An environmental monitoring method and system based on an environmental monitor

Abstract

This application relates to the technical field of environmental monitoring, and particularly to an environmental monitoring method and system based on an environmental monitor. The method includes: obtaining a first concentration sequence of a target polluting gas in the environment to be monitored through a first sensor of the environmental monitor, and determining a first fluctuation degree value of a target data point in the first concentration sequence; comparing the local sequence of the target data point with the concentration in the same time period of the second concentration sequence obtained by the second sensor to determine a credibility degree value of the target data point, so as to obtain a second fluctuation degree value of the target data point; determining a target length corresponding to the target data point according to the second fluctuation degree value of the target data point, and using the fuzzy entropy of the neighborhood sequence of the target length of the target data point to determine the monitoring result at the moment where the target data point is located. Through the above technical solution, it is possible to effectively monitor the anomalies existing in the target polluting gas in the area to be monitored.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, and particularly to an environmental monitoring method and system based on an environmental monitor. Background Art

[0002] During the processes of thermal power generation, industrial production activities, oil extraction, and engine operation, polluting gases such as sulfur dioxide, nitrogen oxides, carbon monoxide, volatile organic compounds, and hydrogen sulfide may be generated. These polluting gases can pose hazards to the surrounding environment and human health. Therefore, in order to avoid the threats posed by polluting gases to the environment or the human body, it is necessary to monitor the polluting gases present in the environment.

[0003] In order to achieve the monitoring of polluting gases in the environment, in the Chinese patent application document with the publication number CN114046822A, a method for monitoring air pollution based on a sensor array module is provided, including: generating an airspace-level particulate matter concentration set by performing hierarchical monitoring of the particulate matter concentration in the first area to be monitored; constructing a longitudinal spatial pollution monitoring network for the first area to be monitored by combining each hierarchical airspace; generating a transverse circumferential pollution monitoring network; building a ground-air integrated pollution monitoring and tracking map for the first area to be monitored; generating real-time wind monitoring data and rendering it to the first corresponding marker on the ground-air integrated pollution monitoring and tracking map; generating real-time temperature and humidity monitoring data and rendering it to the second corresponding marker on the ground-air integrated pollution monitoring and tracking map; generating a ground-air integrated pollution monitoring and tracking network and uploading it to an electronic display screen for dynamic monitoring of the first area to be monitored.

[0004] When the related technology monitors air pollution in the area to be monitored by using the obtained concentration set of pollutants in the area to be monitored, it mainly focuses on displaying the particulate matter concentration in the area to be monitored. Therefore, it is difficult to accurately monitor the anomalies existing in the polluting gases in the area to be monitored. Summary of the Invention

[0005] To overcome the problem that the related technology is difficult to monitor the anomalies existing in the polluting gases in the area to be monitored, this application provides an environmental monitoring method and system based on an environmental monitor.

[0006] According to the first aspect of the embodiments of the present application, there is provided an environmental monitoring method based on an environmental monitor, including: obtaining a first concentration sequence of a target pollutant gas in the environment to be monitored through a first sensor of the environmental monitor, and determining a local sequence where a target data point is located in the first concentration sequence, so as to determine a first fluctuation degree value of the target data point according to the number of extreme points in the local sequence; obtaining a plurality of second concentration sequences of the target pollutant gas in the environment to be monitored through a plurality of second sensors of the environmental monitor; the sensor types of the first sensor and the second sensors are the same; the acquisition time periods of the first concentration sequence and the plurality of second concentration sequences are the same; determining a credibility value of the target data point according to the difference between the local sequence and the concentrations in the second concentration sequences during the same time period, so as to use the product of the first fluctuation degree value and the credibility value of the target data point as the second fluctuation degree value of the target data point; determining a target length corresponding to the target data point according to the second fluctuation degree value of the target data point, and intercepting a neighborhood sequence corresponding to the target data point and equal to the target length from the first concentration sequence, so as to determine the monitoring result of the moment where the target data point is located by using the fuzzy entropy of the neighborhood sequence.

[0007] In this way, by comparing the data obtained by the first sensor with the data obtained by the second sensors of the same sensor type to determine the credibility value of the target data point, it is possible to effectively determine whether the target data point obtained by the first sensor is affected by noise, so as to more accurately monitor the concentration of the target pollutant gas in the area to be monitored while avoiding the influence of noise.

[0008] Optionally, the first fluctuation degree value of the target data point is determined by the following method: When When ; when 1, ; where M is the number of extreme points of the concentration in the local sequence, F is the first fluctuation degree value of the target data point, norm is a normalization processing function, is the concentration of the i-th extreme point in the local sequence, is the average value of the concentration in the local sequence where the target data point is located, is to take the absolute value.

[0009] In this way, by analyzing the number of extreme points of the concentration in the local sequence, the first fluctuation degree value of the target data point can be calculated to detect abnormalities in the environment to be monitored.

[0010] Optionally, the credibility value of the target data point is determined by the following method: , where is the credibility value of the target data point, exp is an exponential function with the natural constant as the base, A is the number of second concentration sequences, B is the number of data points in the local sequence, is the concentration of the b-th data point in the local sequence; is the concentration of the b-th data point corresponding to the local sequence in the a-th second concentration sequence, and corresponds to the same moment; T is the sum of the correlation coefficients between the local sequence and different second concentration sequences, and the correlation coefficient is equal to the Pearson correlation coefficient between the concentration segments with the same time period in the local sequence and the second concentration sequence; is to take the absolute value.

[0011] In this way, based on the Pearson correlation coefficient between the concentration segments with the same time period in the local sequence corresponding to the target data point and the second concentration sequence, the credibility value of the target data point is determined, and the credibility value can reflect the probability that the concentration data near the target data point is affected by noise.

[0012] Optionally, determining the target length corresponding to the target data point according to the second fluctuation degree value of the target data point includes: , is the target length corresponding to the target data point, is the second fluctuation degree value of the target data point, is the first preset length, is to round down; is the second preset length, and the second preset length is less than the first preset length, and the second preset length is the minimum length required for determining the fuzzy entropy of the time series.

[0013] In this way, according to the second fluctuation degree value of the target data point, the size of the target length corresponding to the target data point can be adaptively adjusted to balance the computing resources and computing efficiency.

[0014] Optionally, the fuzzy entropy of the neighborhood sequence is determined by the following method: select multiple neighborhood subsequences with a length equal to the third preset length from the neighborhood sequence, and determine the similarity degree value between any two neighborhood subsequences; according to the similarity degree value between any two neighborhood subsequences, determine the first eigenvalue of the neighborhood sequence at the third preset length, and the first eigenvalue is equal to the average value of different similarity degree values; referring to the determination process of the first eigenvalue of the neighborhood sequence at the third preset length, determine the second eigenvalue of the neighborhood sequence at the fourth preset length, and take the difference between the first eigenvalue and the second eigenvalue as the fuzzy entropy; the fourth preset length is greater than the third preset length.

[0015] In this way, by taking the difference between the eigenvalues corresponding to different preset lengths as the fuzzy entropy, the fuzzy entropy can better reflect the degree of abnormality in the environment to be monitored.

[0016] Optionally, the monitoring result at the moment when the target data point is located is determined by using the fuzzy entropy of the neighborhood sequence, including: determining the abnormal degree value at the moment when the target data point is located by using the fuzzy entropy of the neighborhood sequence, and determining the monitoring result at the moment when the target data point is located by using the relationship between the abnormal degree value at the moment when the target data point is located and a preset threshold value; wherein, the abnormal degree value , norm is a normalization processing function, is the fuzzy entropy corresponding to the target data point, is the fuzzy entropy corresponding to the previous data point of the target data point, is the average value of the concentration in the local sequence where the target data point is located, is the average value of the concentration in the local sequence where the previous data point of the target data point is located.

[0017] Optionally, the monitoring result at the moment when the target data point is located is determined by using the relationship between the abnormal degree value at the moment when the target data point is located and a preset threshold value, including: when the abnormal degree value at the moment when the target data point is located is greater than or equal to the preset threshold value, outputting a prompt message, where the prompt message is used to prompt that the concentration of the target pollutant gas in the environment to be monitored is abnormally increased at the moment when the target data point is located.

[0018] Optionally, the monitoring result at the moment when the target data point is located is determined by using the relationship between the abnormal degree value at the moment when the target data point is located and a preset threshold value, including: when the abnormal degree value at the moment when the target data point is located is less than the preset threshold value, prompting that the concentration at the moment when the target data point is located is in a normal state.

[0019] Optionally, the target pollutant gas is any one of sulfur dioxide, nitrogen oxides, carbon monoxide, volatile organic compounds, and hydrogen sulfide.

[0020] According to the second aspect of the embodiments of the present application, an environmental monitoring system based on an environmental monitor is provided, including: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the steps of the environmental monitoring method based on the environmental monitor provided in the first aspect of the present application are implemented.

[0021] The technical solutions provided by the embodiments of the present application may include the following beneficial effects: For the first concentration sequence of the target pollutant gas in the environment to be monitored obtained by the first sensor of the environmental monitor, the first fluctuation degree value of the target data point in the first concentration sequence can be determined. By comparing the data obtained by the first sensor with the data obtained by the second sensor of the same sensor type, the credibility value of the target data point can be determined to obtain the second fluctuation degree value of the target data point; according to the second fluctuation degree value of the target data point, the target length corresponding to the target data point can be adaptively determined to obtain the monitoring result of the moment when the target data point is located; compared with the display of the concentration of the obtained pollutant gas, the abnormality existing in the concentration of the target pollutant gas in the environment to be monitored can be more effectively identified. Therefore, the abnormality existing in the target pollutant gas in the area to be monitored can be more effectively monitored.

[0022] In the embodiments of the present application, the credibility value of the target data point is determined by comparing the data obtained by the first sensor with the data obtained by the second sensor of the same sensor type, which can effectively determine whether the target data point obtained by the first sensor is affected by noise, so as to realize the monitoring of the concentration of the target pollutant gas in the area to be monitored while avoiding the influence of noise.

[0023] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of an environmental monitoring method based on an environmental monitor shown according to an exemplary embodiment;

[0025] Figure 2 is a schematic structural diagram of an environmental monitoring system based on an environmental monitor shown according to an exemplary embodiment. DETAILED DESCRIPTION

[0026] First, a simple introduction to the application scenario of the embodiments of the present application is given. In the application scenario of the present application, in order to monitor pollutant gases such as particulate matter in the environment, the related technology mainly displays the concentration of the obtained particulate matter and other pollutant gases through a display device, and it is difficult to effectively identify the abnormalities existing in the pollutant gases. Therefore, it is difficult to effectively monitor the pollutant gases.

[0027] To solve the above technical problems, the embodiments of the present application provide an environmental monitoring method and system based on an environmental monitor. Figure 1 is a flowchart of an environmental monitoring method based on an environmental monitor shown according to an exemplary embodiment. As Figure 1 shown, the method includes the following steps.

[0028] In step S101, a first concentration sequence of a target pollutant gas in the environment to be monitored is obtained through a first sensor of an environmental monitor, and a local sequence where a target data point is located in the first concentration sequence is determined, so as to determine a first fluctuation degree value of the target data point according to the number of extreme points in the local sequence.

[0029] The target pollutant gas can be any one of pollutant gases such as sulfur dioxide, nitrogen oxides, carbon monoxide, volatile organic compounds, and hydrogen sulfide.

[0030] A plurality of sensors of the same type can be arranged on the environmental monitor; for example, when the target pollutant gas is sulfur dioxide, a plurality of sulfur dioxide sensors can be arranged on the environmental monitor to measure the concentration of sulfur dioxide in the environment where the environmental monitor is located; or, when the target pollutant gas is nitrogen oxides, a plurality of nitrogen oxide sensors can be arranged on the environmental monitor to measure the concentration of nitrogen oxides in the environment where the environmental monitor is located.

[0031] The first sensor is one of a plurality of sensors arranged in the environmental monitor for obtaining the concentration of the target pollutant gas; through the first sensor, the concentration of the target pollutant gas in the environment to be monitored within a certain time period can be obtained and form a first concentration sequence in chronological order; for example, the concentration of the target pollutant gas in the environment to be monitored obtained by the first sensor within 10 minutes before the current moment can be used to form the first concentration sequence.

[0032] The target data point can be any concentration data point in the first concentration sequence, and a part of the first concentration sequence within a specified time length in the neighborhood range of the target data point can be used as the local sequence where the target data point is located in the first concentration sequence.

[0033] In one embodiment, the first fluctuation degree value of the target data point is determined in the following manner: when , ; when 1, ; where M is the number of extreme points of the concentration in the local sequence, F is the first fluctuation degree value of the target data point, norm is a normalization processing function, is the concentration of the i-th extreme point in the local sequence, is the average value of the concentration in the local sequence where the target data point is located, is to take the absolute value.

[0034] The normalization processing function is used to normalize the variable to be normalized to the range of 0 to 1; for example, the normalization processing function can adopt min-max normalization, logarithmic normalization, or Sigmoid function, etc. The specific form of the normalization processing function in the embodiments of the present application is not limited.

[0035] When there are no extreme concentration points in the local sequence of the target data point, it indicates that the concentration of the target pollutant gas at different times in the local sequence is relatively stable, or the fluctuations in the concentration of the target pollutant gas at different times in the local sequence are small.

[0036] When there are extreme concentration points in the local sequence of the target data point, it indicates that there are certain fluctuations in the concentration of the target pollutant gas at different times in the local sequence, and the more the number of extreme points, at least it indicates that the number of times the concentration fluctuates is more; the extreme concentration points in the local sequence can be the maximum concentration points or the minimum concentration points.

[0037] When there are extreme concentration points in the local sequence of the target data point, the number of extreme points in the local sequence can reflect the number of fluctuations in the concentration; the difference between the extreme point and the average value of the concentration can reflect the amplitude of the concentration fluctuation in the local sequence. Therefore, considering the difference between the extreme point in the local sequence and the average value of the concentration in the local sequence can more comprehensively consider the concentration fluctuation.

[0038] In this way, at least according to the number of extreme concentration points in the local sequence, the first fluctuation degree value of the target data point can be determined to characterize the fluctuation degree in the local sequence where the target data point is located through the first fluctuation degree value.

[0039] In step S102, through a plurality of second sensors of the environmental monitor, a plurality of second concentration sequences of the target pollutant gas in the environment to be monitored are obtained.

[0040] The sensor types of the first sensor and the second sensors are the same; the acquisition time periods of the first concentration sequence and the plurality of second concentration sequences are the same; for example, the first sensor and the plurality of second sensors are used to collect the concentration of the target pollutant gas in the same environment to be monitored during the same time period, so as to calibrate the data obtained by a certain sensor through other sensors among the plurality of sensors.

[0041] Since the sensor types of the plurality of second sensors and the first sensor are the same, and the corresponding acquisition time periods are the same, the plurality of second sensors can respectively obtain the corresponding second concentration sequences, and different second concentration sequences correspond to different second sensors.

[0042] In step S103, according to the difference between the concentrations in the same time period of the local sequence and the second concentration sequence, the credibility value of the target data point is determined, and the product of the first fluctuation degree value and the credibility value of the target data point is used as the second fluctuation degree value of the target data point.

[0043] In an embodiment of the present application, the acquisition of the concentration of the target pollutant gas in the same to-be-monitored environment within the same time period is achieved through a first sensor and multiple second sensors. When the concentration of the target pollutant gas collected by the first sensor is affected by noise, it is difficult for the concentrations of the target pollutant gas obtained by the multiple second sensors to be affected by noise at the same time. Therefore, the local sequence in the first concentration sequence obtained by the first sensor can be compared with the concentrations in the second concentration sequence with the same time period to determine whether there is noise in the local sequence corresponding to the target data point.

[0044] In one embodiment, the credibility value of the target data point is determined by the following method: , where is the credibility value of the target data point, exp is the exponential function with the natural constant as the base, A is the number of second concentration sequences, B is the number of data points in the local sequence, is the concentration of the b-th data point in the local sequence; is the concentration of the b-th data point corresponding to the local sequence in the a-th second concentration sequence, is the same as the moment corresponding to ; T is the sum of the correlation coefficients between the local sequence and different second concentration sequences, and the correlation coefficient is equal to the Pearson correlation coefficient between the local sequence and the concentration segments with the same time period in the second concentration sequence; is to take the absolute value.

[0045] For example, when a first sensor and 3 other second sensors of the same sensor type are set in an environmental monitor, the time period corresponding to the first concentration sequence or the second concentration sequence collected is from the 0th second to the 600th second. The moment corresponding to the target data point can be the 600th second, and the time period of the local sequence corresponding to the target data point can be the time period from the 540th second to the 600th second.

[0046] The concentration data of the target pollutant gas collected by the second sensor during the time period from the 540th second to the 600th second can be compared with the concentration data at the same moment in the local sequence corresponding to the target data point to determine whether the local sequence corresponding to the target data point is affected by noise.

[0047] In the case where the concentration data obtained by the first sensor is not affected by noise, the local sequence corresponding to the target data point in the first concentration sequence has a high consistency with the concentration data in the second concentration sequence within the same time period.

[0048] When the concentration data obtained by the first sensor is affected by noise at the target data point, the consistency between the local sequence corresponding to the target data point and the concentration data in the second concentration sequence within the same time period decreases.

[0049] The correlation coefficient is equal to the Pearson correlation coefficient between the local sequence and the concentration segment with the same time period in the second concentration sequence; for example, when the local sequence of the target data point corresponds to the time period from 12:00:00 to 12:01:00, since the first sensor and the second sensor collect the concentration of the target pollutant gas in the same time period of the environment to be detected, therefore, the concentration segment corresponding to the time period from 12:00:00 to 12:01:00 in the second concentration sequence obtained by the second sensor usually has a correlation with the local sequence of the target data point, and the greater this correlation, the smaller the degree of noise influence on the first sensor near the target data point.

[0050] The credibility value of the target data point can represent the probability that the first sensor is not affected by noise at the moment of the target data point, or the credibility value of the target data point can represent the degree to which the target data point can reflect the actual concentration of the target pollutant gas.

[0051] The greater the credibility value of the target data point, the smaller the probability or degree of noise influence on the first sensor at the moment of the target data point, and the more the target data point can reflect the actual concentration of the target pollutant gas in the environment to be monitored.

[0052] On the contrary, the smaller the credibility value of the target data point, the greater the probability or degree of noise influence on the first sensor at the moment of the target data point, and the more difficult it is for the target data point to reflect the actual concentration of the target pollutant gas in the environment to be monitored.

[0053] In this way, through the Pearson correlation coefficient between the local sequence corresponding to the target data point and the concentration segment with the same time period in the second concentration sequence, and by comparing the concentration data obtained by the first sensor with the concentration data obtained by the second sensor at the same moment respectively, the obtained credibility value can better reflect the probability that the concentration data near the target data point is affected by noise.

[0054] Since the target data point can reflect the fluctuation degree of the concentration of the target pollutant gas obtained by the first sensor in the local sequence, the credibility value of the target data point can represent the probability that the first sensor is not affected by noise at the moment of the target data point, or the credibility value of the target data point can represent the degree to which the target data point can reflect the actual concentration of the target pollutant gas. Therefore, the product of the first fluctuation degree value and the credibility value of the target data point can more effectively reflect the actual fluctuation degree of the concentration of the target pollutant gas in the local sequence.

[0055] In step S104, according to the second fluctuation degree value of the target data point, determine the target length corresponding to the target data point, and intercept the neighborhood sequence corresponding to the target data point and equal to the target length from the first concentration sequence, so as to use the fuzzy entropy of the neighborhood sequence to determine the monitoring result at the moment where the target data point is located.

[0056] According to the second fluctuation degree value of the target data point, a matching target length can be determined for the target data point, so as to further determine whether there is an abnormality in the concentration of the target data point through the neighborhood sequence with a length equal to the target length.

[0057] Among them, the smaller the second fluctuation degree value of the target data point, the more stable the concentration of the environment to be detected at the target data point. Whether there is an abnormality in the concentration of the target pollutant gas at the moment corresponding to the target data point can be determined through a neighborhood sequence with less data volume.

[0058] On the contrary, the larger the second fluctuation degree value of the target data point, the greater the fluctuation degree of the concentration of the environment to be detected at the target data point. Then, whether there is an abnormality in the concentration of the target pollutant gas at the moment corresponding to the target data point can be determined through a neighborhood sequence with a larger data volume, so as to more accurately determine whether there is an abnormality in the concentration of the target pollutant gas at the moment of the target data point.

[0059] In one embodiment, determining the target length corresponding to the target data point according to the second fluctuation degree value of the target data point includes: , is the target length corresponding to the target data point, is the second fluctuation degree value of the target data point, is the first preset length, is rounding down; is the second preset length, and the second preset length is less than the first preset length. The second preset length is the minimum length required for determining the fuzzy entropy of the time series.

[0060] By multiplying the second fluctuation degree value by the first preset length and rounding the multiplication result, it can be ensured that the target length corresponding to the target data point is positively correlated with the second fluctuation degree value. And due to the existence of the second preset length, when the second fluctuation degree value is small, it can be ensured that the target length has a certain basic length, thereby ensuring the smooth progress of the process of determining the fuzzy entropy of the time series of the concentration of the target pollutant gas at the environment to be monitored.

[0061] For example, the first preset length can be between 100 and 120, and the second preset length can be between 35 and 50; both the first preset length and the second preset length are positive integers.

[0062] In this way, by adjusting the target length corresponding to the target data point, when the degree of fluctuation at the target data point is relatively high, the size of the target length can be increased to obtain more detailed information about the environment to be monitored; when the degree of fluctuation at the target data point is relatively low, the size of the target length can be reduced to save computing resources.

[0063] In one embodiment, the fuzzy entropy of the neighborhood sequence is determined as follows: select a plurality of neighborhood subsequences with a length equal to the third preset length from the neighborhood sequence, and determine the similarity degree value between any two neighborhood subsequences among the plurality of neighborhood subsequences; according to the similarity degree value between any two neighborhood subsequences, determine the first eigenvalue of the neighborhood sequence at the third preset length, where the first eigenvalue is equal to the average value of different similarity degree values; referring to the determination process of the first eigenvalue of the neighborhood sequence at the third preset length, determine the second eigenvalue of the neighborhood sequence at the fourth preset length, and use the difference between the first eigenvalue and the second eigenvalue as the fuzzy entropy; the fourth preset length is greater than the third preset length.

[0064] The neighborhood subsequences can be selected from the neighborhood sequence of the target data point according to a preset step size, and the preset step size can be equal to 1; for example, when the neighborhood sequence is [C1, C2, C3, C4, C5, C6, C7, C8], if the third preset length is equal to 2, [C1, C2] can be used as the first selected neighborhood subsequence, [C2, C3] can be used as the second selected neighborhood subsequence, and [C7, C8] can be used as the last selected neighborhood subsequence to complete the selection of all neighborhood subsequences.

[0065] The similarity degree value between any two neighborhood subsequences can be determined according to the maximum value among the differences between the corresponding positions in the two neighborhood subsequences. For example, for the neighborhood subsequence [C1, C2] and the neighborhood subsequence [C3, C4], the maximum value among and can be used as the difference degree value between the neighborhood subsequence [C1, C2] and [C3, C4], and the reciprocal of the difference degree value can be used as the similarity degree value between the neighborhood subsequence [C1, C2] and [C3, C4]; alternatively, the reciprocal of the exponential function of the difference degree value can be used as the similarity degree value.

[0066] Since the first eigenvalue is equal to the average value of different similarity degree values, and the similarity degree value between any two neighborhood subsequences is used to represent the similarity degree between the two neighborhood subsequences, therefore, through the first eigenvalue of the neighborhood sequence at the third preset length, the consistency between different neighborhood subsequences when the neighborhood sequence is selected according to the third preset length can be reflected.

[0067] Referring to the determination process of the first eigenvalue of the neighborhood sequence at the third preset length, the second eigenvalue of the neighborhood sequence at the fourth preset length can be determined. The obtained second eigenvalue can reflect the consistency between different neighborhood subsequences when the neighborhood sequence is selected according to the fourth preset length. The difference between the first eigenvalue and the second eigenvalue can reflect the degree of difference when the neighborhood sequence selects neighborhood subsequences according to different lengths, thereby reflecting the abnormality degree of the environment to be monitored during the time period where the neighborhood sequence is located.

[0068] The third preset length and the fourth preset length can be determined according to actual requirements. For example, the third preset length can be equal to 4, and the fourth preset length can be equal to 5.

[0069] In this way, the neighborhood sequence is divided by different preset lengths respectively to obtain the corresponding eigenvalues. By taking the difference between the eigenvalues corresponding to different preset lengths as the fuzzy entropy, the fuzzy entropy can better reflect the abnormality degree of the environment to be monitored during the time period where the neighborhood sequence is located.

[0070] In one embodiment, using the fuzzy entropy of the neighborhood sequence to determine the monitoring result at the moment of the target data point includes: using the fuzzy entropy of the neighborhood sequence to determine the abnormality degree value at the moment of the target data point, and using the relationship between the abnormality degree value at the moment of the target data point and the preset threshold to determine the monitoring result at the moment of the target data point. Among them, the abnormality degree value , norm is a normalization function, is the fuzzy entropy corresponding to the target data point, is the fuzzy entropy corresponding to the previous data point of the target data point, is the average value of the concentration in the local sequence where the target data point is located, is the average value of the concentration in the local sequence where the previous data point of the target data point is located.

[0071] The normalization function norm is used to normalize the variable to be normalized to the range of 0 to 1. After the normalization process of the normalization function norm on , it can make within the range of 0 to 1. After the normalization process of the normalization function norm on , it can make within the range of 0 to 1.

[0072] For example, when the fuzzy entropy corresponding to the target data point is greater than the fuzzy entropy corresponding to the previous data point of the target data point, is closer to 1; when the fuzzy entropy corresponding to the target data point is less than the fuzzy entropy corresponding to the previous data point of the target data point, is closer to 0.

[0073] When the fuzzy entropy corresponding to the target data point is greater than the fuzzy entropy corresponding to the previous data point of the target data point, it indicates that the fluctuation degree of the concentration of the target pollutant gas in the environment to be monitored has become greater than that of the previous moment, and the concentration of the target pollutant gas in the environment to be monitored requires a higher degree of attention. Here, an abnormal degree value with a higher value can be set so that the concentration of the target pollutant gas in the environment to be monitored can be noticed by the user.

[0074] Moreover, when the fuzzy entropy corresponding to the target data point is greater than the fuzzy entropy corresponding to the previous data point of the target data point, there is a high probability that the concentration of the target pollutant gas will increase in the future time period near the location where the environment to be monitored is located. For example, the pollution source near the location where the environment to be monitored is located increases the release rate of the target pollutant gas. Therefore, comparing the fuzzy entropy corresponding to the target data point with the fuzzy entropy corresponding to the previous data point of the target data point also helps to guide the user to pay attention to the abnormality of the target pollutant gas in the environment to be monitored.

[0075] When the average value of the concentration in the local sequence where the target data point is located is greater than the average value of the concentration in the local sequence where the previous data point of the target data point is located, it indicates that the concentration of the target pollutant gas in the environment to be monitored is greater than the concentration in the previous time period; combined with the change of the fuzzy entropy corresponding to the target data point relative to the fuzzy entropy of the previous data point, the abnormal degree value can better evaluate the abnormality of the concentration of the target pollutant gas in the environment to be monitored.

[0076] In this way, using the fuzzy entropy of the neighborhood sequence to determine the abnormal degree value at the moment when the target data point is located, the abnormal degree value takes into account the change of the concentration of the target data point relative to the concentration of the previous data point. Using the relationship between the abnormal degree value at the moment when the target data point is located and the preset threshold, a relatively accurate monitoring result at the moment when the target data point is located can be obtained, so that the user can monitor the target pollutant gas in the environment to be monitored.

[0077] Using the relationship between the abnormal degree value at the moment when the target data point is located and the preset threshold to determine the monitoring result at the moment when the target data point is located, including: in the case where the abnormal degree value at the moment when the target data point is located is greater than or equal to the preset threshold, a prompt message can be output, and the prompt message is used to prompt that the concentration of the target pollutant gas at the moment when the target data point is located in the environment to be monitored is too high.

[0078] For example, when the target pollutant gas is sulfur dioxide, if the moment corresponding to the target data point is the current moment and the abnormal degree value at the moment when the target data point is located is greater than or equal to the preset threshold, it can be prompted that the concentration of sulfur dioxide at the current moment in the environment to be monitored is abnormally increased.

[0079] When the concentration of sulfur dioxide in the environment to be monitored at the current moment is too high, it is possible to prompt the personnel in the environment to evacuate or take corresponding protective measures. Alternatively, it is possible to control the pollution gas treatment equipment to treat the pollution gas existing in the environment to be monitored.

[0080] When the abnormality degree value at the moment of the target data point is less than the preset threshold, it indicates that the concentration of the target pollution gas in the environment to be monitored at the moment of the target data point is normal, and it is possible to prompt that the concentration at the moment of the target data point is in a normal state.

[0081] Figure 2 It is a schematic structural diagram of an environmental monitoring system 1000 based on an environmental monitor shown according to an exemplary embodiment. Refer to Figure 2 , the environmental monitoring system 1000 based on the environmental monitor includes: a processor 1100 and a memory 1200. The memory 1200 stores computer program instructions, and when the computer program instructions are executed by the processor 1100, all or part of the steps of the environmental monitoring method based on the environmental monitor in the present application are implemented.

[0082] Those skilled in the art will readily think of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary.

[0083] It should be understood that the present application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An environmental monitoring method based on an environmental monitor, characterized in that, Including: Obtain the first concentration sequence of the target polluting gas in the environment to be monitored through the first sensor of the environmental monitor, and determine the local sequence where the target data point is located in the first concentration sequence, so as to determine the first fluctuation degree value of the target data point according to the number of extreme points in the local sequence, including: when When ; when When it is 1, ; where M is the number of extreme points of the concentration in the local sequence, F is the first fluctuation degree value of the target data point, norm is the normalization processing function, is the concentration of the i-th extreme point in the local sequence, is the average value of the concentration in the local sequence where the target data point is located, is to take the absolute value; Obtain a plurality of second concentration sequences of a target polluting gas in the environment to be monitored through a plurality of second sensors of an environmental monitor; the sensor types of the first sensor and the second sensors are the same; the acquisition time period of the first concentration sequence is the same as that of the plurality of second concentration sequences; Determine the credibility value of a target data point according to the difference between the local sequence and the concentration in the same time period of the second concentration sequence, and use the product of the first fluctuation degree value of the target data point and the credibility value as the second fluctuation degree value of the target data point; The credibility value is determined by the following method: , where is the credibility value of the target data point, exp is the exponential function with the natural constant as the base, A is the number of the second concentration sequences, B is the number of data points in the local sequence, is the concentration of the b-th data point in the local sequence; is the concentration of the b-th data point corresponding to the local sequence in the a-th second concentration sequence, and correspond to the same moment; T is the sum of the correlation coefficients between the local sequence and different second concentration sequences, and the correlation coefficient is equal to the Pearson correlation coefficient between the concentration segments with the same time period in the local sequence and the second concentration sequence; determining the target length corresponding to the target data point according to the second fluctuation degree value of the target data point includes: , is the target length corresponding to the target data point, is the second fluctuation degree value of the target data point, is the first preset length, is rounding down; is the second preset length, and the second preset length is less than the first preset length. The second preset length is the minimum length required for determining the fuzzy entropy of the time series, and an adjacent sequence corresponding to the target data point and equal to the target length is intercepted from the first concentration sequence to determine the monitoring result of the moment where the target data point is located by using the fuzzy entropy of the adjacent sequence, including: determining the abnormality degree value of the moment where the target data point is located by using the fuzzy entropy of the adjacent sequence, and determining the monitoring result of the moment where the target data point is located by using the relationship between the abnormality degree value of the moment where the target data point is located and the preset threshold.

2. The environmental monitoring method based on an environmental monitor according to claim 1, wherein The fuzzy entropy of the neighborhood sequence is determined by the following method: Select a plurality of neighborhood subsequences with a length equal to a third preset length from the neighborhood sequence, and determine the similarity degree value between any two neighborhood subsequences among the plurality of neighborhood subsequences; According to the similarity degree value between any two neighborhood subsequences, determine the first eigenvalue of the neighborhood sequence at the third preset length, and the first eigenvalue is equal to the average value of different similarity degree values; Referring to the determination process of the first eigenvalue of the neighborhood sequence at the third preset length, determine the second eigenvalue of the neighborhood sequence at a fourth preset length, and use the difference between the first eigenvalue and the second eigenvalue as the fuzzy entropy; the fourth preset length is greater than the third preset length.

3. The environmental monitoring method based on an environmental monitor according to claim 1, wherein Abnormality degree value , norm is the normalization function, is the fuzzy entropy corresponding to the target data point, is the fuzzy entropy corresponding to the previous data point of the target data point, is the average value of the concentration in the local sequence where the target data point is located, is the average value of the concentration in the local sequence where the previous data point of the target data point is located.

4. The environmental monitoring method based on an environmental monitor according to claim 1, characterized in that Use the relationship between the abnormality degree value at the moment of the target data point and a preset threshold to determine the monitoring result at the moment of the target data point, including: When the abnormality degree value at the moment of the target data point is greater than or equal to the preset threshold, output a prompt message, and the prompt message is used to prompt that the concentration of the target polluting gas at the moment of the target data point in the environment to be monitored has increased abnormally.

5. The environmental monitoring method based on an environmental monitor according to claim 1, wherein Use the relationship between the abnormality degree value at the moment of the target data point and a preset threshold to determine the monitoring result at the moment of the target data point, including: When the abnormality degree value at the moment of the target data point is less than the preset threshold, prompt that the concentration at the moment of the target data point is in a normal state.

6. The environmental monitoring method based on an environmental monitor according to claim 1, wherein, The target polluting gas is any one of sulfur dioxide, nitrogen oxides, carbon monoxide, volatile organic compounds, and hydrogen sulfide.

7. An environmental monitoring system based on an environmental monitor, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the environmental monitoring method based on an environmental monitor according to any one of claims 1-6 is implemented.

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