficient and reliable conformal monitoring capabilities, and realize effective monitoring of the airflow field in various environments and on the surface of objects. Figure 4 As shown, in this embodiment, the data processing and analysis module has a built-in airflow field reconstruction algorithm and a real-time drawing program.

[0021] Optionally, in step S2, a voltage signal obtained by the sensor data acquisition module in the absence of airflow is obtained as baseline data, and a rate of change of the sensor data is calculated based on the baseline data as the preprocessed sensor data. In this embodiment, rate of change = (sensor data - baseline data) / baseline data.

[0022] Specifically, if Figure 5 As shown, in step S2, after obtaining the preprocessed sensor data, radial interpolation is performed based on the sensor data to obtain an interpolated sensor data array. The airflow velocity in the airflow field is reconstructed using the numerical representation of the sensor data in the interpolated sensor data array, and the velocity field gradient is calculated based on the interpolated sensor data. The airflow velocity in the airflow field is reconstructed using the sensor data representation in the interpolated sensor data array to obtain a smoothed velocity field.

[0023] Preferably, the sensing data is radially interpolated by a linear radial interpolation function, and the linear radial interpolation function is: ; in, represents the i-th interpolation point The corresponding sensor data value; Represents the coordinates of the i-th interpolation point; represents the coordinates of the jth known sensor data; Indicates The total number of sensing units in the evenly distributed hair structure flexible airflow sensor array is ; Represents the linear radial basis function, used to calculate the coordinates of the interpolation point Coordinates with sensor data The Euclidean distance of Represents the weight coefficient of the jth known sensor data; represents a first-order polynomial, which is used to eliminate singularities to ensure solvability, where represents the constant term coefficient of the first-order polynomial, represents the coefficient of the first-order polynomial, represents the set of real numbers, express dimensional real number set, given by It consists of real numbers.

[0024] Weight coefficient The following conditions must be met: ; The following linear equations are formed by the linear radial interpolation function: ; Represents the known sensor data coordinates With the known sensor data coordinates The Euclidean distance between points is the Euclidean distance between points in two dimensions. and , Euclidean distance , represents the value of the jth known sensor data; It means from Until The coordinates of all sensor data points, excluding those obtained by linear radial interpolation; ; By solving the linear equations, we get 、 and .

[0025] It is worth noting that in step S2, the coordinate values of the interpolation point coordinates based on the radial interpolation of the sensor data are ; in and Represents the coordinate value of the jth known sensor data , i is and The i-th interpolation point to be inserted between , M represents the number of interpolation points that need to be inserted, and represents the grid step size, , .

[0026] If you want to insert an interpolation point between the coordinate values of the known sensor data (0, 0) and (1, 0), then i=1, then the interpolation point , Note that M here represents the number of interpolation points that need to be inserted, that is, if one interpolation point needs to be inserted between the coordinate values (0, 0) and (1, 0) of the known sensor data, M=1; in this embodiment, (0, 0) and (1, 0) are two adjacent (0, 0) and (1, 0), that is, in the known In the case of , the coordinate values of its adjacent known sensor data are .

[0027] Optionally, in step S2, after obtaining the interpolated sensor data array, the sensor data in the interpolated sensor data array are numbered by rows and columns according to the arrangement order of the interpolated sensor data array, so that each sensor data in the interpolated sensor data array has a unique row number and column number.

[0028] In the row direction, , the interpolated sensor data are arranged in sequence. After a sensor data is inserted between the original sensor unit 1 and sensor unit 2, the data of sensor unit 1 is numbered 1, the inserted data is numbered 2, and the data of sensor unit 2 is numbered 3. This logic is used to sort the interpolated sensor data in the row direction of the array. Similarly, in the column direction, , and the numbers are sorted in the same way.

[0029] In this embodiment, since the coordinate values of the sensor data corresponding to each sensor unit have been obtained, the numerical values and coordinate values of the sensor data corresponding to each interpolation point are obtained through radial interpolation. In this way, the coordinate values of each sensor data in the interpolated sensor data array can be obtained.

[0030] Specifically, in step S2, the formula for calculating the velocity field gradient based on the interpolated sensor data array is: ; ; in , represents the row number in the sensor data array, , respectively represent the column numbers in the sensor data array, Indicates the value of the sensor data with row number g and column number h, Indicates the coordinate value of the sensor data with row number g and column number h, It represents the velocity field gradient of the sensor data with row number g and column number h in the row direction. Represents the velocity field gradient in the column direction of the sensor data with row number g and column number h; is the value of the sensor data with column number h at the row boundary, is the row-direction coordinate of the sensor data with column number h at the row boundary, is the value of the sensor data with row number g at the column boundary, is the column-direction coordinate of the sensor data with row number g at the column boundary. 、 、 and Since they are all parameters corresponding to the boundary, and the parameters corresponding to the interpolation points will not appear on the boundary, 、 、 and These are all parameters corresponding to non-interpolation points.

[0031] In this embodiment, since the coordinate value of each sensor data in the interpolated sensor data array is known, after numbering, the coordinate value of the sensor data with the corresponding number can be obtained. .

[0032] It is worth noting that, in step S3, the visualized flow field cloud map includes the velocity field of the airflow field and the direction field of the airflow field; like Figure 6 As shown, according to the air flow velocity, the velocity field of the air flow field is drawn in the form of a contour map, and the velocity magnitude is represented by color mapping, preferably a rainbow color mapping, from blue to red representing a speed from small to large; like Figure 7 As shown, the direction field of the airflow field is drawn in the form of a streamline diagram according to the direction of the airflow, and the arrow points to the direction of the airflow.

[0033] In this embodiment, step S2 is continuously executed at set time intervals to update the velocity field and the direction field of the airflow field, and to draw an image, thereby achieving the required real-time drawing.

[0034] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and these changes and modifications still fall within the scope of protection of the present invention.

Claims

1. A method for monitoring airflow field based on a hair-structured flexible airflow sensor array, characterized in that: The following steps are involved: S1: collecting sensor data of the monitoring position through the hair structure flexible airflow sensor array to obtain a sensor data array; S2: reconstructing the airflow field according to the sensor data array, extracting the airflow velocity from the reconstructed airflow field, calculating the velocity field gradient by reconstructing the airflow field, and extracting the negative gradient direction from the velocity field gradient as the airflow direction; S3: Draw a visual flow field cloud map containing airflow velocity information and airflow direction information and display it in real time.

2. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 1, characterized in that: In step S1, the hair-structured flexible airflow sensor array includes a plurality of sensor units, each of which includes a base layer, a conductive layer, an adhesive layer, and a fuzz layer. The base layer, the conductive layer, and the adhesive layer are sequentially arranged from bottom to top. The fuzz layer includes a plurality of conductive fibers, which pass through the adhesive layer and are electrically connected to the conductive layer. When the sensing unit is affected by the airflow at the monitoring position, the airflow acts on the conductive fibers of the villi layer, causing the adjacent conductive fibers to repeatedly contact and separate, causing the contact area between the conductive fibers to change, thereby changing the resistance of the villi layer. The resistance of the sensing unit ,in represents the resistance of the base layer and the conductive layer in series, represents the resistance of the villi layer; The resistance value of each sensor unit is converted into a voltage signal as the sensor data of the monitoring position, and the voltage signals of each sensor unit arranged in an array are acquired by the sensor data acquisition module to form a sensor data array.

3. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 2, characterized in that: In step S2, a voltage signal obtained by the sensor data acquisition module in the absence of airflow is obtained as reference data, and a rate of change of the sensor data is calculated based on the reference data as pre-processed sensor data.

4. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 3, characterized in that: In step S2, after obtaining the preprocessed sensor data, radial interpolation is performed based on the sensor data to obtain an interpolated sensor data array, and the airflow velocity in the airflow field is reconstructed using the numerical representation of the sensor data in the interpolated sensor data array, and then the velocity field gradient is calculated based on the interpolated sensor data.

5. The method for monitoring airflow field based on a hair-structured flexible airflow sensor array according to claim 2, characterized in that: The sensing data is radially interpolated using a linear radial interpolation function, which is: ; in, represents the i-th interpolation point The corresponding sensor data value; Represents the coordinates of the i-th interpolation point; represents the coordinates of the jth known sensor data; Indicates The total number of sensing units in the evenly distributed hair structure flexible airflow sensor array is ; Represents the linear radial basis function, used to calculate the coordinates of the interpolation point Coordinates with sensor data The Euclidean distance of Represents the weight coefficient of the jth known sensor data; represents a first-order polynomial, which is used to eliminate singularities to ensure solvability, where represents the constant term coefficient of the first-order polynomial, represents the coefficient of the first-order polynomial, represents the set of real numbers, express dimensional real number set, given by It consists of real numbers.

6. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 5, characterized in that: In step S2, the coordinate values of the interpolation point coordinates are obtained by radial interpolation based on the sensor data. ; in and Represents the coordinate value of the jth known sensor data , i is and The i-th interpolation point to be inserted between , M represents the number of interpolation points that need to be inserted, and represents the grid step size, , .

7. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 6, characterized in that: In step S2, after obtaining the interpolated sensor data array, the sensor data in the interpolated sensor data array are numbered by row and column according to the arrangement order of the interpolated sensor data array, so that each sensor data in the interpolated sensor data array has a unique row number and column number.

8. The method for monitoring airflow field based on a hair-structured flexible airflow sensor array according to claim 7, characterized in that: In step S2, the formula for calculating the velocity field gradient based on the interpolated sensor data array is: ； ； in , represents the row number in the sensor data array, , respectively represent the column numbers in the sensor data array, Indicates the value of the sensor data with row number g and column number h, Indicates the coordinate value of the sensor data with row number g and column number h, It represents the velocity field gradient of the sensor data with row number g and column number h in the row direction. Represents the velocity field gradient in the column direction of the sensor data with row number g and column number h; is the value of the sensor data with column number h at the row boundary, is the row-direction coordinate of the sensor data with column number h at the row boundary, is the value of the sensor data with row number g at the column boundary, is the column-direction coordinate of the sensor data with row number g at the column boundary.

9. The airflow field monitoring method based on a hair-structured flexible airflow sensor array according to claim 1, characterized in that: In step S3, the visualized flow field cloud map includes a velocity field of the airflow field and a direction field of the airflow field; According to the air flow velocity, the velocity field of the air flow field is drawn in the form of a contour map, and the velocity magnitude is represented by color mapping; According to the direction of airflow, the direction field of the airflow field is drawn in the form of a streamline diagram, and the arrow points to the direction of airflow.

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