Coal mine safety data comprehensive analysis and early warning system based on multiple sensors

Abstract

The invention relates to the technical field of data prediction, in particular to a coal mine safety data comprehensive analysis and early warning system based on multiple sensors. The fluctuation degree is obtained according to the gas concentration in the preset nearest short window; obtaining a concentration difference value according to the concentration of the current moment and any historical moment in the gas concentration sequence; obtaining a first concentration predicted value according to the concentration difference value and data in a preset nearest short window; obtaining a second concentration predicted value according to the concentration difference value and the gas concentration sequence; and obtaining a final prediction value according to the first concentration prediction value, the second concentration prediction value and the fluctuation degree. According to the method, an early warning value is obtained according to a final predicted value and concentration change characteristics in a preset nearest short window, and first early warning judgment is carried out; obtaining a ventilation anomaly index according to the wind speed sequence and the wind pressure sequence; and the gas concentration state is subjected to secondary early warning judgment according to the ventilation abnormal index, so that the gas concentration early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a coal mine safety data comprehensive analysis and early warning system based on multiple sensors. Background Art

[0002] Gas is produced during coal mining. When the gas concentration is too high, it is easy to cause mine explosions and accidents. Therefore, in order to detect gas anomalies in time and ensure mine safety, it is necessary to monitor and warn of gas concentration. Because the change of gas concentration shows a trend, the gas concentration at future moments can be predicted based on the historical changes in gas concentration, and early warning analysis can be performed based on the prediction results, thereby increasing the response time and reducing accidents. For data prediction, the existing moving average method can usually be used, but the algorithm has poor prediction accuracy for volatile data sequences, which makes the early warning accuracy of gas concentration low. In addition, the change of gas concentration is related to the ventilation device in the mine. Early warning analysis based only on the single factor of gas concentration prediction results will also affect the accuracy of early warning. Summary of the invention

[0003] In order to solve the above-mentioned technical problems that the prediction of gas concentration by the existing moving average method will lead to low warning accuracy, and the single factor based on the gas concentration prediction result will also affect the accuracy of the warning, the purpose of the present invention is to provide a coal mine safety data comprehensive analysis and early warning system based on multiple sensors, and the technical scheme adopted is as follows: A data acquisition module is used to obtain the gas concentration sequence, wind speed sequence and wind pressure sequence of the current preset period; A concentration analysis module, used to obtain a fluctuation degree according to the gas concentration fluctuation characteristics within a preset recent short window in the gas concentration sequence; obtain a concentration difference according to the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence; obtain a first concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics within the preset recent short window; obtain a second concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics of the gas concentration sequence; obtain a final prediction value according to the first concentration prediction value, the second concentration prediction value and the fluctuation degree; A first warning module is used to obtain a warning value according to the final prediction value and the concentration change characteristics in the preset recent short window; and to make a first warning judgment on the gas concentration state according to the warning value and the final prediction value; The second warning module is used to obtain a ventilation anomaly index according to the change correlation characteristics of the wind speed sequence and the wind pressure sequence; and perform a second warning judgment on the gas concentration state according to the ventilation anomaly index.

[0004] Furthermore, the step of obtaining the degree of fluctuation according to the gas concentration fluctuation characteristics within a preset recent short window in the gas concentration sequence includes: The absolute value of the difference between the gas concentration value at each moment and the average gas concentration value in the preset latest short window is calculated to obtain the concentration difference characteristic value; the sum of the gas concentration standard deviation and a preset minimum positive number in the preset latest short window is calculated to obtain a discrete benchmark; the ratio of the concentration difference characteristic value in the preset latest short window to the discrete benchmark is calculated and normalized to obtain a deviation characteristic value; the concentration difference characteristic value is normalized and the product with the preset first weight is calculated to obtain a first fluctuation factor; the product of the deviation characteristic value and the preset second weight is calculated to obtain a second fluctuation factor; the average value of the sum of the first fluctuation factor and the second fluctuation factor corresponding to each moment in the preset latest short window is calculated to obtain the fluctuation degree.

[0005] Furthermore, the step of obtaining the concentration difference value according to the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence includes: The difference between the gas concentration at the current moment and any historical moment in the gas concentration sequence is calculated to obtain the concentration difference between the current moment and any historical moment.

[0006] Furthermore, the step of obtaining a first concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics in the preset most recent short window includes:

[0007] In the formula, R represents the first concentration prediction value, H represents the average gas concentration in the preset recent short window, N represents the number of historical moments in the preset recent short window, and e represents a natural constant. Indicates the time interval coefficient from the nth historical moment to the current moment, Represents the time interval weight from the nth historical moment to the current moment, represents the concentration difference between the current moment and the nth historical moment, represents the weighted concentration difference at the nth historical moment; Represents the comprehensive weighted concentration difference.

[0008] Furthermore, the step of obtaining a second concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics of the gas concentration sequence includes:

[0009] Wherein, W represents the second concentration prediction value, E represents the average gas concentration of the gas concentration sequence, G represents the number of historical moments in the gas concentration sequence, and e represents a natural constant. Indicates the time interval coefficient from the nth historical moment to the current moment, Represents the time interval weight from the nth historical moment to the current moment, represents the concentration difference between the current moment and the nth historical moment, represents the weighted concentration difference at the nth historical moment, Represents the comprehensive weighted concentration difference.

[0010] Further, the step of obtaining a final predicted value according to the first concentration predicted value, the second concentration predicted value and the degree of fluctuation comprises: Calculate the difference between constant 1 and the degree of fluctuation to obtain the proportionality coefficient; calculate the product of the degree of fluctuation and the first concentration prediction value to obtain the first prediction factor; calculate the product of the proportionality coefficient and the second concentration prediction value to obtain the second prediction factor; calculate the sum of the first prediction factor and the second prediction factor to obtain the final prediction value at the future time.

[0011] Furthermore, the step of obtaining a warning value according to the final predicted value and the concentration change characteristics within the preset recent short window includes: The product of the standard deviation of the gas concentration in the preset most recent short window and the preset coefficient is calculated to obtain the adjustment value; the sum of the final prediction value and the adjustment value is calculated to obtain the warning value at the future moment.

[0012] Furthermore, the step of performing a first warning judgment on the gas concentration state according to the warning value and the final prediction value includes: When the final predicted value or actual gas concentration value at a future moment exceeds the preset safety concentration value, the gas concentration at the future moment is abnormal and an early warning is issued, and no second early warning judgment is made; when the actual gas concentration value at the future moment exceeds the early warning value, the gas concentration at the future moment is suspected to be abnormal, and a second early warning judgment is required.

[0013] Furthermore, the step of obtaining the ventilation anomaly index according to the change correlation characteristics of the wind speed sequence and the wind pressure sequence includes: The joint entropy of the wind speed sequence and the wind pressure sequence is calculated and normalized to obtain a first ventilation abnormality factor; the absolute value of the Pearson correlation coefficient of the wind speed sequence and the wind pressure sequence is calculated and negatively correlated to obtain a second ventilation abnormality factor; the sum of the first ventilation abnormality factor and the second ventilation abnormality factor is calculated to obtain the ventilation abnormality index.

[0014] Furthermore, the step of performing a second early warning judgment on the gas concentration state according to the ventilation abnormality index includes: When the ventilation abnormality index does not exceed the preset abnormality threshold, the gas concentration at a future time will be abnormal and an early warning will be issued.

[0015] The present invention has the following beneficial effects: In the present invention, obtaining the degree of fluctuation can characterize the fluctuation characteristics of the recent gas concentration, and then be used to determine the weight of the prediction results based on different data lengths; obtaining the concentration difference can determine the concentration difference between different historical moments and the current moment, so that the influence of the gas concentration fluctuation on the prediction result is considered in the process of calculating the first concentration prediction value and the second concentration prediction value, thereby improving the accuracy of the prediction and the accuracy of the concentration warning. The first concentration prediction value characterizes the prediction result obtained based on the recent data, and the second concentration prediction value characterizes the prediction result obtained based on the long-term data. Obtaining the final prediction value can reasonably allocate the weights of the first concentration prediction value and the second concentration prediction value according to the degree of fluctuation, further improving the accuracy of the prediction result and the accuracy of the concentration warning. Obtaining the warning value can be used to determine whether the gas concentration has a trend of rapid increase; making the first warning judgment can preliminarily determine whether the gas concentration is abnormal; obtaining the ventilation abnormality index can characterize whether the ventilation device is faulty, and further determine whether the ventilation device is the cause of the rapid increase in concentration. Finally, the accuracy of the concentration warning is improved according to the first warning judgment and the second warning judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A block diagram of a coal mine safety data comprehensive analysis and early warning system based on multiple sensors provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of a coal mine safety data comprehensive analysis and early warning system based on multiple sensors proposed by the present invention are described in detail below 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.

[0019] 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.

[0020] The specific scheme of a coal mine safety data comprehensive analysis and early warning system based on multiple sensors provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a block diagram of a coal mine safety data comprehensive analysis and early warning system based on multiple sensors provided by an embodiment of the present invention, the system includes the following modules: The data acquisition module S1 is used to obtain the gas concentration sequence, wind speed sequence and wind pressure sequence of the current preset time period.

[0022] In the embodiment of the present invention, the implementation scenario is to provide early warning for the gas concentration in the mine to improve the accuracy of the early warning. First, the gas concentration sequence, wind speed sequence and wind pressure sequence of the current preset time period are obtained; the gas concentration in the mine is collected by the gas concentration sensor, the wind speed sensor collects the wind speed at the ventilation equipment, and the differential pressure sensor collects the wind pressure at the ventilation equipment; in the embodiment of the present invention, the current preset time period is a historical three-minute time period range including the current moment. After each update of the current moment, the data in the corresponding preset time period is updated once, and the data is collected and updated every 3 seconds. The implementer can determine it according to the implementation scenario.

[0023] The concentration analysis module S2 is used to obtain the degree of fluctuation based on the gas concentration fluctuation characteristics within a preset recent short window in the gas concentration sequence; to obtain the concentration difference based on the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence; to obtain the first concentration prediction value at a future moment based on the concentration difference and the data distribution characteristics within a preset recent short window; to obtain the second concentration prediction value at a future moment based on the concentration difference and the data distribution characteristics of the gas concentration sequence; and to obtain the final prediction value based on the first concentration prediction value, the second concentration prediction value and the degree of fluctuation.

[0024] When predicting the concentration at a future moment based on the gas concentration sequence, the degree of data fluctuation has a greater impact on the accuracy of the prediction; if the recent concentration is highly volatile, in order to improve accuracy and reflect recent fluctuation characteristics, it is necessary to rely more on recent data when predicting; if the recent concentration is less volatile, more data is needed to consider the overall concentration characteristics when predicting. Therefore, it is necessary to analyze the volatility of the recent gas concentration before prediction, so as to adaptively adjust the prediction strategy; therefore, the degree of fluctuation is obtained based on the gas concentration fluctuation characteristics in the recent short window preset in the gas concentration sequence.

[0025] Preferably, in the embodiment of the present invention, the step of obtaining the degree of fluctuation includes: calculating the absolute value of the difference between the gas concentration value at each moment and the average value of the gas concentration in the preset recent short window to obtain the concentration difference characteristic value; in the embodiment of the present invention, the preset recent short window is the gas concentration data range within the latest 1 minute including the current moment; when the concentration difference characteristic value is larger, it means that the difference between the gas concentration value at this moment and the average value of the gas concentration in the window is larger, and the volatility is more obvious. Calculate the sum of the gas concentration standard deviation in the preset recent short window and the preset minimum positive number to obtain the discrete benchmark; the gas concentration standard deviation reflects the degree of data fluctuation in the preset recent short window. In the embodiment of the present invention, the preset minimum positive number is 0.1, the purpose is to avoid the situation where the constant 0 appears when the standard deviation is used as the denominator. Calculate the ratio of the concentration difference characteristic value in the preset recent short window to the discrete benchmark and normalize it to obtain the deviation characteristic value; when the deviation characteristic value is larger, it means that the gas concentration deviation degree at this moment is higher, and the volatility is more obvious. Normalize the concentration difference characteristic value and calculate the product with the preset first weight to obtain the first fluctuation factor; in the embodiment of the present invention, the preset first weight is 0.6, which can be determined by the implementer according to the implementation scenario. Calculate the product of the deviation characteristic value and the preset second weight to obtain the second fluctuation factor; in the embodiment of the present invention, the preset second weight is 0.4, which can be determined by the implementer according to the implementation scenario. Calculate the average value of the sum of the first fluctuation factor and the second fluctuation factor corresponding to each moment in the preset recent short window to obtain the degree of fluctuation; the larger the sum of the first fluctuation factor and the second fluctuation factor at each moment, the more discrete the gas concentration value at that moment, and the greater the degree of fluctuation, which means that the data volatility in the preset recent short window is stronger.

[0026] Furthermore, since the existing moving average method has poor prediction accuracy when the data volatility is more obvious, the algorithm only smoothes the data fluctuations, which easily drowns out the short-term trend characteristics in the data. If the data fluctuation characteristics are considered in the prediction, the rise and fall of gas concentration can be captured more sensitively, so the moving average method needs to be improved; first, the concentration difference is obtained according to the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence; preferably, in an embodiment of the present invention, the step of obtaining the concentration difference includes: calculating the difference between the gas concentration at the current moment and any historical moment in the gas concentration sequence, and obtaining the concentration difference between the current moment and any historical moment; the current moment is the moment before the future moment, and the gas concentration value at the future moment is closer to the current moment, so the analysis of the concentration difference between the current moment and the historical moment can be used in the prediction process to reflect the impact of recent concentration fluctuations on the concentration at the future moment, thereby improving the prediction accuracy. After obtaining the concentration difference, the first concentration prediction value at the future moment can be obtained according to the concentration difference and the data distribution characteristics in the preset recent short window; preferably, in an embodiment of the present invention, the step and formula for obtaining the first concentration prediction value include:

[0027] In the formula, R represents the first concentration prediction value, H represents the average gas concentration in the preset recent short window, which represents the average level of concentration in the preset recent short window, and the gas concentration value at future moments fluctuates based on the average value. N represents the number of historical moments in the preset recent short window, e represents the natural constant, Indicates the time interval coefficient of the nth historical moment from the current moment. For example, if n of the moment before the current moment is 1, then the time interval coefficient of the moment before is 1. It represents the time interval weight of the nth historical moment from the current moment. The closer to the current moment, the greater the time interval weight, and the higher the corresponding contribution of concentration data. Implementers can determine the values ​​of the time interval coefficient and time interval weight according to the implementation scenario. represents the concentration difference between the current moment and the nth historical moment, Represents the weighted concentration difference at the nth historical moment; the weighted concentration difference represents the concentration difference between the nth historical moment and the current moment in combination with the time interval weight. It represents the comprehensive weighted concentration difference, which characterizes the overall difference between the gas concentration at different times within the preset recent short window and the current time. Then, the sum of the average gas concentration of the preset recent short window and the comprehensive weighted concentration difference is calculated, which can accurately predict the gas concentration value at future times while taking into account the concentration fluctuation characteristics within this period.

[0028] Furthermore, the first concentration prediction value is calculated based on the concentration data in the shorter preset recent short window. If the volatility of the gas concentration is small, the prediction accuracy based on the longer concentration data is higher. Therefore, the second concentration prediction value at the future moment can be obtained based on the concentration difference and the data distribution characteristics of the gas concentration sequence. The specific formula includes:

[0029] In the formula, W represents the second concentration prediction value, E represents the average gas concentration of the gas concentration sequence, and G represents the number of historical moments in the gas concentration sequence. The second concentration prediction value has a longer data calculation range than the first concentration prediction value. e represents a natural constant, Indicates the time interval coefficient from the nth historical moment to the current moment, Represents the time interval weight from the nth historical moment to the current moment, represents the concentration difference between the current moment and the nth historical moment, represents the weighted concentration difference at the nth historical moment, It should be noted that the calculation logic of the second concentration prediction value is the same as that of the first concentration prediction value, and the specific steps will not be repeated.

[0030] If the fluctuation of gas concentration is more obvious, the accuracy of prediction based on the gas concentration data with a closer time is higher; if the fluctuation is weak, prediction based on the longer gas concentration data can better reflect the comprehensive concentration trend. Therefore, the final prediction value can be obtained according to the first concentration prediction value, the second concentration prediction value and the degree of fluctuation; preferably, in the embodiment of the present invention, the step of obtaining the final prediction value includes: calculating the difference between the constant 1 and the degree of fluctuation to obtain the proportional coefficient; when the degree of fluctuation is greater, the proportional coefficient is smaller. Calculate the product of the degree of fluctuation and the first concentration prediction value to obtain the first prediction factor; when the degree of fluctuation is greater, the importance of the concentration data in the preset recent short window is greater, and the weight of the first concentration prediction value is greater. Calculate the product of the proportional coefficient and the second concentration prediction value to obtain the second prediction factor; when the degree of fluctuation is greater, the reliability of the data with a longer interval is lower, and the weight of the second concentration prediction value is smaller. Therefore, the greater the degree of fluctuation, the greater the weight of the first concentration prediction value; the smaller the degree of fluctuation, the greater the weight of the second concentration prediction value. The sum of the first prediction factor and the second prediction factor is calculated to obtain a final prediction value at a future time; the final prediction value is more accurate than the existing moving average method, thereby improving the accuracy of the gas concentration warning.

[0031] The first warning module S3 is used to obtain a warning value based on the final prediction value and the concentration change characteristics within a preset recent short window; and to make a first warning judgment on the gas concentration state based on the warning value and the final prediction value.

[0032] Since the gas in the mine may suddenly leak due to construction, causing the gas concentration to rise rapidly and cause safety accidents, a rapid warning is required for such sudden concentration increases. Therefore, the warning value can be obtained according to the final predicted value and the concentration change characteristics in the preset recent short window; preferably, in the embodiment of the present invention, the step of obtaining the warning value includes: calculating the product of the standard deviation of the gas concentration in the preset recent short window and the preset coefficient to obtain the adjustment value; in the embodiment of the present invention, the preset coefficient is 0.2, which can be determined by the implementer according to the implementation scenario. Calculate the sum of the final predicted value and the adjustment value to obtain the warning value at the future moment; since the gas concentration will fluctuate under normal circumstances, if the floating degree is too high, the gas concentration may rise rapidly, so the adjustment value is set as the floating interval based on the final predicted value. If the actual concentration value at the future moment exceeds the warning value, it means that the floating degree is too high. Even if it does not exceed the set gas concentration upper limit, it is necessary to give a timely warning to avoid explosion.

[0033] Further, the gas concentration state can be judged for the first time according to the warning value and the final prediction value; preferably, in the embodiment of the present invention, the step of making the first warning judgment includes: when the final prediction value or the actual gas concentration value at the future moment exceeds the preset safety concentration value, the gas concentration at the future moment is abnormal and a warning is made, and no second warning judgment is made; this means that the gas concentration is too high and exceeds the safety range, and a timely warning is required, thereby increasing the response time of the on-site personnel; the implementer can determine the preset safety concentration value according to the implementation scenario. When the actual gas concentration value at the future moment exceeds the warning value, the gas concentration at the future moment is suspected to be abnormal, which means that the gas concentration rises too fast. Although it has not reached the preset safety concentration value, there is a trend of abnormal concentration development; and the concentration rises too fast may be due to a sudden and large amount of gas leakage, or it may be due to a failure of the ventilation device in the mine, which affects the gas emission, so a second warning judgment is required to determine the specific cause and improve the accuracy of the warning.

[0034] The second warning module S4 is used to obtain a ventilation anomaly index according to the change correlation characteristics of the wind speed sequence and the wind pressure sequence; and to make a second warning judgment on the gas concentration state according to the ventilation anomaly index.

[0035] When ventilation equipment fails, the gas concentration increases rapidly, which may be due to airflow obstruction or gas accumulation. If an early warning is issued directly, it will lead to a large number of on-site personnel evacuation or even other safety accidents. At this time, it is only necessary to report the ventilation device failure and repair it. When the ventilation device works normally, the data characteristics of the wind speed sequence and the wind pressure sequence are relatively stable, and it is difficult for the data to be complex and changeable. In addition, the correlation between the changes in the wind speed sequence and the wind pressure sequence is more obvious; therefore, the ventilation abnormality index can be obtained based on the correlation characteristics of the changes in the wind speed sequence and the wind pressure sequence.

[0036] Preferably, in an embodiment of the present invention, the step of obtaining the ventilation abnormality index includes: calculating the joint entropy of the wind speed sequence and the wind pressure sequence and normalizing them to obtain the first ventilation abnormality factor; it should be noted that the joint entropy belongs to the prior art, and the specific calculation steps are no longer repeated. When the data combination of the corresponding moments of the two sequences is more random, the joint entropy is larger, and the first ventilation abnormality factor is larger, which means that the changes of the wind speed sequence and the wind pressure sequence are less regular, and the possibility of ventilation device failure is greater. Calculate the absolute value of the Pearson correlation coefficient of the wind speed sequence and the wind pressure sequence and negatively map them to obtain the second ventilation abnormality factor; it should be noted that the calculation of the Pearson correlation coefficient belongs to the prior art, and the specific calculation steps are no longer repeated. When the correlation between wind speed and wind pressure is worse, the absolute value of the Pearson correlation coefficient is closer to 0, the second ventilation abnormality factor is larger, and the ventilation device is more likely to fail. Calculate the sum of the first ventilation abnormality factor and the second ventilation abnormality factor to obtain the ventilation abnormality index; when the ventilation abnormality index is larger, it means that the possibility of ventilation device failure is higher, the effect of gas emission is worse, and it is more likely to cause gas accumulation.

[0037] Furthermore, a second early warning judgment of the gas concentration state can be made according to the ventilation abnormality index, specifically including: when the ventilation abnormality index does not exceed the preset abnormal threshold, it means that the ventilation device is working normally, the rapid increase in gas concentration is due to the occurrence of a large amount of gas leakage, the gas concentration at a future moment is abnormal and an early warning is issued, and on-site personnel need to evacuate quickly. In an embodiment of the present invention, the preset abnormal threshold is 0.6, and the implementer can determine it by himself according to the implementation scenario. If the ventilation abnormality index exceeds the preset abnormal threshold, it is necessary to quickly report the ventilation device failure. So far, the weight of the prediction results under different data lengths is determined by analyzing the volatility of the gas concentration, and the volatility of the gas concentration is considered in the prediction results, thereby improving the accuracy of the concentration prediction and preliminarily improving the accuracy of the gas concentration early warning; at the same time, it is analyzed whether there is a fault in the ventilation device, and the influence of factors other than gas leakage is eliminated, further improving the accuracy of the concentration early warning.

[0038] In summary, the embodiment of the present invention provides a coal mine safety data comprehensive analysis and early warning system based on multiple sensors; obtains the degree of fluctuation according to the gas concentration in the preset recent short window; obtains the concentration difference according to the concentration at the current moment and any historical moment in the gas concentration sequence; obtains the first concentration prediction value according to the concentration difference and the data in the preset recent short window; obtains the second concentration prediction value according to the concentration difference and the gas concentration sequence; obtains the final prediction value according to the first concentration prediction value, the second concentration prediction value and the degree of fluctuation. The present invention obtains the early warning value and performs the first early warning judgment according to the final prediction value and the concentration change characteristics in the preset recent short window; obtains the ventilation abnormality index according to the wind speed sequence and the wind pressure sequence; and performs the second early warning judgment on the gas concentration state according to the ventilation abnormality index, thereby improving the accuracy of the gas concentration early warning.

[0039] 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.

[0040] 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 coal mine safety data comprehensive analysis and early warning system based on multiple sensors, characterized in that: The system includes the following modules: A data acquisition module is used to obtain the gas concentration sequence, wind speed sequence and wind pressure sequence of the current preset period; A concentration analysis module, used to obtain a fluctuation degree according to the gas concentration fluctuation characteristics within a preset recent short window in the gas concentration sequence; obtain a concentration difference according to the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence; obtain a first concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics within the preset recent short window; obtain a second concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics of the gas concentration sequence; obtain a final prediction value according to the first concentration prediction value, the second concentration prediction value and the fluctuation degree; A first warning module, used to obtain a warning value according to the final predicted value and the concentration change characteristics within the preset most recent short window; Performing a first warning judgment on the gas concentration state according to the warning value and the final predicted value; The second warning module is used to obtain a ventilation anomaly index according to the change correlation characteristics of the wind speed sequence and the wind pressure sequence; and perform a second warning judgment on the gas concentration state according to the ventilation anomaly index.

2. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining the degree of fluctuation according to the gas concentration fluctuation characteristics within a preset recent short window in the gas concentration sequence comprises: The absolute value of the difference between the gas concentration value at each moment and the average gas concentration value in the preset latest short window is calculated to obtain the concentration difference characteristic value; the sum of the gas concentration standard deviation and a preset minimum positive number in the preset latest short window is calculated to obtain a discrete benchmark; the ratio of the concentration difference characteristic value in the preset latest short window to the discrete benchmark is calculated and normalized to obtain a deviation characteristic value; the concentration difference characteristic value is normalized and the product with the preset first weight is calculated to obtain a first fluctuation factor; the product of the deviation characteristic value and the preset second weight is calculated to obtain a second fluctuation factor; the average value of the sum of the first fluctuation factor and the second fluctuation factor corresponding to each moment in the preset latest short window is calculated to obtain the fluctuation degree.

3. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining the concentration difference value according to the concentration difference characteristics between the current moment and any historical moment in the gas concentration sequence comprises: The difference between the gas concentration at the current moment and any historical moment in the gas concentration sequence is calculated to obtain the concentration difference between the current moment and any historical moment.

4. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining a first concentration prediction value at a future moment according to the concentration difference value and the data distribution characteristics in the preset most recent short window comprises: ， In the formula, R represents the first concentration prediction value, H represents the average gas concentration in the preset recent short window, N represents the number of historical moments in the preset recent short window, and e represents a natural constant. Indicates the time interval coefficient from the nth historical moment to the current moment, Represents the time interval weight from the nth historical moment to the current moment, represents the concentration difference between the current moment and the nth historical moment, represents the weighted concentration difference at the nth historical moment; Represents the comprehensive weighted concentration difference.

5. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining a second concentration prediction value at a future moment according to the concentration difference and the data distribution characteristics of the gas concentration sequence comprises: ， Wherein, W represents the second concentration prediction value, E represents the average gas concentration of the gas concentration sequence, G represents the number of historical moments in the gas concentration sequence, and e represents a natural constant. Indicates the time interval coefficient from the nth historical moment to the current moment, Represents the time interval weight from the nth historical moment to the current moment, represents the concentration difference between the current moment and the nth historical moment, represents the weighted concentration difference at the nth historical moment, Represents the comprehensive weighted concentration difference.

6. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining a final predicted value according to the first concentration predicted value, the second concentration predicted value and the degree of fluctuation comprises: Calculate the difference between constant 1 and the degree of fluctuation to obtain the proportionality coefficient; calculate the product of the degree of fluctuation and the first concentration prediction value to obtain the first prediction factor; calculate the product of the proportionality coefficient and the second concentration prediction value to obtain the second prediction factor; calculate the sum of the first prediction factor and the second prediction factor to obtain the final prediction value at the future time.

7. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of obtaining the warning value according to the final predicted value and the concentration change characteristics within the preset recent short window comprises: The product of the standard deviation of the gas concentration in the preset most recent short window and the preset coefficient is calculated to obtain the adjustment value; the sum of the final prediction value and the adjustment value is calculated to obtain the warning value at the future moment.

8. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The step of performing a first warning judgment on the gas concentration state according to the warning value and the final prediction value comprises: When the final predicted value or actual gas concentration value at a future moment exceeds the preset safety concentration value, the gas concentration at the future moment is abnormal and an early warning is issued, and no second early warning judgment is made; when the actual gas concentration value at the future moment exceeds the early warning value, the gas concentration at the future moment is suspected to be abnormal, and a second early warning judgment is required.

9. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1, characterized in that: The step of obtaining the ventilation abnormality index according to the change correlation characteristics of the wind speed sequence and the wind pressure sequence comprises: The joint entropy of the wind speed sequence and the wind pressure sequence is calculated and normalized to obtain a first ventilation abnormality factor; the absolute value of the Pearson correlation coefficient of the wind speed sequence and the wind pressure sequence is calculated and negatively correlated to obtain a second ventilation abnormality factor; the sum of the first ventilation abnormality factor and the second ventilation abnormality factor is calculated to obtain the ventilation abnormality index.

10. The multi-sensor based coal mine safety data comprehensive analysis and early warning system according to claim 1, characterized in that: The step of performing a second early warning judgment on the gas concentration state according to the ventilation abnormality index comprises: When the ventilation abnormality index does not exceed the preset abnormality threshold, the gas concentration at a future time will be abnormal and an early warning will be issued.

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