Efficient collecting and monitoring method for coal mine environment data

Abstract

The invention relates to the technical field of data acquisition, in particular to an efficient acquisition and monitoring method for coal mine environment data. Obtaining segment characteristic values according to difference characteristics of data change trends before and after data points in the gas concentration time sequence; obtaining different concentration characteristic time periods according to the segmented characteristic values; discrete data points are obtained according to the influence degree of the data points in the concentration characteristic time period on the overall data change trend; and obtaining a target data point according to the discrete feature of the difference between the adjacent data points in the preset neighborhood range of the discrete data points and the interval feature of the adjacent discrete data points in the preset neighborhood range. The method comprises the following steps: acquiring an acquisition frequency adjustment coefficient according to a quantity characteristic of a concentration characteristic time period in which a target data point exists, a quantity characteristic of the target data point in the concentration characteristic time period and a data fluctuation characteristic; and adaptive data acquisition and analysis are carried out on the concentration characteristic time period according to the acquisition frequency adjustment coefficient, so that the accuracy and efficiency of data analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection, and in particular to a method for efficiently collecting and monitoring coal mine environmental data. Background Art

[0002] In the process of coal mining, harmful gas is prone to occur. When the gas concentration is too high, it will cause explosions and other safety accidents. With the modernization of the mining process and the application of big data, the gas concentration at multiple locations in the mine can be monitored during the mining process to provide early warnings and improve mining safety. After the mining is completed, the gas concentration data of all monitoring points can be analyzed and evaluated, and the law of gas concentration changes and the working conditions of the ventilation system during the mining process can be analyzed to optimize and improve the subsequent mining process.

[0003] Due to the complex structure of the mine, there are a large number of monitoring points for monitoring gas concentration. The amount of data collected at each monitoring point is large, and the gas concentration data is relatively stable in most time periods. Therefore, a large amount of redundant data will appear when analyzing the monitoring data of gas concentration, affecting the efficiency and accuracy of data analysis. Summary of the invention

[0004] In order to solve the technical problem that the redundant data generated in the above monitoring process affects the efficiency and accuracy of data analysis, the purpose of the present invention is to provide a method for efficiently collecting and monitoring coal mine environmental data. The technical solution adopted is as follows: Obtaining the time series of gas concentration at a preset location in the mine within a preset historical period; Obtaining segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series; segmenting the gas concentration time series according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods; obtaining data deviation according to the influence of the data points in the concentration characteristic time period on the overall data change trend; obtaining discrete data points according to the data deviation of the data points; Obtain credibility based on the discrete features of the differences between adjacent data points within a preset neighborhood of the discrete data points and the interval features of adjacent discrete data points within the preset neighborhood; obtain target data points based on the credibility of the discrete data points; obtain acquisition frequency adjustment coefficients based on the quantity features of the concentration feature time period in which the target data points exist, the quantity features of the target data points in the concentration feature time period, and data fluctuation features; Adaptive data collection is performed on the concentration characteristic time period according to the collection frequency adjustment coefficient to obtain a concentration monitoring and analysis data set.

[0005] Furthermore, the step of obtaining segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series includes: Calculate the dynamic time warping distance of the gas concentration segment of the same preset length before and after the data point and normalize it to obtain a first change difference; calculate the slope of the fitting straight line of the gas concentration segment of the preset length before the data point to obtain a first change characteristic value; calculate the slope of the fitting straight line of the gas concentration segment of the preset length after the data point to obtain a second change characteristic value; calculate the absolute value of the difference between the first change characteristic value and the second change characteristic value and normalize it to obtain a second change difference; calculate the average value of the first change difference and the second change difference to obtain the segmented characteristic value of the data point.

[0006] Furthermore, the step of segmenting the gas concentration time series according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods includes: The data points where the segmentation characteristic values ​​exceed the preset segmentation threshold are used as segmentation points, and the gas concentration time series is segmented at the segmentation points to obtain different concentration characteristic time periods.

[0007] Furthermore, the step of obtaining the data deviation according to the influence of the data points in the concentration characteristic period on the overall data change trend includes: The slope of the fitted straight line containing all data points in the concentration characteristic time period is calculated to obtain a first slope value; the slope of the fitted straight line when the concentration characteristic time period does not contain any data point is calculated to obtain a second slope value; the absolute value of the difference between the first slope value and the second slope value is calculated and normalized to obtain the data deviation of the arbitrary data point.

[0008] Furthermore, the step of obtaining discrete data points according to the data deviation of the data points includes: The data points whose data deviation exceeds a preset deviation threshold are regarded as the discrete data points.

[0009] Furthermore, the step of obtaining the credibility according to the discrete features of the differences between adjacent data points within the preset neighborhood of the discrete data points and the interval features of adjacent discrete data points within the preset neighborhood includes:

[0010] Wherein, R represents the credibility of the discrete data point, a represents a preset minimum positive number, and N represents the number of interval segments formed by adjacent discrete data points within the preset neighborhood. represents the number of data points in the nth interval, represents the average number of data points in all intervals, represents the density eigenvalue; Q represents the number of differential values ​​of the differential sequence corresponding to the data within the preset neighborhood range, represents the average value from the first difference value to the qth difference value, Indicates the difference from the first to the The average of the difference values, Indicates the degree of differential fluctuation; represents the differential fluctuation eigenvalue.

[0011] Furthermore, the step of obtaining the target data point according to the credibility of the discrete data point includes: The discrete data points whose credibility exceeds a preset credibility threshold are used as the target data points.

[0012] Furthermore, the step of obtaining the acquisition frequency adjustment coefficient according to the quantity characteristics of the concentration characteristic time period in which the target data point exists, the quantity characteristics of the target data points in the concentration characteristic time period, and the data fluctuation characteristics comprises: Calculate the ratio of the number of concentration feature time periods in which target data points exist to the total number of concentration feature time periods to obtain a time period proportion value; calculate the product of the number proportion of target data points in the concentration feature time period and the time period proportion value and negatively correlate them to obtain a redundant eigenvalue; calculate the inverse of the variance of the differential sequence of the concentration feature time period to obtain a stable eigenvalue; calculate the average of the redundant eigenvalue and the stable eigenvalue to obtain the acquisition frequency adjustment coefficient of the concentration feature time period.

[0013] Furthermore, the step of performing adaptive data acquisition on the concentration characteristic period according to the acquisition frequency adjustment coefficient to obtain a concentration monitoring and analysis data set includes: Calculate the product of a preset constant and the acquisition frequency adjustment coefficient and round it up to obtain the number of deletions; start collecting from the first position in the concentration characteristic period, collect the next data point after each interval of deletion of the number of data points, until the traversal is completed, and obtain the concentration monitoring and analysis data set.

[0014] The present invention has the following beneficial effects: In the present invention, obtaining segmented characteristic values ​​can segment sequence fragments of different concentration change trends, thereby adaptively determining the data acquisition frequency of different concentration characteristic time periods, and preliminarily improving the accuracy of data acquisition frequency calculation. Since the more fluctuating concentration data in the concentration characteristic time period can represent more mining characteristic information, obtaining data deviation can determine the impact of different data points on the overall change trend of the concentration characteristic time period, thereby determining the amount of mining characteristic information reflected by different data points; obtaining discrete data points can determine the data points that represent more mining characteristic information in the concentration characteristic time period, and improve the accuracy of adaptive data acquisition frequency. Since the reasons for the appearance of discrete data points may be caused by multiple factors, obtaining credibility can screen the target data points caused by mining reasons in the discrete data points, and further improve the accuracy of data acquisition frequency. Obtaining the acquisition frequency adjustment coefficient can accurately reflect the amount of data used for data analysis in the concentration characteristic time period, and finally obtain the concentration monitoring and analysis data set according to the acquisition frequency adjustment coefficient, which can improve the accuracy and efficiency of concentration data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a method for efficiently collecting and monitoring coal mine environmental data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a method for efficiently collecting and monitoring coal mine environmental data proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the method for efficiently collecting and monitoring coal mine environmental data provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1, which shows a flow chart of a method for efficiently collecting and monitoring coal mine environmental data provided by an embodiment of the present invention, the method comprising the following steps: Step S1, obtaining the time series of gas concentration at a preset location in the mine within a preset historical period.

[0021] First, obtain the gas concentration time series of a preset position in the mine within a preset historical period. The collection frequency of the gas concentration time series, the preset historical period and the preset position are constructed by the implementer according to the implementation scenario. In an embodiment of the present invention, the collection frequency of the gas concentration time series is once per second, and the calculation steps for other preset positions and preset historical periods are the same.

[0022] Step S2, obtaining segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series; segmenting the gas concentration time series according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods; obtaining data deviation according to the degree of influence of the data points in the concentration characteristic time period on the overall data change trend; obtaining discrete data points according to the data deviation of the data points.

[0023] When the gas concentration in a certain period of time in the gas concentration time series is relatively stable, the gas concentration data in this period reflects similar characteristic states in the mining process, such as similar coal seam characteristics and similar ventilation states. Therefore, such stable data can be considered as redundant data in the data analysis process. If there is a large amount of redundant data, it will affect the accuracy and efficiency of data analysis. In order to improve the accuracy and efficiency of gas concentration data analysis, it can be adaptively deleted according to the data characteristics of the gas concentration time series to reduce the amount of redundant data. Different processes, the complexity of gas distribution and changes in ventilation status in the mining process will cause the gas concentration at the preset position to show different trends; the degree of deletion of data in different trends is different, so it is necessary to first split the data series with different trends, and obtain the segmented feature values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series.

[0024] Preferably, in an embodiment of the present invention, the step of obtaining segmented characteristic values ​​includes: calculating and normalizing the dynamic time warping distance of gas concentration segments of the same preset length before and after the data point to obtain a first change difference; in an embodiment of the present invention, the preset length is the length constructed by 60 data points, which can be determined by the implementer according to the implementation scenario; it should be noted that the dynamic time warping distance is obtained through the existing dynamic time warping algorithm. The more similar the two sequences are, the smaller the dynamic time warping distance is, so the larger the first change difference is, which means that the greater the difference between the gas concentration segments before and after the data point is, the more likely the data point is a segmentation point with different change trends in gas concentration. Calculate the slope of the fitted straight line of the gas concentration segment of the preset length before the data point to obtain the first change characteristic value; calculate the slope of the fitted straight line of the gas concentration segment of the preset length after the data point to obtain the second change characteristic value; calculate the absolute value of the difference between the first change characteristic value and the second change characteristic value and normalize it to obtain the second change difference; the greater the difference in the gas concentration change trend before and after the data point, the greater the difference between the first change characteristic value and the second change characteristic value, and the greater the second change difference, it means that the data point is more likely to be a segmentation point of different gas concentration change trends. Calculate the average value of the first change difference and the second change difference to obtain the segmentation characteristic value of the data point; the larger the segmentation characteristic value, the more likely it is that the gas concentration change trend before and after the data point is different.

[0025] Furthermore, the gas concentration time series can be segmented according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods; preferably, in the embodiment of the present invention, the step of obtaining the concentration characteristic time period includes: taking the data points whose segmented characteristic values ​​exceed the preset segmentation threshold as segmentation points, segmenting the gas concentration time series at the segmentation points, and obtaining different concentration characteristic time periods; there are differences in the change trends of adjacent concentration characteristic time periods. In the embodiment of the present invention, the preset segmentation threshold is 0.7, which can be determined by the implementer according to the implementation scenario.

[0026] Gas exists in coal seams. When mining reaches the gas area, the gas concentration will increase. As the ventilation system works, the gas concentration will slowly decrease. Such obvious fluctuations in gas concentration change characteristics play a greater role in data analysis and can reflect the gas escape trend and ventilation efficiency. At the same time, the distribution of coal seams and mining speed will cause changes in gas concentration. Therefore, when analyzing gas concentration data, when the concentration data changes significantly, more data is required to improve the accuracy of mining analysis. During the mining interval, the gas concentration will remain at a low level for a long time. Such stable gas concentration data plays a smaller role in data analysis. If a large amount of redundant data appears, the data analysis efficiency will be reduced. Therefore, in data analysis, more data can be retained for time periods with more concentration characteristics that can reflect the fluctuations in gas concentration changes, and less data can be retained for time periods with more stable concentration characteristics, thereby improving the efficiency and accuracy of gas concentration data analysis. If any data point in the concentration characteristic period affects the data change trend, it means that the arbitrary data point is relatively discrete and the fluctuation is more obvious; therefore, the data deviation is obtained according to the degree of influence of the data point in the concentration characteristic period on the overall data change trend.

[0027] Preferably, in the embodiment of the present invention, the step of obtaining the data deviation includes: calculating the slope of the fitted straight line containing all data points in the concentration characteristic period to obtain the first slope value; calculating the slope of the fitted straight line when the concentration characteristic period does not contain any data point to obtain the second slope value; if the distribution of the arbitrary data point in the concentration characteristic period is more discrete, it means that the concentration characteristic at this moment is more fluctuating, and the difference between the first slope value and the second slope value is greater. Calculate the absolute value of the difference between the first slope value and the second slope value and normalize them to obtain the data deviation of the arbitrary data point; when the data deviation of the arbitrary data point is greater, it means that the arbitrary data point is more discrete and can better characterize the mining state. Then, discrete data points can be obtained according to the data deviation. Preferably, in the embodiment of the present invention, the step of obtaining discrete data points includes: taking the data point whose data deviation exceeds the preset deviation threshold as a discrete data point. In the embodiment of the present invention, the preset deviation threshold is 0.5, which can be determined by the implementer according to the implementation scenario.

[0028] Step S3, obtaining credibility based on the discrete characteristics of the differences between adjacent data points within a preset neighborhood range of the discrete data points and the interval characteristics of adjacent discrete data points within the preset neighborhood range; obtaining the target data point based on the credibility of the discrete data point; obtaining the acquisition frequency adjustment coefficient based on the quantity characteristics of the concentration characteristic time period in which the target data point exists, the quantity characteristics of the target data points in the concentration characteristic time period and the data fluctuation characteristics.

[0029] Since the deviated data points may be caused by noise data or sensor acquisition errors, in order to improve the accuracy of data acquisition frequency adjustment, it is necessary to further screen the deviated data points to determine the number of data points in the concentration characteristic time period that truly reflect the concentration fluctuation characteristics. When the deviated data point represents the concentration data fluctuation, there will be multiple other deviated data points in the adjacent time period of the deviated data point, and the data distribution in the adjacent time period of the deviated data point is relatively discrete; while the deviated data points caused by noise or acquisition errors are relatively random, and there are fewer other deviated data points in the adjacent time period, and the data distribution in the adjacent time period is relatively stable. Therefore, the credibility can be obtained based on the discrete characteristics of the differences between adjacent data points within the preset neighborhood range of discrete data points and the interval characteristics of adjacent discrete data points within the preset neighborhood range; preferably, in an embodiment of the present invention, the steps and formulas for obtaining credibility include:

[0030] In the formula, R represents the credibility of the discrete data point, a represents a preset minimum positive number, which is 0.1 in the embodiment of the present invention, in order to avoid the situation where the denominator is 0. N represents the number of intervals formed by adjacent discrete data points within a preset neighborhood range. In the embodiment of the present invention, the preset neighborhood range is the range formed by the 100 other data points closest to the discrete data point, which can be determined by the implementer according to the implementation scenario; Indicates the number of data points in the nth interval. The more data points there are in the interval, the more sparsely distributed other discrete data points are in the adjacent time period of the discrete data point, and the less likely it is that the discrete data point reflects the fluctuation characteristics of gas concentration. represents the average number of data points in all intervals, Represents the density characteristic value; when the density characteristic value is larger, it means that the other discrete data points in the adjacent time period of the discrete data point are more densely distributed, and the more likely it is to represent the real gas concentration fluctuation characteristics. Q represents the number of differential values ​​of the differential sequence corresponding to the data within the preset neighborhood range, represents the average value from the first difference value to the qth difference value, Indicates the difference from the first to the The average of the difference values, Indicates the degree of differential fluctuation; if the concentration fluctuation in adjacent time periods of the discrete data point is more obvious, the distribution of the differential value is more discrete and the degree of differential fluctuation is greater; if the concentration change in adjacent time periods of the discrete data point is smaller, the degree of differential fluctuation is smaller, and the discrete data point is more likely to be caused by noise and acquisition errors. It represents the differential fluctuation characteristic value. The larger the differential fluctuation characteristic value is, the more likely the discrete data point is caused by gas concentration fluctuation.

[0031] Further, the target data point can be obtained according to the credibility of the discrete data point, specifically including: taking the discrete data point whose credibility exceeds the preset credibility threshold as the target data point. In the embodiment of the present invention, the preset credibility threshold is 0.6, which can be determined by the implementer according to the implementation scenario. The target data point can characterize the real concentration fluctuation characteristics; when there are more target data points in the concentration characteristic period and the differential value in the differential sequence of the concentration characteristic period is more discrete, it means that the gas concentration fluctuation in the period is more obvious, and the more mining characteristic information is characterized. When the gas concentration data is analyzed after the mining is completed, the amount of data in the concentration characteristic period is more required, thereby improving the accuracy of concentration data analysis. At the same time, the more concentration characteristic periods with target data points in the gas concentration time series, it means that the preset historical period where the gas concentration time series is located is more likely to be in the mining peak period, and the gas concentration changes more frequently, and then more data needs to be provided in the concentration characteristic period to ensure the accuracy of data analysis. Therefore, the acquisition frequency adjustment coefficient is obtained according to the quantity characteristics of the concentration characteristic period with target data points, the quantity characteristics of target data points in the concentration characteristic period, and the data fluctuation characteristics.

[0032] Preferably, in an embodiment of the present invention, the step of obtaining the acquisition frequency adjustment coefficient includes: calculating the ratio of the number of concentration feature time periods in which target data points exist to the total number of concentration feature time periods to obtain a time period proportion value; when the time period proportion value is larger, it means that the preset historical time period is more likely to be in the peak mining period, and more data is required in data analysis. Calculate the product of the number proportion of target data points in the concentration feature time period and the time period proportion value and negatively map them to obtain a redundant characteristic value; when the number proportion of target data points is larger, it means that there are more target data points in the concentration feature time period, and more data should be provided during data analysis. Calculate the inverse of the variance of the differential sequence of the concentration feature time period to obtain a stable characteristic value; when the stable characteristic value is smaller, it means that the concentration change in the time period is stronger, and it can better characterize different situations in the mining process. Calculate the average value of the redundant eigenvalue and the stable eigenvalue to obtain the acquisition frequency adjustment coefficient of the concentration characteristic period; when the acquisition frequency adjustment coefficient is larger, it means that the concentration data in the concentration characteristic period is more stable, the more redundant data there are, and less data can be provided during data analysis to ensure analysis efficiency; when the acquisition frequency adjustment coefficient is smaller, it means that the concentration data in the concentration characteristic period is more volatile, the more data characterizing different conditions in mining are, and more data needs to be provided during data analysis to ensure analysis accuracy.

[0033] Step S4, adaptively collect data for the concentration characteristic period according to the collection frequency adjustment coefficient to obtain a concentration monitoring and analysis data set.

[0034] After obtaining the acquisition frequency adjustment coefficient of the concentration characteristic period, the concentration characteristic period can be adaptively collected according to the acquisition frequency adjustment coefficient to obtain a concentration monitoring and analysis data set; preferably, in an embodiment of the present invention, the step of obtaining a concentration monitoring and analysis data set includes: calculating the product of a preset constant and the acquisition frequency adjustment coefficient and rounding up to obtain a deletion number; in an embodiment of the present invention, the preset constant is 10, which can be determined by the implementer according to the implementation scenario. Start collecting from the first position in the concentration characteristic period, and collect the next data point after deleting a certain number of data points at each interval. The larger the number of deletions, the more data points are in the interval until the traversal is completed to obtain a concentration monitoring and analysis data set. The concentration monitoring and analysis data set has more data that can characterize the mining characteristics, and less redundant data for stable concentration data; then, the concentration monitoring and analysis data sets of all concentration characteristic periods in the mining process are analyzed, which improves the efficiency and accuracy of concentration data analysis.

[0035] In summary, the embodiments of the present invention provide a method for efficiently collecting and monitoring coal mine environmental data; obtain segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series; obtain different concentration characteristic time periods according to the segmented characteristic values; obtain discrete data points according to the degree of influence of the data points in the concentration characteristic time period on the overall data change trend; obtain target data points according to the discrete characteristics of the differences between adjacent data points within a preset neighborhood range of the discrete data points and the interval characteristics of adjacent discrete data points within a preset neighborhood range. The present invention obtains an acquisition frequency adjustment coefficient according to the quantity characteristics of the concentration characteristic time period in which the target data points exist, the quantity characteristics of the target data points in the concentration characteristic time period, and the data fluctuation characteristics; adaptively collects and analyzes data for the concentration characteristic time period according to the acquisition frequency adjustment coefficient, thereby improving the accuracy and efficiency of data analysis.

[0036] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0037] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for efficiently collecting and monitoring coal mine environmental data, characterized in that: The method comprises the following steps: Obtaining the time series of gas concentration at a preset location in the mine within a preset historical period; Obtaining segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series; segmenting the gas concentration time series according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods; obtaining data deviation according to the influence of the data points in the concentration characteristic time period on the overall data change trend; obtaining discrete data points according to the data deviation of the data points; Obtain credibility based on the discrete features of the differences between adjacent data points within a preset neighborhood of the discrete data points and the interval features of adjacent discrete data points within the preset neighborhood; obtain target data points based on the credibility of the discrete data points; obtain acquisition frequency adjustment coefficients based on the quantity features of the concentration feature time period in which the target data points exist, the quantity features of the target data points in the concentration feature time period, and data fluctuation features; Adaptive data collection is performed on the concentration characteristic time period according to the collection frequency adjustment coefficient to obtain a concentration monitoring and analysis data set.

2. A method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining segmented characteristic values ​​according to the difference characteristics of the data change trends before and after the data points in the gas concentration time series includes: Calculate the dynamic time warping distance of the gas concentration segment of the same preset length before and after the data point and normalize it to obtain a first change difference; calculate the slope of the fitting straight line of the gas concentration segment of the preset length before the data point to obtain a first change characteristic value; calculate the slope of the fitting straight line of the gas concentration segment of the preset length after the data point to obtain a second change characteristic value; calculate the absolute value of the difference between the first change characteristic value and the second change characteristic value and normalize it to obtain a second change difference; calculate the average value of the first change difference and the second change difference to obtain the segmented characteristic value of the data point.

3. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of segmenting the gas concentration time series according to the segmented characteristic values ​​of the data points to obtain different concentration characteristic time periods comprises: The data points where the segmentation characteristic values ​​exceed the preset segmentation threshold are used as segmentation points, and the gas concentration time series is segmented at the segmentation points to obtain different concentration characteristic time periods.

4. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining the data deviation according to the influence of the data points in the concentration characteristic period on the overall data change trend comprises: The slope of the fitted straight line containing all data points in the concentration characteristic time period is calculated to obtain a first slope value; the slope of the fitted straight line when the concentration characteristic time period does not contain any data point is calculated to obtain a second slope value; the absolute value of the difference between the first slope value and the second slope value is calculated and normalized to obtain the data deviation of the arbitrary data point.

5. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining discrete data points according to the data deviation of the data points comprises: The data points whose data deviation exceeds a preset deviation threshold are regarded as the discrete data points.

6. A method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining the credibility according to the discrete features of the differences between adjacent data points within the preset neighborhood range of the discrete data points and the interval features of adjacent discrete data points within the preset neighborhood range comprises: ， Wherein, R represents the credibility of the discrete data point, a represents a preset minimum positive number, and N represents the number of interval segments formed by adjacent discrete data points within the preset neighborhood. represents the number of data points in the nth interval, represents the average number of data points in all intervals, represents the density eigenvalue; Q represents the number of differential values ​​of the differential sequence corresponding to the data within the preset neighborhood range, represents the average value from the first difference value to the qth difference value, Indicates the difference from the first to the The average of the difference values, Indicates the degree of differential fluctuation; represents the differential fluctuation eigenvalue.

7. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining the target data point according to the credibility of the discrete data point comprises: The discrete data points whose credibility exceeds a preset credibility threshold are used as the target data points.

8. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of obtaining the acquisition frequency adjustment coefficient according to the quantity characteristics of the concentration characteristic time period in which the target data points exist, the quantity characteristics of the target data points in the concentration characteristic time period, and the data fluctuation characteristics comprises: Calculate the ratio of the number of concentration feature time periods in which target data points exist to the total number of concentration feature time periods to obtain a time period proportion value; calculate the product of the number proportion of target data points in the concentration feature time period and the time period proportion value and negatively correlate them to obtain a redundant eigenvalue; calculate the inverse of the variance of the differential sequence of the concentration feature time period to obtain a stable eigenvalue; calculate the average of the redundant eigenvalue and the stable eigenvalue to obtain the acquisition frequency adjustment coefficient of the concentration feature time period.

9. The method for efficiently collecting and monitoring coal mine environmental data according to claim 1, characterized in that: The step of adaptively collecting data for the concentration characteristic period according to the collection frequency adjustment coefficient to obtain a concentration monitoring and analysis data set comprises: Calculate the product of a preset constant and the acquisition frequency adjustment coefficient and round it up to obtain the number of deletions; start collecting from the first position in the concentration characteristic period, collect the next data point after each interval of deletion of the number of data points, until the traversal is completed, and obtain the concentration monitoring and analysis data set.

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