Smart city data processing method and system based on big data

Abstract

The invention relates to the field of data processing, in particular to a smart city data processing method and system based on big data, and the method comprises the steps: dividing a city region into a plurality of sub-regions, and setting a plurality of monitoring points in the city region; the diffusion fluctuation degree in each sub-region is calculated, wherein the diffusion fluctuation degree represents the influence degree of the sub-region buildings on pollutant diffusion; adjusting diffusion parameters in the Gaussian plume model by using the influence degree to obtain an optimal diffusion coefficient, and obtaining the monitoring concentration of each monitoring point; setting the initial position of a pollution source, calculating the estimated concentration of pollutants at each monitoring point by using a Gaussian plume model, and calculating the difference between the estimated concentration and the monitored concentration; and updating the initial position of the pollution source, and taking the position of the pollution source when the difference is minimum as the final position of the pollution source. According to the invention, precise positioning of the gas pollution source is realized.

Description

Technical Field

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

[0002] With the development of science and technology and the advent of an information society, smart cities are becoming a new direction for future urban planning by optimizing urban management and improving the quality of life of citizens in a scientific, efficient and intelligent way. Air pollution monitoring is one of the important applications of smart cities, which can provide citizens with accurate air quality information in real time.

[0003] The Chinese patent application document with publication number CN115859753A discloses a method for tracing the pollution degree generated by a suspected polluting enterprise at a monitoring point, including: step 1, given the VOCs characteristic pollutant monitoring concentration at the regional boundary monitoring point, delineating the assessment area; step 2, according to meteorological conditions, executing the suspected polluting enterprise selection plan to determine the suspected polluting enterprises in the assessment area; step 3, giving the assumptions of the Gaussian plume diffusion model; step 4, establishing a Gaussian plume diffusion model with the emission source of each suspected polluting enterprise as the respective origin; step 5, according to the Gaussian plume diffusion model established in step 4, calculating the pollutant concentration generated by the emission source of each suspected polluting enterprise at the regional boundary monitoring point in step 1; step 6, according to the pollutant concentration output by the model in step 5 and the monitoring concentration in step 1, obtaining a list of key suspected polluting enterprises and the pollution degree generated by each suspected polluting enterprise at the monitoring point through a comprehensive evaluation method.

[0004] When monitoring and tracing gas pollution in smart cities, the pollution source can be located through the Gaussian plume model or the sensor concentration gradient. However, the traditional Gaussian plume model assumes that the wind speed in all spaces is uniform and stable, and the pollution source intensity is continuous and uniform, and further calculates the concentration of pollutants in different spaces, resulting in large errors in the calculated concentration of the monitoring point, making it impossible to accurately invert the location of the gas pollution source. Therefore, the use of data processing technology to accurately locate the gas pollution source in smart cities for urban management is a problem solved by the present invention. Summary of the invention

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

[0006] In a first aspect, the present invention provides a smart city data processing method based on big data, which adopts the following technical solution: Divide the urban area into multiple sub-areas and set up multiple monitoring points within the urban area; Calculate the diffusion fluctuation degree in each sub-region, which indicates the influence of buildings in the sub-region on the diffusion of pollutants; The diffusion parameters in the Gaussian plume model are adjusted using the degree of influence to obtain the optimal diffusion coefficient and the monitoring concentration at each monitoring point. Set the initial position of the pollution source, use the Gaussian plume model to calculate the estimated concentration of pollutants at each monitoring point, and calculate the difference between the estimated concentration and the monitored concentration; update the initial position of the pollution source, and use the position of the pollution source when the difference is minimal as the final position of the pollution source.

[0007] By adjusting the diffusion coefficient to obtain the optimal diffusion coefficient, the impact of sub-area buildings on pollutants is comprehensively considered, and the accuracy of the calculation results of the Gaussian plume model is improved, thereby further improving the accuracy of calculating the location of pollution sources and facilitating urban management.

[0008] Preferably, the method further comprises: obtaining the location of the monitoring point, the height of the building, the location of the pollution source in the historical pollution incident, the height of the pollution source, and the pollutant emission rate.

[0009] Preferably, the calculation method of the diffusion fluctuation degree is:

[0010] in, represents the diffusion fluctuation degree of the ith sub-region, n represents the number of buildings in the ith sub-region, s represents the area of ​​the ith sub-region, represents the height of the jth building in the i-th sub-area, represents the average height of buildings in the i-th sub-area, It represents the average height of pollution sources in historical pollution events in urban areas, norm represents the normalization function, and exp represents the exponential function with e as the base.

[0011] The degree of diffusion fluctuation can be used to understand the impact of buildings in the sub-area on the diffusion of pollutants, providing a theoretical basis for adjusting the initial diffusion coefficient.

[0012] Preferably, the expression of the optimal diffusion coefficient is:

[0013] In the formula, represents the optimal diffusion coefficient of the ith sub-region, represents the preset initial diffusion coefficient of the ith sub-region, Indicates the diffusion fluctuation degree of the ith sub-region.

[0014] The initial diffusion coefficient is adjusted using the diffusion fluctuation degree to obtain the optimal diffusion coefficient, which further improves the accuracy of the diffusion coefficient.

[0015] Preferably, the method further comprises: calculating the criticality of each monitoring point, the expression being:

[0016] in, Indicates The criticality of the monitoring point, N represents the number of all monitoring points in the urban area, and M represents the criticality of the monitoring point. The number of monitoring points in the same sub-area, NM means the number of monitoring points except the The number of monitoring points in other sub-areas of the sub-area where the monitoring point is located, Indicates The spatial distance between the monitoring point and the monitoring point in the ath domain, Indicates The spatial distance between the bth monitoring point and the bth out-of-domain monitoring point, norm represents the normalization function.

[0017] By calculating the criticality of the monitoring points, it is easy to understand the importance of the corresponding monitoring points, thereby providing a theoretical basis for calculating the difference between the estimated concentration and the monitored concentration.

[0018] Preferably, the difference between the estimated concentration and the monitored concentration is expressed as:

[0019] Where f represents the difference between the estimated concentration and the monitored concentration at the monitoring point in the urban area. Indicates The criticality of each monitoring point, N represents the number of all monitoring points in the urban area. Indicates The monitoring concentration at each monitoring point is Indicates that the Gaussian plume model is used to obtain the The estimated concentration at each monitoring point is x, y, z It 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.

[0020] The difference between the estimated concentration and the monitored concentration makes it easier to correct the initial position and obtain the final position of the pollution source.

[0021] Preferably, the average value of pollutant emission rates in historical pollution events is used as the initial pollution source intensity.

[0022] Preferably, the average value of the pollution source heights in historical pollution events is used as the initial pollution source height.

[0023] Preferably, the method for updating the initial position of the pollution source is: using the Jacobian matrix and the Gauss-Newton algorithm to calculate the change in the initial position, and taking the sum of the initial position and the change as the updated position of the pollution source.

[0024] In a second aspect, the present invention provides a smart city data processing system based on big data, which adopts the following technical solutions: A smart city data processing system based on big data comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the smart city data processing method based on big data is implemented.

[0025] The above-mentioned smart city data processing method based on big data is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0026] The present invention has the following technical effects: The diffusion fluctuation degree of each area is calculated based on the density and height of buildings in each sub-area, and the diffusion coefficient of the Gaussian plume model is adjusted according to the diffusion fluctuation degree of the sub-area. Then, according to the criticality of the calculated monitoring point, the estimated concentration of the monitoring point is calculated through the criticality of the monitoring point, the concentration of the monitoring point and the Gaussian plume model, and the initialization parameters are iteratively updated to obtain the location of the pollution source, thereby achieving precise positioning of the gas pollution source, improving the accuracy of the location of the gas pollution source, and facilitating the management of pollution sources in urban areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a smart city data processing method based on big data of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] The embodiment of the present invention discloses a smart city data processing method based on big data, referring to Figure 1 , including the following steps: S1: Set up multiple monitoring points in the urban area to obtain various parameters of the urban area.

[0030] The parameters include the location of the monitoring point, the location of the building, the location of the pollution source in the historical pollution incident, the height of the pollution source, the pollutant emission rate and the pollutant concentration at each monitoring point. Among them, the monitoring point is used to monitor the concentration of pollutants at the corresponding location.

[0031] S2: Divide the urban area into multiple sub-areas and calculate the diffusion fluctuation degree in each sub-area. The diffusion fluctuation degree indicates the influence of buildings in the sub-area on the diffusion of pollutants.

[0032] The urban area is divided into multiple sub-areas of equal area, each with multiple monitoring points. Due to the different heights and distributions of buildings in different sub-areas in the city, the diffusion rate of gas and the degree of atmospheric stability in different areas are different. Therefore, the urban area is divided, and the degree of diffusion fluctuation of each sub-area is calculated according to the density and height of buildings in different sub-areas.

[0033] The calculation method of diffusion fluctuation degree is:

[0034] in, represents the diffusion fluctuation degree of the ith sub-region, n represents the number of buildings in the ith sub-region, s represents the area of ​​the ith sub-region, represents the height of the jth building in the i-th sub-area, represents the average height of buildings in the i-th sub-area, It represents the average height of pollution sources in historical pollution events in urban areas, norm represents the normalization function, and exp represents the exponential function with e as the base.

[0035] n / s represents the building density of the ith sub-area. The greater the building density, the more stable the air flow in the corresponding sub-area, the slower the diffusion of pollutants, and the greater the degree of diffusion fluctuation; It indicates the complexity of the building height distribution in the i-th sub-area. The larger the value, the more complex the building height distribution. It indicates the influence of buildings on gas diffusion in the ith sub-area. The smaller the value, the higher the average height of the buildings in the i-th sub-region is than the average height of the pollution sources in the historical pollution events, and the greater the impact on the diffusion of the gas, the greater the diffusion fluctuation. In summary, the diffusion fluctuation degree indicates the influence of the buildings in the sub-region on the diffusion of pollutants. The larger the value, the greater the impact of the pollutant gas in the corresponding sub-region, which is not conducive to the diffusion of the pollutant gas.

[0036] S3: Use the influence degree to adjust the diffusion parameters in the Gaussian plume model to obtain the optimal diffusion coefficient.

[0037] The expression of the optimal diffusion coefficient is:

[0038] In the formula, represents the optimal diffusion coefficient of the ith sub-region, represents the preset initial diffusion coefficient of the ith sub-region, It indicates the diffusion fluctuation degree of the ith sub-region. It should be noted that in the Gaussian plume model, the initial diffusion coefficient is divided into a lateral diffusion coefficient and a vertical diffusion coefficient, wherein the adjustment methods of the lateral diffusion coefficient and the vertical diffusion coefficient are the same.

[0039] S4: Calculate the criticality of each monitoring point.

[0040] The expression is:

[0041] in, Indicates The criticality of the monitoring point, N represents the number of all monitoring points in the urban area, and M represents the criticality of the monitoring point. The number of monitoring points in the same sub-area, NM means the number of monitoring points except the The number of monitoring points in other sub-areas of the sub-area where the monitoring point is located, Indicates The spatial distance between the monitoring point and the monitoring point in the ath domain, Indicates The spatial distance between the monitoring point and the bth monitoring point outside the domain, norm represents the normalization function. For each monitoring point, the monitoring point in the first sub-area is an in-domain monitoring point, and the monitoring points in the second, third, fourth, ..., nth sub-areas are out-domain monitoring points.

[0042] Indicates The degree of isolation of each monitoring point within the domain, Indicates The degree of isolation outside the domain of each monitoring point. The smaller the degree of isolation, the The closer the distance between the first monitoring point and the other monitoring points, the The less critical the monitoring point is.

[0043] S5: Obtain the monitoring 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 monitoring concentration.

[0044] In the process of monitoring pollutants in urban areas, the detection concentration of each monitoring point is obtained; the average value of the pollutant emission rate in historical pollution events is used as the initial pollution source intensity Q in the Gaussian plume model, and the average value of the pollution source height in historical pollution events is used as the initial pollution source height H in the Gaussian plume model. The spatial coordinate system of the initial position of the pollution source is set as ( x, y, z ), Q, H, x, y, z Input into the Gaussian plume model to obtain the estimated concentration of pollutants at each monitoring point .

[0045] The difference between the estimated concentration and the monitored concentration is expressed as:

[0046] Where f represents the difference between the estimated concentration and the monitored concentration at the monitoring point in the urban area. Indicates The criticality of each monitoring point, N represents the number of all monitoring points in the urban area. Indicates The monitoring concentration at each monitoring point is Indicates that the Gaussian plume model is used to obtain the The estimated concentration at each monitoring point is x, y, z It 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.

[0047] S6: Update the initial position of the pollution source, and take the position of the pollution source when the difference is minimum as the final position of the pollution source.

[0048] Use the Jacobian matrix and 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 pollution source position, the sum of the initial pollution source intensity and the change as the updated pollution source intensity, and the sum of the initial pollution source height and the change as the updated pollution source height. Input the updated pollution source position, pollution source intensity, and pollution source height 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 pollution source position, pollution source intensity, and pollution source height 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 The iteration stops when , and the final pollution source location is output. The pollution source is checked in the area with a radius of 50 meters around the pollution source location.

[0049] An embodiment of the present invention also discloses a smart city data processing system based on big data, including a processor and a memory, wherein 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 according to the present invention is implemented.

[0050] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0051] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart city data processing method based on big data, characterized in that: Includes steps: Divide the urban area into multiple sub-areas and set up multiple monitoring points within the urban area; Calculate the diffusion fluctuation degree in each sub-region, which indicates the influence of buildings in the sub-region on the diffusion of pollutants; The diffusion parameters in the Gaussian plume model are adjusted using the degree of influence to obtain the optimal diffusion coefficient and the monitoring concentration at each monitoring point. Set the initial position of the pollution source, use the Gaussian plume model to calculate the estimated concentration of pollutants at each monitoring point, and calculate the difference between the estimated concentration and the monitored concentration; update the initial position of the pollution source, and use the position of the pollution source when the difference is minimal as the final position of the pollution source.

2. According to the big data-based smart city data processing method of claim 1, it is characterized in that: The method also includes: obtaining the location of the monitoring point, the height of the building, the location of the pollution source in the historical pollution incident, the height of the pollution source, and the pollutant emission rate.

3. The method for processing smart city data based on big data according to claim 2 is characterized in that: The calculation method of diffusion fluctuation degree is: in, represents the diffusion fluctuation degree of the ith sub-region, n represents the number of buildings in the ith sub-region, s represents the area of ​​the ith sub-region, represents the height of the jth building in the i-th sub-area, represents the average height of buildings in the i-th sub-area, It represents the average height of pollution sources in historical pollution events in urban areas, norm represents the normalization function, and exp represents the exponential function with e as the base.

4. The method for processing smart city data based on big data according to claim 1, characterized in that: The expression of the optimal diffusion coefficient is: In the formula, represents the optimal diffusion coefficient of the ith sub-region, represents the preset initial diffusion coefficient of the ith sub-region, Indicates the diffusion fluctuation degree of the ith sub-region.

5. The method for processing smart city data based on big data according to claim 2 is characterized in that: The method also includes: calculating the criticality of each monitoring point, the expression is: in, Indicates The criticality of the monitoring point, N represents the number of all monitoring points in the urban area, and M represents the criticality of the monitoring point. The number of monitoring points in the same sub-area, NM means the number of monitoring points except the The number of monitoring points in other sub-areas of the sub-area where the monitoring point is located, Indicates The spatial distance between the monitoring point and the monitoring point in the ath domain, Indicates The spatial distance between the bth monitoring point and the bth out-of-domain monitoring point, norm represents the normalization function.

6. A method for processing smart city data based on big data according to claim 5, characterized in that: The difference between the estimated concentration and the monitored concentration is expressed as: Where f represents the difference between the estimated concentration and the monitored concentration at the monitoring point in the urban area. Indicates The criticality of each monitoring point, N represents the number of all monitoring points in the urban area. Indicates The monitoring concentration at each monitoring point is Indicates that the Gaussian plume model is used to obtain the The estimated concentration at each monitoring point is x, y, z It 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.

7. The method for processing smart city data based on big data according to claim 6, characterized in that: The average value of pollutant emission rates in historical pollution events is taken as the initial pollution source intensity.

8. The method for processing smart city data based on big data according to claim 6, characterized in that: The average value of the pollution source heights in historical pollution events is taken as the initial pollution source height.

9. The 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: using the Jacobian matrix and the Gauss-Newton algorithm to calculate the change in the initial position, and taking the sum of the initial position and the change as the updated position of the pollution source.

10. A smart city data processing system based on big data, characterized in that: include: A processor and a memory, wherein 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 according to any one of claims 1 to 9 is implemented.

Images