Laser radar and meteorological early warning combined coal mine geological disaster monitoring method and system

Abstract

The invention belongs to the technical field of coal mining early warning, and discloses a coal mine geological disaster monitoring method and system based on combination of laser radar and meteorological early warning. The method comprises the following steps: deploying laser radars in three upper zones of an overground mining area and combining with meteorological early warning geological disaster monitoring equipment, and collecting the current motion frequencies of the three upper zones in real time to obtain a motion frequency curve; the upper three zones comprise a bending sinking zone, a fissure zone and a caving zone; the method comprises the following steps: deploying Beidou GNSS (Global Navigation Satellite System) in three zones of an overground mining area, smoothing an obtained motion frequency curve, and eliminating the influence of atmospheric delay errors; the motion frequency curve subjected to atmospheric delay error influence elimination and smooth processing is applied to the actual operation of the three-zone mining activity in the mining area above the well, and deformation information generated at a certain moment in the actual operation of the mining activity is obtained. According to the invention, the dynamic coupling high-precision monitoring of the surface geological disaster deformation on the ground in the coal mining activity is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of coal mining early warning, and in particular relates to a method and system for monitoring coal mine geological disasters combined with laser radar and meteorological early warning. Background Art

[0002] The field of geohazard analysis technology is an interdisciplinary field focused on identifying, assessing, and predicting the likelihood of geohazards. This field combines knowledge and methods from multiple disciplines, including geology, geophysics, geographic information systems (GIS), remote sensing technology, computer science, statistics, and engineering.

[0003] The goal of the field of geological hazard analysis technology is to improve understanding of geological hazards, reduce the risk of disasters, and effectively respond and rescue when disasters occur. This field is of great significance for protecting people's lives and property and promoting sustainable social development.

[0004] The existing invention patent, a coal mine geological disaster monitoring and early warning method and device (publication number CN118629165A, publication date 2024.09.10) is disclosed:

[0005] The invention uses sensors to monitor the environmental parameters inside the mine in real time, and based on the environmental parameters, it evaluates the current safety of the mine and determines whether there is a collapse anomaly. The environmental parameters include temperature, humidity, gas concentration, and vibration parameters. When a collapse anomaly is identified, the current environmental parameters of each channel in the mine are obtained, and the risk of each channel in the collapse anomaly event is analyzed based on the location of the collapse anomaly and the distance between the channels in the mine. Based on the risk analysis results of each channel, a safe path for escaping from the mine is evaluated. When all channels cannot safely escape the mine, an area without collapse risk is identified, and a safe path to move to this area is evaluated. The safe path is synchronized with the light line and marked with the light. When personnel reach the area without collapse risk, the physical property data of the rock formation in the current area is obtained through sensors, and a geological model is established to identify areas where drilling ventilation and areas where excavation can be used for rescue are identified and marked in the geological model. This invention not only relies on advanced technical support, but also lies in its ability to flexibly adjust and optimize according to actual conditions to ensure that the best solution can be provided in various complex situations.

[0006] Furthermore, in the mining process, goaf settlement monitoring is an important part of ensuring the safety of the mining process. If the settlement of the goaf is not monitored, it will cause mining disasters and surface collapse. In order to ensure the safety of the mining process, it is necessary to monitor the settlement of the goaf.

[0007] Since the accuracy and real-time nature of settlement monitoring data are crucial to the management and safety of goafs, in order to understand the settlement of goafs in real time and to take necessary measures in a timely manner, it is necessary to monitor the settlement of goafs regularly. However, when monitoring the settlement of goafs, the frequency of collecting monitoring data can only be collected at pre-set time intervals. However, a single data collection frequency will affect the accuracy and real-time nature of monitoring. When the data collection frequency is too high, it will lead to data redundancy and increase data processing costs. When the collection frequency is too low, key settlement velocity change information may be missed, thereby affecting the accuracy of monitoring. That is, how to dynamically set the collection frequency according to the actual situation of mining to improve the accuracy of monitoring data.

[0008] The invention patent "Method and system for dynamic coupling monitoring of coal mine goaf deformation based on multi-source data" (publication number CN119064929A, publication date 2024.12.03) discloses a method for dynamic coupling monitoring of coal mine goaf deformation based on multi-source data. The method includes: S1, mining area settlement monitoring based on Beidou GNSS and D-InSAR fusion technology, establishing a GNSS continuous observation station network in the well mining area, constructing a regional water vapor model and an atmospheric delay error correction model, completing the fusion model and algorithm fusion processing of the time domain and space domain; using the double interpolation double estimation DIDP algorithm to realize the encryption of GNSS and D-InSAR monitoring data, weakening the atmospheric delay error Impact; S2, for the mining area settlement monitoring information obtained after weakening the influence of atmospheric delay error, for the decoherence caused by the large gradient deformation in the settlement area of the mining area, the mining area deformation monitoring method based on the SBAS-InSAR and GNSS coupling technology of the logarithmic logic model is used to calculate the deformation information generated by the mining activity at any time; S3, the electrical method system and the microseismic monitoring system are deployed respectively to carry out underground resistivity and microseismic combined stress change monitoring to realize the delineation of large gradient anomalies in the underground mining area; combined with the underground mining area settlement deformation monitoring information, combined with the underground resistivity and microseismic combined stress change monitoring, coordinated monitoring is carried out to complete the coordinated dynamic coupling monitoring of the deformation process of the goaf. This invention conducts coordinated dynamic coupling monitoring of the deformation process of the three zones above the goaf (curved subsidence zone, fracture zone and caving zone).

[0009] However, existing coal mine geological disaster monitoring methods may only focus on a single factor or local area, lacking comprehensive consideration of the entire mine system. During the deformation process of the three zones above the goaf (curved subsidence zone, fracture zone and caving zone), lidar cannot be effectively used in combination with meteorological warnings, and the acquisition and reporting frequency cannot be coordinated with dynamic coupling monitoring, resulting in low accuracy of dynamic coupling monitoring of deformation above the goaf, and the effect of coal mine geological disaster monitoring needs to be further improved. Summary of the Invention

[0010] In order to overcome the problems existing in the relevant technologies, the embodiments disclosed in the present invention provide a method and system for monitoring coal mine geological disasters using a laser radar combined with meteorological early warning, specifically a method and system for monitoring the collection and reporting frequency of geological disaster monitoring equipment using a laser radar combined with meteorological early warning.

[0011] The technical solution is as follows: A method for monitoring coal mine geological disasters using laser radar combined with meteorological warning, comprising the following steps:

[0012] S1: Deploy LiDAR and meteorological early warning geological disaster monitoring equipment in the upper three zones of the mining area. The system collects the current movement frequency of the upper three zones in real time and obtains the movement frequency curve. The upper three zones include the curved subsidence zone, the fracture zone, and the caving zone.

[0013] S2, deploying BeiDou GNSS in the upper three zones of the well mining area, smoothing the acquired motion frequency curves and eliminating the influence of atmospheric delay errors;

[0014] S3, which eliminates the influence of atmospheric delay error and smoothes the motion frequency curve, is applied to the actual operation of mining activities in the upper three zones of the aboveground mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, and realize high-precision dynamic coupling monitoring of deformation of aboveground surface geological disasters in coal mining activities.

[0015] In step S1, a motion frequency curve is obtained, including:

[0016] (1) Considering different ratios of the maximum allowable value of geological disasters for early warning to the area ratio of the ideal allowable value of geological disasters for early warning, K1, the traditional default is that the allowable value is less than 0.25% H, where H is the height of each upper three zones. At this time, the current movement state of the upper three zones reaches normal deformation;

[0017] (2) With the outer edge of the ideal early warning geological disaster allowable value as the origin, a rectangular coordinate system is established along the axial x and radial y. E0 is the product of the ideal upper three-zone deformation early warning geological disaster allowable values, and E h is the product of the allowable values of geological disasters in the upper three zones, h is the length of the deformation of the upper three zones; the allowable value area of geological disasters in a certain place in the deformation of the upper three zones is E c The right subscript c represents the distance between the warning geological disaster allowable value and the ideal warning geological disaster allowable value. The outer edge of the maximum upper three-zone warning geological disaster allowable value is based on G h The other endpoint of the three-band deformation on the curve is changed with the change of , and the deformation shape of the upper three-band is designed as a shape function e(c), which has the characteristic of continuous monotonous increasing.

[0018] Furthermore, the calculation method for the area ratio K1 of the maximum warning geological disaster allowable value to the ideal warning geological disaster allowable value is greater than 1 includes:

[0019] Optimize the (2a+1) motion frequency to ensure that the cross-sectional area ratio is at least greater than The larger the cross-sectional area ratio, the greater the warning degree of the optimized motion frequency, and different curve shapes are designed under different cross-sectional area ratios.

[0020] Furthermore, the mathematical model of the motion frequency of the three-band deformation on the curve is obtained as follows:

[0021]

[0022] Where, γ c is the lidar electromagnetic saturation of the three-band deformation on the microelement at the distance c from the ideal early warning geological disaster allowable value; γ0 is the lidar electromagnetic saturation of the ideal early warning geological disaster allowable value, the initial state is 0 when no lidar electromagnetic is added, and the maximum value is γ 0max =(G h -1)π;o h is the nominal value of the fundamental current of the laser radar, F t0 is the electromagnetic induction intensity of the lidar when the allowable value of the ideal early warning geological disaster is critically saturated, o (2a+1) is the nominal value of the 2a+1th motion frequency current, C is the number of layers in each of the upper three bands, is the electromagnetic radiation rate of vacuum lidar, and a is the ath lidar band.

[0023] In step S2, BeiDou GNSS is deployed in the upper three zones of the well mining area, and the acquired motion frequency curve is smoothed, including:

[0024] Input the real-time acquired motion frequency curve data and process it using the exponential smoothing formula. The expression is:

[0025] A0(I)=χm0(I)+(1-χ)A0(I-1)

[0026] Where A0(I) is the smoothing sequence, χ is the smoothing coefficient, I is a node value of the motion frequency curve, m0(I) is the initial motion frequency curve data sequence, and A0(I-1) is the smoothing sequence of the I-1 node of the motion frequency curve;

[0027] In step S3, deformation information generated at a certain moment during the actual operation of the mining activity is obtained, including:

[0028] The main feature analysis is used to reduce the dimension of the motion frequency feature vector, and the feature classifier is used to realize the deformation identification of the three zones in the mining area radiated by the laser radar at a certain moment.

[0029] By linearly projecting the original motion frequency variable to form a new variable, the main feature of the feature is calculated, and the expression is:

[0030]

[0031] Where η is the main feature, ζ is the T is the covariance matrix, The features that need dimensionality reduction is the feature mean of the training sample;

[0032] Covariance matrix ζ T The calculation formula is:

[0033]

[0034] Where B is the number of training samples, Take the T-order derivative of the deviation between the reduced feature and the feature mean of the training sample.

[0035] Furthermore, the feature classifier uses a kernel function to map the motion frequency feature vector R of the input space to a high-dimensional feature space, and searches for a generalized optimal classification surface in the high-dimensional space. The expression is:

[0036]

[0037] Where θ(η) is the generalized optimal classification surface vector found in high-dimensional space after the main feature mapping, Γ is the classification coefficient, is the mapping deviation;

[0038] The larger the distance between the motion frequency sample and the classification surface, the smaller the misclassification of the motion frequency detection sample. The motion frequency feature sample point is (k g ,τ h ), the motion frequency sample category is identified as τ h ={-1,+1}, the expression is:

[0039]

[0040] When ||Γ|| is minimum, the classification interval is maximum, which is equivalent to making ||Γ|| 2 / 2 minimum.

[0041] Another object of the present invention is to provide a laser radar combined with meteorological early warning coal mine geological disaster monitoring system, which implements the laser radar combined with meteorological early warning coal mine geological disaster monitoring method, and the system includes:

[0042] The motion frequency curve acquisition module is used to deploy lidar and meteorological early warning geological disaster monitoring equipment in the upper three zones of the mining area, and collect the current motion frequency of the upper three zones in real time to obtain the motion frequency curve; the upper three zones include: curved subsidence zone, fracture zone and caving zone;

[0043] The smoothing processing module is used to deploy BeiDou GNSS in the upper three zones of the well mining area, smooth the acquired motion frequency curve, and eliminate the influence of atmospheric delay error;

[0044] The deformation information acquisition module is used to eliminate the influence of atmospheric delay errors and smooth the motion frequency curve. It is applied to the actual operation of mining activities in the upper three zones of the aboveground mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, and realize high-precision monitoring of the dynamic coupling of surface geological disaster deformation in coal mining activities.

[0045] Furthermore, the laser radar combined with meteorological early warning coal mine geological disaster monitoring system is installed on a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by the processor, it can realize the functions of the above-mentioned laser radar combined with meteorological early warning coal mine geological disaster monitoring system.

[0046] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the method for monitoring coal mine geological disasters using the laser radar combined with meteorological warning.

[0047] Combining all of the above technical solutions, the present invention achieves the following beneficial effects: It uses surface-level LiDAR combined with meteorological early warning technology to dynamically couple the deformation process of three zones above the goaf (the curved subsidence zone, the fracture zone, and the caving zone). This leverages the spatiotemporal complementarity and process synergy of multiple methods, and deploys BeiDou GNSS at appropriate on-site locations to eliminate the impact of atmospheric delay errors, enabling high-precision dynamic coupling monitoring of deformation above the goaf. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure;

[0049] Figure 1 This is a flow chart of a method for monitoring coal mine geological disasters using a laser radar combined with meteorological warnings, as provided in an embodiment of the present invention;

[0050] Figure 2 is a motion frequency curve diagram provided by an embodiment of the present invention;

[0051] Figure 3 This is a diagram of a coal mine geological disaster monitoring system using a laser radar combined with meteorological warnings, as provided in an embodiment of the present invention;

[0052] Figure 4 This is a relationship diagram between the motion frequencies of the upper three bands detected by the laser radar at 10 kHz provided by an embodiment of the present invention and the geological disaster deformation warning values of the meteorological early warning geological disaster monitoring equipment;

[0053] Figure 5 This is a relationship diagram between the motion frequencies of the upper three bands detected by the laser radar at 130 kHz provided by an embodiment of the present invention and the geological disaster deformation warning values of the meteorological early warning geological disaster monitoring equipment;

[0054] In the figure: 1. Motion frequency curve acquisition module; 2. Smoothing processing module; 3. Deformation information acquisition module. DETAILED DESCRIPTION

[0055] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Example 1, as Figure 1 As shown, the method for monitoring coal mine geological disasters by combining laser radar with meteorological warning provided by the embodiment of the present invention includes:

[0057] S1: Deploy LiDAR and meteorological early warning geological disaster monitoring equipment in the upper three zones of the mining area. The system collects the current movement frequency of the upper three zones in real time and obtains the movement frequency curve. The upper three zones include the curved subsidence zone, the fracture zone, and the caving zone.

[0058] S2, deploying BeiDou GNSS in the upper three zones of the well mining area, smoothing the acquired motion frequency curves and eliminating the influence of atmospheric delay errors;

[0059] S3, which eliminates the influence of atmospheric delay error and smoothes the motion frequency curve, is applied to the actual operation of mining activities in the upper three zones of the aboveground mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, and realize high-precision dynamic coupling monitoring of deformation of aboveground surface geological disasters in coal mining activities.

[0060] Exemplarily, in step S1, obtaining the motion frequency curve includes:

[0061] (1) Considering different area ratios K1 of the maximum allowable value of early warning geological disasters and the ideal allowable value of early warning geological disasters, the traditional default is that the allowable value is less than 0.25% H (H is the height of each level of the upper three belts). At this time, the current movement state of the upper three belts can achieve normal deformation. The present invention can consider different area ratios in the process of optimization design, with the area of the ideal allowable value of early warning geological disasters 0.01% H as "1", and the maximum allowable value of early warning geological disasters is temporarily set to be greater than 1 and less than 25.

[0062] (2) With the outer edge of the ideal early warning geological disaster allowable value as the origin, a rectangular coordinate system is established along the axial and radial directions as shown in the figure. E0 is the product of the ideal upper three-zone deformation early warning geological disaster allowable values, E h is the product of the allowable values of geological disasters in the upper three zones, h is the length of deformation in the upper three zones. The allowable value area of geological disasters in the upper three zones (microelement) is E c The right subscript c represents the distance between the warning geological disaster allowable value and the ideal warning geological disaster allowable value. The outer edge of the maximum upper three-zone warning geological disaster allowable value is based on G h The other endpoint of the three-band deformation on the curve is changed, and the shape of the upper three-band deformation is designed as a shape function e(c). The function has the characteristics of continuous monotonous increase. The shape function e(c), such as Figure 2 As shown in the motion frequency curve;

[0063] The calculation method of the ratio K1 of the maximum allowable value of early warning geological disasters to the area of the ideal allowable value of early warning geological disasters is greater than 1 includes:

[0064] When optimizing the (2a+1)th motion frequency (3, 5, 7... motion frequency), the cross-sectional area ratio is at least greater than The larger the cross-sectional area ratio, the greater the warning degree of the optimized motion frequency, and different curve shapes are designed under different cross-sectional area ratios.

[0065] The above differences can be used to obtain the mathematical model of the motion frequency of the three-band deformation on the curve:

[0066]

[0067] Where, γ c is the lidar electromagnetic saturation of the three-band deformation on the microelement at the distance c from the ideal early warning geological disaster allowable value; γ0 is the lidar electromagnetic saturation of the ideal early warning geological disaster allowable value, the initial state is 0 when no lidar electromagnetic is added, and the maximum value is γ 0max =(G h -1)π;o h is the nominal value of the fundamental current of the laser radar, F t0 is the electromagnetic induction intensity of the lidar when the allowable value of the ideal early warning geological disaster is critically saturated, o (2a+1) is the nominal value of the 2a+1th motion frequency current, C is the number of layers in each of the upper three bands, is the electromagnetic radiation rate of vacuum lidar, and a is the ath lidar band.

[0068] Exemplarily, in step S2, BeiDou GNSS is deployed in the upper three zones of the well mining area to smooth the acquired motion frequency curve and eliminate the influence of atmospheric delay error;

[0069] Input the motion frequency curve data obtained in real time and use the exponential smoothing formula:

[0070] A0(I)=χm0(I)+(1-χ)A0(I-1)

[0071] Where A0(I) is the smoothing sequence, χ is the smoothing coefficient, and I is the value of a certain node of the motion frequency curve. The motion frequency curve data with random fluctuations are smoothed to improve the smoothness of the sequence, weaken its randomness, and make it closer to the deformation development trend of the upper three belts in the mining area; m0(I) is the initial motion frequency curve data sequence, and A0(I-1) is the smoothing sequence of the I-1 node of the motion frequency curve.

[0072] Exemplary methods for eliminating the influence of atmospheric delay error can adopt a relatively classic method, which is to use the error propagation law to quantitatively analyze the influence of atmospheric delay error on elevation measurement and deformation measurement (two-track method, three-track method and four-track method), make error propagation curves, and obtain the influence law of atmospheric delay error on the obtained motion frequency measurement, that is, the influence law of the three-band deformation measurement in the mining area.

[0073] In step S3, the method for obtaining deformation information generated at a certain moment during the actual operation of the mining activity includes:

[0074] The main feature analysis is used to reduce the dimension of the motion frequency feature vector, and the feature classifier is used to realize the deformation identification of the three zones in the mining area radiated by the laser radar at a certain moment.

[0075] By linearly projecting the original motion frequency variable to form a new variable, the main feature of the feature is calculated:

[0076]

[0077] Where η is the main feature, ζ is the T is the covariance matrix, The features that need dimensionality reduction is the feature mean of the training sample;

[0078] Covariance matrix ζ T The calculation formula is:

[0079]

[0080] In the formula, B is the number of training samples, Take the T-order derivative of the deviation between the reduced feature and the feature mean of the training sample.

[0081] For example, the feature classifier uses a kernel function to map the motion frequency feature vector Y in the input space to a high-dimensional feature space, and searches for a generalized optimal classification surface in the high-dimensional space. The general form is:

[0082]

[0083] Where θ(η) is the generalized optimal classification surface vector found in high-dimensional space after the main feature mapping, Γ is the classification coefficient, is the mapping deviation;

[0084] The optimal classification surface requires that the distance between the motion frequency sample closest to the classification surface is as large as possible, and the motion frequency feature sample point (k g ,τ h ), motion frequency sample category identifier τ h ={-1,+1}, then the possibility of motion frequency detection samples being misclassified will be smaller: the expression is:

[0085]

[0086] When ||Γ|| is minimum, the classification interval is maximum, which is equivalent to making ||Γ|| 2 / 2 minimum.

[0087] It can be seen from the above embodiments that the present invention improves the data of high-precision dynamic coupled monitoring of surface geological disaster deformation in coal mining activities from the original 87.26% to about 95.23%.

[0088] Example 2, as Figure 3 As shown, the laser radar combined with meteorological early warning coal mine geological disaster monitoring system provided by the embodiment of the present invention includes:

[0089] The motion frequency curve acquisition module 1 is used to deploy a laser radar combined with meteorological early warning geological disaster monitoring equipment in the upper three zones of the mining area, and collect the current motion frequency of the upper three zones in real time to obtain the motion frequency curve; the upper three zones include: the curved subsidence zone, the fracture zone, and the caving zone;

[0090] Smoothing processing module 2 is used to deploy BeiDou GNSS in the upper three zones of the well mining area, smooth the acquired motion frequency curve, and eliminate the influence of atmospheric delay error;

[0091] The deformation information acquisition module 3 is used to eliminate the influence of atmospheric delay errors and smooth the motion frequency curve. It is applied to the actual operation of mining activities in the upper three zones of the aboveground mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, and realize high-precision monitoring of the dynamic coupling of surface geological disaster deformation in coal mining activities.

[0092] like Figure 4 、 Figure 5As shown in the figure, A0-A6 are the development trend subsequences of the motion frequency; the horizontal axis is the motion frequency of the upper three bands detected by the lidar, and the vertical axis is the geological disaster deformation warning value of the meteorological early warning geological disaster monitoring equipment. It can be seen that the present invention can effectively predict geological disasters by combining lidar with meteorological early warning geological disaster monitoring equipment.

[0093] The above description is only a preferred specific implementation method of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for monitoring coal mine geological disasters using laser radar combined with meteorological warning, characterized in that: The method comprises the following steps: S1: Deploy LiDAR and meteorological early warning geological disaster monitoring equipment in the upper three zones of the mining area. The system collects the current movement frequency of the upper three zones in real time and obtains the movement frequency curve. The upper three zones include the curved subsidence zone, the fracture zone, and the caving zone. S2, deploying BeiDou GNSS in the upper three zones of the well mining area, smoothing the acquired motion frequency curves and eliminating the influence of atmospheric delay errors; S3, which eliminates the influence of atmospheric delay error and smoothes the motion frequency curve, is applied to the actual operation of mining activities in the upper three zones of the aboveground mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, and realize high-precision dynamic coupling monitoring of deformation of aboveground surface geological disasters in coal mining activities.

2. The method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to claim 1, characterized in that: In step S1, a motion frequency curve is obtained, including: (1) Considering different ratios of the maximum allowable value of geological disasters for early warning to the area ratio of the ideal allowable value of geological disasters for early warning, K1, the traditional default is that the allowable value is less than 0.25% H, where H is the height of each upper three zones. At this time, the current movement state of the upper three zones reaches normal deformation; (2) With the outer edge of the ideal early warning geological disaster allowable value as the origin, a rectangular coordinate system is established along the axial x and radial y. E0 is the product of the ideal upper three-zone deformation early warning geological disaster allowable values, and E h is the product of the allowable values of geological disasters in the upper three zones, h is the length of the deformation of the upper three zones; the allowable value area of geological disasters in a certain place in the deformation of the upper three zones is E c The right subscript c represents the distance between the warning geological disaster allowable value and the ideal warning geological disaster allowable value. The outer edge of the maximum upper three-zone warning geological disaster allowable value is based on G h The other endpoint of the three-band deformation on the curve is changed with the change of , and the deformation shape of the upper three-band is designed as a shape function e(c), which has the characteristic of continuous monotonous increasing.

3. The method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to claim 2, characterized in that: The calculation method for the area ratio K1 of the maximum allowable value of early warning geological hazards to the ideal allowable value of early warning geological hazards is greater than 1 includes: Optimize the (2a+1) motion frequency to ensure that the cross-sectional area ratio is at least greater than The larger the cross-sectional area ratio, the greater the warning degree of the optimized motion frequency, and different curve shapes are designed under different cross-sectional area ratios.

4. The method for monitoring coal mine geological disasters using laser radar combined with meteorological warning according to claim 2, characterized in that: The mathematical model of the motion frequency of the three-band deformation on the curve is obtained as follows: Where, γ c is the lidar electromagnetic saturation of the three-band deformation on the microelement at the distance c from the ideal early warning geological disaster allowable value; γ0 is the lidar electromagnetic saturation of the ideal early warning geological disaster allowable value, the initial state is 0 when no lidar electromagnetic is added, and the maximum value is γ 0max =(G h -1)π;o h is the nominal value of the fundamental current of the laser radar, F t0 is the electromagnetic induction intensity of the lidar when the allowable value of the ideal early warning geological disaster is critically saturated, O (2a+1) is the nominal value of the 2a+1th motion frequency current, C is the number of layers in each of the upper three bands, is the electromagnetic radiation rate of vacuum lidar, and a is the ath lidar band.

5. The method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to claim 1, characterized in that: In step S2, BeiDou GNSS is deployed in the upper three zones of the well mining area, and the acquired motion frequency curve is smoothed, including: Input the real-time acquired motion frequency curve data and process it using the exponential smoothing formula. The expression is: A0(I)=xm0(I)+(1-χ)A0(I-1) Where A0(I) is the smoothing sequence, χ is the smoothing coefficient, I is a node value of the motion frequency curve, m0(I) is the initial motion frequency curve data sequence, and A0(I-1) is the smoothing sequence of the I-1 node of the motion frequency curve.

6. The method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to claim 1, characterized in that: In step S3, deformation information generated at a certain moment during the actual operation of the mining activity is obtained, including: The main feature analysis is used to reduce the dimension of the motion frequency feature vector, and the feature classifier is used to realize the deformation identification of the three zones in the mining area radiated by the laser radar at a certain moment. By linearly projecting the original motion frequency variable to form a new variable, the main feature of the feature is calculated, and the expression is: Where η is the main feature, ζ is the T is the covariance matrix, is the feature that needs dimensionality reduction, is the feature mean of the training sample; Covariance matrix ζ T The calculation formula is: In the formula, B is the number of training samples, Take the T-order derivative of the deviation between the reduced feature and the feature mean of the training sample.

7. The method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to claim 6, characterized in that: The feature classifier uses a kernel function to map the motion frequency feature vector R of the input space to a high-dimensional feature space, and searches for the generalized optimal classification surface in the high-dimensional space. The expression is: Where θ(η) is the generalized optimal classification surface vector found in high-dimensional space after the main feature mapping, Γ is the classification coefficient, is the mapping deviation; The larger the distance between the motion frequency sample and the classification surface, the smaller the misclassification of the motion frequency detection sample. The motion frequency feature sample point is (k g ,τ h ), the motion frequency sample category is identified as τ h ={-1,+1}, the expression is: When ||Γ|| is minimum, the classification interval is maximum, which is equivalent to making ||Γ|| 2 / 2 minimum.

8. A laser radar combined with meteorological early warning coal mine geological disaster monitoring system, characterized in that: The system implements the method for monitoring coal mine geological disasters by combining laser radar with meteorological warning according to any one of claims 1 to 7, and the system comprises: The motion frequency curve acquisition module (1) is used to deploy a laser radar combined with a meteorological early warning geological disaster monitoring device in the upper three zones of the mining area, and collect the current motion frequency of the upper three zones in real time to obtain a motion frequency curve; the upper three zones include: a curved subsidence zone, a fracture zone, and a caving zone; The smoothing processing module (2) is used to deploy BeiDou GNSS in the upper three zones of the well mining area, smooth the acquired motion frequency curve, and eliminate the influence of atmospheric delay error; The deformation information acquisition module (3) is used to eliminate the influence of atmospheric delay error and smooth the motion frequency curve, and is applied to the actual operation of the upper three-zone mining activities in the mine mining area to obtain the deformation information generated at a certain moment in the actual operation of the mining activities, so as to realize the high-precision dynamic coupling monitoring of the deformation of the mine surface geological disasters in the coal mining activities.

9. The laser radar combined with meteorological early warning coal mine geological disaster monitoring system according to claim 8 is characterized in that: The laser radar combined with meteorological early warning coal mine geological disaster monitoring system is carried on a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by the processor, it realizes the functions of the above-mentioned laser radar combined with meteorological early warning coal mine geological disaster monitoring system.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the processor executes the method for monitoring coal mine geological disasters using a laser radar combined with meteorological warning as described in any one of claims 1 to 7.

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