Method and device for processing and analyzing pressure sensing data

Abstract

A pressure sensing data processing and analyzing method and device, the method comprising: acquiring pressure sensing data, the pressure sensing data comprising varying pressure values and corresponding time; performing smoothing preprocessing on the pressure sensing data to obtain the smoothed pressure sensing data; calculating the smoothed pressure sensing data, and estimating a first-order derivative and a second-order derivative of a pressure change curve of the smoothed pressure sensing data at each time point; and performing analysis according to the pressure change data and the first-order derivative and the second-order derivative at each time point, and marking a plurality of feature points in the pressure change curve. According to the method, the feature points in the pressure change curve at different moments can be judged through a rapid and accurate analysis method, and the method can be applied to processing and analysis of pressure sensing data in membrane separation and membrane processing technologies.

Description

Technical Field

[0001] The present invention relates to the field of pressure sensing data analysis, and in particular to a method and device for processing and analyzing pressure sensing data. Background Art

[0002] In the fields of industry and scientific research, pressure sensing data plays a vital role, especially in membrane separation and membrane treatment technology, the importance of this data is self-evident. Membrane separation technology is widely used in water treatment, food industry, biotechnology and chemical processes. In these applications, pressure is one of the key parameters affecting membrane separation performance, which directly determines the filtration efficiency and processing capacity. Through accurate pressure measurement, it can be ensured that the membrane system operates under optimal pressure conditions, thereby optimizing separation efficiency, extending the service life of the membrane, and reducing energy consumption. In addition, pressure sensing data also provides important information for researchers to study the permeability and selectivity of membrane materials. By systematically changing the operating pressure and observing the impact on separation performance, researchers can optimize the design of membrane materials and develop more efficient and economical membrane separation solutions.

[0003] However, the existing analysis methods for pressure sensing data have the following problems: First, the pressurization module of the pressure threshold detection instrument is affected by various factors during the pressurization process, such as uneven pressurization caused by equipment aging or the pressurization method of the pressurization module itself. This influence may cause the pressure change data to show a state of periodic fluctuation. Such a fluctuating pressure curve will affect the accuracy of the pressure threshold judgment of the membrane; secondly, when judging the pressure threshold of the membrane, it is usually necessary to select a stable pressure value based on the pressure change situation. The existing method of judging the stable position in the change curve by observing the curve with the naked eye cannot accurately and efficiently judge the position and number of stable points; finally, when the pressure is close to the pressure threshold, the state of the membrane may change gradually, and there is a lack of a fast and accurate analysis method to judge the state of the membrane at different times in the pressure change curve. Summary of the invention

[0004] The main purpose of the present invention is to overcome the above-mentioned defects in the prior art and to propose a method and device for processing and analyzing pressure sensor data. By analyzing the changing trend of the pressure sensor data and marking the characteristic points in the pressure change curve, it is convenient for researchers to further analyze the performance of the membrane.

[0005] The present invention adopts the following technical solution:

[0006] A method for processing and analyzing pressure sensing data, characterized by comprising:

[0007] Acquiring pressure sensing data, wherein the pressure sensing data includes a changing pressure value and a corresponding time;

[0008] Performing smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data;

[0009] Calculating the smoothed pressure sensing data to estimate the first-order derivative and the second-order derivative of the pressure variation curve at each time point;

[0010] An analysis is performed based on the pressure variation data and the first-order derivative and the second-order derivative at each time point, and several characteristic points in the pressure variation curve are marked.

[0011] The smoothing preprocessing includes median smoothing based on a sliding window and mean smoothing based on a sliding window.

[0012] Estimate the first-order derivative of the pressure change curve of the smoothed pressure sensor data at each time point, specifically: use the central difference method to calculate the first-order derivative of the smoothed pressure sensor data at time points other than the head and tail of the data, and use the backward difference method and the forward difference method to calculate the first-order derivative of the smoothed pressure sensor data at two time points at the head and tail, respectively.

[0013] The first-order derivative of the pressure variation curve of the smoothed pressure sensing data at each time point is estimated, specifically, the first-order derivative of the point composition of the pressure variation curve near each time point is estimated by segmented least square method.

[0014] The second-order derivative of the pressure variation curve of the smoothed pressure sensing data at each time point is estimated by recalculating the derivative of the calculated first-order derivative of the pressure variation curve at each time point to obtain the second-order derivative at each time point.

[0015] Marking several characteristic points in the pressure change curve includes traversing all pressure values ​​in the pressure sensing data or sorting the pressure values ​​corresponding to all time points, and marking the data point with the maximum pressure value as the maximum pressure point.

[0016] Marking several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points with time t>t0, and comparing the first-order derivative of the pressure change at each data point with the stability threshold k. If the absolute value of the first-order derivative of the data point is less than the stability threshold k, the data point is marked as a pressure stability point.

[0017] Mark several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points in the pressure change curve where the time points are t>t0, and marking the data point where the second-order derivative of the pressure change is the smallest as the opening point of the membrane pore detected by the pressure sensor.

[0018] A pressure sensing data processing and analysis device, comprising:

[0019] A data acquisition module, which acquires pressure sensing data, wherein the pressure sensing data includes a changing pressure value and its corresponding time;

[0020] A data processing module performs smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data;

[0021] A data calculation module calculates the smoothed pressure sensing data to estimate the first-order derivative and the second-order derivative of the pressure change curve at each time point;

[0022] The data analysis module performs analysis based on the pressure change data and the first-order derivative and the second-order derivative at each time point, and marks a number of characteristic points in the pressure change curve.

[0023] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention can reduce the error caused by the periodic fluctuation of pressure in instrument measurement by preprocessing the pressure sensing data, which is conducive to more accurate judgment of pressure changes.

[0025] 2. The present invention can be applied to sensor data processing of membrane separation and membrane treatment technology. By analyzing the trend of data changes and marking the numerical points after the pressure stabilizes, the pressure threshold of the membrane can be obtained more efficiently and accurately.

[0026] 3. The present invention analyzes the changing trend of data and marks characteristic points in the pressure change curve that can describe the opening state of membrane pores, so as to facilitate researchers to further analyze the performance of membranes in membrane separation and membrane treatment technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a pressure sensing data processing and analysis method provided by the present invention;

[0028] Figure 2 It is a schematic diagram of smoothing preprocessing of pressure change data described in the embodiment;

[0029] Figure 3 It is a schematic diagram of the result of smoothing a set of complete pressure change data described in the embodiment;

[0030] Figure 4 It is a schematic diagram of the results of calculating the first-order derivative and the second-order derivative of the pressure change data described in the embodiment, and the positions of the pressure stability point and the membrane pore opening point are marked according to the first-order derivative and the second-order derivative.

[0031] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0032] The present invention is further described below through specific implementation modes.

[0033] See also Figure 1 , a method for processing and analyzing pressure sensing data, comprising the following steps:

[0034] S1 obtains pressure sensing data, which includes a changing pressure value and its corresponding time, that is, the pressure value changes with time, and the time and the pressure value correspond one to one.

[0035] S2 performs smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data.

[0036] In the present invention, the smoothing preprocessing includes median smoothing based on a sliding window and mean smoothing based on a sliding window.

[0037] Specifically, median smoothing includes: assuming that the pressure sensor data is a pressure value sequence P with a length of N = {p1, p2, ..., p i …,p N}, p i represents the i-th pressure value, setting the window size to W. When the window moves to p i When , the sequence in the window is median(·) means finding the median of a set of numbers. If W is an even number, then The window is located at the boundary of the pressure value sequence, that is, or When the points outside the boundary are filled in by reflection, for example, there is a pressure value sequence of {1,2,3,4,5} and the window size is 3. When processing the first and last pressure values, a value needs to be filled in, and the filled sequence is {1,1,2,3,4,5,5}.

[0038] The mean smoothing method is similar to the median smoothing method. Assume that the pressure sensor data is a pressure value sequence P with a length of N = {p1, p2, ..., p i ,…,p N}, p n represents the i-th pressure value, setting the window size to W. When the window moves to p iWhen , the sequence in the window is mean(·) means finding the mean of a group of numbers. When performing mean smoothing, the appropriate window sliding step size can be selected according to the specific pressure fluctuation situation. When the sliding step size is set to k (k>1), the window advances k points each time, and the processed sequences are Pressure values ​​at positions other than 1+jk (j=1, 2, ...) will be skipped, and the skipped values ​​can be omitted or filled with linear interpolation.

[0039] Example:

[0040] refer to Figure 2 When smoothing a set of pressure data, when the window size is set to 3, the window slides in the direction of increasing time, advancing one time unit each time. For a set of sequences {4, 4.2, 4.6, 4.9, 5.2, 5.6, 6.0, 6.2} in the figure, the result after median smoothing and mean smoothing in the sliding window is {4, 4.3, 4.6, 4.9, 5.2, 5.6, 6.0, 6.2}. The effect of overall data smoothing is shown in the reference Figure 3 shown.

[0041] S3 calculates the smoothed pressure sensing data and estimates the first-order derivative and the second-order derivative of the pressure variation curve at each time point.

[0042] In this step, the first-order derivative of the pressure change curve of the smoothed pressure sensor data at each time point is estimated, specifically: the first-order derivative of the smoothed pressure sensor data at time points other than the head and tail of the data is calculated using the central difference method, and the first-order derivative of the two time points at the head and tail of the smoothed pressure sensor data is calculated using the backward difference method and the forward difference method respectively.

[0043] Estimate the first-order derivative of the pressure change data at each time point. For the time point t i , the sampling interval between each data point is t step , use the following central difference method to estimate the first-order derivative at this point:

[0044] Use two points to estimate:

[0045] To improve accuracy, you can also use four points to estimate:

[0046] Among them, d i Represents the first-order derivative at time point i, that is, the slope.

[0047] In practical applications, the first-order derivative of the pressure change curve of the smoothed pressure sensor data at each time point is estimated, and the least squares method can be used in segments to estimate the first-order derivative of the points composed of the pressure change curve near each time point. The least squares method is used to fit the data points in the window, and the slope of the fitting line is obtained as the first-order derivative of the center point in the window.

[0048] In this step, the second-order derivative of the pressure change curve of the smoothed pressure sensor data at each time point is estimated, specifically: the derivative of the calculated first-order derivative of the pressure change curve at each time point is calculated again, so as to obtain the second-order derivative at each time point. The method used to calculate the second-order derivative is to calculate the derivative of the obtained first-order derivative again, so as to obtain the second-order derivative at each time point.

[0049] like Figure 4 As shown, the left figure shows the smoothed pressure data (solid line) and the calculated first-order derivative of the pressure change (short dashed line). Figure 4 The right figure shows the smoothed pressure data (solid line) and the calculated second-order derivative of the pressure change (long dashed line).

[0050] S4 performs analysis based on the pressure change data and the first-order derivative and the second-order derivative at each time point, and marks several characteristic points in the pressure change curve.

[0051] Marking several characteristic points in the pressure change curve includes traversing all pressure values ​​in the pressure sensing data or sorting the pressure values ​​corresponding to all time points, and marking the data point with the maximum pressure value as the maximum pressure point.

[0052] Mark several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points with time t>t0, and comparing the first-order derivative of the pressure change at each data point with the stability threshold k. If the absolute value of the first-order derivative of the data point is less than the stability threshold k, the data point is marked as a pressure stability point.

[0053] Mark several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points in the pressure change curve where the time points are t>t0, and marking the data point where the second-order derivative of the pressure change is the smallest as the initial opening point of the membrane pore detected by the pressure sensor.

[0054] For example: The present invention marks the pressure stabilization point and the initial opening point of the membrane pore according to the first-order derivative of the smoothed pressure change and the second-order derivative of the pressure change, such as Figure 4 The shaded part in the left figure is the marked pressure stability point, and the stability threshold set at this time is 0.015; Figure 4The minimum value of the second-order derivative of the pressure change in the right figure is marked as the initial opening point of the membrane pores. Since the pores on a porous membrane are not completely uniform in size, the pressure required to open a large pore is smaller than that of a small pore. The initial opening point is the opening point of the larger pore among these pores, which can be used to measure the uniformity of the membrane pores.

[0055] Based on this, the present invention also proposes a pressure sensing data processing and analysis device, comprising:

[0056] The data acquisition module acquires pressure sensing data, and the pressure sensing data includes a changing pressure value and its corresponding time.

[0057] The data processing module performs smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data.

[0058] The data calculation module calculates the smoothed pressure sensing data and estimates the first-order derivative and the second-order derivative of the pressure change curve at each time point.

[0059] The data analysis module performs analysis based on the pressure change data and the first-order derivative and the second-order derivative at each time point, and marks several characteristic points in the pressure change curve.

[0060] In the present invention, the data acquisition module, the data processing module, the data calculation module and the data analysis module are respectively used to execute steps S1 to S4 in a method for processing and analyzing pressure sensing data.

[0061] In the present invention, the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. In the description, the directions or positional relationships indicated by "upper", "lower", "left", "right", "front", and "back" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention, and are not intended to indicate or imply that the device referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the scope of protection of the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0062] In addition, in the description of this application, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0063] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited to this. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A method for processing and analyzing pressure sensing data, characterized in that: include: Acquiring pressure sensing data, wherein the pressure sensing data includes a changing pressure value and a corresponding time; Performing smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data; Calculating the smoothed pressure sensing data to estimate the first-order derivative and the second-order derivative of the pressure variation curve at each time point; An analysis is performed based on the pressure variation data and the first-order derivative and the second-order derivative at each time point, and several characteristic points in the pressure variation curve are marked.

2. A method for processing and analyzing pressure sensing data according to claim 1, characterized in that: The smoothing preprocessing includes median smoothing based on a sliding window and mean smoothing based on a sliding window.

3. A method for processing and analyzing pressure sensing data according to claim 1, characterized in that: Estimate the first-order derivative of the pressure change curve of the smoothed pressure sensor data at each time point, specifically: use the central difference method to calculate the first-order derivative of the smoothed pressure sensor data at time points other than the head and tail of the data, and use the backward difference method and the forward difference method to calculate the first-order derivative of the smoothed pressure sensor data at two time points at the head and tail, respectively.

4. A method for processing and analyzing pressure sensing data according to claim 1, characterized in that: The first-order derivative of the pressure variation curve of the smoothed pressure sensing data at each time point is estimated, specifically, the first-order derivative of the point composition of the pressure variation curve near each time point is estimated by segmented least square method.

5. A method for processing and analyzing pressure sensing data as claimed in claim 1, characterized in that: The second-order derivative of the pressure variation curve of the smoothed pressure sensing data at each time point is estimated by recalculating the derivative of the calculated first-order derivative of the pressure variation curve at each time point to obtain the second-order derivative at each time point.

6. A method for processing and analyzing pressure sensing data according to claim 1, characterized in that: Marking several characteristic points in the pressure change curve includes traversing all pressure values ​​in the pressure sensing data or sorting the pressure values ​​corresponding to all time points, and marking the data point with the maximum pressure value as the maximum pressure point.

7. A method for processing and analyzing pressure sensing data according to claim 1, characterized in that: Marking several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points with time t>t0, and comparing the first-order derivative of the pressure change at each data point with the stability threshold k. If the absolute value of the first-order derivative of the data point is less than the stability threshold k, the data point is marked as a pressure stability point.

8. A method for processing and analyzing pressure sensing data as claimed in claim 1, characterized in that: Mark several characteristic points in the pressure change curve, including marking the time of the maximum point of the first-order derivative in the pressure change curve as t0, traversing the data points in the pressure change curve where the time points are t>t0, and marking the data point where the second-order derivative of the pressure change is the smallest as the opening point of the membrane pore detected by the pressure sensor.

9. A pressure sensing data processing and analysis device, characterized in that: include A data acquisition module, which acquires pressure sensing data, wherein the pressure sensing data includes a changing pressure value and its corresponding time; A data processing module performs smoothing preprocessing on the pressure sensing data to obtain smoothed pressure sensing data; A data calculation module calculates the smoothed pressure sensing data to estimate the first-order derivative and the second-order derivative of the pressure change curve at each time point; The data analysis module performs analysis based on the pressure change data and the first-order derivative and the second-order derivative at each time point, and marks a number of characteristic points in the pressure change curve.

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