Self-calibration method of air quality micro monitoring station and related device

Abstract

The invention discloses a self-calibration method for an air quality micro monitoring station, the air quality micro monitoring station, environment monitoring equipment and a computer readable storage medium, and the method comprises the steps: carrying out the data cleaning of obtained historical data through the air quality micro monitoring station based on historical environment parameters and sensor use condition data; obtaining normal operation data and deviation operation data; performing classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model; based on the time point of obtaining the current monitoring data and the classification detection model, performing classification detection processing on the current monitoring data to obtain a sensor classification result; when the sensor classification result is deviation operation data, parameter calibration processing is carried out based on current environment parameters and sensor aging condition data; and when the calibration is completed, collecting the monitoring data again. Automatic calibration of the monitoring station is realized, operators are prevented from going to the field for calibration, and the calibration efficiency of the monitoring station is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and more specifically, relates to a self-calibration method of an air quality micro-monitoring station, an air quality micro-monitoring station, an environmental monitoring device, and a computer-readable storage medium. Background Art

[0002] With the continuous development of information technology, electronic information technology has been widely used in the field of environmental monitoring. Among them, the air quality micro-monitoring station is a miniaturized, low-cost monitoring device designed for real-time air quality monitoring in urban environments, industrial areas, schools, residential areas and other locations. These monitoring stations usually include a variety of sensors that can detect particulate matter in the air (such as PM2.5 and PM10), gaseous pollutants (such as NOx, SO2, CO, O3) and other environmental parameters (such as temperature, humidity, air pressure).

[0003] In the related art, after the air quality micro-monitoring station has been in operation for a period of time, there will be problems with inaccurate detection. Generally, the staff manually calibrates the detection data of the air quality micro-monitoring station to achieve the calibration of the air quality micro-monitoring station. However, manual calibration will waste labor costs and the calibration efficiency is not high.

[0004] Therefore, how to improve the calibration efficiency of air quality micro-monitoring stations is a key issue that technicians in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide a self-calibration method for an air quality micro-monitoring station, an air quality micro-monitoring station, an environmental monitoring device, and a computer-readable storage medium, so as to realize an automatic calibration of a monitoring station, avoid operators going to the site for calibration, and improve the calibration efficiency of the monitoring station. It also avoids calibration errors caused by subjective factors and improves the accuracy of calibration.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a self-calibration method of an air quality micro-monitoring station, which is applied to the air quality micro-monitoring station, comprising:

[0007] The air quality micro-monitoring station cleans the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data;

[0008] Perform classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model;

[0009] Based on the time point of acquiring the current monitoring data and the classification detection model, the current monitoring data is subjected to classification detection processing to obtain a sensor classification result;

[0010] When the sensor classification result is deviation operation data, parameter calibration processing is performed based on current environmental parameters and sensor aging data; wherein the current environmental parameters include temperature, humidity, and air pressure;

[0011] When calibration is complete, re-collect monitoring data.

[0012] Optionally, parameter calibration is performed based on current environmental parameters and sensor aging data, including:

[0013] The air quality micro-monitoring station performs monitoring in a pollution-free environment to obtain corresponding zero-point response data;

[0014] Calculating deviation data between the zero-point response data and the zero-point standard data;

[0015] The sensor is calibrated based on a compensation algorithm, the current environmental parameters, the sensor aging data, and the deviation data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

[0016] Optionally, parameter calibration is performed based on current environmental parameters and sensor aging data, including:

[0017] The air quality micro-monitoring station is used to monitor in a known concentration environment to obtain the response range and linearity of the sensor;

[0018] When the linearity is less than a preset linearity, calculating deviation range data between the response range and known concentration data;

[0019] The sensor is calibrated based on a compensation algorithm, the current environmental parameters, the sensor aging data, and the deviation range data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

[0020] Optionally, also include:

[0021] Obtain environmental change data according to the preset cycle;

[0022] Calculate sensor aging data;

[0023] The parameters of the sensor are compensated and adjusted based on the environmental change data and the sensor aging condition data.

[0024] Optionally, performing classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model includes:

[0025] The initial linear regression model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

[0026] Optionally, performing classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model includes:

[0027] The initial polynomial regression model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

[0028] Optionally, performing classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model includes:

[0029] The initial decision tree model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

[0030] The present application also provides an air quality micro monitoring station, comprising:

[0031] A data cleaning module is used to clean the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data;

[0032] A model training module, used to perform classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model;

[0033] A classification detection module, used to perform classification detection processing on the current monitoring data based on the time point of acquiring the current monitoring data and the classification detection model to obtain a sensor classification result;

[0034] A parameter calibration module, used for performing parameter calibration processing based on current environmental parameters and sensor aging data when the sensor classification result is deviation operation data; wherein the current environmental parameters include temperature, humidity, and air pressure;

[0035] The data acquisition module is used to re-collect monitoring data when the calibration is completed.

[0036] The present application also provides an environmental monitoring device, comprising:

[0037] Memory for storing computer programs;

[0038] A processor is used to implement the steps of the self-calibration method described above when executing the computer program.

[0039] The present application also provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the self-calibration method described above are implemented.

[0040] A self-calibration method for an air quality micro-monitoring station provided in the present application is applied to the air quality micro-monitoring station, comprising: the air quality micro-monitoring station performs data cleaning on the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operating data and deviation operating data; performs classification model training based on the normal operating data and the deviation operating data to obtain a classification detection model; performs classification detection processing on the current monitoring data based on the time point at which the current monitoring data is acquired and the classification detection model to obtain a sensor classification result; when the sensor classification result is deviation operating data, performs parameter calibration processing based on the current environmental parameters and sensor aging data; wherein the current environmental parameters include temperature, humidity, and air pressure; when the calibration is completed, re-collects the monitoring data.

[0041] It has the following beneficial effects:

[0042] Improve the efficiency of calibration of the testing station: avoid manual calibration by operators, thus improving the efficiency of calibration.

[0043] Improve the reliability of monitoring stations: Through the self-calibration method, the monitoring station can timely identify and calibrate the deviation data, thereby improving the accuracy of air quality monitoring data.

[0044] Enhanced system stability: Regular automatic calibration and parameter adjustments help maintain the long-term stability of the monitoring station and reduce performance degradation due to sensor aging or environmental changes.

[0045] Extend the service life of the sensor: By compensating for sensor aging through compensation algorithms, the service life of the sensor can be extended, the replacement frequency can be reduced, and the maintenance cost can be reduced.

[0046] Improve the adaptive capability of monitoring stations: The monitoring stations can automatically adjust parameters according to current environmental parameters and sensor aging, enhancing their adaptive capability to environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1A flowchart of a self-calibration method for an air quality micro-monitoring station provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the structure of an air quality micro monitoring station provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of an environmental monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The core of this application is to provide a self-calibration method of an air quality micro-monitoring station, an air quality micro-monitoring station, an environmental monitoring device, and a computer-readable storage medium to realize an automatic calibration of a monitoring station, avoid operators going to the site for calibration, and improve the calibration efficiency of the monitoring station. It also avoids calibration errors caused by subjective factors and improves the accuracy of calibration.

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0053] The self-calibration method of the air quality micro-monitoring station provided in the present application is described below through an embodiment.

[0054] Please refer to Figure 1 , Figure 1 A flow chart of a self-calibration method for an air quality micro-monitoring station provided in an embodiment of the present application.

[0055] In this embodiment, the method may include:

[0056] S101, the air quality micro-monitoring station cleans the acquired raw data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data;

[0057] This step aims to clean the acquired raw data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data. It can be seen that this step mainly cleans the acquired historical data and filters out inappropriate and large deviation data. At the same time, it is necessary to mark the data with deviations in the historical data, and finally obtain normal operation data and deviation operation data.

[0058] S102, performing classification model training based on normal operation data and deviation operation data to obtain a classification detection model;

[0059] Based on S101, this step aims to train the classification model based on the normal operation data and the deviation operation data to obtain the classification detection model. It can be seen that this step extracts the features of the normal operation data and the deviation operation data and then trains the classification model to obtain the corresponding classification detection model.

[0060] Among them, the classification detection model can classify the acquired data, and then determine whether the data is normal operation data or deviation operation data.

[0061] Furthermore, this step may include:

[0062] The initial linear regression model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

[0063] Among them, the linear regression model is a mathematical regression model that determines the correlation between variables. The linear regression model can be used to effectively classify the obtained data and accurately determine whether there is a problem with the data.

[0064] Furthermore, this step may include:

[0065] The initial polynomial regression model is trained based on the normal operation data and the deviation operation data to obtain a classification detection model.

[0066] Among them, the polynomial regression model is a kind of linear regression model, and the regression function is linear with respect to the regression coefficient. It can be seen that the monitoring data obtained can be accurately and effectively classified through the polynomial regression model.

[0067] Furthermore, this step may include:

[0068] The initial decision tree model is trained based on the normal operation data and the deviation operation data to obtain a classification detection model.

[0069] Among them, the decision tree model is a simple and easy-to-use non-parametric classifier. It does not require any prior assumptions about the data, has a fast calculation speed, is easy to interpret, and is robust. It can be seen that the decision tree model can effectively classify data and avoid the problem of misclassification.

[0070] S103, performing classification detection processing on the current monitoring data based on the time point of acquiring the current monitoring data and the classification detection model to obtain a sensor classification result;

[0071] Based on S102, this step aims to classify and detect the current monitoring data based on the time point of obtaining the current monitoring data and the classification detection model to obtain the sensor classification result. It can be seen that this step mainly explains the classification and judgment of the current monitoring data in order to identify it as normal operation data or deviation operation data.

[0072] Among them, an intelligent classification detection model is used to infer the current monitoring data, which can effectively determine whether there is a problem. At the same time, it also avoids manual judgment of the operating status of the sensor, improving the effectiveness of problem judgment.

[0073] S104, when the sensor classification result is deviation operation data, parameter calibration processing is performed based on current environmental parameters and sensor aging data; wherein the current environmental parameters include temperature, humidity, and air pressure;

[0074] Based on S103, this step aims to perform parameter calibration based on current environmental parameters and sensor aging data when the sensor classification result is deviation operation data; the current environmental parameters include temperature, humidity, and air pressure. In other words, when deviation operation data appears, calibration operation is automatically performed.

[0075] Among them, due to the differences in temperature, humidity, and air pressure, the current environmental parameters can also be obtained during the calibration process for calibration, so as to compensate for the sensor calibration process. Similarly, the aging of the sensor will also affect the calibration process. Therefore, corresponding compensation operations should also be performed according to the sensor aging data.

[0076] Furthermore, this step may include:

[0077] Step 1: The air quality micro-monitoring station performs monitoring in a pollution-free environment to obtain corresponding zero-point response data;

[0078] Step 2, calculating the deviation data between the zero-point response data and the zero-point standard data;

[0079] Step 3, calibrating the sensor parameters based on the compensation algorithm, current environmental parameters, sensor aging data, and deviation data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

[0080] It can be seen that in this optional solution, the calibration is mainly performed in a pollution-free environment. Among them, the pollution-free environment can be obtained by performing air evolution through an air quality micro-monitoring station and constructing a closed space. Then, the zero point calibration operation is performed in the pollution-free environment.

[0081] Furthermore, this step may include:

[0082] Step 1: The air quality micro-monitoring station is used to monitor in an environment with known concentration to obtain the response range and linearity of the sensor;

[0083] Step 2, when the linearity is less than the preset linearity, calculating the deviation range data between the response range and the known concentration data;

[0084] Step 3, calibrating the sensor parameters based on the compensation algorithm, current environmental parameters, sensor aging data, and deviation range data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

[0085] It can be seen that in this optional solution, the calibration is mainly performed in a known concentration environment. The known concentration environment can be obtained by releasing the preset pollution source in the air quality micro-monitoring station in a confined space without pollution. Then, the calibration operation is performed in the known concentration environment.

[0086] S105, when the calibration is completed, re-collect monitoring data.

[0087] Based on S104, this step aims to re-collect monitoring data when the calibration is completed. It can be seen that in this optional solution, data can be re-collected after the calibration operation is completed. At the same time, the newly collected data is classified and judged to determine the effect of the calibration.

[0088] Furthermore, this embodiment may also include:

[0089] Step 1, obtaining environmental change data according to a preset period;

[0090] Step 2, calculating sensor aging data;

[0091] Step 3: Compensate and adjust the sensor parameters based on the environmental change data and the sensor aging data.

[0092] It can be seen that this optional solution mainly explains that in daily work, the parameters of the sensor can be compensated and adjusted through environmental change data and aging data so that the sensor operates in the best state. Among them, the environmental change data can be temperature change data, humidity change data, and air pressure change data.

[0093] In summary, this embodiment has the following beneficial effects:

[0094] Improve the efficiency of calibration of the testing station: avoid manual calibration by operators, thus improving the efficiency of calibration.

[0095] Improve the reliability of monitoring stations: Through the self-calibration method, the monitoring station can timely identify and calibrate the deviation data, thereby improving the accuracy of air quality monitoring data.

[0096] Enhanced system stability: Regular automatic calibration and parameter adjustments help maintain the long-term stability of the monitoring station and reduce performance degradation due to sensor aging or environmental changes.

[0097] Extend the service life of the sensor: By compensating for sensor aging through compensation algorithms, the service life of the sensor can be extended, the replacement frequency can be reduced, and the maintenance cost can be reduced.

[0098] Improve the adaptive capability of monitoring stations: The monitoring stations can automatically adjust parameters according to current environmental parameters and sensor aging, enhancing their adaptive capability to environmental changes.

[0099] The self-calibration method of an air quality micro-monitoring station provided by the present application is further described below through another specific embodiment.

[0100] Preparations before implementation may include the following:

[0101] Monitoring station configuration: Ensure that the hardware and software configuration of the air quality micro-monitoring station is complete, including sensors, data acquisition systems and communication modules.

[0102] Data collection: Collect historical environmental parameters and sensor usage data for subsequent data cleaning and model training.

[0103] Environmental parameter monitoring: equipment prepared to monitor current environmental parameters (temperature, humidity, air pressure).

[0104] Zero point and standard data preparation: Determine the zero point standard data and standard data of the known concentration environment for subsequent calibration processing.

[0105] Model selection: Select an appropriate classification model, such as linear regression, polynomial regression, or decision tree model, based on the specific needs of the monitoring station.

[0106] Compensation algorithm development: Develop temperature compensation algorithm and sensor aging compensation algorithm for parameter calibration processing.

[0107] Preset cycle setting: Determine the preset cycle for automatic adjustment of sensor parameters.

[0108] On the basis of having made preparations for implementation, the implementation process may include:

[0109] Step 1, data cleaning: Collect the raw monitoring data from the air quality micro-monitoring station. Clean the raw data based on historical environmental parameters and sensor usage data to identify normal operation data and deviation operation data.

[0110] Step 2, classification model training: Use the normal operation data and deviation operation data obtained in step 1 to train a classification model, which can be a linear regression model, a polynomial regression model, or a decision tree model, depending on the actual use case, which is not limited here.

[0111] Step 3: Classification detection processing: Use the classification detection model to classify the current monitoring data. According to the time point of the current monitoring data, the classification result of the sensor is obtained.

[0112] Step 4, deviation operation data processing: If the sensor classification result is deviation operation data, monitor in a pollution-free environment and obtain zero-point response data. Calculate the deviation data between the zero-point response data and the zero-point standard data. Apply the compensation algorithm (including temperature compensation algorithm and sensor aging compensation algorithm) to calibrate the sensor parameters. Or step 5, response range and linearity detection: Monitor in a known concentration environment to obtain the response range and linearity of the sensor. If the linearity is less than the preset linearity, calculate the deviation range data between the response range and the known concentration data. Perform parameter calibration processing on the sensor based on the compensation algorithm, current environmental parameters (temperature, humidity, air pressure), sensor aging data and deviation range data.

[0113] Step 6: Periodic automatic adjustment: According to a preset period, obtain environmental change data and calculate sensor aging data. Automatically adjust sensor parameters based on the environmental change data and sensor aging data.

[0114] Step 7, re-collect monitoring data: After calibration is completed, re-collect monitoring data to verify the calibration effect.

[0115] Step 8, Continuous Monitoring and Maintenance: Continuously monitor the performance of the monitoring station to ensure data accuracy. Perform regular maintenance and calibration to ensure long-term stable operation of the monitoring station.

[0116] When the implementation is complete, it may also include:

[0117] Calibration effect evaluation: Analyze the re-collected monitoring data and evaluate the calibration effect.

[0118] Data accuracy verification: Verify whether the calibrated monitoring data accurately reflects the environmental quality.

[0119] Performance stability test: Test the stability and reliability of the monitoring station over a period of time.

[0120] Maintenance and optimization: Based on the implementation results, carry out necessary maintenance and optimization to improve the performance of the monitoring station.

[0121] Reporting and Records: Prepare implementation reports to record the implementation process, results and any issues found.

[0122] User training and instruction: If necessary, provide training to operators of the monitoring stations to ensure they can understand and perform the self-calibration process.

[0123] Follow-up monitoring: Continuously monitor the performance of the monitoring station to ensure long-term stable operation.

[0124] The air quality micro monitoring station provided in the embodiment of the present application is introduced below. The air quality micro monitoring station and the self-calibration method of the air quality micro monitoring station described below can be referenced to each other.

[0125] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of an air quality micro monitoring station provided in an embodiment of the present application.

[0126] In this embodiment, the monitoring station may include:

[0127] A data cleaning module 100 is used to clean the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data;

[0128] A model training module 200 is used to perform classification model training based on normal operation data and deviation operation data to obtain a classification detection model;

[0129] The classification detection module 300 is used to perform classification detection processing on the current monitoring data based on the time point of acquiring the current monitoring data and the classification detection model to obtain the sensor classification result;

[0130] The parameter calibration module 400 is used to perform parameter calibration processing based on current environmental parameters and sensor aging data when the sensor classification result is deviation operation data; wherein the current environmental parameters include temperature, humidity, and air pressure;

[0131] The data collection module 500 is used to re-collect monitoring data when the calibration is completed.

[0132] This application also provides environmental monitoring equipment, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of an environmental monitoring device provided in an embodiment of the present application, the environmental monitoring device may include:

[0133] Memory for storing computer programs;

[0134] The processor is used to implement the steps of any of the above-mentioned self-calibration methods for the air quality micro-monitoring station when executing the computer program.

[0135] like Figure 3, which is a schematic diagram of the composition structure of an environment monitoring device, the environment monitoring device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 all communicate with each other through the communication bus 13.

[0136] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0137] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0138] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0139] The air quality micro-monitoring station cleans the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data;

[0140] Classification model training is performed based on normal operation data and deviation operation data to obtain a classification detection model;

[0141] Based on the time point of acquiring the current monitoring data and the classification detection model, the current monitoring data is classified and detected to obtain the sensor classification result;

[0142] When the sensor classification result is deviation operation data, parameter calibration processing is performed based on the current environmental parameters and sensor aging data; the current environmental parameters include temperature, humidity, and air pressure;

[0143] When calibration is complete, re-collect monitoring data.

[0144] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0145] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0146] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0147] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the environmental monitoring device in the embodiment of the present application. In actual applications, the environmental monitoring device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0148] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the self-calibration method of any of the above-mentioned air quality micro-monitoring stations can be implemented.

[0149] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0150] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0151] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0152] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0153] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0154] The above is a detailed introduction to the self-calibration method of an air quality micro-monitoring station, an air quality micro-monitoring station, an environmental monitoring device, and a computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A self-calibration method for an air quality micro-monitoring station, applied to an air quality micro-monitoring station, characterized in that: include: The air quality micro-monitoring station cleans the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data; Perform classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model; Based on the time point of acquiring the current monitoring data and the classification detection model, the current monitoring data is subjected to classification detection processing to obtain a sensor classification result; When the sensor classification result is deviation operation data, parameter calibration processing is performed based on current environmental parameters and sensor aging data; wherein the current environmental parameters include temperature, humidity, and air pressure; When calibration is complete, re-collect monitoring data.

2. The self-calibration method according to claim 1, characterized in that: Parameter calibration is performed based on current environmental parameters and sensor aging data, including: The air quality micro-monitoring station performs monitoring in a pollution-free environment to obtain corresponding zero-point response data; Calculating deviation data between the zero-point response data and the zero-point standard data; The sensor is calibrated based on a compensation algorithm, the current environmental parameters, the sensor aging data, and the deviation data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

3. The self-calibration method according to claim 1, characterized in that: Parameter calibration is performed based on current environmental parameters and sensor aging data, including: The air quality micro-monitoring station is used to monitor in a known concentration environment to obtain the response range and linearity of the sensor; When the linearity is less than a preset linearity, calculating deviation range data between the response range and known concentration data; The sensor is calibrated based on a compensation algorithm, the current environmental parameters, the sensor aging data, and the deviation range data; wherein the compensation algorithm includes a temperature compensation algorithm and a sensor aging compensation algorithm.

4. The self-calibration method according to any one of claims 1 to 3, characterized in that: Also includes: Obtain environmental change data according to the preset cycle; Calculate sensor aging data; The parameters of the sensor are compensated and adjusted based on the environmental change data and the sensor aging condition data.

5. The self-calibration method according to claim 4, characterized in that: The classification model is trained based on the normal operation data and the deviation operation data to obtain a classification detection model, including: The initial linear regression model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

6. The self-calibration method according to claim 4, characterized in that: The classification model is trained based on the normal operation data and the deviation operation data to obtain a classification detection model, including: The initial polynomial regression model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

7. The self-calibration method according to claim 4, characterized in that: The classification model is trained based on the normal operation data and the deviation operation data to obtain a classification detection model, including: The initial decision tree model is trained based on the normal operation data and the deviation operation data to obtain the classification detection model.

8. An air quality micro monitoring station, characterized in that: include: A data cleaning module is used to clean the acquired historical data based on historical environmental parameters and sensor usage data to obtain normal operation data and deviation operation data; A model training module, used to perform classification model training based on the normal operation data and the deviation operation data to obtain a classification detection model; A classification detection module, used to perform classification detection processing on the current monitoring data based on the time point of acquiring the current monitoring data and the classification detection model to obtain a sensor classification result; A parameter calibration module, used for performing parameter calibration processing based on current environmental parameters and sensor aging data when the sensor classification result is deviation operation data; wherein the current environmental parameters include temperature, humidity, and air pressure; The data acquisition module is used to re-collect monitoring data when the calibration is completed.

9. An environmental monitoring device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the self-calibration method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the self-calibration method according to any one of claims 1 to 7 are implemented.

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