A method and system for processing smart city data based on big data

Abstract

The present invention relates to the field of data processing, and particularly to a method and system for processing smart city data based on big data. The method includes: dividing the urban area into multiple sub-areas and setting multiple monitoring points within the urban area; calculating the diffusion fluctuation degree within each sub-area, where the diffusion fluctuation degree represents the influence degree of the sub-area buildings on pollutant diffusion; adjusting the diffusion parameters in the Gaussian plume model using the influence degree to obtain the optimal diffusion coefficient, and obtaining the monitoring concentration of each monitoring point; setting the initial position of the pollution source, calculating the estimated concentration of pollutants at each monitoring point using the Gaussian plume model, and calculating the difference between the estimated concentration and the monitoring concentration; updating the initial position of the pollution source, and taking the position of the pollution source when the difference is the smallest as the final position of the pollution source. The present invention realizes the accurate positioning of the gas pollution source.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for processing smart city data based on big data. Background Art

[0002] With the development of technology and the advent of the information society, smart cities optimize urban management and improve the quality of life of citizens in a scientific, efficient, and intelligent way, and gradually become a new direction for future urban planning. Air pollution monitoring is one of the important applications of smart cities, which can provide accurate air quality information for citizens in real time.

[0003] The Chinese patent application document with the publication number CN115859753A discloses a method for tracing the pollution degree produced by a suspected polluting enterprise to a monitoring point, including: Step 1, given the monitoring concentration of VOCs characteristic pollutants at the monitoring points on the regional boundary, delimit the evaluation area; Step 2, according to the meteorological conditions, execute the suspected polluting enterprise selection scheme to determine the suspected polluting enterprises in the evaluation area; Step 3, give the assumption conditions of the Gaussian plume diffusion model; Step 4, establish a Gaussian plume diffusion model with the emission source of each suspected polluting enterprise as its respective origin; Step 5, according to the Gaussian plume diffusion model established in Step 4, calculate the pollutant concentration generated by the emission sources of each suspected polluting enterprise at the regional boundary monitoring points in Step 1; Step 6, according to the pollutant concentration output by the model in Step 5 and the monitoring concentration in Step 1, obtain the list of key suspected polluting enterprises and the pollution degree produced by each suspected polluting enterprise to the monitoring point through a comprehensive evaluation method.

[0004] When monitoring and tracing gas pollution in a smart city, the location of pollution sources can be determined through the Gaussian plume model or the sensor concentration gradient. However, the traditional Gaussian plume model assumes that the wind speed is uniformly stable in all spaces and the pollution source intensity is continuous and uniform, and further calculates the pollutant concentration in different spaces, resulting in a large calculation concentration error at the monitoring point and unable to accurately invert the location of the gas pollution source. Therefore, using data processing technology to accurately locate gas pollution sources in a smart city for urban management is the problem solved by the present invention. Summary of the Invention

[0005] In order to solve the problem of accurately locating gas pollution sources in a smart city, the present invention provides a method and system for processing smart city data based on big data.

[0006] In a first aspect, the present invention provides a method for processing smart city data based on big data, adopting the following technical solution:

[0007] Divide the urban area into multiple sub-areas and set multiple monitoring points within the urban area;

[0008] Calculate the degree of diffusion fluctuation within each sub-region, where the degree of diffusion fluctuation represents the impact of the buildings in the sub-region on pollutant diffusion;

[0009] Adjust the diffusion parameters in the Gaussian plume model using the degree of impact to obtain the optimal diffusion coefficient, and obtain the monitoring concentration at each monitoring point;

[0010] Set the initial position of the pollution source, calculate the estimated concentration of pollutants at each monitoring point using the Gaussian plume model, and calculate the difference between the estimated concentration and the monitoring concentration; update the initial position of the pollution source, and take the position of the pollution source when the difference is the smallest as the final position of the pollution source.

[0011] By adjusting the diffusion coefficient to obtain the optimal diffusion coefficient, comprehensively considering the impact of the buildings in the sub-region on pollutants, the accuracy of the calculation results of the Gaussian plume model is improved, thereby further improving the accuracy of calculating the position of the pollution source, which is convenient for urban management.

[0012] Preferably, the method further includes: obtaining the positions of the monitoring points, the heights of the buildings, the positions of the pollution sources, the heights of the pollution sources, and the pollutant emission rates in historical pollution events.

[0013] Preferably, the calculation method of the degree of diffusion fluctuation is:

[0014]

[0015] where, represents the degree of diffusion fluctuation of the i-th sub-region, n represents the number of buildings in the i-th sub-region, s represents the area of the i-th sub-region, represents the height of the j-th building in the i-th sub-region, represents the average height of the buildings in the i-th sub-region, represents the average height of the pollution sources in historical pollution events in the urban area, norm represents the normalization function, and exp represents the exponential function with e as the base.

[0016] Through the degree of diffusion fluctuation, the impact of the buildings in the sub-region on pollutant diffusion can be understood, providing a theoretical basis for adjusting the initial diffusion coefficient.

[0017] Preferably, the expression of the optimal diffusion coefficient is:

[0018]

[0019] In the formula, represents the optimal diffusion coefficient of the i-th sub-region, represents the preset initial diffusion coefficient of the i-th sub-region, represents the degree of diffusion fluctuation of the i-th sub-region.

[0020] The optimal diffusion coefficient is obtained by adjusting the initial diffusion coefficient according to the degree of diffusion fluctuation, further improving the accuracy of the diffusion coefficient.

[0021] Preferably, the method further includes: calculating the key degree of each monitoring point, and the expression is:

[0022]

[0023] Wherein, represents the key degree of the th monitoring point, N represents the number of all monitoring points in the urban area, M represents the number of monitoring points in the same sub-region as the th monitoring point, N-M represents the number of monitoring points in other sub-regions except the sub-region where the th monitoring point is located, represents the spatial distance between the th monitoring point and the th monitoring point within the domain, represents the spatial distance between the

[0024] th monitoring point and the

[0025] th monitoring point outside the domain, and norm represents the normalization function.

[0026]

[0027] In the formula, f represents the difference between the estimated concentration and the monitored concentration of the monitoring points in the urban area, represents the key degree of the th monitoring point, N represents the number of all monitoring points in the urban area, represents the monitored concentration of the th monitoring point, represents the estimated concentration of the th monitoring point obtained by using the Gaussian plume model, x, y, z represents the spatial coordinate point of the pollution source in the Gaussian plume model, H represents the initial pollution source height in the preset Gaussian plume model, and Q represents the initial pollution source intensity in the preset Gaussian plume model.

[0028] By calculating the difference between the estimated concentration and the monitored concentration, it is convenient to correct the initial position to obtain the final position of the pollution source.

[0029] Preferably, the average value of the pollutant emission rate in historical pollution events is used as the initial pollution source intensity.

[0030] Preferably, the average value of the source height in historical pollution events is used as the initial source height.

[0031] Preferably, the method for updating the initial position of the pollution source is as follows: calculate the change amount of the initial position using the Jacobian matrix and the Gauss-Newton algorithm, and take the sum of the initial position and the change amount as the position of the updated pollution source.

[0032] In a second aspect, the present invention provides a smart city data processing system based on big data, adopting the following technical solution:

[0033] A smart city data processing system based on big data includes: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a smart city data processing method based on big data as described above is implemented.

[0034] Generate a computer program for the smart city data processing method based on big data as described above and store it in the memory to be loaded and executed by the processor. Thus, a system is made according to the memory and the processor, which is convenient to use.

[0035] The present invention has the following technical effects:

[0036] Calculate the diffusion fluctuation degree of each area based on the density and height of buildings in each sub-area, adjust the diffusion coefficient of the Gaussian plume model according to the diffusion fluctuation degree of the sub-area, and then calculate the estimated concentration of the monitoring point through the key degree of the monitoring point, the concentration of the monitoring point, and the Gaussian plume model according to the calculated key degree of the monitoring point. Iteratively update the initial parameters to obtain the pollution source position, realizing the precise positioning of the gas pollution source, improving the accuracy of the gas pollution source position, and facilitating the management of pollution sources in urban areas. Description of the Drawings

[0037] Figure 1 is a flowchart of a smart city data processing method based on big data according to the present invention. Detailed Embodiments

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0039] An embodiment of the present invention discloses a smart city data processing method based on big data. Referring to Figure 1 , it includes the following steps:

[0040] S1: Set multiple monitoring points within the urban area to obtain various parameters of the urban area.

[0041] The parameters include the positions of the monitoring points, the positions of the buildings, the positions of the pollution sources, the heights of the pollution sources, the pollutant emission rates, and the pollutant concentrations at each monitoring point in historical pollution incidents. Among them, the monitoring points are used to monitor the pollutant concentrations at the corresponding positions.

[0042] S2: Divide the urban area into multiple sub - areas, and calculate the diffusion fluctuation degree within each sub - area. The diffusion fluctuation degree represents the influence degree of the sub - area buildings on pollutant diffusion.

[0043] Divide the urban area into multiple sub - areas with the same area, and each sub - area has multiple monitoring points. Since the heights and distributions of the buildings in different sub - areas of the city are different, the diffusion rates of gases and the atmospheric stability degrees in different areas are different. Therefore, divide the urban area, and calculate the diffusion fluctuation degree of each sub - area according to the density and height of the buildings in different sub - areas.

[0044] The calculation method of the diffusion fluctuation degree is as follows:

[0045]

[0046] Among them, represents the diffusion fluctuation degree of the i - th sub - area, n represents the number of buildings in the i - th sub - area, s represents the area of the i - th sub - area, represents the height of the j - th building in the i - th sub - area, represents the average height of the buildings in the i - th sub - area, represents the average height of the pollution sources in historical pollution incidents in the urban area, norm represents the normalization function, and exp represents the exponential function with base e.

[0047] n / s represents the building density of the i - th sub - area. The greater the building density, the more stable the air flow in the corresponding sub - area, the slower the pollutant diffusion, and the greater the diffusion fluctuation degree; represents the complexity of the building height distribution in the i - th sub - area, and the larger its value, the more complex the building height distribution; represents the influence degree of the buildings in the i - th sub - area on gas diffusion. When and the smaller it is, it means that the average height of the buildings in the i - th sub - area is higher than the average height of the pollution sources in historical pollution incidents, and the greater the influence on gas diffusion, then the greater the diffusion fluctuation degree. To sum up, the diffusion fluctuation degree represents the influence degree of the sub - area buildings on pollutant diffusion. The larger its value, the greater the influence on the pollutant gas in the corresponding sub - area, and it is not conducive to the diffusion of polluted gas.

[0048] S3: Adjust the diffusion parameters in the Gaussian plume model using the influence degree to obtain the optimal diffusion coefficient.

[0049] The expression for the optimal diffusion coefficient is:

[0050]

[0051] Where, represents the optimal diffusion coefficient of the i-th sub-region, represents the preset initial diffusion coefficient of the i-th sub-region, represents the diffusion fluctuation degree of the i-th sub-region. It should be noted that in the Gaussian plume model, the initial diffusion coefficient is divided into the horizontal diffusion coefficient and the vertical diffusion coefficient, and the adjustment methods for the horizontal diffusion coefficient and the vertical diffusion coefficient are the same.

[0052] S4: Calculate the criticality of each monitoring point.

[0053] The expression is:

[0054]

[0055] Among them, represents the criticality of the -th monitoring point, N represents the number of all monitoring points in the urban area, M represents the number of monitoring points in the same sub-region as the -th monitoring point, N - M represents the number of monitoring points in other sub-regions except the sub-region where the -th monitoring point is located, represents the spatial distance between the -th monitoring point and the a-th monitoring point within the domain, represents the spatial distance between the -th monitoring point and the b-th monitoring point outside the domain, norm represents the normalization function. Exemplarily, for the -th monitoring point in the first sub-region, the monitoring points in the first sub-region are the monitoring points within the domain, and the monitoring points in the remaining second, third, fourth,..., n-th sub-regions are the monitoring points outside the domain.

[0056] represents the degree of isolation within the domain of the -th monitoring point, represents the degree of isolation outside the domain of the -th monitoring point. The smaller the degree of isolation, the closer the -th monitoring point is to the other monitoring points, and the smaller the criticality of the -th monitoring point.

[0057] S5: Obtain the monitored concentration at each monitoring point, set the initial position of the pollution source, calculate the estimated concentration of pollutants at each monitoring point using the Gaussian plume model, and calculate the difference between the estimated concentration and the monitored concentration.

[0058] During the process of monitoring pollutants in the urban area, obtain the detected concentration at each monitoring point; use the average value of the pollutant emission rate in historical pollution events as the initial pollution source intensity Q in the Gaussian plume model, and use the average value of the pollution source height in historical pollution events as the initial pollution source height H in the Gaussian plume model. Set the spatial coordinate system of the initial position of the pollution source as ( x, y, z ), input Q, H, x, y, z into the Gaussian plume model to obtain the estimated concentration of pollutants at each monitoring point .

[0059] The expression for the difference between the estimated concentration and the monitored concentration is:

[0060]

[0061] In the formula, f represents the difference between the estimated concentration and the monitored concentration at the monitoring points in the urban area, represents the key degree of the th monitoring point, N represents the number of all monitoring points in the urban area, represents the th monitored concentration of the monitoring point, represents the estimated concentration obtained for the th monitoring point using the Gaussian plume model, x, y, z represents the spatial coordinate point of the pollution source in the Gaussian plume model, H represents the initial pollution source height in the preset Gaussian plume model, and Q represents the initial pollution source intensity in the preset Gaussian plume model.

[0062] S6: Update the initial position of the pollution source, and use the position of the pollution source when the difference is the smallest as the final position of the pollution source.

[0063] Use the Jacobian matrix and the Gauss-Newton algorithm to calculate the change in the initial position, the change in the initial pollution source intensity, and the change in the initial pollution source height; use the sum of the initial position and the change as the updated position of the pollution source, use the sum of the initial pollution source intensity and the change as the updated pollution source intensity, and use the sum of the initial pollution source height and the change as the updated pollution source height. Input the updated position, pollution source intensity, and pollution source height of the pollution source into the Gaussian plume model to obtain the estimated concentration, repeatedly calculate the difference between the estimated concentration and the monitored concentration, and iteratively update the position, pollution source intensity, and pollution source height of the pollution source until the number of iterations is greater than or equal to 100 or the change in the position parameter is less than or equal to Stop the iteration and output the final location of the pollution source. Conduct a search for the pollution source in the area with a radius of 50 meters centered on the location of the pollution source.

[0064] An embodiment of the present invention also discloses a smart city data processing system based on big data, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a method for processing smart city data based on big data according to the present invention.

[0065] The above system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0066] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for processing smart city data based on big data, characterized in that, Including the steps: Dividing the urban area into multiple sub-areas and setting multiple monitoring points within the urban area; Calculating the degree of diffusion fluctuation within each sub-area, where the degree of diffusion fluctuation represents the degree of influence of the sub-area buildings on pollutant diffusion; Adjusting the diffusion parameters in the Gaussian plume model using the degree of influence to obtain the optimal diffusion coefficient and obtaining the monitoring concentration of each monitoring point; Setting the initial position of the pollution source, calculating the estimated concentration of pollutants at each monitoring point using the Gaussian plume model, and calculating the difference between the estimated concentration and the monitoring concentration; Updating the initial position of the pollution source and taking the position of the pollution source when the difference is the smallest as the final position of the pollution source; It also includes: obtaining the positions of the monitoring points, the heights of the buildings, the positions of the pollution sources, the heights of the pollution sources, and the pollutant emission rates in historical pollution events; The calculation method of the degree of diffusion fluctuation is: Among them, represents the diffusion fluctuation degree of the i-th sub-region, n represents the number of buildings in the i-th sub-region, s represents the area of the i-th sub-region, represents the height of the j-th building in the i-th sub-region, represents the average height of the buildings in the i-th sub-region, represents the average height of the pollution sources in the historical pollution events in the urban area, norm represents the normalization function, and exp represents the exponential function with e as the base.

2. The method for processing smart city data based on big data according to claim 1, wherein, The expression of the optimal diffusion coefficient is: Wherein, represents the optimal diffusion coefficient of the i-th sub-region, represents the preset initial diffusion coefficient of the i-th sub-region, represents the diffusion fluctuation degree of the i-th sub-region.

3. A method for processing smart city data based on big data according to claim 1, characterized in that, The method also includes: calculating the key degree of each monitoring point, and the expression is: Among them, represents the key degree of the th monitoring point, N represents the number of all monitoring points in the urban area, M represents the number of monitoring points in the same sub-area as the th monitoring point, N - M represents the number of monitoring points in other sub-areas except the sub-area where the th monitoring point is located, represents the th monitoring point and the spatial distance between the th monitoring point and the a-th in-domain monitoring point, represents the th monitoring point and the spatial distance between the th monitoring point and the b-th out-of-domain monitoring point, and norm represents the normalization function.

4. A method for processing smart city data based on big data according to claim 3, characterized in that The expression of the difference between the estimated concentration and the monitoring concentration is: In the formula, f represents the difference between the estimated concentration and the monitored concentration at the monitoring points within the urban area. represents the key degree of the th monitoring point, N represents the number of all monitoring points within the urban area, represents the monitored concentration of the th monitoring point, represents the estimated concentration of the th monitoring point obtained by using the Gaussian plume model, x, y, z represents the spatial coordinate point of the pollution source in the Gaussian plume model, H represents the initial pollution source height in the preset Gaussian plume model, and Q represents the initial pollution source intensity in the preset Gaussian plume model.

5. A method for processing smart city data based on big data according to claim 4, characterized in that, Taking the average value of the pollutant emission rates in historical pollution events as the initial pollution source intensity.

6. A method for processing smart city data based on big data according to claim 4, characterized in that, Taking the average value of the heights of the pollution sources in historical pollution events as the initial pollution source height.

7. A method for processing smart city data based on big data according to claim 1, characterized in that, The method for updating the initial position of the pollution source is: calculating the change amount of the initial position using the Jacobian matrix and the Gauss-Newton algorithm, and taking the sum of the initial position and the change amount as the position of the updated pollution source.

8. A smart city data processing system based on big data, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data processing method for a smart city based on big data according to any one of claims 1-7 is implemented.

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