Ring network data transmission method and diagnosis system applied to underground coal mine

Abstract

The invention relates to the technical field of mine data transmission and supervision, in particular to a ring network data transmission method applied to an underground coal mine and a diagnosis system. The method comprises the following steps: firstly, acquiring gas concentration data, humidity data and a mining starting moment; further in the current monitoring period, according to the fluctuation stability characteristics of the target data, in combination with the time difference between the sampling moment of each data point and the latest mining starting moment, obtaining a reference coefficient of the target data; according to the similar characteristics of the gas concentration data and the similar characteristics of the humidity data between the two adjacent monitoring nodes, in combination with the reference coefficient, obtaining correction effective coefficients corresponding to the two adjacent monitoring nodes; and according to the sum value of all the correction effective coefficients between any two monitoring nodes, mutual monitoring coefficients corresponding to the two monitoring nodes are obtained, and a two-dimensional data set is obtained and transmitted by using a preset dimension reduction algorithm in combination with the corresponding covariance. And finally, performing security diagnosis according to the two-dimensional data set.

Description

Technical Field

[0001] The present invention relates to the technical field of mine data transmission and supervision, and in particular to a ring network data transmission method and a diagnosis system applied to underground coal mines. Background Art

[0002] Due to the complex underground environment of coal mines, the gas concentration in different areas may be different, so it is necessary to install multiple sensors underground to transmit data to ensure the monitoring of the entire mine environment. In addition, for the data obtained by multiple sensors, in order to reduce the impact of electromagnetic interference and environmental changes, ring network transmission is generally used to ensure data accuracy.

[0003] In the existing technology, the gas concentration data obtained by all sensors needs to be transmitted in a loop in the ring network until it finally reaches the control center, and then the data is used for dimensionality reduction analysis to speed up the analysis efficiency and conduct safety diagnosis in the coal mine. However, due to the complex environment, the data obtained by some sensors has low availability, and the reference value of the correlation between the data of two different sensors also fluctuates, which ultimately leads to inaccurate dimensionality reduction results and inaccurate safety diagnosis results. Summary of the invention

[0004] In order to solve the technical problem that inaccurate data dimensionality reduction affects the safety diagnosis results, the purpose of the present invention is to provide a ring network data transmission method and diagnosis system applied to coal mines. The technical scheme adopted is as follows: A ring network data transmission method used in coal mines, the method comprising: Obtain the gas concentration data and humidity data of each monitoring node in the coal mine in the preset monitoring period; obtain the mining start time; In the current monitoring cycle, the gas concentration data of any of the monitoring nodes is selected as the target data, and the reference coefficient of the target data is obtained according to the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the most recent mining start time; according to the similar characteristics of the gas concentration data between two adjacent monitoring nodes and the reference coefficient, the effective coefficient corresponding to the two adjacent monitoring nodes is obtained; according to the similar characteristics of the humidity data between two adjacent monitoring nodes and the effective coefficient, the corrected effective coefficient corresponding to the two adjacent monitoring nodes is obtained; In the current monitoring cycle, the mutual monitoring coefficient of the corresponding two monitoring nodes is obtained based on the sum of the modified effective coefficients of all adjacent monitoring nodes between any two of the monitoring nodes; based on the covariance of the gas concentration data between all any two of the monitoring nodes, combined with the mutual monitoring coefficient, a two-dimensional data set is obtained and transmitted using a preset dimensionality reduction algorithm.

[0005] Furthermore, the method for obtaining the reference coefficient includes: In the target data, the local stability parameter of each data point is obtained according to the difference characteristics between each data point and the adjacent data points in time series; the difference characteristics between the data point and the adjacent data points in time series are negatively correlated with the local stability parameter; the fluctuation reference weight of each data point is obtained according to the time difference between the collection time of each data point and the most recent mining start time; the time difference between the collection time of the data point and the most recent mining start time is negatively correlated with the fluctuation reference weight; The reference coefficient of the target data is obtained according to the local stability parameters and the fluctuation reference weights of all data points in the target data; the local stability parameters and the fluctuation reference weights of the data points are positively correlated with the reference coefficient of the target data.

[0006] Furthermore, the method for obtaining the mutual monitoring coefficient includes: Each of the monitoring nodes transmits data to the adjacent monitoring nodes on both sides to obtain a data transmission path between any two of the monitoring nodes; On each of the data transmission paths between any two of the monitoring nodes, the sum of the modified effective coefficients of all adjacent monitoring nodes is used as the monitoring sub-coefficient of each of the data transmission paths corresponding to the two monitoring nodes; According to all the monitoring sub-coefficients between the two monitoring nodes, a mutual monitoring coefficient corresponding to the two monitoring nodes is obtained; the monitoring sub-coefficients and the mutual monitoring coefficient are positively correlated.

[0007] Furthermore, the method of obtaining and transmitting a two-dimensional data set by using a preset dimensionality reduction algorithm based on the covariance of the gas concentration data between any two of the monitoring nodes in combination with the mutual monitoring coefficient includes: The covariance of the gas concentration data between any two of the monitoring nodes and the mutual monitoring coefficient are multiplied to obtain a corrected covariance between the corresponding two monitoring nodes; wherein the mutual monitoring coefficient is also normalized; Based on all the modified covariances, a two-dimensional data set is acquired and transmitted using a preset dimensionality reduction algorithm.

[0008] Furthermore, the method for obtaining the effective coefficient includes: In the current monitoring cycle, based on the similar characteristics of the gas concentration data of the two adjacent monitoring nodes, the similarity coefficients corresponding to the two adjacent monitoring nodes are obtained; the product of the similarity coefficient and the reference coefficient and value corresponding to the two adjacent monitoring nodes is used as the effective coefficient corresponding to the two adjacent monitoring nodes.

[0009] Furthermore, the method for obtaining the modified effective coefficient includes: In the current monitoring cycle, in the humidity data of two adjacent monitoring nodes, according to the difference characteristics of all the humidity data with the same time series, the humidity consistency coefficient corresponding to the two adjacent monitoring nodes is obtained; the difference characteristics of the humidity data are negatively correlated with the humidity consistency coefficient; According to the humidity consistency coefficient and the effective coefficient of two adjacent monitoring nodes, the corrected effective coefficient corresponding to the two adjacent monitoring nodes is obtained; the humidity consistency coefficient and the effective coefficient are both positively correlated with the corrected effective coefficient.

[0010] Furthermore, the preset dimensionality reduction algorithm is a PCA dimensionality reduction algorithm.

[0011] The present invention also proposes a ring network data diagnosis system for underground coal mines, the system comprising: The data acquisition module is used to obtain the gas concentration data and humidity data of each monitoring node in the coal mine in the current preset monitoring period; obtain the mining start time; A data analysis module is used to select the gas concentration data of any of the monitoring nodes as the target data in the current monitoring cycle, and obtain the reference coefficient of the target data based on the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the most recent mining start time; obtain the effective coefficient corresponding to the two adjacent monitoring nodes based on the similar characteristics of the gas concentration data between the two adjacent monitoring nodes and the reference coefficient; obtain the corrected effective coefficient corresponding to the two adjacent monitoring nodes based on the similar characteristics of the humidity data between the two adjacent monitoring nodes and the effective coefficient; A data transmission module, used to select the corrected effective coefficients of all adjacent monitoring nodes between any two monitoring nodes in the current monitoring cycle, and obtain the mutual monitoring coefficient of the corresponding two monitoring nodes; according to the covariance of the gas concentration data between all any two monitoring nodes, combined with the mutual monitoring coefficient, a preset dimensionality reduction algorithm is used to obtain and transmit a two-dimensional data set; The safety diagnosis module is used to perform safety diagnosis based on the two-dimensional data set of the current monitoring cycle.

[0012] Furthermore, the method for performing safety diagnosis includes: A scatter plot of the two-dimensional data set in the current monitoring period is obtained, and the outlier degree of the data points in the scatter plot is obtained; when the outlier degree is greater than a preset danger threshold, a safety alarm is issued.

[0013] Furthermore, the method for obtaining the outlier degree of the data point includes: The outlier degree of the data points in the scatter plot is obtained using the LOF algorithm.

[0014] The present invention has the following beneficial effects: The present invention first obtains the gas concentration data and humidity data of each monitoring node in the coal mine in a preset monitoring period and obtains the mining start time to obtain a data analysis basis; further, in the current monitoring period, according to the fluctuation stability characteristics of the target data, combined with the time difference between the sampling time of each data point and the latest mining start time, obtains the reference coefficient of the target data, characterizes the reference of the target data, and reduces the interference of the gas concentration data with low reference that contains more environmental noise; further, according to the similar characteristics of the gas concentration data between two adjacent monitoring nodes, combined with the reference coefficient, obtains the effective coefficient corresponding to the two adjacent monitoring nodes, characterizes the effectiveness of the mutual monitoring of the adjacent monitoring nodes, and reduces the interference of the data containing noise or distortion on the dimensionality reduction analysis; further, from the perspective of the similarity of the humidity data, the effective coefficient is analyzed. The number is further corrected to obtain the corrected effective coefficient of the corresponding two adjacent monitoring nodes, improve the accuracy of the effective coefficient, and then improve the accuracy of the dimensionality reduction result; further in the current monitoring period, according to the sum of the corrected effective coefficients of all adjacent monitoring nodes between any two monitoring nodes, the mutual monitoring coefficient of the corresponding two monitoring nodes is obtained, which characterizes the effectiveness of mutual monitoring between any two monitoring nodes, and also reflects the effectiveness of the correlation between the gas concentration data of any two monitoring nodes, so as to prepare for the final accurate dimensionality reduction result; further according to the covariance of the gas concentration data between all any two monitoring nodes, combined with the mutual monitoring coefficient, a two-dimensional data set is obtained and transmitted using a preset dimensionality reduction algorithm, which reduces data redundancy, reduces data transmission pressure, and provides data support for safety diagnosis. Finally, safety diagnosis is performed according to the two-dimensional data set of the current monitoring period. The present invention analyzes the reference of the data itself and the effectiveness of the data correlation of the adjacent monitoring nodes, determines the effectiveness of mutual monitoring between the monitoring nodes during ring network transmission, corrects the covariance between the data, improves the accuracy of the dimensionality reduction result, and makes the safety diagnosis result more accurate. 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 ring network data transmission method applied to an underground coal mine provided by an embodiment of the present invention; Figure 2A schematic diagram of the relationship between adjacent monitoring substations in a ring network structure provided by an embodiment of the present invention; Figure 3 A system block diagram of a ring network data diagnostic system applied to underground coal mines provided by one embodiment of the present invention; Figure 4 A scatter plot of a two-dimensional data set 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 ring network data transmission method and diagnostic system for underground coal mines proposed by the present invention, its specific implementation method, structure, features and effects 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 following describes in detail a specific scheme of a ring network data transmission method and a diagnostic system for underground coal mines provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a ring network data transmission method applied to a coal mine provided by an embodiment of the present invention, specifically comprising: Step S1: Obtain the gas concentration data and humidity data of each monitoring node in the coal mine in a preset monitoring period; obtain the mining start time.

[0021] In an embodiment of the present invention, taking into account the influence of gas release and ventilation system, the coal seam in the mine will gradually release gas during the mining process. This process is usually relatively smooth. At the same time, the ventilation system of the mine can effectively adjust the gas concentration by introducing fresh air and discharging exhaust gas to avoid drastic fluctuations. Therefore, the gas concentration data changes relatively smoothly over a period of time. Therefore, the data is analyzed based on a preset monitoring period. Considering that the humidity data reflects the humidity characteristics in the environment, the gas concentration data and humidity data of each monitoring node underground in the coal mine in the preset monitoring period are obtained; considering that during coal mining, the fracture of rocks may cause rapid release of gas, which will affect the change of gas concentration data, the start time of mining is also obtained.

[0022] As an example, multiple monitoring substations are set up underground in a coal mine, and electrochemical sensors and humidity sensors are installed to collect gas concentration data and humidity data in real time. Mechanical vibration sensors are installed and thresholds are set to monitor the mining status (whether mining is in progress), obtain the start time of mining, and record the locations of these substations. Both electrochemical sensors and humidity sensors collect data at a frequency of 1 Hz at each monitoring substation. Multiple monitoring substations are connected through the network to form a closed ring network structure. The preset monitoring cycle length is 5 minutes, and the current monitoring cycle is the latest and complete monitoring cycle; each monitoring substation is a monitoring node.

[0023] It should be noted that the number of monitoring nodes and the setting thresholds of mechanical vibration sensors are related to the actual operating scenarios. The installation and management of the monitoring system can refer to the "Summary of 24 Systems for Coal Mine Gas Monitoring Systems" of the International Coal Network. In other embodiments of the present invention, the implementer can set other data collection frequencies and monitoring cycle lengths.

[0024] Step S2: In the current monitoring cycle, select the gas concentration data of any monitoring node as the target data, and obtain the reference coefficient of the target data based on the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the start time of the most recent mining; according to the similar characteristics of the gas concentration data between two adjacent monitoring nodes, combined with the reference coefficient, obtain the effective coefficient corresponding to the two adjacent monitoring nodes; according to the similar characteristics of the humidity data between two adjacent monitoring nodes, combined with the effective coefficient, obtain the corrected effective coefficient corresponding to the two adjacent monitoring nodes.

[0025] In order to facilitate the analysis of the data of the monitoring nodes one by one, the gas concentration data of any monitoring node is selected as the target data in the current monitoring cycle; considering the influence of gas release and ventilation system, the fluctuation of gas concentration data is relatively stable. At the same time, since coal mining will affect the change of gas concentration data, the time difference between the sampling time of each data point in the target data and the start time of the most recent mining is combined to analyze the fluctuation stability characteristics of the target data, obtain the reference coefficient of the target data, characterize the reference of the target data, and reduce the interference of gas concentration data with low reference that contains more environmental noise.

[0026] Preferably, in one embodiment of the present invention, the smaller the difference between a data point and a time-series adjacent data point, the more stable the data fluctuation and the stronger the local stability; at the same time, mining may cause rapid release of gas, which will cause large fluctuations in the time-domain adjacent data points at the time of mining. At this time, the data fluctuation is the fluctuation during normal operation, not affected by environmental noise, and still has a high reference value; Based on this, in the target data, the local stability parameter of each data point is obtained according to the difference characteristics between each data point and the adjacent data points in time series; the difference characteristics between the data point and the adjacent data points in time series are negatively correlated with the local stability parameter; the fluctuation reference weight of each data point is obtained according to the time difference between the collection time of each data point and the latest mining start time; the time difference between the collection time of the data point and the latest mining start time is negatively correlated with the fluctuation reference weight; The reference coefficient of the target data is obtained according to the local stability parameters and fluctuation reference weights of all data points in the target data; the local stability parameters and fluctuation reference weights of the data points are positively correlated with the reference coefficient of the target data.

[0027] As an example, the calculation formula of the reference coefficient includes: ; In the formula, represents the reference coefficient of the target data; Represents the time series number of the data point in the target data; Indicates the number of data points in the target data; Indicates the target data The time when the data points are collected; Indicates the most recent mining start time; Indicates the target data The data value of the data point; Indicates the target data The data value of the data point; Indicates the target data The data value of the data point; represents the first preset divide-by-zero positive parameter; Indicates the target data Local stability parameters for data points; Indicates the target data The fluctuation reference weight of each data point; Indicates taking the absolute value.

[0028] In the calculation formula of the reference coefficient, the difference characteristics between the data points are expressed by the absolute value of the difference, and then the negative correlation mapping is performed by taking the reciprocal method to adjust the correlation relationship; The smaller it is, the smaller the difference characteristics between the data point and the adjacent data points in the time series are, which reflects that the local stability of the data is higher, the local stability parameter is larger, the possibility of containing noise is smaller, and the reference coefficient is larger; The smaller it is, the smaller the time difference between the data point collection time and the most recent mining start time is, and the more likely the local fluctuation of the data is the normal fluctuation caused by the mining operation. The higher the reference value and the larger the reference coefficient.

[0029] It should be noted that in the calculation formula of the reference coefficient, and When there is only one data point adjacent to the data point in time series, only the absolute value of the difference between the data point and the data point adjacent to the data point in time series is calculated to obtain the local stability parameter; in one embodiment of the present invention, In other embodiments of the present invention, the implementer may also use a negative correlation mapping function such as a natural constant Exponential function with base Perform negative correlation mapping, Represents the independent variable, and other first preset positive parameters other than zero can also be set.

[0030] See also Figure 2 , which shows a schematic diagram of the relationship between adjacent monitoring substations in a ring network structure provided by an embodiment of the present invention, Figure 2 The monitoring substation 1 and the monitoring substation 2 are included in the monitoring substation. Each monitoring substation is a monitoring node. Data processing is performed in the monitoring substation. The arrow represents the direction of data transmission. Figure 2 It can be seen that in the ring network structure, adjacent monitoring nodes are directly connected and can transmit data to each other. The data sent by one node will be received and processed by the next adjacent node, and then forwarded to the next node. If a node fails, the design of the ring network structure allows data to be transmitted through the reverse path, thereby ensuring the reliability and continuity of the transmission, so adjacent nodes can monitor the validity of each other's gas concentration data.

[0031] In order to determine the effectiveness of the gas concentration data monitored by adjacent nodes, it is first necessary to reflect the effectiveness of mutual monitoring from the perspective of the reference of each other's data based on the reference coefficient that characterizes the reference of the gas concentration data; considering that the ventilation system will make the air flow evenly and the gas diffuses in the air, the gas concentrations of adjacent monitoring nodes are similar. Therefore, based on the similar characteristics of the gas concentration data between two adjacent monitoring nodes and combined with the reference coefficient, the effective coefficient corresponding to the two adjacent monitoring nodes is obtained to characterize the effectiveness of the mutual monitoring of adjacent monitoring nodes, reduce the interference of data containing noise or distortion on the dimensionality reduction analysis, and prepare for obtaining accurate dimensionality reduction results.

[0032] Preferably, in one embodiment of the present invention, considering that in the current monitoring period, the greater the similarity characteristics of the gas concentration data of two adjacent monitoring nodes, the stronger the consistency of the gas concentration data of the adjacent monitoring nodes, and the stronger the effectiveness of mutual monitoring; at the same time, the larger the reference coefficient, the higher the reference value of the gas concentration data, the higher the reference value of the similarity characteristics of the gas concentration data of the adjacent monitoring nodes, and the stronger the effectiveness of mutual monitoring. Based on this, according to the similarity characteristics of the gas concentration data of the two adjacent monitoring nodes, the similarity coefficients corresponding to the two adjacent monitoring nodes are obtained; the product of the similarity coefficient and the reference coefficient and value corresponding to the two adjacent monitoring nodes is used as the effective coefficient corresponding to the two adjacent monitoring nodes.

[0033] As an example, the Pearson correlation coefficient of the gas concentration data of two adjacent monitoring nodes is obtained, and the Pearson correlation coefficient is linearly normalized, and the normalized result is used as the similarity coefficient corresponding to the two adjacent monitoring nodes, so as to obtain the effective coefficient.

[0034] In another embodiment of the present invention, the DTW distance of the gas concentration data of two adjacent monitoring nodes can also be obtained, and the DTW distance can be calculated by After the function negative correlation mapping, it is used as the similarity coefficient; the cosine similarity of the gas concentration data of two adjacent monitoring nodes can also be obtained, and the cosine similarity is linearly normalized, and the normalized result is used as the similarity coefficient corresponding to the two adjacent monitoring nodes. Among them, the Pearson correlation coefficient, DTW distance and cosine similarity are all existing technologies and will not be repeated here.

[0035] Taking into account that the ambient humidity between adjacent monitoring nodes is relatively consistent, the effective coefficient can be further corrected from the perspective of humidity data similarity. At the same time, humidity is related to air flow. If the humidity consistency between adjacent nodes is strong, it means that the air flow state is better. The similar characteristics of the gas concentration of adjacent monitoring nodes can be supplemented and verified. Therefore, according to the similar characteristics of the humidity data between two adjacent monitoring nodes, combined with the effective coefficient, the corrected effective coefficient of the corresponding two adjacent monitoring nodes is obtained to improve the accuracy of the effective coefficient, thereby improving the accuracy of the dimensionality reduction results, and ultimately improving the accuracy and diagnostic effect of coal mine safety monitoring.

[0036] Preferably, in one embodiment of the present invention, considering that the smaller the difference of humidity data with the same time series, the more similar the humidity data of two adjacent monitoring nodes are, the more consistent the humidity is, reflecting the better the air flow state is, the higher the accuracy of the effective coefficient is, and the higher the credibility is, so in the current monitoring period, in the humidity data of two adjacent monitoring nodes, according to the difference characteristics of all humidity data with the same time series, the humidity consistency coefficient of the corresponding two adjacent monitoring nodes is obtained; the difference characteristics of the humidity data are negatively correlated with the humidity consistency coefficient; According to the humidity consistency coefficient and effectiveness coefficient of two adjacent monitoring nodes, the corrected effectiveness coefficient of the corresponding two adjacent monitoring nodes is obtained; the humidity consistency coefficient and effectiveness coefficient are both positively correlated with the corrected effectiveness coefficient.

[0037] As an example, assuming that monitoring node A and monitoring node B are adjacent and A transmits to B, the calculation formula for the modified effective coefficient includes: ; In the formula, is the serial number of the monitoring node; is the serial number of the monitoring node; Represents two adjacent monitoring nodes and monitoring nodes The modified effective coefficient of Indicates the time sequence number of the data point in the current monitoring cycle; Indicates the number of data points in the current monitoring cycle; Indicates monitoring node In the current monitoring cycle Humidity value of data points; Indicates monitoring node In the current monitoring cycle Humidity value of data points; represents the second preset zero-divided positive parameter; Represents two adjacent monitoring nodes and monitoring nodes The effective coefficient of Indicates taking the absolute value; Represents two adjacent monitoring nodes and monitoring nodes The humidity consistency coefficient, in this example, .

[0038] In the calculation formula of the modified effective coefficient, the difference characteristics of the humidity data with the same time series are expressed by the absolute value of the difference, and then the negative correlation mapping is performed by taking the reciprocal method to adjust the correlation relationship, reflect the similar characteristics of the humidity data, and obtain the humidity consistency coefficient; The smaller it is, the smaller the difference of humidity data with the same time series is, the stronger the similarity characteristics are, reflecting that the air flow state is better, the accuracy of the effective coefficient is higher, the credibility is higher, the larger the humidity consistency coefficient is, the larger the corrected effective coefficient is; The larger it is, the stronger the similarity characteristics of the gas concentration data are, the greater the effectiveness of mutual monitoring between adjacent monitoring nodes is, and the larger the correction effectiveness coefficient is.

[0039] It should be noted that humidity data is only used to calculate the corrected effective coefficient. It is transmitted and processed between adjacent monitoring nodes. There is no need to transmit humidity data to other non-adjacent monitoring nodes. Only the corrected effective coefficient is transmitted to reduce transmission redundancy. For example, if the transmission order is ABCD, the humidity data of A does not need to be transmitted to C and D. Only the corrected effective coefficient is transmitted. To C and D.

[0040] In other embodiments of the present invention, implementers may also use a negative correlation mapping function such as The function performs negative correlation mapping, and other preset positive parameters other than zero can also be set.

[0041] Step S3: In the current monitoring cycle, the mutual monitoring coefficient of the corresponding two monitoring nodes is obtained according to the sum of the modified effective coefficients of all adjacent monitoring nodes between any two monitoring nodes; according to the covariance of the gas concentration data between all any two monitoring nodes, combined with the mutual monitoring coefficient, a preset dimensionality reduction algorithm is used to obtain and transmit a two-dimensional data set.

[0042] Through the ring network transmission, the gas concentration data of each monitoring node in the current monitoring period and the corrected effective coefficient between each monitoring node and the adjacent monitoring node are received. However, the effectiveness of mutual monitoring between non-adjacent transmission nodes is still unknown. Since the ring network transmission is transmitted layer by layer, each node only forwards the received data to the next adjacent node after receiving the data. This layer-by-layer forwarding mechanism limits the direct communication between nodes and can only be transmitted through adjacent nodes. Therefore, the effectiveness of mutual supervision between two non-adjacent nodes is the cumulative sum of the effectiveness of each pair of adjacent nodes during transmission. Therefore, in the current monitoring period, according to the sum of the corrected effective coefficients of all adjacent monitoring nodes between any two monitoring nodes, the mutual monitoring coefficient of the corresponding two monitoring nodes is obtained, which characterizes the effectiveness of mutual monitoring between any two monitoring nodes, and also reflects the effectiveness of the correlation between the gas concentration data of any two monitoring nodes, preparing for the final accurate dimensionality reduction results.

[0043] Preferably, in one embodiment of the present invention, considering that when the ring network structure is intact, there are two transmission paths between any two monitoring nodes, and both transmission paths need to be analyzed, so each monitoring node transmits data to the adjacent monitoring nodes on both sides to obtain the data transmission path between any two monitoring nodes; On each data transmission path between any two monitoring nodes, the sum of the modified effective coefficients of all adjacent monitoring nodes is used as the monitoring sub-coefficient of each data transmission path corresponding to the two monitoring nodes; According to all monitoring sub-coefficients between two monitoring nodes, the mutual monitoring coefficients of the corresponding two monitoring nodes are obtained; the monitoring sub-coefficients and the mutual monitoring coefficients are positively correlated.

[0044] As an example, the average value of all monitoring sub-coefficients between two monitoring nodes is used as the mutual monitoring coefficient of the corresponding two monitoring nodes.

[0045] For example, the ring network structure is ABCDE, where E and A are adjacent. There are two data transmission paths between A and C, namely ABC and AEDC. The monitoring sub-coefficients are , ,Will and The average value of is taken as the mutual monitoring coefficient between A and C.

[0046] It should be noted that a fault may occur in the ring network structure. At this time, there is only one data transmission path between the two monitoring nodes. For example, the transmission line between B and C is interrupted. At this time, there is only one data transmission path between A and C. As the mutual monitoring coefficient between A and C.

[0047] Since the ring network equipment and lines are regularly maintained and inspected, the possibility of failure is low. In the embodiment of the present invention, only one failure at a time is considered. When two or more failures occur at the same time, it means that the ring network transmission equipment cannot perform safe monitoring of the gas concentration in the coal mine and needs to be shut down for maintenance in time.

[0048] After characterizing the effectiveness of the correlation between the gas concentration data of two monitoring nodes through the mutual monitoring coefficient, the two-dimensional data set can be acquired and transmitted using a preset dimensionality reduction algorithm based on the covariance of the gas concentration data between any two monitoring nodes and the mutual monitoring coefficient, thereby reducing data redundancy and data transmission pressure.

[0049] Preferably, in one embodiment of the present invention, it is considered that the covariance represents the correlation between the gas concentration data of two monitoring nodes, and the mutual monitoring coefficient reflects the transmission reliability between them. By multiplying the covariance with the mutual monitoring coefficient, the modified covariance not only reflects the similarity between the data, but also comprehensively considers the reliability of data transmission. This correction method can effectively reduce the impact of noise or low-reliability transmission on the final dimensionality reduction result, and ensure that the data input into the dimensionality reduction algorithm is more accurate. Based on this, the covariance of the gas concentration data between any two monitoring nodes and the mutual monitoring coefficient are multiplied as the modified covariance between the corresponding two monitoring nodes; wherein the mutual monitoring coefficient is also normalized; Based on all corrected covariances, a two-dimensional data set is obtained and transmitted using a preset dimensionality reduction algorithm.

[0050] As an example, based on the mutual monitoring coefficients between all two monitoring nodes, the mutual monitoring coefficients are linearly normalized; the normalized mutual monitoring coefficient is multiplied by the covariance product of the gas concentration data between the corresponding two monitoring nodes as the corrected covariance between the corresponding two monitoring nodes, and based on all corrected covariances, an optimized covariance matrix is ​​obtained, thereby using the principal component analysis algorithm (Principal Component Analysis, PCA) to obtain a two-dimensional data set after dimensionality reduction and transmit it.

[0051] It should be noted that in other embodiments of the present invention, the implementer may also perform dimensionality reduction based on other dimensionality reduction algorithms of the covariance matrix, such as Linear Discriminant Analysis (LDA) algorithm.

[0052] The embodiment of the present invention also proposes a ring network data diagnosis system for use in coal mines, see Figure 3 , which shows a system block diagram of a ring network data diagnosis system applied to coal mines provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a data analysis module 102, a data transmission module 103 and a safety diagnosis module 104, specifically including: The data acquisition module 101 is used to obtain the gas concentration data and humidity data of each monitoring node in the coal mine in the current preset monitoring period; obtain the mining start time; The data analysis module 102 is used to select the gas concentration data of any monitoring node as the target data in the current monitoring cycle, and obtain the reference coefficient of the target data according to the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the latest mining start time; obtain the effective coefficient corresponding to the two adjacent monitoring nodes according to the similar characteristics of the gas concentration data between two adjacent monitoring nodes and the reference coefficient; obtain the corrected effective coefficient corresponding to the two adjacent monitoring nodes according to the similar characteristics of the humidity data between the two adjacent monitoring nodes and the effective coefficient; The data transmission module 103 is used to select the corrected effective coefficients of all adjacent monitoring nodes between any two monitoring nodes in the current monitoring cycle to obtain the mutual monitoring coefficient between the two corresponding monitoring nodes; according to the covariance of the gas concentration data between all any two monitoring nodes, combined with the mutual monitoring coefficient, a preset dimensionality reduction algorithm is used to obtain and transmit a two-dimensional data set; The safety diagnosis module 104 is used to perform safety diagnosis according to the two-dimensional data set of the current monitoring period.

[0053] The implementation process of the data acquisition module 101, the data analysis module 102, and the data transmission module 103 has been described in a ring network data transmission method applied to underground coal mines, and will not be repeated here.

[0054] After obtaining the two-dimensional data set after dimensionality reduction, safety diagnosis can be performed based on the two-dimensional data set of the current monitoring period to ensure the safety of coal mine operations.

[0055] Preferably, in one embodiment of the present invention, considering that the scatter plot can easily and intuitively analyze the distribution characteristics of data points, thereby identifying abnormal distribution, identifying abnormal gas concentration in coal mines, and issuing a safety alarm, a scatter plot of the two-dimensional data set of the current monitoring period is obtained, and the outlier degree of the data points in the scatter plot is obtained; when the outlier degree is greater than the preset danger threshold, a safety alarm is issued.

[0056] As an example, see Figure 4 , which shows a scatter plot of a two-dimensional data set provided by an embodiment of the present invention, with the horizontal axis being dimension 1 and the vertical axis being dimension 2. The scatter plot is obtained using the t-SNE (t-distributed Stochastic Neighbor Embedding) method, and the outlier degree of the data points in the scatter plot is obtained using the LOF algorithm. The preset danger threshold is set to 10. When the outlier degree is greater than 10, it is considered that there is an abnormality, and a safety alarm is automatically issued to remind the staff to promptly check for safety hazards and improve the safety of underground coal mine operations.

[0057] In other embodiments of the present invention, implementers may also use a density-based clustering algorithm, such as a DBSCAN algorithm, to identify the number of outliers, set a safety threshold for the number of outliers, and issue a safety alert.

[0058] In summary, in order to solve the technical problem that data dimensionality reduction is inaccurate and affects the safety diagnosis results, the present invention proposes a ring network data transmission method and diagnostic system applied to underground coal mines. The present invention first obtains gas concentration data, humidity data and the start time of mining; further, in the current monitoring cycle, according to the fluctuation stability characteristics of the target data, combined with the time difference between the sampling time of each data point and the most recent start time of mining, the reference coefficient of the target data is obtained; according to the similar characteristics of the gas concentration data and the similar characteristics of the humidity data between two adjacent monitoring nodes, combined with the reference coefficient, the corrected effective coefficient corresponding to the two adjacent monitoring nodes is obtained; further, according to the sum of all the corrected effective coefficients between any two monitoring nodes, the mutual monitoring coefficient corresponding to the two monitoring nodes is obtained, combined with the corresponding covariance, and a preset dimensionality reduction algorithm is used to obtain and transmit the two-dimensional data set. Finally, safety diagnosis is performed based on the two-dimensional data set.

[0059] 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.

[0060] 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 ring network data transmission method used in coal mines, characterized in that: The method comprises: Obtain the gas concentration data and humidity data of each monitoring node in the coal mine in the preset monitoring period; obtain the mining start time; In the current monitoring cycle, the gas concentration data of any of the monitoring nodes is selected as the target data, and the reference coefficient of the target data is obtained according to the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the most recent mining start time; according to the similar characteristics of the gas concentration data between two adjacent monitoring nodes and the reference coefficient, the effective coefficient corresponding to the two adjacent monitoring nodes is obtained; according to the similar characteristics of the humidity data between two adjacent monitoring nodes and the effective coefficient, the corrected effective coefficient corresponding to the two adjacent monitoring nodes is obtained; In the current monitoring cycle, the mutual monitoring coefficient of the corresponding two monitoring nodes is obtained based on the sum of the modified effective coefficients of all adjacent monitoring nodes between any two of the monitoring nodes; based on the covariance of the gas concentration data between all any two of the monitoring nodes, combined with the mutual monitoring coefficient, a two-dimensional data set is obtained and transmitted using a preset dimensionality reduction algorithm.

2. The ring network data transmission method used in coal mines according to claim 1, characterized in that: The method for obtaining the reference coefficient includes: In the target data, the local stability parameter of each data point is obtained according to the difference characteristics between each data point and the adjacent data points in time series; the difference characteristics between the data point and the adjacent data points in time series are negatively correlated with the local stability parameter; the fluctuation reference weight of each data point is obtained according to the time difference between the collection time of each data point and the most recent mining start time; the time difference between the collection time of the data point and the most recent mining start time is negatively correlated with the fluctuation reference weight; The reference coefficient of the target data is obtained according to the local stability parameters and the fluctuation reference weights of all data points in the target data; the local stability parameters and the fluctuation reference weights of the data points are positively correlated with the reference coefficient of the target data.

3. The ring network data transmission method used in coal mines according to claim 1, characterized in that: The method for obtaining the mutual monitoring coefficient includes: Each of the monitoring nodes transmits data to the adjacent monitoring nodes on both sides to obtain a data transmission path between any two of the monitoring nodes; On each of the data transmission paths between any two of the monitoring nodes, the sum of the modified effective coefficients of all adjacent monitoring nodes is used as the monitoring sub-coefficient of each of the data transmission paths corresponding to the two monitoring nodes; According to all the monitoring sub-coefficients between the two monitoring nodes, a mutual monitoring coefficient corresponding to the two monitoring nodes is obtained; the monitoring sub-coefficients and the mutual monitoring coefficient are positively correlated.

4. The ring network data transmission method used in coal mines according to claim 3 is characterized in that: The method of obtaining and transmitting a two-dimensional data set by using a preset dimensionality reduction algorithm based on the covariance of the gas concentration data between any two of the monitoring nodes and in combination with the mutual monitoring coefficient includes: The covariance of the gas concentration data between any two of the monitoring nodes and the mutual monitoring coefficient are multiplied to obtain a corrected covariance between the corresponding two monitoring nodes; wherein the mutual monitoring coefficient is also normalized; Based on all the modified covariances, a two-dimensional data set is acquired and transmitted using a preset dimensionality reduction algorithm.

5. The ring network data transmission method used in coal mines according to claim 1, characterized in that: The method for obtaining the effective coefficient includes: In the current monitoring cycle, based on the similar characteristics of the gas concentration data of the two adjacent monitoring nodes, the similarity coefficients corresponding to the two adjacent monitoring nodes are obtained; the product of the similarity coefficient and the reference coefficient and value corresponding to the two adjacent monitoring nodes is used as the effective coefficient corresponding to the two adjacent monitoring nodes.

6. The ring network data transmission method used in coal mines according to claim 1, characterized in that: The method for obtaining the modified effective coefficient includes: In the current monitoring cycle, in the humidity data of two adjacent monitoring nodes, according to the difference characteristics of all the humidity data with the same time series, the humidity consistency coefficient corresponding to the two adjacent monitoring nodes is obtained; the difference characteristics of the humidity data are negatively correlated with the humidity consistency coefficient; According to the humidity consistency coefficient and the effective coefficient of two adjacent monitoring nodes, the corrected effective coefficient corresponding to the two adjacent monitoring nodes is obtained; the humidity consistency coefficient and the effective coefficient are both positively correlated with the corrected effective coefficient.

7. The ring network data transmission method used in coal mines according to claim 1, characterized in that: The preset dimensionality reduction algorithm is the PCA dimensionality reduction algorithm.

8. A ring network data diagnostic system used in coal mines, characterized in that: The system comprises: The data acquisition module is used to obtain the gas concentration data and humidity data of each monitoring node in the coal mine in the current preset monitoring period; obtain the mining start time; A data analysis module is used to select the gas concentration data of any of the monitoring nodes as the target data in the current monitoring cycle, and obtain the reference coefficient of the target data based on the fluctuation stability characteristics of the target data and the time difference between the sampling time of each data point in the target data and the most recent mining start time; obtain the effective coefficient corresponding to the two adjacent monitoring nodes based on the similar characteristics of the gas concentration data between the two adjacent monitoring nodes and the reference coefficient; obtain the corrected effective coefficient corresponding to the two adjacent monitoring nodes based on the similar characteristics of the humidity data between the two adjacent monitoring nodes and the effective coefficient; A data transmission module, used to select the corrected effective coefficients of all adjacent monitoring nodes between any two monitoring nodes in the current monitoring cycle, and obtain the mutual monitoring coefficient of the corresponding two monitoring nodes; according to the covariance of the gas concentration data between all any two monitoring nodes, combined with the mutual monitoring coefficient, a preset dimensionality reduction algorithm is used to obtain and transmit a two-dimensional data set; The safety diagnosis module is used to perform safety diagnosis based on the two-dimensional data set of the current monitoring cycle.

9. The ring network data diagnosis system used in coal mines according to claim 8, characterized in that: The method for performing safety diagnosis comprises: A scatter plot of the two-dimensional data set in the current monitoring period is obtained, and the outlier degree of the data points in the scatter plot is obtained; when the outlier degree is greater than a preset danger threshold, a safety alarm is issued.

10. The ring network data diagnosis system used in coal mines according to claim 9, characterized in that: The method for obtaining the outlier degree of the data point includes: The outlier degree of the data points in the scatter plot is obtained using the LOF algorithm.

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