LORA-based portable air quality detection system and detection device

Abstract

The LORA-based portable air quality detection system comprises a plurality of modules, and the LORA-based portable air quality detection system structurally comprises a main control chip, a power supply module, an LORA module and an acquisition module which are embedded on a printed circuit board. The acquisition module comprises a PM1.0 sensor, a PM2.5 sensor, a PM10 sensor, a temperature and humidity sensor and a carbon dioxide sensor. The invention discloses a main control chip integrated data optimization method. The main control chip is connected with the LORA module, the positioning module and the acquisition module, and the main control chip is used for optimizing data measured by each sensor and receiving position information, and sending the data to a computer end through the LORA module for real-time monitoring and visual display of pollutant concentration. The device can quickly and accurately measure the air quality, and when the air quality is detected, the GPS module can help to position the specific position of a pollution source, so that the source and diffusion condition of the pollution can be tracked conveniently; the requirement for measuring air quality in a specific area can be met, and the problem that a fixed device is single in measuring point is solved.

Description

Technical Field

[0001] The present invention belongs to the field of air quality monitoring within a region, and particularly relates to the fields of air quality monitoring methods and data processing technologies, as well as a detection device using the air quality detection system. Background Art

[0003] LORA is a low-power local area network wireless standard that can transmit over a longer distance than other wireless methods under the same power consumption conditions, achieving the unity of low power consumption and long distance, and having good performance in long-distance wireless communication.

[0004] Environmental noise, non-steady signals, and other interference factors can cause the monitoring data to be contaminated by noise, thereby reducing the accuracy of air quality monitoring.

[0005] Wavelet transform and empirical mode decomposition denoising algorithms, as common signal denoising methods, cannot adaptively represent multi-type data when there are various types of polluting gases in the monitoring data. Therefore, the traditional denoising methods cannot achieve good denoising effects in various monitoring scenarios. How to adaptively represent the monitoring data and cope with different noise situations has become the research focus of the present invention. Summary of the Invention

[0006] The purpose of the embodiments of the present application is to provide a portable air quality detection system and a detection device based on LORA, which have low cost, accurate measurement, simple structure, high efficiency, and long data transmission distance. The purpose is to quickly measure the air quality within a region and provide timely feedback, solving the problems existing in the related technologies: the current detection device has high cost, fixed monitoring points, and noise in the monitoring data.

[0007] To solve the problems in the background art, the technical solution adopted in the embodiments of the present application is as follows:

[0008] In the first aspect, a method for optimizing portable air quality monitoring data based on LORA is provided, including:

[0009] Obtaining the time series of noisy gas concentration at the monitoring location;

[0010] Performing improved complete ensemble empirical mode decomposition with adaptive noise on the time series of gas concentration;

[0011] When the present invention selects the noise-dominated component and the effective signal-dominated component, it considers the characteristic differences existing in the two categories of components, and adaptively separates the noise-dominated component and the effective signal-dominated component, thereby obtaining a better separation result.

[0012] Preferably, the cross-validation method for separating the noise-dominated component and the effective signal-dominated component includes:

[0013] Check the length of the original data containing noise. If it is an odd number, add a zero at the end to make the number even, denoted as N. Divide the observation time series into odd samples and even samples. Take the odd samples as the filtering samples, and randomly sample the even samples as the identification samples. To preserve the high-frequency characteristics and global statistical characteristics of the original signal, the number of identification samples is taken as N1 = 0.1N. Perform improved complete adaptive noise ensemble empirical mode decomposition on the filtering samples to obtain N2 IMF components and a trend component. Assume that the sum of the IMF components from k (k = 1, 2, …, N2) to N2 is the denoised filtering value f’, and obtain the value f’(t2,i) at the corresponding time of the identification samples through cubic spline interpolation. Calculate the variance of the identification samples with respect to the filtering value: Calculate the mean value of the variances of W identification samples, and select the k value corresponding to the minimum average value. Since the filtering samples are half of the original data, take the IMF components from k + 1 to N2 after the decomposition of the original data as the dominant part of the effective signal of the original data.

[0014] Use the cross-identification method to distinguish the decomposed components of the signal into the dominant component of the high-frequency noise signal and the dominant component of the low-frequency effective signal. Apply the improved wavelet adaptive threshold method to the high-frequency noise components to remove high-frequency noise, and dynamically adjust the threshold according to the signal energy distribution to avoid over-smoothing or residual noise caused by the traditional fixed threshold.

[0015] Preferably, the improved wavelet adaptive threshold function includes:

[0016]

[0017] where δ is the noise variance and i is the decomposition level.

[0018] Apply singular value decomposition (SVD) to the low-frequency effective signal components to further extract the effective signal. Perform singular value decomposition on the low-frequency component matrix X to obtain the singular values and corresponding singular vectors of matrix X: Select the components with a singular value contribution rate greater than 90%, filter out the low-frequency ineffective components, and reconstruct the processed low-frequency part and high-frequency part to obtain the denoised gas concentration time series.

[0019] Through the above technical solution, generate a computer program for the data optimization method of the above-mentioned LORA-based portable air quality detection system and store it in the memory to be loaded and executed by the main control chip.

[0020] In a second aspect, provide a LORA-based portable air quality detection system, including:

[0021] Modular design, integrating an acquisition module, a LORA communication module, a power supply module, and a GPS module, and each of the modules is controlled by a main control chip.

[0022] The LORA-based portable air quality measurement system includes a main control chip, a positioning module, a collection module, a LORA module, and a power module embedded on a printed circuit board, which can conveniently perform mobile measurements on the measurement system.

[0023] In one embodiment, the collection module includes a PM1.0 sensor, a PM2.5 sensor, a PM10 sensor, a temperature and humidity sensor, and a carbon dioxide sensor. Each of the sensors includes a control main board and a sensor body mounted on the control main board.

[0024] In one embodiment, the GPS positioning module, the collection module, and the power module are respectively connected to the main control chip.

[0025] In one embodiment, the air quality detection system further includes a LORA wireless communication module for wireless communication with the external computer platform. The wireless communication module is respectively connected to the main control chip and the computer terminal, realizing wireless communication between the entire detection system and the computer.

[0026] In a third aspect, there is provided an efficient, low-cost, and portable detection device, including a housing and the air quality detection system provided in any of the above embodiments. The air quality detection system is installed in the housing.

[0027] In one embodiment, the housing is a packaged integrated housing. The device is small in size and compact in modules, facilitating portability. The collection module, the LORA module, the power module, the GPS module, and the main control chip are respectively arranged in the accommodation chamber of the housing.

[0028] The LORA-based portable air quality detection system and detection device provided by the embodiments of the present application at least have the following beneficial effects: The LORA-based portable air quality measurement system has the advantages of low cost, accurate measurement, simple structure, high efficiency, and long data transmission distance. The data optimization method can accurately remove noise information in the time series of various polluted gas concentrations in multiple scenarios. The GPS module can help locate the specific position of the pollution source, facilitating tracking the source and diffusion of pollution. The present application can be directly applied to the accurate measurement of air quality in a specific area. The measurement results can be optimized and sent to the computer in real time through the LORA module for the user to view, which can well meet people's needs for detecting the environmental quality of the surrounding area. Description of the Drawings

[0029] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments or exemplary technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is an equivalent structural schematic diagram of a LORA-based portable air quality detection system provided by an embodiment of the present application.

[0031] Among them, the main labels of each drawing in the figure are:

[0032] 1. LORA wireless communication module; 2. Main control chip; 3. Acquisition module; 4. External server platform;

[0033] 5. GPS positioning module; 6. Power module.

[0034] Figure 2 It is a flowchart of a data optimization method for a LORA-based portable air quality detection system provided by an embodiment of the present application. Detailed implementation manners

[0035] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer and more understandable, the following further details the embodiments of the present application with reference to the drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] Throughout the specification, reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the phrases "in one embodiment" or "in some embodiments" appear in various places throughout the specification. Not all references are to the same embodiment. Additionally, in one or more embodiments, the particular features, structures, or characteristics may be combined in any suitable manner.

[0037] The present invention is a portable air quality detection system and detection device based on LORA. The air quality detection system includes multiple modules, and its structure includes a main control chip, a power supply module, a LORA module, and a collection module embedded on a printed circuit board. The collection module includes a PM1.0 sensor, a PM2.5 sensor, a PM10 sensor, a temperature and humidity sensor, and a carbon dioxide sensor. The main control chip integrates a data optimization method, which includes the steps of: obtaining the gas concentration time series of the monitoring point; performing improved complete ensemble empirical mode decomposition with adaptive noise on the gas concentration time series; selecting the noise-dominated component and the effective signal-dominated component based on the cross-validation method; applying the improved wavelet adaptive threshold method to remove high-frequency noise from the high-frequency noise component, and applying singular value decomposition (SVD) to the low-frequency effective signal component to further extract the effective signal, so as to effectively remove the high and low frequency noise of the monitoring data. The main control chip is connected to the LORA module, the positioning module, and the collection module. The main control chip is used for optimizing the measured data of each sensor and receiving the position information, and sending the data to the computer terminal through the LORA module for real-time monitoring and visual display of the pollutant concentration. The device is not only small in size, can quickly and accurately measure the air quality, but also has a low cost; when performing air quality detection, the GPS module can help locate the specific position of the pollution source, facilitating the tracking of the source and diffusion of the pollution; it can meet the measurement requirements of the air quality in a specific area, avoiding the problem of a single measurement point of the fixed device.

[0038] Refer to Figure 1 , the portable air quality detection system based on LORA provided by the embodiment of the present application will be described below. The portable air quality detection system based on LORA includes four parts: a main control chip, a collection module, a power supply module, and a LORA module, and the four parts are embedded on a printed circuit board. Among them: the main control chip: used to read the measurement values of the GPS positioning module, PM1.0 sensor, PM2.5 sensor, PM10 sensor, temperature and humidity sensor, and carbon dioxide sensor, and send the measurement values to the computer through the LORA module; the positioning module: used to detect the longitude and latitude information of the system and upload it to the main control chip; the PM1.0 sensor, PM2.5 sensor, and PM10 sensor: used to measure the particulate matter concentration information of the area and upload it to the main control chip; the power supply module: used to supply power to the detection system; the LORA module: used for wireless transmission of direct data between the main control chip and the computer.

[0039] In one embodiment, as a specific implementation manner of the LORA-based portable air quality detection system provided by the embodiments of the present application, the above PM1.0 sensor, PM2.5 sensor, PM10 sensor, temperature and humidity sensor, carbon dioxide sensor are connected to the main control chip, the main control chip is connected to the LORA module, and the power supply and the positioning module are connected to the main control chip. The LORA module realizes the function of wirelessly transmitting measurement data from the detection system to a computer over a long distance.

[0040] In one embodiment, as a specific implementation manner of the LORA-based portable air quality measurement system provided by the embodiments of the present application. This system has the advantages of low cost, convenient measurement, simple structure, high efficiency, long data transmission distance, etc. It can quickly and accurately measure the air quality in a specified area and send the results to the computer terminal in real time.

[0041] In one embodiment, as a specific implementation manner of the LORA-based portable air quality detection system provided by the embodiments of the present application, the housing is of an integrated design and is fixed by a front shell and a rear shell. A compact integrated chamber is formed by enclosing between the front shell and the rear shell. The acquisition module, the LORA communication module, the power supply module and the GPS positioning module are respectively installed in this chamber. Specifically, the front shell and the rear shell can be connected by screws, buckles or other fasteners to ensure the stability and reliability of the device, and at the same time facilitate disassembly and maintenance. This structural design integrates the acquisition module, the LORA communication module, the power supply module, and the GPS positioning module in the same housing, ensuring that the device can effectively integrate multi-functional modules while maintaining the characteristics of being compact and portable, and is suitable for detection requirements in various environments. Through the reasonable layout and modular design of the housing, the device has the characteristics of convenient operation and easy portability, and can meet the requirements of efficient and flexible detection.

[0042] The embodiments of the present application disclose a data optimization method for a LORA-based portable air quality detection system, referring to Figure 2 , including Step 1 - Step 6;

[0043] 1: Obtain time series of gas concentrations of various types at the monitoring point.

[0044] Specifically, use the detection device to collect the gas concentrations of various types once every n seconds, collect N times, and arrange the collected gas concentrations of various types in chronological order to obtain the time series of gas concentrations at the monitoring point location.

[0045] 2. Perform improved complete ensemble empirical mode decomposition with adaptive noise on the time series of gas concentrations.

[0046] Using the improved complete ensemble empirical mode decomposition with adaptive noise to decompose the time series of gas concentrations can obtain several intrinsic mode function components.

[0047] 3. Apply the cross-validation method to the gas concentration time series to adaptively identify the components obtained by modal decomposition.

[0048] It should be noted that currently, it is not known how to separate the noise-dominated component from the effective signal-dominated component. Next, it is necessary to judge according to the difference characteristics between the noise and the normal signal. In the embodiments of the present application, the cross-validation method is used to judge the noise-dominated component and the effective signal-dominated component.

[0049] Preferably, as an example, constructing the cross-validation method includes:

[0050] Check the length of the original data containing noise. If it is an odd number, append zeros at the end to make the number even N; divide the observation time series into odd samples and even samples. Take the odd samples as the filtering samples, and randomly sample the even samples as the validation samples. To retain the high-frequency characteristics and global statistical characteristics of the original signal, the number of validation samples is taken as N1 = 0.1N; perform improved complete adaptive noise ensemble empirical mode decomposition on the filtering samples to obtain N2 IMF components and a trend component; assume that the sum of the IMF components from k (k = 1, 2,..., N2) to N2 is the denoised filtering value f', and obtain the value f'(t2,i) corresponding to the validation samples through cubic spline interpolation; calculate the variance of the validation samples with respect to the filtering value: Calculate the mean value of the variances of W validation samples, and select the k value corresponding to the minimum average value. Since the filtering samples are half of the original data, take the IMF components from k + 1 to N2 after the decomposition of the original data as the effective signal-dominated part of the original data.

[0051] 4. Obtain the noise-dominated component and the effective signal-dominated component by the cross-validation method.

[0052] 5. Further process the high- and low-frequency components.

[0053] Apply the improved wavelet adaptive threshold method to the noise-dominated component to remove high-frequency noise, dynamically adjust the threshold according to the signal energy distribution, and avoid over-smoothing or residual noise caused by traditional fixed thresholds. The improved wavelet adaptive threshold function includes:

[0054]

[0055] Apply singular value decomposition (SVD) to the low-frequency effective signal component to further extract the effective signal. Perform singular value decomposition on the low-frequency component matrix X to obtain the singular values and corresponding singular vectors of the matrix X: Select the components with a singular value contribution rate greater than 90%, and filter out the low-frequency invalid components.

[0056] 6. Reconstruct the processed high- and low-frequency components to obtain the gas concentration time series with noise removed.

[0057] The above embodiments describe the basic principles, main features, and advantages of the LORA-based portable air quality detection system and data optimization method provided by the embodiments of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present application. Without departing from the spirit and scope of the present application, the present application will have various changes and improvements, and these changes and improvements all fall within the scope of the present application claimed. The scope of protection required by the present application is defined by the appended claims and their equivalents.

Claims

1. A portable air quality detection system based on LORA, characterized in that, Comprising: A main control chip, a positioning module, a collection module, a LORA module, and a power supply module embedded on a printed circuit board. The collection module is used to measure the particulate matter concentration, temperature and humidity, and carbon dioxide concentration in the area; the positioning module is used to measure the location information of the system; the information of the collection module and the positioning module is wirelessly transmitted to a computer through the LORA module; the main control chip is used to optimize the data measured by each sensor and receive the location information, and transmit the data to the LORA module.

2. The data optimization method of the LORA-based portable air quality detection system according to claim 1, characterized in that Including steps: Obtain the gas concentration time series of each sensor; perform improved complete ensemble empirical mode decomposition with adaptive noise on the gas concentration time series; adaptively select decomposition components based on the cross-validation method, including: check the length of the original data containing noise, if it is an odd number, append zero at the end to make the number even N; divide the observation time series into odd samples and even samples; take the odd samples as the filtering samples, and randomly sample the even samples as the cross-validation samples. To retain the high-frequency characteristics and global statistical characteristics of the original signal, the number of cross-validation samples is taken as N1 = 0.1N; perform improved complete ensemble empirical mode decomposition with adaptive noise on the filtering samples to obtain N2 IMF components and a trend component; assume that the sum of the IMF components from k (k = 1, 2,..., N2) to N2 is the denoised filtering value f', and obtain the value f'(t2,i) at the corresponding time of the cross-validation samples through cubic spline interpolation; calculate the variance of the cross-validation samples with respect to the filtering value: Calculate the mean variance of W cross-validation samples, and select the k value corresponding to the minimum average value. Since the filtering samples are half of the original data, take the IMF components from k + 1 to N2 after the decomposition of the original data as the dominant part of the effective signal of the original data; distinguish the signal decomposition components into the dominant component of high-frequency noise signal and the dominant component of low-frequency effective signal through the cross-validation method, and apply the improved wavelet adaptive threshold method to remove high-frequency noise from the high-frequency noise components. Dynamically adjust the threshold according to the signal energy distribution to avoid over-smoothing or residual noise caused by the traditional fixed threshold. The improved wavelet adaptive threshold function is: Apply singular value decomposition (SVD) to the low-frequency effective signal components to extract the effective signal. Perform singular value decomposition on the low-frequency component matrix X to obtain the singular values and corresponding singular vectors of matrix X: Select the components with a singular value contribution rate greater than 90%, and filter out the low-frequency ineffective components, so as to effectively remove the high and low frequency noises in the monitoring data.

3. The LORA-based portable air quality detection system according to claim 1, wherein: A positioning module, a PM1.0 sensor, a PM2.5 sensor, a PM10 sensor, a temperature and humidity sensor, and a carbon dioxide sensor are connected to the main control chip, which are used to detect location information and particulate matter concentration, temperature and humidity, and carbon dioxide concentration information.

4. The LORA-based portable air quality detection system according to claim 1, characterized in that: The air quality detection system includes a LORA module for wireless communication with an external server platform. The LORA module is connected to the main control chip and is used for wireless transmission of data between the main control chip and the computer.

5. The LORA-based portable air quality detection system according to claim 1, wherein the power supply module includes a battery and power management; Detection device, characterized in that: Comprising a housing and the air quality detection system according to any one of claims 1-4, and the air quality detection system is installed in the housing.

6. The LORA-based portable air quality detection system according to claim 5, characterized in that: The housing of the detection device is a sealed integrated housing; the collection module, the LORA module, the power supply module, the GPS module, and the main control chip are respectively arranged in the accommodation chamber of the housing.

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