Data processing method, system and device based on atmospheric environment remote sensing monitoring

Abstract

The application discloses a data processing method, system and device based on atmospheric environment remote sensing monitoring, and the method comprises the following steps: acquiring monitoring data of a geographical environment; screening final monitoring data required for process simulation from the monitoring data; acquiring input data of a prediction object; performing process simulation on the prediction object according to the final monitoring data and the input data, and determining a simulation result; and judging whether the current geographical environment state meets the requirements of the prediction object according to the simulation result. The application can obtain the required final monitoring data by gradually screening the acquired monitoring data, can provide more accurate basic data for subsequent process simulation, makes the process simulation more real and accurate, makes the simulation result more accurate, and improves the accuracy of judging whether the current geographical environment state meets the requirements of the prediction object.

Description

Technical Field

[0001] This application belongs to the field of geographic environment and remote sensing applications, and specifically relates to a data processing method, system and device based on atmospheric environment remote sensing monitoring. Background Technology

[0002] With the rapid development of computer software technology and aerospace technology, geographic remote sensing information technology has become an important means for people to understand and explore geographic information, and has been well applied in various fields, such as environmental remote sensing, atmospheric remote sensing, resource remote sensing, marine remote sensing, geological remote sensing, agricultural remote sensing, and forestry remote sensing.

[0003] In geographic remote sensing, remote sensing technology is primarily used as a means of collecting and updating data for geographic information systems. Specifically, relevant spatial environmental data is typically acquired using appropriate remote sensing techniques, classified simply, and then processed and analyzed by a geographic information system.

[0004] In practice, the application of geographic environmental data requires the simulation and prediction of the evolution of the predicted object under current atmospheric environmental conditions. For example, the simulation of the diffusion process of various pollutants in the atmosphere under current meteorological conditions, and the prediction results after diffusion. Although there are some models in the existing technology that simulate this process, they all use fixed model parameters for simulation and prediction. Obviously, they cannot simulate the evolution process of the predicted object in real time and dynamically according to the actual atmospheric environmental conditions. They also do not combine with increasingly mature geographic remote sensing technology to obtain accurate and real-time data to further improve the accuracy of the simulation and prediction results. Therefore, they inevitably cannot meet people's needs and requirements for the application of geographic environmental data. Summary of the Invention

[0005] In view of the above problems, this application provides a data processing method, system and device based on atmospheric environment remote sensing monitoring to solve the above technical problems.

[0006] This application provides the following technical solutions:

[0007] Firstly, this application provides a data processing method based on atmospheric environment remote sensing monitoring, the method comprising:

[0008] Obtain monitoring data of the geographical environment;

[0009] From the monitoring data, the final monitoring data required for process simulation is selected;

[0010] Obtain the input data for the prediction object;

[0011] Based on the final monitoring data and the input data, a process simulation is performed on the predicted object to determine the simulation results;

[0012] Based on the simulation results, determine whether the current geographical environment meets the requirements of the predicted object;

[0013] The monitoring data refers to monitoring data that characterizes the state of the geographical environment.

[0014] The input data is the state data of the object to be predicted;

[0015] The prediction object is the specific object that needs to be simulated.

[0016] The process simulation is a simulation of the evolution of the predicted object in the current geographical environment;

[0017] Furthermore, the step of filtering out the final monitoring data required for process simulation from the monitoring data includes:

[0018] To acquire the first monitoring data related to the state of the atmospheric environment;

[0019] After filtering the acquired first monitoring data, second monitoring data is obtained;

[0020] The second monitoring data will be used as the final monitoring data;

[0021] The first monitoring data is monitoring data related to the state of the atmospheric environment;

[0022] The second monitoring data is the relevant monitoring data extracted from the first monitoring data to determine the simulation results;

[0023] Furthermore, the step of filtering the acquired first monitoring data to obtain second monitoring data includes:

[0024] From the first monitoring data, extract the first diffusion data, the second diffusion data, and other monitoring data related to the atmospheric environment status;

[0025] The first diffusion data, the second diffusion data, and other monitoring data are used as the second monitoring data;

[0026] The first diffusion data is data related to atmospheric diffusion in the first state of the atmospheric environment;

[0027] The second diffusion data is data related to atmospheric diffusion in the second state of the atmospheric environment;

[0028] The other monitoring data refers to other monitoring data besides the first diffusion data and the second diffusion data, which are also used to determine the simulation results;

[0029] The first state refers to the atmospheric environment state at the time of the previous sampling in the atmospheric environment monitoring data sampling cycle.

[0030] The second state refers to the atmospheric environment state at the time of the last sampling in the monitoring data sampling cycle during the atmospheric environment monitoring process;

[0031] The sampling time interval between the first state and the second state is a sampling period for one monitoring data.

[0032] This application uses the atmospheric environment data related to atmospheric diffusion in the first state from the first monitoring data as the first diffusion data, and the atmospheric environment data related to atmospheric diffusion in the second state from the first monitoring data as the second diffusion data. Then, the first diffusion data and the second diffusion data are selected from the first monitoring data, and other monitoring data used to determine the simulation results are also selected from the first monitoring data. The first diffusion data, the second diffusion data, and the other monitoring data are then used as the second monitoring data. Since the process simulation is a simulation of the diffusion process of the predicted object under the current atmospheric environment, and the second monitoring data extracted in this application includes diffusion data from two adjacent states related to atmospheric diffusion, it can more accurately reflect the current atmospheric environment state. Moreover, due to the real-time acquisition of data, the real-time nature of the subsequent process simulation can be guaranteed, so that the simulation results determined by the process simulation of the predicted object more accurately reflect the real atmospheric environment state, improve the real-time performance and accuracy of the process simulation, and make the final simulation results more accurate and have better dynamic response performance.

[0033] Furthermore, the step of extracting first diffusion data, second diffusion data, and other monitoring data related to atmospheric environmental conditions from the first monitoring data includes:

[0034] From the first monitoring data, data related to the first grid in the first state, data related to the second grid in the first state, and other data related to the first state are extracted respectively as the first diffusion data;

[0035] From the first monitoring data, data related to the first grid in the second state, data related to the second grid in the second state, and other data related to the second state are extracted respectively as the second diffusion data;

[0036] The first grid is the monitoring grid that completely covers the monitored objects in the atmospheric remote sensing monitoring grid;

[0037] The second grid is the monitoring grid that is partially covered by the monitored object in the atmospheric remote sensing monitoring grid;

[0038] Specifically, since the size of the remote sensing monitoring grid is equivalent to the resolution of the remote sensing monitoring, using the relevant data from the monitoring grid in the first and second diffusion data is equivalent to directly using data with the same precision as the remote sensing monitoring as the basis for subsequent process simulations. This ensures the accuracy of the data to the greatest extent. This application uses the relevant data from the atmospheric remote sensing monitoring grid as part of the first and second diffusion data, and divides it into a first grid and a second grid based on the specific atmospheric environmental conditions. This not only closely integrates with remote sensing monitoring technology but also maintains the same data precision as the remote sensing monitoring technology, thus ensuring the accuracy of the data to the greatest extent.

[0039] Furthermore, the first diffusion data includes: the number of first grids in the first state, the coverage of each category of the second grids in the first state and their corresponding numbers, and the wind speed in the first state;

[0040] The second diffusion data includes: the number of first grids in the second state, the coverage of each category of the second grids in the second state and their corresponding numbers, and the wind speed in the second state;

[0041] The other monitoring data includes: the sampling period of the monitoring data, and the maximum value of the diffusion distance under different wind directions from the first state to the second state;

[0042] Furthermore, the step of performing process simulation on the predicted object based on the final monitoring data and the input data, and determining the simulation results, includes:

[0043] Based on the final monitoring data, the first parameter is determined;

[0044] Based on the first parameter and the input data, determine the simulation result;

[0045] The first parameter is a parameter related to atmospheric diffusion characteristics and is used to determine the simulation results;

[0046] Specifically, depending on the specific circumstances, different simulation results may correspond to different or the same simulation models.

[0047] Furthermore, the first parameter is the atmospheric diffusion rate under the current meteorological conditions, specifically:

[0048]

[0049] Wherein, δ represents the atmospheric diffusion rate under the current meteorological conditions;

[0050] n1 is the number of the first grid cells in the first state;

[0051] α i The value represents the coverage of the i-th class in the second grid in the first state.

[0052] n 1i This represents the number of second grid cells in the first state that also have the i-th type of coverage.

[0053] m1 is the total number of coverage categories in the second grid division in the first state;

[0054] n2 is the number of the first grid cells in the second state;

[0055] β j The value represents the coverage of the j-th class in the second grid in the second state.

[0056] n 2j This represents the number of second grid cells in the second state that also have the j-th type of coverage.

[0057] m2 is the total number of coverage categories in the second grid division in the second state;

[0058] d0 is the maximum diffusion distance under different wind directions from the first state to the second state;

[0059] v f1 The wind speed in the first state;

[0060] v f2 The wind speed in the second state;

[0061] The coverage rate refers to the proportion of the monitored objects within the atmospheric remote sensing monitoring grid; changes in the monitored objects reflect the state of the atmospheric environment.

[0062] The diffusion distance is the distance the monitored object moves during the diffusion process in the atmospheric environment;

[0063] Furthermore, the simulation results include: a first simulation result and its model, specifically:

[0064] T t =[log (1+δ) C0-log (1+δ) C t ]T0

[0065] Among them, T t The first simulation result shows that, under the current meteorological conditions, the concentration changes from C0 to C during the diffusion process. t Time required;

[0066] δ represents the atmospheric diffusion rate under current meteorological conditions;

[0067] T0 is the time elapsed from the first state to the second state, which is the sampling period of the monitoring data;

[0068] C0 represents the initial concentration of the target object under current meteorological conditions before diffusion;

[0069] C t The target concentration of the object after diffusion under current meteorological conditions;

[0070] Specifically, the input data is the state data of the object to be predicted, specifically the initial state data and target state data of the object in the atmospheric environment.

[0071] Furthermore, the simulation results also include: a second simulation result and its model, specifically:

[0072]

[0073] d t Under current meteorological conditions, the concentration after diffusion changes from C0 to C t At that time, the maximum distance of diffusion in the same direction as the wind direction in the second state;

[0074] Specifically, because the monitoring data sampling period is short, the wind direction in the first and second states is the same or very close. In reality, the wind direction usually changes little in a short period of time, so it is considered to be the same wind direction in the process simulation.

[0075] Specifically, the determined simulation results are used to measure the specific state of the predicted object after the diffusion process under current meteorological conditions. Therefore, the simulation results are: simulation results (including the first simulation result and the second simulation result) predicted based on the state data of the predicted object (i.e., initial state data and target state data) under the current timeframe reflected by atmospheric environmental monitoring data. Therefore, when determining whether the current geographical environment state meets the requirements of the predicted object based on the simulation results, the simulation results can be compared with the requirements of the predicted object to draw a conclusion on whether the requirements are met. For example, if the requirement of the predicted object is that the concentration changes from C0 to C during the diffusion process under current meteorological conditions... t The required time does not exceed T t0 When its first simulation result T t Less than or equal to T t0 When the result of the first simulation meets the requirements of the predicted object, it indicates that the result of the first simulation meets the requirements of the predicted object; if the requirement of the predicted object is that the concentration after diffusion changes from X0 to C under the current meteorological conditions. t At that time, in the same direction as the wind direction in the second state, the maximum diffusion distance does not exceed d. t0 When its second simulation result d t Greater than d t0 When the result of the second simulation does not meet the requirements of the object being predicted, it indicates that the result of the second simulation does not meet the requirements of the object being predicted.

[0076] Secondly, this application provides a data processing system based on atmospheric environment remote sensing monitoring, the system comprising:

[0077] The monitoring data acquisition unit is used to acquire monitoring data of the geographical environment and filter out the final monitoring data required for process simulation from the monitoring data.

[0078] The input data acquisition unit is used to acquire the input data of the prediction object;

[0079] The simulation result determination unit is used to perform process simulation on the predicted object based on the final monitoring data and the input data, and determine the simulation result;

[0080] The simulation result judgment unit is used to determine whether the current geographical environment status meets the requirements of the prediction object based on the simulation results.

[0081] The monitoring data refers to monitoring data that characterizes the state of the geographical environment.

[0082] The input data is the state data of the object to be predicted;

[0083] The prediction object is the specific object that needs to be simulated.

[0084] The process simulation is a simulation of the evolution of the predicted object in the current geographical environment.

[0085] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0086] Fourthly, this application provides a computer device including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the method as described in the first aspect.

[0087] In summary, this application can obtain the final monitoring data required by progressively filtering the acquired monitoring data, which can provide more accurate basic data for subsequent process simulation, making the process simulation more realistic and accurate, and the simulation results more precise, thereby improving the accuracy of judging whether the current geographical environment meets the requirements of the prediction object. Attached Figure Description

[0088] For ease of explanation, this application is described in detail below with reference to specific embodiments and accompanying drawings.

[0089] Figure 1 This is one of the flowcharts illustrating the method of this application;

[0090] Figure 2This is the second schematic diagram of the method flow of this application;

[0091] Figure 3 This is the third schematic diagram of the method flow of this application;

[0092] Figure 4 This is a schematic diagram of the system architecture of this application;

[0093] Figure 5 This is a schematic diagram of a computer-readable storage medium according to this application;

[0094] Figure 6 This is a schematic diagram of the computer device described in this application. Detailed Implementation

[0095] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the figures. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0096] Example 1

[0097] like Figure 1 As shown in the figure, this embodiment provides a data processing method based on atmospheric environment remote sensing monitoring, the method including:

[0098] Obtain monitoring data of the geographical environment;

[0099] From the monitoring data, the final monitoring data required for process simulation is selected;

[0100] Obtain the input data for the prediction object;

[0101] Based on the final monitoring data and the input data, a process simulation is performed on the predicted object to determine the simulation results;

[0102] Based on the simulation results, determine whether the current geographical environment meets the requirements of the predicted object;

[0103] The monitoring data refers to monitoring data that characterizes the state of the geographical environment.

[0104] The input data is the state data of the object to be predicted;

[0105] The prediction object is the specific object that needs to be simulated.

[0106] The process simulation is a simulation of the evolution of the predicted object in the current geographical environment;

[0107] This application uses monitoring data of the geographic environment and input data of the predicted object to simulate the process of the predicted object. It can reflect the evolution process of the predicted object in the current geographic environment and provide data support for judging whether the current geographic environment meets the requirements of the predicted object. Since the process simulation uses real-time monitoring data and the input data is the state data of the predicted object, the real-time performance and accuracy of the judgment are further improved.

[0108] Furthermore, such as Figure 2 As shown, the step of filtering the final monitoring data required for process simulation from the monitoring data includes:

[0109] To acquire the first monitoring data related to the state of the atmospheric environment;

[0110] After filtering the acquired first monitoring data, second monitoring data is obtained;

[0111] The second monitoring data will be used as the final monitoring data;

[0112] The first monitoring data is monitoring data related to the state of the atmospheric environment;

[0113] The second monitoring data is the relevant monitoring data extracted from the first monitoring data to determine the simulation results;

[0114] This application first obtains first monitoring data related to the atmospheric environment state. Then, it selects relevant monitoring data from the first monitoring data to determine the simulation results, which are used as the final monitoring data required for the process simulation. This improves the availability of relevant data, facilitates subsequent data analysis, and enhances the accuracy of the data used in the process simulation. As a result, the determined simulation results are more consistent with the actual atmospheric environment state, and the accuracy of the simulation results is higher.

[0115] Furthermore, the step of filtering the acquired first monitoring data to obtain second monitoring data includes:

[0116] From the first monitoring data, extract the first diffusion data, the second diffusion data, and other monitoring data related to the atmospheric environment status;

[0117] The first diffusion data, the second diffusion data, and other monitoring data are used as the second monitoring data;

[0118] The first diffusion data is data related to atmospheric diffusion in the first state of the atmospheric environment;

[0119] The second diffusion data is data related to atmospheric diffusion in the second state of the atmospheric environment;

[0120] The other monitoring data refers to other monitoring data besides the first diffusion data and the second diffusion data, which are also used to determine the simulation results;

[0121] The first state refers to the atmospheric environment state at the time of the previous sampling in the atmospheric environment monitoring data sampling cycle.

[0122] The second state refers to the atmospheric environment state at the time of the last sampling in the monitoring data sampling cycle during the atmospheric environment monitoring process;

[0123] The sampling time interval between the first state and the second state is a sampling period for one monitoring data.

[0124] This application uses the atmospheric environment data related to atmospheric diffusion in the first state from the first monitoring data as the first diffusion data, and the atmospheric environment data related to atmospheric diffusion in the second state from the first monitoring data as the second diffusion data. Then, the first diffusion data and the second diffusion data are selected from the first monitoring data, and other monitoring data used to determine the simulation results are also selected from the first monitoring data. The first diffusion data, the second diffusion data, and the other monitoring data are then used as the second monitoring data. Since the process simulation is a simulation of the diffusion process of the predicted object under the current atmospheric environment, and the second monitoring data extracted in this application includes diffusion data from two adjacent states related to atmospheric diffusion, it can more accurately reflect the current atmospheric environment state. Moreover, due to the real-time acquisition of data, the real-time nature of the subsequent process simulation can be guaranteed, so that the simulation results determined by the process simulation of the predicted object more accurately reflect the real atmospheric environment state, improve the real-time performance and accuracy of the process simulation, and make the final simulation results more accurate and have better dynamic response performance.

[0125] Furthermore, such as Figure 3 As shown, the step of extracting first diffusion data, second diffusion data, and other monitoring data related to atmospheric environmental conditions from the first monitoring data includes:

[0126] From the first monitoring data, data related to the first grid in the first state, data related to the second grid in the first state, and other data related to the first state are extracted respectively as the first diffusion data;

[0127] From the first monitoring data, data related to the first grid in the second state, data related to the second grid in the second state, and other data related to the second state are extracted respectively as the second diffusion data;

[0128] The first grid is the monitoring grid that completely covers the monitored objects in the atmospheric remote sensing monitoring grid;

[0129] The second grid is the monitoring grid that is partially covered by the monitored object in the atmospheric remote sensing monitoring grid;

[0130] Specifically, since the size of the remote sensing monitoring grid is equivalent to the resolution of the remote sensing monitoring, using the relevant data of the monitoring grid in the first and second diffusion data is equivalent to directly using data with the same accuracy as the remote sensing monitoring as the basis data for subsequent process simulation, which can ensure the accuracy of the data to the greatest extent.

[0131] This application incorporates relevant data from atmospheric remote sensing monitoring grids as part of the first and second diffusion data, and divides them into the first and second grids based on specific atmospheric environmental conditions. This not only closely integrates with remote sensing monitoring technology, but also ensures that the data accuracy is consistent with that of remote sensing monitoring technology, thus maximizing the accuracy of the data.

[0132] Furthermore, the first diffusion data includes: the number of first grids in the first state, the coverage of each category of the second grids in the first state and their corresponding numbers, and the wind speed in the first state;

[0133] The second diffusion data includes: the number of first grids in the second state, the coverage of each category of the second grids in the second state and their corresponding numbers, and the wind speed in the second state;

[0134] The other monitoring data includes: the sampling period of the monitoring data, and the maximum value of the diffusion distance under different wind directions from the first state to the second state;

[0135] Because this application uses first diffusion data, second diffusion data, and other monitoring data, it takes the number of first grids under atmospheric conditions, the coverage of each category of the second grid and its corresponding quantity, real-time wind speed, sampling period, and diffusion distance as the second monitoring data, which further improves the real-time performance and accuracy of the various parameters required in the process simulation. This makes the data used in the process simulation more realistic and accurate in reflecting the current atmospheric conditions, and the simulation results can accurately reflect the diffusion process of the predicted object under the current atmospheric conditions, thus ensuring the accuracy of the simulation results.

[0136] Example 2

[0137] like Figure 1 As shown in the figure, this embodiment provides a data processing method based on atmospheric environment remote sensing monitoring, the method including:

[0138] Obtain monitoring data of the geographical environment;

[0139] From the monitoring data, the final monitoring data required for process simulation is selected;

[0140] Obtain the input data for the prediction object;

[0141] Based on the final monitoring data and the input data, a process simulation is performed on the predicted object to determine the simulation results;

[0142] Based on the simulation results, determine whether the current geographical environment meets the requirements of the predicted object;

[0143] The monitoring data refers to monitoring data that characterizes the state of the geographical environment.

[0144] The input data is the state data of the object to be predicted;

[0145] The prediction object is the specific object that needs to be simulated.

[0146] The process simulation is a simulation of the evolution of the predicted object in the current geographical environment;

[0147] This application uses monitoring data of the geographic environment and input data of the predicted object to simulate the process of the predicted object. It can reflect the evolution process of the predicted object in the current geographic environment and provide data support for judging whether the current geographic environment meets the requirements of the predicted object. Since the process simulation uses real-time monitoring data and the input data is the state data of the predicted object, the real-time performance and accuracy of the judgment are further improved.

[0148] Furthermore, such as Figure 2 As shown, the step of filtering the final monitoring data required for process simulation from the monitoring data includes:

[0149] To acquire the first monitoring data related to the state of the atmospheric environment;

[0150] After filtering the acquired first monitoring data, second monitoring data is obtained;

[0151] The second monitoring data will be used as the final monitoring data;

[0152] The first monitoring data is monitoring data related to the state of the atmospheric environment;

[0153] The second monitoring data is the relevant monitoring data extracted from the first monitoring data to determine the simulation results;

[0154] This application first obtains first monitoring data related to the atmospheric environment state. Then, it selects relevant monitoring data from the first monitoring data to determine the simulation results, which are used as the final monitoring data required for the process simulation. This improves the availability of relevant data, facilitates subsequent data analysis, and enhances the accuracy of the data used in the process simulation. As a result, the determined simulation results are more consistent with the actual atmospheric environment state, and the accuracy of the simulation results is higher.

[0155] Furthermore, the step of filtering the acquired first monitoring data to obtain second monitoring data includes:

[0156] From the first monitoring data, extract the first diffusion data, the second diffusion data, and other monitoring data related to the atmospheric environment status;

[0157] The first diffusion data, the second diffusion data, and other monitoring data are used as the second monitoring data;

[0158] The first diffusion data is data related to atmospheric diffusion in the first state of the atmospheric environment;

[0159] The second diffusion data is data related to atmospheric diffusion in the second state of the atmospheric environment;

[0160] The other monitoring data refers to other monitoring data besides the first diffusion data and the second diffusion data, which are also used to determine the simulation results;

[0161] The first state refers to the atmospheric environment state at the time of the previous sampling in the atmospheric environment monitoring data sampling cycle.

[0162] The second state refers to the atmospheric environment state at the time of the last sampling in the monitoring data sampling cycle during the atmospheric environment monitoring process;

[0163] The sampling time interval between the first state and the second state is a sampling period for one monitoring data.

[0164] This application uses the atmospheric environment data related to atmospheric diffusion in the first state from the first monitoring data as the first diffusion data, and the atmospheric environment data related to atmospheric diffusion in the second state from the first monitoring data as the second diffusion data. Then, the first diffusion data and the second diffusion data are selected from the first monitoring data, and other monitoring data used to determine the simulation results are also selected from the first monitoring data. The first diffusion data, the second diffusion data, and the other monitoring data are then used as the second monitoring data. Since the process simulation is a simulation of the diffusion process of the predicted object under the current atmospheric environment, and the second monitoring data extracted in this application includes diffusion data from two adjacent states related to atmospheric diffusion, it can more accurately reflect the current atmospheric environment state. Moreover, due to the real-time acquisition of data, the real-time nature of the subsequent process simulation can be guaranteed, so that the simulation results determined by the process simulation of the predicted object more accurately reflect the real atmospheric environment state, improve the real-time performance and accuracy of the process simulation, and make the final simulation results more accurate and have better dynamic response performance.

[0165] Furthermore, such as Figure 3 As shown, the step of extracting first diffusion data, second diffusion data, and other monitoring data related to atmospheric environmental conditions from the first monitoring data includes:

[0166] From the first monitoring data, data related to the first grid in the first state, data related to the second grid in the first state, and other data related to the first state are extracted respectively as the first diffusion data;

[0167] From the first monitoring data, data related to the first grid in the second state, data related to the second grid in the second state, and other data related to the second state are extracted respectively as the second diffusion data;

[0168] The first grid is the monitoring grid that completely covers the monitored objects in the atmospheric remote sensing monitoring grid;

[0169] The second grid is the monitoring grid that is partially covered by the monitored object in the atmospheric remote sensing monitoring grid;

[0170] Specifically, since the size of the remote sensing monitoring grid is equivalent to the resolution of the remote sensing monitoring, using the relevant data of the monitoring grid in the first and second diffusion data is equivalent to directly using data with the same accuracy as the remote sensing monitoring as the basis data for subsequent process simulation, which can ensure the accuracy of the data to the greatest extent.

[0171] This application incorporates relevant data from atmospheric remote sensing monitoring grids as part of the first and second diffusion data, and divides them into the first and second grids based on specific atmospheric environmental conditions. This not only closely integrates with remote sensing monitoring technology, but also ensures that the data accuracy is consistent with that of remote sensing monitoring technology, thus maximizing the accuracy of the data.

[0172] Furthermore, the first diffusion data includes: the number of first grids in the first state, the coverage of each category of the second grids in the first state and their corresponding numbers, and the wind speed in the first state;

[0173] The second diffusion data includes: the number of first grids in the second state, the coverage of each category of the second grids in the second state and their corresponding numbers, and the wind speed in the second state;

[0174] The other monitoring data includes: the sampling period of the monitoring data, and the maximum value of the diffusion distance under different wind directions from the first state to the second state;

[0175] Because this application uses first diffusion data, second diffusion data, and other monitoring data, it takes the number of first grids under atmospheric conditions, the coverage of each category of the second grid and its corresponding quantity, real-time wind speed, sampling period, and diffusion distance as the second monitoring data, which further improves the real-time performance and accuracy of the various parameters required in the process simulation. This makes the data used in the process simulation more realistic and accurate in reflecting the current atmospheric conditions, and the simulation results can accurately reflect the diffusion process of the predicted object under the current atmospheric conditions, thus ensuring the accuracy of the simulation results.

[0176] Furthermore, the step of performing process simulation on the predicted object based on the final monitoring data and the input data, and determining the simulation results, includes:

[0177] Based on the final monitoring data, the first parameter is determined;

[0178] Based on the first parameter and the input data, determine the simulation result;

[0179] The first parameter is a parameter related to atmospheric diffusion characteristics and is used to determine the simulation results;

[0180] Specifically, depending on the specific circumstances, different simulation results may correspond to different or the same simulation models.

[0181] This application determines the simulation results based on the first parameter and the input data. Since the first parameter is a parameter related to atmospheric diffusion characteristics and is determined by the final monitoring data, the accuracy of the first parameter can be effectively improved. This enables the simulation of the predicted object to accurately reflect the diffusion process of the predicted object under the current atmospheric environment, thus improving the accuracy of the final simulation results.

[0182] Furthermore, the first parameter is the atmospheric diffusion rate under the current meteorological conditions, specifically:

[0183]

[0184] Wherein, δ represents the atmospheric diffusion rate under the current meteorological conditions;

[0185] n1 is the number of the first grid cells in the first state;

[0186] α i The value represents the coverage of the i-th class in the second grid in the first state.

[0187] n 1i This represents the number of second grid cells in the first state that also have the i-th type of coverage.

[0188] m1 is the total number of coverage categories in the second grid division in the first state;

[0189] n2 is the number of the first grid cells in the second state;

[0190] β j The value represents the coverage of the j-th class in the second grid in the second state.

[0191] n 2j This represents the number of second grid cells in the second state that also have the k-th type of coverage.

[0192] m2 is the total number of coverage categories in the second grid division in the second state;

[0193] d0 is the maximum diffusion distance under different wind directions from the first state to the second state;

[0194] v f1 The wind speed in the first state;

[0195] v f2 The wind speed in the second state;

[0196] The coverage rate refers to the proportion of the monitored objects within the atmospheric remote sensing monitoring grid; changes in the monitored objects reflect the state of the atmospheric environment.

[0197] The diffusion distance is the distance the monitored object moves during the diffusion process in the atmospheric environment;

[0198] Specifically, the first grid and the second grid have the same range, both being atmospheric remote sensing monitoring grids; the second grid is a monitoring grid in the atmospheric remote sensing monitoring grid that is partially covered by the monitored object, and its corresponding coverage rate is not 100%, i.e., not full coverage; while the first grid is a monitoring grid in the atmospheric remote sensing monitoring grid that is fully covered by the monitored object, and its corresponding coverage rate is 100%, i.e., full coverage.

[0199] Specifically, this application divides the coverage of the second grid into different categories according to certain rules, such as classifying them according to different segments of coverage size;

[0200] This application uses the atmospheric diffusion rate under current meteorological conditions as the first parameter, and this first parameter accurately reflects the rate of change of atmospheric diffusion under current meteorological conditions. It provides key model parameters for subsequent process simulation and determination of simulation results, thereby enabling the subsequent process simulation to be more realistic and accurate, and further improving the accuracy of the simulation results.

[0201] Furthermore, the simulation results include: a first simulation result and its model, specifically:

[0202] T t =[log (1+δ) C0-log (1+δ) C t ]T0

[0203] Among them, T t The first simulation result shows that, under the current meteorological conditions, the concentration changes from C0 to C during the diffusion process. t Time required;

[0204] δ represents the atmospheric diffusion rate under current meteorological conditions;

[0205] T0 is the time elapsed from the first state to the second state, which is the sampling period of the monitoring data;

[0206] C0 represents the initial concentration of the target object under current meteorological conditions before diffusion;

[0207] C t The target concentration of the object after diffusion under current meteorological conditions;

[0208] Specifically, the input data is the state data of the object to be predicted, specifically the initial state data and target state data of the object in the atmospheric environment.

[0209] Specifically, the first simulation results and its model, combined with the second monitoring data and input data, can not only simulate and predict the evolution of the object in the atmospheric environment, but also obtain relatively accurate simulation results in real time, provided that the second monitoring data can be updated in real time.

[0210] The first simulation result of this application is that, under the current meteorological conditions, the concentration of the target substance changes from C0 to C during the diffusion process. t The required time can reflect the diffusion process of the predicted object under current meteorological conditions. Combined with the real-time nature of the second monitoring data, more accurate simulation results can be obtained.

[0211] Furthermore, the simulation results also include: a second simulation result and its model, specifically:

[0212]

[0213] d t Under current meteorological conditions, the concentration after diffusion changes from C0 to C t At that time, the maximum distance of diffusion in the same direction as the wind direction in the second state;

[0214] Specifically, because the monitoring data sampling period is short, the wind direction in the first and second states is the same or very close. In reality, the wind direction usually changes little in a short period of time, so it is considered to be the same wind direction in the process simulation.

[0215] Specifically, the determined simulation results are used to measure the specific state of the predicted object after the diffusion process under current meteorological conditions. Therefore, the simulation results are: simulation results (including the first simulation result and the second simulation result) predicted based on the state data of the predicted object (i.e., initial state data and target state data) under the current timeframe reflected by atmospheric environmental monitoring data. Therefore, when determining whether the current geographical environment state meets the requirements of the predicted object based on the simulation results, the simulation results can be compared with the requirements of the predicted object to draw a conclusion on whether the requirements are met. For example, if the requirement of the predicted object is that the concentration changes from C0 to C during the diffusion process under current meteorological conditions... t The required time does not exceed T t0 When its first simulation result T t Less than or equal to T t0 When the result of the first simulation meets the requirements of the predicted object, it indicates that the result of the first simulation meets the requirements of the predicted object; if the requirement of the predicted object is that the concentration after diffusion changes from C0 to C under the current meteorological conditions. t At that time, in the same direction as the wind direction in the second state, the maximum diffusion distance does not exceed d. t0 When its second simulation result d t Greater than dt0 When the result of the second simulation does not meet the requirements of the object being predicted, it indicates that the result of the second simulation does not meet the requirements of the object being predicted.

[0216] Example 3

[0217] like Figure 4 As shown, this embodiment provides a data processing system based on atmospheric environment remote sensing monitoring, the system comprising:

[0218] The monitoring data acquisition unit is used to acquire monitoring data of the geographical environment and filter out the final monitoring data required for process simulation from the monitoring data.

[0219] The input data acquisition unit is used to acquire the input data of the prediction object;

[0220] The simulation result determination unit is used to perform process simulation on the predicted object based on the final monitoring data and the input data, and determine the simulation result;

[0221] The simulation result judgment unit is used to determine whether the current geographical environment status meets the requirements of the prediction object based on the simulation results.

[0222] The monitoring data refers to monitoring data that characterizes the state of the geographical environment.

[0223] The input data is the state data of the object to be predicted;

[0224] The prediction object is the specific object that needs to be simulated.

[0225] The process simulation is a simulation of the evolution of the predicted object in the current geographical environment.

[0226] Furthermore, such as Figure 2 As shown, the step of filtering the final monitoring data required for process simulation from the monitoring data includes:

[0227] To acquire the first monitoring data related to the state of the atmospheric environment;

[0228] After filtering the acquired first monitoring data, second monitoring data is obtained;

[0229] The second monitoring data will be used as the final monitoring data;

[0230] The first monitoring data is monitoring data related to the state of the atmospheric environment;

[0231] The second monitoring data is the relevant monitoring data extracted from the first monitoring data to determine the simulation results;

[0232] This application first obtains first monitoring data related to the atmospheric environment state. Then, it selects relevant monitoring data from the first monitoring data to determine the simulation results, which are used as the final monitoring data required for the process simulation. This improves the availability of relevant data, facilitates subsequent data analysis, and enhances the accuracy of the data used in the process simulation. As a result, the determined simulation results are more consistent with the actual atmospheric environment state, and the accuracy of the simulation results is higher.

[0233] Furthermore, the step of filtering the acquired first monitoring data to obtain second monitoring data includes:

[0234] From the first monitoring data, extract the first diffusion data, the second diffusion data, and other monitoring data related to the atmospheric environment status;

[0235] The first diffusion data, the second diffusion data, and other monitoring data are used as the second monitoring data;

[0236] The first diffusion data is data related to atmospheric diffusion in the first state of the atmospheric environment;

[0237] The second diffusion data is data related to atmospheric diffusion in the second state of the atmospheric environment;

[0238] The other monitoring data refers to other monitoring data besides the first diffusion data and the second diffusion data, which are also used to determine the simulation results;

[0239] The first state refers to the atmospheric environment state at the time of the previous sampling in the atmospheric environment monitoring data sampling cycle.

[0240] The second state refers to the atmospheric environment state at the time of the last sampling in the monitoring data sampling cycle during the atmospheric environment monitoring process;

[0241] The sampling time interval between the first state and the second state is a sampling period for one monitoring data.

[0242] This application uses the atmospheric environment data related to atmospheric diffusion in the first state from the first monitoring data as the first diffusion data, and the atmospheric environment data related to atmospheric diffusion in the second state from the first monitoring data as the second diffusion data. Then, the first diffusion data and the second diffusion data are selected from the first monitoring data, and other monitoring data used to determine the simulation results are also selected from the first monitoring data. The first diffusion data, the second diffusion data, and the other monitoring data are then used as the second monitoring data. Since the process simulation is a simulation of the diffusion process of the predicted object under the current atmospheric environment, and the second monitoring data extracted in this application includes diffusion data from two adjacent states related to atmospheric diffusion, it can more accurately reflect the current atmospheric environment state. Moreover, due to the real-time acquisition of data, the real-time nature of the subsequent process simulation can be guaranteed, so that the simulation results determined by the process simulation of the predicted object more accurately reflect the real atmospheric environment state, improve the real-time performance and accuracy of the process simulation, and make the final simulation results more accurate and have better dynamic response performance.

[0243] Furthermore, such as Figure 3 As shown, the step of extracting first diffusion data, second diffusion data, and other monitoring data related to atmospheric environmental conditions from the first monitoring data includes:

[0244] From the first monitoring data, data related to the first grid in the first state, data related to the second grid in the first state, and other data related to the first state are extracted respectively as the first diffusion data;

[0245] From the first monitoring data, data related to the first grid in the second state, data related to the second grid in the second state, and other data related to the second state are extracted respectively as the second diffusion data;

[0246] The first grid is the monitoring grid that completely covers the monitored objects in the atmospheric remote sensing monitoring grid;

[0247] The second grid is the monitoring grid that is partially covered by the monitored object in the atmospheric remote sensing monitoring grid;

[0248] Specifically, since the size of the remote sensing monitoring grid is equivalent to the resolution of the remote sensing monitoring, using the relevant data of the monitoring grid in the first and second diffusion data is equivalent to directly using data with the same accuracy as the remote sensing monitoring as the basis data for subsequent process simulation, which can ensure the accuracy of the data to the greatest extent.

[0249] This application incorporates relevant data from atmospheric remote sensing monitoring grids as part of the first and second diffusion data, and divides them into the first and second grids based on specific atmospheric environmental conditions. This not only closely integrates with remote sensing monitoring technology, but also ensures that the data accuracy is consistent with that of remote sensing monitoring technology, thus maximizing the accuracy of the data.

[0250] Furthermore, the first diffusion data includes: the number of first grids in the first state, the coverage of each category of the second grids in the first state and their corresponding numbers, and the wind speed in the first state;

[0251] The second diffusion data includes: the number of first grids in the second state, the coverage of each category of the second grids in the second state and their corresponding numbers, and the wind speed in the second state;

[0252] The other monitoring data includes: the sampling period of the monitoring data, and the maximum value of the diffusion distance under different wind directions from the first state to the second state;

[0253] Because this application uses first diffusion data, second diffusion data, and other monitoring data, it takes the number of first grids under atmospheric conditions, the coverage of each category of the second grid and its corresponding quantity, real-time wind speed, sampling period, and diffusion distance as the second monitoring data, which further improves the real-time performance and accuracy of the various parameters required in the process simulation. This makes the data used in the process simulation more realistic and accurate in reflecting the current atmospheric conditions, and the simulation results can accurately reflect the diffusion process of the predicted object under the current atmospheric conditions, thus ensuring the accuracy of the simulation results.

[0254] Furthermore, the step of performing process simulation on the predicted object based on the final monitoring data and the input data, and determining the simulation results, includes:

[0255] Based on the final monitoring data, the first parameter is determined;

[0256] Based on the first parameter and the input data, determine the simulation result;

[0257] The first parameter is a parameter related to atmospheric diffusion characteristics and is used to determine the simulation results;

[0258] Specifically, depending on the specific circumstances, different simulation results may correspond to different or the same simulation models.

[0259] This application determines the simulation results based on the first parameter and the input data. Since the first parameter is a parameter related to atmospheric diffusion characteristics and is determined by the final monitoring data, the accuracy of the first parameter can be effectively improved. This enables the simulation of the predicted object to accurately reflect the diffusion process of the predicted object under the current atmospheric environment, thus improving the accuracy of the final simulation results.

[0260] Furthermore, the first parameter is the atmospheric diffusion rate under the current meteorological conditions, specifically:

[0261]

[0262] Wherein, δ represents the atmospheric diffusion rate under the current meteorological conditions;

[0263] n1 is the number of the first grid cells in the first state;

[0264] α i The value represents the coverage of the i-th class in the second grid in the first state.

[0265] n 1i This represents the number of second grid cells in the first state that also have the i-th type of coverage.

[0266] m1 is the total number of coverage categories in the second grid division in the first state;

[0267] n2 is the number of the first grid cells in the second state;

[0268] β j The value represents the coverage of the j-th class in the second grid in the second state.

[0269] n 2j This represents the number of second grid cells in the second state that also have the j-th type of coverage.

[0270] m2 is the total number of coverage categories in the second grid division in the second state;

[0271] d0 is the maximum diffusion distance under different wind directions from the first state to the second state;

[0272] v f1 The wind speed in the first state;

[0273] v f2 The wind speed in the second state;

[0274] The coverage rate refers to the proportion of the monitored objects within the atmospheric remote sensing monitoring grid; changes in the monitored objects reflect the state of the atmospheric environment.

[0275] The diffusion distance is the distance the monitored object moves during the diffusion process in the atmospheric environment;

[0276] Specifically, the first grid and the second grid have the same range, both being atmospheric remote sensing monitoring grids; the second grid is a monitoring grid in the atmospheric remote sensing monitoring grid that is partially covered by the monitored object, and its corresponding coverage rate is not 100%, i.e., not full coverage; while the first grid is a monitoring grid in the atmospheric remote sensing monitoring grid that is fully covered by the monitored object, and its corresponding coverage rate is 100%, i.e., full coverage.

[0277] Specifically, this application divides the coverage of the second grid into different categories according to certain rules, such as classifying them according to different segments of coverage size;

[0278] This application uses the atmospheric diffusion rate under current meteorological conditions as the first parameter, and this first parameter accurately reflects the rate of change of atmospheric diffusion under current meteorological conditions. It provides key model parameters for subsequent process simulation and determination of simulation results, thereby enabling the subsequent process simulation to be more realistic and accurate, and further improving the accuracy of the simulation results.

[0279] Furthermore, the simulation results include: a first simulation result and its model, specifically:

[0280] T t =[log (1+δ) C0-log (1+δ) C t ]T0

[0281] Among them, T t The first simulation result shows that, under the current meteorological conditions, the concentration changes from C0 to C during the diffusion process. t Time required;

[0282] δ represents the atmospheric diffusion rate under current meteorological conditions;

[0283] T0 is the time elapsed from the first state to the second state, which is the sampling period of the monitoring data;

[0284] C0 represents the initial concentration of the target object under current meteorological conditions before diffusion;

[0285] C t The target concentration of the object after diffusion under current meteorological conditions;

[0286] Specifically, the input data is the state data of the object to be predicted, specifically the initial state data and target state data of the object in the atmospheric environment.

[0287] Specifically, the first simulation results and its model, combined with the second monitoring data and input data, can not only simulate and predict the evolution of the object in the atmospheric environment, but also obtain relatively accurate simulation results in real time, provided that the second monitoring data can be updated in real time.

[0288] The first simulation result of this application is that, under the current meteorological conditions, the concentration of the target substance changes from C0 to C during the diffusion process. t The required time can reflect the diffusion process of the predicted object under current meteorological conditions. Combined with the real-time nature of the second monitoring data, more accurate simulation results can be obtained.

[0289] Furthermore, the simulation results also include: a second simulation result and its model, specifically:

[0290]

[0291] d t Under current meteorological conditions, the concentration after diffusion changes from C0 to C t At that time, the maximum distance of diffusion in the same direction as the wind direction in the second state;

[0292] Specifically, because the monitoring data sampling period is short, the wind direction in the first and second states is the same or very close. In reality, the wind direction usually changes little in a short period of time, so it is considered to be the same wind direction in the process simulation.

[0293] Specifically, the determined simulation results are used to measure the specific state of the predicted object after the diffusion process under current meteorological conditions. Therefore, the simulation results are: simulation results (including the first simulation result and the second simulation result) predicted based on the state data of the predicted object (i.e., initial state data and target state data) under the current timeframe reflected by atmospheric environmental monitoring data. Therefore, when determining whether the current geographical environment state meets the requirements of the predicted object based on the simulation results, the simulation results can be compared with the requirements of the predicted object to draw a conclusion on whether the requirements are met. For example, if the requirement of the predicted object is that the concentration changes from C0 to C during the diffusion process under current meteorological conditions... t The required time does not exceed T t0 When its first simulation result T t Less than or equal to T t0 When the result of the first simulation meets the requirements of the predicted object, it indicates that the result of the first simulation meets the requirements of the predicted object; if the requirement of the predicted object is that the concentration after diffusion changes from C0 to C under the current meteorological conditions. t At that time, in the same direction as the wind direction in the second state, the maximum diffusion distance does not exceed d. t0 When its second simulation result d t Greater than dt0 When the result of the second simulation does not meet the requirements of the object being predicted, it indicates that the result of the second simulation does not meet the requirements of the object being predicted.

[0294] Example 4

[0295] like Figure 5 As shown, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1 or Embodiment 2.

[0296] Example 5

[0297] like Figure 6 As shown, this embodiment provides a computer device, including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the method described in Embodiment 1 or Embodiment 2.

[0298] In summary, this application can obtain the final monitoring data required by progressively filtering the acquired monitoring data, which can provide more accurate basic data for subsequent process simulation, making the process simulation more realistic and accurate, and the simulation results more precise, thereby improving the accuracy of judging whether the current geographical environment meets the requirements of the prediction object.

[0299] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, media, devices, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0300] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units 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 through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0301] The modules or units described as separate components may or may not be physically separate. The components shown as modules or units may or may not be physical modules or units; that is, they may be located in one place or distributed across multiple network modules or units. Some or all of the modules or units can be selected to achieve the purpose of this embodiment according to actual needs.

[0302] Furthermore, the functional modules or units in the various embodiments of this application can be integrated into one processing module or unit, or each module or unit can exist physically separately, or two or more modules or units can be integrated into one module or unit. The integrated modules or units described above can be implemented in hardware or as software functional units.

[0303] The integrated systems, modules, and units, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or 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 described in the various embodiments of this application. 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.

[0304] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method based on remote sensing monitoring of the atmospheric environment, characterized in that, The method includes: Obtain monitoring data of the geographical environment; From the monitoring data, the final monitoring data required for process simulation is selected; Obtain the input data for the prediction object; Based on the final monitoring data and the input data, a process simulation is performed on the predicted object to determine the simulation results; Based on the simulation results, determine whether the current geographical environment meets the requirements of the predicted object; The monitoring data refers to monitoring data that characterizes the state of the geographical environment. The input data is the state data of the object to be predicted; The prediction object is the specific object that needs to be simulated. The process simulation is a simulation of the evolution of the predicted object in the current geographical environment; The step of filtering out the final monitoring data required for process simulation from the monitoring data includes: To acquire the first monitoring data related to the state of the atmospheric environment; From the first monitoring data, data related to the first grid in the first state, data related to the second grid in the first state, and other data related to the first state are extracted respectively as the first diffusion data; From the first monitoring data, data related to the first grid in the second state, data related to the second grid in the second state, and other data related to the second state are extracted respectively as the second diffusion data; The first diffusion data, the second diffusion data, and other monitoring data are used as the second monitoring data; The second monitoring data will be used as the final monitoring data; The first monitoring data is monitoring data related to the state of the atmospheric environment; The second monitoring data is the relevant monitoring data extracted from the first monitoring data to determine the simulation results; The first diffusion data is data related to atmospheric diffusion in the first state of the atmospheric environment; The second diffusion data is data related to atmospheric diffusion in the second state of the atmospheric environment; The other monitoring data refers to other monitoring data besides the first diffusion data and the second diffusion data, which are also used to determine the simulation results; The first state refers to the atmospheric environment state at the time of the previous sampling in the atmospheric environment monitoring data sampling cycle. The second state refers to the atmospheric environment state at the time of the last sampling in the monitoring data sampling cycle during the atmospheric environment monitoring process; The sampling time interval between the first state and the second state is a sampling period for one monitoring data. The first grid is the monitoring grid that completely covers the monitored objects in the atmospheric remote sensing monitoring grid; The second grid is the monitoring grid that is partially covered by the monitored object in the atmospheric remote sensing monitoring grid.

2. The data processing method based on atmospheric environment remote sensing monitoring according to claim 1, characterized in that, The first diffusion data includes: the number of first grids in the first state, the coverage of each category of second grids in the first state and their corresponding numbers, and the wind speed in the first state; The second diffusion data includes: the number of first grids in the second state, the coverage of each category of the second grids in the second state and their corresponding numbers, and the wind speed in the second state; The other monitoring data includes: the sampling period of the monitoring data, and the maximum value of the diffusion distance under different wind directions from the first state to the second state; The step of performing process simulation on the predicted object based on the final monitoring data and the input data, and determining the simulation results, includes: Based on the final monitoring data, the first parameter is determined; Based on the first parameter and the input data, the process of the object to be predicted is simulated, and the simulation results are determined. The first parameter is a parameter related to atmospheric diffusion characteristics and is used to determine the simulation results.

3. The data processing method based on atmospheric environment remote sensing monitoring according to claim 2, characterized in that, The first parameter is the atmospheric diffusion rate under the current meteorological conditions, specifically: in, The atmospheric diffusion rate under current meteorological conditions; This represents the number of cells in the first state. For the second grid in the first state The size of class coverage; In the first state, both are of the first degree. The number of second grid cells for class coverage; This represents the total number of coverage categories divided by the second grid in the first state; This represents the number of the first grid cells in the second state. For the second grid in the second state The size of class coverage; In the second state, both are the first The number of second grid cells for class coverage; The total number of coverage categories in the second grid division of the second state; This represents the maximum diffusion distance under different wind directions from the first state to the second state; The wind speed in the first state; The wind speed in the second state; The coverage rate refers to the proportion of the monitored objects within the atmospheric remote sensing monitoring grid; changes in the monitored objects reflect the state of the atmospheric environment. The diffusion distance is the distance the monitored object moves during the diffusion process in the atmospheric environment.

4. The data processing method based on atmospheric environment remote sensing monitoring according to claim 3, characterized in that, The simulation results include: a first simulation result and its model, specifically: in, The first simulation result, i.e., under the current meteorological conditions, predicts the target concentration from... Change to Time required; The atmospheric diffusion rate under current meteorological conditions; This refers to the time elapsed from the first state to the second state, i.e., the sampling period for the monitoring data; The initial concentration of the target object before diffusion under current meteorological conditions; This refers to the target concentration of the object after diffusion under current meteorological conditions.

5. The data processing method based on atmospheric environment remote sensing monitoring according to claim 4, characterized in that, The simulation results also include: a second simulation result and its model, specifically: Under current meteorological conditions, the concentration after diffusion is from Change to At that time, the maximum distance of diffusion in the same direction as the wind direction in the second state.

6. A data processing system based on remote sensing monitoring of the atmospheric environment, characterized in that, The system includes: A monitoring data acquisition unit is used to acquire monitoring data of the geographical environment by any one of claims 1-5, and to filter out the final monitoring data required for process simulation from the monitoring data; An input data acquisition unit is used to acquire input data of the prediction object according to any one of claims 1-5; The simulation result determination unit is used to perform process simulation on the predicted object using the final monitoring data and the input data according to any one of claims 1-5, and determine the simulation result; The simulation result judgment unit is used to determine whether the current geographic environment state meets the requirements of the prediction object by using the simulation result according to any one of claims 1-5; The monitoring data refers to monitoring data that characterizes the state of the geographical environment. The input data is the state data of the object to be predicted; The prediction object is the specific object that needs to be simulated. The process simulation is a simulation of the evolution of the predicted object in the current geographical environment.

7. A computer device comprising a memory and a processor; the memory being used to store a computer program; the processor being used to execute the computer program to implement the method as described in any one of claims 1-5.

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