Air pollution simulation method and system, electronic equipment and storage medium

Abstract

The invention discloses an air pollution simulation method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring preset pollutant data in a preset simulation area; performing prediction through a particle filtering algorithm according to the preset pollutant data to obtain first pollutant prediction data; performing prediction through a Kalman filtering algorithm according to the preset pollutant data to obtain second pollutant prediction data; and performing fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result. According to the embodiment of the invention, the accuracy and stability of air pollution simulation can be effectively improved. The method can be widely applied to the technical field of environment monitoring.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, and particularly to an air pollution simulation method, system, electronic device, and storage medium. Background Art

[0002] Air pollution simulation technology aims to simulate the diffusion, transformation, and deposition processes of pollutants in the atmosphere to evaluate air pollution conditions. In related technologies, under complex and variable pollution sources and diffusion patterns, the accuracy of air pollution simulation results is relatively low, there are problems such as simulation results deviating from reality, and the stability of simulation algorithms is poor.

[0003] In summary, the technical problems existing in related technologies need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose an air pollution simulation method, system, electronic device, and storage medium, which can effectively improve the accuracy and stability of air pollution simulation.

[0005] To achieve the above object, on the one hand, an embodiment of this application proposes an air pollution simulation method, and the method includes the following steps:

[0006] Obtain preset pollutant data within a preset simulation area;

[0007] Perform prediction through a particle filter algorithm based on the preset pollutant data to obtain first pollutant prediction data;

[0008] Perform prediction through a Kalman filter algorithm based on the preset pollutant data to obtain second pollutant prediction data;

[0009] Perform fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result.

[0010] In some embodiments, the obtaining of the preset pollutant data within the preset simulation area includes:

[0011] Dynamically collect pollutant concentration data within the preset simulation area through a preset pollutant sensor;

[0012] Perform data preprocessing on the pollutant concentration data to obtain the preset pollutant data.

[0013] In some embodiments, the performing of data preprocessing on the pollutant concentration data to obtain the preset pollutant data includes:

[0014] Perform denoising processing on the pollutant concentration data through a wavelet transform algorithm to obtain denoised data;

[0015] Normalize the denoised data to obtain the preset pollutant data.

[0016] In some embodiments, the predicting the first pollutant prediction data through a particle filter algorithm according to the preset pollutant data includes:

[0017] Simulate the movement of pollutants through a Monte Carlo algorithm to predict pollutant position data;

[0018] Calculate pollutant weight data according to the pollutant position data and the preset pollutant data;

[0019] Calculate the first pollutant prediction data through the pollutant weight data; wherein, the first pollutant prediction data includes first position prediction data.

[0020] In some embodiments, the predicting the second pollutant prediction data through a Kalman filter algorithm according to the preset pollutant data includes:

[0021] Calculate prediction state data according to historical position prediction data through a preset Kalman prediction formula; wherein, the historical position prediction data includes the estimated pollutant position data at the end of the previous time step;

[0022] Calculate predicted error covariance according to historical error covariance; wherein, the historical error covariance includes the error covariance at the previous moment;

[0023] Calculate Kalman gain data according to the predicted error covariance and the observation matrix;

[0024] Calculate the second pollutant prediction data according to the Kalman gain data, the prediction state data and the preset pollutant data; wherein, the second pollutant prediction data includes second position prediction data.

[0025] In some embodiments, the fusing and calculating the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result includes:

[0026] Fuse and calculate according to the first pollutant prediction data and the second pollutant prediction data through a weighted fusion algorithm to obtain the pollutant simulation result; wherein, the pollutant simulation result includes pollutant prediction position data.

[0027] In some embodiments, before performing the fusing and calculating according to the first pollutant prediction data and the second pollutant prediction data through a weighted fusion algorithm to obtain the pollutant simulation result, the method further includes:

[0028] Construct a preset pollutant dataset;

[0029] Determine the weight coefficients according to the preset pollutant dataset through a cross-validation algorithm.

[0030] To achieve the above object, another aspect of the embodiments of the present application provides an air pollution simulation system, the system includes:

[0031] A first module, configured to obtain preset pollutant data within a preset simulation area;

[0032] A second module, configured to perform prediction through a particle filter algorithm according to the preset pollutant data to obtain first pollutant prediction data;

[0033] A third module, configured to perform prediction through a Kalman filter algorithm according to the preset pollutant data to obtain second pollutant prediction data;

[0034] A fourth module, configured to perform fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result.

[0035] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, the electronic device includes:

[0036] At least one processor;

[0037] At least one memory, configured to store at least one program;

[0038] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0039] To achieve the above object, another aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0040] The embodiments of the present application at least include the following beneficial effects: The present application provides an air pollution simulation method, system, electronic device and storage medium. The solution first obtains preset pollutant data within a preset simulation area. Then, according to the preset pollutant data, the embodiments of the present invention perform prediction through a particle filter algorithm to obtain first pollutant prediction data. At the same time, according to the preset pollutant data, the embodiments of the present invention perform prediction through a Kalman filter algorithm to obtain second pollutant prediction data. Finally, the embodiments of the present invention perform fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result, realizing air pollution simulation. It is easy to understand that by combining the particle filter algorithm and the Kalman filter algorithm to process the preset pollutant data, the embodiments of the present invention can effectively improve the accuracy and stability of air pollution simulation, and achieve stable and accurate air pollution simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flowchart of the steps of the air pollution simulation method provided by the embodiments of the present invention;

[0042] Figure 2 is a schematic diagram of the system architecture of the air pollution simulation provided by the embodiments of the present invention;

[0043] Figure 3 is a schematic overall working flowchart of the air pollution simulation provided by the embodiments of the present invention;

[0044] Figure 4 is a schematic diagram of the structure of the air pollution simulation system provided by the embodiments of the present invention;

[0045] Figure 5 is a schematic diagram of the hardware structure of the electronic device provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods that are consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0047] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0048] The terms "at least one", "a plurality of", "each", "any one", etc. used in this application, at least one includes one, two or more than two, a plurality of includes two or more than two, each refers to each of the corresponding plurality, and any one refers to any one of the plurality.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] Before elaborating on the embodiments of this application in detail, some nouns and terms involved in the embodiments of this application are first explained, and the nouns and terms involved in the embodiments of this application are applicable to the following explanations.

[0051] Small-scale air pollution simulation: It refers to simulating the processes of diffusion, transformation, deposition, etc. of pollutants in a small-scale space (such as urban blocks, industrial areas, etc.).

[0052] Particle Filter (PF) algorithm: It is an algorithm used to estimate the state of a nonlinear and non-Gaussian dynamic system from a series of noisy observation data.

[0053] Kalman Filter (KF) algorithm: It is a linear minimum variance estimator used to estimate a linear stochastic process or system in the presence of noise.

[0054] Air pollution simulation technology aims to simulate the processes of diffusion, transformation, and deposition of pollutants in the atmosphere to evaluate the air pollution situation. In related technologies, under complex and variable pollution sources and diffusion patterns, the accuracy of air pollution simulation results is relatively low, there are problems such as the simulation results deviating from the actual situation, and the stability of the simulation algorithm is poor.

[0055] Exemplarily, for instance, the small-scale air pollution simulation technology is one of the core technologies in the field of environmental monitoring and pollution control. Currently, this technology mainly relies on the data collection of air quality monitoring stations and conducts the simulation and prediction of air pollutants through a single filtering algorithm. These monitoring stations can real-time monitor the concentrations of various pollutants in the air, such as PM2.5, PM10, sulfur dioxide, nitrogen dioxide, etc. However, in the face of complex and variable pollution sources and diffusion patterns, the related air pollution simulation methods often cannot provide high-precision simulation results. For example, in the urban microenvironment, due to the complex layout of buildings, roads, and human flows, the diffusion and transmission of air pollutants exhibit strong non-linear characteristics, which makes it difficult to directly apply traditional linear Gaussian simulation systems, such as the Kalman filter. Among them, under complex and variable pollution sources and diffusion patterns, traditional filtering methods, such as the extended Kalman filter, unscented Kalman filter, etc., due to the need to locally linearize the system, lead to a reduction in the approximation accuracy under strong non-linear conditions, thus affecting the accuracy of the simulation results. In addition, when the non-linear degree of the pollution source and diffusion pattern increases, it will lead to the problem of filter inconsistency, that is, the estimated error covariance does not match the true simulation error, and there is a possibility that the simulation result deviates from the actual situation. At the same time, the related simulation algorithms are easily affected by abnormal data and environmental changes. In practical applications, due to problems such as the failure of monitoring equipment, data transmission delay or error, etc., abnormal values may occur in the monitoring data, and these abnormal values will seriously affect the stability of the air pollution simulation system. In addition, a single filter is difficult to adapt to the rapidly changing small-scale air pollution environment, especially when the pollution source intensity or diffusion conditions change significantly, the performance of the single filter will significantly decline.

[0056] In view of this, in the embodiments of the present application, an air pollution simulation method, system, electronic device, and storage medium are provided. This solution first obtains the preset pollutant data within the preset simulation area. Then, the embodiments of the present invention predict through the particle filter algorithm according to the preset pollutant data to obtain the first pollutant prediction data. At the same time, the embodiments of the present invention predict through the Kalman filter algorithm according to the preset pollutant data to obtain the second pollutant prediction data. Finally, the embodiments of the present invention perform a fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain the pollutant simulation result, realizing air pollution simulation and effectively improving the accuracy and stability of air pollution simulation.

[0057] The air pollution simulation method provided by the embodiments of this application relates to the field of environmental monitoring technology. The air pollution simulation method provided by the embodiments of this application can be applied to a terminal, a server, or software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the air pollution simulation method, etc., but is not limited to the above forms.

[0058] This application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0059] Figure 1 is an optional flowchart of the air pollution simulation method provided by the embodiments of this application, Figure 1 The method in can include but is not limited to steps S110 to S140.

[0060] Step S110: Obtain preset pollutant data within a preset simulation area.

[0061] Step S120: Perform prediction through a particle filter algorithm according to the preset pollutant data to obtain first pollutant prediction data.

[0062] Step S130: Perform prediction through a Kalman filter algorithm according to the preset pollutant data to obtain second pollutant prediction data.

[0063] Step S140: Perform fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain the pollutant simulation result.

[0064] During the working process of this specific embodiment, the embodiment of the present invention first obtains the preset pollutant data within the preset simulation area. Specifically, in the embodiment of the present invention, the preset simulation area refers to the area where air pollution simulation needs to be carried out, such as relevant street areas, industrial parks, etc. Correspondingly, in the embodiment of the present invention, the preset pollutant data refers to the pollutant-related data that needs to be simulated, such as pollutant information, pollution source data, and meteorological condition data, etc. The embodiment of the present invention first collects the preset pollutant data within the relevant area (preset simulation area) to facilitate subsequent air pollution simulation and prediction. Then, the embodiment of the present invention performs prediction through the particle filter algorithm according to the preset pollutant data to obtain the first pollutant prediction data. Specifically, the embodiment of the present invention processes the preset pollutant data through the particle filter algorithm to estimate the position information and movement trajectory of the pollutant, so as to predict the first pollutant prediction data by combining the nonlinear processing ability of the particle filter, thereby alleviating the problem of reduced approximation accuracy under strong nonlinear conditions, affecting the accuracy of the simulation result and the non-consistency of filtering. At the same time, the embodiment of the present invention performs prediction through the Kalman filter algorithm according to the preset pollutant data to obtain the second pollutant prediction data. Specifically, the embodiment of the present invention processes the preset pollutant data through the Kalman filter algorithm to smooth the movement state of the pollutant to improve the stability of air pollution simulation. Finally, the embodiment of the present invention performs fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain the pollutant simulation result. Specifically, the embodiment of the present invention combines the results of the particle filter and the Kalman filter to fuse the first pollutant prediction data and the second pollutant prediction data, so as to alleviate the difficulty of a single filter in providing a high-precision simulation result when facing complex and variable pollution sources and diffusion patterns. Correspondingly, the embodiment of the present invention effectively improves the accuracy and stability of air pollution simulation by combining the nonlinear processing ability of the particle filter and the stability of the Kalman filter.

[0065] In some embodiments of the present invention, obtaining the preset pollutant data within the preset simulation area includes, but is not limited to, the following steps:

[0066] Dynamically collect the pollutant concentration data within the preset simulation area through a preset pollutant sensor.

[0067] Perform data preprocessing on the pollutant concentration data to obtain the preset pollutant data.

[0068] In this specific embodiment, the embodiment of the present invention first dynamically collects pollutant concentration data in a preset simulation area through a preset pollutant sensor, and performs data preprocessing on the pollutant concentration data to obtain preset pollutant data. Specifically, the preset pollutant sensor in the embodiment of the present invention refers to a grid air pollutant concentration sensor deployed inside the preset simulation area. For example, in the preset simulation area, the east-west and north-south intervals at the center of each grid are both 100 meters. The embodiment of the present invention collects the pollutant concentration data in the area in real time through the deployed grid air pollutant concentration sensor, including parameters such as PM2.5, PM10, sulfur dioxide, and nitrogen dioxide. Then, in order to facilitate subsequent data processing and improve the accuracy of air pollution simulation, the embodiment of the present invention performs data preprocessing on the collected pollutant concentration data, such as noise filtering and outlier processing.

[0069] In some embodiments of the present invention, performing data preprocessing on the pollutant concentration data to obtain preset pollutant data includes, but is not limited to, the following steps:

[0070] Performing denoising processing on the pollutant concentration data through a wavelet transform algorithm to obtain denoised data.

[0071] Performing normalization processing on the denoised data to obtain preset pollutant data.

[0072] In this specific embodiment, the embodiment of the present invention first performs denoising processing on the pollutant concentration data through a wavelet transform algorithm to obtain denoised data, and then performs normalization processing on the denoised data to obtain preset pollutant data. Specifically, the data preprocessing performed on the pollutant concentration data in the embodiment of the present invention includes denoising processing and normalization processing. Among them, the embodiment of the present invention removes high-frequency noise in the pollutant concentration data through a wavelet transform algorithm to obtain denoised data. Correspondingly, the wavelet transform algorithm in the embodiment of the present invention is a signal processing algorithm that decomposes a signal by using a wavelet function through stretching and translation operations. Then, in order to facilitate subsequent data processing, the embodiment of the present invention normalizes the denoised data after removing high-frequency noise to scale the data to the range of [0,1], thereby eliminating the order-of-magnitude difference between different features and improving the accuracy of subsequent processing.

[0073] In some embodiments of the present invention, predicting through a particle filter algorithm according to the preset pollutant data to obtain first pollutant prediction data includes, but is not limited to, the following steps:

[0074] Simulating the movement of pollutants through a Monte Carlo algorithm to predict pollutant position data.

[0075] Calculating pollutant weight data according to the pollutant position data and the preset pollutant data.

[0076] The first pollutant prediction data is calculated from the pollutant weight data. Among them, the first pollutant prediction data includes the first position prediction data.

[0077] In this specific embodiment, the embodiment of the present invention first simulates the movement of pollutants through the Monte Carlo algorithm to predict the pollutant position data, and calculates the pollutant weight data based on the pollutant position data and the preset pollutant data, and then calculates the first pollutant prediction data through the pollutant weight data. Specifically, the embodiment of the present invention first initializes a number of particles, such as initializing 1000 particles. Among them, each particle in the embodiment of the present invention represents the possible position and speed of the pollutant. Correspondingly, the embodiment of the present invention simulates the movement of pollutants through the Monte Carlo algorithm to process non-linear and non-Gaussian noise. Among them, the particle weight update formula in the embodiment of the present invention is shown in the following formula (1):

[0078]

[0079] Where, in the formula w i is the weight of the i-th particle, N is the total number of particles, x i is the particle position, z is the observation value, and σ is the standard deviation.

[0080] Correspondingly, the first pollutant prediction data in the embodiment of the present invention includes the first position prediction data, that is, the pollutant position data predicted based on the pollutant weight data. For example, the embodiment of the present invention calculates the predicted first position prediction data by performing a weighted average on the weight data and the corresponding position data of each particle.

[0081] Exemplarily, in the embodiment of the present invention, in a gas trajectory with a length of 100 meters, the initial position of the pollutant is x i = 50 meters, and the initial velocity v 0 = 0.5 m / s. The embodiment of the present invention estimates the initial position and movement trajectory of the pollutant through the particle filter algorithm. The embodiment of the present invention first initializes 1000 particles, and the particle positions are randomly distributed near the initial position, and the velocities are randomly distributed between 0.4 and 0.6 m / s. Then, the embodiment of the present invention simulates the movement of 100 time steps through the Monte Carlo method, and each time step is 1 second. Correspondingly, when at the 50th time step, the embodiment of the present invention observes that the pollutant position z = 55 meters and the standard deviation σ = 1 meter. Correspondingly, the embodiment of the present invention updates the weight of each particle according to the observation value, as shown in the following formula (2):

[0082]

[0083] Correspondingly, when the position x of a certain particle iIf it is 55.2 meters, the weight data of the particle can be calculated as shown in the following formula (3):

[0084]

[0085] It is easy to understand that for this specific particle, its weight w i is 0.00098019867, and this value is the weight relative to other particles. Accordingly, in the embodiments of the present invention, the weight data of each particle is calculated to construct the particle weight data, and then the weighted average of all the particles is performed to obtain the first position prediction data.

[0086] In some embodiments of the present invention, prediction is performed according to preset pollutant data through the Kalman filtering algorithm to obtain second pollutant prediction data, including but not limited to the following steps:

[0087] Calculate the predicted state data according to the historical position prediction data through a preset Kalman prediction formula. Among them, the historical position prediction data includes the pollutant position estimation data at the end of the previous time step.

[0088] Calculate the predicted error covariance according to the historical error covariance. Among them, the historical error covariance includes the error covariance at the previous moment.

[0089] Calculate the Kalman gain data according to the predicted error covariance and the observation matrix.

[0090] Calculate the second pollutant prediction data according to the Kalman gain data, the predicted state data, and the preset pollutant data. Among them, the second pollutant prediction data includes the second position prediction data.

[0091] In this specific embodiment, the embodiments of the present invention first calculate the predicted state data according to the historical position prediction data through a preset Kalman prediction formula, and calculate the predicted error covariance according to the historical error covariance, so as to calculate the Kalman gain data according to the predicted error covariance and the observation matrix, and then calculate the second pollutant prediction data according to the Kalman gain data, the predicted state data, and the preset pollutant data. Specifically, the second position prediction data in the embodiments of the present invention refers to the pollutant position data predicted through the Kalman filtering algorithm. Before smoothing the motion state of the pollutant through the Kalman filtering algorithm in the embodiments of the present invention, the state transition matrix A and the observation matrix H, as well as the process noise covariance Q and the observation noise covariance R, are first set. Among them, the embodiments of the present invention improve the stability of the estimation through the prediction and update steps. Accordingly, the prediction and update formulas of the Kalman filter in the embodiments of the present invention are as shown in the following formula (4):

[0092]

[0093] Among them, in the formula is the predicted state, P k|k-1 is the prediction error covariance, K k is the Kalman gain, z k is the observed value, I is the identity matrix, represents the updated pollutant state, P k|k represents the updated error covariance.

[0094] Exemplarily, in the embodiments of the present invention, the state transition matrix A is set to 1 (indicating that the pollutant position changes linearly with time), the observation matrix H is set to 1 (indicating that the observed value is the same as the pollutant position), the process noise covariance Q is set to 0.1 (indicating the uncertainty of position change), and the observation noise covariance R is set to 1 (indicating the uncertainty of the observed value). Accordingly, starting from the first time step, the embodiments of the present invention predict and update the pollutant position at each time step. Among them, if at the 50th time step, the observed pollutant position z = 55 meters, then the embodiments of the present invention calculate the predicted state, prediction error covariance, Kalman gain, updated state, and updated error covariance according to the prediction and update formulas of the Kalman filter.

[0095] Among them, the embodiments of the present invention first calculate the predicted state data according to the historical position prediction data through the prediction formula of the Kalman filter. Accordingly, the historical position data in the embodiments of the present invention includes the pollutant position estimation data at the end of the previous time step. For example, at the end of the 49th time step, the estimated pollutant position obtained is And at the beginning of the 50th time step, the embodiments of the present invention predict the state at the current moment as That is, in the absence of new observation data, the embodiments of the present invention assume that the position of the pollutant remains unchanged.

[0096] Then, the embodiments of the present invention calculate the prediction error covariance through the historical error covariance, that is, the error covariance at the previous moment, which represents the uncertainty of the predicted state, as shown in the following formula (5):

[0097] P 50|49 = P 49|49 + Q(5)

[0098] Among them, in the formula P 49|49 is the error covariance at the previous moment, and Q is the process noise covariance, which reflects the estimation of the possible random disturbances in the movement process of the pollutant.

[0099] Accordingly, the embodiments of the present invention calculate the Kalman gain based on the prediction error covariance and the observation noise covariance (R) to determine the contribution degree of the observation data to the updated state, as shown in the following formula (6):

[0100] K 50 = P 50|49 H T (HP 50|49 H T + R) -1 (6)

[0101] Wherein, in the formula, H is an identity matrix, indicating that the directly observed is the position of the pollutant.

[0102] Then, after obtaining the new observation data z 50 = 55 meters, the embodiment of the present invention uses the Kalman gain to update the position estimate of the pollutant, as shown in the following formula (7):

[0103]

[0104] Wherein, the embodiment of the present invention incorporates the observation data into the predicted state, thereby obtaining a more accurate position estimate of the pollutant. Then, the embodiment of the present invention updates the error covariance to reduce the uncertainty of the updated state and improve the accuracy of the estimate, as shown in the following formula (8):

[0105] P 50|50 = (I - H 50 H)P 50|49 (8)

[0106] It is easy to understand that the embodiment of the present invention completes the prediction and update of the pollutant position at the 50th time step through the above steps. Among them, if at the end of the 49th time step, the position estimate of the pollutant obtained by the embodiment of the present invention is meters, the process noise covariance Q = 1 meter 2 , the observation noise covariance R = 2 meters 2 , the predicted state, predicted error covariance, Kalman gain, updated state, and updated error covariance at the 50th time step can be calculated. For example, in the embodiment of the present invention, the calculated predicted state is meters. In addition, the predicted error covariance is P 50|49 = P 49|49 + Q = (hypothetical error covariance value, such as 1 meter 2 ) + 1 meter 2 = 2 meters 2 . Correspondingly, the Kalman gain is K 50 = P 50|49 H T (HP 50|49 H T + R) -1 = 2×1 / (1×2 + 2) = 2 / 4 = 0.5. At the same time, the updated state is meters, and the updated error covariance is P 50|50=(I - K 50 H)P 50|49 =(1 - 0.5×1)×2 = 1 meter 2 。Among them, in the formula, H is the identity matrix.

[0107] In some embodiments of the present invention, the first pollutant prediction data and the second pollutant prediction data are fused and calculated to obtain a pollutant simulation result, including but not limited to the following steps:

[0108] The first pollutant prediction data and the second pollutant prediction data are fused and calculated through a weighted fusion algorithm to obtain a pollutant simulation result. Among them, the pollutant simulation result includes pollutant prediction position data.

[0109] In this specific embodiment, the embodiment of the present invention fuses the first pollutant prediction data and the second pollutant prediction data through a weighted fusion algorithm to obtain a pollutant simulation result. Specifically, the pollutant simulation result in the embodiment of the present invention includes pollutant prediction position data, that is, the prediction position information of relevant pollutants. Correspondingly, the embodiment of the present invention combines the results of particle filtering and Kalman filtering, that is, the first pollutant prediction data and the second pollutant prediction data, through a weighted fusion method to improve the tracking accuracy and robustness. For example, the weighted fusion formula in the embodiment of the present invention is shown in the following formula (9):

[0110]

[0111] Among them, in the formula is the fused position estimate, is the position estimate of particle filtering, is the position estimate of Kalman filtering, α represents the weight of the particle filtering result (the first pollutant prediction data), and β represents the weight of the Kalman filtering result (the second pollutant prediction data).

[0112] Exemplarily, in the embodiment of the present invention, the weight coefficients α = 0.6 and β = 0.4 are set. If at the 50th time step, the position estimate of particle filtering is 53 meters and the position estimate of Kalman filtering is 54 meters, then the final position is calculated according to the fusion formula: meters.

[0113] In some embodiments of the present invention, before performing the fusion calculation of the first pollutant prediction data and the second pollutant prediction data through a weighted fusion algorithm to obtain a pollutant simulation result, the air pollution simulation method provided by the embodiment of the present invention further includes but not limited to the following steps:

[0114] Construct a preset pollutant data set.

[0115] Determine the weight coefficient according to the preset pollutant data set through a cross-validation algorithm.

[0116] Specifically, in the embodiments of the present invention, a preset pollutant dataset is first constructed, and the weight coefficients are determined according to the preset pollutant data through a cross-validation algorithm. Specifically, in the embodiments of the present invention, according to the characteristics of the actual tracking environment and related pollutants, the weight data (weight coefficients) of the weighted fusion algorithm are determined through a cross-validation algorithm, such as the weight α of the first pollutant prediction data and the weight β of the second pollutant prediction data. Correspondingly, in the embodiments of the present invention, the relevant data of the pollutants are first obtained, such as the diffusion data, historical position data, movement speed, etc. of the pollutants, and a preset pollutant dataset is constructed. Then, the embodiments of the present invention divide the preset pollutant dataset into a training set and a test set, and use the training set to evaluate different weight combinations to select the weight combination with the smallest tracking error, and then verify it through the test set. For example, when after cross-validation, it is determined that when α = 0.6 and β = 0.4, the tracking error is the smallest, then the weight coefficients are determined to be α = 0.6 and β = 0.4 respectively.

[0117] It should be noted that the embodiments of the present invention output the fused tracking result (pollutant simulation result) for guiding pollutant treatment and environmental management. Among them, the output results in the embodiments of the present invention include the estimated position, movement speed, and direction of the pollutants. For example, in the embodiments of the present invention, it is predicted that at the 50th time step, the estimated position of the pollutant is 53.4 meters, the movement speed is 0.5 m / s (the same as the initial speed), and the direction is downstream of the wind direction.

[0118] Next, in combination with a specific air pollution simulation scenario, the solutions of the embodiments of the present invention will be introduced and described in detail:

[0119] Exemplarily, in a small-scale air pollution simulation application scenario, the air pollution simulation accuracy is low, there is a possibility that the simulation result deviates from the actual situation, and the stability of the simulation algorithm is poor. Correspondingly, the embodiments of the present invention perform small-scale air pollutant simulation by fusing particle filtering and Kalman filtering to combine the non-linear processing ability of particle filtering and the stability of Kalman filtering, and at the same time improve the tracking accuracy and robustness through a weighted fusion algorithm, effectively alleviating the problems of low air pollution simulation accuracy and poor stability. Specifically, as Figure 2 shown, the system module in the embodiments of the present invention includes an air pollutant sensor network, a data preprocessing unit, a particle filtering algorithm module, a Kalman filtering algorithm module, a weighted fusion unit, and a tracking result output module. At the same time, referring to Figure 3, in the embodiments of the present invention, an air pollutant sensor network is first deployed to collect pollutant concentration data in the simulated area in real time. Then, the data preprocessing unit in the embodiments of the present invention performs denoising processing on the collected original data. Next, the initial position and movement trajectory of the pollutants are estimated through the particle filter algorithm module to obtain the first pollutant prediction data. At the same time, the movement state of the pollutants is smoothed through the Kalman filter algorithm module to obtain the second pollutant prediction data. Further, the results of the particle filter and the Kalman filter are weighted and fused through the weighted fusion unit to obtain the pollutant simulation result. Among them, the weight coefficient is determined through the cross-validation algorithm. Accordingly, the embodiments of the present invention output the fused tracking result to guide pollutant treatment and small-scale environmental management.

[0120] It is easy to understand that through the fusion of the particle filter and the Kalman filter, the embodiments of the present invention significantly improve the accuracy and robustness of small-scale atmospheric pollutant tracking. In a non-linear and non-Gaussian noise environment, the traditional filtering method requires local linearization of the system, resulting in a reduced approximation accuracy under strong non-linear conditions, thus affecting the tracking accuracy. The embodiments of the present invention process non-linear and non-Gaussian noise through the particle filter algorithm and simulate the movement of pollutants through the Monte Carlo method, effectively improving the tracking accuracy. At the same time, the movement state of the pollutants is smoothed through the Kalman filter algorithm, improving the stability of the estimation. Accordingly, through the weighted fusion method, the embodiments of the present invention combine the results of the particle filter algorithm and the Kalman filter algorithm to further improve the tracking accuracy and robustness. The application of the embodiments of the present invention can reduce the occurrence of atmospheric pollution events in small-scale areas such as industrial parks and blocks, reduce environmental pollution risks, and has important social and economic value. At the same time, by using big data technology, combining historical monitoring data and real-time data, a comprehensive database can be established for long-term trend analysis and evaluation. This method can not only reveal the change rules of the atmospheric environment system, but also provide support for the decision-making process. Among them, by establishing a mathematical model to simulate the dynamic changes of the small-scale atmospheric environment system in the region and predict future development trends, the efficiency and effect of atmospheric environment management are improved, and strong technical support can be provided for environmental protection and resource management.

[0121] Please refer to Figure 4 , the embodiments of the present application also provide an air pollution simulation system that can implement the above air pollution simulation method. The system includes:

[0122] The first module 210 is used to obtain preset pollutant data within a preset simulation area.

[0123] The second module 220 is configured to perform prediction through a particle filter algorithm based on preset pollutant data to obtain first pollutant prediction data.

[0124] The third module 230 is configured to perform prediction through a Kalman filter algorithm based on preset pollutant data to obtain second pollutant prediction data.

[0125] The fourth module 240 is configured to perform fusion calculation on the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result.

[0126] It can be understood that the content in the above method embodiments is applicable to the system embodiments of the present application. The functions specifically implemented by the system embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0127] The embodiments of the present application further provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above air pollution simulation method is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0128] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented by the device embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0129] Please refer to Figure 5 , Figure 5 which schematically shows the hardware structure of an electronic device according to another embodiment. The electronic device includes:

[0130] A processor 310, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0131] The memory 320 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 320 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 320 and are called by the processor 310 to execute the air pollution simulation method of the embodiments of this application;

[0132] The input / output interface 330 is used to implement information input and output;

[0133] The communication interface 340 is used to implement communication and interaction between this device and other devices. It can communicate through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0134] The bus 350 transmits information between various components of the device (such as the processor 310, the memory 320, the input / output interface 330, and the communication interface 340);

[0135] Among them, the processor 310, the memory 320, the input / output interface 330, and the communication interface 340 achieve communication connections with each other inside the device through the bus 350.

[0136] The embodiments of this application also provide a computer-readable storage medium. This computer-readable storage medium stores a computer program, and when this computer program is executed by a processor, it implements the above-mentioned air pollution simulation method.

[0137] It can be understood that the content in the above method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0138] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory can optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0139] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0140] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0143] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0144] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist simultaneously. Here, A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the associated objects before and after. "At least one (item) of the following" or its similar expressions refer to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0145] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0146] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0148] If the integrated unit is implemented in the form of 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 application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The foregoing storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0149] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings. However, this does not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall fall within the scope of the rights of the embodiments of this application.

Claims

1. An air pollution simulation method, characterized in that: The method comprises the following steps: Obtaining preset pollutant data within a preset simulation area; Perform prediction based on the preset pollutant data by using a particle filtering algorithm to obtain first pollutant prediction data; Performing prediction based on the preset pollutant data by using a Kalman filter algorithm to obtain second pollutant prediction data; The first pollutant prediction data and the second pollutant prediction data are fused and calculated to obtain a pollutant simulation result.

2. The method according to claim 1, characterized in that The step of obtaining the preset pollutant data in the preset simulation area includes: Dynamically collect pollutant concentration data in the preset simulation area through a preset pollutant sensor; The pollutant concentration data is preprocessed to obtain the preset pollutant data.

3. The method according to claim 2, characterized in that The performing data preprocessing on the pollutant concentration data to obtain the preset pollutant data includes: De-noising the pollutant concentration data by using a wavelet transform algorithm to obtain de-noised data; The denoised data is normalized to obtain the preset pollutant data.

4. The method according to claim 1, characterized in that: The step of performing prediction based on the preset pollutant data by using a particle filtering algorithm to obtain first pollutant prediction data includes: The movement of pollutants is simulated by Monte Carlo algorithm to predict the location data of pollutants; Calculate pollutant weight data according to the pollutant location data and the preset pollutant data; The first pollutant prediction data is calculated by using the pollutant weight data; wherein the first pollutant prediction data includes first location prediction data.

5. The method according to claim 1, characterized in that The step of performing prediction based on the preset pollutant data by using a Kalman filter algorithm to obtain second pollutant prediction data includes: Calculating predicted state data using a preset Kalman prediction formula based on historical position prediction data; wherein the historical position prediction data includes pollutant position estimation data at the end of the previous time step; The forecast error covariance is calculated based on the historical error covariance; wherein the historical error covariance includes the error covariance at the previous moment; The Kalman gain data is calculated based on the prediction error covariance and the observation matrix; The second pollutant prediction data is calculated according to the Kalman gain data, the predicted state data and the preset pollutant data; wherein the second pollutant prediction data includes second position prediction data.

6. The method according to claim 1, characterized in that The step of fusing the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result includes: The pollutant simulation result is obtained by performing fusion calculation based on the first pollutant prediction data and the second pollutant prediction data through a weighted fusion algorithm; wherein the pollutant simulation result includes pollutant prediction location data.

7. The method according to claim 6, characterized in that Before performing the fusion calculation based on the first pollutant prediction data and the second pollutant prediction data by using a weighted fusion algorithm to obtain the pollutant simulation result, the method further includes: Construct a preset pollutant dataset; The weight coefficient is determined by a cross-validation algorithm according to the preset pollutant data set.

8. An air pollution simulation system, characterized in that: The system comprises: The first module is used to obtain preset pollutant data in a preset simulation area; The second module is used to predict the preset pollutant data through a particle filtering algorithm to obtain first pollutant prediction data; A third module is used to predict the preset pollutant data through a Kalman filter algorithm to obtain second pollutant prediction data; The fourth module is used to fuse the first pollutant prediction data and the second pollutant prediction data to obtain a pollutant simulation result.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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