Mine earthquake intensity monitoring method and system based on multi-source data fusion

Abstract

The invention provides a mine earthquake intensity monitoring method and system based on multi-source data fusion, and the method comprises the steps: determining target monitoring equipment disposed in a to-be-monitored region, and collecting the multi-source data of the to-be-monitored region based on the target monitoring equipment; performing preprocessing operation on the multi-source data to obtain target multi-source data; performing feature extraction on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data; and obtaining the mine earthquake intensity of the to-be-monitored area according to the multi-dimensional characteristic parameters, thereby breaking through the limitation of a single data source by obtaining the multi-source data, obtaining the mine earthquake intensity of the to-be-monitored area by fusing the multi-source data, and improving the accuracy and reliability of obtaining the mine earthquake intensity of the to-be-monitored area.

Description

Technical Field

[0001] This invention relates to the field of mine seismic monitoring technology, and in particular to a method and system for monitoring mine seismic intensity based on multi-source data fusion. Background Technology

[0002] Existing methods for monitoring mine seismic intensity generally rely on single data types, which cannot comprehensively capture the multi-dimensional impact of mine seismic events on the structural stability of underground roadways, the safety of workers, and surrounding facilities in coal mines. Different monitoring methods operate independently, and there is a lack of effective correlation and verification mechanisms between the data. In complex coal mine geological environments (such as high-stress zones, fault zones, and water-rich areas), assessment biases are prone to occur, resulting in insufficient accuracy and reliability of the monitored mine seismic intensity. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] According to a first aspect of this application, a method for monitoring seismic intensity based on multi-source data fusion is provided, comprising: determining a target monitoring device deployed in a monitoring area; collecting multi-source data of the monitoring area based on the target monitoring device; performing preprocessing operations on the multi-source data to obtain target multi-source data; extracting features from the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data; and obtaining the seismic intensity of the monitoring area based on the multi-dimensional feature parameters.

[0005] According to a second aspect of this application, a monitoring system for seismic intensity based on multi-source data fusion is provided, comprising: a data acquisition module for identifying target monitoring equipment deployed in a monitoring area, and acquiring multi-source data of the monitoring area based on the target monitoring equipment; a preprocessing module for performing preprocessing operations on the multi-source data to obtain target multi-source data; a feature extraction module for extracting features from the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data; and an acquisition module for acquiring the seismic intensity of the monitoring area based on the multi-dimensional feature parameters.

[0006] According to a third aspect of this application, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the monitoring method for seismic intensity based on multi-source data fusion proposed in the first aspect above.

[0007] According to a fourth aspect of this application, a non-transitory computer-readable storage medium storing computer instructions is proposed, wherein the computer instructions are used to cause the computer to execute the monitoring method for seismic intensity based on multi-source data fusion proposed in the first aspect above.

[0008] According to the fifth aspect of this application, a computer program product is proposed, comprising a computer program that, when executed by a processor, implements the method for monitoring seismic intensity based on multi-source data fusion proposed in the first aspect above.

[0009] The method and system for monitoring seismic intensity based on multi-source data fusion provided in this application determine the target monitoring equipment to be deployed in the area to be monitored, collect multi-source data of the area to be monitored based on the target monitoring equipment, perform preprocessing operations on the multi-source data to obtain target multi-source data, extract features from the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data, and obtain the seismic intensity of the area to be monitored based on the multi-dimensional feature parameters. Thus, this application breaks through the limitations of a single data source by acquiring multi-source data, and improves the accuracy and reliability of obtaining the seismic intensity of the area to be monitored by fusing multi-source data.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein: Figure 1 A flowchart illustrating a method for monitoring seismic intensity based on multi-source data fusion, provided in an embodiment of this application; Figure 2 A flowchart illustrating another method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment; Figure 3 A flowchart illustrating another method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment; Figure 4 A flowchart illustrating another method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a mine seismic intensity monitoring system based on multi-source data fusion, provided in an embodiment of this application. Detailed Implementation

[0012] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0013] Among the related technologies, (1) the method of monitoring the intensity of mine seismic waves based on mine seismic waveform data: the mine seismic waveform signal is collected by the monitoring equipment deployed in the roadway, and the peak particle velocity (PPV) value is extracted from it as the core indicator. The intensity of mine seismic waves in the monitoring area is determined by using the pre-established correspondence between PPV value and mine seismic intensity. Among them, the correspondence between PPV value and mine earthquake intensity needs to be determined by constructing a mechanical model containing coal seam, roof and floor surrounding rock, and simulating the stress distribution, deformation characteristics and failure mode of roadway under different intensity mine earthquakes; (2) Monitoring mine earthquake intensity based on acceleration sensor: use multi-axis acceleration sensing device to collect vibration acceleration signals in the X, Y and Z directions caused by mine earthquake, correct the original signal through preset sensitivity coefficient, and use the processed acceleration data as the basis for judging mine earthquake intensity; (3) Determining mine earthquake intensity based on macro survey data: conduct surveys of the mine earthquake-affected area along the preset route through manual inspection or automated image acquisition equipment, collect macro data such as roadway support structure damage (such as anchor bolt breakage, spray layer peeling, surrounding rock fissure development), pipeline equipment deformation status and on-site workers' earthquake sensation description, and classify mine earthquake intensity based on the above data.

[0014] The above scheme has the following disadvantages: (1) Significant limitations of single data: relying solely on waveform data for evaluation makes it difficult to reflect the actual damage degree of mine tremors to the actual engineering structure. Monitoring based on acceleration signals can only reflect the vibration intensity and cannot be associated with the damage evolution process of the surrounding rock in the roadway. Macroscopic survey data has collection lag and is greatly affected by subjective judgment, resulting in insufficient real-time performance and objectivity. (2) Lack of data correlation: data obtained by different monitoring methods are stored and analyzed independently, and no cross-type data verification and complementarity mechanism has been established. When a certain type of data is disturbed and becomes abnormal, it cannot be corrected by other data, leading to a decrease in the credibility of the evaluation results. (3) Insufficient scene adaptability: existing technologies are difficult to meet the needs of complex and ever-changing coal mine environments. For example, in soft rock roadways, the evaluation results based on waveform data may deviate significantly from the actual damage situation. In areas with high electromagnetic interference, acceleration sensor data is prone to distortion. None of the above problems can be solved by a single technical means.

[0015] The following examples illustrate the method for monitoring seismic intensity based on multi-source data fusion proposed in this application.

[0016] Figure 1 This is a flowchart illustrating the method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment.

[0017] like Figure 1 As shown in the figure, the method for monitoring seismic intensity based on multi-source data fusion proposed in this embodiment specifically includes the following steps: S101. Determine the target monitoring equipment to be deployed in the area to be monitored, and collect multi-source data of the area to be monitored based on the target monitoring equipment.

[0018] In this embodiment of the application, geological information of the area to be monitored can be obtained, and target monitoring equipment to be deployed in the area to be monitored can be determined based on the geological information.

[0019] For example, it is difficult to deploy monitoring equipment in soft rock tunnels. In such cases, intensity meters and strain sensors can be deployed at pre-set monitoring points in the area to be monitored as target monitoring equipment. For hard rock tunnels, intensity meters, strain sensors, stress sensors, and monitoring equipment for acquiring tunnel damage data can be deployed at pre-set monitoring points in the area to be monitored as target monitoring equipment.

[0020] It should be noted that the target monitoring equipment deployed at pre-set monitoring points in the area to be monitored can adapt to different geological conditions and monitoring environments, and can still stably collect multi-source data from the area to be monitored in areas with high interference and complex structures.

[0021] Optionally, waveform data during the mine seismic process can be collected based on an intensity meter, strain data during the mine seismic process can be collected based on a strain sensor, stress data during the mine seismic process can be collected based on a stress sensor, and roadway damage data during the mine seismic process can be obtained based on an image acquisition device, an acoustic wave tester, and a displacement sensor. The multi-source data of the area to be monitored includes waveform data, strain data, stress data, and roadway damage data.

[0022] For example, based on strain sensors, strain data such as deformation data of the surrounding rock in the roadway (such as the amount of convergence of the roof and floor plates and the amount of convergence of the two sides) and deformation data of the support structure (strain data of anchor bolts / anchor cables and deformation of U-shaped steel supports) are collected during the mine seismic process.

[0023] For example, stress sensors can be used to collect stress data such as stress change data of the rock mass surrounding the roadway during mine seismic events (e.g., changes in the original rock stress of the rock mass surrounding the roadway and changes in the load borne by the support structure) and stress concentration area distribution data during coal seam mining.

[0024] For example, tunnel damage data can be obtained by using images of surface crack development in tunnels captured by high-definition cameras, data on rock mass integrity obtained by acoustic testing instruments, and data on the movement of key points determined by displacement sensors.

[0025] S102. Perform preprocessing operations on the multi-source data to obtain the target multi-source data.

[0026] It should be noted that, in order to improve the accuracy of obtaining the seismic intensity of the area to be monitored, preprocessing can be performed on the multi-source data to reduce anomalies and improve the quality and consistency of the multi-source data in order to obtain the target multi-source data.

[0027] Optionally, wavelet transform and other algorithms can be used to denoise and filter the waveform data during the mine seismic process, eliminating the influence of noise such as mechanical vibration and electromagnetic interference, retaining effective signal characteristics, performing temperature compensation and drift correction on the strain and stress data collected by strain and stress sensors, eliminating outliers caused by strain and stress sensor failures or instantaneous interference, unifying data dimensions through standardization, performing crack identification and quantitative analysis on image data, converting acoustic test data into rock mass integrity index, and smoothing displacement data to obtain target multi-source data.

[0028] S103. Perform feature extraction on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data.

[0029] In this embodiment of the application, after obtaining the target multi-source data, feature extraction is performed on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data.

[0030] It should be noted that by working together with the target monitoring equipment, comprehensive data on the impact of mine seismic activity on the surrounding rock and support structure of the roadway can be collected, covering multi-dimensional characteristics, namely "energy characteristics, deformation state characteristics, stress change characteristics and damage characteristics", providing rich data support for obtaining the intensity of mine seismic activity and breaking the limitations of a single data source.

[0031] For example, key feature parameters can be extracted from the preprocessed target multi-source data to obtain key feature parameters such as PPV peak value, maximum strain value of surrounding rock, maximum deformation of support structure, peak stress of rock mass, and fracture development level, so as to obtain multi-dimensional feature parameters of target multi-source data.

[0032] S104. Obtain the seismic intensity of the area to be monitored based on multi-dimensional feature parameters.

[0033] In this embodiment of the application, the current seismic stage of the area to be monitored can be obtained. Based on the seismic stage, the weight value corresponding to each dimension feature parameter is determined, and the seismic intensity corresponding to each dimension feature parameter is determined. Based on the weight value, the seismic intensity corresponding to each dimension feature parameter is weighted and averaged to obtain the initial seismic intensity of the area to be monitored. The initial seismic intensity is then optimized to obtain the seismic intensity of the area to be monitored.

[0034] The seismic intensity monitoring method based on multi-source data fusion provided in this application involves identifying target monitoring equipment deployed within the monitoring area, collecting multi-source data from the monitoring area based on the target monitoring equipment, preprocessing the multi-source data to obtain target multi-source data, extracting features from the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data, and obtaining the seismic intensity of the monitoring area based on the multi-dimensional feature parameters. Thus, this application overcomes the limitations of a single data source by acquiring multi-source data and improves the accuracy and reliability of obtaining the seismic intensity of the monitoring area by fusing multi-source data.

[0035] Figure 2 This is a flowchart illustrating the method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment.

[0036] like Figure 2 As shown in the figure, the method for monitoring seismic intensity based on multi-source data fusion proposed in this embodiment specifically includes the following steps: S201. Determine the target monitoring equipment to be deployed in the area to be monitored, and collect multi-source data of the area to be monitored based on the target monitoring equipment.

[0037] S202. Perform preprocessing operations on the multi-source data to obtain the target multi-source data.

[0038] S203. Perform feature extraction on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data.

[0039] S204. Obtain the current seismic stage of the area to be monitored.

[0040] The mine earthquake stage includes the mine earthquake incubation stage, the mine earthquake occurrence stage, and the mine earthquake dissipation stage.

[0041] Optionally, the determination features of each seismic stage can be obtained, and the multi-dimensional feature parameters of the target multi-source data can be matched with the determination features of each seismic stage. Based on the matching results, the current seismic stage of the area to be monitored can be determined.

[0042] It should be noted that the core disaster-causing factor in the gestation stage of a mine earthquake is the accumulation of stress concentration. The characteristics of the gestation stage of a mine earthquake are that the stress data shows a continuous and slow upward trend (without sudden fluctuations), microseismic events show the precursor characteristics of "increasing frequency but extremely low energy", vibration signals remain at normal levels, and rock mass damage parameters (fractures, integrity) do not change significantly. The gestation stage of a mine earthquake is generally in a steady-state state of risk accumulation, with no signs of sudden energy release.

[0043] It should be noted that the core disaster-causing factor in the occurrence stage of a mine earthquake is the instantaneous release of energy. The defining characteristics of the occurrence stage of a mine earthquake are a sudden drop in stress data (energy release leads to stress unloading), the generation of significant dynamic response signals (consistent with the signal abrupt change characteristics of impact dynamic phenomena), and the simultaneous instantaneous deformation of the support structure or surrounding rock. The occurrence stage of a mine earthquake is characterized by rapid energy release.

[0044] It should be noted that the core disaster-causing factor in the retreat phase of a mine earthquake is the continuous evolution of damage. The defining characteristics of the retreat phase are: stress data decreases to a new stable level (without significant fluctuations), dynamic response signals continue to decay to a stable level, the frequency of microseismic events decreases and is dominated by extremely low-energy events, and the deformation rate of rock mass and support structure gradually decreases but continues (damage develops slowly over time). In the retreat phase of a mine earthquake, the energy has been largely dissipated, and damage becomes the core characteristic.

[0045] S205. Based on the mining seismic stage, determine the weight value corresponding to the feature parameter of each dimension.

[0046] In this embodiment of the application, a mapping relationship between each seismic stage and the weight value corresponding to each dimension feature parameter can be constructed. By querying the mapping relationship through the current seismic stage of the area to be monitored, the weight value corresponding to each dimension feature parameter can be determined.

[0047] For example, during the tremor incubation stage, the weight of the characteristic parameter corresponding to waveform data is 0.2 (capturing early weak vibration signals), the weight of the characteristic parameter corresponding to stress data is 0.5, the weight of the characteristic parameter corresponding to roadway damage data is 0.1, and the weight of the characteristic parameter corresponding to strain data is 0.1; during the tremor occurrence stage, the weight of the characteristic parameter corresponding to waveform data is 0.7, the weight of the characteristic parameter corresponding to strain data is 0.2, the weight of the characteristic parameter corresponding to stress data is 0.05, and the weight of the characteristic parameter corresponding to roadway damage data is 0.05; during the tremor dissipation stage, the weight of the characteristic parameter corresponding to strain data is 0.3, the weight of the characteristic parameter corresponding to roadway damage data is 0.3, the weight of the characteristic parameter corresponding to waveform data is 0.1, and the weight of the characteristic parameter corresponding to stress data is 0.1.

[0048] S206. Determine the seismic intensity corresponding to each dimension of the characteristic parameters.

[0049] In this embodiment of the application, after obtaining the feature parameters of each dimension, the feature parameters of each dimension are analyzed and processed to determine the seismic intensity corresponding to each feature parameter of each dimension.

[0050] S207. Based on the weight values, perform a weighted average fusion of the seismic intensity corresponding to each dimension feature parameter to obtain the initial seismic intensity of the area to be monitored.

[0051] In this embodiment of the application, in order to improve the accuracy of the seismic intensity of the area to be monitored, a weighted average calculation can be performed based on the weight value of each dimension feature parameter and the corresponding seismic intensity level to obtain the initial seismic intensity of the area to be monitored.

[0052] S208. Optimize the initial seismic intensity to obtain the seismic intensity of the area to be monitored.

[0053] In this embodiment of the application, the initial seismic intensity is optimized based on Kalman filtering to obtain the first seismic intensity of the area to be monitored, a correction coefficient for the first seismic intensity is determined, and the first seismic intensity is corrected according to the correction coefficient to obtain the seismic intensity of the area to be monitored.

[0054] For example, state equations can be constructed. Observation equation ,in, For the present The intensity of the mine earthquake at any given moment. For the present Multidimensional feature parameters at time, For the present The initial seismic intensity at that moment. For the present Process noise at any given moment (inherent sensor error). For the present Observation noise at any given time (environmental interference error). The state transition matrix (used to characterize the correlation between intensity at different times; the diagonal elements of the precipitating and receding phases are taken as identity matrices of 0.95-0.98; the precipitating phase is determined by linear regression fitting of historical data of similar precipitates over the past 3 years), B is the control matrix (used to quantify the influence coefficient of monitoring parameters on intensity, obtained by normalization after calculation through Pearson correlation analysis), and H is the observation matrix (matrix elements are the confidence weights of sensor parameters). Through iterative calculation of the above equations, the denoised intensity assessment value, i.e., the first precipitating intensity, is output. The intensity assessment value is then corrected by combining it with the current precipitating phase of the area to be monitored. That is, the correlation between multi-dimensional characteristic parameters and precipitating intensity is determined to obtain the goodness of fit. The correction coefficient is determined based on the goodness of fit. For example, when the goodness of fit is greater than or equal to 0.9, the correction coefficient is 0.05; when the goodness of fit is less than 0.9, the correction coefficient is 0.15. Based on the correction coefficient and the first precipitating intensity, the precipitating intensity of the area to be monitored is determined.

[0055] The method for monitoring seismic intensity in mines based on multi-source data fusion provided in this application This paper identifies target monitoring equipment to be deployed within the monitored area. Based on this equipment, multi-source data of the monitored area is collected. Preprocessing of the multi-source data yields target multi-source data. Feature extraction is performed on the target multi-source data to obtain multi-dimensional feature parameters. The current seismic stage of the monitored area is determined. Based on the seismic stage, the weight value corresponding to each dimension feature parameter is determined, as well as the seismic intensity corresponding to each dimension feature parameter. A weighted average fusion of the seismic intensities corresponding to each dimension feature parameter is performed based on the weight values ​​to obtain the initial seismic intensity of the monitored area. This initial seismic intensity is then optimized to obtain the final seismic intensity of the monitored area. Therefore, this application determines the weight value corresponding to each dimension feature parameter based on the seismic stage, improving the flexibility of weight value acquisition. This allows for responses to changes in core influencing factors at different seismic stages, improving the monitoring accuracy of seismic intensity under complex working conditions. Furthermore, optimizing the initial seismic intensity to obtain the final seismic intensity of the monitored area achieves continuous monitoring of seismic intensity throughout its entire lifecycle, improving the accuracy and comprehensiveness of seismic intensity acquisition and meeting the monitoring needs of the entire lifecycle of coal mine safety production. Figure 3 This is a flowchart illustrating the method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment.

[0056] like Figure 3 As shown in the figure, the method for monitoring seismic intensity based on multi-source data fusion proposed in this embodiment specifically includes the following steps: S301. Determine the target monitoring equipment to be deployed in the area to be monitored, and collect multi-source data of the area to be monitored based on the target monitoring equipment.

[0057] S302. Perform preprocessing operations on the multi-source data to obtain the target multi-source data.

[0058] S303. Perform feature extraction on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data.

[0059] S304. Obtain the seismic intensity of the area to be monitored based on multi-dimensional feature parameters.

[0060] S305. Generate isoseismal lines based on the seismic intensity of the area to be monitored.

[0061] S306. Align the roadway plan and isoseismal lines of the area to be monitored with coordinates, and merge the roadway plan and isoseismal lines to generate a mine seismic impact isoseismal line map.

[0062] For example, the roadway plan and isoseismal lines of the area to be monitored are aligned with coordinates and merged. The distribution range of different seismic intensity areas, key characteristic parameters (such as maximum PPV value and maximum deformation of surrounding rock) and damage images of key monitoring points are marked. At the same time, multi-source data of the area to be monitored are added to ensure that the process of obtaining the seismic intensity of the area to be monitored is traceable, so as to generate a seismic impact isoseismal map and realize a three-dimensional dynamic visualization of the seismic impact isoseismal map.

[0063] The method for monitoring seismic intensity based on multi-source data fusion provided in this application involves identifying target monitoring equipment deployed within the monitoring area, collecting multi-source data from the monitoring area based on the target monitoring equipment, preprocessing the multi-source data to obtain target multi-source data, extracting features from the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data, obtaining the seismic intensity of the monitoring area based on the multi-dimensional feature parameters, generating isoseismic lines based on the seismic intensity of the monitoring area, aligning the roadway plan and isoseismic lines of the monitoring area with coordinates, and merging the roadway plan and isoseismic lines to generate a seismic impact isoseismic map. Therefore, this application provides a more intuitive and clear understanding of the seismic intensity of the monitoring area through the seismic impact isoseismic map, improving the user experience and contributing to improved emergency response efficiency.

[0064] Figure 4 This is a flowchart illustrating the method for monitoring seismic intensity based on multi-source data fusion provided in this application embodiment.

[0065] like Figure 4 As shown in the figure, the method for monitoring seismic intensity based on multi-source data fusion proposed in this embodiment specifically includes the following steps: S401. Determine the target monitoring equipment to be deployed in the area to be monitored, and collect multi-source data of the area to be monitored based on the target monitoring equipment.

[0066] S402. Perform preprocessing operations on the multi-source data to obtain the target multi-source data.

[0067] S403. Perform feature extraction on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data.

[0068] S404. Obtain the seismic intensity of the area to be monitored based on multi-dimensional feature parameters.

[0069] S405. Based on the seismic intensity of the area to be monitored and multi-source data of the area to be monitored, generate seismic early warning information that matches the seismic stage.

[0070] For example, the probability of mine tremors is determined by multi-source data of the area to be monitored. Based on the probability of mine tremors, the intensity of mine tremors, the three-dimensional coordinate range of high-risk areas, the location of stress concentration core points, and recommended measures (such as increasing monitoring frequency and temporary support reinforcement), mine tremor early warning information matching the mine tremor incubation stage is generated and pushed to the safety management platform and field terminals.

[0071] For example, by comparing the deviation of data from each monitoring point with the baseline value, the validity of multi-source data in the area to be monitored is determined. Based on the spatial distribution of each monitoring point, the core impact area of ​​the seismic event is accurately located using the time difference positioning method. Based on the seismic intensity, epicenter coordinates, core impact area range, validity of multi-source data in the area to be monitored, and abnormal alarms from abnormal monitoring points, a seismic early warning message matching the stage of the seismic event is generated and pushed to the safety management platform and on-site terminals.

[0072] For example, starting 30 minutes after a mine tremor, multi-source data from the monitored area is used to predict the increase in surrounding rock deformation, crack propagation length, and propagation direction over the next 72 hours. Based on the prediction results, high-risk areas for secondary damage are identified, and corresponding recommended measures are obtained. For example, reinforcement is recommended within 1 hour for Level 1 areas (extremely high risk), within 4 hours for Level 2 areas (high risk), and within 12 hours for Level 3 areas (medium risk). Based on the mine tremor intensity, high-risk areas for secondary damage, and corresponding recommended measures, mine tremor early warning information matching the mine tremor receding stage is generated and pushed to the safety management platform and on-site terminals.

[0073] The method for monitoring mine seismic intensity based on multi-source data fusion provided in this application involves: identifying target monitoring equipment deployed within the monitoring area; collecting multi-source data of the monitoring area based on the target monitoring equipment; preprocessing the multi-source data to obtain target multi-source data; extracting features from the target multi-source data to obtain multi-dimensional feature parameters; obtaining the mine seismic intensity of the monitoring area based on the multi-dimensional feature parameters; and generating mine seismic early warning information matching the seismic stage based on the mine seismic intensity and the multi-source data of the monitoring area. Therefore, this application improves the accuracy of mine seismic early warning by generating mine seismic early warning information matching the mine seismic stage. Based on the mine seismic early warning information, corresponding measures can be taken in a timely manner, which is conducive to ensuring safe coal mine production. To implement the above embodiments, this embodiment provides a monitoring system for mine seismic intensity based on multi-source data fusion. Figure 5 This is a schematic diagram of the structure of a mine seismic intensity monitoring system based on multi-source data fusion, provided in an embodiment of this application.

[0074] like Figure 5 As shown, the monitoring system 1000 for seismic intensity based on multi-source data fusion includes: a data acquisition module 110, a preprocessing module 120, a feature extraction module 130, and an acquisition module 140.

[0075] The acquisition module 110 is used to identify target monitoring devices deployed in the area to be monitored, and to acquire multi-source data of the area to be monitored based on the target monitoring devices; Preprocessing module 120 is used to perform preprocessing operations on the multi-source data to obtain target multi-source data; Feature extraction module 130 is used to extract features from the target multi-source data and obtain multi-dimensional feature parameters of the target multi-source data; The acquisition module 140 is used to acquire the seismic intensity of the area to be monitored based on the multi-dimensional feature parameters.

[0076] According to one embodiment of this application, the acquisition module 110 is further configured to: acquire geological information of the area to be monitored; and determine the target monitoring equipment to be deployed in the area to be monitored based on the geological information.

[0077] According to one embodiment of this application, the acquisition module 110 is further configured to: acquire waveform data during the mine seismic process based on an intensity meter; acquire strain data during the mine seismic process based on a strain sensor; acquire stress data during the mine seismic process based on a stress sensor; and acquire roadway damage data during the mine seismic process based on an image acquisition device, an acoustic wave tester, and a displacement sensor; wherein the multi-source data of the area to be monitored includes the waveform data, the strain data, the stress data, and the roadway damage data.

[0078] According to one embodiment of this application, the acquisition module 140 is further configured to: acquire the current seismic stage of the area to be monitored; determine the weight value corresponding to each dimension feature parameter according to the seismic stage; determine the seismic intensity corresponding to each dimension feature parameter; perform weighted average fusion of the seismic intensity corresponding to each dimension feature parameter according to the weight value to acquire the initial seismic intensity of the area to be monitored; and optimize the initial seismic intensity to acquire the seismic intensity of the area to be monitored.

[0079] According to one embodiment of this application, the acquisition module 140 is further configured to: optimize the initial seismic intensity based on Kalman filtering to obtain a first seismic intensity of the area to be monitored; determine a correction coefficient for the first seismic intensity; and correct the first seismic intensity according to the correction coefficient to obtain the seismic intensity of the area to be monitored.

[0080] According to one embodiment of this application, the system 1000 is further configured to: generate isoseismal lines based on the seismic intensity of the area to be monitored; align the roadway plan of the area to be monitored with the isoseismal lines, and merge the roadway plan and the isoseismal lines to generate a seismic influence isoseismal line map.

[0081] According to one embodiment of this application, the system 1000 is further configured to: generate a mine earthquake early warning message matching the mine earthquake stage based on the mine earthquake intensity of the area to be monitored and multi-source data of the area to be monitored.

[0082] The seismic intensity monitoring system based on multi-source data fusion provided in this application identifies target monitoring equipment deployed within the monitoring area. Based on the target monitoring equipment, multi-source data of the monitoring area is collected. The multi-source data is preprocessed to obtain target multi-source data. Feature extraction is performed on the target multi-source data to obtain multi-dimensional feature parameters. Based on the multi-dimensional feature parameters, the seismic intensity of the monitoring area is obtained. Thus, this application breaks through the limitations of a single data source by acquiring multi-source data, and improves the accuracy and reliability of obtaining the seismic intensity of the monitoring area by fusing multi-source data.

[0083] It should be noted that the explanation of the above-mentioned embodiment of the monitoring method for seismic intensity based on multi-source data fusion also applies to the monitoring system for seismic intensity based on multi-source data fusion in this embodiment, and will not be repeated here.

[0084] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0085] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0086] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0088] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0090] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0091] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring mine seismic intensity based on multi-source data fusion, characterized in that, The method includes: Identify the target monitoring devices to be deployed in the area to be monitored, and collect multi-source data of the area to be monitored based on the target monitoring devices; Preprocessing operations are performed on the multi-source data to obtain target multi-source data; Feature extraction is performed on the target multi-source data to obtain multi-dimensional feature parameters of the target multi-source data; The seismic intensity of the monitored area is obtained based on the multi-dimensional feature parameters.

2. The method of claim 1, wherein, The determination of the target monitoring equipment deployed in the area to be monitored also includes: Obtain the geological information of the area to be monitored; Based on the geological information, the target monitoring equipment to be deployed in the area to be monitored is determined.

3. The method of claim 2, wherein, The step of collecting multi-source data of the area to be monitored based on the target monitoring device also includes: Waveform data during the mine seismic process were collected using an intensity meter. Based on strain sensors, strain data is collected during the mine seismic process; Stress data is collected during the mine seismic process based on stress sensors; Based on image acquisition equipment, acoustic wave tester and displacement sensor, roadway damage data during mine seismic processes are obtained; The multi-source data of the area to be monitored includes the waveform data, the strain data, the stress data, and the roadway damage data.

4. The method of claim 1, wherein, The step of obtaining the seismic intensity of the monitored area based on the multi-dimensional feature parameters includes: Obtain the current seismic stage of the monitored area; Based on the described mining shock stage, determine the weight value corresponding to each dimension feature parameter; Determine the seismic intensity corresponding to each dimension of feature parameters; Based on the weight values, the seismic intensity corresponding to each dimension feature parameter is weighted and averaged to obtain the initial seismic intensity of the area to be monitored. The initial seismic intensity is optimized to obtain the seismic intensity of the area to be monitored.

5. The method according to claim 4, characterized in that, The optimization of the initial seismic intensity to obtain the seismic intensity of the area to be monitored includes: Based on Kalman filtering, the initial seismic intensity is optimized to obtain the first seismic intensity of the area to be monitored; Determine the correction factor for the seismic intensity of the first mine; The first seismic intensity is corrected according to the correction coefficient to obtain the seismic intensity of the area to be monitored.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the seismic intensity of the area to be monitored, isoseismal lines are generated; The roadway plan and the isoseismic lines of the area to be monitored are aligned in coordinates, and the roadway plan and the isoseismic lines are merged to generate a mine seismic impact isoseismic line map.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the seismic intensity of the monitored area and multi-source data of the monitored area, a seismic early warning message matching the seismic stage is generated.

8. A monitoring system for mine seismic intensity based on multi-source data fusion, characterized in that, The system includes: The data acquisition module is used to identify target monitoring devices deployed in the area to be monitored, and to acquire multi-source data of the area to be monitored based on the target monitoring devices. The preprocessing module is used to perform preprocessing operations on the multi-source data to obtain target multi-source data; The feature extraction module is used to extract features from the target multi-source data and obtain multi-dimensional feature parameters of the target multi-source data; The acquisition module is used to acquire the seismic intensity of the area to be monitored based on the multi-dimensional feature parameters.

9. An electronic device comprising a processor and a memory; characterized in that, The processor runs a program corresponding to the executable program code stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

Images