Method, device, equipment and medium for fitting pollution risk multiplying power curve

Abstract

The invention relates to a method, a device, equipment and a medium for fitting a pollution risk multiplying power curve, and belongs to the technical field of environmental protection, the method comprises the following steps: obtaining historical data, the historical data comprising daily particulate matter concentration data in autumn and winter of a to-be-fitted area in continuous N years; performing data processing on the historical data, and calculating a first mean value of particulate matter concentration of the processed data; calculating daily concentration multiplying power in each year according to the historical data and the first mean value of the particulate matter concentration to obtain a first set; screening the concentration multiplying power of which the concentration multiplying power is greater than 1 in the first set, and setting the concentration multiplying power as risk concentration multiplying power; calculating a second mean value of each risk concentration multiplying power; and drawing a broken line graph according to the second mean value, and fitting a pollution risk multiplying power curve according to the broken line graph. The method provides a reliable basis for guiding off-peak production of enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental protection, and particularly relates to a method, device, equipment and medium for fitting a pollution risk magnification curve. Background Art

[0002] In the prior art, peak-shifting production of high-emission industries such as cement, steel, and coking is usually dynamically guided based on air quality forecasts and meteorological data. However, due to the lack of scientific analysis of data, this method will lead to economic contradictions such as high environmental protection facility maintenance costs still being borne during peak-shifting periods for some industries, and large damage to enterprises caused by temporarily shutting down production lines during sudden pollution events. It is not conducive to guiding joint prevention and control and scientific peak-shifting production among key regions, reducing the emission reduction costs and economic burdens of enterprises, and there are technical problems of unscientific guiding basis and inability to provide a reliable basis for peak-shifting production. Summary of the Invention

[0003] In view of the above analysis, embodiments of the present invention aim to provide a method, device, equipment and medium for fitting a pollution risk magnification curve to solve the technical problems of unscientific guiding basis and inability to provide a reliable basis for peak-shifting production in the prior art.

[0004] The object of the present invention is achieved as follows: A first aspect embodiment of the present invention provides a method for fitting a pollution risk magnification curve, including: Obtaining historical data, where the historical data includes particulate matter concentration data of each day in autumn and winter for N consecutive years in the area to be fitted; Performing data processing on the historical data and calculating the first mean value of the particulate matter concentration of the processed data; Calculating the concentration magnification of each day in each year according to the historical data and the first mean value of the particulate matter concentration to obtain a first set; Screening out the concentration magnifications greater than 1 in the first set and setting them as risk concentration magnifications; Calculating the second mean value of each risk concentration magnification; Drawing a line graph according to the second mean value and fitting a pollution risk magnification curve according to the line graph.

[0005] Further, the data processing includes: setting the particulate matter concentration with concentration changes caused by non-particulate matter in the historical data to zero, and filling and correcting the missing and invalid daily particulate matter concentrations.

[0006] Further, calculating the first mean value of the particulate matter concentration of the processed data includes: calculating the ratio of the sum of the particulate matter concentrations in autumn and winter of each year in the historical data to the number of days in the current year to obtain N a first mean value of the particulate matter concentration, expressed as: Among them, n represents the n th day of autumn and winter, represents the number of days in the m th year, represents the particulate matter concentration on the m th day of the n th year.

[0007] Furthermore, calculating the concentration multiple of each day in each year according to the historical data and the first mean value of the particulate matter concentration to obtain a first set, including: calculating the ratio of the particulate matter concentration of each day in the autumn and winter of each year in the historical data to the first mean value of the particulate matter concentration of the current year, obtaining a first set, and dividing the first set into N subsets according to years, and each subset includes 120 concentration multiples, expressed as: .

[0008] Furthermore, calculating the second mean value of each of the risk concentration multiples includes: calculating the mean value of the risk concentration multiples of each day in the autumn and winter within N years in sequence as the second mean value, expressed as: .

[0009] Furthermore, drawing a line chart according to the second mean value and fitting a pollution risk multiple curve according to the line chart includes: taking the dates of autumn and winter as the abscissa, taking the range of concentration multiples as the left ordinate, plotting all the concentration multiples in the first set as a scatter plot, taking the range of the second mean value as the right ordinate, and connecting the second mean values corresponding to each day to obtain a line chart; fitting the line chart into a curve chart through polynomial fitting based on the concentration multiples in the scatter plot to represent the pollution risk multiple.

[0010] Furthermore, it further includes: taking 30 days as the window length and 1 day as the step size, and selecting the 30 days with the highest pollution risk multiples in autumn and winter through the sliding window method.

[0011] An embodiment of the second aspect of the present invention provides a device for fitting a pollution risk multiple curve, including: A data acquisition module, configured to acquire historical data, where the historical data includes particulate matter concentration data of each day in autumn and winter for N consecutive years in the area to be fitted; A first mean value calculation module, configured to perform data processing on the historical data and calculate the first mean value of the particulate matter concentration of the processed data; A concentration multiple calculation module, configured to calculate the concentration multiple of each day in each year according to the historical data and the first mean value of the particulate matter concentration to obtain a first set; A concentration multiple processing module is used to screen out the concentration multiples greater than 1 in the first set, which are set as risk concentration multiples. A second mean value calculation module is used to calculate the second mean value of each of the risk concentration multiples. A curve fitting module is used to draw a line graph based on the second mean value and fit a pollution risk multiple curve according to the line graph.

[0012] An embodiment of the third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the method for fitting a pollution risk multiple curve according to any embodiment.

[0013] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for fitting a pollution risk multiple curve according to any embodiment.

[0014] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects: The method for fitting a pollution risk multiple curve provided by the present invention calculates the first mean value of the particulate matter concentration, calculates the concentration multiple according to the historical data and the first mean value, screens out the risk concentration multiples among them, calculates the second mean value of the risk concentration multiples, draws a line graph according to the second mean value, and fits it into a pollution risk multiple curve for measuring the average change trend of the risk concentration multiple with the date, so as to provide a reliable basis for guiding enterprises to stagger production peaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the embodiments of this specification. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the method for fitting a pollution risk multiple curve provided by Embodiment 1 of the present invention; Figure 2 It is a line graph drawn according to the second mean value provided by Embodiment 1 of the present invention; Figure 3 It is a pollution risk multiple curve fitted according to the line graph provided by Embodiment 1 of the present invention; Figure 4 It is an effect diagram of selecting the 30 days with the highest pollution risk multiples provided by Embodiment 1 of the present invention; Figure 5 It is a schematic diagram of the device for fitting a pollution risk multiple curve provided by Embodiment 2 of the present invention; Figure 6 This is a schematic diagram of the electronic device architecture provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. It should be noted that, in the absence of conflict, the embodiments and features in the embodiments disclosed in this disclosure can be combined, separated, interchanged and / or rearranged with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Example 1 A specific embodiment of the present invention, as Figures 1-4 As shown, a method for fitting a pollution risk multiplier curve is disclosed, comprising the following steps: S1. Obtain historical data, wherein the historical data includes daily particulate matter concentration data in autumn and winter in the area to be fitted for N consecutive years; For example, the daily concentration data of particulate matter (such as PM2.5) in a certain area in the autumn and winter seasons (November 1st to February 28th) in the past 10 years (2015-2024) are obtained.

[0019] S2. Processing the historical data to calculate a first mean value of the particle concentration of the processed data; In this embodiment, step S2 specifically includes: S201, data processing, specifically: The particle concentrations of the concentration changes caused by non-particles in the historical data were set to zero, and the missing and invalid daily particle concentrations were filled and corrected.

[0020] For example, during the four months of autumn and winter, on dates such as New Year's Eve, the first day of the first lunar month, and the fifteenth day of the first lunar month, there are fireworks that affect the particle matter concentration. Therefore, the particle matter concentration on these dates is set to zero, and interpolation and other methods are used to fill in or correct missing or invalid data to ensure the reliability of the data.

[0021] S202. Calculate the ratio of the sum of the autumn and winter particulate matter concentrations in each year in the historical data to the number of days in the current year, and obtain N The first mean value of the particle concentration is expressed as: in, n Indicates the autumn and winter season nDay represents the number of days in the m year, and represents the particulate matter concentration on the m day of the n year.

[0022] Specifically, the number of days in the current year is the remaining days in autumn and winter after excluding the dates with concentration changes caused by non-particulate matter. Taking step S1 as an example, m ranges from 1 to 10, representing the years 2015 - 2024, and n ranges from 1 to 120, representing November 1st to February 28th. The calculation result of this step is the average value of particulate matter concentration for each year from 2015 to 2024.

[0023] S3. Calculate the concentration ratio of each day in each year based on the historical data and the first mean value of particulate matter concentration to obtain the first set; In this embodiment, step S3 specifically includes: Calculate the ratio of the particulate matter concentration on each day in the autumn and winter of each year in the historical data to the first mean value of particulate matter concentration in the current year to obtain the first set. The first set is divided into N subsets according to years. Each subset includes 120 concentration ratios, which is expressed as: .

[0024] Specifically, the concentration ratio reflects whether the particulate matter concentration on each day in autumn and winter of a year exceeds the average particulate matter concentration of that year.

[0025] S4. Screen out the concentration ratios greater than 1 in the first set and set them as risk concentration ratios; Specifically, the concentration ratios greater than 1 in the first set indicate that the particulate matter concentration on that date is greater than the average particulate matter concentration of that year, and they are set as risk concentration ratios for subsequent guidance of peak-shifting production of enterprises in this area.

[0026] S5. Calculate the second mean value of each of the risk concentration ratios; In this embodiment, step S5 specifically includes: Calculate the mean value of the risk concentration ratios on each day in autumn and winter within N years in sequence as the second mean value, which is expressed as: .

[0027] Exemplarily, taking 2015 - 2024 as an example, add the risk concentration ratios belonging to the same date within ten years and then divide by ten to obtain the second mean value to measure the average change trend of the risk concentration ratio with the date.

[0028] S6. Draw a line graph based on the second mean value, and fit a pollution risk magnification curve according to the line graph.

[0029] Specifically, step S6 includes: S601. Use the dates in autumn and winter as the abscissa, the range of concentration magnification as the left ordinate, plot all the concentration magnifications in the first set as scatter points, and use the range of the second mean value as the right ordinate, and connect the corresponding second mean values of each day to obtain a line graph; Exemplarily, as Figure 2 shown, the dots of different colors in the figure represent the concentration magnifications on the current date in different years, and the black line represents the second mean value.

[0030] S602. Based on the concentration magnifications in the scatter plot, fit the line graph into a curve graph through polynomial fitting, which represents the pollution risk magnification.

[0031] The line graph obtained in step S601 is a discrete curve. In order to be able to predict the mean value of the risk concentration magnification according to the date subsequently, it is necessary to fit the line graph into a continuous curve. Exemplarily, as Figure 3 shown, the black dotted line represents the line graph, and the black solid line represents the pollution risk magnification curve, which is obtained through quartic polynomial fitting. The curve is expressed as: , where y represents the second mean value, X represents the day in the four months of autumn and winter.

[0032] Compared with the prior art, the pollution risk magnification curve fitted in this embodiment calculates the first mean value of the particulate matter concentration, calculates the concentration magnification according to the historical data and the first mean value, screens the risk concentration magnification among them, calculates the second mean value of the risk concentration magnification, draws a line graph according to the second mean value, and fits it into a pollution risk magnification curve that measures the average change trend of the risk concentration magnification with the date, thereby providing a reliable basis for guiding the enterprise to stagger production peaks.

[0033] In some embodiments, step S6 further includes: S603. Use a 30-day window length and a 1-day step size, and select the 30 days with the highest pollution risk magnification in autumn and winter through the sliding window method.

[0034] Specifically, by screening out 30 consecutive days with the relatively highest pollution degree in autumn and winter, it provides reliable support for guiding the enterprise to stagger production peaks subsequently. As Figure 4 shown, the purple frame part is the 30 days with the highest pollution risk magnification.

[0035] Embodiment 2 This embodiment provides a device for fitting a pollution risk magnification curve, asFigure 5 As shown in the figure, it includes: A data acquisition module for acquiring historical data, where the historical data includes particulate matter concentration data for each day in autumn and winter for N consecutive years in the area to be fitted; A first mean calculation module for processing the historical data and calculating the first mean of the particulate matter concentration of the processed data; A concentration multiple calculation module for calculating the concentration multiple for each day in each year based on the historical data and the first mean of the particulate matter concentration to obtain a first set; A concentration multiple processing module for screening the concentration multiples greater than 1 in the first set and setting them as risk concentration multiples; A second mean calculation module for calculating the second mean of each of the risk concentration multiples; A curve fitting module for plotting a line graph based on the second mean and fitting a pollution risk multiple curve based on the line graph.

[0036] Embodiment 3 This embodiment provides an electronic device, as Figure 6 shown in the figure, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for fitting a pollution risk multiple curve as described in any one of the above embodiments.

[0037] Embodiment 4 This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for fitting a pollution risk multiple curve as described in any one of the above embodiments.

[0038] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0039] Those skilled in the art should also be able to further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0040] The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well-known in the technical field.

[0041] The specific implementation manners described above have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent corrections, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for fitting a pollution risk magnification curve, characterized in that, Including: Obtain historical data, where the historical data includes the particulate matter concentration data of each day in autumn and winter for N consecutive years in the area to be fitted; Perform data processing on the historical data and calculate the first mean value of the particulate matter concentration of the processed data; Calculate the concentration multiple of each day in each year based on the historical data and the first mean value of the particulate matter concentration to obtain a first set; Screen the concentration multiples greater than 1 in the first set and set them as risk concentration multiples; Calculate the second mean value of each of the risk concentration multiples; Draw a line chart based on the second mean value and fit a pollution risk multiple curve based on the line chart.

2. The method for fitting a pollution risk magnification curve according to claim 1, characterized in that The data processing includes: Set the particulate matter concentration with concentration changes caused by non-particulate matter in the historical data to zero, and fill and correct the missing and invalid daily particulate matter concentrations.

3. The method for fitting a pollution risk magnification curve according to claim 1, wherein The calculation of the first mean value of the particulate matter concentration of the processed data includes: Calculate the ratio of the sum of the particulate matter concentrations in autumn and winter of each year in the historical data to the number of days in the current year to obtain N the first mean value of the particulate matter concentration, denoted as: Among them, n represents the n th day of autumn and winter, represents the number of days in the m th year, represents the particulate matter concentration on the m th day of the n th year.

4. The method for fitting a pollution risk magnification curve according to claim 3, characterized in that The calculation of the concentration multiple of each day in each year based on the historical data and the first mean value of the particulate matter concentration to obtain a first set includes: Calculate the ratio of the particulate matter concentration of each day in the autumn and winter of each year in the historical data to the first mean value of the particulate matter concentration in the current year to obtain a first set. The first set is divided into N subsets according to the year. Each subset includes 120 concentration multiples, expressed as: 。 5. The method for fitting a pollution risk magnification factor curve according to claim 1, wherein The calculation of the second mean value of each of the risk concentration multiples includes: Calculate the mean value of the daily risk concentration multiples in each day of autumn and winter in sequence within N the year as the second mean value, denoted as: 。 6. The method for fitting a pollution risk magnification curve according to claim 5, wherein, The drawing of a line chart based on the second mean value and the fitting of a pollution risk multiple curve based on the line chart includes: Taking the dates in autumn and winter as the abscissa, the range of concentration multiples as the left ordinate, plotting all the concentration multiples in the first set as a scatter plot, and taking the range of the second mean value as the right ordinate, connecting the corresponding second mean values of each day to obtain a line chart; Based on the concentration multiples in the scatter plot, fit the line chart into a curve through polynomial fitting to represent the pollution risk multiple.

7. The method for fitting a pollution risk magnification curve according to claim 6, characterized in that, Also including: Taking 30 days as the window length and 1 day as the step size, select the 30 days with the highest pollution risk multiples in autumn and winter through the sliding window method.

8. An apparatus for fitting a pollution risk magnification curve, characterized in that The device includes: A data acquisition module for acquiring historical data, where the historical data includes the particulate matter concentration data of each day in autumn and winter for N consecutive years in the area to be fitted; A first mean value calculation module for performing data processing on the historical data and calculating the first mean value of the particulate matter concentration of the processed data; A concentration multiple calculation module for calculating the concentration multiple of each day in each year based on the historical data and the first mean value of the particulate matter concentration to obtain a first set; A concentration multiple processing module for screening the concentration multiples greater than 1 in the first set and setting them as risk concentration multiples; A second mean value calculation module for calculating the second mean value of each of the risk concentration multiples; A curve fitting module for drawing a line chart based on the second mean value and fitting a pollution risk multiple curve based on the line chart.

9. An electronic device, characterized in that, Including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for fitting a pollution risk multiple curve as described in any one of claims 1-7.

10. A storage medium, characterized in that, Stored thereon is a computer program, and when the program is executed by the processor, it implements the method for fitting a pollution risk multiple curve as described in any one of claims 1-7.

Images