Coal mine operation risk assessment method, computer program product, coal mine operation risk assessment system and storage medium

Abstract

The invention provides a coal mine operation risk assessment method, device and equipment, relates to the technical field of coal mine safety, and aims to solve the problem that gas safety accidents are difficult to recognize in advance. The coal mine operation risk assessment method comprises the following steps: receiving real-time monitoring data; real-time monitoring sequences of different gas monitoring points are obtained according to the detection time sequence; if the average value of one real-time monitoring sequence is greater than an early warning threshold value, carrying out concentration standard exceeding early warning; performing time calibration on the real-time monitoring sequences to obtain the similarity of the real-time monitoring sequences of different gas monitoring points under different concentration change time delays; obtaining a ventilation effect evaluation value and a diffusion effect evaluation value by taking the concentration change time lag and the gas diffusion time threshold when the similarity is maximum as references; obtaining an operation risk assessment value according to the ventilation effect assessment value and the diffusion effect assessment value; and if the operation risk assessment value is smaller than the diffusion risk threshold value, diffusion early warning is performed. According to the coal mine operation risk assessment method provided by the invention, the gas safety accident can be identified in advance.

Description

Technical Field

[0001] This application relates to the field of coal mine safety technology, and more specifically, to a coal mine operation risk assessment method, a computer program product, a coal mine operation risk assessment system, and a storage medium. Background Technology

[0002] The underground working environment in coal mines is complex and variable, with gas concentration changes exhibiting sudden, regional, and time-lag characteristics. Abnormal ventilation or untimely handling of gas accumulation can easily lead to serious safety accidents such as gas exceeding limits and gas explosions. Therefore, establishing a real-time monitoring and risk assessment mechanism for underground gas concentration and ventilation diffusion is a crucial technical means to ensure coal mine operational safety.

[0003] For example, CN119333247A (publication date: January 21, 2025) discloses a gas monitoring device, but lacks an automated and precise monitoring method; patent number CN103590855A (publication date: February 19, 2014) provides a local gas monitoring method, but lacks global control. Currently, coal mine gas safety monitoring mostly adopts single-point or small-scale concentration collection methods, using threshold judgment to achieve over-limit alarms. However, traditional solutions have the following problems: Lacking multi-point collaborative monitoring capabilities, it is difficult to reflect the overall ventilation status: Traditional gas monitoring mostly relies on single-point concentration data of fixed working faces or mining points, which cannot achieve linkage analysis of multiple areas such as wellhead, working face, tunneling face and ventilation roadway, making it difficult to accurately determine the gas diffusion path and ventilation effectiveness.

[0004] The lack of quantitative analysis of the time lag characteristics of gas diffusion is a significant issue. Gas diffusion underground is influenced by various factors, including ventilation speed, roadway structure, and working conditions, resulting in a clear time lag between different monitoring points. Traditional monitoring methods cannot model and analyze the temporal characteristics of gas concentration changes and diffusion lag patterns, making it difficult to determine potential diffusion risks at their source.

[0005] The evaluation method for ventilation effect is singular and lacks integrated analysis capability: Existing technology usually judges the ventilation effect based on the concentration change at a certain monitoring point. It cannot combine multi-source data from coal mining face monitoring points, tunneling face monitoring points and ventilation monitoring points for joint evaluation, which makes the evaluation results susceptible to local fluctuations and not comprehensive and reliable enough.

[0006] The lack of data-driven multimodal fusion methods makes it difficult to achieve dynamic risk assessment: Current operational risk judgments rely heavily on experience or single indicator thresholds, and no data fusion model based on multiple monitoring points, multiple time scales, and time lag characteristics has been established. This makes it impossible to calculate ventilation effects, diffusion status, and comprehensive risk values ​​in real time, thus making it difficult to detect potential diffusion anomalies in a timely manner.

[0007] The early warning mechanism is lagging behind and cannot achieve ultra-early warning: Most systems only generate an alarm when the concentration reaches the warning threshold. For dangerous situations that are spreading but have not yet reached the threshold, they often cannot be identified and alerted in advance, which can easily lead to safety hazards caused by untimely handling. Summary of the Invention

[0008] The first objective of this application is to provide a method for assessing risks in coal mine operations to address the technical problem of the difficulty in early identification of gas safety accidents.

[0009] To address the above technical issues, the coal mine operation risk assessment method provided in this application includes: receiving real-time monitoring data; obtaining real-time monitoring sequences of different gas monitoring points according to the detection time sequence; and issuing a concentration exceedance warning if the average value of one of the real-time monitoring sequences is greater than the warning threshold. The real-time monitoring sequence is time-calibrated to obtain the similarity of the real-time monitoring sequences of different gas monitoring points under different concentration change time lags; based on the concentration change time lag and gas diffusion time threshold when the similarity is the largest, the ventilation effect evaluation value and diffusion effect evaluation value are obtained. Based on the ventilation effect assessment value and the diffusion effect assessment value, an operational risk assessment value is obtained; if the operational risk assessment value is less than the diffusion risk threshold, a diffusion warning is issued.

[0010] The beneficial effects of the coal mine operation risk assessment method proposed in this application are: When the average value of the real-time monitoring sequence exceeds the warning threshold, a concentration exceedance warning can be issued to stop mining operations and ensure the safety of workers. By using ventilation effect assessment values ​​and diffusion effect estimates to obtain operational risk assessment values, the effectiveness of the ventilation system in actively intervening in gas concentration and the passive diffusion effect of gas underground can be quantitatively evaluated to determine whether the response capability to gas diffusion is sufficient. If the response capability is insufficient, an early warning can be issued. Dangerous situations that have formed a diffusion trend but have not yet reached the threshold can be identified and alerted in advance, thereby improving the pre-treatment capability for gas leaks.

[0011] In the optional technical solution, the real-time monitoring data comes from gas monitoring devices at multiple gas monitoring points in the underground working space; the multiple gas monitoring points include wellhead monitoring points, coal mining face monitoring points, tunneling face monitoring points, and ventilation monitoring points.

[0012] In the optional technical solution, receiving real-time monitoring data; obtaining real-time monitoring sequences of different gas monitoring points according to the detection time sequence; and issuing a concentration exceedance warning when the average value of one of the real-time monitoring sequences is greater than the warning threshold include: Obtain the real-time monitoring data a(t) of the ventilation monitoring point. Coal mine operations are carried out when a(t)∈[0,B0], where B0 is the safety threshold. During coal mine operations, real-time monitoring data a from the monitoring points at the coal mining face is acquired. T0 (t) and the real-time monitoring data a of the monitoring points at the tunneling face. T1 (t); based on the monitoring time sequence and the aforementioned a T0 (t) The monitoring sequence r of the monitoring points at the coal mining face is obtained. T0 According to the monitoring time sequence and a T1 (t) Obtain the monitoring sequence r of the monitoring points at the tunneling face. T1 ; Simultaneously, real-time monitoring data a(t) from the ventilation monitoring points are collected, and the real-time monitoring sequence r of the ventilation monitoring points is obtained based on the monitoring time sequence and a(t). a If the real-time monitoring sequence r a If the average value is greater than the warning threshold B1, a concentration exceeding the standard warning will be issued. If the r a When the average value is less than or equal to B1, then r T0 and the r T1 As the base, according to the r a Calculate the concentration change lag and evaluate the ventilation effect based on the concentration change lag.

[0013] In the optional technical solution, the r T0 and the r T1 As the base, according to the r a Calculating the concentration change lag and evaluating the ventilation effect based on the concentration change lag includes: For the r T0 The r T1 and the r a Perform time calibration; A first ventilation weight w1 is set based on the Euclidean distance between the monitoring point of the coal mining face and the ventilation system, and a second ventilation weight w2 is set based on the Euclidean distance between the monitoring point of the tunneling face and the ventilation system. The monitoring sequence R of the monitoring point of the coal mining face is then time-calibrated based on w1 and w2. T0 The monitoring sequence R after time calibration with the monitoring points at the tunneling face T1 The fusion is performed to obtain the fused monitoring sequence R; The R and R are calculated based on the Pearson correlation coefficient method. a Similarity under different concentration changes over time lags, R a This is the monitoring sequence after time calibration for the ventilation monitoring points; Obtain the average gas diffusion rate v under conditions of no ventilation system operation; based on v, B0, and B1, obtain the concentration change lag τ, τ = (B1 - B0) / v; based on τ and τ max1 Obtain the ventilation effect evaluation value s1, s1=τ max1 / τ, the τ max1 The concentration change lag is the time lag when the similarity is highest.

[0014] In an optional technical solution, real-time monitoring data from the wellhead monitoring point is obtained, and a real-time monitoring sequence r of the wellhead monitoring point is obtained based on the monitoring time sequence. out Based on the R a For the r out Time calibration is performed to obtain the real-time monitoring sequence R of the wellhead monitoring point after time calibration. out ; The R is calculated based on the Pearson correlation coefficient method. a With the R out The similarity under different concentration changes over time lags is used to obtain the diffusion effect evaluation value s2, where s2=τ. max2 / τ, the τ max2 The concentration change lag with the highest similarity.

[0015] In the optional technical solution, the operation risk assessment value S is obtained based on s1 and s2, where S = α*s1 + β*s2, α is the ventilation effect harmonization factor, β is the diffusion effect harmonization factor, and α + β = 1.

[0016] In the optional technical solution, the calculation of R and R based on the Pearson correlation coefficient method a The similarity under different concentration changes over time lags is:

[0017] f RRa (τ) represents the relationship between R and R a Similarity under the time lag τ of concentration change; R i This represents the i-th monitoring data in R, where i represents the data identifier of the monitoring sequence; L represents the total number of monitoring data in the monitoring sequence; R represents the average value of all monitoring data in R; ai R represents a The i-th monitoring data in the data; R represents a The average value of all monitored data.

[0018] The second objective of this application is to provide a computer program product that solves the technical problem of difficulty in early identification of gas safety accidents.

[0019] The computer program product provided in this application includes a computer program / instruction that, when executed by a processor, implements the steps of the coal mine operation risk assessment method described above.

[0020] The third objective of this application is to provide a coal mine operation risk assessment system to address the technical problem of difficulty in early identification of gas safety accidents.

[0021] The coal mine operation risk assessment system provided in this application includes a computer-readable storage medium storing a computer program and a processor. The computer program is read and executed by the processor to implement the control method described above.

[0022] The fourth objective of this application is to provide a computer-readable storage medium to address the technical problem of difficulty in early identification of gas safety incidents.

[0023] The computer-readable storage medium provided in this application stores a computer program, which is read and executed by a processor to implement the above-described control method.

[0024] The computer program product, coal mine operation risk assessment system, and computer-readable storage medium of this application can achieve the same technical effect as the aforementioned coal mine operation risk assessment method. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments or background art of this application, the drawings used in the description of the embodiments or background art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 A schematic diagram of the physical equipment composition for applying an embodiment of the coal mine operation risk assessment method provided in this application.

[0027] Figure 2 This is a schematic flowchart illustrating another coal mine operation risk assessment method in one embodiment of this application. Detailed Implementation

[0028] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 A schematic flowchart of a coal mine operation risk assessment method provided as an embodiment of this application; as follows: Figure 1 As shown, the risk assessment method for coal mine operations includes: S210. Select gas monitoring points in the underground working space and install gas monitoring devices at each gas monitoring point. Specifically, multiple gas monitoring points include wellhead monitoring points, coal mining face monitoring points, tunneling face monitoring points, and ventilation monitoring points.

[0030] In this embodiment, considering the comprehensiveness and sensitivity of monitoring, the more gas monitoring points set up in the downhole working space lock, the better. The more comprehensive the locations of the gas monitoring points, the more beneficial it is to cover all gas risk areas, so as to build a three-dimensional monitoring network covering the entire area from the wellhead to the downhole. However, in reality, considering the cost of the equipment and the processing power of the controller and processor, it is necessary to ensure that gas monitoring points are set up at at least the above locations.

[0031] In this embodiment, the wellhead monitoring point is used to monitor the methane concentration in the incoming airflow and the methane concentration in the ventilation airflow inside the well, which can prevent surface methane from rushing into the well when there is an abnormality in surface methane. Specifically, the wellhead monitoring point is set at the airflow inlet of the main and auxiliary wellheads, for example, within a range of 20m from the wellhead. The location of the wellhead monitoring point needs to be determined based on the size of the wellhead, wind speed, and well length, etc., and no special restrictions are imposed here.

[0032] The monitoring points at the coal mining face are equipped with a T0 upper corner sensor and a working face sensor. The monitoring data from the coal mining face monitoring points is a. T0 (t) Take the average value of the upper corner sensor and the working surface sensor of T0.

[0033] The tunneling face monitoring points are equipped with a T1 tunneling head sensor and a return air flow sensor. The monitoring data from the tunneling face monitoring points is a. T1 (t) Take the average value of the T1 tunneling head sensor and the return air flow sensor.

[0034] Ventilation monitoring points are used to monitor the methane concentration in the total return airflow of the underground working space. These monitoring points can be located in the middle of the shaft. If the underground shaft is long, multiple monitoring points can be set up according to the actual situation. If multiple ventilation monitoring points are set up, the monitoring data a(t) is taken as the average of the actual data monitored by all the monitoring points.

[0035] Each monitoring point can be equipped with a methane sensor and a wind speed sensor, or a portable monitoring device with methane monitoring capabilities; this application does not impose specific restrictions on this. The ground-based monitoring and analysis system can connect to the methane monitoring devices at each monitoring point via a multi-mode mine-grade flame-retardant optical fiber and receive real-time monitoring data from the devices. Alternatively, a mine-grade intrinsically safe 4G / 5G module can be used to receive signals via an underground explosion-proof antenna, transmit the data to an operator's base station, and then connect to the ground-based monitoring and analysis system. The specific data transmission method can be selected based on the actual situation.

[0036] S220, Optionally, receive real-time monitoring data; obtain real-time monitoring sequences of different gas monitoring points according to the detection time sequence; if the average value of one of the real-time monitoring sequences is greater than the warning threshold, issue a concentration exceedance warning, including: Obtain real-time monitoring data a(t) from ventilation monitoring points. Coal mine operations are carried out when a(t) ∈ [0, B0], where B0 is the safety threshold. During coal mine operations, real-time monitoring data from monitoring points at the coal mining face is acquired. T0 (t) and real-time monitoring data a from monitoring points at the tunneling face T1 (t); based on the monitoring time sequence and a T0 (t) Obtain the monitoring sequence r of the monitoring points at the coal mining face. T0 According to the monitoring time sequence and a T1 (t) Obtain the monitoring sequence r of the monitoring points at the tunneling face. T1 ; Simultaneously, real-time monitoring data a(t) from ventilation monitoring points are collected, and the real-time monitoring sequence r of the ventilation monitoring points is obtained based on the monitoring time sequence and a(t). a If the sequence r is monitored in real time a If the average value is greater than the warning threshold B1, a concentration exceeding the standard warning will be issued. If r a When the average value is ≤ B1, r T0 and r T1 As the basis, according to r a Calculate the time lag of concentration change and evaluate the ventilation effect based on the time lag of concentration change.

[0037] Acquire real-time monitoring data from monitoring points at coal mining faces and tunneling faces. T0 (t) and a T1 (t), and form the corresponding sequence r. T0 (t) and r T1 (t), and the sequence r obtained based on the real-time monitoring data a(t) collected from the ventilation monitoring point. aBy comparing a(t) with the safety threshold B0, coal mine operations can be carried out when a(t) ≤ B0, ensuring operational efficiency. If r a When the average value is greater than B1, an alarm is triggered to indicate that the gas concentration at the ventilation monitoring point is already quite high. This alarm is triggered to improve the safety of coal mine operations.

[0038] Since the real-time monitoring data a(t) from the ventilation monitoring point is not the real-time monitoring data from the tunneling face or the coal mining face, the ventilation monitored by the ventilation monitoring point is already mixed with the air already inside the roadway. If there is a gas leak, the gas concentration is already diluted. If such a gas concentration exceeds the warning threshold, it means that there is already a considerable danger and it needs to be dealt with immediately.

[0039] S230, optionally, with r T0 and r T1 As the basis, according to r a Calculating the time lag of concentration changes and evaluating ventilation effectiveness based on the time lag of concentration changes includes: For r T0 r T1 and r a Perform time calibration; A first ventilation weight w1 is set based on the Euclidean distance between the monitoring points at the coal mining face and the ventilation system, and a second ventilation weight w2 is set based on the Euclidean distance between the monitoring points at the tunneling face and the ventilation system. The monitoring sequence R of the coal mining face monitoring points is then time-calibrated based on w1 and w2. T0 The monitoring sequence R after time calibration with the monitoring points at the tunneling face T1 The fusion is performed to obtain the fused monitoring sequence R; R and R are calculated based on the Pearson correlation coefficient method. a Similarity under different concentration changes over time lags, R a This is the monitoring sequence after time calibration of the ventilation monitoring points; Obtain the average gas diffusion rate v under no-ventilation system operation conditions, and obtain the concentration change time lag τ based on v, B0, and B1, where τ = (B1 - B0) / v; based on τ and τ max1 Obtain the ventilation effect evaluation value s1, s1=τ max1 / τ,τ max1 The concentration change lag is the time lag when the similarity is highest.

[0040] Because the ventilation system facilitates gas flow in multiple locations within the roadway, sudden changes in methane concentration at ventilation monitoring points lag behind those at the coal mining and tunneling faces. Therefore, a fusion monitoring sequence, which integrates real-time data from monitoring points at both the coal mining and tunneling faces, is used to compare this fusion monitoring sequence with R... a By comparing and calculating the similarity, the concentration change lag with the highest similarity is selected as the lag time of gas concentration at the ventilation monitoring point. This lag is then compared with the theoretical concentration change lag to evaluate the ventilation effect. The purpose of ventilation is to slow down the diffusion rate of gas concentration in the roadway and inhibit the rise of gas concentration at various locations. Therefore, dividing the concentration change lag with the highest similarity by the concentration change lag calculated based on distance and time confirms the ventilation effect.

[0041] A higher ventilation effectiveness assessment value indicates that a sudden change in gas concentration in one area affects gas concentration changes in other locations more slowly. This is because once a gas concentration rises, the air supplied by the ventilation system can quickly dilute the change, preventing a rapid increase in gas concentration at other ventilation monitoring points. In other words, even if the gas concentration at a ventilation monitoring point rises, the rate of increase is slower, and the lag time is longer. Conversely, low ventilation capacity leads to a gradual increase in gas concentration in surrounding areas after a leak, and even at ventilation monitoring points. The smaller the lag time of this increase compared to the concentration change lag without a ventilation system, the weaker the ventilation system's ability to suppress the rise in gas concentration, resulting in a lower ventilation effectiveness assessment value.

[0042] The calculation methods for w1 and w2 are as follows: the Euclidean distance between the monitoring point at the coal mining face and the main ventilation system is m1, and the Euclidean distance between the monitoring point at the tunneling face and the main ventilation system is m2. Therefore, the first ventilation weight for the monitoring point at the coal mining face is w1 = m2 / (m1 + m2), and the second ventilation weight for the monitoring point at the tunneling face is w2 = m1 / (m1 + m2). The main ventilation system is the largest airflow channel for dispersing methane underground.

[0043] The monitoring sequence R after time calibration of the monitoring points at the coal mining face using w1 and w2 is as follows: T0 The monitoring sequence R after time calibration with the monitoring points at the tunneling face T1 The fusion is performed to obtain the fused monitoring sequence R, specifically including: if R T0 The monitoring data at a certain time t is n1, and the monitoring sequence at the same time is R. T1If the monitoring data in the sequence is n2, then the monitoring data at time t in the monitoring sequence R is n = w1*n1 + w2*n2. Here, w1 and w2 are used to weight the monitoring data from the coal mining face and the tunneling face respectively to obtain the fused monitoring sequence R, which can simulate the concentration diffusion during ventilation system operation. If the gas monitoring point is located near the main ventilation system duct, the real-time monitoring sequence of that gas monitoring point can be directly selected.

[0044] Optionally, R and R can be calculated based on the Pearson correlation coefficient method. a The similarity under different concentration changes over time lags is:

[0045] f RRa (τ) represents R and R a Similarity under the time lag τ of concentration change; R i Let R represent the i-th monitoring data point, where i represents the data identifier of the monitoring sequence; L represents the total number of monitoring data points in the monitoring sequence. R represents the average value of all monitoring data in R; ai R represents a The i-th monitoring data in the data; R represents a The average value of all monitored data.

[0046] S240, Optionally, acquire real-time monitoring data from the wellhead monitoring point, and obtain the real-time monitoring sequence r of the wellhead monitoring point based on the monitoring time sequence. out Based on R a For r out Time calibration is performed to obtain the real-time monitoring sequence R of the wellhead monitoring point after time calibration. out ; R is calculated based on the Pearson correlation coefficient method. a With R out The similarity under different concentration changes over time lags is used to obtain the diffusion effect evaluation value s2, where s2=τ. max2 / τ,τ max2 The concentration change lag with the highest similarity.

[0047] Real-time monitoring sequence R from wellhead monitoring points after time calibration out Compared to R a By comparing the concentration change lag with the highest similarity with the calculated concentration change lag, the diffusion effect assessment value s2 of the gas in the wellhead area can be obtained. If the diffusion effect assessment value s2 is low, it means that after the air is introduced through the ventilation system, the air returning from the wellhead is difficult to diffuse sufficiently in the wellhead area, and the gas concentration cannot be reduced enough, so an alarm needs to be triggered.

[0048] Among them, calculate Ra With R out For similarity at different concentration lags, the algorithm can use the similarity calculation method in S230, which will not be elaborated further.

[0049] S250, Optionally, the operational risk assessment value S is obtained based on s1 and s2, where S = α*s1 + β*s2, α is the ventilation effect harmonization factor, β is the diffusion effect harmonization factor, and α + β = 1. When the operational risk assessment value S is less than S0, a diffusion warning is issued.

[0050] Among them, the ventilation effect evaluation value s1 and the diffusion effect evaluation value s2 represent the ventilation intervention effect of the gas leakage source after passing through the underground ventilation system; the ventilation effect evaluation value s1 represents the active intervention effect of the ventilation system; while the diffusion effect evaluation value s2 represents the passive diffusion effect underground.

[0051] Because the gas leak source is close to the ventilation system, active intervention has a major influence on the ventilation effect assessment value s1, while passive diffusion has a major influence on the diffusion effect assessment value s2. In this embodiment, α=0.6 and β=0.4. In practical applications, these values ​​can be set according to the air volume of the ventilation system and the size of the underground space, without specific limitations. The diffusion risk threshold S0 represents the diffusion threshold at which the gas concentration is reduced to the safe threshold based on ventilation system intervention when the gas concentration is higher than the safe threshold. In this embodiment, S0=0.6, but those skilled in the art can adjust it according to actual conditions. When the operational risk assessment value S is less than S0, a diffusion warning is issued.

[0052] Figure 2 This is a schematic flowchart illustrating another coal mine operation risk assessment method in one embodiment of this application. The method includes: S301. Gas monitoring devices shall be installed in the underground working space. The gas monitoring devices may be installed at the wellhead, coal mining face, tunneling face and ventilation monitoring point.

[0053] S302, the ground monitoring and analysis system receives real-time monitoring data from each monitoring point.

[0054] S303. Are the monitoring data of the ventilation monitoring point within the safe threshold range? If not, proceed to S304; if yes, proceed to S305.

[0055] S304. No coal mining operations are permitted.

[0056] S305. Conduct underground operations, collect real-time data from monitoring points at coal mining faces, tunneling faces, and ventilation monitoring points, and generate real-time monitoring sequences.

[0057] S306. Is the average value of the monitoring sequence of the ventilation monitoring point greater than the warning threshold? If yes, proceed to S307; otherwise, proceed to S308.

[0058] S307, triggering a concentration exceeding the standard warning.

[0059] S308. Time calibration is performed on the monitoring sequences of coal mining face monitoring points, tunneling face monitoring points, and ventilation monitoring points.

[0060] S309. Calculate the similarity between the fused monitoring sequence and the ventilation monitoring sequence under different concentration change time lags based on the Pearson correlation coefficient, and determine the concentration change time lag corresponding to the maximum similarity.

[0061] S310. Calculate the ventilation effect evaluation value and the diffusion effect evaluation value.

[0062] S311. Calculate the operational risk assessment value by combining the harmonization factor.

[0063] S312. Is the operational risk assessment value less than the diffusion risk threshold? If yes, proceed to S313; otherwise, proceed to S302.

[0064] S313, triggering a diffusion warning.

[0065] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the laser annealing temperature monitoring method described above.

[0066] Among them, the processor is a device with data processing capabilities, including but not limited to the central processing unit (CPU); the memory is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) is connected between the processor and the memory, enabling information exchange between the memory and the processor, including but not limited to the data bus (Bus).

[0067] This application also provides a coal mine operation risk assessment system, including a computer-readable storage medium storing a computer program and a processor, wherein the computer program is read and run by the processor to implement the above-described control method.

[0068] This application also provides a computer-readable storage medium storing a computer program. When the computer program is read and executed by a processor, it implements the control method provided in the above embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0069] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing a control device. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The storage medium can be a memory, a disk, an optical disk, etc.

[0070] While this application discloses the above information, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application shall be determined by the scope defined in the claims.

[0071] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Regarding the computer program product and air conditioner disclosed in the embodiments, since they correspond to the air conditioner control method disclosed in the above embodiments, the description is relatively simple; relevant parts can be referred to the method section. While this application discloses the above information, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application shall be determined by the scope defined in the claims.

Claims

1. A method for assessing risks in coal mine operations, characterized in that, include: Receive real-time monitoring data; obtain real-time monitoring sequences for different gas monitoring points based on the detection time order; like When the average value of one of the real-time monitoring sequences is greater than the warning threshold, a concentration exceeding the standard warning is issued; The real-time monitoring sequence is time-calibrated to obtain the similarity of the real-time monitoring sequences of different gas monitoring points under different concentration change time lags; based on the concentration change time lag and gas diffusion time threshold when the similarity is the largest, the ventilation effect evaluation value and diffusion effect evaluation value are obtained. Based on the ventilation effect assessment value and the diffusion effect assessment value, the operational risk assessment value is obtained; If the operational risk assessment value is less than the diffusion risk threshold, a diffusion warning will be issued.

2. The coal mine operation risk assessment method according to claim 1, characterized in that, The real-time monitoring data comes from gas monitoring devices at multiple gas monitoring points in the underground working space; the multiple gas monitoring points include wellhead monitoring points, coal mining face monitoring points, tunneling face monitoring points, and ventilation monitoring points.

3. The coal mine operation risk assessment method according to claim 2, characterized in that, The process of receiving real-time monitoring data; obtaining real-time monitoring sequences for different gas monitoring points according to the detection time sequence; and issuing a concentration exceedance warning when the average value of one of the real-time monitoring sequences is greater than the warning threshold includes: Obtain the real-time monitoring data a(t) of the ventilation monitoring point. Coal mine operations are carried out when a(t)∈[0,B0], where B0 is the safety threshold. During coal mine operations, real-time monitoring data a from the monitoring points at the coal mining face is acquired. T0 (t) and the real-time monitoring data a of the monitoring points at the tunneling face. T1 (t); based on the monitoring time sequence and the aforementioned a T0 (t) The monitoring sequence r of the monitoring points at the coal mining face is obtained. T0 According to the monitoring time sequence and a T1 (t) Obtain the monitoring sequence r of the monitoring points at the tunneling face. T1 ; Simultaneously, real-time monitoring data a(t) from the ventilation monitoring points are collected, and the real-time monitoring sequence r of the ventilation monitoring points is obtained based on the monitoring time sequence and a(t). a If the real-time monitoring sequence r a If the average value is greater than the warning threshold B1, a concentration exceeding the standard warning will be issued. If the r a When the average value is less than or equal to B1, then r T0 and the r T1 As the base, according to the r a Calculate the concentration change lag and evaluate the ventilation effect based on the concentration change lag.

4. The coal mine operation risk assessment method according to any one of claims 3, characterized in that, The r T0 and the r T1 As the base, according to the r a Calculating the concentration change lag and evaluating the ventilation effect based on the concentration change lag includes: For the r T0 The r T1 and the r a Perform time calibration; A first ventilation weight w1 is set based on the Euclidean distance between the monitoring point of the coal mining face and the ventilation system, and a second ventilation weight w2 is set based on the Euclidean distance between the monitoring point of the tunneling face and the ventilation system. The monitoring sequence R of the monitoring point of the coal mining face is then time-calibrated based on w1 and w2. T0 The monitoring sequence R after time calibration with the monitoring points at the tunneling face T1 The fusion is performed to obtain the fused monitoring sequence R; The R and R are calculated based on the Pearson correlation coefficient method. a Similarity under different concentration changes over time lags, R a This is the monitoring sequence after time calibration for the ventilation monitoring points; Obtain the average gas diffusion rate v under conditions of no ventilation system operation; based on v, B0, and B1, obtain the concentration change lag τ, τ = (B1 - B0) / v; based on τ and τ max1 Obtain the ventilation effect evaluation value s1, s1=τ max1 / τ, the τ max1 The concentration change lag is the time lag when the similarity is highest.

5. The coal mine operation risk assessment method according to claim 4, characterized in that, Obtain real-time monitoring data from the wellhead monitoring point, and obtain the real-time monitoring sequence r of the wellhead monitoring point according to the monitoring time sequence. out Based on the R a For the r out Time calibration is performed to obtain the real-time monitoring sequence R of the wellhead monitoring point after time calibration. out ; The R is calculated based on the Pearson correlation coefficient method. a With the R out The similarity under different concentration changes over time lags is used to obtain the diffusion effect evaluation value s2, where s2=τ. max2 / τ, the τ max2 The concentration change lag with the highest similarity.

6. The coal mine operation risk assessment method according to claim 5, characterized in that, The operational risk assessment value S is obtained based on s1 and s2, where S = α*s1 + β*s2, α is the ventilation effect harmonization factor, β is the diffusion effect harmonization factor, and α + β = 1.

7. The coal mine operation risk assessment method according to claim 4, characterized in that, The similarity between R and Ra under different concentration change time lags is calculated based on the Pearson correlation coefficient method as follows: f RRa (τ) represents the relationship between R and R a Similarity under the time lag τ of concentration change; R i This represents the i-th monitoring data in R, where i represents the data identifier of the monitoring sequence; L represents the total number of monitoring data in the monitoring sequence; R represents the average value of all monitoring data in R; ai R represents a The i-th monitoring data in the data; R represents a The average value of all monitored data.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the method of claim 1.

9. A coal mine operation risk assessment system, characterized in that, The device includes a readable storage medium and a processor, wherein the readable storage medium stores a computer program, characterized in that the computer program is read and executed by the processor to implement the control method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is read and executed by a processor to implement the control method of any one of claims 1-7.

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