A smart humidity detection system based on fiber optic sensors

Abstract

The invention discloses an intelligent humidity detection system based on an optical fiber sensor, relates to the technical field of optical fiber sensing and information processing, solves the technical problem that required data cannot be viewed in real time during the existing humidity detection, and the process of calculating the humidity is relatively complicated, collects images through a data acquisition module, and sends the images to a data processing module, the data processing module processes the images to obtain image sensitivity, and sends the image sensitivity to a data analysis module, the data analysis module analyzes the image sensitivity to obtain a standard image sensitivity, and sends the standard image sensitivity to a control center for storage, and sends the standard image sensitivity to an execution module, the execution module obtains a corresponding sensing function through the standard image sensitivity, and obtains the humidity of the environment in the image through the sensing function.

Description

Technical Field

[0001] The invention relates to the technical field of optical fiber sensing and information processing, and in particular to an intelligent humidity detection system based on an optical fiber sensor. Background Art

[0002] Humidity sensors have certain applications in various industrial safety fields such as aviation, national defense, and food manufacturing. Although the preparation principle of traditional capacitive and resistive humidity sensors is simple and easy to understand, and the cost is low, they have the disadvantages of large size, long response time, susceptibility to electromagnetic interference, and unsuitable for flammable and explosive environments. Optical fiber humidity sensors have become a research hotspot due to their fast response speed, no electromagnetic interference, safety and reliability. At the same time, smartphones have become one of the "must-have" items; smartphones have the advantages of integration, popularization, and simple operation. Since the idea of ​​measuring spectra on mobile phones was proposed in 2008, smartphones have gradually stood out in various sensor measurement and analysis systems; smartphones have certain uses in the field of optical imaging, such as smartphone fluorescence spectrometers realized by endoscopes and optical fibers, which can be used for agricultural product analysis and detection, and smartphone fluorescence spectrometers realized by laser light sources, optical fibers, lenses, and pinholes, which can be used for virus detection. In addition, there are other ways to realize smartphone laboratories, such as designing new sensor peripherals to integrate refractive index, electrochemical signals, light absorption and luminescence signals, and then adding portable chemical analysis instruments realized by smartphones to quickly detect parameters such as oxygen and pH in marine scenes.

[0003] When using the existing optical fiber humidity sensor, people perform real-time measurement and analysis on the device. When detecting humidity, the required data cannot be viewed in real time, and the process of calculating humidity is relatively complicated. Therefore, an intelligent humidity detection system based on optical fiber sensor is now proposed. Summary of the invention

[0004] In order to solve the deficiencies mentioned in the above background technology, the object of the present invention is to provide an intelligent humidity detection system based on optical fiber sensors.

[0005] The object of the present invention can be achieved by the following technical solutions: an intelligent humidity detection system based on an optical fiber sensor, comprising a data acquisition module, a data processing module, a data analysis module, a control center and an execution module, wherein the data acquisition module is used to acquire images and send the acquired images to the data processing module for processing;

[0006] After receiving the image sent by the data acquisition module, the data processing module processes the image, obtains the image sensitivity through processing, and sends the image sensitivity to the data analysis module for analysis;

[0007] The data analysis module analyzes the received image sensitivity, marks the image sensitivity that meets the requirements as the standard image sensitivity after the analysis, sends the standard image sensitivity to the execution module for execution operation, and sends the standard image sensitivity to the control center for storage;

[0008] After receiving the standard image sensitivity sent by the data analysis module, the execution module obtains the corresponding sensing function according to the standard image sensitivity, then obtains the humidity of the environment corresponding to the image through the sensing function, and sends the humidity to the control center for storage.

[0009] Furthermore, the data acquisition module includes an LED light and an OTG camera.

[0010] Furthermore, the LED lamp is used as an incident light source, and the OTG camera is used to collect images.

[0011] Furthermore, the processing process of the data processing module includes the following steps:

[0012] Step S1: grayscale processing is performed on the image to obtain the grayscale value of each row of the image;

[0013] Step S2: Select the gray value of a row;

[0014] Step S3: The two-dimensional drawing waveform graph is superimposed to obtain a wavelength grayscale curve graph, and the image sensitivity is obtained by performing linear fitting on the wavelength grayscale curve graph.

[0015] Furthermore, the analysis process of the data analysis module includes the following steps:

[0016] Step W1: Assume the image sensitivity L, and set the image sensitivity base value to Lz, then analyze the image sensitivity L to determine whether the image sensitivity meets the requirements;

[0017] Step W2: According to the formula The standard image sensitivity coefficient P is calculated, where α is the image sensitivity influence coefficient and β is the image sensitivity base value influence coefficient;

[0018] Step W3: Analyze the standard image sensitivity coefficient P. If P < 1, it means that the standard image sensitivity coefficient does not meet the usage standard, and the image corresponding to the image sensitivity cannot be used. If P ≥ 1, it means that the standard image sensitivity coefficient meets the usage standard. The data analysis module marks the image sensitivity as the standard image sensitivity, and the data analysis module sends the standard image sensitivity to the execution module.

[0019] Beneficial effects of the present invention:

[0020] During use of the present invention, images are collected by a data acquisition module, and the images are sent to a data processing module. The data processing module processes the images to obtain image sensitivity, and the image sensitivity is sent to a data analysis module. The data analysis module analyzes the image sensitivity to obtain standard image sensitivity, and the standard image sensitivity is sent to a control center for storage, and the standard image sensitivity is sent to an execution module. The execution module obtains a corresponding sensing function through the standard image sensitivity, and obtains the humidity of the environment in the image through the sensing function, and the humidity is sent to the control center for storage, thereby realizing the function of viewing the processed data in real time and being able to quickly calculate the humidity. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, an intelligent humidity detection system based on an optical fiber sensor includes a data acquisition module, a data processing module, a data analysis module and an execution module. The data acquisition module is used to collect images. It needs to be further explained that in the specific implementation process, the data acquisition module includes an LED lamp and an OTG camera. First, a fixed-wavelength LED lamp is used as an incident light source, and an image is collected through the OTG camera, and the captured image is imported; the data acquisition module sends the collected image to the data processing module for processing;

[0025] The data processing module is used to process the image. Specifically, the process of the data processing module processing the image includes the following steps:

[0026] Step S1: grayscale processing is performed on the image to obtain the grayscale value of each row of the image;

[0027] It needs to be further explained that, in the specific implementation process, the grayscale processing process is to unify the RGB value of each pixel into the same value, and the grayscale image will be changed from three channels to a single channel. The advantage of this processing is that single-channel data processing is much simpler than three-channel data processing.

[0028] Step S2: Select the gray value of a row;

[0029] Step S3: superimposing the two-dimensional drawing waveform to obtain a wavelength grayscale curve graph, and performing linear fitting on the wavelength grayscale curve graph to obtain image sensitivity;

[0030] The data processing module sends the image sensitivity obtained after processing to the data analysis module for analysis. The data analysis module is used to perform data analysis on the received image sensitivity. Specifically, the analysis process of the data analysis module includes the following steps:

[0031] Step W1: Assume the image sensitivity L, and set the image sensitivity base value to Lz, then analyze the image sensitivity L to determine whether the image sensitivity meets the requirements;

[0032] Step W2: According to the formula The standard image sensitivity coefficient P is calculated, where α is the image sensitivity influence coefficient and β is the image sensitivity base value influence coefficient;

[0033] Step W3: Analyze the standard image sensitivity coefficient P. If P < 1, it means that the standard image sensitivity coefficient does not meet the usage standard, and the image corresponding to the image sensitivity cannot be used. If P ≥ 1, it means that the standard image sensitivity coefficient meets the usage standard. The data analysis module marks the image sensitivity as standard image sensitivity. The data analysis module sends the standard image sensitivity to the execution module and sends the standard image sensitivity to the control center for storage.

[0034] The execution module is used to receive the standard image sensitivity sent by the data analysis module, and select the corresponding sensing function based on the standard image sensitivity. It needs to be further explained that in the specific implementation process, the sensing function is the relationship between wavelength and humidity. The corresponding humidity can be obtained through the sensing function, and the humidity is sent to the control center for storage. The control center is a mobile phone that controls the storage in real time.

[0035] It needs to be further explained that, in the specific implementation process, the principle adopted by the present invention is to use PVA humidity-sensitive film as the sensitive film. When the incident light passes through the optical fiber and enters the Fabry-Perot cavity, part of the light will be directly reflected back by the concave surface of the optical fiber, and the other part of the light will enter the air cavity and be reflected back by the PVA film. These two beams of reflected light will produce interference inside the optical fiber. When the humidity of the external environment changes, it will cause the refractive index of the PVA film to change, thereby causing the reflected spectrum signal to change. The size of the external humidity can be demodulated by tracking the peak value.

[0036] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent humidity detection system based on optical fiber sensor, characterized in that: It includes a data acquisition module, a data processing module, a data analysis module, a control center and an execution module. The data acquisition module is used to acquire images and send the acquired images to the data processing module for processing; After receiving the image sent by the data acquisition module, the data processing module processes the image, obtains the image sensitivity through processing, and sends the image sensitivity to the data analysis module for analysis; The data analysis module analyzes the received image sensitivity, marks the image sensitivity that meets the requirements as the standard image sensitivity after the analysis, sends the standard image sensitivity to the execution module for execution operation, and sends the standard image sensitivity to the control center for storage; After receiving the standard image sensitivity sent by the data analysis module, the execution module obtains the corresponding sensing function according to the standard image sensitivity, then obtains the humidity of the environment corresponding to the image through the sensing function, and sends the humidity to the control center for storage.

2. According to claim 1, an intelligent humidity detection system based on optical fiber sensor is characterized in that: The data acquisition module includes an LED light and an OTG camera.

3. The intelligent humidity detection system based on optical fiber sensor according to claim 2 is characterized in that: The LED lamp is used as an incident light source, and the OTG camera is used to collect images.

4. The intelligent humidity detection system based on optical fiber sensor according to claim 1 is characterized in that: The processing process of the data processing module includes the following steps: Step S1: grayscale processing is performed on the image to obtain the grayscale value of each row of the image; Step S2: Select the gray value of a row; Step S3: The two-dimensional drawing waveform graph is superimposed to obtain a wavelength grayscale curve graph, and the image sensitivity is obtained by performing linear fitting on the wavelength grayscale curve graph.

5. The intelligent humidity detection system based on optical fiber sensor according to claim 1 is characterized in that: The analysis process of the data analysis module includes the following steps: Step W1: Assume the image sensitivity L, and set the image sensitivity base value to Lz, then analyze the image sensitivity L to determine whether the image sensitivity meets the requirements; Step W2: According to the formula The standard image sensitivity coefficient P is calculated, where α is the image sensitivity influence coefficient and β is the image sensitivity base value influence coefficient; Step W3: Analyze the standard image sensitivity coefficient P. If P < 1, it means that the standard image sensitivity coefficient does not meet the usage standard, and the image corresponding to the image sensitivity cannot be used. If P ≥ 1, it means that the standard image sensitivity coefficient meets the usage standard. The data analysis module marks the image sensitivity as the standard image sensitivity, and the data analysis module sends the standard image sensitivity to the execution module.

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