Methods, devices, equipment and media for stress monitoring of waterproof and airtight walls in underground goaf areas.

Abstract

This invention relates to the field of mining engineering technology, and more particularly to a method for stress monitoring of waterproof and airtight walls in underground goaf areas. The method includes obtaining multiple sets of stress data by monitoring the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices; calculating the sum, mean, within-group sum of squares, and sample variance of the stress data in each set; calculating the test statistic H-value, level number r, and degrees of freedom f of the sample variance for each set of stress data using the Hartley test model combined with the sample variance; and then combining the test statistic H-value with H... 0.95 Pairwise comparisons (r, f) are used to determine whether there are significant differences in the overall variance of each group of stress data. Based on the comparison results, the sum, mean, within-group sum of squares, and sample variance of the stress data are analyzed to obtain the stress hazard level, and an alarm is triggered based on the stress hazard level. This invention enables the effective utilization and post-processing of stress data, achieving the ability to distinguish significant differences in data among similar stress data, and providing high accuracy in stress monitoring results.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a method, device, equipment and medium for stress monitoring of waterproof and airtight walls in underground goaf areas. Background Technology

[0002] A coal mine goaf refers to the void or cavity left after underground coal or gangue has been extracted during coal mining operations. Because coal mining requires the removal of underground coal resources, tunnels, similar to those used in road construction, are typically constructed during excavation to gradually connect the underground coal site to the mine shaft. The ore and coal encountered during mining are usually transported to the surface to create a suitable transport and mining face. As coal and other ores are continuously removed, these coal mine goafs are formed underground.

[0003] Currently, during coal mine operations, after the underground working face is mined, both the return airway and transport roadways need to be sealed. Therefore, over time, old goaf water accumulates in the goaf, and the water level rises continuously, seriously affecting the safety of the goaf sealing wall. The pressure-bearing capacity of the goaf sealing wall varies depending on its location. Determining the stress state of the goaf waterproof sealing wall is crucial for assessing its pressure-bearing capacity. Stress changes are a key parameter in determining this stress state. However, existing stress monitoring equipment only collects and monitors stress data, lacking effective utilization and post-processing of this data. It cannot distinguish significant differences in data within the same type of stress data, meaning the reliability of identifying abnormal stress data is low, resulting in low accuracy of stress monitoring results. Summary of the Invention

[0004] The technical problem this invention aims to solve is: to address the issue that existing stress monitoring equipment only focuses on the acquisition and monitoring of stress data, lacks effective utilization and post-processing of stress data, cannot distinguish significant differences in data among similar stress data, and has low accuracy in stress monitoring results. This invention provides a stress monitoring method for waterproof and airtight walls in underground goaf areas, which can effectively utilize and post-process stress data, distinguish significant differences in data among similar stress data, and achieve high accuracy in stress monitoring results.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for monitoring the stress of a waterproof and airtight wall in an underground goaf, the method comprising: S1, stress monitoring is performed on the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices to obtain multiple sets of stress data. ,in, Let N be the set of stress data monitored by multiple stress monitoring devices, where N is the number of stress data collected. S2, Calculate the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Q m and sample variance ,in, m ∈{ A, B, C, ...}; S3, using the Hartley test method model combined with the sample variance. Calculate the H-value and level number of the test statistic for each group of stress data. r degrees of freedom of sample variance f The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance The stress hazard level of the waterproof and airtight wall was obtained through analysis. S4, issue an alarm warning for the stress data based on the stress hazard level.

[0006] Furthermore, specifically, when the value of H is greater than or equal to H 0.95 ( r , f When there are significant differences in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the mean y m and the sample variance , combined u The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

[0007] Furthermore, specifically, when the value of H is less than H 0.95 ( r , f When there is no significant difference in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the sum of stress data in each set of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups S t and its degrees of freedom f t ; Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; The F-ratio is calculated based on the mean square error and the mean square factor. p value; Will F Comparison and F 0.95 ( f t , f e If pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

[0008] A stress monitoring device for a waterproof and airtight wall in an underground goaf, comprising: The data acquisition module uses multiple stress monitoring devices to monitor the stress in the same area of ​​the waterproof and airtight wall, obtaining multiple sets of stress data. ,in, Let N be the set of stress data monitored by multiple stress monitoring devices, where N is the number of stress data collected. The data calculation module, connected to the data acquisition module, calculates the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Qm and sample variance ,in, m ∈{ A, B, C, ...}; The data discrimination and diagnosis module is connected to the data calculation module, and combines the sample variance with the Hartley test method model. Calculate the H-value and level number of the test statistic for each group of stress data. r degrees of freedom of sample variance f The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance The stress hazard level of the waterproof and airtight wall was obtained through analysis. The warning module is connected to the data discrimination and diagnosis module and provides alarm warnings for the stress data based on the stress hazard level.

[0009] Furthermore, specifically, when there are significant differences in the overall variance among the various groups of stress data, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the mean y m and the sample variance , combined u The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

[0010] Furthermore, specifically, when there is no significant difference in the overall variance among the stress data sets, the determination of the stress hazard level of the waterproof and airtight wall includes the following steps: Based on the sum of stress data in each set of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups St and its degrees of freedom f t ; Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; The F-ratio is calculated based on the mean square error and the mean square factor. p value; Will F Comparison and F 0.95 ( f t , f e If pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

[0011] Furthermore, specifically, the warning module includes an audible and visual alarm unit, which provides alarm warnings, with alarm levels ranging from low to high: blue, yellow, orange, and red.

[0012] Furthermore, specifically, it also includes a storage module for real-time storage of the collected stress data, the processed judgment results, the calculated confidence interval, the determined stress hazard level, and the alarm time.

[0013] A computer device, comprising: processor; Memory, used to store executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the stress monitoring method for waterproof and airtight walls in underground goaf areas as described above.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the stress monitoring method for waterproof and airtight walls in underground goaf areas as described above.

[0015] The beneficial effects of this invention are that it provides a method for stress monitoring of waterproof and airtight walls in underground goaf areas. This method enables effective utilization and post-processing of stress data, distinguishes significant differences in data among similar stress data, and achieves high accuracy in stress monitoring results. It provides a data analysis method for the safety of water storage and use in coal mine goaf areas, and improves the reliability of waterproof and airtight operation in goaf areas. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Figure 1 This is a flowchart illustrating Embodiment 1 of the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of Embodiment 2 of the present invention.

[0019] Figure 3 This is a schematic diagram of the computer device structure according to Embodiment 3 of the present invention.

[0020] In the figure, 20 is the stress monitoring equipment; 21 is the data acquisition module; 22 is the data calculation module; 23 is the data discrimination and diagnosis module; 24 is the warning module; 25 is the storage module; 241 is the audible and visual alarm unit; 10 is the computer equipment; 1002 is the processor; 1004 is the memory; and 1006 is the transmission device. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0023] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0024] Example 1 This application provides a method for monitoring the stress of a waterproof and airtight wall in an underground goaf, such as... Figure 1 As shown, the method includes: S1, stress monitoring is performed on the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices 20, and multiple sets of stress data are obtained. ,in, Let N be the set of stress data monitored by multiple stress monitoring devices 20, where N is the number of stress data collected.

[0025] S2, calculates the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Q m and sample variance ,in, m ∈{ A, B, C, ...}

[0026] S3, using the Hartley test method model combined with sample variance Calculate the H-value and level number of the test statistic for each group of stress data. r ( r (Number of variances) and degrees of freedom of the sample variance f(f=i-1) The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance The analysis yielded the stress hazard levels of the waterproof and airtight wall. These stress hazard levels are categorized into four levels: Level IV, Level III, Level II, and Level I.

[0027] The formula for calculating the H-value of the test statistic for each set of stress data is as follows: .

[0028] S4, issue an alarm warning for the stress data based on the stress hazard level.

[0029] In this embodiment, when the value of H is greater than or equal to H 0.95 ( r , f When there are significant differences in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: A1, based on mean y m and sample variance , combined u The test method calculates the confidence intervals for each group of stress data at 95%, 90%, 85%, and 80%; furthermore, u The test method is Take respectively α Given values ​​of 0.05, 0.1, 0.15, and 0.2, the corresponding confidence intervals for 95%, 90%, 85%, and 80% for each group are calculated as follows: , , , , .

[0030] It should be noted that, i To the total number of stress data obtained, according to α The value is obtained The value is retrieved from the quantile table. The specific value.

[0031] A2. Based on the calculated confidence intervals, determine the stress hazard level intervals. Further, determine the intervals for each group of four hazard levels, from lowest to highest: The intervals for the four hazard levels are as follows: The third-level hazard range is: The second-level hazard range is The first-level hazard range is: .

[0032] In this embodiment, when the value of H is less than H 0.95 ( r , f When there is no significant difference in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: B1, based on the sum of stress data in each set of stress data. T m Calculate the sum T Total deviation sum of squaresS T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups S t and its degrees of freedom f t ; B2, based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; B3, Calculate the F-ratio based on mean square error and mean square factor. p Values; and generate an analysis of variance table: Table 1. Analysis of Variance Table

[0033] B4, will F Comparison and F 0.95 ( f t , f e If pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals for each group of stress data at 95%, 90%, 85%, and 80%; the corresponding confidence intervals for 95%, 90%, 85%, and 80% are as follows: .

[0034] B5. Based on the calculated confidence intervals, determine the stress hazard level intervals. Further, determine the intervals for each group of four hazard levels, from lowest to highest: The intervals for the four hazard levels are as follows: The third-level hazard range is: The second-level hazard range is The first-level hazard range is: .

[0035] Furthermore, the implementation process of this embodiment is explained in detail using example data, specifically when there are significant differences in the overall variance of the stress data from each group.

[0036] I. Ten stress data points were collected from four stress monitoring devices. The data are summarized in the following table: Table 2 Statistical Table of Monitoring Data

[0037] The variances of the four samples were obtained by summing the squares within each group. Q m Calculation, that is: .

[0038] 2. Calculate the test statistic H value for each group of stress data;

[0039] Given a significance level of α = 0.05, find the quantiles of H. Table Since H < 6.31, there is no significant difference in the overall variance among the four groups of stress data.

[0040] Based on the sum of stress data from four sets of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T ( f T = i -1) Sum of squared errors S e and its degrees of freedom f e ( f e = i - r ), between-groups sum of squared deviations S t and its degrees of freedom f t The calculation formulas are as follows:

[0041]

[0042]

[0043] Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom fCalculate the factor mean square, and then calculate the F-ratio based on the error mean square and the factor mean square. p Values; and generate an analysis of variance table:

[0044] Given a significance level of α = 0.05, by consulting the 0.95 quantile table of the F-distribution, we can find that... Since F>2.87, the degree of change in the four sets of stress monitoring data is significantly different.

[0045] Based on mean y m Stress data with large variation characteristics were obtained and analyzed as follows:

[0046] It can be seen that the second set of stress monitoring data shows the greatest degree of change. According to Table 3 above, σ... 2 The estimate is , Combining t The test method was used to obtain the mean of the second set of profit monitoring data. y A2 The 95% confidence interval is calculated as follows: , The number of stress monitoring data in the second set n =10, ,but

[0047] We can obtain, y A2 The 95% confidence interval is [87.85, 91.03]. Similarly, the 90%, 85%, and 80% confidence intervals are [88.12, 90.76], [88.42, 90.46], and [88.77, 90.11], respectively. Based on the confidence intervals, the stress hazard levels can be determined. The intervals for Level IV hazard are [87.85, 88.77], Level III hazard is (88.77, 90.46], Level II hazard is (90.46, 90.76], and Level I hazard is (90.76, 91.03]. Similarly, the Level IV intervals for the other three sets of stress monitoring data can also be calculated using the above steps, which will not be elaborated here for the sake of brevity.

[0048] Example 2 This application provides a stress monitoring device for waterproof and airtight walls in underground goaf areas, such as... Figure 2 As shown, it includes: The data acquisition module 21 monitors the stress in the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices 20, obtaining multiple sets of stress data. ,in, N represents the set of stress data monitored by multiple stress monitoring devices 20, where N is the number of stress data collected. Data calculation module 22, connected to data acquisition module 21, calculates the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Q m and sample variance ,in, m ∈{ A, B, C, ...}; The data discrimination and diagnosis module 23 is connected to the data calculation module 22, and combines the Hartley test method model with sample variance. S m 2 Calculate the H-value and level number of the test statistic for each group of stress data. r degrees of freedom of sample variance f The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance The analysis yielded the stress hazard level of the waterproof and airtight wall; The warning module 24 is connected to the data discrimination and diagnosis module 23, and provides an alarm warning for the stress data according to the stress hazard level.

[0049] In this embodiment, when the value of H is less than H 0.95 ( r , f When there are significant differences in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on mean y m and sample variance , combined u The test method calculates the confidence intervals for each group of stress data at 95%, 90%, 85%, and 80%; furthermore, u The test method is Take respectively α Given values ​​of 0.05, 0.1, 0.15, and 0.2, the corresponding confidence intervals for 95%, 90%, 85%, and 80% for each group are calculated as follows: , , , , .

[0050] Based on the calculated confidence intervals, the stress hazard level intervals are determined. Further, the intervals for each group of four hazard levels are determined, from lowest to highest: The intervals for the four hazard levels are as follows: The third-level hazard range is: The second-level hazard range is The first-level hazard range is: .

[0051] In this embodiment, when the value of H is less than H 0.95 ( r , f When there is no significant difference in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the sum of stress data in each set of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups S t and its degrees of freedom f t ; Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; Calculate the F-ratio based on the mean square error and the mean square factor. p value; Will F Comparison and F 0.95 ( f t , f eIf pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals for each group of stress data at 95%, 90%, 85%, and 80%; the corresponding confidence intervals for 95%, 90%, 85%, and 80% are as follows: .

[0052] Based on the calculated confidence intervals, the stress hazard level intervals are determined. Further, the intervals for each group of four hazard levels are determined, from lowest to highest: The intervals for the four hazard levels are as follows: The third-level hazard range is: The second-level hazard range is The first-level hazard range is: ...

[0053] In this embodiment, the warning module 24 includes an audible and visual alarm unit 241, which provides alarm warnings. The alarm levels, from low to high, are blue, yellow, orange, and red. Specifically, if stress data belongs to the first-level range, a red audible and visual alarm is issued; otherwise, it proceeds to the second-level range for judgment, and if found, an orange audible and visual alarm is issued; otherwise, it proceeds to the third-level range for judgment, and if found, a yellow audible and visual alarm is issued; otherwise, it proceeds to the fourth-level range for judgment, and if found, a blue audible and visual alarm is issued; otherwise, the data is considered normal, the data is overwritten, and the judgment process for the next data begins again.

[0054] In this embodiment, a storage module 25 is also included to store the collected stress data, the processed judgment results, the calculated confidence interval, the determined stress hazard level, and the alarm time in real time.

[0055] In summary, this invention provides a method and device for stress monitoring of waterproof and airtight walls in underground goaf areas. It enables effective utilization and post-processing of stress data, distinguishing significant differences among similar stress data, and achieving high accuracy in stress monitoring results. This provides a data analysis method for the safety of water storage and use in coal mine goaf areas, improving the reliability of waterproof and airtight operation in goaf areas.

[0056] Example 3 This application provides a computer device including a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement a method for monitoring stress in a waterproof and airtight wall in an underground goaf, as provided in the above-described method embodiments.

[0057] Figure 3This diagram illustrates a hardware structure of an apparatus for implementing a stress monitoring method for a waterproof and airtight wall in an underground goaf, as provided in an embodiment of this application. The apparatus may constitute or include the device or system provided in the embodiment of this application. Figure 3 As shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer device 10 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0058] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuit may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer device 10 (or mobile device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0059] The memory 1004 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to a method for monitoring the stress of a waterproof and airtight wall in an underground goaf according to an embodiment of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, thereby implementing the aforementioned method. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1004 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0060] The transmission device 1006 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer device 10. In one example, the transmission device 1006 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 1006 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0061] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer device 10 (or mobile device).

[0062] Example 4 This application embodiment also provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to implementing a method for monitoring stress of a waterproof and airtight wall in an underground goaf in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method for monitoring stress of a waterproof and airtight wall in an underground goaf provided in the above method embodiment.

[0063] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0064] Example 5 This invention also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a stress monitoring method for a waterproof and airtight wall in an underground goaf provided in the various optional embodiments described above.

[0065] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0067] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0068] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for monitoring stress in a waterproof and airtight wall in an underground goaf, characterized in that, The method includes: S1, stress monitoring is performed on the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices (20) to obtain multiple sets of stress data. ,in, N represents the set of stress data monitored by multiple stress monitoring devices (20), where N is the number of stress data collected. S2, Calculate the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Q m and sample variance ,in, m ∈{ A, B, C, ... }; S3, using the Hartley test method model combined with the sample variance. Calculate the H-value and level number of the test statistic for each group of stress data. r degrees of freedom of sample variance f The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance S m 2 The stress hazard level of the waterproof and airtight wall was obtained through analysis. S4, issue an alarm warning for the stress data based on the stress hazard level; Among them, the test statistic H value and H 0.95 ( r , f Pairwise comparisons include: When H value is greater than or equal to H 0.95 ( r , f When there are significant differences in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the mean y m and the sample variance , combined u The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval; When H value is less than H 0.95 ( r , f When there is no significant difference in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the sum of stress data in each set of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups S t and its degrees of freedom f t ; Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; The F-ratio is calculated based on the mean square error and the mean square factor. p value; Will F Comparison and F 0.95 ( f t , f e If pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

2. A stress monitoring device for waterproof and airtight walls in underground goaf areas, characterized in that, The stress monitoring device for the waterproof and airtight wall in the underground goaf adopts the stress monitoring method for the waterproof and airtight wall in the underground goaf as described in claim 1, and the monitoring device includes: The data acquisition module (21) monitors the stress in the same area of ​​the waterproof and airtight wall using multiple stress monitoring devices (20) to obtain multiple sets of stress data. ,in, N represents the set of stress data monitored by multiple stress monitoring devices (20), where N is the number of stress data collected. The data calculation module (22) is connected to the data acquisition module (21) and calculates the sum of stress data in each set of stress data based on the stress data. T m mean y m Sum of squares within groups Q m and sample variance ,in, m ∈{ A, B, C，…， }; The data discrimination and diagnosis module (23) is connected to the data calculation module (22) and combines the sample variance through the Hartley test method model. Calculate the H-value and level number of the test statistic for each group of stress data. r degrees of freedom of sample variance f The test statistic H value and H 0.95 ( r , f Pairwise comparisons were performed to determine if there were significant differences in the overall variance of the stress data across different groups. Based on the comparison results, and in conjunction with the summation of the stress data... T m mean y m Sum of squares within groups Q m and sample variance The stress hazard level of the waterproof and airtight wall was obtained through analysis. The warning module (24) is connected to the data discrimination and diagnosis module (23) and provides an alarm warning for the stress data according to the stress hazard level.

3. The stress monitoring device for waterproof and airtight walls in underground goaf areas as described in claim 2, characterized in that, When H value is greater than or equal to H 0.95 ( r , f When there are significant differences in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the mean y m and the sample variance , combined u The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard zone is determined based on the calculated confidence interval.

4. The stress monitoring device for waterproof and airtight walls in underground goaf areas as described in claim 2, characterized in that, When H value is less than H 0.95 ( r , f When there is no significant difference in the overall variance among the stress data of each group, the determination of the stress hazard level of the waterproof and airtight wall specifically includes the following steps: Based on the sum of stress data in each set of stress data T m Calculate the sum T Total deviation sum of squares S T and its degrees of freedom f T Sum of squared errors S e and its degrees of freedom f e Sum of squared deviations between groups S t and its degrees of freedom f t ; Based on the sum of squared errors S e and its degrees of freedom f e Calculate the mean square error based on the sum of squares of between-group deviations. S t and its degrees of freedom f Calculate the factor mean square; The F-ratio is calculated based on the mean square error and the mean square factor. p value; Will F Comparison and F 0.95 ( f t , f e If pairwise comparisons are made and differences are found, the result is based on the mean. y m Obtain stress data with large variation characteristics, and combine them with t The test method was used to calculate the confidence intervals of each group of stress data at 95%, 90%, 85%, and 80%. The stress hazard level range is determined based on the calculated confidence interval.

5. The stress monitoring device for waterproof and airtight walls in underground goaf areas as described in claim 2, characterized in that, The warning module (24) includes an audible and visual alarm unit (241), which provides an alarm warning. The alarm levels are blue, yellow, orange and red, from low to high.

6. The stress monitoring device for waterproof and airtight walls in underground goaf areas as described in claim 5, characterized in that, It also includes a storage module (25) for real-time storage of collected stress data, processed judgment results, calculated confidence intervals, determined stress hazard levels and alarm times.

7. A computer device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the stress monitoring method for waterproof and airtight walls in underground goaf areas as described in claim 1.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the stress monitoring method for waterproof and airtight walls in underground goaf areas as described in claim 1.

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