Method and device for improving environmental monitoring accuracy and readable storage medium

Abstract

The invention provides a method and device for improving environment monitoring accuracy and a readable storage medium, and belongs to the technical field of atmospheric environment detection quality control. The method comprises the following steps: acquiring environment monitoring data of a target area, wherein the environment monitoring data comprises concentration monitoring data of a monitored object; according to the acquired concentration monitoring data, determining a probability distribution function of a corresponding relationship between concentration and probability; and utilizing a local linear regression method to predict a probability density function value of a preset range of a preset breakpoint based on the probability distribution function, and determining function approximation values of two sides of the preset breakpoint. According to the method, the breakpoints are accurately determined, and discrete intervals are reasonably divided; by means of local linear regression, a probability density function is accurately predicted, and a function approximation value is determined; by calculating the difference between the probability densities of the two sides of the breakpoint, the data continuity is effectively judged, the quality and reliability of the data are guaranteed, and the accuracy of environment monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air environment detection quality control, and particularly to a method, device and readable storage medium for improving the accuracy of environmental monitoring. Background Art

[0002] In order to accurately grasp the degree of air pollution and formulate scientific treatment plans, a large number of ambient air automatic monitoring stations have been deployed everywhere, and high-frequency monitoring is carried out on pollution factors such as particulate matter (PM2.5 and PM10) and gaseous pollutants (such as SO 2 , NO 2 , O 3 , CO, etc.). Among them, the five-minute value data, as an important basis for real-time monitoring, can reflect the short-term fluctuation characteristics of pollution factors and provide support for pollution source analysis and refined control. In the field of environmental monitoring, accurately obtaining and analyzing environmental monitoring data is crucial for evaluating the environmental situation. Currently, the accuracy and reliability of environmental monitoring data face many challenges.

[0003] Existing environmental monitoring data often contains invalid data, such as exceeding the instrument range or having missing values, etc., which will affect the integrity of the data and the accuracy of the analysis results. Moreover, to accurately judge the characteristics of environmental monitoring data, such as determining data breakpoints and analyzing data continuity, etc., traditional methods have certain limitations. And the existing environmental monitoring methods are difficult to accurately achieve effective analysis of environmental monitoring data, unable to provide a reliable basis for environmental monitoring, thus affecting the accurate assessment of the environment in the target area. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method for improving the accuracy of environmental monitoring, which aims to solve many problems existing in the current environmental monitoring process to achieve more accurate and reliable environmental monitoring.

[0005] To achieve the above purpose, the embodiments of the present invention provide a method for improving the accuracy of environmental monitoring, the method includes: obtaining environmental monitoring data of a target area, the environmental monitoring data including concentration monitoring data of a monitored object; determining a probability distribution function of the relationship between the corresponding concentration and probability according to the obtained concentration monitoring data; using a local linear regression method, based on the probability distribution function, predicting the probability density function values in a preset range of a preset breakpoint, and determining the function approximation values on both sides of the preset breakpoint to calculate the difference in probability density on both sides of the preset breakpoint; judging the continuity of the environmental monitoring data according to the difference in probability density on both sides of the preset breakpoint to improve the accuracy of environmental monitoring of the target area.

[0006] Optionally, obtaining the environmental monitoring data of the target area includes: obtaining concentration monitoring data with timestamps; recording the obtained concentration monitoring data with timestamps as a time series; removing invalid data from the time series and determining the removed invalid data as missing points; calculating supplementary values through a linear interpolation algorithm according to the linear relationship between the adjacent data before and after the missing points.

[0007] Optionally, determining the probability distribution function of the relationship between the corresponding concentration and probability according to the obtained concentration monitoring data includes: dividing the environmental monitoring data according to the preset breakpoints to obtain discrete intervals; obtaining the probability distribution function based on the discrete intervals.

[0008] On the basis of the above solution, before dividing the environmental monitoring data according to the preset breakpoints, the method further includes: determining the preset breakpoints according to the distribution characteristics of the concentration monitoring data of the monitored substance, and the preset breakpoints are used to indicate the critical value of environmental pollution of the monitored substance.

[0009] Optionally, the timestamps of the concentration monitoring data are at preset time intervals.

[0010] Optionally, dividing the environmental monitoring data according to the preset breakpoints to obtain discrete intervals includes: dividing at equal intervals according to the concentration of the monitored substance according to the preset breakpoints to obtain the discrete intervals; calculating the corresponding concentration after dividing the concentration of the monitored substance at equal intervals through the following formula:

[0011]

[0012] where g(R i ) represents the corresponding concentration after dividing the concentration of the monitored substance at equal intervals, R i is the concentration of the monitored substance within the discrete interval, b represents the width of the divided concentration interval, c is the pollutant concentration of the preset breakpoint, represents the largest integer;

[0013] Solving for the width of the concentration interval using the following formula:

[0014]

[0015] In the formula, x i represents the concentration point, μ represents the concentration mean of x i , that is n represents the number of concentration points.

[0016] Optionally, the calculation of the probability density difference on both sides of the preset break point includes: using the McCrary test method and determining the probability density estimation values on both sides of the preset break point based on the function approximation values on both sides of the preset break point; performing a logarithmic calculation on the determined probability density estimation values on both sides of the preset break point to obtain the logarithmic difference of the probability density on both sides of the preset break point.

[0017] Optionally, the judgment of the continuity of the preset break point includes: judging whether there is discontinuity in the preset break point according to the magnitude of the logarithmic difference of the probability density on both sides and in combination with the one-sided test method.

[0018] Optionally, the judgment of whether there is discontinuity in the preset break point includes: comparing the obtained logarithmic difference with a preset first threshold and a second threshold, and performing the following steps according to the comparison result: the logarithmic difference < the first threshold, it is determined that there is discontinuity in the preset break point; the first threshold < the logarithmic difference < the second threshold, mark the judgment result; the second threshold < the logarithmic difference, it is determined that there is no discontinuity in the preset break point.

[0019] On the other hand, the present invention provides a control device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method for improving the accuracy of environmental monitoring as described above.

[0020] On the other hand, the present invention provides a readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the method for improving the accuracy of environmental monitoring as described above.

[0021] The method for improving the accuracy of environmental monitoring provided by the present invention can scientifically and accurately determine the preset break point, reasonably divide the discrete interval, accurately predict the probability density function and determine the function approximation value by means of local linear regression, effectively judge the data continuity by calculating the logarithmic difference, ensure the data quality and reliability, so as to improve the accuracy of environmental monitoring.

[0022] The method of the present invention has strong versatility, is easy to operate and promote, can serve different environmental monitoring scenarios, provide a basis for environmental decision-making, help with refined environmental management, and comprehensively improve the accuracy and effectiveness of environmental monitoring.

[0023] The present invention innovatively applies local linear regression to construct the probability density function estimation of monitoring data at a preset breakpoint. This method can avoid misjudgment that may be caused by the overall trend model while maintaining the local characteristics of the data, thereby improving the accuracy of data distribution estimation. By performing regression analysis on the probability density functions on both sides of the preset breakpoint, the potential discontinuity of the data at the preset breakpoint can be captured more meticulously.

[0024] Secondly, based on the McCrary test method, the present invention quantitatively determines the continuity of monitoring data at a specified preset breakpoint by calculating the logarithmic difference of the regression values on both sides of the preset breakpoint. Compared with traditional anomaly detection methods such as mean or variance analysis, this method is more suitable for processing non-normal distributed data and can directly reflect the specific location where the data may be manipulated or abnormal, significantly improving the sensitivity and robustness of detection.

[0025] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. Brief Description of the Drawings

[0026] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used to explain the embodiments of the present invention together with the following specific implementation manners, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0027] Figure 1 is a flowchart of the method for improving the accuracy of environmental monitoring in the embodiments of the present invention.

[0028] Figure 2 is a schematic diagram of the breakpoint continuity test provided by the embodiments of the present invention. Detailed Description of the Invention

[0029] The following will describe in detail the specific implementation manners of the embodiments of the present invention with reference to the drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0030] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of laws and regulations. In the embodiments of this application, certain industry-existing solutions such as software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0031] Figure 1 is a flowchart of the method for improving the accuracy of environmental monitoring in the embodiments of the present invention. As Figure 1 shown, the method for improving the accuracy of environmental monitoring may include the following steps:

[0032] Step S100: Acquire environmental monitoring data of a target area, where the environmental monitoring data includes concentration monitoring data of monitored objects.

[0033] In an embodiment of the present invention, the environmental monitoring data may include concentration monitoring data of monitored objects. The monitored objects may include pollution factors that affect the environment, such as particulate matter (PM2.5 and PM10), gaseous pollutants (such as SO 2 、NO 2 , O 3 It should be noted that the embodiment of the present invention uses PM2.5 as an example of the monitored object, which is not intended to limit the embodiment of the present invention.

[0034] The preferred breakpoint of the environmental monitoring data in the embodiment of the present invention may be a critical value of a high-pollution area of ​​the monitored object.

[0035] For example, the analysis breakpoint c is determined based on the ambient air quality standard of PM2.5 and the distribution characteristics of the monitoring data. In this embodiment, c is set to 75 μg / m 3 is the breakpoint, i.e. the critical value of the high pollution area.

[0036] In a preferred embodiment of the present invention, step S100 may include:

[0037] Acquire concentration monitoring data with timestamps; record the acquired concentration monitoring data with timestamps as a time series; eliminate invalid data in the time series, and determine the eliminated invalid data as missing points; calculate supplementary values ​​according to the linear relationship between the preceding and following adjacent data of the missing points through a linear interpolation algorithm.

[0038] In the embodiment of the present invention, the timestamp of the concentration monitoring data may be a preset time interval, for example, the preset time interval may be 3 minutes, 5 minutes or 8 minutes. For example, five minutes of PM2.5 concentration monitoring data with a timestamp is obtained from an environmental monitoring device and recorded as a time series {X t}, where X t is the PM2.5 concentration value at time t. Invalid data that exceeds the instrument range is eliminated, and the missing data is supplemented by linear interpolation. For example, the linear interpolation algorithm is used to calculate the supplementary value based on the linear relationship between the adjacent data before and after the missing point, thereby ensuring the integrity of the time series data.

[0039] Step S200: Determine a probability distribution function of the relationship between the corresponding concentration and probability according to the acquired concentration monitoring data.

[0040] In a preferred embodiment of the present invention, step S200 may include: dividing the environmental monitoring data according to a preset break point to obtain discrete intervals; and obtaining a probability distribution function based on the discrete intervals.

[0041] Preferably, before dividing the environmental monitoring data according to the preset break point, the method further includes: determining the preset break point according to the distribution characteristics of the concentration monitoring data of the monitored object, and the preset break point is used to indicate the critical value of environmental pollution of the monitored object.

[0042] Since the probability density function corresponding to PM2.5 at each concentration is relatively discrete, in order to better perform the next local linear regression, PM2.5 can be first divided according to the concentration interval to obtain discrete intervals.

[0043] Specifically, dividing the environmental monitoring data according to the preset break point to obtain discrete intervals includes: dividing at equal intervals according to the concentration of the monitored object according to the preset break point to obtain discrete intervals; and calculating the corresponding concentration after dividing at equal intervals of the concentration of the monitored object through the following formula:

[0044]

[0045] where g(R i ) represents the corresponding concentration after dividing at equal intervals of the concentration of the monitored object, R i is the concentration of the monitored object within the discrete interval, b represents the width of the divided concentration interval, c is the pollutant concentration of the preset break point, represents the largest integer.

[0046] To avoid the influence of human factors to the greatest extent, the width of the concentration interval is solved using the following formula:

[0047]

[0048] In the formula, x i represents the concentration point, μ represents the concentration mean of x i , that is n represents the number of concentration points.

[0049] Then, define the equally spaced grid points X 1 , X 2 , …, X J as points with a spacing of b, which cover all concentration ranges. Then the frequency distribution of the jth interval is:

[0050]

[0051] Its probability distribution can be expressed as a scatter plot form composed of (X j , Y j ).

[0052] Step S300: Using the locally linear regression method, based on the probability distribution function, predict the probability density function values within the preset range of the preset breakpoint, and determine the function approximation values on both sides of the preset breakpoint, so as to calculate the difference in probability density on both sides of the preset breakpoint.

[0053] In a preferred embodiment of the present invention, step S300 may include: using the locally linear regression method to estimate the probability density function near the breakpoint. Its density estimate at point x is Where Obtained by minimizing the objective function.

[0054] Minimize the objective function:

[0055]

[0056] In the formula, L(φ 1 , φ 2 , x) is the objective function with respect to point x, and φ 1 , φ 2 Are the parameters to be solved;

[0057] Among them, K(·) is the Gaussian kernel function, expressed as:

[0058]

[0059] For example, h = 15b, where h is the bandwidth.

[0060] For example, by solving the minimum value of the above objective function, the function approximation values on the left and right sides of x = c can be obtained as follows:

[0061]

[0062] Where:

[0063]

[0064] In the formula, f - (c) and f + (c) are the function approximation values on the left and right sides of the target point x = c, Is the local polynomial kernel function estimate of degree k on the left side of x = c, Is the local polynomial kernel function estimate of degree k on the right side of x = c, and k is the degree of the local polynomial kernel function. For example, k = 1, 2.

[0065] Step S400: According to the difference in probability density on both sides of the preset breakpoint, judge the continuity of the environmental monitoring data, so as to improve the accuracy of environmental monitoring of the target area.

[0066] In a preferred embodiment of the present invention, step S400 may include:

[0067] S410. Using the McCrary test method, based on the function approximation values on both sides of the breakpoint, obtain the probability density estimates on both sides of the breakpoint. For example, use the McCrary test method to verify whether the data distribution at the breakpoint c is continuous. The McCrary test method tests whether there is discontinuity in the data by calculating the logarithmic difference of the probability density on both sides of the breakpoint. Its test formula is:

[0068]

[0069] where and are the probability density estimates on the left and right sides of the breakpoint c, respectively.

[0070] S420. Perform a logarithmic operation on the obtained probability density estimates on both sides of the breakpoint to obtain the logarithmic difference of the probability density on both sides of the breakpoint.

[0071] For example, the judgment of the continuity of the breakpoint includes: judging whether there is a significant discontinuity at the breakpoint according to the magnitude of the logarithmic difference of the probability density on both sides, in combination with the one-sided test method.

[0072] Judging whether there is discontinuity at the breakpoint includes: comparing the obtained logarithmic difference with the preset first threshold and second threshold, and according to the comparison result, perform the following steps: logarithmic difference < first threshold, determine that there is discontinuity at the breakpoint; first threshold < logarithmic difference < second threshold, mark the judgment result, and subsequently, it can be determined whether there is discontinuity at the breakpoint through expert judgment, or it can be determined whether there is discontinuity at the breakpoint by setting a third threshold and comparing the logarithmic difference with the third threshold; second threshold < logarithmic difference, determine that there is no discontinuity at the breakpoint.

[0073] As Figure 2 shown, judge whether there is a significant discontinuity at the breakpoint by the magnitude of the logarithmic difference Δ. Since the test breakpoint c = 75 μg / m of the PM2.5 concentration 3 is usually located on the right side of the peak of the normal distribution, a one-sided t-test can be used for the test.

[0074] For example, if Δ < -2, it is considered that the possibility of having a breakpoint is relatively high, that is, the possibility of artificial manipulation is relatively high;

[0075] If -2 < Δ < -1.64, it is considered that there may be a distribution breakpoint, but the possibility is relatively low and further manual identification is required;

[0076] If Δ > -1.64, it is considered that there is no breakpoint in the data distribution, that is, there is no possibility of artificial manipulation.

[0077] The present invention innovatively applies local linear regression to construct the probability density function estimation of monitoring data at breakpoints. This method can maintain the local characteristics of the data while avoiding misjudgments that may be caused by the overall trend model, thereby improving the accuracy of data distribution estimation. By performing regression analysis on the probability density functions on both sides of the breakpoint, the potential discontinuity of the data at the breakpoint can be captured more meticulously. Secondly, based on the McCrary test method, by calculating the logarithmic difference of the regression values on both sides of the breakpoint, the present invention quantitatively determines the continuity of the monitoring data at the specified breakpoint (such as 35 μg / m 3 and 75 μg / m 3 ). Compared with traditional anomaly detection methods, such as mean or variance analysis, this method is more suitable for processing non-normal distributed data and can directly reflect the specific positions where the data may be manipulated or abnormal, significantly improving the sensitivity and robustness of detection. By eliminating invalid data and optimizing the breakpoint selection process, the present invention can adapt to the characteristics of monitoring points in different regions, improving the operability of the algorithm in large-scale point applications.

[0078] The present invention provides a control device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method for improving environmental monitoring accuracy as described above.

[0079] An embodiment of the present invention provides a readable storage medium, on which instructions are stored, and these instructions cause a machine to execute the method for improving environmental monitoring accuracy as described above.

[0080] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the processFigure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the operations in the process Figure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0084] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0085] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0086] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. 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 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.

[0087] It should also be noted that the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising the element.

[0088] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the present application.

Claims

1. A method for improving the accuracy of environmental monitoring, characterized in that: The method includes: Acquiring environmental monitoring data of a target area, wherein the environmental monitoring data includes concentration monitoring data of a monitored object; Determine a probability distribution function of the relationship between the corresponding concentration and probability according to the acquired concentration monitoring data; Using a local linear regression method, based on the probability distribution function, the probability density function value of a preset range of a preset breakpoint is predicted, and the function approximation values ​​on both sides of the preset breakpoint are determined to calculate the difference in probability density on both sides of the preset breakpoint; The continuity of the environmental monitoring data is judged according to the difference in probability density on both sides of the preset breakpoint to improve the accuracy of environmental monitoring of the target area.

2. The method according to claim 1, characterized in that The obtaining of environmental monitoring data of the target area includes: Obtain concentration monitoring data with time stamp; The acquired concentration monitoring data with time stamps are recorded as time series; Eliminate invalid data in the time series, and determine the eliminated invalid data as missing points; The supplementary value is calculated by a linear interpolation algorithm according to the linear relationship between the preceding and following adjacent data of the missing point.

3. The method according to claim 1, characterized in that Determining the probability distribution function of the relationship between the corresponding concentration and probability according to the acquired concentration monitoring data includes: According to the preset breakpoints, the environmental monitoring data is divided to obtain discrete intervals; Based on the discrete interval, the probability distribution function is obtained.

4. The method according to claim 3, characterized in that Before dividing the environmental monitoring data according to the preset breakpoints, the method further includes: The preset breakpoint is determined according to the distribution characteristics of the concentration monitoring data of the monitored object, and the preset breakpoint is used to indicate the critical value of the environmental pollution of the monitored object.

5. The method according to claim 3, characterized in that: The step of dividing the environmental monitoring data according to the preset breakpoints to obtain discrete intervals includes: According to the preset breakpoints, the discrete intervals are divided into equal intervals according to the concentration of the monitored object to obtain the discrete intervals; The concentration of the monitored object after being divided into equal intervals is calculated by the following formula: Among them, g(R i ) represents the concentration of the monitored object after the concentration is divided into equal intervals, R i is the concentration of the monitored object in the discrete interval, b represents the width of the divided concentration interval, and c is the pollutant concentration at the preset breakpoint. express The largest integer of ; Use the following formula to solve for the concentration interval width: In the formula, x i represents the concentration point, μ represents x i is the mean concentration of , and n represents the number of concentration points.

6. The method according to claim 5, characterized in that The calculation of the probability density difference on both sides of the preset breakpoint includes: Using the McCrary test method, and based on the function approximation values ​​on both sides of the preset breakpoint, determining the probability density estimation values ​​on both sides of the preset breakpoint; The probability density estimates on both sides of the determined preset breakpoint are logarithmically calculated to obtain the logarithmic difference of the probability density on both sides of the preset breakpoint.

7. The method according to claim 6, characterized in that The determination of the continuity of the preset breakpoints includes: According to the size of the logarithmic difference of the probability density on both sides, combined with the one-sided test method, it is determined whether the preset breakpoint has discontinuity.

8. The method according to claim 7, characterized in that The determining whether the preset breakpoint has discontinuity includes: The obtained logarithmic difference is compared with the preset first threshold and second threshold, and the following steps are performed according to the comparison result: If the logarithmic difference is less than the first threshold, it is determined that the preset breakpoint has discontinuity; The first threshold value is less than the logarithmic difference and less than the second threshold value, marking the determination result; If the second threshold is less than the logarithmic difference, it is determined that there is no discontinuity at the preset breakpoint.

9. A control device, characterized in that: The control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the method for improving the accuracy of environmental monitoring according to any one of claims 1-8.

10. A readable storage medium, characterized in that: The readable storage medium stores instructions, which enable the machine to execute the method for improving the accuracy of environmental monitoring according to any one of claims 1-8.

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