Environment data processing method, device and equipment

Abstract

The embodiment of the invention provides an environment data processing method, device and equipment. The method comprises the following steps: acquiring a plurality of environment data packets of each preset grid of a target area in a target time period; wherein each environment data packet comprises at least one kind of air pollutant concentration data collected by the vehicle; for each preset grid, determining initial environment data of the preset grid in the target time period according to the air pollutant concentration data in the environment data packet of the preset grid in the target time period; acquiring environment monitoring data of an environment monitoring station to which the preset grid belongs in a target time period; based on the environment monitoring data of the target time period, performing correction processing on the initial environment data of the preset grid in the target time period to obtain target environment data of the preset grid in the target time period; and determining an environment pollution area of the target area according to the target environment data of each preset grid in the target time period. The method is used for achieving the effect of improving the accuracy of the environmental data.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, apparatus and equipment for processing environmental data. Background Technology

[0002] Environmental monitoring of air pollutants is a core foundation for safeguarding ecological security, protecting public health, and promoting refined urban governance. This includes monitoring PM2.5, PM10, NOx, and other pollutants in the air. Pollutants such as these not only harm human health but also disrupt the ecological balance; therefore, it is necessary to monitor air pollutants.

[0003] In related technologies, environmental monitoring primarily utilizes fixed monitoring stations to collect air pollutant concentration data from the environment. This data is then processed and analyzed to achieve environmental monitoring. However, this method suffers from limited station density, making it difficult for single-point monitoring data to represent the true air quality of a large surrounding area. This leads to discrepancies between the monitoring data and the actual pollution situation, failing to provide reliable data support for accurate source tracing and real-time remediation. Summary of the Invention

[0004] The environmental data processing methods, apparatus, and devices provided in this application are intended to improve the accuracy of environmental data.

[0005] In a first aspect, embodiments of this application provide a method for processing environmental data, the method being applied to a cloud server in an environmental monitoring system, comprising:

[0006] Acquire multiple environmental data packets for each preset grid in the target area during the target time period; wherein each environmental data packet includes at least one air pollutant concentration data collected by the vehicle;

[0007] For each preset grid, the initial environmental data for that preset grid during the target time period is determined based on the air pollutant concentration data in the environmental data packet for that preset grid during the target time period.

[0008] Obtain environmental monitoring data from the environmental monitoring station to which the preset grid belongs during the target time period;

[0009] Based on the environmental monitoring data for the target time period, the initial environmental data of the preset grid during the target time period is corrected to obtain the target environmental data of the preset grid during the target time period.

[0010] Based on the target environmental data of each preset grid during the target time period, the environmental pollution area of ​​the target region is determined.

[0011] In one possible implementation, determining the initial environmental data for each preset grid during the target time period based on the air pollutant concentration data in the environmental data packet for that preset grid during the target time period includes:

[0012] For each preset grid, the air pollutant concentration data in each environmental data packet of the preset grid are weighted and averaged to obtain the initial environmental data of the preset grid in the target time period.

[0013] In one possible implementation, the step of correcting the initial environmental data of the preset grid during the target time period based on the environmental monitoring data for the target time period to obtain the target environmental data of the preset grid during the target time period includes:

[0014] The environmental monitoring data for the target time period and the initial environmental data of the preset grid during the target time period are input into the preset association model, and the intermediate environmental data of the preset grid during the target time period are output.

[0015] Determine the distance from the center point of the preset grid to the environmental monitoring station;

[0016] Based on the distance, determine the distance correction factor;

[0017] Based on the distance correction coefficient, the intermediate environmental data of the preset grid in the target time period is corrected to obtain the target environmental data of the preset grid in the target time period.

[0018] In one possible implementation, the method further includes:

[0019] If it is determined that the number of environmental data packets in a preset grid is less than a preset threshold, then the target environmental data of the preset grid in the target time period is determined based on the target environmental data of the adjacent preset grids in the target time period.

[0020] In one possible implementation, determining the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period includes:

[0021] An environmental map is generated based on the target environmental data of each preset grid during the target time period;

[0022] Based on the environmental map, the environmental pollution areas of the target area are determined.

[0023] In one possible implementation, acquiring multiple environmental data packets for each preset grid in the target area during a target time period includes:

[0024] The system receives multiple environmental data packets sent by the edge device of the environmental monitoring system during a target time period; each environmental data packet includes the location information of the vehicle when collecting air pollutant concentration data;

[0025] Based on the location information of the environmental data packets and the preset grid, multiple environmental data packets for each preset grid in the target time period are obtained.

[0026] Secondly, embodiments of this application provide a method for processing environmental data, the method being applied to an edge device in an environmental monitoring system, comprising:

[0027] Acquire multiple initial environmental data packets sent by the vehicle during the target time period; wherein each initial environmental data packet includes collected environmental data, vehicle status data, and at least one initial air pollutant concentration data;

[0028] For each initial environmental data packet, the initial air pollutant concentration data of the initial environmental data packet is corrected based on the collected environmental data and vehicle status data of the initial environmental data packet to obtain the air pollutant concentration data.

[0029] Based on the air pollutant concentration data, an environmental data package corresponding to the initial environmental data package is generated;

[0030] The environmental data packet corresponding to the initial environmental data packet for the target time period will be sent to the cloud server of the environmental monitoring system; wherein, the environmental data packet is the environmental data packet described in any of the first aspects.

[0031] In one possible implementation, the step of correcting the initial air pollutant concentration data of the initial environmental data packet based on the collected environmental data and vehicle status data to obtain air pollutant concentration data includes:

[0032] Based on the vehicle status data in the initial environmental data packet, the initial air pollutant concentration data in the initial environmental data packet is cleaned to obtain the processed air pollutant concentration data.

[0033] Based on the collected environmental data and the pre-set baseline collected environmental data, the processed air pollutant concentration data is compensated to obtain the air pollutant concentration data.

[0034] In one possible implementation, the vehicle status data includes vehicle speed, engine speed, air conditioning status, window status, and windshield wiper status; the step of cleaning the initial air pollutant concentration data of the initial environmental data packet based on the vehicle status data of the initial environmental data packet to obtain processed air pollutant concentration data includes:

[0035] The exhaust gas interference level is determined based on the vehicle speed and the generator rotation speed during the target time period;

[0036] Based on the exhaust gas interference level, outliers in the initial air pollutant concentration data of the initial environmental data packet are deleted to obtain intermediate air pollutant concentration data.

[0037] A first compensation coefficient is determined based on the air conditioning status, the window status, and the windshield wiper status.

[0038] The intermediate air pollutant concentration data is compensated based on the first compensation coefficient to obtain the processed air pollutant concentration data.

[0039] In one possible implementation, the step of compensating the processed air pollutant concentration data based on the collected environmental data and preset benchmark collected environmental data to obtain air pollutant concentration data includes:

[0040] The second compensation coefficient is determined based on the collected environmental data and the preset benchmark collected environmental data;

[0041] The air pollutant concentration data is compensated according to the second compensation coefficient to obtain the air pollutant concentration data.

[0042] Thirdly, embodiments of this application provide an environmental data processing apparatus, which is applied to a cloud server in an environmental monitoring system, comprising:

[0043] The first acquisition module is used to acquire multiple environmental data packets for each preset grid in the target area during the target time period; wherein each environmental data packet includes at least one air pollutant concentration data collected by the vehicle;

[0044] The first determining module is used to determine the initial environmental data of each preset grid in the target time period based on the air pollutant concentration data in the environmental data packet of the preset grid in the target time period.

[0045] The second acquisition module is used to acquire environmental monitoring data of the environmental monitoring station to which the preset grid belongs during the target time period;

[0046] The correction module is used to correct the initial environmental data of the preset grid during the target time period based on the environmental monitoring data during the target time period, so as to obtain the target environmental data of the preset grid during the target time period.

[0047] The second determining module is used to determine the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period.

[0048] Fourthly, embodiments of this application provide an environmental data processing apparatus, which is applied to an edge device in an environmental monitoring system, comprising:

[0049] The acquisition module is used to acquire multiple initial environmental data packets sent by the vehicle during the target time period; wherein each initial environmental data packet includes collected environmental data, vehicle status data, and at least one initial air pollutant concentration data.

[0050] The correction module is used to correct the initial air pollutant concentration data of each initial environmental data packet based on the collected environmental data and vehicle status data of the initial environmental data packet, so as to obtain the air pollutant concentration data.

[0051] The generation module is used to generate an environmental data packet corresponding to the initial environmental data packet based on the air pollutant concentration data.

[0052] The sending module is used to send the environmental data packet corresponding to the initial environmental data packet for the target time period to the cloud server of the environmental monitoring system.

[0053] Fifthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0054] The memory stores computer-executed instructions;

[0055] The processor executes computer execution instructions stored in the memory, causing the processor to perform various possible implementations of the first and / or second aspects described above.

[0056] Sixthly, embodiments of this application provide an environmental monitoring system, which includes a cloud server, at least one edge device, and multiple vehicles; the cloud server is communicatively connected to the edge device, and each edge device is communicatively connected to the vehicle.

[0057] The vehicle is used to acquire multiple initial environmental data packets during a target time period and send them to the edge device; wherein each initial environmental data packet includes environmental data collected by the vehicle, vehicle status data, and at least one initial air pollutant concentration data;

[0058] The cloud server is used to perform the method as described in any of the first aspects;

[0059] The edge device is used to perform the method as described in any of the second aspects.

[0060] In a seventh aspect, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement various possible implementations of the first and / or second aspects described above.

[0061] Eighthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements various possible implementations of the first and / or second aspects described above.

[0062] The environmental data processing method, apparatus, and equipment provided in this application receive initial environmental data packets sent by vehicles through edge devices. The initial pollutant concentration data is corrected by combining collected environmental data with vehicle status data, generating standardized environmental data packets, and then transmitted to a cloud server. The cloud server divides the target area into preset grids, aggregates the environmental data packets from each preset grid, calculates the initial environmental data for each preset grid, and then combines authoritative data from the environmental monitoring station to which each preset grid belongs to obtain accurate target environmental data through correction. Finally, the environmental pollution area is determined based on the target environmental data. This approach overcomes the limitations of traditional fixed monitoring station deployment by enabling full-area, high-resolution monitoring of the target area based on air pollutant concentration data collected by mobile vehicles, filling monitoring blind spots in suburban areas and secondary roads. Furthermore, the hierarchical correction between the edge device and the cloud reduces errors caused by vehicle interference, environmental factors, and sensor drift, improving the accuracy of environmental data. This allows for the rapid and accurate capture of sudden pollution events, providing reliable data support for precise pollution source tracing and real-time governance. Attached Figure Description

[0063] 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.

[0064] Figure 1 A schematic diagram illustrating one application scenario provided in this application;

[0065] Figure 2 Flowchart of the method for processing environmental data provided in this application Figure 1 ;

[0066] Figure 3 Flowchart of the method for processing environmental data provided in this application Figure 2 ;

[0067] Figure 4 A schematic diagram illustrating another application scenario provided by this application;

[0068] Figure 5Schematic diagram of the structure of the environmental data processing device provided in this application Figure 1 ;

[0069] Figure 6 Schematic diagram of the structure of the environmental data processing device provided in this application Figure 2 ;

[0070] Figure 7 A schematic diagram of the structure of the electronic device provided in this application.

[0071] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0072] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0073] The inventors, recognizing the limitations of fixed monitoring stations in related technologies—limited station density, inability of single-point data to accurately represent the true air quality over a wide area, leading to monitoring bias—realized the potential for vehicles to be mobile and cover a broad range of scenarios. They realized that vehicles could be used as mobile monitoring terminals to collect air pollutant concentration data, thus filling the monitoring blind spots of fixed stations and addressing insufficient coverage. Considering the discrete nature of data collected by massive numbers of vehicles, which makes it difficult to accurately reflect the pollution status of specific areas, they further conceived of dividing the target area into multiple grids using a cloud server. This allowed them to centrally process the discrete vehicle-collected data, assigning it to the corresponding grid based on location, achieving spatial data aggregation. Furthermore, recognizing the potential for individual errors in vehicle-collected data, while fixed monitoring station data provides authoritative benchmarks, they ultimately decided to use data from the monitoring stations belonging to each grid to correct the processed environmental data within that grid, thereby improving the accuracy of environmental monitoring.

[0074] Figure 1 A schematic diagram illustrating an application scenario provided in this application, such as... Figure 1 As shown, the environmental monitoring system includes a cloud server, at least one edge device, and multiple vehicles. The cloud server communicates with the edge device, and each edge device communicates with each vehicle. It should be noted that... Figure 1This example uses two edge devices, each connected to two vehicles. The number of edge devices and vehicles is not limited in this embodiment.

[0075] The vehicle is used to collect data on air pollutant concentrations, vehicle status, and other environmental data. It generates initial environmental data packets from the collected data and sends them to edge devices via an onboard communication module. The vehicle can be equipped with a multi-module environmental sensor array and a navigation system. The multi-module environmental sensor array is used to collect data on PM2.5, PM10, NOx, and other pollutants. The system collects air pollutant concentrations such as CO, and simultaneously gathers environmental data including ambient temperature, humidity, atmospheric pressure, and ambient noise levels in decibels. The navigation system may include, for example, a positioning module and an inertial measurement unit, to acquire information such as the timestamp, latitude and longitude coordinates, and altitude of the collected data. It should be noted that this application does not limit the type of vehicle; for example, it can be a new energy vehicle or a fuel-powered vehicle.

[0076] An edge device is used to receive air pollutant concentration data, vehicle status data, and environmental data collected by vehicles, and preprocesses the data to generate environmental data packets which are then sent to a cloud server. This edge device can be an electronic device with processing capabilities and can be deployed in each vehicle or in a roadside unit; this application does not limit the scope of the embodiment.

[0077] A cloud server is used to fuse environmental data packets and monitoring data from monitoring stations to obtain more accurate environmental data, thereby enabling precise location of polluted areas. It should be noted that this application does not limit the deployment method of the cloud server; for example, it can be an integrated deployment or a distributed deployment.

[0078] It should be noted that the vehicle status data, air pollutant concentration data, and collected environmental data involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of the relevant data must comply with relevant laws, regulations, and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 2 Flowchart of the method for processing environmental data provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0081] S101, The edge device acquires multiple initial environmental data packets sent by the vehicle during the target time period.

[0082] For example, the target time period refers to a preset data analysis time window, which can be set to 1 minute, 5 minutes, etc. according to actual needs. This application embodiment does not limit this.

[0083] An initial environmental data packet refers to the raw, unprocessed data set collected by a vehicle. This initial environmental data packet can be collected by the same vehicle or by multiple vehicles. Each initial environmental data packet includes environmental data collected by the vehicle, vehicle status data, and at least one initial air pollutant concentration data. Optionally, the initial environmental data packet also includes spatiotemporal information such as timestamps and location information, which characterize the time and location when the vehicle collected the initial air pollutant concentration data.

[0084] The environmental data refers to the basic parameters of the surrounding environment collected by the multimodal environmental sensor array deployed in the vehicle. These parameters characterize the environment at the time of initial air pollutant concentration data collection, and may include ambient temperature, humidity, atmospheric pressure, and noise levels in decibels. Vehicle status data refers to parameters reflecting the vehicle's operating conditions and equipment status, such as vehicle speed, engine speed, air conditioning status (off / recirculation / external circulation), window status (fully closed / half open / fully open), and wiper status (on / off / gear position). Initial air pollutant concentration data refers to the raw air pollutant data directly collected by the multimodal environmental sensor array deployed in the vehicle. This initial air pollutant may include, for example, PM2.5, PM10, NOx, etc. It should be noted that the embodiments of this application do not limit the types of air pollutants, such as CO.

[0085] In one example, the edge device can receive initial environmental data packets sent by vehicles within a preset distance range. After receiving the packets, it first verifies the integrity of the data packet format, checks whether there is any missing environmental data, vehicle status data, or initial pollutant concentration data, and discards invalid packets with incomplete formats.

[0086] S102. For each initial environmental data packet, the edge device corrects the initial air pollutant concentration data of the initial environmental data packet based on the collected environmental data and vehicle status data of the initial environmental data packet, and obtains the air pollutant concentration data.

[0087] For example, the goal of correction processing is to eliminate interference from vehicle exhaust emissions and correct sensor biases caused by environmental factors, thereby improving the accuracy of air pollutant concentration data. Air pollutant concentration data refers to pollutant concentration values ​​that have been corrected and possess higher accuracy and reliability. It is understandable that correction processing needs to be adapted to the different characteristics of various pollutants; for example, NOx is easily affected by engine speed, while PM is easily affected by the condition of the vehicle windows.

[0088] In one example, for each initial environmental data packet, the edge device can first perform interference removal processing based on vehicle status data. For instance, when the vehicle speed in the vehicle status data is less than a preset speed threshold and the engine speed is greater than a preset speed threshold, it is determined to be a high-interference condition. The PM2.5 and NOx data in the initial air pollutant concentration data under this condition are compared with the average values ​​under normal vehicle conditions. If the deviation exceeds three times the standard deviation, outliers are removed. At the same time, since the sensor collects the air inside the vehicle, it needs to be corrected to the equivalent concentration outside the vehicle. The first compensation coefficient can be determined based on the air conditioning status and window status in the vehicle status data. If the air conditioning is on with recirculation and all windows are closed, the PM pollutant data is multiplied by the preset first compensation coefficient. Furthermore, the deviation can be compensated based on the collected environmental data. Based on the preset benchmark collected environmental data (temperature 25℃, humidity 50%RH, atmospheric pressure 1013hPa), the deviation between the current collected environment and the benchmark environment can be calculated. For example, for every 10℃ decrease in temperature, the PM2.5 sensor sensitivity decreases by 8%, so the corresponding second compensation coefficient is set to 1.08. Multiplicative compensation is then performed on the pollutant data after interference removal to finally obtain the air pollutant concentration data.

[0089] S103. The edge device generates an environmental data packet corresponding to the initial environmental data packet based on the air pollutant concentration data.

[0090] For example, an environmental data packet refers to a standardized data carrier output by an edge device after processing. It is understood that this environmental data packet not only contains corrected air pollutant concentration data, but also includes corresponding collected environmental data, vehicle status data, collection timestamps, and vehicle anonymity identifiers.

[0091] Optionally, each environmental data packet may also carry a data quality score. The quality score can be determined based on the aforementioned correction process results. For example, if there is no exhaust gas interference and the absolute value of the compensation coefficient is <10%, a score of 90-100 is obtained; if slight interference has been eliminated and the absolute value of the compensation coefficient is 10%-20%, a score of 70-89 is obtained; and if the absolute value of the compensation coefficient is >20%, a score of 50-69 is obtained.

[0092] In one example, the edge device encapsulates the corrected air pollutant concentration data with the original collected environmental data, vehicle status data, spatiotemporal information, and other related data into an environmental data packet according to a preset format.

[0093] S104. The edge device will send the environmental data packet corresponding to the initial environmental data packet of the target time period to the cloud server of the environmental monitoring system.

[0094] Edge devices can send the environmental data packets corresponding to the initial environmental data packets in the target time period to the cloud server according to a preset communication method.

[0095] S105, The cloud server obtains multiple environmental data packets for each preset grid in the target area during the target time period.

[0096] For example, as mentioned above, each environmental data packet includes at least one air pollutant concentration data collected by the vehicle. The target area refers to the geographical area requiring environmental monitoring, which can be set as a city, district / county, or specific functional area (such as an industrial zone or transportation hub) according to actual needs. This application embodiment does not limit the scope of the target area. The preset grid refers to the smallest analytical unit that divides the target area according to a fixed spatial resolution. For example, it can be a 100m × 100m square grid. Each grid is assigned a unique ID and bound to the authoritative environmental monitoring station to which the preset grid belongs.

[0097] In one example, the cloud server deploys distributed receiving nodes to receive environmental data packets uploaded by each edge device in segments according to the target area. After receiving the data packets, it first performs format standardization verification and removes duplicate data. Based on the location information in the environmental data packets, it uses a geocoding mapping algorithm to assign each environmental data packet to the corresponding preset grid, and then aggregates all environmental data packets of each preset grid within the target time period.

[0098] S106. For each preset grid, the cloud server determines the initial environmental data for that preset grid during the target time period based on the air pollutant concentration data in the environmental data packet of that preset grid during the target time period.

[0099] For example, the initial environmental data refers to the statistical values ​​of pollutant concentrations for each preset grid within the target time period. It is understood that the initial environmental data includes statistical values ​​of at least one air pollutant concentration; in other words, it can be calculated separately for each type of air pollutant to ensure that the concentration status of each pollutant can be accurately characterized.

[0100] In one example, for each preset grid, the cloud server first filters environmental data packets for that grid whose quality score is greater than or equal to a preset score threshold within the target time period. If the amount of filtered data is greater than or equal to a preset quantity, weights are assigned according to the quality score, with a weight of 1.0 for a score of 100 and a weight decrease of 0.1 for every 10 points decrease. The weighted average of the concentration data for each air pollutant is calculated, and this weighted average is used as the initial environmental data for the corresponding air pollutant for that preset grid. Optionally, if the amount of filtered data is less than the preset quantity (insufficient data), the initial environmental data from the previous target time period for that grid can be used.

[0101] S107. The cloud server obtains environmental monitoring data for the target time period from the environmental monitoring station to which the preset grid belongs.

[0102] For example, an environmental monitoring station refers to a fixed monitoring facility with authoritative monitoring qualifications, such as a national, provincial, or municipal level monitoring station. It is understood that the monitoring data from environmental monitoring stations undergoes professional calibration and possesses benchmark authority. Environmental monitoring data refers to the air pollutant concentration data collected by the environmental monitoring station within a time window consistent with the target time period of the preset grid, and the air pollutant types correspond one-to-one with the initial environmental data of that preset grid.

[0103] In one example, the cloud server connects to the database of all environmental monitoring stations in the target area in real time through a preset data interface, and extracts the environmental monitoring data of the corresponding environmental monitoring station for the target time period according to the environmental monitoring station ID to which the preset grid belongs.

[0104] Optionally, if a certain environmental monitoring station has missing data within the target time period, such as due to equipment maintenance, monitoring data from the station in an adjacent time period (the previous or subsequent target time period) can be selected and interpolated with data from other environmental monitoring stations adjacent to that station to ensure the integrity of the environmental monitoring data.

[0105] S108. Based on the environmental monitoring data for the target time period, the cloud server corrects the initial environmental data of the preset grid for the target time period to obtain the target environmental data of the preset grid for the target time period.

[0106] For example, the target environmental data refers to the corrected air pollutant concentration data for the preset grid. It is understood that the correction process needs to be performed separately for each type of air pollutant to ensure the accuracy of the correction for each pollutant.

[0107] In one example, the cloud server can first input the initial environmental data and the environmental monitoring data from the corresponding environmental monitoring station into a pre-trained association model for each preset grid to obtain the target environmental data. This pre-trained association model can be a random forest algorithm model, capable of learning the mapping relationship between sample environmental data and sample environmental monitoring data for each grid. This step utilizes historical data to learn the relationship between the grid's environmental data and monitoring data, and uses this relationship to correct the input initial environmental data, resulting in more accurate target environmental data.

[0108] S109. The cloud server determines the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period.

[0109] For example, an environmentally polluted area refers to a grid area within the target area where the concentration of pollutants significantly exceeds the standard. For example, it could be a sudden pollution hotspot (such as illegal industrial emissions or straw burning) or a persistently polluted area (such as a traffic-congested road or an industrial zone).

[0110] In one example, the cloud server can preset the concentration thresholds for various air pollutants in different functional areas, such as a PM2.5 increase of ≥50 mg / L in a residential area over 10 minutes. Industrial zones ≥80 It iterates through the target environmental data of all grids and identifies grids with concentration increases exceeding the threshold as environmentally polluted areas.

[0111] The environmental data processing method provided in this application embodiment receives initial environmental data packets sent by vehicles through an edge device. It then corrects the initial pollutant concentration data by combining collected environmental data with vehicle status data, generating standardized environmental data packets and transmitting them to a cloud server. The cloud server divides the target area into preset grids, aggregates the environmental data packets from each preset grid, calculates the initial environmental data for each preset grid, and combines it with authoritative data from the environmental monitoring station to which each preset grid belongs. This corrects the data to obtain accurate target environmental data, and finally determines the polluted area based on the target environmental data. This approach overcomes the limitations of traditional fixed monitoring station deployment by enabling full-area, high-resolution (street-level) monitoring of the target area based on air pollutant concentration data collected by mobile vehicles, filling monitoring blind spots in suburbs and secondary roads. Furthermore, the hierarchical correction between the edge device and the cloud reduces errors caused by vehicle interference, environmental factors, and sensor drift, improving the accuracy of environmental monitoring data. This allows for the rapid and accurate capture of sudden pollution events, providing reliable data support for precise pollution source tracing and real-time governance.

[0112] Figure 3 Flowchart of the method for processing environmental data provided in this application Figure 2 , Figure 4 A schematic diagram illustrating another application scenario provided in this application, such as... Figure 3 and Figure 4 As shown, in this embodiment... Figure 2 Based on the embodiments, the method for processing environmental data is described in detail, which includes:

[0113] S201, The edge device acquires multiple initial environmental data packets sent by the vehicle during the target time period.

[0114] It should be noted that this step is similar to the aforementioned step S101, and will not be repeated here.

[0115] S202. For each initial environmental data packet, the edge device cleans the initial air pollutant concentration data of the initial environmental data packet based on the vehicle status data of the initial environmental data packet, and obtains the processed air pollutant concentration data.

[0116] For example, cleaning treatment refers to a process of removing interference from initial air pollutant concentration data to eliminate abnormal data caused by factors such as vehicle exhaust and equipment operating conditions, ensuring data authenticity. The processed air pollutant concentration data refers to intermediate data after exhaust interference removal and operating condition compensation. It is understandable that cleaning treatment needs to be adapted to the characteristics of different air pollutants; for example, PM2.5 and PM10 are easily affected by vehicle exhaust and window conditions, while NOx and CO are strongly correlated with engine speed.

[0117] Specifically, this step may include the following steps:

[0118] S2021. The edge device determines the exhaust gas interference level within the target time period based on the vehicle speed and the transmitter speed.

[0119] For example, as mentioned above, vehicle status data may include vehicle speed and engine speed. Vehicle speed refers to the vehicle's speed at the time of data acquisition, which can be read in real-time by the vehicle communication bus. Engine speed refers to the number of revolutions per minute of the engine crankshaft. Exhaust gas interference refers to the degree of interference between the vehicle's own exhaust emissions and the air pollutant concentration data collected by the sensors. It should be noted that the quantitative grading standard for interference in this application is not uniquely limited and can be dynamically adjusted according to the exhaust emission characteristics of different vehicle models.

[0120] In one example, the edge device has a pre-defined mapping table of vehicle speed, engine speed, and interference level, as shown in Table 1. The edge device can extract the time-series data of vehicle speed and engine speed within the target time period from the initial environmental data packet to determine the time-series data of interference level. Then, it can calculate the average exhaust gas interference level for the target time period by weighting it according to time proportions. For example, if the interference level is 0.9 for 30% of the target time period and 0.2 for 70% of the time, then 0.9 × 30% + 0.2 × 70% = 0.41 is used as the exhaust gas interference level for the target time period. Alternatively, the edge device can calculate the average vehicle speed and the average engine speed within the target time period, and then combine this with Table 1 to determine the interference level corresponding to the average vehicle speed and the average engine speed as the exhaust gas interference level for the target time period.

[0121] Table 1 Mapping Table of Vehicle Speed-RPM-Interference Level

[0122]

[0123] S2022. The edge device deletes outliers in the initial air pollutant concentration data of the initial environmental data packet based on the exhaust gas interference level, and obtains intermediate air pollutant concentration data.

[0124] For example, outliers refer to abnormal data points that deviate from the normal range of environmental pollutant concentrations due to severe interference from vehicle exhaust emissions. Intermediate air pollutant concentration data refers to the data after removing outliers caused by exhaust emissions. It is understood that outlier deletion should be performed separately for each type of air pollutant.

[0125] In one example, the edge device can first set a dynamic anomaly threshold for each air pollutant based on the exhaust gas interference level. For instance, when the interference level is >0.8, the anomaly threshold is ±4 standard deviations of the historical average pollutant concentration under the same operating conditions for that vehicle; when the interference level is 0.4~0.8, the anomaly threshold is ±3 standard deviations of the historical average pollutant concentration under the same operating conditions for that vehicle; and when the interference level is <0.4, the anomaly threshold is ±2 standard deviations of the historical average pollutant concentration under the same operating conditions for that vehicle. Furthermore, based on the initial air pollutant concentration data and the exhaust gas interference level, the corresponding anomaly threshold can be determined. If the value exceeds the anomaly threshold, it is judged as an anomaly and directly deleted, resulting in intermediate air pollutant concentration data after removing anomalies.

[0126] S2023. The edge device determines the first compensation coefficient based on the air conditioning status, window status, and windshield wiper status.

[0127] For example, as mentioned above, vehicle status data may include air conditioning status, window status, and windshield wiper status. Air conditioning status refers to the operating mode of the air conditioning system when the data is collected, which may include, for example, off, recirculation, and external circulation. Window status refers to the degree to which the windows are open when the data is collected, which may include, for example, fully closed, half-open, and fully open. When fully closed, the sensor is more affected by the interior environment; when fully open, it approximates the actual exterior environment. Windshield wiper status refers to the operating state of the windshield wipers when the data is collected, which may include, for example, off, low-speed on, and high-speed on. The on state indicates the presence of precipitation, which dilutes the concentration of air pollutants, leading to lower sensor readings. The first compensation coefficient is a correction coefficient used to correct the influence of air conditioning, window, and windshield wiper status on the air pollutant concentration data.

[0128] In one example, the edge device pre-defines a mapping table between vehicle states and a first compensation coefficient, as shown in Table 2. It should be noted that... Figure 2 This is merely an illustration; in actual applications, different first compensation coefficients can be set according to different types of air pollutants. Furthermore, the edge device can extract the air conditioning status, window status, and windshield wiper status from the initial environmental data packet, match them with the combined scenarios shown in Table 2, and determine the first compensation coefficient for each type of air pollutant.

[0129] Table 2 Mapping Table of Vehicle Status and First Compensation Coefficient

[0130]

[0131] S2024. The edge device performs compensation processing on the intermediate air pollutant concentration data according to the first compensation coefficient to obtain the processed air pollutant concentration data.

[0132] For example, compensation processing involves multiplying the first compensation coefficient with intermediate air pollutant concentration data to correct system deviations caused by the status of the air conditioner, windows, and wipers, making the data closer to the actual air pollutant concentration outside the vehicle. The processed air pollutant concentration data refers to intermediate data after dual purification through exhaust gas interference anomaly removal and status deviation compensation, possessing high environmental realism. It is understood that compensation processing needs to be performed separately for each type of air pollutant, with different first compensation coefficients corresponding to different air pollutants.

[0133] In one example, the edge device can first impute missing values ​​in the intermediate air pollutant concentration data, that is, calculate the missing values ​​using linear interpolation of adjacent data. Then, each intermediate pollutant concentration data is multiplied by its corresponding first compensation coefficient to obtain the processed air pollutant concentration data. Optionally, the processed air pollutant concentration data can be range-checked. If it exceeds the reasonable environmental concentration range for the pollutant, the maximum value within the reasonable range is taken as the final processed air pollutant concentration data to avoid new deviations caused by over-compensation.

[0134] S203. The edge device collects environmental data based on the collected environmental data and the preset benchmark, and performs compensation processing on the processed air pollutant concentration data to obtain air pollutant concentration data.

[0135] For example, the preset benchmark acquisition environment data refers to the standard environmental parameters for sensor calibration, which are set based on the sensor's factory calibration data. For example, it could be a temperature of 25°C, a relative humidity of 50%RH, and an atmospheric pressure of 1013hPa. The accuracy of the sensor acquisition data is highest under this environment.

[0136] Compensation processing refers to correcting the sensor's sensitivity deviation when environmental temperature, humidity, and air pressure deviate from baseline values, ensuring data consistency under different environmental conditions. Air pollutant concentration data refers to the final pollutant concentration data with high reliability after both cleaning and compensation processing. It is understandable that the compensation coefficient can be dynamically adjusted according to the sensor type, as different types of sensors have different environmental adaptability deviations.

[0137] In one example, the edge device has a pre-set mapping table of air pollutants, environmental parameters, sensor bias rates, and compensation factors, as shown in Table 3. Each type of sensor has a different bias rate under different environmental parameters, allowing for the setting of compensation factors for different environmental parameters. The second compensation coefficient can be the product of the compensation factors for each environmental parameter. It is understandable that different types of air pollutants are affected differently by temperature, humidity, and atmospheric pressure. It should be noted that Table 3 only shows the compensation factor for PM2.5; the principle is similar for other types of air pollutants, and specific settings can be made based on actual needs. Furthermore, the processed air pollutant concentration data can be compensated based on the second compensation coefficient to obtain the final air pollutant concentration data.

[0138] Table 3 Mapping table of air pollutants, environmental parameters, sensor bias rate and compensation factor

[0139]

[0140] Specifically, the edge device determines a second compensation coefficient based on the collected environmental data and the preset benchmark collected environmental data; based on the second compensation coefficient, it performs compensation processing on the processed air pollutant concentration data to obtain air pollutant concentration data.

[0141] The second compensation coefficient is a comprehensive correction coefficient used to correct sensor sensitivity deviations when environmental parameters such as temperature, humidity, and air pressure deviate from the reference value.

[0142] For example, edge devices can first query a preset mapping table, determine the compensation factor for each environmental parameter of air pollutant based on the deviation rate between the collected environmental data and the benchmark collected environmental data, and use the product of the compensation factors of each environmental parameter as the second compensation coefficient. Then, the product of the second compensation coefficient and the processed air pollutant concentration data is used as the air pollutant concentration data. This method achieves accurate calculation of the compensation coefficient when environmental parameters deviate, quantifies and superimposes the influence of environmental factors such as temperature, humidity, and air pressure on the sensor, effectively reducing sensor sensitivity deviation under different environmental conditions. This allows the air pollutant concentration data to be restored to the equivalent value under the benchmark environment, improving the comparability and accuracy of data in complex scenarios such as high temperature, high humidity, and high altitude.

[0143] Optionally, the air pollutant concentration data can be range-checked. If it exceeds the reasonable environmental concentration range of the pollutant, the maximum value of the reasonable range is taken as the final air pollutant concentration data to avoid new deviations caused by over-compensation.

[0144] S204. The edge device generates an environmental data packet corresponding to the initial environmental data packet based on the air pollutant concentration data.

[0145] It should be noted that this step is similar to the aforementioned step S103, and will not be repeated here.

[0146] S205. The edge device will send the environmental data packet corresponding to the initial environmental data packet of the target time period to the cloud server of the environmental monitoring system.

[0147] It should be noted that this step is similar to the aforementioned step S104, and will not be repeated here.

[0148] Correspondingly, the cloud server receives multiple environmental data packets sent by the edge devices of the environmental monitoring system during the target time period; each environmental data packet includes the location information of the vehicle when collecting air pollutant concentration data.

[0149] S206. The cloud server obtains multiple environmental data packets for each preset grid within the target time period based on the location information of the environmental data packets and the preset grid.

[0150] For example, location information refers to the location coordinates of the vehicle when data is collected.

[0151] In one example, the cloud server deploys distributed receiving nodes to receive environmental data packets uploaded by each edge device in segments according to the target area. After receiving the data packets, the location information in each environmental data packet is extracted, and the location coordinates are assigned to the corresponding preset grids through a geocoding mapping algorithm. The results are then aggregated to obtain all environmental data packets for each preset grid within the target time period.

[0152] S207. For each preset grid, the cloud server performs a weighted average of the air pollutant concentration data in each environmental data packet of the preset grid to obtain the initial environmental data of the preset grid in the target time period.

[0153] For example, as mentioned above, the environmental data package may include a quality score. Weighted average processing refers to setting weights based on the quality score of the environmental data package and calculating the statistical mean of air pollutant concentrations within a preset grid to highlight the reference value of high-quality data. Initial environmental data refers to the basic statistical values ​​of air pollutant concentrations for each preset grid within the target time period, calculated separately for each air pollutant type.

[0154] In one example, the cloud server filters environmental data packets for each preset grid where the quality score is greater than or equal to a preset score threshold within a target time period. If the amount of filtered data is greater than or equal to a preset quantity, a weight is set according to the quality score. A score of 100 points has a weight of 1.0, and the weight decreases by 0.1 for every 10 points decrease in the score. Then, based on the weight of each environmental data packet, the weighted average of each air pollutant is calculated, and this weighted average is used as the initial environmental data for the air pollutant corresponding to the preset grid.

[0155] S208. The cloud server obtains environmental monitoring data for the target time period from the environmental monitoring station to which the preset grid belongs.

[0156] It should be noted that this step is similar to the aforementioned step S107, and will not be repeated here.

[0157] S209. The cloud server inputs the environmental monitoring data for the target time period and the initial environmental data of the preset grid for the target time period into the preset association model, and outputs the intermediate environmental data of the preset grid for the target time period.

[0158] For example, the preset association model refers to an artificial intelligence model capable of learning the mapping relationship between environmental data of the grid and monitoring data of the monitoring station. It can learn the mapping relationship between the initial environmental data of different grids and the environmental monitoring data of the monitoring station. Intermediate environmental data refers to the air pollutant concentration data of the grid after preliminary correction by the model. It can be understood that the training samples of the association model may include environmental data packets of the grid in historical periods, historical environmental monitoring data of the corresponding monitoring station, and real environmental data of the grid. The training objective is to minimize the error between the model output and the real environmental data of the grid.

[0159] In one example, the training samples for the association model are historical data of all grids in the target area over the past year, including initial environmental data of the grids and environmental monitoring data of the monitoring stations, labeled as the historical real environmental data of the grids. After training, the initial environmental data of the grids in the target period and the environmental monitoring data of the monitoring stations to which they belong are input into the association model, and the model outputs intermediate environmental data.

[0160] S210, The cloud server determines the distance from the center point of the preset grid to the environmental monitoring station.

[0161] For example, the center point of the grid refers to the latitude and longitude coordinates of the preset grid geometric center, which can be calculated from the latitude and longitude of the grid boundary. The distance refers to the straight-line distance between the grid center point and the environmental monitoring station to which it belongs.

[0162] In one example, the cloud server first extracts the latitude and longitude information of the boundary of the preset grid and calculates the coordinates of the center point; then it obtains the latitude and longitude coordinates of the environmental monitoring station to which it belongs and calculates the straight-line distance between the two to obtain the distance.

[0163] S211. The cloud server determines the distance correction factor based on the distance.

[0164] For example, the distance correction factor is a weighted coefficient based on the straight-line distance between the grid and the monitoring station. It is used to balance the reference value of intermediate environmental data and the environmental monitoring data from the monitoring station. The closer the distance, the stronger the spatial representativeness of the monitoring station data, and the larger the correction factor. It can be understood that the threshold for the distance correction factor can be dynamically adjusted according to the terrain features of the target area.

[0165] In one example, the cloud server can pre-define a mapping table between the distance correction factor and the distance, as shown in Table 4.

[0166] Table 4. Mapping Table of Distance Correction Factor and Distance

[0167]

[0168] S212. The cloud server corrects the intermediate environmental data of the preset grid during the target time period based on the distance correction coefficient, and obtains the target environmental data of the preset grid during the target time period.

[0169] For example, the correction process refers to a secondary correction procedure based on the distance attenuation effect to further improve the spatial representativeness and accuracy of the data. The target environmental data refers to the final air pollutant concentration data of the grid, which possesses high accuracy and high authority after undergoing dual processing of model correlation correction and distance attenuation correction. It can be understood that the target environmental data can be output separately according to the type of air pollutant.

[0170] In one example, the cloud server uses the product of intermediate environment data and distance correction factor as the target environment data.

[0171] S213. The cloud server generates an environmental map based on the target environmental data of each preset grid during the target time period.

[0172] For example, an environmental map is a visual map that uses a preset grid as the smallest unit to intuitively display the distribution of air pollutant concentrations within a target area. It supports layered display, dynamic updates, and interactive queries. Understandably, environmental maps can use color gradients to indicate air pollutant concentration levels, with colors transitioning from green (low concentration) to red (high concentration), making it easier for users to quickly identify pollution distribution.

[0173] In one example, a cloud server can generate layered maps based on air pollutant type, such as PM2.5 and NOx layers. Each layer uses a uniform color gradient standard to obtain an environmental map. Optionally, the map update frequency can be set, and interactive functions can be added, allowing users to click on any grid to query specific target environmental data, as well as the concentration change trend curve of that grid over a preset time period.

[0174] Optionally, the generated environmental map can be connected to downstream applications such as vehicle head-up displays, environmental protection department monitoring platforms, and map manufacturers through preset interfaces.

[0175] Optionally, if it is determined that the number of environmental data packets in a preset grid is less than a preset threshold, then the target environmental data of the preset grid in the target time period is determined based on the target environmental data of the adjacent preset grids in the target time period.

[0176] For example, the preset threshold refers to the minimum number of data packets required to ensure the statistical validity of the grid environment data. It should be noted that the specific value of the preset threshold is not uniquely limited in this application embodiment and can be dynamically adjusted according to the vehicle density of the target area. Adjacent preset grids refer to preset grids that are geographically directly adjacent to grids with insufficient current data. For example, it can be an 8-neighbor grid consisting of 4 directly adjacent grids and 4 diagonally adjacent grids (with the current grid as the center, the top and bottom, left and right are directly adjacent, and the diagonal is diagonally adjacent). Specifically, a weight coefficient can be preset for each adjacent preset grid position, for example, 0.1 for the top left grid and 0.3 for the top grid. If it is determined that the number of environmental data packets in a preset grid is less than the preset threshold, a weighted summation can be performed based on the weight coefficients of each adjacent preset grid position and the target environment data corresponding to the grid to obtain the target environment data for the preset grid whose current number is less than the preset threshold during the target time period. By using weighted interpolation to supplement the target environmental data of 8 neighboring grids, the monitoring blind spots in remote and low-traffic areas in traditional monitoring methods are effectively filled, ensuring the integrity of environmental data in the entire grid of the target area and reducing misjudgments of blank or polluted areas on the environmental map due to missing local data.

[0177] S214. The cloud server determines the environmental pollution areas of the target area based on the environmental map.

[0178] For example, the cloud server can traverse the target environmental data of all grids in the environmental map and identify grids where the concentration increase exceeds a threshold as environmental pollution areas.

[0179] Optionally, the cloud server can also combine the vehicle status data and light status in the environmental data packet of the grid of the environmental pollution area to further determine whether there is smog. For example, if the proportion of lights in the grid that are on is greater than a preset threshold, then it is determined that there is smog in the grid.

[0180] Optionally, the navigation device can obtain an environmental map of the target area through a preset interface, and then, based on the environmental map of the target area and the preset map, combined with the user's input of the starting point and destination, output a healthy travel route plan to reduce the passage through polluted areas.

[0181] Optionally, the transportation department's planning platform can obtain an environmental map of the target area through a preset interface, and then dynamically adjust the duration of traffic lights based on the pollution situation (areas with concentrated exhaust pollution) provided by the environmental map of the target area.

[0182] Optionally, the environmental protection department's monitoring platform can obtain an environmental map of the target area through a preset interface, and then determine the pollution source based on the pollution situation provided by the environmental map of the target area, and generate an environmental assessment report for the target area.

[0183] The environmental data processing method provided in this application embodiment acquires the initial environmental data packets sent by vehicles through edge devices. First, it cleans and removes exhaust gas interference anomalies based on vehicle status data. Then, it performs compensation processing by combining the collected environmental data with benchmark data to generate standardized environmental data packets and transmits them to a cloud server. The cloud server divides the target area into preset grids, aggregates the environmental data packets of each grid based on positioning information, and obtains the initial environmental data through weighted averaging. Subsequently, it combines authoritative data from the monitoring station to obtain intermediate environmental data through a preset correlation model. Then, it determines the distance correction coefficient based on the distance between the grid and the monitoring station to complete the second correction and obtain the target environmental data. Finally, it generates a high-resolution environmental map based on the target environmental data of each grid, thereby determining the environmental pollution area of ​​the target area. This method fills the monitoring blind spots of traditional fixed monitoring stations by collecting data through vehicle movement, achieving full coverage of the target area. The hierarchical correction processing at the edge and cloud effectively reduces errors caused by vehicle interference, environmental factors, and sensor drift. Combined with data from authoritative monitoring stations and distance attenuation effect, it further improves data accuracy. The minute-level update frequency and street-level spatial resolution environmental map ensure the real-time and refined nature of the monitoring results, enabling rapid capture of sudden pollution events and providing reliable data support for accurate pollution source tracing and real-time governance.

[0184] Figure 5 Schematic diagram of the structure of the environmental data processing device provided in this application Figure 1 ,like Figure 5 As shown, the environmental data processing device 300 provided in this embodiment can be applied to a cloud server. The device includes:

[0185] The first acquisition module 301 is used to acquire multiple environmental data packets for each preset grid in the target area during a target time period; wherein each environmental data packet includes at least one air pollutant concentration data collected by the vehicle;

[0186] The first determining module 302 is used to determine the initial environmental data of each preset grid in the target time period based on the air pollutant concentration data in the environmental data packet of the preset grid in the target time period.

[0187] The second acquisition module 303 is used to acquire environmental monitoring data of the environmental monitoring station to which the preset grid belongs during the target time period;

[0188] The correction module 304 is used to correct the initial environmental data of the preset grid in the target time period based on the environmental monitoring data of the target time period, so as to obtain the target environmental data of the preset grid in the target time period.

[0189] The second determining module 305 is used to determine the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period.

[0190] In one possible implementation, the first determining module 302 is configured to:

[0191] For each preset grid, the air pollutant concentration data in each environmental data packet of the preset grid are weighted and averaged to obtain the initial environmental data of the preset grid in the target time period.

[0192] In one possible implementation, the correction module 304 is configured to:

[0193] The environmental monitoring data for the target time period and the initial environmental data of the preset grid for the target time period are input into the preset correlation model, and the intermediate environmental data of the preset grid for the target time period are output.

[0194] Determine the distance from the center point of the preset grid to the environmental monitoring station;

[0195] Determine the distance correction factor based on the distance;

[0196] Based on the distance correction coefficient, the intermediate environmental data of the preset grid in the target time period is corrected to obtain the target environmental data of the preset grid in the target time period.

[0197] In one possible implementation, the second determining module 305 is configured to:

[0198] If it is determined that the number of environmental data packets in a preset grid is less than a preset threshold, then the target environmental data of the preset grid in the target time period is determined based on the target environmental data of the adjacent preset grids in the target time period.

[0199] In one possible implementation, the second determining module 305 is configured to:

[0200] An environmental map is generated based on the target environmental data of each preset grid during the target time period;

[0201] Based on the environmental map, identify the areas of environmental pollution within the target region.

[0202] In one possible implementation, the first acquisition module 301 is configured to:

[0203] The system receives multiple environmental data packets sent by edge devices of the environmental monitoring system during a target time period; each environmental data packet includes the location information of the vehicle when collecting air pollutant concentration data.

[0204] Based on the location information of the environmental data packets and the preset grid, multiple environmental data packets for each preset grid in the target time period are obtained.

[0205] The environmental data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0206] Figure 6 Schematic diagram of the structure of the environmental data processing device provided in this application Figure 2 .like Figure 6 As shown, the environmental data processing device 400 provided in this embodiment can be applied to edge devices, and the device includes:

[0207] The acquisition module 401 is used to acquire multiple initial environmental data packets sent by the vehicle during the target time period; wherein each initial environmental data packet includes collected environmental data, vehicle status data and at least one initial air pollutant concentration data collected by the vehicle.

[0208] The correction module 402 is used to correct the initial air pollutant concentration data of each initial environmental data packet based on the collected environmental data and vehicle status data of the initial environmental data packet, so as to obtain the air pollutant concentration data.

[0209] The generation module 403 is used to generate an environmental data packet corresponding to the initial environmental data packet based on the air pollutant concentration data.

[0210] The sending module 404 is used to send the environmental data packet corresponding to the initial environmental data packet of the target time period to the cloud server of the environmental monitoring system.

[0211] In one possible implementation, the correction module 402 is configured to:

[0212] Based on the vehicle status data in the initial environmental data packet, the initial air pollutant concentration data in the initial environmental data packet is cleaned to obtain the processed air pollutant concentration data.

[0213] Based on the collected environmental data and the pre-set baseline collected environmental data, the processed air pollutant concentration data is compensated to obtain the air pollutant concentration data.

[0214] In one possible implementation, vehicle status data includes vehicle speed, engine speed, air conditioning status, window status, and windshield wiper status; correction module 402 is used for:

[0215] Determine the exhaust gas interference level within the target time period based on vehicle speed and generator speed;

[0216] Based on the exhaust gas interference level, outliers in the initial air pollutant concentration data of the initial environmental data package are removed to obtain intermediate air pollutant concentration data.

[0217] The first compensation coefficient is determined based on the status of the air conditioning, windows, and windshield wipers.

[0218] Based on the first compensation coefficient, the intermediate air pollutant concentration data is compensated to obtain the processed air pollutant concentration data.

[0219] In one possible implementation, the correction module 402 is configured to:

[0220] The second compensation coefficient is determined based on the collected environmental data and the preset benchmark collected environmental data;

[0221] Based on the second compensation coefficient, the processed air pollutant concentration data is compensated to obtain the air pollutant concentration data.

[0222] The environmental data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0223] Figure 7 A schematic diagram of the structure of the electronic device provided in this application. Figure 7 As shown, the electronic device 500 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 500 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus. This electronic device can be the aforementioned edge device or a cloud server.

[0224] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0225] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0226] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0227] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0228] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0229] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0230] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0231] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0232] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0233] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0234] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0235] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0236] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0237] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0238] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing environmental data, characterized in that, The method is applied to a cloud server in an environmental monitoring system, and the method includes: Acquire multiple environmental data packets for each preset grid in the target area during the target time period; wherein each environmental data packet includes at least one air pollutant concentration data collected by the vehicle; For each preset grid, the initial environmental data for that preset grid during the target time period is determined based on the air pollutant concentration data in the environmental data packet for that preset grid during the target time period. Obtain environmental monitoring data from the environmental monitoring station to which the preset grid belongs during the target time period; Based on the environmental monitoring data for the target time period, the initial environmental data of the preset grid during the target time period is corrected to obtain the target environmental data of the preset grid during the target time period. Based on the target environmental data of each preset grid during the target time period, the environmental pollution area of ​​the target region is determined.

2. The method according to claim 1, characterized in that, For each preset grid, the initial environmental data for the target time period is determined based on the air pollutant concentration data in the environmental data packet for that preset grid, including: For each preset grid, the air pollutant concentration data in each environmental data packet of the preset grid are weighted and averaged to obtain the initial environmental data of the preset grid in the target time period.

3. The method according to claim 1, characterized in that, The method of correcting the initial environmental data of the preset grid during the target time period based on the environmental monitoring data of the target time period to obtain the target environmental data of the preset grid during the target time period includes: The environmental monitoring data for the target time period and the initial environmental data of the preset grid during the target time period are input into the preset association model, and the intermediate environmental data of the preset grid during the target time period are output. Determine the distance from the center point of the preset grid to the environmental monitoring station; Based on the distance, determine the distance correction factor; Based on the distance correction coefficient, the intermediate environmental data of the preset grid in the target time period is corrected to obtain the target environmental data of the preset grid in the target time period.

4. The method according to claim 1, characterized in that, The method further includes: If it is determined that the number of environmental data packets in a preset grid is less than a preset threshold, then the target environmental data of the preset grid in the target time period is determined based on the target environmental data of the adjacent preset grids in the target time period.

5. The method according to any one of claims 1-4, characterized in that, The step of determining the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period includes: An environmental map is generated based on the target environmental data of each preset grid during the target time period; Based on the environmental map, the environmental pollution areas of the target area are determined.

6. The method according to any one of claims 1-4, characterized in that, The acquisition of multiple environmental data packets for each preset grid in the target area during the target time period includes: The system receives multiple environmental data packets sent by the edge device of the environmental monitoring system during a target time period; each environmental data packet includes the location information of the vehicle when collecting air pollutant concentration data; Based on the location information of the environmental data packets and the preset grid, multiple environmental data packets for each preset grid in the target time period are obtained.

7. A method for processing environmental data, characterized in that, The method is applied to edge devices in an environmental monitoring system, and the method includes: Acquire multiple initial environmental data packets sent by the vehicle during the target time period; wherein each initial environmental data packet includes collected environmental data, vehicle status data, and at least one initial air pollutant concentration data; For each initial environmental data packet, the initial air pollutant concentration data of the initial environmental data packet is corrected based on the collected environmental data and vehicle status data of the initial environmental data packet to obtain the air pollutant concentration data. Based on the air pollutant concentration data, an environmental data package corresponding to the initial environmental data package is generated; The environmental data packet corresponding to the initial environmental data packet for the target time period is sent to the cloud server of the environmental monitoring system; wherein, the environmental data packet is the environmental data packet as described in any one of claims 1-6.

8. The method according to claim 7, characterized in that, The step of correcting the initial air pollutant concentration data in the initial environmental data packet based on the collected environmental data and vehicle status data to obtain air pollutant concentration data includes: Based on the vehicle status data in the initial environmental data packet, the initial air pollutant concentration data in the initial environmental data packet is cleaned to obtain the processed air pollutant concentration data. Based on the collected environmental data and the pre-set baseline collected environmental data, the processed air pollutant concentration data is compensated to obtain the air pollutant concentration data.

9. The method according to claim 8, characterized in that, The vehicle status data includes vehicle speed, engine speed, air conditioning status, window status, and windshield wiper status; based on the vehicle status data of the initial environmental data packet, the initial air pollutant concentration data of the initial environmental data packet is cleaned to obtain processed air pollutant concentration data, including: The exhaust gas interference level is determined based on the vehicle speed and the generator rotation speed during the target time period; Based on the exhaust gas interference level, outliers in the initial air pollutant concentration data of the initial environmental data packet are deleted to obtain intermediate air pollutant concentration data. A first compensation coefficient is determined based on the air conditioning status, the window status, and the windshield wiper status. The intermediate air pollutant concentration data is compensated based on the first compensation coefficient to obtain the processed air pollutant concentration data.

10. The method according to claim 8, characterized in that, The process of compensating the processed air pollutant concentration data based on the collected environmental data and preset benchmark collected environmental data to obtain air pollutant concentration data includes: The second compensation coefficient is determined based on the collected environmental data and the preset benchmark collected environmental data; The air pollutant concentration data is compensated according to the second compensation coefficient to obtain the air pollutant concentration data.

11. An environmental data processing device, characterized in that, The device is used in a cloud server of an environmental monitoring system, and the device includes: The first acquisition module is used to acquire multiple environmental data packets for each preset grid in the target area during the target time period; wherein each environmental data packet includes at least one air pollutant concentration data collected by the vehicle; The first determining module is used to determine the initial environmental data of each preset grid in the target time period based on the air pollutant concentration data in the environmental data packet of the preset grid in the target time period. The second acquisition module is used to acquire environmental monitoring data of the environmental monitoring station to which the preset grid belongs during the target time period; The correction module is used to correct the initial environmental data of the preset grid during the target time period based on the environmental monitoring data during the target time period, so as to obtain the target environmental data of the preset grid during the target time period. The second determining module is used to determine the environmental pollution area of ​​the target region based on the target environmental data of each preset grid during the target time period.

12. An environmental data processing device, characterized in that, The device is used as an edge device in an environmental monitoring system, and the device includes: The acquisition module is used to acquire multiple initial environmental data packets sent by the vehicle during the target time period; wherein each initial environmental data packet includes collected environmental data, vehicle status data, and at least one initial air pollutant concentration data. The correction module is used to correct the initial air pollutant concentration data of each initial environmental data packet based on the collected environmental data and vehicle status data of the initial environmental data packet, so as to obtain the air pollutant concentration data. The generation module is used to generate an environmental data packet corresponding to the initial environmental data packet based on the air pollutant concentration data. The sending module is used to send the environmental data packet corresponding to the initial environmental data packet for the target time period to the cloud server of the environmental monitoring system.

13. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-10.

14. An environmental monitoring system, characterized in that, The environmental monitoring system includes a cloud server, at least one edge device, and multiple vehicles; the cloud server is communicatively connected to the edge device, and each edge device is communicatively connected to the vehicle. The vehicle is used to acquire multiple initial environmental data packets during a target time period and send them to the edge device; wherein each initial environmental data packet includes environmental data collected by the vehicle, vehicle status data, and at least one initial air pollutant concentration data; The cloud server is used to perform the method as described in any one of claims 1-6; The edge device is used to perform the method as described in any one of claims 7-10.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

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