Ozone concentration monitoring method, device, equipment and storage medium
By acquiring historical data of the target area and performing ozone generation simulation and multi-source data fusion, an ozone prediction model was constructed, which solved the problem of low accuracy in ozone concentration prediction and achieved more accurate ozone concentration prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA WEST NORMAL UNIVERSITY
- Filing Date
- 2026-02-24
- Publication Date
- 2026-06-05
AI Technical Summary
The accuracy of ozone concentration prediction results in the existing technology is low, mainly due to measurement errors in single-source ozone concentration data.
By acquiring historical ozone concentration, meteorological, and pollutant concentration data for the target area, simulating the process using an ozone generating device, and combining multi-source data fusion and model training, an ozone prediction model is constructed to accurately predict ozone concentration.
The accuracy of ozone concentration prediction has been improved by combining multi-source data and model training.
Smart Images
Figure CN121703368B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of pollutant monitoring, and in particular to a method, apparatus, equipment and storage medium for ozone concentration monitoring. Background Technology
[0002] Air pollution is a significant public health issue affecting human health, the ecological environment, and climate change. In recent years, ozone has become the leading pollutant hindering urban air quality improvement and compliance after SO2 and PM2.5. Ozone pollution is severe in many regions both domestically and internationally, with persistently high ground-level ozone concentrations. Therefore, ozone forecasting is a crucial task that can help people avoid dangerously high ozone concentrations.
[0003] In related technologies, there are already technical concepts for using machine learning models to predict ozone concentration. However, training samples are usually constructed using single-source ozone concentration data. Single-source ozone concentration data are ground-based observation ozone concentrations collected by ground monitoring stations or satellite remote sensing ozone concentrations collected by satellites. Both of these have certain measurement errors, which leads to distortion of the final prediction results. Summary of the Invention
[0004] The main objective of this application is to provide an ozone concentration monitoring method, device, equipment, and storage medium, aiming to solve the technical problem of low accuracy in ozone concentration prediction results.
[0005] To achieve the above objectives, this application proposes a method for monitoring ozone concentration, comprising:
[0006] Acquire historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area;
[0007] Using an ozone generating device, ozone generation simulation is performed based on the historical meteorological data and historical pollutant concentration data to obtain ozone simulation data.
[0008] By fusing ozone simulation data and historical ozone concentration data, the fused ozone concentration data is calculated.
[0009] Historical meteorological data, historical pollutant concentration data, and fused ozone concentration data were used as training samples to train the ozone prediction model and obtain a well-trained ozone prediction model.
[0010] Using a trained ozone prediction model, ozone concentration is predicted based on meteorological data and pollutant concentration data of the area to be predicted, so as to obtain the ozone concentration of the target area.
[0011] In one embodiment, the historical ozone concentration data includes ground-based ozone concentrations collected by monitoring stations within the target area and satellite-based remote sensing ozone concentrations collected by satellites.
[0012] The process of fusing ozone simulation data and historical ozone concentration data to calculate the fused ozone concentration data includes:
[0013] The ozone concentration data is obtained by fusing ozone simulation data, ground-based ozone concentration observation data, and satellite remote sensing ozone concentration data.
[0014] In one embodiment, the historical ozone concentration data includes ground-based ozone concentrations collected by monitoring stations within the target area and satellite-based remote sensing ozone concentrations collected by satellites.
[0015] The process of fusing ozone simulation data and historical ozone concentration data to calculate the fused ozone concentration data includes:
[0016] The ozone concentration data is obtained by fusing ozone simulation data, ground-based ozone concentration observation data, and satellite remote sensing ozone concentration data.
[0017] In one embodiment, the step of fusing ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration to calculate the fused ozone concentration data further includes:
[0018] An initial grid is constructed, and ozone simulation data, cleaned ground-observed ozone concentration, and satellite remote sensing ozone concentration are mapped onto the initial grid.
[0019] When ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are all included at the grid points, the ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are fused together.
[0020] When both ozone simulation data and satellite remote sensing ozone concentration are included at grid points, the ozone simulation data and satellite remote sensing ozone concentration are fused.
[0021] When the grid points include both ground-based ozone concentrations and satellite-sensed ozone concentrations, the ground-based ozone concentrations and satellite-sensed ozone concentrations are fused.
[0022] In one embodiment, the process of normalizing ozone simulation data, cleaned ground-based ozone concentration, and satellite remote sensing ozone concentration, and then fusing the data to calculate the fused ozone concentration includes:
[0023] Based on the maximum-minimum normalization method, the ozone simulation data, the cleaned ground-observed ozone concentration, and the satellite remote sensing ozone concentration are normalized and then fused to calculate the fused ozone concentration.
[0024] In one embodiment, the step of interpolating the data missing areas in the target region to calculate the interpolated ozone concentration data includes:
[0025] Based on the Cresman interpolation method, interpolation is performed on the data missing areas in the target region to calculate the interpolated ozone concentration data.
[0026] In addition, to achieve the above objectives, this application also proposes a monitoring device, comprising:
[0027] The data acquisition module is used to acquire historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area.
[0028] The data simulation module is used to simulate ozone generation using the ozone generating device based on the historical meteorological data and historical pollutant concentration data, so as to obtain ozone simulation data.
[0029] The data fusion module is used to fuse ozone simulation data and historical ozone concentration data to calculate fused ozone concentration data.
[0030] The model training module is used to train the ozone prediction model using historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples, so as to obtain a well-trained ozone prediction model.
[0031] The prediction module is used to predict ozone concentration based on meteorological data and pollutant concentration data of the area to be predicted using a trained ozone prediction model, so as to obtain the ozone concentration of the target area.
[0032] The monitoring device is configured to implement the steps of the ozone concentration monitoring method described above.
[0033] In addition, to achieve the above objectives, this application also proposes a monitoring device, including: a memory, a processor, and a computer program stored in the memory and running on the processor, the computer program being configured to implement the steps of the ozone concentration monitoring method described above.
[0034] In addition, to achieve the above objectives, this application also proposes a storage medium that is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the ozone concentration monitoring method described above.
[0035] One or more technical solutions proposed in this application have at least the following technical effects:
[0036] A method for monitoring ozone concentration is proposed. This method involves acquiring historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for a target area. Using an ozone generation device, ozone generation simulation is performed based on the historical meteorological and pollutant concentration data to obtain simulated ozone data. The simulated ozone data and historical ozone concentration data are then fused to calculate a fused ozone concentration. Using the historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples, an ozone prediction model is trained to obtain a well-trained ozone prediction model. Finally, using the trained ozone prediction model, the ozone concentration is predicted based on the meteorological and pollutant concentration data of the area to be predicted, thus improving the accuracy of ozone concentration prediction. Attached Figure Description
[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is a flowchart illustrating the first embodiment of the ozone concentration monitoring method provided in this application.
[0040] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0041] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application. To better understand the technical solutions of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0042] This application provides a method for monitoring ozone concentration. In a first embodiment of the ozone concentration monitoring method of this application, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the ozone concentration monitoring method of this application. The ozone concentration monitoring method may include steps S10 to S50:
[0043] Step S10: Obtain historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area.
[0044] It should be noted that the target area can be the province, city, or county. The ozone concentration data is the sparse ozone concentration distribution collected by ozone monitoring stations and / or the global ozone concentration distribution of the target area collected by satellite. Meteorological data includes meteorological parameters such as solar radiation intensity, temperature, humidity, wind speed, and wind direction, which can be collected by meteorological stations. The pollutant concentration data is the concentration distribution of pollutants that generate ozone, collected by atmospheric component stations, such as nitrogen oxide (NOx) concentration and volatile organic compound (VOCs) concentration.
[0045] Step S20: Using an ozone generating device, ozone generation simulation is performed based on the historical meteorological data and historical pollutant concentration data to obtain ozone simulation data.
[0046] It should be noted that the ozone generating device is used to simulate ozone concentration under target meteorological data and target pollutant concentration. For example, the ozone generating device may include a reaction chamber equipped with an air conditioner, a light source, and a fan. Based on the pollutant concentration at the target time, VOCs and NOx are introduced into the reaction chamber. Based on the meteorological data at the target time, the temperature of the reaction chamber is controlled by the air conditioner, solar radiation is simulated by the light source, and the wind speed in the reaction chamber is controlled by the fan. After ozone is generated, it is introduced into the ozone monitoring equipment through the output gas path to measure the simulated ozone data at the target time.
[0047] Step S30: The ozone simulation data and historical ozone concentration data are fused together to calculate the fused ozone concentration data.
[0048] In one feasible implementation, the historical ozone concentration data includes ground-based ozone concentrations collected by monitoring stations within the target area and satellite remote sensing ozone concentrations collected by satellites. Accordingly, step S30 may include step S301:
[0049] Step S301: The ozone simulation data, ground-observed ozone concentration and satellite remote sensing ozone concentration are fused to calculate the fused ozone concentration data.
[0050] Specifically, step S301 may include steps A11 to A13;
[0051] Step A11: Clean the satellite remote sensing ozone concentration and the ground observation ozone concentration, and calculate the cleaned satellite remote sensing ozone concentration and the ground observation ozone concentration.
[0052] It should be noted that monitoring station equipment may experience drift and malfunctions, leading to distortion of ground-based ozone concentration observations. Satellite ozone concentrations may also be missing or distorted due to issues such as cloud thickness, light scattering, and transmission failures. Therefore, anomaly detection algorithms can be used to clean both satellite-sensed and ground-based ozone concentrations to remove excessively distorted data. It is worth noting that the cleaned satellite-sensed and ground-based ozone concentrations may lack local ozone concentration information for the target area.
[0053] Step A12 involves normalizing the ozone simulation data, the cleaned ground-based ozone concentration, and the satellite remote sensing ozone concentration, then fusing the data to calculate the fused ozone concentration.
[0054] Preferably, before fusion, the multi-source data is preprocessed to ensure spatiotemporal alignment. Then, based on the maximum-minimum normalization method, the ozone simulation data, the cleaned ground-based ozone concentration, and the satellite remote sensing ozone concentration are normalized before data fusion, and the fused ozone concentration is calculated using the following formula:
[0055]
[0056] In the formula, x i This represents the i-th data. max ( x j () represents the maximum value in the corresponding data sequence. min ( x j () represents the minimum value in the corresponding data sequence. y i This represents the normalized value of the i-th data point.
[0057] In one feasible implementation, step S301 further includes:
[0058] An initial grid is constructed, and ozone simulation data, ground-based ozone concentrations after cleaning, and satellite remote sensing ozone concentrations are mapped onto the initial grid.
[0059] Accordingly, step A12 may include:
[0060] When ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are all included at the grid points, the ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are fused together.
[0061] When both ozone simulation data and satellite remote sensing ozone concentration are included at grid points, the ozone simulation data and satellite remote sensing ozone concentration are fused.
[0062] When the grid points include both ground-based ozone concentrations and satellite-sensed ozone concentrations, the ground-based ozone concentrations and satellite-sensed ozone concentrations are fused.
[0063] Step A13: Perform interpolation on the data missing areas in the target region to calculate the interpolated ozone concentration data.
[0064] It should be noted that, since the data was cleaned in step A11, some ozone concentrations in the target area were missing. In this step, based on the fused data of the target area, the ozone concentrations in the missing data areas were interpolated to obtain the interpolated ozone concentration data, which is the complete ozone concentration distribution of the target area.
[0065] Specifically, based on the Cresman interpolation method, interpolation is performed on the data missing areas in the target region to calculate the interpolated ozone concentration data. The interpolation formula is as follows:
[0066]
[0067] In the formula, α0 is the first guess of the variable α at the grid point (i, j). α ij It is the difference between the observed value at observation point k and the first guess.
[0068] Step S40: Use historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples to train the ozone prediction model to obtain a trained ozone prediction model.
[0069] It should be noted that ozone prediction models can be built based on neural networks, random forests, or vector machines.
[0070] For example, the ozone prediction model is built based on the random forest algorithm. By constructing multiple decision trees, it reduces the overfitting of individual models and improves the generalization ability of the model. Specifically, it uses a bootstrap random sampling method with replacement to ensure that the training data for each decision tree is different. When constructing a decision tree each time, it randomly samples some features and selects the optimal features from them to build the decision tree. This reduces the dependence of a single decision tree on specific features, thereby ensuring the diversity and robustness of the model. In this way, each decision tree constructed by the random forest is independent and different, and can still maintain its expected performance or function when facing uncertainty, disturbances, or changes.
[0071] Step S50: Using the trained ozone prediction model, the ozone concentration is predicted based on meteorological data and pollutant concentration data of the area to be predicted, so as to obtain the ozone concentration of the target area.
[0072] It should be noted that the meteorological data for the area to be predicted can refer to real-time observation data or forecast data, such as the meteorological forecast data of the area to be predicted for the next week from the meteorological bureau. Similarly, pollutant concentration data can be real-time observation data or forecast data.
[0073] In one feasible implementation, after predicting the ozone concentration distribution in the target area at the target time, the ozone concentration model can be iteratively optimized based on actual multi-source ozone concentration data.
[0074] This embodiment provides an ozone concentration monitoring method. It acquires historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for a target area. Using an ozone generation device, it simulates ozone generation based on the historical meteorological and pollutant concentration data to obtain simulated ozone data. The simulated ozone data and historical ozone concentration data are then fused to calculate a fused ozone concentration. Using the historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples, an ozone prediction model is trained to obtain a trained ozone prediction model. Finally, using the trained ozone prediction model, the ozone concentration is predicted based on real-time meteorological and pollutant concentration data for the target area, thereby improving the accuracy of ozone concentration prediction.
[0075] This application also provides a monitoring device, which may include:
[0076] The data acquisition module is used to acquire historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area.
[0077] The data simulation module is used to simulate ozone generation using the ozone generating device based on the historical meteorological data and historical pollutant concentration data, so as to obtain ozone simulation data.
[0078] The data fusion module is used to fuse ozone simulation data and historical ozone concentration data to calculate fused ozone concentration data.
[0079] The model training module is used to train the ozone prediction model using historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples, so as to obtain a well-trained ozone prediction model.
[0080] The prediction module is used to predict ozone concentration based on meteorological data and pollutant concentration data of the area to be predicted using a trained ozone prediction model, so as to obtain the ozone concentration of the target area.
[0081] The monitoring device is configured to implement the steps of the ozone concentration monitoring method described above.
[0082] The monitoring device provided in this application, employing the ozone concentration monitoring method described in the above embodiments, can solve the main technical problems. Compared with related technologies, the beneficial effects of the monitoring device provided in this application are the same as those of the ozone concentration monitoring method provided in the above embodiments, and other technical features of the monitoring device are the same as those disclosed in the ozone concentration monitoring method of the above embodiments, and will not be repeated here.
[0083] This application also provides a monitoring device, which may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the ozone concentration monitoring method in the above embodiments.
[0084] The monitoring device provided in this application, employing the ozone concentration monitoring method described in the above embodiments, can solve the main technical problems. Compared with related technologies, the beneficial effects of the monitoring device provided in this application are the same as those of the ozone concentration monitoring method provided in the above embodiments, and other technical features of the monitoring device are the same as those disclosed in the ozone concentration monitoring method of the above embodiments, and will not be repeated here.
[0085] This application also provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the ozone concentration monitoring method in the above embodiments.
[0086] The storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described ozone concentration monitoring method, thereby solving the main technical problem. Compared with related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the ozone concentration monitoring method provided in the above embodiments, and will not be repeated here.
[0087] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A method for monitoring ozone concentration, characterized in that, The ozone concentration monitoring method includes: Historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area are acquired. The historical ozone concentration data includes ground-based ozone concentrations collected by monitoring stations within the target area and satellite remote sensing ozone concentrations collected by satellites. Using an ozone generating device, ozone generation simulation is performed based on historical meteorological data and historical pollutant concentration data to obtain ozone simulation data. The ozone generating device includes a reaction chamber equipped with an air conditioner, a light source, and a fan. The ozone generating device is used to introduce VOCs and NOx into the reaction chamber based on the pollutant concentration at a target time. Based on the meteorological data at the target time, the temperature of the reaction chamber is controlled by the air conditioner, solar radiation is simulated by the light source, and the wind speed in the reaction chamber is controlled by the fan. After ozone is generated, it is introduced into an ozone monitoring device through an output gas path to measure the ozone simulation data at the target time. By fusing ozone simulation data and historical ozone concentration data, the fused ozone concentration data is calculated, including: The ozone concentration data is obtained by fusing ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration. Historical meteorological data, historical pollutant concentration data, and fused ozone concentration data were used as training samples to train the ozone prediction model and obtain a well-trained ozone prediction model. Using a trained ozone prediction model, ozone concentration is predicted based on meteorological data and pollutant concentration data of the area to be predicted, so as to obtain the ozone concentration of the target area.
2. The ozone concentration monitoring method as described in claim 1, characterized in that, The process of fusing ozone simulation data, ground-observed ozone concentrations, and satellite remote sensing ozone concentrations to calculate the fused ozone concentration data includes: Data cleaning was performed on the ozone concentrations from satellite remote sensing and ground observation, and the cleaned ozone concentrations from satellite remote sensing and ground observation were calculated. The ozone simulation data, the cleaned ground-observed ozone concentration, and the satellite remote sensing ozone concentration were normalized and then fused together to calculate the fused ozone concentration. Interpolation is performed on the data missing areas in the target region to calculate the interpolated ozone concentration data.
3. The ozone concentration monitoring method as described in claim 2, characterized in that, The process of fusing ozone simulation data, ground-observed ozone concentrations, and satellite remote sensing ozone concentrations to calculate the fused ozone concentration data also includes: An initial grid is constructed, and ozone simulation data, cleaned ground-observed ozone concentration, and satellite remote sensing ozone concentration are mapped onto the initial grid. When ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are all included at the grid points, the ozone simulation data, ground-observed ozone concentration, and satellite remote sensing ozone concentration are fused together. When both ozone simulation data and satellite remote sensing ozone concentration are included at grid points, the ozone simulation data and satellite remote sensing ozone concentration are fused. When the grid points include both ground-based ozone concentrations and satellite-sensed ozone concentrations, the ground-based ozone concentrations and satellite-sensed ozone concentrations are fused.
4. The ozone concentration monitoring method as described in claim 2, characterized in that, The process involves normalizing ozone simulation data, cleaned ground-based ozone concentration, and satellite remote sensing ozone concentration, then fusing the data to calculate the fused ozone concentration, which includes: Based on the maximum-minimum normalization method, the ozone simulation data, the cleaned ground-observed ozone concentration, and the satellite remote sensing ozone concentration are normalized and then fused to calculate the fused ozone concentration.
5. The ozone concentration monitoring method as described in claim 2, characterized in that, The step of interpolating the data missing areas in the target region to calculate the interpolated ozone concentration data includes: Based on the Cresman interpolation method, interpolation is performed on the data missing areas in the target region to calculate the interpolated ozone concentration data.
6. A monitoring device, characterized in that, The monitoring device includes: The data acquisition module is used to acquire historical ozone concentration data, historical meteorological data, and historical pollutant concentration data for the target area. The data simulation module is used to simulate ozone generation using the ozone generating device based on the historical meteorological data and historical pollutant concentration data, so as to obtain ozone simulation data. The data fusion module is used to fuse ozone simulation data and historical ozone concentration data to calculate fused ozone concentration data. The model training module is used to train the ozone prediction model using historical meteorological data, historical pollutant concentration data, and fused ozone concentration data as training samples, so as to obtain a well-trained ozone prediction model. The prediction module is used to predict ozone concentration based on meteorological data and pollutant concentration data of the area to be predicted using a trained ozone prediction model, so as to obtain the ozone concentration of the target area. The monitoring device is configured to implement the steps of the ozone concentration monitoring method as described in any one of claims 1 to 5.
7. A monitoring device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and running on the processor, the computer program being configured to implement the steps of the ozone concentration monitoring method as described in any one of claims 1 to 5.
8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the ozone concentration monitoring method as described in any one of claims 1 to 5.