Coal seam mining water hazard prevention method, system, electronic device and readable storage medium

Abstract

The application provides a coal seam mining water disaster prevention method, system, electronic equipment and readable storage medium, and belongs to the technical field of coal seam mining water disaster prevention. The method comprises the following steps: acquiring geological structure information in a target area range of coal seam mining; generating a three-dimensional space structure diagram based on the geological structure information, and unitizing the three-dimensional space structure diagram to process into a plurality of grid units, each type of grid unit corresponds to one laying strategy; generating a laying track of a distributed optical fiber in the target area range based on the grid unit, the laying strategy and the number of spiral turns; then determining a coal seam stress change amplitude and a defect position in the target area range based on a feedback signal and a trained signal recognition model, the feedback signal being obtained by real-time collection after injecting an optical signal into the distributed optical fiber; and generating a water disaster prevention report (for water disaster prevention) based on the coal seam stress change amplitude and the defect position. The application can improve the real-time performance and accuracy of water disaster risk identification in coal seam mining.

Description

Technical Field

[0001] This application relates to the field of water hazard prevention and control technology in coal seam mining, specifically to a method, system, electronic device, and readable storage medium for water hazard prevention and control in coal seam mining. Background Technology

[0002] Coal seam mining water hazards refer to water inrushes, surges, and subsidence caused by high water content in the coal seam or complex seepage conditions in the surrounding rock during coal mining. These water hazards can not only cause flooding of the coal mine working face, equipment damage, and production interruption, but also lead to safety accidents, endangering the safety of personnel and equipment, and affecting the normal progress and economic benefits of coal seam mining.

[0003] Currently, existing technologies for water hazard prevention mainly rely on prior geological exploration, experience-based judgment, and fixed prevention measures. In other words, the assessment of the water-bearing structure of coal seams and their surrounding rocks is mostly based on static hydrogeological data, which is insufficient to reflect stress changes, fracture evolution, and dynamic changes in aquifers during the mining process, resulting in inadequate real-time performance and accuracy in water hazard risk identification.

[0004] Therefore, improving the real-time performance and accuracy of water hazard risk identification in coal seam mining remains an unresolved issue. Summary of the Invention

[0005] The purpose of this application is to provide a method for preventing and controlling water hazards in coal seam mining, which can solve the problems of insufficient real-time performance and accuracy in identifying water hazard risks in existing coal seam mining technologies.

[0006] In a first aspect, embodiments of this application provide a method for preventing and controlling water hazards in coal seam mining, the method comprising: Obtain geological structure information within the target area of ​​coal seam mining; A three-dimensional spatial structure map is generated based on geological structural information, and the three-dimensional spatial structure map is processed into units according to the preset spatial division rules to obtain several grid units. Each type of grid unit corresponds to a laying strategy. Based on the grid cells, laying strategy, and number of spiral turns, a laying trajectory for distributed optical fiber is generated within the target area; the number of spiral turns corresponds to the risk level of the grid cells, and the laying trajectory is used to lay the distributed optical fiber. Based on the feedback signal and the trained signal recognition model, the stress variation amplitude and defect location of the coal seam within the target area are determined; the feedback signal is obtained in real time after injecting optical signal into the distributed optical fiber. A water hazard prevention report is generated based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of coal seam mining to carry out water hazard prevention.

[0007] In one possible implementation of the first aspect, the mesh element includes: internal elements and boundary elements, wherein the internal elements are laid out in a linear pattern and the boundary elements are laid out in a spiral pattern.

[0008] In one possible implementation of the first aspect, the method further includes: Obtain the attribute data of each boundary unit; Determine the risk level corresponding to each boundary unit based on attribute data; Establish a mapping relationship between risk level and the number of spiral turns.

[0009] In one possible implementation of the first aspect, the method further includes: If two adjacent grid cells both have a risk level of Level 3, then those two grid cells will be merged. If the risk level of a certain grid cell is the first level, then the grid cell is divided into several sub-grid cells; Each subgrid unit corresponds to a risk coefficient, and each risk coefficient corresponds to a monitoring frequency.

[0010] In one possible implementation of the first aspect, the method further includes: Optical signals are injected into the distributed optical fiber based on the target monitoring frequency, and the target feedback signal is collected in real time; the target monitoring frequency is the highest monitoring frequency in the sub-grid cell. Based on the target feedback signal and the trained signal recognition model, update the stress variation amplitude and defect location of the coal seam within the target area; Based on the updated coal seam stress variation range and defect location, the water hazard prevention report is updated in conjunction with the grid cell comparison table. The grid cell lookup table is generated based on the grid cell's corresponding number and length range.

[0011] In one possible implementation of the first aspect, the method further includes: Acquire sensor data within the target area; Based on sensor data, the monitoring frequency corresponding to each sub-grid unit is dynamically adjusted.

[0012] In one possible implementation of the first aspect, the method further includes: Obtain information on coal seam changes within the target area; Based on the changes in the coal seam, the change unit is determined, and a deployment task is created and sent to the management personnel terminal. The variable unit is a grid unit where the coal seam changes, and the deployment task is to redeploy backup distributed optical fibers into the variable unit.

[0013] In one possible implementation of the first aspect, the method further includes: A signal recognition model is constructed based on feedback signals, the magnitude of coal seam stress changes, and the location of defects. Historical feedback signals are acquired, abnormal features in the historical feedback signals are labeled, a training set is generated based on the labeled historical feedback signals, and the signal recognition model is trained based on the training set.

[0014] In one possible implementation of the first aspect, the method further includes: Receives reports uploaded by management personnel; the reports are feedback information for water hazard prevention and control reports. Feature extraction was performed on the report feedback and water hazard prevention report to obtain the target features; The target characteristics, coal seam stress variation range, and defect location are written into a preset template to obtain the water hazard event.

[0015] In one possible implementation of the first aspect, the target features include: the time of occurrence of the flood and a summary of the flood; the method further includes: A reference set is generated based on the reference signal; the reference set is used to reflect the response patterns of water-related events. The reference signal is the feedback information corresponding to the time of the water damage.

[0016] Secondly, embodiments of this application provide a coal seam mining water hazard prevention system, the system comprising: The acquisition module is used to acquire geological structure information within the target area of ​​coal seam mining; The first processing module is used to generate a three-dimensional spatial structure map based on geological structural information, and to perform unitization processing on the three-dimensional spatial structure map according to the preset spatial division rules to obtain several grid units. Each type of grid unit corresponds to a laying strategy. The second processing module is used to generate the laying trajectory of distributed optical fiber within the target area based on the grid cell, laying strategy and number of spiral turns; the number of spiral turns corresponds to the risk level of the grid cell, and the laying trajectory is used to lay the distributed optical fiber. The third processing module is used to determine the stress variation amplitude and defect location of the coal seam within the target area based on the feedback signal and the trained signal recognition model; the feedback signal is obtained in real time after injecting optical signals into the distributed optical fiber. The fourth processing module is used to generate a water hazard prevention report based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of coal seam mining to carry out water hazard prevention.

[0017] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the coal seam mining water hazard prevention method described in the first aspect above.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the coal seam mining water hazard prevention method described in the first aspect above.

[0019] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the coal seam mining water hazard prevention method described in the first aspect.

[0020] This application first obtains geological structure information within the target area of ​​coal seam mining; secondly, it generates a three-dimensional spatial structure map based on the geological structure information, and then divides the three-dimensional spatial structure map into several grid units, each type of grid unit corresponding to a laying strategy; thirdly, it generates the laying trajectory of distributed optical fibers within the target area based on the grid units, laying strategies, and the number of spiral turns; then, it determines the stress variation amplitude and defect location of the coal seam within the target area based on feedback signals and a trained signal recognition model, with the feedback signals being acquired in real time after injecting optical signals into the distributed optical fibers; finally, it generates a water hazard prevention report based on the stress variation amplitude and defect location of the coal seam, and the management personnel terminal of the coal seam mining performs water hazard prevention based on the water hazard prevention report.

[0021] This proposed solution does not rely on prior geological exploration, experience-based judgment, or fixed prevention and control measures. Instead, it acquires feedback signals from distributed optical fibers in real time and identifies the stress variation range and defect location of the coal seam within the target area based on the feedback signals and signal recognition models. Water hazard prevention and control is then implemented for the mined coal seam based on the stress variation range and defect location, thereby improving the real-time performance and accuracy of water hazard risk identification in coal seam mining. It also has strong usability and practicality.

[0022] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a schematic diagram of the steps of the coal seam mining water hazard prevention method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the coal seam mining water hazard prevention system provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or photovoltaic modules, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, photovoltaic modules and / or combinations thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0030] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in some other embodiments," "in other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] Coal seam mining water hazards refer to water inrushes, surges, and subsidence caused by high water content in the coal seam or complex seepage conditions in the surrounding rock during coal mining. These water hazards can not only cause flooding of the coal mine working face, equipment damage, and production interruption, but also lead to safety accidents, endangering the safety of personnel and equipment, and affecting the normal progress and economic benefits of coal seam mining.

[0033] Currently, existing technologies for water hazard prevention mainly rely on pre-exploration geological surveys, empirical judgments, and fixed prevention measures. In other words, the assessment of the water-bearing structure of coal seams and their surrounding rocks is largely based on static hydrogeological data, which is insufficient to reflect stress changes, fracture evolution, and dynamic changes in aquifers during mining, resulting in inadequate real-time performance and accuracy in water hazard risk identification. Existing technologies cannot achieve dynamic monitoring of water hazard risks during coal seam mining.

[0034] To address the aforementioned deficiencies, this application provides a method for preventing water hazards in coal seam mining. First, geological structure information within the target area of ​​the coal seam is acquired. Second, a three-dimensional spatial structure map is generated based on the geological structure information, and this map is modularized into several grid units, each corresponding to a laying strategy. Third, the laying trajectory of distributed optical fibers within the target area is generated based on the grid units, laying strategy, and number of spiral turns. Then, the stress variation amplitude and defect location of the coal seam within the target area are determined based on feedback signals and a trained signal recognition model. The feedback signals are obtained in real-time after injecting optical signals into the distributed optical fibers. Finally, a water hazard prevention report is generated based on the stress variation amplitude and defect location of the coal seam, and the management personnel terminal of the coal seam mining implements water hazard prevention measures based on the report.

[0035] This proposed solution does not rely on prior geological exploration, experience-based judgment, or fixed prevention and control measures. Instead, it acquires feedback signals from distributed optical fibers in real time and identifies the stress variation range and defect location of the coal seam within the target area based on the feedback signals and signal recognition models. Water hazard prevention and control is then implemented for the mined coal seam based on the stress variation range and defect location, thereby improving the real-time performance and accuracy of water hazard risk identification in coal seam mining. It also has strong usability and practicality.

[0036] The specific process implemented in this application is described below through specific embodiments.

[0037] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of the coal seam mining water hazard prevention method provided in this application embodiment. This application embodiment uses a sensor host as the execution subject to describe the method. Figure 1 As shown, the method includes the following steps: S101, Obtain geological structure information within the target area of ​​coal seam mining.

[0038] S102, a three-dimensional spatial structure map is generated based on geological structural information, and the three-dimensional spatial structure map is processed into units according to the preset spatial division rules to obtain several grid units. Each type of grid unit corresponds to a laying strategy.

[0039] According to one embodiment of this application, the mesh element includes: internal elements and boundary elements, wherein the laying strategy corresponding to the internal elements is linear laying and the laying strategy corresponding to the boundary elements is spiral laying.

[0040] In one embodiment, the boundary of the coal seam mining area is determined based on information such as the mine area design data, geological exploration results, and mining plan, and the mining range formed by the boundary is defined as the target area. Key geological structural information such as faults, collapse columns, and aquifers within the target area is collected to create a three-dimensional spatial structure map. The three-dimensional spatial structure map is mainly used to reflect the spatial morphology of the coal seam and its relationship with the surrounding rock.

[0041] In one embodiment, the three-dimensional spatial structure diagram is modularized according to a preset spatial division rule to obtain several grid units. The preset spatial division rule can be equal-volume partitioning or partitioning based on functional areas. For example, equal-volume partitioning can be performed by modularizing the three-dimensional spatial structure diagram with sides of 1 meter in length, width, and height to obtain several grid units of equal volume. Functional areas can include geological structure areas, safe coal pillar protection areas, water inrush threat / danger areas, and normal mining functional areas.

[0042] In one embodiment, grid cells are divided into internal cells and boundary cells based on their specific locations. A grid cell entirely within the area is defined as an internal cell, while grid cells located at the edge of the area, near surrounding rock, faults, or aquifers, are defined as boundary cells. Different laying strategies are set for different grid cells, including spiral and straight laying strategies.

[0043] For example, boundary units are usually located at the interface between the coal seam and the surrounding rock, or in areas near faults or aquifers. These are high-risk areas that require close monitoring. Distributed optical fibers can be spirally deployed to increase their length and coverage density within a limited space, allowing them to make full contact with the surrounding rock in multiple directions and thus sensitively capture minute strain and multi-directional deformation characteristics. In contrast, internal units are usually located in relatively stable areas of the coal seam with relatively low risk. A straight-line laying strategy is adopted, that is, the distributed optical fibers are directly laid through the area where the internal unit is located.

[0044] This embodiment can optimize the allocation of monitoring resources by setting different laying strategies. While ensuring the monitoring accuracy in high-risk areas, it can improve the economy and accuracy of monitoring in the overall area and provide a reliable data foundation for flood prevention and control.

[0045] S103 generates a laying trajectory for distributed optical fiber within the target area based on grid cells, laying strategy, and number of spiral turns; the number of spiral turns corresponds to the risk level of the grid cells, and the laying trajectory is used to lay the distributed optical fiber.

[0046] According to one embodiment of this application, the method further includes: acquiring attribute data of each boundary unit; determining the risk level corresponding to each boundary unit based on the attribute data; and establishing a mapping relationship between the risk level and the number of spiral turns.

[0047] In one embodiment, attribute data for each boundary cell is collected, including at least: coal seam thickness, burial depth parameters, spatial coordinate information, and coal seam boundary environment. A risk level is set for each boundary cell, including at least high, medium, and low risk levels, establishing a one-to-one correspondence between risk levels and the number of spiral turns. The grid cells, laying strategy, and spiral turn number are aggregated to generate the laying trajectory of the distributed optical fiber within the region, allowing for the laying of the distributed optical fiber according to the trajectory.

[0048] In one embodiment, multiple evaluation indicators are set based on the attribute data of each boundary unit, and each evaluation indicator corresponds to a risk level. The risk level of each boundary unit is determined based on the evaluation indicators. For example, when the coal seam thickness of a boundary unit is less than a first preset threshold, the corresponding risk level is determined to be high; when the coal seam thickness of a boundary unit is greater than or equal to the first preset threshold and less than or equal to a second preset threshold, the corresponding risk level is determined to be medium; when the coal seam thickness of a boundary unit is greater than the second preset threshold, the corresponding risk level is determined to be low.

[0049] It should be noted that determining the risk level of a boundary unit based on burial depth parameters is essentially the same as determining the risk level of a boundary unit based on coal seam thickness. Both are determined based on preset thresholds, which can be understood by analogy, and will not be elaborated here.

[0050] For example, the risk level of a boundary unit is determined based on spatial coordinate information: when the spatial coordinates of a boundary unit are within the range of the first spatial coordinates, the risk level of the boundary unit is determined to be high; when the spatial coordinates of a boundary unit are within the range of the second spatial coordinates, the risk level of the boundary unit is determined to be medium; when the spatial coordinates of a boundary unit are within the range of the third spatial coordinates, the risk level of the boundary unit is determined to be low.

[0051] For example, the risk level of boundary units is determined based on the coal seam boundary environment: Risk of confined aquifer inrush at the floor boundary. When a confined aquifer (such as Ordovician limestone) exists beneath the coal seam, mining disturbance may damage the floor aquitard, triggering a floor water inrush. The risk level is determined based on the water inrush coefficient; values ​​greater than a critical value indicate high risk. The formula for the water inrush coefficient is as follows: T s = P / T b ,in, T s Indicates the inrush coefficient. P Indicates the water pressure of the pressurized water. T b This indicates the thickness of the waterproof layer on the base plate.

[0052] It should be noted that the risk level of the internal unit is low by default, while the boundary unit with a high risk level represents an area prone to water damage or stress abrupt changes.

[0053] In one embodiment, a corresponding number of spiral turns is assigned to each risk level, with higher-risk boundary units corresponding to more spiral turns, to increase the coverage density of distributed optical fibers in space. The laying path within each boundary unit is determined based on the boundary unit, its corresponding laying strategy, and the number of spiral turns, and distributed optical fibers are installed along this path, generating the laying trajectory of the distributed optical fibers throughout the entire coal seam mining area.

[0054] S104, based on the feedback signal and the trained signal recognition model, determines the stress variation amplitude and defect location of the coal seam within the target area; the feedback signal is obtained in real time after injecting optical signals into the distributed optical fiber.

[0055] In one embodiment, one end of the distributed optical fiber is connected to a sensor host, which injects optical signals into the distributed optical fiber and collects feedback signals (return signals) in real time. When the coal seam, rock strata, and other geological structures in a certain grid cell undergo stress changes, it will cause a small strain or deformation in the distributed optical fiber within the corresponding grid cell. This strain will cause changes in the scattering characteristics or time delay of the optical signal in the distributed optical fiber.

[0056] In one embodiment, the sensor host collects feedback signals in real time and monitors abnormal fluctuations in the optical signal based on the feedback signals and a trained signal recognition model. This allows for precise location of the amplitude and position of stress changes in the coal seam, and the corresponding signal offset position (the location of the stress change in the coal seam) is identified as the defect location. When the stress change amplitude exceeds a safety threshold, it indicates that the corresponding grid cell is at risk of crack propagation, water seepage, or local instability.

[0057] According to one embodiment of this application, the method further includes: constructing a signal recognition model based on feedback signals, coal seam stress variation amplitude, and defect location; acquiring historical feedback signals, labeling abnormal features in the historical feedback signals, generating a training set based on the labeled historical feedback signals, and training the signal recognition model based on the training set.

[0058] In one embodiment, a deep learning algorithm is used to construct a signal recognition model based on feedback signals, coal seam stress variation amplitude, and defect location. Historical feedback signals from distributed optical fibers, i.e., historical data, are collected. Anomalies in the historical data are determined based on waveform changes and labeled. A training set is then generated based on the labeled historical data.

[0059] In one embodiment, the signal recognition model is trained based on a training set, enabling it to learn the mapping relationship between optical signal characteristics and the magnitude of coal seam stress changes and defect locations. In practical applications, real-time feedback signals acquired by distributed optical fibers are input into the trained signal recognition model. The model analyzes and identifies the input signals based on the learned characteristics, determines the magnitude of coal seam stress changes and defect locations, and outputs the results.

[0060] S105 generates a water hazard prevention report based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of coal seam mining to carry out water hazard prevention.

[0061] In one embodiment, the stress variation range and defect location of the coal seam within the target area are recorded in the water hazard prevention report. The management terminal of the coal seam mining can then implement water hazard prevention measures based on the report. For example, within grid cells where the stress variation range of the coal seam exceeds the safety threshold and defects are present, grouting and dewatering / pressure reduction are performed to address floor water.

[0062] Grouting modification involves injecting cement grout into the impermeable layer of the foundation slab to seal cracks and reinforce the rock strata, enabling it to withstand greater water pressure. Engineering practice shows that precise drilling and grouting can effectively seal the water-conducting channels of the foundation slab. Drainage and pressure reduction involves draining water from the confined aquifer through boreholes to reduce its water pressure and decrease the risk of sudden water inrush.

[0063] According to one embodiment of this application, the method further includes: if the risk level of two adjacent grid units is both at level three, then the two grid units are merged; if the risk level of a certain grid unit is at level one, then the grid unit is divided into several sub-grid units; wherein each sub-grid unit corresponds to a risk coefficient, and each risk coefficient corresponds to a monitoring frequency.

[0064] In one embodiment, during actual flood control, if the sensor host continuously injects high-frequency optical signals into the distributed optical fiber, it may create a data processing burden. Therefore, a hierarchical mechanism can be constructed to differentiate the processing of grid units. Specifically, when two adjacent grid units have low risk levels, they are merged to reduce the number of monitoring points and data redundancy in low-risk areas; for grid units with high risk levels, they are finely divided into several smaller sub-grid units, and a risk coefficient is assigned to each sub-grid unit. The risk coefficient can also be determined based on evaluation indicators. Each risk coefficient corresponds to a monitoring frequency; the higher the risk coefficient, the higher the monitoring frequency.

[0065] This embodiment, by combining merging and fine segmentation, not only achieves a balance between monitoring accuracy and efficiency, but also provides a scientific basis for dynamically adjusting laying strategies and optimizing distributed optical fiber laying, significantly improving the reliability of water hazard monitoring in coal seam mining.

[0066] According to one embodiment of this application, the method further includes: injecting optical signals into a distributed optical fiber based on a target monitoring frequency, and acquiring target feedback signals in real time; the target monitoring frequency is the highest monitoring frequency in the sub-grid cell; updating the coal seam stress variation amplitude and defect location within the target area based on the target feedback signal and the trained signal recognition model; and updating the water hazard prevention report based on the updated coal seam stress variation amplitude and defect location, combined with a grid cell reference table; wherein the grid cell reference table is generated based on the corresponding number and length range of the grid cell.

[0067] In one embodiment, the grid cells are numbered, a lookup table consisting of numbered items and length range items is drawn, and written into the sensor host. The highest monitoring frequency in the sub-grid cell is queried as the target monitoring frequency, and the sensor host is activated according to the target monitoring frequency. The coal seam stress change amplitude and defect location are updated according to the monitoring frequency corresponding to the grid cell. The lookup table is queried, the water hazard prevention report is updated, and it is sent to the terminal of the coal seam mining management personnel.

[0068] For example, each grid cell is assigned a number according to a preset numbering rule, the length range corresponding to each numbered grid cell is determined, a correspondence between the number and the length range is established, and a lookup table is drawn. For instance, a grid cell is numbered G12, and its corresponding length range is 12.5 to 15.0 meters, meaning that the 12.5 to 15.0 meters of the distributed optical fiber are located in grid cell G12.

[0069] In one embodiment, the number and length range are labeled as notes in the corresponding area of ​​the three-dimensional spatial structure diagram.

[0070] For example, the monitoring frequency corresponding to each sub-grid cell is determined according to a lookup table. The highest monitoring frequency is found and defined as the target monitoring frequency. The sensor host is activated according to this target monitoring frequency, injecting optical signals into the distributed optical fiber and collecting target feedback signals. The target feedback signals are input into the trained signal recognition model to update the coal seam stress change amplitude and defect location within the target area. The updated coal seam stress change amplitude and defect location are written into the water hazard prevention report, and the specific grid cells where coal seam stress changes and defect locations occur are written according to the lookup table to update the water hazard prevention report.

[0071] It should be noted that during actual monitoring, some distributed optical fibers in the grid cells may shift. While the sensor host physically detects the entire optical fiber, the software and data processing only need to determine and alarm for the sub-grid cells within the monitoring period. The advantage of this method is that it can eliminate the influence of background noise and achieve refined monitoring.

[0072] According to one embodiment of this application, the method further includes: acquiring sensing data within the target area; and dynamically adjusting the monitoring frequency corresponding to each sub-grid unit based on the sensing data.

[0073] In one embodiment, sensing data is collected using sensing devices deployed over a region. This data includes temperature, stress (measured by a stress gauge), gas concentration, and other geological parameters. The sensor host analyzes and processes the collected data and adjusts the monitoring frequency for each subgrid cell.

[0074] For example, the monitoring frequency corresponding to each sub-grid cell is adjusted according to the temperature. Regular monitoring involves continuously acquiring the temperature distribution along the fiber optic cable using a fixed scanning frequency (e.g., 1 Hz). The frequency adjustment trigger conditions are: the local temperature change rate exceeds a set threshold (e.g., >0.5℃ / min), the temperature approaches a preset alarm threshold (e.g., the extreme temperature specified in coal mines), or the temperature gradient is abnormal (excessive temperature difference between adjacent measuring points). The adjustment strategy is: when the trigger conditions are met, the sampling frequency of that channel is increased to 10~100 Hz to achieve high-frequency tracking.

[0075] For example, the monitoring frequency corresponding to each subgrid cell is adjusted according to the stress. Conventional monitoring is as follows: a lower scanning frequency (e.g., 0.1~1 Hz) is used to monitor the background stress value of the mining area; the frequency adjustment trigger conditions are: the strain change rate exceeds the threshold (e.g., >10με / min), the stress concentration factor λ>1.5 (medium risk area), and microseismic events are detected (strain fluctuation amplitude increases sharply); the adjustment strategy is to increase the sampling frequency to 100~500 Hz when entering the high stress area or the mining-affected area.

[0076] For example, the monitoring frequency of each sub-grid unit is adjusted according to the gas concentration. Routine monitoring involves periodically inspecting each monitoring point using a fixed scanning cycle (e.g., 5-10 seconds / channel). Frequency adjustment is triggered when: the rate of change in gas concentration increases (dC / dt > threshold), the concentration approaches the alarm threshold (e.g., 0.5%, 1% CH4), or mining activities approach a high-gas area. The adjustment strategy is to increase the real-time monitoring frequency to 1 second / time in hazardous areas.

[0077] According to one embodiment of this application, the method further includes: acquiring coal seam changes within a target area; determining change units based on coal seam changes, creating deployment tasks, and sending the deployment tasks to management personnel terminals; wherein, the change unit is a grid unit where the coal seam changes, and the deployment task is to redeploy backup distributed optical fibers to the change unit.

[0078] In one embodiment, when a coal seam changes, the grid cell where the change occurs is identified and defined as the changing cell. A deployment task is then created, which refers to redeploying backup distributed optical fibers into the changing cell, monitoring the changing cell, and sending the deployment task to the terminal of the coal seam mining management personnel.

[0079] According to one embodiment of this application, the method further includes: receiving report feedback uploaded by the management personnel terminal; the report feedback is feedback information of a water hazard prevention and control report; extracting features from the report feedback and the water hazard prevention and control report to obtain target features; and writing the target features, the coal seam stress change range, and the defect location into a preset template to obtain a water hazard event.

[0080] In one embodiment, feedback information regarding the water hazard prevention report is received from the management terminal, i.e., report feedback. The report feedback can include annotations and corrections of errors, as well as new content or supplementary explanations. Key features are extracted from the report feedback and the water hazard prevention report. These key features include the occurrence time and a summary of the water hazard. The key features, the magnitude of coal seam stress changes, and the location of defects are written into a preset template to obtain the water hazard event.

[0081] According to one embodiment of this application, the method further includes: generating a reference set based on a reference signal; the reference set is used to reflect the response pattern of a flood event; wherein the reference signal is feedback information corresponding to the time of the flood event.

[0082] In one embodiment, the feedback information corresponding to the occurrence time is defined as a reference signal. The reference signal includes the temporal characteristics, amplitude changes, and related response information of the flood event. By integrating the reference signals of all flood events, a reference set is generated. The reference set can comprehensively reflect the typical response patterns of flood events, providing basic data support for subsequent flood event early warning.

[0083] Among them, the relevant response information when a water hazard occurs can be: (1) Visual signals: sweat and red (water rust) on the roadway wall and coal wall, fog, increased water spray on the roof, bulging of the floor or cracks and water seepage; (2) Tactile signals: coal seam temperature becomes cold and air humidity increases abnormally; (3) Auditory signals: hear hissing water flow or water call, bottom explosion sound; (4) Operation signals: when drilling for water exploration, soft coal and rock, rock spalling, pressure, sudden increase in drilling water pressure and volume, top drilling and other abnormalities are found.

[0084] According to one embodiment of this application, edge devices are deployed within a target area. These edge devices can be edge servers or smart gateways. A signal recognition model is deployed within the edge devices, and feedback signals are analyzed and processed at the edge.

[0085] In one embodiment, the sensor host is connected to an edge device, which processes and analyzes the collected feedback signals in real time. At the same time, the device allows for the adjustment of risk levels and risk coefficients, thereby enabling rapid feedback and adjustment at the edge and improving the response speed and decision-making efficiency of flood control.

[0086] This application provides a method for preventing water hazards in coal seam mining. First, it acquires geological structure information within the target area of ​​the coal seam mining. Second, it generates a three-dimensional spatial structure map based on the geological structure information and divides the map into several grid units, each type of grid unit corresponding to a laying strategy. Third, it generates the laying trajectory of distributed optical fibers within the target area based on the grid units, laying strategy, and number of spiral turns. Then, it determines the stress variation amplitude and defect location of the coal seam within the target area based on feedback signals and a trained signal recognition model. The feedback signals are obtained in real-time after injecting optical signals into the distributed optical fibers. Finally, it generates a water hazard prevention report based on the stress variation amplitude and defect location of the coal seam, and the management personnel terminal of the coal seam mining performs water hazard prevention based on the report.

[0087] This proposed solution does not rely on prior geological exploration, experience-based judgment, or fixed prevention and control measures. Instead, it acquires feedback signals from distributed optical fibers in real time and identifies the stress variation range and defect location of the coal seam within the target area based on the feedback signals and signal recognition models. Water hazard prevention and control is then implemented for the mined coal seam based on the stress variation range and defect location, thereby improving the real-time performance and accuracy of water hazard risk identification in coal seam mining. It also has strong usability and practicality.

[0088] This application's solution optimizes the allocation of monitoring resources by setting different deployment strategies, ensuring the accuracy and reliability of monitoring in high-risk areas, improving the overall precision of coal seam monitoring, and significantly reducing the data processing burden. By deploying distributed optical fibers, it enables large-scale real-time monitoring of coal seams, facilitating early warning and providing effective data support for coal seam water hazard prevention, greatly enhancing the scientific rigor and proactiveness of prevention and control decisions. By setting monitoring frequencies, it enables tiered response and tiered handling, ensuring timely detection of potential water hazards while avoiding the introduction of excessively high monitoring intensity and thus reducing the data processing burden and improving water hazard monitoring efficiency. By creating a comparison table, it accurately locates the specific positions of abnormal strains, further improving the data processing efficiency and real-time response capability for water hazard prevention and control.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0090] Corresponding to the method in the above embodiments, Figure 2 This is a schematic diagram of the coal seam mining water hazard prevention system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0091] Please see Figure 2 The system includes: The acquisition module 201 is used to acquire geological structure information within the target area of ​​coal seam mining; The first processing module 202 is used to generate a three-dimensional spatial structure map based on geological structural information, and to perform unitization processing on the three-dimensional spatial structure map according to the preset spatial division rules to obtain a number of grid units. Each type of grid unit corresponds to a laying strategy. The second processing module 203 is used to generate a laying trajectory of distributed optical fiber within a target area based on grid cells, laying strategy and number of spiral turns; the number of spiral turns corresponds to the risk level of the grid cells, and the laying trajectory is used to lay the distributed optical fiber. The third processing module 204 is used to determine the stress variation amplitude and defect location of the coal seam within the target area based on the feedback signal and the trained signal recognition model; the feedback signal is obtained in real time after injecting optical signal into the distributed optical fiber. The fourth processing module 205 is used to generate a water hazard prevention report based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of coal seam mining to carry out water hazard prevention.

[0092] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0093] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0094] Figure 3 This is a schematic diagram of the structure of the electronic device 3 provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 of this embodiment includes: at least one processor 301 ( Figure 3Only one is shown in the diagram), memory 303, and computer program 302 stored in memory 303 and executable on at least one processor 301, which implements the steps in the above method embodiments when the processor 301 executes computer program 302.

[0095] Electronic device 3 may be a sensor host or similar device. This electronic device 3 may include, but is not limited to, a processor 301 and a memory 303. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0096] The processor 301 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware photovoltaic modules, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0097] In some embodiments, memory 303 may be an internal storage unit of electronic device 3, such as a hard disk or memory of electronic device 3. In other embodiments, memory 303 may be an external storage device of electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on electronic device 3. Furthermore, memory 303 may include both internal storage units and external storage devices of electronic device 3. Memory 303 is used to store operating system, application programs, boot loader, data, and other programs, such as program code of computer programs. Memory 303 may also be used to temporarily store data that has been output or will be output.

[0098] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, when implementing all or part of the processes in the methods of the above embodiments, this application can use a computer program to instruct related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps applied in the method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a computing device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0099] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various method embodiments described above.

[0100] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the steps described in the various method embodiments above.

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0103] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. The device / electronic device embodiments described above are merely illustrative, and the division of modules or units described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or photovoltaic modules may be combined or integrated into another system, and some features may be ignored. Furthermore, the indirect coupling, direct coupling, or communication connection shown or discussed between them can be through some interfaces, devices, or units, and can be electrical, mechanical, or other forms.

[0104] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the above embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for preventing water hazards in coal seam mining, characterized in that, The method includes: Obtain geological structure information within the target area of ​​coal seam mining; A three-dimensional spatial structure map is generated based on the geological structure information, and the three-dimensional spatial structure map is processed into a unit according to the preset spatial division rules to obtain a number of grid units. Each type of grid unit corresponds to a laying strategy. Based on the grid cells, laying strategy, and number of spiral turns, a laying trajectory for the distributed optical fiber is generated within the target area; the number of spiral turns corresponds to the risk level of the grid cells, and the laying trajectory is used to lay the distributed optical fiber. Based on the feedback signal and the trained signal recognition model, the stress variation amplitude and defect location of the coal seam within the target area are determined; the feedback signal is obtained in real time after injecting optical signal into the distributed optical fiber. A water hazard prevention report is generated based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of coal seam mining to carry out water hazard prevention.

2. The method for preventing and controlling water hazards in coal seam mining according to claim 1, characterized in that, The grid unit includes: internal units and boundary units. The laying strategy corresponding to the internal units is linear laying, and the laying strategy corresponding to the boundary units is spiral laying.

3. The method for preventing and controlling water hazards in coal seam mining according to claim 2, characterized in that, The method further includes: Obtain the attribute data of each boundary unit; The risk level corresponding to each boundary unit is determined based on the attribute data. Establish a mapping relationship between risk level and the number of spiral turns.

4. The method for preventing and controlling water hazards in coal seam mining according to claim 1, characterized in that, The method further includes: If two adjacent grid cells both have a risk level of Level 3, then those two grid cells will be merged. If the risk level of a certain grid cell is the first level, then the grid cell is divided into several sub-grid cells; Each subgrid unit corresponds to a risk coefficient, and each risk coefficient corresponds to a monitoring frequency.

5. The method for preventing and controlling water hazards in coal seam mining according to claim 4, characterized in that, The method further includes: Optical signals are injected into the distributed optical fiber based on the target monitoring frequency, and the target feedback signal is collected in real time; the target monitoring frequency is the highest monitoring frequency in the sub-grid cell. Based on the target feedback signal and the trained signal recognition model, update the coal seam stress variation amplitude and defect location within the target area; Based on the updated coal seam stress variation range and defect location, the water hazard prevention report is updated in conjunction with the grid cell comparison table. The grid cell lookup table is generated based on the grid cell's corresponding number and length range.

6. The method for preventing and controlling water hazards in coal seam mining according to claim 4, characterized in that, The method further includes: Acquire sensor data within the target area; Based on the sensor data, the monitoring frequency corresponding to each sub-grid unit is dynamically adjusted.

7. The method for preventing and controlling water hazards in coal seam mining according to claim 1, characterized in that, The method further includes: Obtain information on coal seam changes within the target area; Based on the changes in the coal seam, the change unit is determined, and a deployment task is created and sent to the management personnel terminal. The change unit is a grid unit where the coal seam changes, and the deployment task is to redeploy backup distributed optical fibers into the change unit.

8. The method for preventing and controlling water hazards in coal seam mining according to claim 1, characterized in that, The method further includes: A signal recognition model is constructed based on feedback signals, the magnitude of coal seam stress changes, and the location of defects. Historical feedback signals are acquired, abnormal features in the historical feedback signals are labeled, a training set is generated based on the labeled historical feedback signals, and the signal recognition model is trained based on the training set.

9. The method for preventing and controlling water hazards in coal seam mining according to any one of claims 1-8, characterized in that, The method further includes: Receive the report feedback uploaded by the management personnel terminal; the report feedback is the feedback information of the water hazard prevention and control report; Feature extraction is performed on the report feedback and the water hazard prevention report to obtain target features; The target features, coal seam stress variation range, and defect location are written into a preset template to obtain a water hazard event.

10. The method for preventing and controlling water hazards in coal seam mining according to claim 9, characterized in that, The target features include: the time of flood occurrence and a summary of the flood; the method further includes: A reference set is generated based on the reference signal; the reference set is used to reflect the response pattern of water-related events. The reference signal is the feedback information corresponding to the time of the water damage.

11. A coal seam mining water hazard prevention system, characterized in that, The system includes: The acquisition module is used to acquire geological structure information within the target area of ​​coal seam mining; The first processing module is used to generate a three-dimensional spatial structure map based on the geological structure information, and to perform unitization processing on the three-dimensional spatial structure map according to the preset spatial division rules to obtain a number of grid units, each type of grid unit corresponding to a laying strategy. The second processing module is used to generate a laying trajectory of the distributed optical fiber within the target area based on the grid cells, laying strategy, and number of spiral turns; the number of spiral turns corresponds to the risk level of the grid cells, and the laying trajectory is used to lay the distributed optical fiber. The third processing module is used to determine the stress variation amplitude and defect location of the coal seam within the target area based on the feedback signal and the trained signal recognition model; the feedback signal is obtained in real time after injecting optical signal into the distributed optical fiber; The fourth processing module is used to generate a water hazard prevention report based on the stress variation range and defect location of the coal seam; the water hazard prevention report is used to instruct the management personnel of the coal seam mining to carry out water hazard prevention.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for preventing and controlling water hazards in coal seam mining as described in any one of claims 1-10.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for preventing and controlling water hazards in coal seam mining as described in any one of claims 1-10.

14. A computer program product, characterized in that, When the computer program product is run on an electronic device, the electronic device performs the coal seam mining water hazard prevention method according to any one of claims 1-10.

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