CN115235676B - Automatic alarm method for pressure abnormity of mine hydraulic support - Google Patents
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Abstract
The invention relates to the technical field of data identification and processing, in particular to an automatic alarm method for pressure abnormity of a mine hydraulic support. The method comprises the following steps: collecting a pressure value of each supporting point on the mine hydraulic support to obtain a quality evaluation index of the hydraulic support; acquiring a quality evaluation index sequence of each hydraulic support within a period of time, and acquiring a tracking index according to the quality evaluation index sequence to obtain the hydraulic support which is preliminarily judged to be in a normal state; acquiring a horizontal inclination angle of each hydraulic support in a normal state in multiple time periods, fitting all the horizontal inclination angles to obtain a Gaussian model so as to obtain probability distribution of each hydraulic support, and obtaining abnormal pressure evaluation according to the probability distribution and quality evaluation indexes; acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, and inputting the abnormal sequence into a neural network to predict to obtain the time of abnormity warning; the detection result is ensured to be more accurate and timely.
Description
Technical Field
The invention relates to the technical field of data identification processing, in particular to an automatic alarm method for pressure abnormity of a mine hydraulic support.
Background
In mine operation, the hydraulic support is widely applied to support the pressure of the upper stratum, and the mining face mine pressure acts on the hydraulic support in an external load mode; the problem of abnormal pressure usually occurs when the hydraulic support for the mine is used along with the lapse of the using time, so that the maintenance of the support is very important when the hydraulic support is used.
At present, the hydraulic support is usually maintained by early warning when the hydraulic support is in a problem during working and then is maintained by workers, abnormal discovery is not timely enough, and the arrangement of the hydraulic support is possibly dangerous due to mine operation caused by the fact that the hydraulic support is abnormal and not timely discovered.
Disclosure of Invention
In order to solve the technical problem, the invention aims to provide an automatic alarm method for pressure abnormity of a mine hydraulic support, which comprises the following steps:
collecting the pressure value of each supporting point on the mine hydraulic support in a plurality of time periods; obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period;
acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and primarily judging the hydraulic support to be in a normal state when the tracking index is smaller than a preset index;
acquiring a horizontal inclination angle of the hydraulic support in each normal state in multiple time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, obtaining probability distribution of the hydraulic support based on the Gaussian model, and obtaining abnormal pressure evaluation according to the probability distribution and the quality evaluation index;
acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, inputting the abnormal sequence into a trained neural network to obtain a predicted abnormal sequence, and obtaining an abnormal alarm time based on the predicted abnormal sequence;
the method for obtaining the quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period comprises the following steps:
acquiring a pressure sequence corresponding to the pressure value of the supporting point in the current time period and a pressure sequence corresponding to the pressure value in the adjacent previous time period;
the pressure sequence is obtained by performing high-pass filtering on a complete pressure sequence consisting of all pressure values of the supporting point in the current time period; if the length of the pressure sequence in the current time period is not consistent with that of the pressure sequence in the adjacent previous time period, element supplement is carried out on the pressure sequence with fewer elements at the position of the point screened out by the high-pass filter, the element value is the mean value of the element values on the two adjacent sides of the point, and the length of the pressure sequence with fewer elements after the element supplement is carried out on the pressure sequence with fewer elements is consistent with that of the pressure sequence with more elements;
calculating a correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period; splicing the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period to obtain a spliced sequence, calculating the standard deviation of the spliced sequence, and obtaining the working state corresponding to the supporting point based on the correlation coefficient and the standard deviation;
the working state calculating method comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
representing the working state of the supporting point;
is shown as
A pressure sequence corresponding to the daily supporting point, namely a pressure sequence corresponding to the supporting point in the current time period;
is shown as
The pressure sequence corresponding to the daily supporting point, namely the pressure sequence corresponding to the supporting point of the adjacent previous time period;
representing the Pearson correlation coefficient calculation;
represents the standard deviation calculation;
represents a natural constant;
representing the concatenation of pressure sequences, i.e. a concatenation sequence;
representing a correlation coefficient;
the minimum value of the working state corresponding to all the supporting points in the hydraulic support is a quality evaluation index of the hydraulic support;
the method for obtaining the tracking index by performing phase space reconstruction on the quality evaluation index sequence comprises the following steps:
performing phase space reconstruction on the quality evaluation index sequence, and predicting a phase point value of a different phase space at the next moment based on the phase space reconstruction;
calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence and recording the difference as a phase difference; the phase points are elements in the quality evaluation index sequence; calculating a standard deviation according to phase differences corresponding to all phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence;
the method for obtaining the probability distribution of the hydraulic support based on the Gaussian model comprises the following steps:
substituting each horizontal inclination angle into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired every second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in a convergence domain; and calculating the ratio of the number in the convergence domain to the total number of all horizontal tilt angles to be a probability distribution, wherein the probability distribution is the probability distribution of each hydraulic support.
Preferably, the method for obtaining the pressure anomaly evaluation according to the probability distribution and the quality evaluation index includes:
and calculating the product of the probability distribution and the quality evaluation index, wherein the product is the pressure anomaly evaluation.
Preferably, the neural network is a time convolution network.
The invention has the following beneficial effects: analyzing the working state corresponding to each supporting point on the hydraulic support by the pressure value corresponding to each supporting point, further reflecting the quality evaluation index of the hydraulic support according to the minimum value of the working state in all the supporting points, and carrying out phase space reconstruction by combining the quality evaluation indexes of the hydraulic support in multiple days so as to increase the accuracy of analysis, thereby judging whether the quality problem occurs in the hydraulic support by the obtained tracking index and carrying out maintenance in time; when the abnormality of the hydraulic supports is not found based on the tracking index, acquiring probability distribution corresponding to each hydraulic support by combining horizontal inclined included angles corresponding to all the hydraulic supports, wherein the probability distribution is used for reflecting whether the hydraulic supports incline or not; acquiring pressure abnormity evaluation corresponding to the hydraulic support according to the product of the probability distribution and the quality evaluation index; the abnormal sequence is obtained according to the pressure abnormal evaluation in the historical time period, the time needing alarming is obtained by predicting according to the abnormal sequence, the detection accuracy is high, the abnormal condition is found more timely, and the occurrence of accidents is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an automatic alarm method for pressure abnormality of a mine hydraulic support according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following detailed description, with reference to the accompanying drawings and preferred embodiments, describes a method for automatically alarming pressure abnormality of a hydraulic support for a mine according to the present invention, and the specific implementation manner, structure, features and effects thereof. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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.
This application is applicable to the analysis to hydraulic support behavior, through the corresponding pressure value of the last bearing point of every hydraulic support and this hydraulic support horizontal inclination combination analysis, judge the unusual evaluation of pressure that hydraulic support corresponds every day, the unusual evaluation of pressure that this hydraulic support corresponds carries out the prediction based on in the historical period, mark the unusual moment of hydraulic support work and in time remind the staff to pay close attention to the maintenance through the result of prediction, the promptness of discovering hydraulic support abnormity has been improved, the emergence of accident has been avoided.
The following specifically describes a specific scheme of the automatic alarm method for the pressure abnormality of the mine hydraulic support provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an automatic alarm method for pressure abnormality of a mine hydraulic support according to an embodiment of the present invention is shown, where the method includes the following steps:
s100, collecting a pressure value of each supporting point on the mine hydraulic support in multiple time periods; and obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the adjacent previous time period.
The hydraulic support of the mine is commonly supported through a plurality of hydraulic supports, each supporting point is used for supporting surrounding rocks at the top, and the hydraulic support is subjected to load sharing by a plurality of hydraulic supports, so that whether the supporting state of the hydraulic support is good or not can be preliminarily judged according to the pressure condition at each supporting point.
Specifically, a pressure sensor is installed at each supporting point in the hydraulic support and used for acquiring a pressure value at the supporting point, in order to improve the accuracy of analysis, in the embodiment of the invention, a time period of one day is set, the pressure value corresponding to each supporting point in the hydraulic support is acquired every day, and the acquisition frequency is 1 second; from this, pressure values per 1 second per 1 day per support point can be obtained.
Considering that the rock burst caused by the formation change can cause abnormal data of overlarge pressure of a supporting point on the hydraulic support, but the data of overlarge pressure often appears instantly and has no universality, the high-pass filtering processing is carried out on the pressure values of the supporting point obtained in each day, namely the pressure values of the supporting point obtained in each day form a complete pressure sequence, and the pressure sequence processed by the high-pass filter is obtained by screening the larger values in the complete pressure sequence based on the high-pass filter, so that the subsequent analysis based on the pressure sequence is more accurate.
Further, acquiring the pressure change of each supporting point within two days, namely reflecting the working state of the supporting point according to the difference between the pressure sequence of the supporting point in the current day and the pressure sequence of the supporting point in the previous day; considering that the pressure sequences respectively corresponding to the two days are sequences processed by a high-pass filter, the lengths of the pressure sequences are possibly inconsistent; therefore, in the embodiment of the invention, the pressure sequence with fewer elements is subjected to element supplement, namely, the element supplement is carried out at the position of the point screened by the high-pass filter, the element value is the average value of the element values at two adjacent sides of the point, and by analogy, the pressure sequence with fewer elements is subjected to element supplement and then is consistent with the pressure sequence with more elements in length.
Obtaining a correlation coefficient between the pressure sequence of the supporting point on the current day and the pressure sequence of the supporting point on the previous day, wherein the Pearson correlation coefficient is adopted in the method for calculating the correlation coefficient in the embodiment of the invention; because the pressure value difference born by each supporting point under the normal environment is very small, the pressure sequence of the supporting point on the same day is spliced with the pressure sequence of the supporting point on the previous day to obtain a spliced sequence, the standard deviation corresponding to the spliced sequence is calculated, and the working state corresponding to the supporting point is obtained according to the correlation coefficient and the standard deviation corresponding to each supporting point, wherein the calculation method of the working state comprises the following steps:
wherein the content of the first and second substances,
representing the working state of the supporting point;
is shown as
The pressure sequence corresponding to the daily supporting point is the pressure sequence corresponding to the daily supporting point;
is shown as
The pressure sequence corresponding to the day supporting point, namely the pressure sequence corresponding to the supporting point of the previous day;
representing the Pearson correlation coefficient calculation;
represents the standard deviation calculation;
represents a natural constant;
representing a splice of the pressure sequence;
representing the correlation coefficient.
By analogy, the working state corresponding to each supporting point on the hydraulic support is obtained, and the minimum value of the working states in all the supporting points is used as the quality evaluation index of the hydraulic support at the moment; the larger the quality evaluation index is, the better the working quality of the hydraulic support is, and the internal quality problem of the hydraulic support does not occur.
And S200, acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and when the tracking index is smaller than a preset index, primarily judging the hydraulic support to be in a normal state.
In step S100, the quality evaluation index corresponding to each hydraulic support on the current day is obtained, and in order to identify the abnormality of the hydraulic support more accurately, the quality evaluation index of each hydraulic support for a plurality of consecutive days is obtained, so that a corresponding quality evaluation index sequence is constructed according to the quality evaluation indexes on each day, and then phase space reconstruction is performed on the quality evaluation index sequence; the embedding dimension of the phase space reconstruction is selected by an implementer, and the delay time is calculated according to an autocorrelation function method, wherein the specific method is the prior art and is not described any more.
Predicting a phase point value of a different phase space at the next moment based on the phase space reconstruction, and calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence to be recorded as a phase difference; obtaining the phase difference between the predicted phase point value and the true value of each phase point in the quality evaluation index sequence; calculating a standard deviation according to phase differences corresponding to all phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence, and when the tracking index is smaller than a preset index, indicating that no actual quality problem occurs in the hydraulic support at the moment, primarily judging the hydraulic support to be in a normal state and enabling the hydraulic support to be normally used; when the index is larger than the preset index, the quality problem of the hydraulic support at the moment is shown, and the hydraulic support is required to be immediately repaired and maintained.
It should be noted that, in the embodiment of the present invention, a phase point in a quality evaluation index sequence is an element in the quality evaluation index sequence.
Step S300, acquiring the horizontal inclination angle of the hydraulic support in each normal state in a plurality of time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, acquiring the probability distribution of the hydraulic support based on the Gaussian model, and acquiring abnormal pressure evaluation according to the probability distribution and the quality evaluation index.
Specifically, the quality of the hydraulic support is preliminarily evaluated based on the pressure value of each supporting point in the steps S100 and S200; however, considering the complexity of the upper stratum, when the upper stratum is impacted, the hydraulic support bearing the lower pressure is unevenly settled, so that the whole body of the hydraulic support may incline, and the abrasion of the hydraulic support is further increased. Therefore, in the embodiment of the invention, the level meter is arranged at the center of the hydraulic support and used for detecting whether the hydraulic support inclines, one day is taken as a time period, the acquisition frequency is 1 second, and namely the horizontal inclination angle corresponding to each 1 second of the hydraulic support in one day is acquired.
Acquiring horizontal inclination angles of all hydraulic supports in a day during mine operation; performing Gaussian fitting on the basis of the horizontal inclination angles of all the hydraulic supports acquired in one day to obtain corresponding Gaussian models, wherein the Gaussian fitting method is a conventional known means and is not repeated; acquiring a convergence domain of the Gaussian model, substituting all horizontal inclination angles in the hydraulic support into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired every second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in the convergence domain; the hydraulic support does not have an inclination phenomenon under normal conditions, so that the obtained convergence domain of the Gaussian model almost comprises all horizontal inclination angle data, when the hydraulic support has the inclination phenomenon, the quantity of the horizontal inclination angle data in the convergence domain of the Gaussian model fitted by the horizontal inclination angle of the hydraulic support has deviation, and the larger the deviation is, the more serious the inclination problem of the hydraulic support is; and recording the ratio of the number of the horizontal inclination angle data in the convergence domain of the fitted Gaussian model to the number of all the horizontal inclination angle data as the probability distribution of the Gaussian model, namely that all the hydraulic supports correspond to the probability distribution during mine operation.
Further, calculating a product between the quality evaluation index corresponding to the hydraulic support and the probability distribution of the Gaussian model corresponding to the horizontal inclination angle, and taking the product as the pressure anomaly evaluation corresponding to the hydraulic support in the current day, wherein the smaller the value of the pressure anomaly evaluation is, the more possible problems exist in the hydraulic support at the moment.
And S400, acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, inputting the abnormal sequence into a trained neural network to obtain a predicted abnormal sequence, and obtaining an abnormal alarm time based on the predicted abnormal sequence.
In the step S300, the pressure anomaly evaluation of each hydraulic support within one day is obtained, the pressure anomaly evaluation construction sequence of the hydraulic supports for a plurality of consecutive days in the historical time period is obtained to obtain an anomaly sequence, and the anomaly sequence is input into the trained neural network to obtain a predicted anomaly sequence.
The method comprises the steps that a time convolution network TCN which is trained can obtain prediction abnormity evaluation of the hydraulic support every day subsequently, so that a prediction abnormity sequence is formed, when the prediction abnormity evaluation is smaller than a preset threshold value, the problem of the hydraulic support at the moment is shown, the prediction time is marked, early warning is timely sent to relevant workers, and the working condition of the hydraulic support can be better monitored.
In summary, in the embodiment of the present invention, the pressure value of each supporting point on the hydraulic support in each day is collected, and the quality evaluation index corresponding to the hydraulic support is obtained according to the difference between the pressure values corresponding to each supporting point on the current day and the adjacent previous day; acquiring corresponding quality evaluation indexes of the hydraulic support within multiple days to form a quality evaluation index sequence, then performing phase space reconstruction on the quality evaluation index sequence, and primarily judging the working state of the hydraulic support based on a result after the phase space reconstruction; the method further comprises the steps of analyzing based on the horizontal inclination angle on the hydraulic support, fitting through the horizontal inclination angle within one day to obtain a Gaussian model, obtaining probability distribution of the Gaussian model based on fitting, multiplying the probability distribution by a quality evaluation index to obtain pressure anomaly evaluation, inputting a pressure anomaly evaluation sequence of the hydraulic support obtained for multiple days into a time convolution network for prediction, obtaining more accurate alarm time, finding and processing anomalies in time, and avoiding accidents.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit of the present invention are intended to be included therein.
Claims (3)
1. An automatic alarm method for pressure abnormity of a mine hydraulic support is characterized by comprising the following steps:
collecting the pressure value of each supporting point on the mine hydraulic support in a plurality of time periods; obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period;
acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and primarily judging the hydraulic support to be in a normal state when the tracking index is smaller than a preset index;
acquiring a horizontal inclination angle of the hydraulic support in each normal state in multiple time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, obtaining probability distribution of the hydraulic support based on the Gaussian model, and obtaining abnormal pressure evaluation according to the probability distribution and the quality evaluation index;
acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormity sequence, inputting the abnormity sequence into a trained neural network to obtain a prediction abnormity sequence, and obtaining an abnormity alarm moment based on the prediction abnormity sequence;
the method for obtaining the quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period comprises the following steps:
acquiring a pressure sequence corresponding to the pressure value of the supporting point in the current time period and a pressure sequence corresponding to the pressure value in the adjacent previous time period;
the pressure sequence is obtained by performing high-pass filtering on a complete pressure sequence consisting of all pressure values of the supporting point in the current time period; if the length of the pressure sequence in the current time period is not consistent with that of the pressure sequence in the adjacent previous time period, element supplement is carried out on the pressure sequence with fewer elements at the position of the point screened out by the high-pass filter, the element value is the mean value of the element values on the two adjacent sides of the point, and the length of the pressure sequence with fewer elements after the element supplement is carried out on the pressure sequence with fewer elements is consistent with that of the pressure sequence with more elements;
calculating a correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period; splicing the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period to obtain a spliced sequence, calculating the standard deviation of the spliced sequence, and obtaining the working state corresponding to the supporting point based on the correlation coefficient and the standard deviation;
the working state calculation method comprises the following steps:
wherein the content of the first and second substances,
representing the working state of the supporting point;
is shown as
A pressure sequence corresponding to the daily supporting point, namely a pressure sequence corresponding to the supporting point in the current time period;
is shown as
The pressure sequence corresponding to the daily supporting point, namely the pressure sequence corresponding to the supporting point of the adjacent previous time period;
representing the Pearson correlation coefficient calculation;
represents the standard deviation calculation;
represents a natural constant;
representing a mosaic of pressure sequencesSplicing, namely splicing sequences;
representing a correlation coefficient;
the minimum value of the working state corresponding to all the supporting points in the hydraulic support is a quality evaluation index of the hydraulic support;
the method for obtaining the tracking index by performing phase space reconstruction on the quality evaluation index sequence comprises the following steps:
performing phase space reconstruction on the quality evaluation index sequence, and predicting a phase point value of a different phase space at the next moment based on the phase space reconstruction;
calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence and recording the difference as a phase difference; the phase point is an element in the quality evaluation index sequence; calculating a standard deviation according to phase differences corresponding to all phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence;
the method for obtaining the probability distribution of the hydraulic support based on the Gaussian model comprises the following steps:
substituting each horizontal inclination angle into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired every second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in a convergence domain; and calculating the ratio of the number in the convergence domain to the total number of all horizontal inclination angles to be probability distribution, wherein the probability distribution is the probability distribution of each hydraulic support.
2. The automatic alarm method for the pressure abnormity of the mine hydraulic support according to claim 1, wherein the method for obtaining the pressure abnormity evaluation according to the probability distribution and the quality evaluation index comprises the following steps:
and calculating the product of the probability distribution and the quality evaluation index, wherein the product is the pressure anomaly evaluation.
3. The automatic alarm method for the pressure abnormality of the hydraulic support in the mine as claimed in claim 1, wherein the neural network is a time convolution network.
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