Mine pollution state monitoring system

Abstract

The invention relates to the technical field of pollution monitoring, in particular to a mine pollution state monitoring system which comprises a concentration sudden change recognition module, a disturbance frequency calculation module, a pollution area positioning module, a diffusion path judgment module and a monitoring result output module. According to the method, the time points with significant change differences in the concentration time sequence are identified and the mutation appearing position is calibrated, so that the pollution emergency state is accurately locked, and the time focusing of the disturbance behavior is completed by dividing the operation period and counting the concentration trend inversion frequency; in combination with the trend consistency of adjacent induction points, the mine pollution concentration area is subjected to spatial judgment, and the connectivity of pollution paths is determined based on the continuity of the trend direction, so that the pollution evolution process is dynamically reduced in two dimensions of time sequence and spatial distribution. A pollution source display position, a propagation link and an influence area are completely marked in a unified map, and the effects of clear mine pollution state positioning, traceable diffusion path and visual intervention decision are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pollution monitoring, in particular to a mine pollution state monitoring system. BACKGROUND

[0002] The technical field of pollution monitoring involves the identification, detection, and concentration evaluation of pollutants in the environment, including pollution source identification, pollutant type analysis, pollution diffusion trend monitoring, and data collection and processing, covering gas pollution detection, dust particle monitoring, water pollution analysis, and comprehensive environmental index evaluation, and is widely used in industrial production, urban environmental governance, mine safety management, and other scenarios. Among them, the mine pollution state monitoring system refers to a system for real-time monitoring of harmful pollutants in the underground mine environment, which is aimed at monitoring and identifying the concentration of harmful gases such as dust, gas, and carbon monoxide in the mine operating space. It usually uses fixed electrochemical sensors, light scattering detectors, and catalytic combustion probes to collect pollutant concentration data, and through the inductive devices placed in the main ventilation channels or operating points of the mine, the environmental pollution data is transmitted to the ground monitoring platform to realize the acquisition and update of pollution state information.

[0003] In the existing mine pollution state monitoring process, although sensor monitoring data can be collected and transmitted in real time, there is a lack of detailed analysis mechanism for pollution concentration change patterns, and it is difficult to effectively identify high-risk time periods when pollution state changes suddenly or fluctuates frequently. When the pollution evolution process reflected by the data presents complex fluctuations, it is difficult to reflect whether the pollution source is persistent or diffusive by relying only on fixed point concentration values, resulting in unclear pollution area determination and delayed response, such as in areas with frequent abnormal gas fluctuations, the trend convergence phenomenon cannot be identified, leading to the neglect of pollution concentrated distribution, affecting the overall safety monitoring efficiency and accuracy. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide a mine pollution state monitoring system.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: a mine pollution state monitoring system comprises:

[0006] The concentration mutation identification module obtains the continuous concentration time series data of the underground gas sensor in the sampling period, calculates the gas concentration change value and compares the change difference value, when the change difference value is greater than or equal to the concentration mutation rate threshold, marks the current time point as a mutation point, and generates a pollution mutation appearance position table;

[0007] The disturbance frequency calculation module divides a work cycle into time segments with equal length based on the pollution mutation appearance position table, detects the number of reversals of the direction of concentration change in each segment, screens time segments greater than or equal to a disturbance frequency threshold, and generates a disturbance high-frequency segment list;

[0008] The pollution area positioning module obtains a concentration change trend sequence of the gas sensor at a corresponding time segment at a corresponding spatial position based on the disturbance high-frequency segment list, counts the number of sensors with consistent trends, selects a spatial position satisfying a region concentration judgment reference value as a pollution suspected area, and generates a pollution suspected area list;

[0009] The diffusion path judgment module obtains the concentration trend change direction of the gas sensor in the pollution suspected area at three continuous time segments before and after the pollution appearance point based on the pollution suspected area list, marks the corresponding path as a pollution diffusion channel if the continuous trend is consistent, and generates a pollution diffusion channel structure diagram;

[0010] The monitoring result output module draws a distribution diagram according to the spatial position of the mine based on the pollution diffusion channel structure diagram, labels all pollution propagation links, and obtains a mine pollution state monitoring atlas.

[0011] As a further scheme of the present application, the pollution mutation appearance position table includes a mutation point spatial position, mutation point time information, and mutation intensity level, the disturbance high-frequency segment list includes a high-frequency time segment number, a frequently reversed sensor number, and a reversal frequency index, the pollution suspected area list includes a pollution concentration area number, a number of trend-consistent sensors in the area, and a suspected pollution level, the pollution diffusion channel structure diagram includes a diffusion path number, a path start and end position, and a continuous trend maintenance time segment number, and the mine pollution state monitoring atlas includes a pollution source appearance position, a pollution propagation link path diagram, and a pollution influence area distribution record.

[0012] As a further scheme of the present application, the concentration mutation recognition module includes:

[0013] The data stream receiving submodule obtains continuous concentration time sequence data of the underground fixed-point gas sensor in a sampling period, stores the concentration data in a numbered manner according to time sequence, forms a structured time sequence concentration sequence, establishes a data receiving cache list, and generates a continuous concentration sequence value set;

[0014] The change rate calculation submodule selects any three time points to form two adjacent time segments based on the continuous concentration sequence value set, respectively calculates the gas concentration change values in the two time segments, performs difference calculation on the two change values, combines the current sampling time interval, calculates the concentration change difference value per unit time, and generates a concentration change difference rate value set;

[0015] The mutation point determination submodule determines the mutation points according to the concentration change difference rate value set and a concentration mutation rate threshold value, compares the concentration change difference rate values corresponding to each time point, and marks the time point as a mutation point when the rate value of the time point is greater than or equal to the concentration mutation rate threshold value, records the position number and time information of the mutation point, and generates a pollution mutation appearance position table.

[0016] As a further scheme of the present application, the disturbance frequency calculation module comprises:

[0017] The concentration data extraction submodule locates the sensor number and mutation occurrence time corresponding to each mutation point based on the pollution mutation appearance position table, extracts the complete gas concentration data sequence of each sensor in the corresponding operation cycle, organizes the concentration data in chronological order and unifies the sampling time interval, divides the operation cycle into time segments of equal length, and generates an operation cycle segmented concentration data sequence;

[0018] The reverse number detection submodule traverses the continuous concentration data in each time segment according to the operation cycle segmented concentration data sequence, compares the concentration change directions of adjacent two time points in sequence, records the direction sequence of the positive change and the reverse change, counts the number of direction continuous reversals, and assigns a disturbance frequency value to each time segment to generate a segmented disturbance frequency value set;

[0019] The high-frequency segment screening submodule compares the disturbance frequency value corresponding to each time segment with a set disturbance frequency threshold value according to the segmented disturbance frequency value set, screens the time segments with disturbance frequency values greater than or equal to the disturbance frequency threshold value, extracts the corresponding time segment number and sensor number information, and generates a disturbance high-frequency segment list.

[0020] As a further scheme of the present application, the pollution area positioning module comprises:

[0021] The concentration trend extraction submodule extracts the concentration data of all sensors at the corresponding spatial position in each time segment according to the disturbance high-frequency segment list, constructs the concentration change sequence of each sensor in each time segment in the sampling order, calculates the concentration difference value between adjacent time points in each sequence and extracts the difference direction label, establishes the concentration change trend structure of each sensor in each time segment, and generates a spatial concentration trend sequence matrix;

[0022] The trend consistency determination submodule compares the concentration trend vectors corresponding to adjacent sensors in each time segment based on the spatial concentration trend sequence matrix, and if the trend symbol sequence positions of any two adjacent sensors remain consistent, it is determined that the trends are consistent, the number of sensors satisfying the trend consistency determination condition is counted in sequence, and the cumulative number under the current spatial position unit is recorded to generate a spatial position consistent sensor number value set;

[0023] The spatial gathering identification sub-module judges whether the number of consistent sensors at each spatial position is greater than or equal to a region concentration judgment reference value according to the set of spatial position consistent sensor number values, and if the condition is met, marks the corresponding position as a pollution suspected area, records the corresponding spatial position number and corresponding time period, and establishes a pollution suspected area list.

[0024] As a further scheme of the present application, the diffusion path judgment module comprises:

[0025] The trend direction extraction sub-module extracts each sensor number and corresponding pollution appearance point time period under each suspected area based on the pollution suspected area list, traces back two time periods and extends one time period forward, constructs a concentration trend data set containing three consecutive time periods before and after the pollution appearance point, assigns a value to each concentration trend direction in the set and arranges it into a direction sequence matrix, and generates a three-segment concentration trend direction sequence matrix;

[0026] The continuous consistency judgment sub-module judges the position consistency of each trend direction sequence based on the three-segment concentration trend direction sequence matrix, marks a consistent path if the direction values of the three time periods are the same, calculates the start point and end point index positions of each trend consistent path in the spatial position, obtains a trend consistency distance quantity, establishes a path segment index for a sensor pair with a set difference threshold, and obtains a trend consistent path segment index set;

[0027] The path structure construction sub-module connects sensor numbers in index order according to the trend consistent path segment index set and records each pair of start point and end point positions as a channel path, numbers all the paths and establishes a start point-end point path mapping table, and generates a pollution diffusion channel structure diagram.

[0028] As a further scheme of the present application, the calculation formula of the trend consistency distance quantity is:

[0029] ;

[0030] wherein, indicates the trend direction value of the sensor in the first time period, and is assigned a value of +1 or -1, 、 indicates the spatial position coordinates of the sensor , indicates the trend consistency distance quantity.

[0031] As a further scheme of the present application, the monitoring result output module comprises:

[0032] The pollution node extraction submodule extracts the spatial position index and time label of the starting pollution source appearance point, the path continuity confirmation point and the terminal point pollution influence area in each channel based on the pollution diffusion channel structure diagram, establishes three types of key node sets, and generates a pollution channel key node index set;

[0033] The spatial path drawing submodule connects the starting pollution source appearance point to the path continuity confirmation point and to the terminal point pollution influence area according to the pollution channel key node index set, draws each path according to a unique number, marks the path segment type with different colors, records the node coordinates, connection order and path number index information, and establishes a pollution propagation path topology structure diagram.

[0034] The state atlas generation submodule superimposes all path structures into a mine three-dimensional space structure diagram according to the pollution propagation path topology structure diagram, maps the path line segments to the mine plan according to spatial layering, marks the pollution node attributes, path number and flow direction arrow, draws a pollution link distribution diagram, and establishes a mine pollution state monitoring atlas.

[0035] As a further scheme of the present application, the starting pollution source appearance point is specifically a node where the pollution concentration mutation occurs earliest in each path, the path continuity confirmation point is specifically an intermediate position node where the concentration trend direction is continuous and consistent, and the terminal point pollution influence area is specifically a concentration rising area at the end of the path.

[0036] Compared with the prior art, the present application has the following advantages and positive effects:

[0037] In the present application, the pollution burst state is accurately locked by identifying the time points with significant changes in the concentration time sequence and marking the mutation appearance position, the time focus of the disturbance behavior is completed by dividing the operation period and counting the concentration trend reversal frequency, the spatial judgment of the mine pollution concentration area is made in combination with the trend consistency of adjacent sensing points, and the continuity of the pollution path is determined based on the continuity of the trend direction, so that the pollution evolution process is dynamically restored in the time sequence and spatial distribution dimensions, the pollution source appearance position, propagation link and influence area are completely marked in the unified atlas, and the effects of clear mine pollution state positioning, traceable pollution diffusion path and intuitive intervention decision are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The system flowchart of the present application is shown in the figure;

[0039] Figure 2 The concentration mutation recognition module flowchart of the present application is shown in the figure;

[0040] Figure 3 The disturbance frequency calculation module flowchart of the present application is shown in the figure;

[0041] Figure 4 The flow chart of the pollution area positioning module of the present application;

[0042] Figure 5 The flow chart of the diffusion path judgment module of the present application;

[0043] Figure 6 The flow chart of the monitoring result output module of the present application. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0045] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0046] Please refer to Figure 1 A mine pollution state monitoring system comprises:

[0047] The concentration mutation identification module obtains the continuous concentration time series data of the underground fixed point gas sensor in the sampling period, selects any three points to form two time periods, calculates the gas concentration change value of each time period respectively, compares the change difference value and judges in combination with the concentration mutation rate threshold value, marks the current time point as a mutation point when the change difference value is greater than or equal to the concentration mutation rate threshold value, records the position and time information of the mutation point, and generates a pollution mutation appearance position table;

[0048] The disturbance frequency calculation module obtains the complete gas concentration data sequence in the corresponding work period based on the pollution mutation appearance position table, divides the work period into time periods of equal length, detects whether the concentration change direction is continuously reversed in each period, counts the number of direction reversals, selects the period greater than or equal to the disturbance frequency threshold value, records the time period number and sensor number, and generates a disturbance high-frequency section list;

[0049] The pollution area positioning module obtains a concentration change trend sequence of all sensors at a corresponding time period on a corresponding spatial position according to the disturbance high-frequency section list, judges whether there is a trend consistency phenomenon of adjacent sensors at the same time period, counts the number of sensors with trend consistency, selects a spatial position satisfying a region concentration judgment reference value as a pollution suspected area, and generates a pollution suspected area list;

[0050] The diffusion path judgment module obtains a concentration trend change direction of all sensors in each pollution suspected area at three continuous time periods before and after a pollution appearance point based on the pollution suspected area list, judges whether the trend direction is continuously consistent, marks a corresponding path as a pollution diffusion channel if it is continuously consistent, records a starting position and an end position, and generates a pollution diffusion channel structure diagram;

[0051] The monitoring result output module determines a starting pollution source appearance point, a path continuity confirmation point and an end pollution influence area on each channel based on the pollution diffusion channel structure diagram, draws a distribution diagram according to a mine spatial position, labels all pollution propagation links, and obtains a mine pollution state monitoring atlas.

[0052] The pollution mutation appearance position table includes a mutation point spatial position, mutation point time information and mutation intensity level, the disturbance high-frequency section list includes a high-frequency time period number, a frequently reversed sensor number and a reversal frequency index, the pollution suspected area list includes a pollution concentration area number, a number of trend consistency sensors in the area and a suspected pollution level, the pollution diffusion channel structure diagram includes a diffusion path number, a path starting and end position and a continuous trend maintaining time period number, and the mine pollution state monitoring atlas includes a pollution source appearance position, a pollution propagation link path diagram and a pollution influence area distribution record.

[0053] Please refer to Figure 2 , the concentration mutation identification module includes:

[0054] The data flow receiving submodule obtains continuous concentration time sequence data of the underground fixed point gas sensor in a sampling period, numbers and stores the concentration data in time order, forms a structured time sequence concentration sequence, establishes a data receiving cache list, and generates a continuous concentration sequence value set;

[0055] To obtain continuous concentration time sequence data of the underground fixed point gas sensor in a sampling period, the gas concentration signal needs to be collected in real time from the underground installed gas concentration sensor, and the sampling terminal needs to sample at a fixed interval, for example, to obtain a concentration reading every 10 seconds. All collected gas concentration values are written into a concentration sequence array in time order. Assuming that the current monitoring period is 5 minutes, that is, a total of 30 groups of data can be obtained, denoted as , wherein represents the i-th group of data, and The gas concentration value corresponding to the second corresponds to the second, and the data collected is uploaded to the ground server through wired or wireless communication, and then the time tag is mapped one by one and forms a data structure For example, the concentration value obtained at a fixed point within 5 minutes is: The data is temporarily stored in the memory structure through the cache list for subsequent operation; in order to solve the data compatibility problem between different sensing devices and calculation modules, a unified formatting structure is used to standardize the concentration data, and a flag bit is set to distinguish the data source of multiple points. Finally, a continuous concentration sequence value set is generated.

[0056] The change rate calculation submodule is based on the continuous concentration sequence value set, selects any three time points to form two adjacent time periods, calculates the gas concentration change value in the two time periods respectively, calculates the difference value of the two change values, and calculates the concentration change difference value in unit time combined with the current sampling time interval. A concentration change difference rate value set is generated;

[0057] Based on the established continuous concentration sequence value set, any three adjacent time points corresponding to the gas concentration value , two time periods , are composed, , , the concentration change value in the two time periods is calculated, , , the difference rate value is calculated , for example, in a group of data:

[0058] , , ;

[0059] , ;

[0060] , ;

[0061] , ;

[0062] ;

[0063] The above process is pushed forward in a sliding window manner in the entire concentration sequence to form the change rate difference of multiple time periods, which constitutes an array Each item represents the degree of difference in the rate of concentration change within adjacent time periods. In order to unify the dimensions of the rate of difference and the judgment criteria, the absolute value processing method is used to exclude the factor of the direction of change, and the time interval is fixed at 10 seconds to finally generate a set of concentration change rate of difference values.

[0064] The mutation point determination submodule compares the concentration change difference rate value set with the concentration mutation rate threshold at each time point. When the rate value at a time point is greater than or equal to the concentration mutation rate threshold, the corresponding time point is marked as a mutation point. The location number and time information of the mutation point are recorded, and a pollution mutation manifestation location table is generated.

[0065] Based on the acquired set of concentration change rate values, for each item... Compared with the preset concentration mutation rate threshold Perform a step-by-step comparison and judgment; if a certain time point satisfies... If a mutation occurs, the point is marked as a mutation point, and its position number in the original data sequence and its corresponding time information are recorded. For example, setting a threshold If a certain point If the threshold is met, the judgment condition is satisfied. This threshold can be set based on the frequency distribution of mutation rates in historical monitoring data. For example, if the rate difference is extracted from 100 historical mutation events, the 95th percentile in the statistical distribution can be used. That is, if 95% of the normal fluctuation rates are less than 0.0028, then a threshold of 0.003 is representative. The actual sampling situation is shown below:

[0066] Table 1. Statistical table of concentration change rate differences Time number Current time / s Concentration difference rate value (% / s) Whether mutation point 8 80 0.002 No 9 90 0.0031 Yes 10 100 0.0029 No 11 110 0.0032 Yes

[0067] As shown in Table 1, the rate difference between the 9th and 11th seconds both exceeded the set threshold, and therefore were recorded as mutation points; the output structure of the mutation points is a record array. Each item includes the mutation point time, concentration value, and serial number information, and finally a pollution mutation manifestation location table is established.

[0068] Please see Figure 3 The disturbance frequency calculation module includes:

[0069] The concentration data extraction submodule is based on the pollution mutation manifestation location table. It locates the sensor number and mutation occurrence time corresponding to each mutation point, extracts the complete gas concentration data sequence of each sensor in the corresponding operation cycle, organizes the concentration data in chronological order and unifies the sampling time interval, divides the operation cycle into equal-length time periods, and generates segmented concentration data sequences for the operation cycle.

[0070] Based on the pollution mutation position table, first extract the timestamp and corresponding sensor number of the mutation point listed in the table, determine the start and end time range of the mutation point by comparing the occurrence time of each mutation point with the work account record, and then call the full gas concentration original record of the corresponding sensor in the time range, organize it into a continuous time sequence according to the second-level sampling period, and then divide the whole work cycle into several equal interval subsegments according to the set time length, for example, a 300-second work cycle is divided into 60-second segments, which can get 5 subsegments, and are numbered as D1 to D5 in turn, then the concentration sequence in each subsegment needs to be aligned in time, if some time points are missing due to transmission delay or equipment failure, linear interpolation is used to complete the missing data, ensure the integrity and equal interval characteristics of the data in each time segment, and record the corresponding time start and end point, sensor number and segment number of each segment, the following is an example of actual sampling data:

[0071] Table 2 work cycle segmentation concentration data table Section number Starting time / s End time / s Concentration sequence (%) Sensor number D1 0 60 0.32，0.33，0.35，0.34，0.36，... S001 D2 60 120 0.36，0.37，0.38，0.39，0.38，... S001 D3 120 180 0.38，0.37，0.36，0.34，0.35，... S001

[0072] As shown in Table 2, the concentration data in each segment is in the form of one-dimensional array, with uniform sampling interval and time sequence structure, which will be used for disturbance direction analysis in the next module to generate work cycle segmentation concentration data sequence.

[0073] The reverse frequency detection submodule traverses the continuous concentration data in each time segment according to the work cycle segmentation concentration data sequence, compares the concentration change direction of adjacent two time points in turn, records the direction sequence of positive change and negative change, counts the number of continuous direction reversal and assigns a disturbance frequency value to each time segment, and generates a set of segmented disturbance frequency values;

[0074] According to the work cycle segmentation concentration data sequence, the concentration sequence in each time segment is processed, first two consecutive time points are taken as a group, the concentration change direction is calculated respectively, if the latter value is greater than the former value, it is defined as positive, otherwise it is defined as negative, if the two values are equal, the direction change is not recorded, then the continuous direction sequence in the current segment is traversed and judged, if the current direction changes with the previous direction, a reversal is recorded, finally the number of direction reversal in the time segment is accumulated, assuming that the concentration change direction sequence in D1 segment is: positive, positive, negative, negative, positive, negative, the number of reversal is 4, this value is the disturbance frequency value, in order to avoid repeated counting, a flag bit is set to record the current direction state, only when the direction value changes, the flag bit is updated and counted, the disturbance frequency value needs to be recorded under the corresponding segment number, at the same time, a one-to-one mapping relationship between segment number and disturbance frequency value needs to be established, finally the following structure is constructed:

[0075] Table 3 Segment disturbance frequency value table Section number Concentration change direction sequence Disturbance frequency value (times) D1 Positive→positive→negative→negative→positive→negative 4 D2 Positive→positive→positive→positive→positive 0 D3 Negative→negative→negative→positive→negative→positive 3

[0076] As shown in Table 3, segment numbers D1 and D3 record 4 and 3 times of inversion behavior respectively, and the values will be used in the threshold screening operation in the next sub-module to finally generate a segment disturbance frequency value set.

[0077] The high-frequency segment screening sub-module compares the disturbance frequency value corresponding to each time segment with the set disturbance frequency threshold according to the segment disturbance frequency value set, screens the time segment whose disturbance frequency value is greater than or equal to the disturbance frequency threshold, extracts the corresponding time segment number and sensor number information, and generates a disturbance high-frequency segment list;

[0078] According to the segment disturbance frequency value set, the disturbance frequency value of each time segment is extracted in turn, and the disturbance frequency threshold is set as the screening reference value. It is compared whether each segment disturbance frequency value is greater than or equal to the threshold. If the condition is met, the time segment is marked as a disturbance high-frequency segment and its segment number and sensor number information are recorded. The setting of the disturbance frequency threshold needs to be determined according to the 90% quantile point principle in the data distribution. Assuming that 1000 time segment disturbance frequency values are collected and it is found that 900 values are less than 3 times, the disturbance frequency threshold is set to 3, that is, the time segment whose disturbance frequency is greater than or equal to 3 times is a high-frequency segment. The screening process uses a sequential comparison method to individually judge and mark each disturbance frequency value, and the results are presented in the form of a table as follows:

[0079] Table 4 Disturbance high-frequency segment list Section number Disturbance frequency value (times) Whether high frequency section Sensor number D1 4 Yes S001 D2 0 No S001 D3 3 Yes S001

[0080] Referring to Table 4, the disturbance frequency values of two time segments D1 and D3 are 4 and 3 respectively, which meet the screening condition, so they are marked as disturbance high-frequency segments, and finally a disturbance high-frequency segment list is generated.

[0081] Please refer to Figure 4 , the pollution area positioning module comprises:

[0082] The concentration trend extraction sub-module extracts the concentration data of all sensors at corresponding spatial positions in each time segment according to the disturbance high-frequency segment list, constructs the concentration change sequence of each sensor in each time segment in the sampling order, calculates the concentration difference between adjacent time points in each sequence and extracts the difference direction label, establishes the concentration change trend structure of each sensor in each time segment, and generates a spatial concentration trend sequence matrix.

[0083] According to the disturbance high-frequency section list, the time period number and the corresponding sensor spatial position of each record item recorded in the list are extracted, the spatial coordinate index of the sensor in the monitoring system is mapped through the sensor position information, the original concentration sampling data of each sensor at the spatial position in the time period is obtained, a one-dimensional sequence is formed in time sequence, the positive and negative labels are given to the concentration difference value direction of adjacent points, if the concentration value at the next time is greater than that at the previous time, it is marked as "up", otherwise as "down", if equal, it is marked as "stable", the direction mark sequence is defined as the concentration change trend vector, then the trend vectors of all sensors are uniformly stored as a two-dimensional structure, wherein the first dimension is the spatial position number, and the second dimension is the trend vector of each sensor at the position, for example, for the sensor S101 located in the A1 section, the concentration sequence in 120-180 seconds is {0.28, 0.31, 0.32, 0.30, 0.29}, and the corresponding trend vector is {up, up, down, down}, which is represented by digital coding as {+1, +1, -1, -1}, this trend vector will be used as the basis data structure for subsequent trend consistency judgment, and is uniformly classified according to the spatial position, and finally a concentration trend structured matrix is constructed.

[0084] Table 5 Concentration trend sequence matrix table Spatial position Sensor number Concentration trend sequence A1 S101 +1，+1，-1，-1 A1 S102 +1，+1，-1，-1 A2 S201 -1，-1，+1，+1

[0085] As shown in Table 5, the sensors at different positions have independent trend sequences, which will be input into the next sub-module for consistency judgment to generate a spatial concentration trend sequence matrix.

[0086] The trend consistency judgment sub-module compares the concentration trend vectors corresponding to adjacent sensors in each time period based on the spatial concentration trend sequence matrix, if the trend symbol sequence positions of any two adjacent sensors remain consistent, it is determined that the trends are consistent, the number of sensors satisfying the trend consistency judgment condition is counted in turn, and the cumulative number under the current spatial position unit is recorded to generate a spatial position consistent sensor number value set;

[0087] According to the spatial concentration trend sequence matrix, the concentration trend sequences of all adjacent sensors in the same spatial position unit in the matrix are pairwise compared. First, any two groups of trend sequences are selected in order, and the trend direction label values in the same time period are compared in position correspondence. If a sequence pair has the same direction label at more than half of the time points, it is determined that the pair of trend sequences is consistent. For example, between the sequence pair {+1, +1, -1, -1} and {+1, +1, -1, -1}, all positions are completely consistent, and it is determined to be consistent. If it is {+1, +1, -1, -1} and {+1, -1, -1, +1}, only the first and third positions are consistent, which is less than 50% of the 4 time points, and it is determined to be inconsistent. The number of consistent sensor combinations in the same spatial position is counted, and the number of sensors with consistent trends in the space is deduced by counting the number of consistent sensor pairs. For example, in position A1, if there are 3 sensors, 3 groups of trend pairs are formed by pairing them two by two, and 2 groups meet the consistency judgment condition, then it is determined that there are 3 sensors with consistent trends in this position. The number of consistent sensors in all positions is recorded, and the position number and the corresponding number value are indexed to form the following structure:

[0088] Table 6: Spatial Consistent Sensor Statistics Spatial position Number of trend consistent sensors (pcs) A1 3 A2 1 A3 4

[0089] Referring to Table 6, the trend consistency performance in each spatial position is recorded to provide a quantitative basis for subsequent spatial determination, and a set of spatial position consistent sensor quantity values is finally generated.

[0090] The spatial aggregation recognition submodule determines whether the number of consistent sensors in each spatial position is greater than or equal to the regional concentration judgment reference value according to the set of spatial position consistent sensor quantity values. If the condition is met, the corresponding position is marked as a pollution suspected area, the corresponding spatial position number and the corresponding time period are recorded, and a pollution suspected area list is established.

[0091] According to the spatial position consistent sensor quantity value set, the trend consistent sensor quantity value at each spatial position is read point by point, the regional concentration judgment reference value is set and compared with the quantity value at each position, if the value of a certain spatial position is greater than or equal to the reference value, it is determined that the spatial unit has pollution trend aggregation, the judgment reference value is obtained according to historical concentration event statistics, and the commonly used set value is 3, that is, at least 3 sensors in the same spatial unit are required to be consistent in trend to be determined as an aggregation area, in the comparison operation, the number of the spatial position meeting the condition is extracted and the corresponding time period identifier is recorded to form a pollution space candidate list, and the number of all spatial positions meeting the condition is summarized to construct a pollution positioning index list, for example, when the position quantity values of A1 and A3 are 3 and 4 respectively, both of which meet the reference value requirement, and A2 is only 1, which does not meet the reference value limit, only A1 and A3 are recorded:

[0092] Table 7 pollution suspected area judgment table Spatial position Trend consistent number Judgment reference value Whether it is a suspected area A1 3 3 Yes A2 1 3 No A3 4 3 Yes

[0093] As shown in Table 7, A1 and A3 are assigned suspected labels, and a pollution suspected area list is finally established.

[0094] Please refer to Figure 5 , the diffusion path judgment module includes:

[0095] The trend direction extraction submodule extracts the sensor number and corresponding pollution appearance point time period under each suspected area based on the pollution suspected area list, traces back two time periods and extends one time period, constructs a concentration trend data set containing three consecutive time periods before and after the pollution appearance point, assigns a value to each concentration trend direction in the set and arranges it into a direction sequence matrix, and generates a three-segment concentration trend direction sequence matrix;

[0096] Based on the pollution suspected area list, the corresponding pollution appearance point spatial position number and time period index in each record are extracted, and 3 time period indexes are obtained by centering on the pollution appearance time period, tracing back 2 time period and extending 1 time period forward and backward. Then, the corresponding sensor number is indexed according to the spatial position, and the concentration sampling sequence of the sensor in each time period is extracted. The concentration sequence is organized according to the fixed sampling frequency, for example, the sampling point number in each time period is 5 points, and the sampling frequency is 1 Hz. The sample data is as follows: the concentration values of S01 in t1-t3 are {0.30, 0.31, 0.32, 0.34, 0.35}, {0.35, 0.35, 0.36, 0.38, 0.39}, and {0.39, 0.38, 0.37, 0.36, 0.35}. The direction judgment is based on the difference value of adjacent sampling points. If the difference value is greater than 0, the direction is +1, the difference value is less than 0, and the direction is -1, and the difference value is equal to 0, the direction is 0. The trend judgment of t1 is {+1, +1, +1, +1}, the overall trend direction is the mode of all directions in this section, so the direction of t1 is +1, t2 is +1, and t3 is -1. Finally, the direction sequence of S01 is {+1, +1, -1}. The operation is performed on all sensors in the suspected area to form a direction vector matrix. Part of the example data is as follows:

[0097] Table 8 Three-section concentration trend direction sequence matrix table Spatial position Sensor number Time period t1 Time period t2 Time period t3 A1 S101 +1 +1 -1 A1 S102 +1 +1 +1 A2 S201 -1 -1 -1 A2 S202 +1 -1 0 A2 S203 -1 -1 -1

[0098] As shown in Table 8, the direction data is unified to a direction label value by sampling difference method, and finally forms a three-section concentration trend direction sequence matrix which can be used for comparison and judgment.

[0099] The continuous consistency judgment submodule judges the position consistency of each trend direction sequence based on the three-section concentration trend direction sequence matrix. If the direction values of the three time periods are the same, it is marked as a consistent path. The start and end index positions of each trend consistent path in the spatial position are calculated using the formula:

[0100] ;

[0101] The trend consistency distance is obtained by calculating the path segment index of the sensor pair with a difference less than the set difference threshold. The trend consistent path segment index set is obtained. Wherein, represents the trend direction value of the sensor in the first time period, which is assigned as +1 or -1, , is the spatial position coordinate of the sensor , represents the fusion difference quantity formed by the trend consistent direction value difference and the spatial position Euclidean distance. The set threshold is 2.5, and The trend direction and the spatial continuity are considered consistent when <2.5;

[0102] Based on the three-segment concentration trend direction sequence matrix, the consistency determination operation is performed on the trend sequence of each group of sensors, the consistency condition is defined as the three time period direction values being completely same, that is, {+1, +1, +1} or {-1, -1, -1}, the sensors satisfying the condition are extracted, and all possible combination pairs are constructed with the spatial adjacent sensors, then the joint calculation of the trend difference and the spatial distance is performed in the combination pair unit, the trend difference value is set as the absolute value of the sum of the three direction value differences, the spatial position difference is the two-dimensional Euclidean distance, and the fusion difference value is formed by the weighted combination of the two, and the formula is calculated.

[0103] Taking S101 and S102 as examples, the trend directions thereof are {+1, +1, -1} and {+1, +1, +1} respectively, and the trend difference is:

[0104] ;

[0105] If the spatial coordinates thereof are S01 (x=2.0, y=3.0) and S02 (x=3.5, y=2.0) respectively, the spatial distance is:

[0106] ;

[0107] Finally, it is obtained that:

[0108] ;

[0109] If the preset fusion difference threshold is 2.5, then =3.803>2.5, the consistency connection condition is not satisfied, and the combination is discarded; taking S201 and S203 as examples, the trend directions thereof are both {-1, -1, -1}, the spatial coordinates thereof are (5.0, 6.0) and (6.0, 6.0) respectively, the trend difference is 0, and the spatial distance is 1.0, then =0+1.0=1.0<2.5, the condition is satisfied, and it is recorded as an effective path segment and written into the index structure, and finally all combination path segments satisfying the condition <2.5 are obtained, and the trend consistent path segment index set is generated.

[0110] The operation logic of the formula is that the trend direction difference item and the spatial position distance item are superimposed together to form the fusion difference degree index Δij, the trend direction difference item is used to measure the trend difference between the sensor and ​The difference in the concentration change trend direction in three consecutive time periods is obtained by summing the difference between the trend values of each segment and taking the absolute value, so as to quantize the trend direction. If the three directions are completely consistent, the value is 0, otherwise, the value increases according to the difference, reflecting the inconsistency of the trend. The spatial position distance term is used to measure the spatial distance of the sensor in the two-dimensional monitoring area, which is calculated in the form of Euclidean distance, ensuring that the physical proximity of adjacent sensors is quantized into the fusion index. The two are added by addition, and the purpose is to regard "trend consistency" and "spatial continuity" as parallel and equally important criteria to jointly determine whether there is a connectable diffusion path segment between sensors. Therefore, no weighting factor is set in the formula structure, but the two parameters are synchronized in the structure and added by operation. The path with consistent direction but too large spatial span will be discarded due to the increase of the Euclidean term, and vice versa. The square root operation is used for the standard definition of Euclidean distance to ensure that the spatial difference is squared back to the original dimension, avoiding the imbalance between the direction term and the distance term in scale, so as to make the whole fusion judgment symmetrical and comparable.

[0111] The trend consistency distance quantity is used to quantize the comprehensive degree of the consistency of the concentration change trend and the tightness of the spatial distance between two sensors in the pollution monitoring process. The quantity reflects whether the two monitoring points belong to the same continuous propagation chain in the pollution diffusion path. From the definition, the distance quantity is composed of two parts. One is the direction trend difference term, which measures the difference between the concentration change trend directions of the two sensors in three consecutive time periods, reflecting the behavior consistency of the two in the time evolution process. The other is the spatial Euclidean distance term, which measures the actual physical distance between the two in the two-dimensional monitoring area, reflecting the spatial proximity. By directly adding the two values, the distance quantity can effectively identify the sensor pair that meets the trend direction continuity and spatial proximity at the same time, thereby providing criterion support for the identification of the pollution diffusion path segment by segment. The smaller the value, the more consistent the trend and the closer the position, the more likely it is to form an effective diffusion segment. Conversely, the connection is excluded. Therefore, the index is used to judge whether to connect the sensor nodes on the basis of trend and position continuity in the path construction stage, which is the core decision quantity of path segment screening.

[0112] The path structure construction submodule connects the sensor numbers according to the index order according to the trend consistent path segment index set, and records the start and end positions of each pair as a channel path. All paths are numbered and an start-end path mapping table is established to generate a pollution diffusion channel structure diagram.

[0113] According to the trend consistent path segment index set, the starting sensor number and the ending sensor number recorded in each pair of combination are extracted one by one, and the corresponding spatial coordinate information is obtained by calling the sensor layout table. The path node sequence is combined in the order of sensor number, and the continuous path linked list structure is constructed by splicing multiple paragraphs in turn according to the spatial topology connection mode. If a certain end point number appears repeatedly as a starting point in the next path segment, the two segments can be spliced into a longer path. In this way, all connectable path channels are constructed. In this process, each channel structure is constructed, and the path number is assigned and the starting point and end point number are recorded. At the same time, the pollution diffusion channel structure diagram index table is written. Finally, all channel numbers and corresponding path information are summarized to establish the pollution diffusion channel structure diagram.

[0114] Please refer to Figure 6 The monitoring result output module comprises:

[0115] The pollution node extraction submodule extracts the spatial position index and time label of the starting pollution source appearance point, the path continuity confirmation point and the end point pollution influence area in each channel based on the pollution diffusion channel structure diagram (determines the node where the pollution concentration mutation first appears in each path as the starting pollution source appearance point, extracts the intermediate position node with consistent concentration trend direction in the path as the path continuity confirmation point, and extracts the concentration rising area at the end of the path as the end point pollution influence area), establishes three key node sets, and generates a pollution channel key node index set;

[0116] Based on the pollution diffusion channel structure diagram, the path number and the corresponding node sequence are read one by one, the spatial index and the corresponding time period number of each node on each path are extracted, the pollution appearance time record table and the concentration trend structure table are called in turn, the first sensor number where the concentration mutation appears in the path node is found and the position is recorded as the starting pollution source appearance point, the concentration trend sequence of the path intermediate paragraph node is called, the continuity is judged according to the direction change symbol between paragraphs, if the trend direction of three consecutive paragraphs does not change (such as +1 or -1), the current node is marked as the path continuity confirmation point, then the concentration sequence corresponding to the end node of the path is found, if the concentration value is monotonously increasing at the last three time points (for example, 0.34→0.36→0.38), the end node of the pollution influence area is recorded, the spatial index, the path number, the direction label of the above three types of nodes are recorded, and they are uniformly summarized as a pollution channel node index structure, for example:

[0117] Table 9 Pollution channel key node index set table Path number Node type Node number Spatial position Concentration trend direction Appearance time / s P001 Starting appearance point S21 A3 +1 360 P001 Coherence confirmation point S24 A5 +1 420 P001 End point influence area S27 A9 +1 480

[0118] As shown in Table 9, the three key points of path P001 have been clearly marked as three types of functional nodes, and the pollution channel key node index set is finally generated.

[0119] The space path drawing submodule connects the starting pollution source appearance point to the continuity confirmation point and to the terminal point pollution influence area according to the pollution channel key node index set, and draws each path according to a unique number, labels the path segment type with different colors, records the node coordinates, connection order and path number index information, and establishes the pollution propagation path topology structure diagram;

[0120] According to the pollution channel key node index set, the spatial position coordinates of the three key nodes in each path are extracted, the spatial coordinates pair of the connection operation between the starting appearance point and the continuity confirmation point is recorded as path segment A, and the same connection operation is performed on the continuity confirmation point and the terminal point pollution influence area as path segment B, each path forms a two-segment continuous line segment structure, is stored in layers according to the path number, each segment is given a direction label, the path direction arrow is uniformly labeled according to the trend direction, and the path segment type is identified with different colors, for example, red represents the source segment and orange represents the diffusion segment. In order to facilitate the three-dimensional atlas nesting, the spatial coordinate pair and the path number mapping relationship of each segment are recorded, such as S21-S24 for segment A and S24-S27 for segment B. After the above structured processing of all paths, the following path topology table is finally constructed:

[0121] Table 10 Pollution propagation path topology structure diagram Path number Path section starting point Path section end point Starting point coordinates (m) End point coordinates (m) Path section type Direction P001 S21 S24 （12，30，5） （20，36，5） Source section → P001 S24 S27 （20，36，5） （28，40，5） Diffusion section →

[0122] As shown in Table 10, the path number P001 complete path has been visualized and structured, and the pollution propagation path topology structure diagram is finally established.

[0123] The state atlas generation submodule superimposes all path structures into the mine three-dimensional space structure diagram according to the pollution propagation path topology structure diagram, maps the path line segment to the mine plan according to the spatial layer, marks the pollution node attribute, path number and flow arrow, draws the pollution link distribution diagram, and establishes the mine pollution state monitoring atlas;

[0124] According to the pollution propagation path topology structure diagram, the starting and ending point coordinates in the path segment record are read one by one, the corresponding position layer data in the mine space structure diagram is obtained, the path segment is projected to the mine main roadway plan in three-dimensional space coordinates, and the path number is labeled. All path segments are superimposed into a unified layer, and different graphical symbols are marked for the starting point, ending point and continuity node of each path segment, for example, the source point is marked as a red circle, the confirmation point is marked as a blue diamond, and the terminal point is marked as a green square. The direction arrow uses a unified vector representation, the flow direction is consistent with the trend direction, the path number is labeled in the middle of the path segment with a black label, and a legend is added at the bottom of the image to explain the meaning of each graph. Finally, the whole visual diagram is output as follows:

[0125] Table 11 Mine pollution state atlas node summary table Figure element number Node number Spatial coordinates (m) Type name Belonging path number G001 S21 （12，30，5） Starting appearance point P001 G002 S24 （20，36，5） Coherence confirmation point P001 G003 S27 （28，40，5） End point influence area P001

[0126] As shown in Table 11, the node attributes corresponding to each graph element have been sorted out, and finally the mine pollution state monitoring atlas is established.

[0127] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, in accordance with the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A mine contamination status monitoring system, characterized in that the system The method comprises the following steps: The concentration mutation recognition module obtains the continuous concentration time series data of the underground gas sensor in the sampling period, calculates the gas concentration change value and compares the change difference value, marks the current time point as a mutation point when the change difference value is greater than or equal to the concentration mutation rate threshold, and generates a pollution mutation appearance position table; The disturbance frequency calculation module divides the work period into time segments of equal length based on the pollution mutation appearance position table, detects and counts the number of times of the reversal of the concentration change direction in each segment, selects the time segments greater than or equal to the disturbance frequency threshold, and generates a disturbance high-frequency segment list; The pollution area positioning module obtains the concentration change trend sequence of the gas sensor at the corresponding time segment on the corresponding spatial position according to the disturbance high-frequency segment list, counts the number of sensors with consistent trends, selects the spatial position satisfying the area concentration judgment reference value as a pollution suspected area, and generates a pollution suspected area list; The diffusion path judgment module obtains the concentration trend change direction of the gas sensor in the pollution suspected area in the three continuous time segments before and after the pollution appearance point based on the pollution suspected area list, marks the corresponding path as a pollution diffusion channel if the trend is consistent, and generates a pollution diffusion channel structure diagram; The monitoring result output module draws a distribution diagram according to the spatial position of the mine based on the pollution diffusion channel structure diagram, labels all pollution propagation links, and obtains a mine pollution state monitoring atlas.

2. The mine contamination status monitoring system of claim 1, wherein, The pollution mutation appearance position table comprises mutation point spatial position, mutation point time information and mutation intensity level, the disturbance high-frequency segment list comprises high-frequency time segment number, frequently reversed sensor number and reversal frequency index, the pollution suspected area list comprises pollution concentration area number, number of trend consistency sensors in the area and suspected pollution level, the pollution diffusion channel structure diagram comprises diffusion path number, path starting and ending position and continuous trend maintenance time segment number, and the mine pollution state monitoring atlas comprises pollution source appearance position, pollution propagation link path diagram and pollution influence area distribution record.

3. The mine contamination status monitoring system of claim 1, wherein, The concentration mutation recognition module comprises: The data stream receiving submodule obtains the continuous concentration time series data of the underground fixed-point gas sensor in the sampling period, stores the concentration data in number according to time sequence, forms a structured time series concentration sequence, establishes a data receiving buffer list, and generates a continuous concentration sequence value set; The change rate calculation submodule selects any three time points to form two adjacent time segments based on the continuous concentration sequence value set, calculates the gas concentration change value in the two time segments respectively, calculates the difference value of the two change values, calculates the concentration change difference value in unit time combined with the current sampling time interval, and generates a concentration change difference rate value set; The mutation point determination submodule compares the concentration change difference rate value corresponding to each time point combined with the concentration mutation rate threshold, marks the corresponding time point as a mutation point when the rate value of the time point is greater than or equal to the concentration mutation rate threshold, records the position number and time information of the mutation point, and generates a pollution mutation appearance position table.

4. The mine contamination status monitoring system of claim 1, wherein, The disturbance frequency calculation module comprises: The concentration data extraction submodule locates the sensor number corresponding to each mutation point and the mutation occurrence time based on the pollution mutation appearance position table, extracts the complete gas concentration data sequence of each sensor in the corresponding work cycle, organizes the concentration data in chronological order and unifies the sampling time interval, divides the work cycle into time segments of equal length, and generates a work cycle segmented concentration data sequence; The reverse number detection submodule traverses the continuous concentration data in each time segment according to the work cycle segmented concentration data sequence, compares the concentration change direction of adjacent two time points in turn, records the direction sequence of positive change and reverse change, counts the number of continuous direction reversals and assigns a disturbance frequency value to each time segment, and generates a segmented disturbance frequency value set; The high-frequency segment screening submodule compares the disturbance frequency value corresponding to each time segment with the set disturbance frequency threshold according to the segmented disturbance frequency value set, screens the time segments with disturbance frequency values greater than or equal to the disturbance frequency threshold, extracts the corresponding time segment number and sensor number information, and generates a disturbance high-frequency segment list.

5. The mine contamination status monitoring system of claim 1, wherein, The pollution area positioning module comprises: The concentration trend extraction submodule extracts the concentration data of all sensors at the corresponding spatial position in each time segment according to the disturbance high-frequency segment list, constructs the concentration change sequence of each sensor in each time segment in the sampling order, calculates the concentration difference between adjacent time points in each sequence and extracts the difference direction label, establishes the concentration change trend structure of each sensor in each time segment, and generates a spatial concentration trend sequence matrix; The trend consistency determination submodule compares the concentration trend vectors corresponding to adjacent sensors in each time segment based on the spatial concentration trend sequence matrix, and if the trend symbol sequence positions of any two adjacent sensors remain consistent, it is determined that the trends are consistent, the number of sensors satisfying the trend consistency determination condition is counted in turn, and the cumulative number under the current spatial position unit is recorded, generating a spatial position consistent sensor number value set; The spatial aggregation recognition submodule determines whether the number of consistent sensors at each spatial position is greater than or equal to the regional aggregation judgment reference value point by point according to the spatial position consistent sensor number value set, and if the condition is met, marks the corresponding position as a pollution suspected area, records the corresponding spatial position number and corresponding time segment, and establishes a pollution suspected area list.

6. The mine contamination status monitoring system of claim 1, wherein, The diffusion path judgment module comprises: The trend direction extraction submodule extracts each sensor number and the time segment where the corresponding pollution appearance point is located under each suspected area based on the pollution suspected area list, traces back two time segments and extends one time segment forward, constructs a concentration trend data set containing three consecutive time segments before and after the pollution appearance point, assigns a direction to each concentration trend in the set and organizes it into a direction sequence matrix, and generates a three-segment concentration trend direction sequence matrix; The continuous consistency determination sub-module determines the position consistency of each group of trend direction sequences based on the three-segment concentration trend direction sequence matrix. If the direction values of the three time periods are the same, the path is marked as consistent. The start and end index positions of each consistent trend path in the spatial position are calculated. The trend consistency distance is obtained. The path segment index is established for the sensor pairs with a difference less than the set threshold. The trend consistent path segment index set is obtained; The path structure construction sub-module connects the sensor numbers in the index order according to the trend consistent path segment index set, and records each pair of start and end positions as a channel path. All paths are numbered and an start-end path mapping table is established. A pollution diffusion channel structure diagram is generated.

7. The mine contamination status monitoring system of claim 6, wherein, The calculation formula of the trend consistency distance is: ； wherein, represents a sensor trend direction value of the first time period, assigned a value of +1 or -1, , spatial position coordinates of the sensor , represents a trend consistency distance measure.

8. The mine contamination status monitoring system of claim 1, wherein, The monitoring result output module includes: The pollution node extraction sub-module extracts the spatial position index and time label of the starting pollution source appearance point, path continuity confirmation point and end pollution influence area in each channel based on the pollution diffusion channel structure diagram. Three key node sets are established. A pollution channel key node index set is generated; The spatial path drawing sub-module connects the starting pollution source appearance point to the path continuity confirmation point and to the end pollution influence area according to the pollution channel key node index set. Each path is drawn separately according to the unique number and marked with different path segment types. The node coordinates, connection order and path number index information are recorded. A pollution propagation path topology structure diagram is established. The state atlas generation sub-module superimposes all path structures onto the mine three-dimensional space structure diagram according to the pollution propagation path topology structure diagram. The path line segments are mapped to the mine plan according to the spatial layering. The pollution node attributes, path numbers and flow direction arrows are marked. A pollution link distribution diagram is drawn. A mine pollution state monitoring atlas is established.

9. The mine contamination status monitoring system of claim 8, wherein, The starting pollution source appearance point is the node where the pollution concentration mutation appears earliest in each path. The path continuity confirmation point is the intermediate position node where the concentration trend direction is continuous and consistent in the path. The end pollution influence area is the concentration rising area at the end of the path.

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