Urban air quality estimation optimization method and system based on mobile station operation status

Abstract

The present invention discloses a method and system for optimizing urban air quality estimation based on the operation status of mobile stations. The method divides a target monitoring area into grids and queries the distribution of monitoring stations in each grid in the target area. If a fixed station exists in the grid, the data of the fixed station within the acquisition frequency length is used as the air quality monitoring value of the grid at the current time. If there is no fixed station in the grid, the real-time monitoring data of all mobile stations passing through the grid within the current acquisition frequency length is obtained. The calibration compensation value corresponding to each atmospheric monitoring value of each mobile station is called to calibrate the real-time monitoring data of each mobile station respectively to obtain parameter calibration data, and the average value of the parameter calibration data of all mobile stations within the acquisition frequency length is used as the air quality monitoring value of the grid. The mobile station data is calibrated by combining the fixed station and grid data to improve the accuracy of the mobile station monitoring value, thereby improving the accuracy of the description of the air quality in the target area.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality monitoring, and in particular to a method and system for optimizing urban air quality estimation based on the operating status of a mobile station. Background Art

[0002] Traditional air pollution source monitoring generally uses standard fixed station monitoring or fixed micro-station monitoring. However, both have their own advantages and disadvantages. Not only is the cost of a single device too high, but it cannot be installed on a large scale in urban areas due to cost constraints, resulting in the inability to achieve comprehensive domain monitoring in urban space, and the inability to achieve ideal and refined monitoring from the pollution source monitoring level. With the construction of urbanization, the flexibility of vehicle-mounted mobile equipment can be used to build a new mobile air monitoring network, clarify the cause and severity of pollution sources, enrich and refine urban grid management skills based on the original fixed station data management, and provide a big data foundation for the efficient improvement of urban grid supervision.

[0003] Air quality monitoring microstation (mobile station for short) is a new type of environmental monitoring method. Compared with traditional atmospheric stations or small stations, it has the characteristics of flexible deployment, low cost, wide monitoring range and fast frequency update. However, the monitoring data of mobile stations may be inaccurate due to various reasons such as equipment or transmission, so further optimization and adjustment are needed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing urban air quality estimation based on the operating status of mobile stations. For grids without fixed stations, the static monitoring data of all mobile stations passing through the grid are matched, and the static monitoring data are calibrated to improve the accuracy of the mobile station monitoring values, thereby improving the accuracy of the characterization of the air quality in the target area.

[0005] To achieve the above objectives, this application adopts the following scheme:

[0006] On the one hand, the present invention provides a method for optimizing urban air quality estimation based on the operating status of a mobile station, which specifically includes the following steps:

[0007] S1. Divide the target monitoring area into grids and query the distribution of monitoring stations in each grid within the target area;

[0008] S2. If there is a fixed station in the grid, the data within the collection frequency length of the fixed station is used as the air quality monitoring value of the grid at the current time;

[0009] S3. If there is no fixed station in the grid, obtain real-time monitoring data of all mobile stations passing through the grid within the current acquisition frequency length;

[0010] S4. Call the calibration compensation value corresponding to each atmospheric monitoring value of each mobile station to calibrate the real-time monitoring data of each mobile station to obtain parameter calibration data, and take the average value of the parameter calibration data of all mobile stations within the acquisition frequency length as the air quality monitoring value of the grid.

[0011] In some optional implementations, for each monitoring parameter of each mobile station, the process of obtaining the calibration compensation value is:

[0012] S31, acquiring monitoring data of mobile stations in the target monitoring area within one day before the current time and filtering out historical static monitoring data therefrom, and grouping the historical static monitoring data in sequence according to the acquisition frequency length to obtain a number of static data groups;

[0013] S32, performing preliminary calibration on the historical monitoring data in each static data group within a preset range of each fixed station to obtain historical calibration data of each static data group;

[0014] S33, for each grid in the target monitoring area, calculating the average value of all historical calibration data located in the grid in each static data group to obtain a simulated monitoring value;

[0015] S34. Use simulated monitoring values ​​to calibrate the first historical monitoring data in each static data group within a preset range of each grid to obtain a calibration compensation value of the mobile station corresponding to a monitoring parameter. The first historical monitoring data is the uncalibrated historical monitoring data remaining after deleting the historical calibration data in the static data group.

[0016] In some optional implementations, the specific process of step S31 is:

[0017] S311, calculating the vehicle speed of each monitoring data according to the vehicle longitude and latitude and data collection time in each monitoring data, and marking the monitoring data with a vehicle speed less than a preset threshold as historical static monitoring data;

[0018] S312. Group the historical static monitoring data according to the acquisition frequency length of the fixed station to obtain several groups of static data within one day.

[0019] In some optional implementations, the preliminary calibration process in step S32 includes:

[0020] For each static data group:

[0021] Search for historical monitoring data in the static data group that is within the preset range of each fixed station;

[0022] Determine the calibration fixed station where the mobile station stays based on the amount of historical monitoring data within a preset range of each fixed station;

[0023] Taking the monitoring value of the calibration fixed station where the mobile station stays as the reference value, calculating the deviation between the reference value and the average value of the historical monitoring data within the preset range of each fixed station, and obtaining the preliminary calibration coefficient;

[0024] A preliminary calibration coefficient is added to each historical monitoring data within the preset range of each fixed station to obtain the historical calibration data of each static data group.

[0025] In some optional implementations, the process of obtaining the preliminary calibration coefficients is:

[0026] For each static data group, execute steps S321-S324;

[0027] S321, calculating the Euclidean distance between each historical static monitoring data in the static data group and all fixed stations in the target monitoring area;

[0028] S322, using the Euclidean distance within the preset range of the fixed station as a screening condition, determining the calibration fixed station near which the mobile station stays, and obtaining the monitoring value of each calibration fixed station in the corresponding time of the static data group;

[0029] S323, calculating the average value of all historical static monitoring data within a preset range of the calibration fixed station, and subtracting the monitoring value of the calibration fixed station from the average value to obtain a first calibration coefficient;

[0030] S324, traverse each calibration fixed station, repeat step S323, obtain the first calibration coefficient corresponding to each calibration fixed station, and average all the first calibration coefficients to obtain the second calibration coefficient;

[0031] S325, traverse all static data groups to obtain the second calibration coefficient corresponding to each static data group of the mobile station, average all the second calibration coefficients, and obtain the preliminary calibration coefficient of the monitoring value of this type of the mobile station.

[0032] In some optional implementations, for each fixed station, the number of historical static monitoring data that meets the screening condition in each fixed station is counted, and if the number of data in the fixed station exceeds a number threshold, it is determined that the mobile station stays near the fixed station, and the fixed station is used as a calibration fixed station;

[0033] If the amount of static data does not exceed the data amount threshold, it is determined that the mobile station is not staying near the fixed station.

[0034] In some optional implementations, the specific process of step S34 is:

[0035] For each static data group:

[0036] Find the first historical monitoring data in the static data group that is within a preset range of the grid;

[0037] Determining a calibration grid where the mobile station stays according to the amount of data of the first historical monitoring data within a preset range of the grid;

[0038] Taking the simulated monitoring value of the calibration grid where the mobile station stays as the reference value, calculating the deviation between the reference value and the average value of all first historical monitoring data within the preset range of each fixed station, and obtaining the calibration compensation value;

[0039] A calibration compensation value is added to each first historical monitoring data within a preset range of each fixed station to obtain historical calibration data of each static data group.

[0040] In some optional implementations, the process of obtaining the calibration compensation value is:

[0041] For each static data group, execute steps S341-S344;

[0042] S341, calculating the Euclidean distance between each first historical static monitoring data in the static data group and all grids in the target monitoring area;

[0043] S342, using the Euclidean distance within the preset range of the grid as a screening condition, counting the number of first historical static monitoring data in the grid that meets the screening condition, and if the number of data in the grid exceeds the number threshold, using the grid as a calibration grid near the mobile station, and obtaining simulated monitoring values ​​of each calibration grid and the static data group in the corresponding time;

[0044] S343, calculating the average value of all first historical static monitoring data within a preset range of the calibration grid, and subtracting the simulated monitoring value of the calibration grid from the average value to obtain a first calibration compensation value;

[0045] S344, traverse each calibration grid, repeat step S343, obtain the first calibration compensation value corresponding to each calibration grid, average all the first calibration compensation values, and obtain the second calibration compensation value;

[0046] S345 , traverse all static data groups to obtain second calibration compensation values ​​corresponding to each static data group of the mobile station, average all second calibration compensation values, and obtain calibration compensation values ​​of the monitoring values ​​of the mobile station.

[0047] In some optional implementations, the process of calibrating the real-time monitoring data of each mobile station in step S4 is:

[0048] The real-time static monitoring data is selected from the real-time monitoring data. For each type of monitoring value in the real-time static monitoring data, the corresponding calibration compensation value is called respectively. The monitoring value of each real-time static monitoring data is added with the calibration compensation value respectively to obtain parameter calibration data.

[0049] In a second aspect, the present application provides a system for optimizing urban air quality monitoring values ​​based on the operating status of a mobile station, including:

[0050] The monitoring station distribution query module is used to divide the target monitoring area into grids and query the distribution of monitoring stations in each grid within the target area;

[0051] The mobile station calibration module obtains the real-time monitoring data of all mobile stations passing through the grid within the current acquisition frequency length; calls the calibration compensation value corresponding to each atmospheric monitoring value of each mobile station to calibrate the real-time monitoring data of each mobile station respectively, and obtains parameter calibration data;

[0052] The monitoring value calculation module, if there is a fixed station in the grid, uses the data within the collection frequency length of the fixed station as the air quality monitoring value of the grid at the current time;

[0053] If there is no fixed station in the grid, the average value of the parameter calibration data of all mobile stations within the acquisition frequency length is used as the air quality monitoring value of the grid.

[0054] The inventive concept of this application is:

[0055] Currently, most of the air pollution monitoring in urban areas is carried out by combining mobile stations and fixed stations. However, due to the mobility of mobile stations and the high frequency of data upload, there may be a delay between the uploaded location data and the monitoring data. Therefore, when performing grid-based supervision, if the monitoring values ​​of the combination of mobile station and fixed station data are directly used to determine the air monitoring values ​​in each grid, errors will occur in the calculated air monitoring values.

[0056] In this application, if there is a fixed station in the grid, the fixed station data is directly used to estimate the air quality to avoid the influence of the mobile station data on the fixed station data. For grids without fixed stations, the stationary data of the calibrated mobile station is used for aggregation. On the one hand, the position of the stationary data of the mobile station changes less, which can reduce the delay error between the frequency of uploading data and the mobile position. In addition, in the calibration method, the fixed station is used to perform preliminary calibration on the historical stationary data passing near the fixed station, and for the historical stationary data that has not passed near the fixed station, the grid aggregation data is used to simulate the data of the fixed station, so as to complete the calibration of all the data of the mobile station and improve the accuracy of the air quality description in the target area.

[0057] The present invention has the beneficial effects:

[0058] The present invention proposes a joint calibration method for the situation where the monitoring values ​​of the mobile station monitoring data are inaccurate due to various reasons such as equipment or transmission. The characteristics of the mobile station when running on the road are used to calculate the vehicle speed of the mobile station through the latitude and longitude of the mobile station, the equipment collection time and other information, and the static state data of the mobile station is analyzed and processed, and then combined with the monitoring data of the fixed station for real-time calibration, so as to improve the accuracy of the monitoring value of the mobile station, thereby improving the accuracy of the description of the air quality in a certain area. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a method for optimizing urban air quality estimation provided by an embodiment of the present invention;

[0060] Figure 2 A flow chart of calibration of historical data of a mobile station provided by an embodiment of the present invention;

[0061] Figure 3 A schematic diagram of performing preliminary calibration on a mobile station provided by an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of a fixed station performing preliminary calibration on a mobile station according to an embodiment of the present invention;

[0063] Figure 5 A schematic diagram of calibrating a mobile station using a grid provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] The relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0066] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0067] Additionally, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness.One of ordinary skill in the art will recognize that various changes and modifications may be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0068] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0069] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment provides a method for optimizing urban air quality estimation based on the operating status of a mobile station, which specifically includes the following steps:

[0072] S1. Divide the target monitoring area into grids and query the distribution of monitoring stations in each grid within the target area;

[0073] The global space of the monitoring area is gridded according to the set spatial scale. This embodiment takes the spatial scale of 1×1 km as an example; the grid sizes mentioned below are all 1×1 km, and then the spatial range of each grid is appropriately expanded, that is, the monitoring range is formed with the grid as the center and the set distance as the radius.

[0074] S2. If there is a fixed station in the grid, the data within the collection frequency length of the fixed station is used as the air quality monitoring value of the grid at the current time;

[0075] The fixed station referred to in the present invention is an atmospheric fixed monitoring station that meets national standards, including national control stations, provincial control stations, and regional control stations; the mobile station is a mobile monitoring station that collects air quality data through vehicle-mounted gas and particulate matter indicator sensors and low-power embedded processors. The air quality data of the fixed station is hourly data, and the air quality data of the mobile monitoring station is real-time data at the second level. In this embodiment, the acquisition frequency length referred to below refers to 1 hour of data;

[0076] S3. If there is no fixed station in the grid, match the real-time static monitoring data of all mobile stations passing through the grid in that hour, and calculate the average value after aggregation as the air quality monitoring value of the grid in that hour;

[0077] S4. Call the calibration compensation value corresponding to each atmospheric monitoring value of each mobile station to calibrate the real-time monitoring data of each mobile station to obtain parameter calibration data, and take the average value of the parameter calibration data of all mobile stations within the acquisition frequency length as the air quality monitoring value of the grid.

[0078] The monitoring data of all mobile stations passing through the grid are obtained, and the monitoring data include the monitoring values ​​of the six atmospheric parameters (PM2.5, PM10, CO, NO2, SO2, O3), the longitude and latitude of the vehicle, and the data collection time. The longitude and latitude of the vehicle at the mobile station can be matched with the longitude and latitude range of the grid to obtain all mobile stations passing through the grid, and the vehicle speed v within the collection frequency length is calculated according to the longitude and latitude of each mobile station and the data collection time. If v is small, that is, 0 or close to 0, the monitoring data is marked as static monitoring data, and the monitoring data when v is large is marked as motion monitoring data.

[0079] The real-time static monitoring data is selected from the real-time monitoring data. For each type of monitoring value in the real-time static monitoring data, the corresponding calibration compensation value is called respectively. The monitoring value of each real-time static monitoring data is added with the calibration compensation value respectively to obtain parameter calibration data.

[0080] Specifically, the calibration compensation value is obtained by analyzing the historical static data of the mobile station and calibrating the mobile station in combination with the data of the fixed station, so as to obtain the calibration compensation value of each mobile station for each monitoring parameter (monitoring parameters include PM2.5 concentration, PM10 concentration, CO concentration, NO2 concentration, SO2 concentration, and O3 concentration).

[0081] Therefore, for the calibration compensation value of each monitoring parameter, taking the monitoring value of CO concentration as an example, Figure 2 As shown, the calculation process of obtaining the calibration compensation value of CO concentration is:

[0082] S31, acquiring monitoring data of mobile stations in the target monitoring area within one day before the current time and filtering out historical static monitoring data therefrom, and grouping the historical static monitoring data in sequence according to the acquisition frequency length to obtain a number of static data groups;

[0083] Specifically, when the sampling frequency length is 1 hour, the historical monitoring data is grouped according to each hour to obtain 24 groups of static data, one of which is t i ∈[t1,t2..t 24 ],i∈[1,24];

[0084] S311. Calculate the vehicle speed of each monitoring data according to the vehicle longitude and latitude and data collection time in each monitoring data, and mark the monitoring data with a vehicle speed less than a preset threshold as historical static monitoring data; if the vehicle speed is relatively small, that is, 0 or close to 0, then mark the monitoring data as static monitoring data, and the monitoring data with a larger v is marked as motion monitoring data; only retain the static monitoring data for subsequent calculations.

[0085] S312. Group the historical static monitoring data according to the acquisition frequency length of the fixed station to obtain several groups of static data within one day.

[0086] S32, performing preliminary calibration on the historical monitoring data in each static data group within a preset range of each fixed station to obtain historical calibration data of each static data group;

[0087] Among them, for each static data group t i ,like Figure 3 As shown, the initial calibration process includes:

[0088] S321, find static data group t i The historical monitoring data within the preset range of each fixed station: Calculate the static data group t i The Euclidean distance d between each historical static monitoring data and each fixed station in the target monitoring area j , j∈[all fixed station numbers];

[0089] S322, the Euclidean distance within the preset range of the fixed station is used as the screening condition. In this application, the preset range of the fixed station is within 1 km of the fixed station as the center. Therefore, the screening condition can be expressed as: d j <1km, determine the calibration fixed station near the mobile station, and obtain the monitoring value of each calibration fixed station and the static data group during the corresponding time;

[0090] For each fixed station, the number of historical static monitoring data that meets the screening conditions in the fixed station is counted. If the number of data in fixed station j exceeds the number threshold (which can be set to 100, indicating that the mobile station stays near the fixed station), it is determined that the mobile station stays near the fixed station, and the fixed station is used as a calibration fixed station;

[0091] If the amount of static data does not exceed the data amount threshold, it is determined that the mobile station is not staying near the fixed station.

[0092] S323, calculating the average value of all historical static monitoring data within the preset range of the calibration fixed station , and compare the monitoring value of the calibration fixed station with y j The average Subtract and get the first calibration coefficient =y j - ;

[0093] S324, traverse each calibration fixed station, repeat step S323, obtain the first calibration coefficient corresponding to each calibration fixed station, average all the first calibration coefficients, and obtain the second calibration coefficient ;

[0094] S325, traverse all static data groups, obtain the second calibration coefficient corresponding to each static data group of the mobile station, average all the second calibration coefficients, and obtain the preliminary calibration coefficient of the monitoring value of the mobile station .

[0095] For example, assuming that the static data group t1 of the first hour includes 300 historical static monitoring data, and assuming that 101 historical static monitoring data satisfy the requirement that the distance from fixed station 1 is less than 1 km, that is, within the preset range of fixed station 1, then the average of these 100 historical static monitoring data is obtained. , the corresponding monitoring value of fixed station 1 in the first hour is y1, y1 - Recorded as calibration coefficient ε1; Similarly, if t1 has 101 historical static monitoring data that satisfy the distance from fixed station 2 is less than 1 km, that is, within the preset range of fixed station 2, follow the above steps to obtain the calibration coefficient ε2, and follow the above steps to average all the first calibration coefficients that meet the conditions in t1 to obtain = (ε1+ε2) / 2;

[0096] By traversing 24 static data groups, 24 second calibration coefficients can be obtained. , the 24 second calibration coefficients are averaged to obtain the preliminary calibration coefficient of one of the monitoring parameters of a certain mobile station ; Traverse each monitoring parameter, the initial calibration coefficient of each monitoring parameter Independently of each other, traverse each mobile station and repeat the above steps to obtain the preliminary calibration coefficients of each monitoring parameter of each mobile station.

[0097] After obtaining the preliminary calibration coefficient, the historical static monitoring data of the mobile station near the fixed station can be calibrated. Each historical static monitoring data within the preset range of each fixed station (i.e., the 202 historical static monitoring data mentioned above, the data of the two fixed stations may be repeated, this application is just an example) is added with the preliminary calibration coefficient to obtain the historical calibration data of each static data group. The mobile station calibrates a certain air quality monitoring indicator x, and the result after calibration is x+ .

[0098] Since more than 200 pieces of static data in the first hour t1 are located near the fixed station, they can be calibrated directly using the preliminary calibration coefficient. As for the remaining 100 pieces of data, since they are not near the fixed station, they cannot be calibrated using the fixed station data, but they must pass through the grid, and since the size of the grid is small, there must be calibrated historical static monitoring data passing through the grid. Therefore, the average value of the historical calibration data passing through the grid every hour can be used to simulate the monitoring data of the fixed station as a calibration for the remaining 100 pieces of historical static monitoring data that have not been calibrated by the fixed station. The specific process is:

[0099] S33, for each grid in the target monitoring area, calculating the average value of all historical calibration data located in the grid in each static data group to obtain a simulated monitoring value;

[0100] Obtain the calibrated historical calibration data. The target monitoring area is divided into 1km*1km grids. The calibrated historical calibration data in the static data group is matched with the grids according to the latitude and longitude of the vehicle. All historical calibration data of the mobile station passing through the grid every hour can be obtained. Each grid aggregates the monitoring values ​​of the mobile station every hour, and takes the average value as the CO pollutant concentration of this grid every hour, which is used as the monitoring data of the fixed station simulated by this grid in this hour.

[0101] S34. Use simulated monitoring values ​​to calibrate the first historical monitoring data in each static data group within a preset range of each grid to obtain a calibration compensation value of the mobile station corresponding to a monitoring parameter. The first historical monitoring data is the uncalibrated historical monitoring data remaining after deleting the historical calibration data in the static data group.

[0102] Specifically, the monitoring values ​​aggregated every hour in the grid are used as the monitoring values ​​of the simulated fixed station, and the uncalibrated historical static monitoring data in the static data group are calibrated again. The calibration process is similar to the calibration process using the fixed station mentioned above. For each static data group (at this time, each static data group only includes historical static detection data that have not been calibrated with the preliminary calibration coefficient), the specific process is:

[0103] S341, calculating the Euclidean distance between each first historical static monitoring data (updated data, i.e., only uncalibrated data) in the static data group and all grids in the target monitoring area;

[0104] S342, taking the Euclidean distance within the preset range of the grid as the screening condition, similarly, in the present application, the preset range of the grid is within the range of 1 km with the center of the grid as the origin, counting the number of first historical static monitoring data that meet the screening condition in the grid (which can be set to 100, indicating that the mobile station stays near the grid), if the number of data in the grid exceeds the number threshold, the grid is used as a calibration grid near the mobile station, and the simulated monitoring value of each calibration grid and the static data group corresponding to the time is obtained;

[0105] S343, calculating the average value of all first historical static monitoring data within a preset range of the calibration grid, and subtracting the simulated monitoring value of the calibration grid from the average value to obtain a first calibration compensation value;

[0106] S344, traverse each calibration grid, repeat step S343, obtain the first calibration compensation value corresponding to each calibration grid, average all the first calibration compensation values, and obtain the second calibration compensation value;

[0107] S345 , traverse all static data groups to obtain second calibration compensation values ​​corresponding to each static data group of the mobile station, average all second calibration compensation values, and obtain calibration compensation values ​​of the monitoring values ​​of the mobile station.

[0108] Similarly, taking the static data group t1 of the first hour as an example, 202 historical static monitoring data stay around the fixed site and have been calibrated using the preliminary calibration coefficients. However, these 202 historical calibration data may not be in a grid. Therefore, these 202 historical calibration data are matched with the grid and divided again according to the grid passed by the longitude and latitude. Assuming that grid 1 contains 120 historical calibration data, the simulated monitoring value corresponding to grid 1 in the first hour is y ‘ 1, then for the 100 data that have not been calibrated by the fixed station, if they are within the 1km range of grid 1, these 100 static monitoring data within the preset range of grid 1 are averaged to obtain x ‘ 1, y ‘ 1 - x ‘ 1 is recorded as the first calibration compensation value ε 11; Similarly, if t1 has 100 first historical static monitoring data that satisfy the distance from grid 2 is less than 1Km, that is, within the preset range of grid 2, follow the above steps to obtain the calibration coefficient ε 22. According to the above steps, all the first calibration compensation values ​​that meet the conditions within t1 are averaged to obtain = ( ε 11+ ε 22) / 2;

[0109] By traversing 24 static data groups, 24 second calibration compensation values ​​can be obtained. , the calibration compensation value of one of the monitoring parameters of a certain mobile station is obtained by averaging the 24 second calibration coefficients = , traverse each monitoring parameter, the calibration compensation value of each monitoring parameter is independent of each other, traverse each mobile station, repeat the above steps, and obtain the calibration compensation value of each monitoring parameter of each mobile station. And use the calibration compensation value as the calibration parameter of the mobile station for real-time static data.

[0110] It can be understood that the method for calibrating the historical data of the mobile station to obtain the calibration compensation value in this embodiment is: firstly use the data of the fixed station that the mobile station passes through to perform preliminary calibration on the data of the mobile station, and for the data of the mobile station that the mobile station does not pass through the fixed station, use the grid that the mobile station passes through, use the grid to aggregate the calibrated static data to simulate the fixed station data to calibrate the remaining data of the mobile station, and obtain the final calibration compensation value. Figure 4 As shown in , for a mobile station, if it only passes through the preset range of a fixed station, such as Figure 4 As shown in the figure, only the historical data of the mobile station passing through the fixed station range is preliminarily calibrated. It can also be seen that in this hour, there are still many static historical data of the mobile station outside the fixed station range. Therefore, a grid is constructed and the grid data is used to simulate the fixed station data to fully calibrate the uncalibrated historical static data of the mobile station. Figure 5 As shown, for a certain mobile station, its data may pass through multiple grids. These grids aggregate the historical calibration data of the mobile station that has passed through within one hour and average them to obtain the simulated monitoring value of the grid, which is used to simulate the monitoring value of the fixed station. In this way, the monitoring values ​​of each grid that the mobile station has passed through (the data is within the preset range of the grid and the data volume is greater than 100, which is defined as passing through the grid) can be used to integrate and calibrate the historical monitoring data of the mobile station. It can be seen that for the grid, the calibrated data of the mobile station will be aggregated during aggregation, and then the calibrated data will be used to calibrate the uncalibrated data again. In this way, all the data of the mobile station can be calibrated comprehensively to obtain an accurate calibration compensation value for the calibration of real-time static monitoring data. It can be understood that the calibration compensation value in this application is not fixed, and an update program can be set, such as re-updating the calibration compensation value every day using the data of the previous day, or updating it once a month.

[0111] Example 2

[0112] This embodiment provides an intelligent atmospheric monitoring and management platform, and applies the method for processing the operating status of a mobile station in Embodiment 1, including:

[0113] The monitoring station distribution query module is used to divide the target monitoring area into grids and query the distribution of monitoring stations in each grid within the target area;

[0114] The mobile station calibration module obtains the real-time monitoring data of all mobile stations passing through the grid within the current acquisition frequency length; calls the calibration compensation value corresponding to each atmospheric monitoring value of each mobile station to calibrate the real-time monitoring data of each mobile station respectively, and obtains parameter calibration data;

[0115] The monitoring value calculation module, if there is a fixed station in the grid, uses the data within the collection frequency length of the fixed station as the air quality monitoring value of the grid at the current time;

[0116] If there is no fixed station in the grid, the average value of the parameter calibration data of all mobile stations within the acquisition frequency length is used as the air quality monitoring value of the grid.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. According to the technical essence of the present invention, within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing urban air quality estimation based on the operating status of a mobile station, characterized in that: The specific steps include: S1. Divide the target monitoring area into grids and query the distribution of monitoring stations in each grid in the target area; S2. If there is a fixed station in the grid, the data within the collection frequency length of the fixed station is used as the air quality monitoring value of the grid at the current time; S3. If there is no fixed station in the grid, obtain real-time static monitoring data of all mobile stations passing through the grid within the current acquisition frequency length; S4, calling the calibration compensation value corresponding to each atmospheric monitoring value of each mobile station to calibrate the real-time static monitoring data of each mobile station respectively, to obtain parameter calibration data, and taking the average value of the parameter calibration data of all mobile stations within the acquisition frequency length as the air quality monitoring value of the grid; In step S4, for each monitoring parameter of each mobile station, the process of obtaining the calibration compensation value is as follows: S31, acquiring monitoring data of mobile stations in the target monitoring area within one day before the current time and filtering out historical static monitoring data therefrom, and grouping the historical static monitoring data in sequence according to the acquisition frequency length to obtain a number of static data groups; The specific process of step S31 is: S311, calculating the vehicle speed of each monitoring data according to the vehicle longitude and latitude and data collection time in each monitoring data, and marking the monitoring data with a vehicle speed less than a preset threshold as historical static monitoring data; S312, grouping the historical static monitoring data according to the collection frequency length of the fixed station to obtain several groups of static data within a day; S32, performing preliminary calibration on the historical monitoring data in each static data group within a preset range of each fixed station to obtain historical calibration data of each static data group; The process of preliminary calibration in step S32 includes: For each static data group: Search for historical monitoring data in the static data group that is within the preset range of each fixed station; Determine the calibration fixed station where the mobile station stays based on the amount of historical monitoring data within a preset range of each fixed station; Taking the monitoring value of the calibration fixed station where the mobile station stays as the reference value, calculating the deviation between the reference value and the average value of the historical monitoring data within the preset range of each fixed station, and obtaining the preliminary calibration coefficient; Adding a preliminary calibration coefficient to each historical monitoring data within a preset range of each fixed station to obtain historical calibration data for each static data group; S33, for each grid in the target monitoring area, calculating the average value of all historical calibration data located in the grid in each static data group to obtain a simulated monitoring value; S34, using the simulated monitoring value to calibrate the first historical monitoring data in each static data group within the preset range of each grid, to obtain a calibration compensation value corresponding to a monitoring parameter of the mobile station, wherein the first historical monitoring data is the uncalibrated historical monitoring data remaining after deleting the historical calibration data in the static data group; the specific process of step S34 is: For each static data group: Find the first historical monitoring data in the static data group that is within a preset range of the grid; Determining a calibration grid where the mobile station stays according to the amount of data of the first historical monitoring data within a preset range of the grid; Taking the simulated monitoring value of the calibration grid where the mobile station stays as the reference value, calculating the deviation between the reference value and the average value of all first historical monitoring data within the preset range of each fixed station, and obtaining the calibration compensation value; A calibration compensation value is added to each first historical monitoring data within a preset range of each fixed station to obtain historical calibration data of each static data group.

2. The urban air quality estimation and optimization method based on the mobile station operation status according to claim 1 is characterized in that: The process of obtaining the preliminary calibration coefficients is: For each static data group, execute steps S321-S324; S321, calculating the Euclidean distance between each historical static monitoring data in the static data group and all fixed stations in the target monitoring area; S322, using the Euclidean distance within the preset range of the fixed station as a screening condition, determining the calibration fixed station near which the mobile station stays, and obtaining the monitoring value of each calibration fixed station in the corresponding time of the static data group; S323, calculating the average value of all historical static monitoring data within a preset range of the calibration fixed station, and subtracting the monitoring value of the calibration fixed station from the average value to obtain a first calibration coefficient; S324, traverse each calibration fixed station, repeat step S323, obtain the first calibration coefficient corresponding to each calibration fixed station, and average all the first calibration coefficients to obtain the second calibration coefficient; S325, traverse all static data groups to obtain the second calibration coefficient corresponding to each static data group of the mobile station, average all the second calibration coefficients, and obtain the preliminary calibration coefficient of the monitoring value of this type of the mobile station.

3. The urban air quality estimation and optimization method based on the mobile station operation status according to claim 2 is characterized in that: For each fixed station, the number of historical static monitoring data that meets the screening conditions in each fixed station is counted. If the number of data in the fixed station exceeds the number threshold, it is determined that the mobile station stays near the fixed station, and the fixed station is used as a calibration fixed station; If the amount of static data does not exceed the data amount threshold, it is determined that the mobile station is not staying near the fixed station.

4. The urban air quality estimation and optimization method based on the mobile station operation status according to claim 1 is characterized in that: The process of obtaining the calibration compensation value is: For each static data group, execute steps S341-S344; S341, calculating the Euclidean distance between each first historical static monitoring data in the static data group and all grids in the target monitoring area; S342, using the Euclidean distance within the preset range of the grid as a screening condition, counting the number of first historical static monitoring data in the grid that meets the screening condition, and if the number of data in the grid exceeds the number threshold, using the grid as a calibration grid near the mobile station, and obtaining simulated monitoring values ​​of each calibration grid and the static data group in the corresponding time; S343, calculating the average value of all first historical static monitoring data within a preset range of the calibration grid, and subtracting the simulated monitoring value of the calibration grid from the average value to obtain a first calibration compensation value; S344, traverse each calibration grid, repeat step S343, obtain the first calibration compensation value corresponding to each calibration grid, average all the first calibration compensation values, and obtain the second calibration compensation value; S345 , traverse all static data groups to obtain second calibration compensation values ​​corresponding to each static data group of the mobile station, average all second calibration compensation values, and obtain calibration compensation values ​​of the monitoring values ​​of the mobile station.

5. The urban air quality estimation and optimization method based on the mobile station operation status according to claim 1 is characterized in that: The process of calibrating the real-time static monitoring data of each mobile station in step S4 is as follows: The real-time static monitoring data is selected from the real-time monitoring data. For each type of monitoring value in the real-time static monitoring data, the corresponding calibration compensation value is called respectively. The monitoring value of each real-time static monitoring data is added with the calibration compensation value respectively to obtain parameter calibration data.

6. A system for estimating and optimizing urban air quality based on the operating status of a mobile station, applying the method for estimating and optimizing urban air quality based on the operating status of a mobile station as claimed in claim 1, characterized in that: include: The monitoring station distribution query module is used to divide the target monitoring area into grids and query the distribution of monitoring stations in each grid within the target area; Mobile station calibration module, which obtains real-time monitoring data of all mobile stations passing through the grid within the current acquisition frequency length; The calibration compensation value corresponding to each atmospheric monitoring value of each mobile station is called to calibrate the real-time monitoring data of each mobile station to obtain parameter calibration data; The monitoring value calculation module, if there is a fixed station in the grid, uses the data within the collection frequency length of the fixed station as the air quality monitoring value of the grid at the current time; If there is no fixed station in the grid, the average value of the parameter calibration data of all mobile stations within the acquisition frequency length is used as the air quality monitoring value of the grid.

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