Multi-parameter correlation warning method and system for dangerous rock based on comprehensive perception in air, space, and ground

Abstract

The present invention provides a multi-parameter correlation warning method and system for dangerous rocks based on comprehensive perception of air, space, ground and interior, which belongs to the technical field of dangerous rock monitoring. The invention deploys an integrated "air-space-ground-interior" monitoring system in the dangerous rock monitoring area, integrates multiple and multi-scale dangerous rock monitoring data, and uses precursor information of dangerous rock instability to construct a multi-parameter warning system with data complementarity, data fusion and data association. It solves the problem of one-sided data when predicting with a single factor or a single scale information, and realizes data complementarity and comprehensive warning. At the same time, based on multiple dangerous rock instability cases and corrected data streams, a training database for monitoring dangerous rock bodies is generated, and a warning function model containing multi-parameter information is constructed, so as to evaluate and predict the development trend of the dangerous rock monitoring data stream and the stable state of the dangerous rock body, and make warning instructions according to the warning level of the dangerous rock body, thereby improving the warning accuracy and timeliness of dangerous rock collapse disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of dangerous rock mass monitoring, and in particular to a dangerous rock multi-parameter correlation early warning method and system based on integrated perception in the air, space, and ground. Background Art

[0002] Crustal movement and open-pit mining activities can easily form high and steep rock masses. Under the combined effects of vibration, rain erosion, external forces and other factors, adverse phenomena such as tensile cracks, local rock shedding, and rock layer structure damage will appear on the high and steep rock masses, thus forming dangerous rock masses and eventually causing collapse disasters, posing a serious threat to human activities and safe production.

[0003] Accurately predicting the deformation and failure of dangerous rock masses is extremely difficult due to their complexity, randomness, and uncertainty. The key to accurately predicting the deformation evolution of dangerous rock mass disasters and providing accurate forecasts before they occur is to establish an intelligent early warning system for dangerous rock masses. However, existing technologies mostly rely on single dangerous rock mass data parameters and often operate independently, with data not being complementary. The setting of early warning thresholds often lacks scientific basis, resulting in inadequate utilization of monitoring data and limited early warning effectiveness. Summary of the Invention

[0004] In order to solve the problems in the above-mentioned prior art, the present invention provides a multi-parameter correlation warning method and system for dangerous rocks based on comprehensive perception in the air, space, ground and interior. The invention deploys an integrated "air-space-ground-interior" monitoring system in the dangerous rock monitoring area, integrates various and multi-scale dangerous rock monitoring data, and uses the precursor information of dangerous rock instability to build a multi-parameter warning system with data complementarity, data fusion, and data association. It solves the problem of one-sided data when predicting with a single factor or a single scale information, and realizes data complementarity and comprehensive warning. At the same time, a training database for monitoring dangerous rock bodies is generated based on multiple dangerous rock instability cases and corrected data streams, and a warning function model containing multi-parameter information is constructed, so as to evaluate and predict the development trend of the dangerous rock monitoring data stream and the stable state of the dangerous rock body, and make warning instructions according to the warning level of the dangerous rock body, thereby improving the warning accuracy and timeliness of dangerous rock collapse disasters. To achieve the above purpose, the technical solution is as follows:

[0005] On the one hand, the present invention provides a dangerous rock multi-parameter correlation early warning method based on integrated perception in the air, space, and ground, the method comprising:

[0006] S1. By collating and analyzing the precursor information of dangerous rock mass instability cases, the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters are obtained using the orthogonal design method;

[0007] S2. Based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters, a training database of multi-parameter correlation is obtained through training with a machine learning algorithm;

[0008] S3. Obtain image data of the dangerous rock mass in the monitoring area through a drone to obtain a three-dimensional model of the monitored dangerous rock mass;

[0009] S4. Based on the three-dimensional model of the monitored dangerous rock mass, a correction data stream is obtained by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process;

[0010] S5. Retrain and update the corrected data stream and the multi-parameter associated training database to obtain a training database for monitoring dangerous rock masses;

[0011] S6. Deploy a monitoring system for the dangerous rock mass in the monitoring area to obtain monitoring data information of the dangerous rock mass;

[0012] S7. Based on the monitoring data information of the monitored dangerous rock body and the training database of the monitored dangerous rock body, normalization and weighting processing are performed to obtain a monitoring data stream for monitoring the dangerous rock body;

[0013] S8. According to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, the development trend and warning level of the monitored dangerous rock body are obtained, and a warning indication is made.

[0014] Optionally, in S1, by collating and analyzing precursor information of dangerous rock mass instability cases, an orthogonal design method is used to obtain weight factors of parameters related to dangerous rock mass instability and correlation coefficients between the parameters, including:

[0015] S11. Based on the precursor information of the dangerous rock mass instability case, parameters related to the dangerous rock mass instability are extracted and normalized to obtain dimensionless parameters of multiple precursor information. The parameters related to the dangerous rock mass instability include: three-dimensional coordinate change of characteristic points, displacement, inclination, acceleration, microseismic energy, and structural surface slip;

[0016] S12. Based on the dimensionless parameters of the multiple precursor information, an orthogonal design method is used to obtain weight factors of parameters related to instability of the dangerous rock mass;

[0017] S13. Based on the dimensionless parameter and the weight factor of the parameter related to the instability of the dangerous rock mass, an orthogonal design method is used to perform a correlation analysis to obtain a correlation coefficient between the parameters.

[0018] Optionally, in S3, image data of the dangerous rock mass in the monitoring area is obtained by using a drone to obtain a three-dimensional model of the monitored dangerous rock mass, including:

[0019] S31. Deploy multiple image control points on the dangerous rock mass in the monitoring area, and use RTK positioning technology to obtain the three-dimensional coordinates of the multiple image control points;

[0020] S32, collecting multi-angle images of the dangerous rock mass in the monitoring area by using a drone to obtain two-dimensional image data of the monitored dangerous rock mass;

[0021] S33 , obtaining a three-dimensional model of the monitored dangerous rock body using SFM three-dimensional image reconstruction technology based on the two-dimensional image data of the monitored dangerous rock body and the three-dimensional coordinates of the plurality of image control points.

[0022] Optionally, in S4, based on the three-dimensional model of the monitored dangerous rock mass, a correction data stream is obtained by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process, including:

[0023] S41, obtaining a numerical model of a finite element simulation of the monitored dangerous rock mass based on the three-dimensional model of the monitored dangerous rock mass;

[0024] S42. Based on the numerical model of the finite element simulation of the monitored dangerous rock mass, numerical simulation software is used to simulate the instability process of the monitored dangerous rock mass to obtain correction parameter information related to the instability of the monitored dangerous rock mass, wherein the correction parameter information related to the instability of the monitored dangerous rock mass includes: a change in the three-dimensional coordinates of a characteristic point related to the instability of the monitored dangerous rock mass, a displacement related to the instability of the monitored dangerous rock mass, an inclination angle related to the instability of the monitored dangerous rock mass, an acceleration related to the instability of the monitored dangerous rock mass, a microseismic energy related to the instability of the monitored dangerous rock mass, and a structural surface slip related to the instability of the monitored dangerous rock mass;

[0025] S43. By arranging and analyzing the correction parameter information related to the monitoring of the instability of the dangerous rock mass, an orthogonal design method is used to obtain the weight factors of the correction parameters related to the monitoring of the instability of the dangerous rock mass and the correlation coefficients between the correction parameters;

[0026] S44. Obtain a correction data stream according to the weight factors of the correction parameters related to the monitoring of the instability of the dangerous rock mass and the correlation coefficients between the correction parameters.

[0027] Optionally, the monitoring system includes:

[0028] GNSS positioning equipment is used to achieve three-dimensional coordinate positioning of the dangerous rock mass feature points in the monitoring area on an airspace scale;

[0029] UAVs and take-off and landing platforms are used to build a three-dimensional model of the dangerous rock mass in the monitoring area at a celestial scale and conduct local photography;

[0030] Surface monitoring instruments are used to obtain microscopic information of dangerous rock masses in the monitoring area at a regional scale, including displacement, inclination, acceleration, and microseismicity of the surface of dangerous rock masses in the monitoring area;

[0031] The large in-hole flexible deformation meter is used to realize the sliding perception of the internal structural surface of the dangerous rock mass in the monitoring area on the internal domain scale;

[0032] The data acquisition unit is used to collect data information.

[0033] Optionally, in S7, normalization and weighting processing are performed based on the monitoring data information of the monitoring dangerous rock body and the training database of the monitoring dangerous rock body to obtain a monitoring data stream for monitoring the dangerous rock body, including:

[0034] S71. Obtaining a monitoring parameter set related to monitoring instability of the dangerous rock mass based on the monitoring data information of the dangerous rock mass;

[0035] S72. Performing normalization processing on the monitoring parameter set related to monitoring the instability of the dangerous rock mass to obtain a dimensionless monitoring parameter set;

[0036] S73. Performing Gaussian filtering and equalization processing on the dimensionless monitoring parameter set to obtain a processed dimensionless monitoring parameter set.

[0037] S74. Based on the processed dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body, supplement the monitoring parameters that were not collected or have erroneous information by using the correlation coefficient in the training database for monitoring the dangerous rock body to obtain a corrected dimensionless monitoring parameter set.

[0038] S75 , weighting is performed according to the corrected dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body according to the weight factors in the training database for monitoring the dangerous rock body to obtain a monitoring data stream for monitoring the dangerous rock body.

[0039] Optionally, in S74, based on the processed dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body, monitoring parameters that have not been collected or have erroneous information are supplemented by using correlation coefficients in the training database for monitoring the dangerous rock body to obtain a corrected dimensionless monitoring parameter set, including:

[0040] S741. Obtaining correlation coefficients between parameters related to instability of the monitored dangerous rock mass based on the training database of the monitored dangerous rock mass;

[0041] S742: Based on the correlation coefficients between the parameters related to the monitoring of the instability of the dangerous rock mass and the processed dimensionless monitoring parameter set, three correlation coefficients with the largest correlation coefficients of the monitoring parameters that were not collected or had erroneous information are selected to obtain supplementary correlation coefficients;

[0042] S743. Calculate and average the supplementary correlation coefficient and the processed dimensionless monitoring parameter set to obtain a supplementary parameter.

[0043] S744. Obtain a revised dimensionless monitoring parameter set based on the supplementary parameter and the processed dimensionless monitoring parameter set.

[0044] Optionally, in S8, based on the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, a development trend and warning level of the monitored dangerous rock body are obtained, and a warning indication is made, including:

[0045] S81, obtaining a multi-parameter bandwidth through cumulative processing based on the monitoring data stream of the dangerous rock mass;

[0046] S82. Separate the interference phase in the multi-parameter bandwidth by filtering according to the multi-parameter bandwidth, and obtain a changing relationship between the multi-parameter bandwidth and time;

[0047] S83, obtaining a bandwidth threshold according to the training database for monitoring the dangerous rock mass;

[0048] S84. Obtaining a development trend of the monitored dangerous rock mass based on the bandwidth threshold and the relationship between the multi-parameter bandwidth and time, wherein the development trend of the monitored dangerous rock mass includes: an imminent trend prediction of the monitored dangerous rock mass, a short-term trend prediction of the monitored dangerous rock mass, and a long-term trend prediction of the monitored dangerous rock mass;

[0049] S85. Obtain an early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and a training database of the monitored dangerous rock body, and make an early warning indication.

[0050] Optionally, in S85, obtaining the warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and making a warning indication, includes:

[0051] S851. Obtain a three-dimensional coordinate variation data stream, a displacement data stream, an inclination data stream, an acceleration data stream, a microseismic energy data stream, and a structural surface sliding data stream based on the monitoring data stream of the dangerous rock mass.

[0052] S852. Based on the training database of the monitored dangerous rock mass, a three-dimensional coordinate change weight, a displacement weight, a tilt weight, an acceleration weight, a microseismic energy weight, and a structural surface sliding weight are obtained. The relationship between the three-dimensional coordinate change weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight, and the structural surface sliding weight conforms to formula (1).

[0053] (1)

[0054] Where: For the i The weight of the three-dimensional coordinate change of the point position, For the iThe displacement weight of the point position, For the i The tilt weight of the point location, For the i The acceleration weight of the point position, For the i The microseismic energy weight at the point position, For the i Structural surface sliding weight at point location;

[0055] S853. Based on the three-dimensional coordinate variation data stream, the displacement data stream, the tilt data stream, the acceleration data stream, the microseismic energy data stream, the structural surface sliding data stream, the three-dimensional coordinate variation weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight, and the structural surface sliding weight, a multi-parameter function model is obtained by formula (2).

[0056] (2)

[0057] Where: is a multi-parameter function model, For the i The data stream of the three-dimensional coordinate change of the point position, , For the i Displacement data stream of point positions, , For the i Tilt data stream at point locations, , For the i Acceleration data stream of point positions, , For the i Microseismic energy data stream at point locations, , For the i Structural surface sliding data flow at point position, ;

[0058] S854. Obtain an early warning level for monitoring dangerous rock masses according to the multi-parameter function model and early warning response rules. The early warning response rules include:

[0059] The danger level is IV, which is a blue warning.

[0060] The danger level is level Ⅲ, which is a yellow warning.

[0061] The danger level is level II, which is an orange warning.

[0062] , the danger level is Level Ⅰ, which is a red alert;

[0063] When the danger level is Ⅰ~Ⅲ, the drone is started to cruise and shoot the location with the danger level Ⅰ~Ⅲ.

[0064] On the other hand, the present invention provides a dangerous rock multi-parameter correlation warning system based on integrated perception in air, space, and ground. The system is applied to a dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground. The system includes:

[0065] The data acquisition module is used to collate and analyze the precursor information of dangerous rock mass instability cases, and use the orthogonal design method to obtain the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters;

[0066] A training module is used to obtain a multi-parameter correlation training database through machine learning algorithm training based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters;

[0067] The model generation module is used to obtain the image data of the dangerous rock mass in the monitoring area through the UAV and obtain the three-dimensional model of the monitored dangerous rock mass;

[0068] A correction module is used to obtain a correction data stream by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process based on the three-dimensional model of the monitored dangerous rock mass;

[0069] An updating module is used to retrain and update the training database associated with the correction data stream and the multi-parameters to obtain a training database for monitoring dangerous rock masses;

[0070] A monitoring module is used to deploy a monitoring system on the dangerous rock mass in the monitoring area and obtain monitoring data information of the dangerous rock mass;

[0071] A processing module is used to obtain a monitoring data stream for monitoring the dangerous rock body by performing normalization and weighting processing based on the monitoring data information of the monitoring dangerous rock body and the training database of the monitoring dangerous rock body;

[0072] The early warning module is used to obtain the development trend and early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and to make early warning instructions.

[0073] Compared with the prior art, the technical solution of the invention has at least the following beneficial effects:

[0074] On the one hand, the above scheme deploys an integrated "air-space-ground-interior" monitoring system in the dangerous rock mass monitoring area, integrates various and multi-scale dangerous rock mass monitoring data, and uses the precursor information of dangerous rock mass instability to build a multi-parameter early warning system with data complementarity, data fusion, and data correlation. It solves the problem of one-sided data when predicting with a single factor or a single scale information, and realizes data complementarity and comprehensive early warning. On the other hand, it generates a training database for monitoring dangerous rock masses based on multiple dangerous rock mass instability cases and corrected data streams, and constructs an early warning function model containing multi-parameter information, so as to evaluate and predict the development trend of the dangerous rock mass monitoring data stream and the stable state of the dangerous rock mass, and make early warning instructions according to the early warning level of the dangerous rock mass monitoring, thereby improving the early warning accuracy and timeliness of dangerous rock mass collapse disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0076] Figure 1 This is a flow chart of an embodiment of a dangerous rock multi-parameter correlation early warning method based on integrated perception in the air, space, and ground of the present invention;

[0077] Figure 2 This is a flow chart of obtaining weight factors of parameters related to the instability of dangerous rock masses and correlation coefficients between the parameters in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0078] Figure 3 This is a flow chart of obtaining a three-dimensional model of a monitored dangerous rock body in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0079] Figure 4 This is a flow chart of obtaining a correction data stream in an embodiment of a dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0080] Figure 5 This is a flow chart of obtaining a monitoring data stream for monitoring dangerous rock bodies in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0081] Figure 6 This is a flow chart of obtaining a corrected dimensionless monitoring parameter set in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0082] Figure 7This is a flow chart of obtaining the development trend of monitored dangerous rock masses in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0083] Figure 8 This is a flow chart for obtaining the warning level of a monitored dangerous rock body in an embodiment of the dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground of the present invention;

[0084] Figure 9 This is a schematic diagram of multi-parameter bandwidth and time variation obtained in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention;

[0085] Figure 10 It is a system block diagram of an embodiment of the dangerous rock multi-parameter correlation early warning system based on comprehensive perception in the air, space, and ground of the present invention. DETAILED DESCRIPTION

[0086] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0087] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0088] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0089] like Figure 1 The flowchart of an embodiment of a dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground according to the present invention is shown. The present invention provides a dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground. The method is implemented by a dangerous rock multi-parameter correlation warning system based on integrated perception in air, space, and ground, and the method includes:

[0090] S1. By collating and analyzing the precursor information of dangerous rock mass instability cases, the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters are obtained using the orthogonal design method;

[0091] Specifically, if Figure 2The flowchart of obtaining weight factors of parameters related to dangerous rock mass instability and correlation coefficients between parameters in an embodiment of the dangerous rock mass multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention is shown. In S1, by collating and analyzing precursor information of dangerous rock mass instability cases, the weight factors of parameters related to dangerous rock mass instability and correlation coefficients between parameters are obtained using the orthogonal design method, including:

[0092] S11. Based on the precursor information of the dangerous rock mass instability case, parameters related to the dangerous rock mass instability are extracted and normalized to obtain dimensionless parameters of multiple precursor information. The parameters related to the dangerous rock mass instability include: three-dimensional coordinate change of characteristic points, displacement, inclination, acceleration, microseismic energy, and structural surface slip;

[0093] S12. Based on the dimensionless parameters of the multiple precursor information, an orthogonal design method is used to obtain weight factors of parameters related to instability of the dangerous rock mass;

[0094] S13. Based on the dimensionless parameter and the weight factor of the parameter related to the instability of the dangerous rock mass, an orthogonal design method is used to perform a correlation analysis to obtain a correlation coefficient between the parameters.

[0095] Furthermore, the correlation coefficient calculation method is shown in formula (3):

[0096] (3)

[0097] Where, Number m The dangerous rock mass t Time parameters x and parameters y The correlation coefficient of t For a certain moment, For t Time parameters x data, For t Time parameters y data, Number m The parameters of the dangerous rock mass at different times x The average value of the data, Number m The parameters of the dangerous rock mass at different times y The average value of the data, m is the number of the dangerous rock mass, is the number of time points of dangerous rock mass.

[0098] S2. Based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters, a training database of multi-parameter correlation is obtained through training with a machine learning algorithm;

[0099] S3. Obtain image data of the dangerous rock mass in the monitoring area through a drone to obtain a three-dimensional model of the monitored dangerous rock mass;

[0100] Specifically, if Figure 3 The flowchart of obtaining a three-dimensional model of a monitored dangerous rock mass in an embodiment of the dangerous rock mass multi-parameter correlation early warning method based on integrated air-space-ground-intra-air perception of the present invention is shown. In step S3, image data of the dangerous rock mass in the monitored area is obtained by using a drone to obtain the three-dimensional model of the monitored dangerous rock mass, including:

[0101] S31. Deploy multiple image control points on the dangerous rock mass in the monitoring area, and use RTK positioning technology to obtain the three-dimensional coordinates of the multiple image control points;

[0102] S32, collecting multi-angle images of the dangerous rock mass in the monitoring area by using a drone to obtain two-dimensional image data of the monitored dangerous rock mass;

[0103] S33 , obtaining a three-dimensional model of the monitored dangerous rock body using SFM three-dimensional image reconstruction technology based on the two-dimensional image data of the monitored dangerous rock body and the three-dimensional coordinates of the plurality of image control points.

[0104] S4. Based on the three-dimensional model of the monitored dangerous rock mass, a correction data stream is obtained by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process;

[0105] Specifically, if Figure 4 The flowchart of obtaining a correction data stream in an embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground of the present invention is shown. In S4, based on the three-dimensional model of the monitored dangerous rock body, the correction data stream is obtained by numerically simulating the instability process of the dangerous rock body and extracting data information of relevant parameters in the instability process, including:

[0106] S41, obtaining a numerical model of a finite element simulation of the monitored dangerous rock mass based on the three-dimensional model of the monitored dangerous rock mass;

[0107] S42. Based on the numerical model of the finite element simulation of the monitored dangerous rock mass, numerical simulation software is used to simulate the instability process of the monitored dangerous rock mass to obtain correction parameter information related to the instability of the monitored dangerous rock mass, wherein the correction parameter information related to the instability of the monitored dangerous rock mass includes: a change in the three-dimensional coordinates of a characteristic point related to the instability of the monitored dangerous rock mass, a displacement related to the instability of the monitored dangerous rock mass, an inclination angle related to the instability of the monitored dangerous rock mass, an acceleration related to the instability of the monitored dangerous rock mass, a microseismic energy related to the instability of the monitored dangerous rock mass, and a structural surface slip related to the instability of the monitored dangerous rock mass;

[0108] S43. By arranging and analyzing the correction parameter information related to the monitoring of the instability of the dangerous rock mass, an orthogonal design method is used to obtain the weight factors of the correction parameters related to the monitoring of the instability of the dangerous rock mass and the correlation coefficients between the correction parameters;

[0109] S44. Obtain a correction data stream according to the weight factors of the correction parameters related to the monitoring of the instability of the dangerous rock mass and the correlation coefficients between the correction parameters.

[0110] S5. Retrain and update the corrected data stream and the multi-parameter associated training database to obtain a training database for monitoring dangerous rock masses;

[0111] S6. Deploy a monitoring system for the dangerous rock mass in the monitoring area to obtain monitoring data information of the dangerous rock mass;

[0112] Specifically, the monitoring system includes:

[0113] GNSS positioning equipment is used to achieve three-dimensional coordinate positioning of the dangerous rock mass feature points in the monitoring area on an airspace scale;

[0114] UAVs and take-off and landing platforms are used to build a three-dimensional model of the dangerous rock mass in the monitoring area at a celestial scale and conduct local photography;

[0115] Surface monitoring instruments are used to obtain microscopic information of dangerous rock masses in the monitoring area at a regional scale, including displacement, inclination, acceleration, and microseismicity of the surface of dangerous rock masses in the monitoring area;

[0116] The large in-hole flexible deformation meter is used to realize the sliding perception of the internal structural surface of the dangerous rock mass in the monitoring area on the internal domain scale;

[0117] The data acquisition unit is used to collect data information.

[0118] Furthermore, a GNSS positioning device is installed at a location with an open field of view of the dangerous rock mass in the monitoring area, a take-off and landing platform of a UAV is installed at a location with an open field of view of the dangerous rock mass in the monitoring area, and a UAV equipped with an RTK positioning module and a visual sensor device is deployed, a displacement meter, an inclinometer, an accelerometer and a microseismic monitor are installed at a location with cracks in the dangerous rock mass in the monitoring area, a large flexible deformation meter is installed by drilling in an area with an upper and lower inclined structural surface of the dangerous rock mass in the monitoring area, and a data acquisition module is installed in an area with a stable dangerous rock mass and an open field of view in the monitoring area. The data acquisition module is connected to each monitoring instrument through wireless transmission to form a monitoring system.

[0119] S7. Based on the monitoring data information of the monitored dangerous rock body and the training database of the monitored dangerous rock body, normalization and weighting processing are performed to obtain a monitoring data stream for monitoring the dangerous rock body;

[0120] Specifically, if Figure 5 The flowchart of obtaining a monitoring data stream for monitoring a dangerous rock body in an embodiment of the method for multi-parameter correlation early warning of dangerous rock based on integrated perception in air, space, and ground of the present invention is shown. In step S7, normalization and weighting processing are performed based on the monitoring data information of the dangerous rock body and the training database of the dangerous rock body to obtain the monitoring data stream for monitoring the dangerous rock body, including:

[0121] S71. Obtaining a monitoring parameter set related to monitoring instability of the dangerous rock mass based on the monitoring data information of the dangerous rock mass;

[0122] S72. Performing normalization processing on the monitoring parameter set related to monitoring the instability of the dangerous rock mass to obtain a dimensionless monitoring parameter set;

[0123] Furthermore, the dimensionless monitoring parameter is concentrated in a data form with a parameter range of [0, 1].

[0124] S73. Performing Gaussian filtering and equalization processing on the dimensionless monitoring parameter set to obtain a processed dimensionless monitoring parameter set.

[0125] S74. Based on the processed dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body, supplement the monitoring parameters that were not collected or have erroneous information by using the correlation coefficient in the training database for monitoring the dangerous rock body to obtain a corrected dimensionless monitoring parameter set.

[0126] S75 , weighting is performed according to the corrected dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body according to the weight factors in the training database for monitoring the dangerous rock body to obtain a monitoring data stream for monitoring the dangerous rock body.

[0127] Further, if Figure 6 The flowchart of obtaining a revised dimensionless monitoring parameter set in an embodiment of the dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground of the present invention is shown. In S74, based on the processed dimensionless monitoring parameter set and the training database for monitoring the dangerous rock body, the monitoring parameters that have not been collected or have erroneous information are supplemented by the correlation coefficient in the training database for monitoring the dangerous rock body to obtain the revised dimensionless monitoring parameter set, including:

[0128] S741. Obtaining correlation coefficients between parameters related to instability of the monitored dangerous rock mass based on the training database of the monitored dangerous rock mass;

[0129] S742. Based on the correlation coefficients between the parameters related to the monitoring of the instability of the dangerous rock mass and the processed dimensionless monitoring parameter set, three correlation coefficients with the largest correlation coefficients of the monitoring parameters that were not collected or had erroneous information are selected to obtain supplementary correlation coefficients.

[0130] S743. Calculate and average the supplementary correlation coefficient and the processed dimensionless monitoring parameter set to obtain a supplementary parameter.

[0131] S744. Obtain a revised dimensionless monitoring parameter set based on the supplementary parameter and the processed dimensionless monitoring parameter set.

[0132] S8. According to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, the development trend and warning level of the monitored dangerous rock body are obtained, and a warning indication is made.

[0133] Specifically, if Figure 7 The flowchart of the embodiment of the multi-parameter correlation early warning method for dangerous rock based on integrated perception in air, space, and ground of the present invention for obtaining the development trend of the monitored dangerous rock mass is shown. In S8, the development trend and early warning level of the monitored dangerous rock mass are obtained based on the monitoring data stream of the monitored dangerous rock mass and the training database of the monitored dangerous rock mass, and an early warning indication is issued, including:

[0134] S81, obtaining a multi-parameter bandwidth through cumulative processing based on the monitoring data stream of the dangerous rock mass;

[0135] S82. Based on the multi-parameter bandwidth, filtering is used to separate the interference phase in the multi-parameter bandwidth to obtain the relationship between the multi-parameter bandwidth and time, such as Figure 9 The schematic diagram of multi-parameter bandwidth and time variation obtained in the embodiment of the dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, ground and space of the present invention is shown;

[0136] S83, obtaining a bandwidth threshold according to the training database for monitoring the dangerous rock mass;

[0137] S84. Obtaining a development trend of the monitored dangerous rock mass based on the bandwidth threshold and the relationship between the multi-parameter bandwidth and time, wherein the development trend of the monitored dangerous rock mass includes: an imminent trend prediction of the monitored dangerous rock mass, a short-term trend prediction of the monitored dangerous rock mass, and a long-term trend prediction of the monitored dangerous rock mass;

[0138] S85. Obtain an early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and a training database of the monitored dangerous rock body, and make an early warning indication.

[0139] Furthermore, if Figure 8 The flowchart of obtaining the warning level of a monitored dangerous rock body in an embodiment of the multi-parameter correlation warning method for dangerous rock based on integrated perception in air, space, and ground of the present invention is shown. In S85, the warning level of the monitored dangerous rock body is obtained based on the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and a warning indication is issued, including:

[0140] S851. Obtain a three-dimensional coordinate variation data stream, a displacement data stream, an inclination data stream, an acceleration data stream, a microseismic energy data stream, and a structural surface sliding data stream based on the monitoring data stream of the dangerous rock mass.

[0141] S852. Based on the training database of the monitored dangerous rock mass, a three-dimensional coordinate change weight, a displacement weight, a tilt weight, an acceleration weight, a microseismic energy weight, and a structural surface sliding weight are obtained. The relationship between the three-dimensional coordinate change weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight, and the structural surface sliding weight conforms to formula (1).

[0142] (1)

[0143] Where: For the i The weight of the three-dimensional coordinate change of the point position, For the i The displacement weight of the point position, For the i The tilt weight of the point location, For the i The acceleration weight of the point position, For the i The microseismic energy weight at the point position, For the i Structural surface sliding weight at point location;

[0144] S853. Based on the three-dimensional coordinate variation data stream, the displacement data stream, the tilt data stream, the acceleration data stream, the microseismic energy data stream, the structural surface sliding data stream, the three-dimensional coordinate variation weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight, and the structural surface sliding weight, a multi-parameter function model is obtained by formula (2).

[0145] (2)

[0146] Where: is a multi-parameter function model, , For the i The data stream of the three-dimensional coordinate change of the point position, , For the i Displacement data stream of point positions, , For the i Tilt data stream at point locations, , For the i Acceleration data stream of point positions, , For the i Microseismic energy data stream at point locations, , For the i Structural surface sliding data flow at point position, ;

[0147] S854. Obtain an early warning level for monitoring dangerous rock masses according to the multi-parameter function model and early warning response rules. The early warning response rules include:

[0148] The danger level is IV, which is a blue warning.

[0149] The danger level is level Ⅲ, which is a yellow warning.

[0150] The danger level is level II, which is an orange warning.

[0151] , the danger level is Level Ⅰ, which is a red alert;

[0152] When the danger level is level I to III, the drone is started to cruise and shoot the location of the abnormal point to obtain the image data of the abnormal point. The obtained image data is compared and analyzed with the data in the three-dimensional model, and the rock displacement and deformation speed in the image area are calculated for further detailed analysis and prediction.

[0153] like Figure 10 The system block diagram of an embodiment of the dangerous rock multi-parameter correlation warning system based on integrated perception in air, space, and ground of the present invention is shown. The present invention provides a dangerous rock multi-parameter correlation warning system based on integrated perception in air, space, and ground. The system is applied to a dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground. The system includes a data acquisition module, a training module, a model generation module, a correction module, an update module, a monitoring module, a processing module, and an early warning module. Specifically,

[0154] The data acquisition module is used to collate and analyze the precursor information of dangerous rock mass instability cases, and use the orthogonal design method to obtain the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters;

[0155] A training module is used to obtain a multi-parameter correlation training database through machine learning algorithm training based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters;

[0156] The model generation module is used to obtain the image data of the dangerous rock mass in the monitoring area through the UAV and obtain the three-dimensional model of the monitored dangerous rock mass;

[0157] A correction module is used to obtain a correction data stream by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process based on the three-dimensional model of the monitored dangerous rock mass;

[0158] An updating module is used to retrain and update the training database associated with the correction data stream and the multi-parameters to obtain a training database for monitoring dangerous rock masses;

[0159] A monitoring module is used to deploy a monitoring system on the dangerous rock mass in the monitoring area and obtain monitoring data information of the dangerous rock mass;

[0160] A processing module is used to obtain a monitoring data stream for monitoring the dangerous rock body by performing normalization and weighting processing based on the monitoring data information of the monitoring dangerous rock body and the training database of the monitoring dangerous rock body;

[0161] The early warning module is used to obtain the development trend and early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and to make early warning instructions.

[0162] The present invention provides a multi-parameter correlation warning method and system for dangerous rocks based on comprehensive perception in air, space, ground and interior. The invention deploys an integrated "air-space-ground-interior" monitoring system in the dangerous rock monitoring area, integrates multiple and multi-scale dangerous rock monitoring data, and uses precursor information of dangerous rock instability to construct a multi-parameter warning system with data complementarity, data fusion and data correlation. It solves the problem of one-sided data when predicting with a single factor or a single scale information, and realizes data complementarity and comprehensive warning. At the same time, based on multiple dangerous rock instability cases and corrected data streams, a training database for monitoring dangerous rock bodies is generated, and a warning function model containing multi-parameter information is constructed, so as to evaluate and predict the development trend of the data stream for monitoring dangerous rock bodies and the stable state of dangerous rock bodies, and make warning instructions according to the warning level of the monitored dangerous rock bodies, thereby improving the warning accuracy and timeliness of dangerous rock collapse disasters.

[0163] It will be appreciated that the present invention is described by way of the above embodiments and should not be construed as limiting the embodiments of the present invention and the scope of the present invention. It will be appreciated by those skilled in the art that various changes or equivalent replacements may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application fall within the scope protected by the present invention.

Claims

1. A multi-parameter correlation early warning method for dangerous rocks based on comprehensive perception in the air, space, and ground, characterized by: The method comprises: S1. By collating and analyzing the precursor information of dangerous rock mass instability cases, the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters are obtained using the orthogonal design method; S2. obtaining a multi-parameter correlation training database by training a machine learning algorithm based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters; S3. Obtain image data of the dangerous rock mass in the monitoring area through a drone to obtain a three-dimensional model of the monitored dangerous rock mass; S4. Based on the three-dimensional model of the monitored dangerous rock mass, a correction data stream is obtained by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process; S5. Retrain and update the training database based on the corrected data stream and the multi-parameter association to obtain a training database for monitoring dangerous rock masses; S6. Deploy a monitoring system for the dangerous rock mass in the monitoring area to obtain monitoring data information of the dangerous rock mass; S7. Based on the monitoring data information of the dangerous rock body and the training database of the dangerous rock body, normalization and weighting processing are performed to obtain a monitoring data stream for monitoring the dangerous rock body; S8. Obtain the development trend and warning level of the monitored dangerous rock mass according to the monitoring data stream of the monitored dangerous rock mass and the training database of the monitored dangerous rock mass, and make a warning indication.

2. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space and ground according to claim 1 is characterized in that: In S1, by collating and analyzing the precursor information of dangerous rock mass instability cases, the orthogonal design method is used to obtain the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters, including: S11. Extracting parameters related to the instability of the dangerous rock mass based on precursor information of the dangerous rock mass instability case and performing normalization processing to obtain dimensionless parameters of multiple precursor information, wherein the parameters related to the instability of the dangerous rock mass include: three-dimensional coordinate change of characteristic points, displacement, inclination, acceleration, microseismic energy, and structural surface slip; S12. Obtaining weight factors of parameters related to instability of the dangerous rock mass using an orthogonal design method based on the dimensionless parameters of the plurality of precursor information; S13. Based on the dimensionless parameters and the weight factors of the parameters related to the instability of the dangerous rock mass, an orthogonal design method is used to perform a correlation analysis to obtain a correlation coefficient between the parameters.

3. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space and ground according to claim 1 is characterized in that: In S3, the image data of the dangerous rock mass in the monitoring area is obtained by using a drone to obtain a three-dimensional model of the monitored dangerous rock mass, including: S31. Deploy multiple image control points on the dangerous rock mass in the monitoring area, and use RTK positioning technology to obtain the three-dimensional coordinates of the multiple image control points; S32, collecting multi-angle images of the dangerous rock mass in the monitoring area by using a drone to obtain two-dimensional image data of the monitored dangerous rock mass; S33 , obtaining a three-dimensional model of the monitored dangerous rock body using SFM three-dimensional image reconstruction technology based on the two-dimensional image data of the monitored dangerous rock body and the three-dimensional coordinates of the plurality of image control points.

4. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground according to claim 1 is characterized in that: In S4, based on the three-dimensional model of the monitored dangerous rock mass, a correction data stream is obtained by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process, including: S41, obtaining a numerical model of a finite element simulation of the monitored dangerous rock mass according to the three-dimensional model of the monitored dangerous rock mass; S42. Using numerical simulation software, simulate the instability process of the monitored dangerous rock mass based on the finite element simulation numerical model, and obtain correction parameter information related to the instability of the monitored dangerous rock mass, wherein the correction parameter information related to the instability of the monitored dangerous rock mass includes: a change in the three-dimensional coordinates of a characteristic point related to the instability of the monitored dangerous rock mass, a displacement related to the instability of the monitored dangerous rock mass, an inclination angle related to the instability of the monitored dangerous rock mass, an acceleration related to the instability of the monitored dangerous rock mass, a microseismic energy related to the instability of the monitored dangerous rock mass, and a structural surface slip related to the instability of the monitored dangerous rock mass; S43. By arranging and analyzing the correction parameter information related to monitoring the instability of the dangerous rock mass, using an orthogonal design method, obtaining weight factors of the correction parameters related to monitoring the instability of the dangerous rock mass and correlation coefficients between the correction parameters; S44. Obtain a correction data stream according to the weight factors of the correction parameters related to monitoring the instability of the dangerous rock mass and the correlation coefficients between the correction parameters.

5. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground according to claim 1 is characterized in that: The monitoring system comprises: GNSS positioning equipment, used to achieve three-dimensional coordinate positioning of characteristic points of dangerous rock masses in the monitoring area on an airspace scale; UAVs and take-off and landing platforms are used to build a three-dimensional model of the dangerous rock mass in the monitoring area at a celestial scale and to take local photos; Surface monitoring instruments are used to obtain microscopic information of dangerous rock masses in the monitoring area at a regional scale, including displacement, inclination, acceleration and microseismicity of the surface of dangerous rock masses in the monitoring area; A large in-hole flexible deformation meter is used to sense the sliding of the internal structural surface of the dangerous rock mass in the monitoring area on an internal scale; The data acquisition unit is used to collect data information.

6. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground according to claim 1 is characterized in that: In S7, the monitoring data information of the dangerous rock body and the training database of the dangerous rock body are normalized and weighted to obtain a monitoring data stream for monitoring the dangerous rock body, including: S71. Obtaining a monitoring parameter set related to monitoring instability of the dangerous rock mass based on the monitoring data information of the dangerous rock mass; S72. Performing normalization processing on the monitoring parameter set related to monitoring the instability of the dangerous rock mass to obtain a dimensionless monitoring parameter set; S73. Performing Gaussian filtering and equalization processing on the dimensionless monitoring parameter set to obtain a processed dimensionless monitoring parameter set. S74. Based on the processed dimensionless monitoring parameter set and the training database for monitoring dangerous rock bodies, supplement the monitoring parameters that were not collected or have erroneous information by using the correlation coefficient in the training database for monitoring dangerous rock bodies to obtain a corrected dimensionless monitoring parameter set. S75 , weighting the modified dimensionless monitoring parameter set and the training database for monitoring dangerous rock bodies according to weight factors in the training database for monitoring dangerous rock bodies to obtain a monitoring data stream for monitoring dangerous rock bodies.

7. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground according to claim 6 is characterized in that: In S74, based on the processed dimensionless monitoring parameter set and the training database for monitoring dangerous rock bodies, monitoring parameters that have not been collected or have erroneous information are supplemented by using correlation coefficients in the training database for monitoring dangerous rock bodies to obtain a corrected dimensionless monitoring parameter set, including: S741. Obtaining correlation coefficients between parameters related to instability of the monitored dangerous rock mass based on the training database for monitoring the dangerous rock mass; S742. Based on the correlation coefficients between the parameters related to monitoring the instability of the dangerous rock mass and the processed dimensionless monitoring parameter set, three correlation coefficients with the largest correlation coefficients of the monitoring parameters that were not collected or had erroneous information are selected to obtain supplementary correlation coefficients. S743. Calculate and average the supplementary correlation coefficient and the processed dimensionless monitoring parameter set to obtain a supplementary parameter. S744. Obtain a revised dimensionless monitoring parameter set based on the supplementary parameters and the processed dimensionless monitoring parameter set.

8. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, and ground according to claim 1 is characterized in that: In S8, the development trend and warning level of the monitored dangerous rock mass are obtained based on the monitoring data stream of the monitored dangerous rock mass and the training database of the monitored dangerous rock mass, and a warning indication is made, including: S81, obtaining a multi-parameter bandwidth through cumulative processing based on the monitoring data stream of the dangerous rock mass; S82. Separate the interference phase in the multi-parameter bandwidth by filtering according to the multi-parameter bandwidth, and obtain a changing relationship between the multi-parameter bandwidth and time; S83, obtaining a bandwidth threshold according to the training database for monitoring dangerous rock masses; S84. Obtaining a development trend of the monitored dangerous rock mass based on the bandwidth threshold and the relationship between the multi-parameter bandwidth and time, wherein the development trend of the monitored dangerous rock mass includes: an imminent trend prediction of the monitored dangerous rock mass, a short-term trend prediction of the monitored dangerous rock mass, and a long-term trend prediction of the monitored dangerous rock mass; S85. Obtaining an early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and making an early warning indication.

9. The dangerous rock multi-parameter correlation early warning method based on integrated perception in air, space, ground and space according to claim 1 is characterized in that: The step S85 obtains the warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and makes a warning indication, including: S851. Obtaining a three-dimensional coordinate variation data stream, a displacement data stream, an inclination data stream, an acceleration data stream, a microseismic energy data stream, and a structural surface sliding data stream based on the monitoring data stream of the dangerous rock mass; S852. Based on the training database for monitoring dangerous rock masses, a three-dimensional coordinate variation weight, a displacement weight, a tilt weight, an acceleration weight, a microseismic energy weight, and a structural surface sliding weight are obtained. The relationship among the three-dimensional coordinate variation weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight, and the structural surface sliding weight conforms to formula (1). （1） Where: For the i The weight of the three-dimensional coordinate change of the point position, For the i The displacement weight of the point position, For the i The tilt weight of the point location, For the i The acceleration weight of the point position, For the i The microseismic energy weight at the point position, For the i Structural surface sliding weight at point location; S853, according to the three-dimensional coordinate variation data stream, the displacement data stream, the tilt data stream, the acceleration data stream, the microseismic energy data stream, the structural surface sliding data stream, the three-dimensional coordinate variation weight, the displacement weight, the tilt weight, the acceleration weight, the microseismic energy weight and the structural surface sliding weight, a multi-parameter function model is obtained by formula (2): （2） Where: is a multi-parameter function model, For the i The data stream of the three-dimensional coordinate change of the point position, , For the i Displacement data stream of point positions, , For the i Tilt data stream at point locations, , For the i Acceleration data stream of point positions, , For the i Microseismic energy data stream at point locations, , For the i Structural surface sliding data flow at point position, ; S854: Obtain an early warning level for monitoring dangerous rock masses according to the multi-parameter function model and early warning response rules. The early warning response rules include: The danger level is IV, which is a blue warning. The danger level is level III, which is a yellow warning. The danger level is Level II, which is an orange warning. , the danger level is Level Ⅰ, which is a red alert; When the danger level is level I to level III, the drone is started to cruise and shoot the location with the danger level I to level III.

10. A dangerous rock multi-parameter correlation warning system based on integrated perception in air, space, and ground, for implementing the dangerous rock multi-parameter correlation warning method based on integrated perception in air, space, and ground as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The data acquisition module is used to collate and analyze the precursor information of dangerous rock mass instability cases, and use the orthogonal design method to obtain the weight factors of parameters related to dangerous rock mass instability and the correlation coefficients between the parameters; A training module is used to obtain a multi-parameter correlation training database through machine learning algorithm training based on the weight factors of the parameters related to the instability of the dangerous rock mass and the correlation coefficients between the parameters; The model generation module is used to obtain the image data of the dangerous rock mass in the monitoring area through the UAV and obtain the three-dimensional model of the monitored dangerous rock mass; A correction module is used to obtain a correction data stream by numerically simulating the instability process of the dangerous rock mass and extracting data information of relevant parameters in the instability process based on the three-dimensional model of the monitored dangerous rock mass; An updating module, configured to retrain and update the training database associated with the correction data stream and the multi-parameters to obtain a training database for monitoring dangerous rock masses; A monitoring module, configured to deploy a monitoring system on the dangerous rock mass in the monitoring area and obtain monitoring data information of the dangerous rock mass; a processing module for obtaining a monitoring data stream for monitoring the dangerous rock body by performing normalization and weighting processing based on the monitoring data information of the dangerous rock body and the training database for monitoring the dangerous rock body; The early warning module is used to obtain the development trend and early warning level of the monitored dangerous rock body according to the monitoring data stream of the monitored dangerous rock body and the training database of the monitored dangerous rock body, and to make early warning instructions.

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