Mine impact risk troubleshooting method and device

Abstract

The invention provides a mine impact risk checking method and a mine impact risk checking device. Counting a first cluster of the microseismic events in a preset time period; screening and counting a second micro-seismic event cluster in the first micro-seismic event cluster; and comparing the percentage of the total number of the second clusters of the micro-seismic events in the total number of the first clusters of the micro-seismic events with a first percentage threshold, and evaluating the effectiveness of the second clusters of the micro-seismic events. Compared with micro-seismic event clustering analysis only aiming at a specific structural area, the method has the advantages that the contained micro-seismic data volume is richer, the data analysis is more comprehensive, the risk of rock burst disasters is effectively reduced, and efficient and safe production of a mine is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of prevention and control of rock burst disasters in coal mines, and in particular to a method and device for investigating rock burst risks in mines. Background Art

[0002] The specific data of microseismic events obtained by the microseismic monitoring system need to be analyzed and processed to have guiding practical significance. For the selection and partitioning of large-scale spatiotemporal data, one part directly selects unstable areas of dynamic loads such as faults, synclines, and tunnels from a geological perspective, and the other part selects data of unrelated vibration areas with a wide spatial distribution according to the time series or performs simple artificial regional division according to the mining face. Therefore, the effectiveness of using the cluster analysis method to analyze the distribution of microseismic events in the mine as a whole (regardless of the excavation or mining stage) can comprehensively and accurately screen out abnormal production activities, which is of great significance for the investigation of mine impact risks.

[0003] At present, the research on mine impact risk investigation based on microseismic monitoring data by domestic and foreign scholars can be divided into the following two categories:

[0004] 1) One part focuses on the formation mechanism of rock burst and conducts mathematical and statistical analysis on the mechanical characteristics of coal-rock instability. Conventional statistical parameters and comprehensive processing parameters are used to analyze the variation law and trend of microseismic energy and the range of temporal and spatial damage.

[0005] 2) The other part focuses on the establishment of prediction models, including fitting prediction and identification prediction. For the selection and partitioning of large-scale spatiotemporal strong data, one part directly selects unstable areas of dynamic and static loads such as faults, synclines, and tunnels from a geological perspective, and the other part selects data of widely distributed and unrelated vibration areas according to time series or performs simple artificial regional division based on the mining face.

[0006] Since coal mining conditions are different, there are many types of impact mine pressure and influencing factors, and microseismic systems are mostly recorded after the fact, if these mine earthquake data are not deeply mined and classified, it will be difficult to improve the accuracy of subsequent anti-impact decisions.

[0007] The SOS microseismic monitoring system monitors large areas underground in coal mines, and obtains information on microseismic events in a wide range of time and space, but these microseismic events are often scattered in multiple mining working faces and located in different geological structures. Microseismic events in parallel time and space are independent of each other and do not affect each other. Summary of the invention

[0008] In order to solve the deficiencies in the prior art, the present application proposes a mine impact risk investigation method and device.

[0009] In the first aspect, the present application proposes a method for investigating the risk of mine impact, comprising:

[0010] Counting the first cluster of microseismic events within a preset time period;

[0011] Screening and counting the second cluster of microseismic events from the first cluster of microseismic events;

[0012] The percentage of the total number of microseismic events in the second cluster to the total number of microseismic events in the first cluster is compared with the first percentage threshold to evaluate the validity of the second cluster of microseismic events.

[0013] Optionally, counting the first cluster of microseismic events within a preset time period includes:

[0014] By determining the cluster center of microseismic events according to the mine excavation and mining work areas within a preset time period, and delineating the first cluster of microseismic events according to the cluster center of microseismic events;

[0015] Determine whether the first cluster of microseismic events identified is valid.

[0016] Optionally, the method of determining the microseismic event cluster center based on the mine excavation and mining work area includes: determining an excavation working face or a mining working face as a microseismic event cluster number, and selecting the center position of the excavation working face or the mining working face as the microseismic event cluster center.

[0017] Optionally, determining whether the defined first cluster of microseismic events is valid includes: screening a valid first cluster of microseismic events by comparing the percentage of the total number of the first cluster of microseismic events to the total number of microseismic events in a preset time period with a second percentage threshold.

[0018] Optionally, the screening and counting of the second cluster of microseismic events in the first cluster of microseismic events includes: screening the second cluster of microseismic events from microseismic events occurring within a preset radius of the center of the microseismic event cluster.

[0019] Optionally, the step of counting a first cluster of microseismic events within a preset time period or screening a second cluster of microseismic events from the first cluster of microseismic events comprises:

[0020] Calculate the sum of the squares of the distances from each microseismic event in the monitoring area to the center of the monitoring area;

[0021] Select any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold.

[0022] Optionally, the step of selecting any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold comprises the following steps:

[0023] Step S1: Select M initial monitoring area centers;

[0024] Step S2: Allocate the microseismic event to one of the M monitoring area centers according to the shortest distance principle;

[0025] Step S3: Calculate the new vector value between the microseismic event and the center of each monitoring area and the average value of the new vector value, and take the center of the monitoring area where the vector value between the center of the monitoring area and the microseismic event and the average value of the new vector value are equal as the new center of the monitoring area;

[0026] Step S4: If the new monitoring area center is not equal to one of the M monitoring area centers, return to step S2 and redivide the monitoring areas for the microseismic events one by one. If they are equal, the microseismic event clustering is determined.

[0027] In a second aspect, a mine impact risk investigation device is provided, which is implemented by the mine impact risk investigation method described in the first aspect, and includes:

[0028] A microseismic event first cluster statistical module, used for counting the first cluster of microseismic events within a preset time period;

[0029] A microseismic event second cluster statistical module, used for screening and statistically analyzing the second microseismic event cluster in the first microseismic event cluster;

[0030] The microseismic event judgment module is used to compare the percentage of the total number of the second cluster of microseismic events to the total number of the first cluster of microseismic events with a first percentage threshold to evaluate the effectiveness of the second cluster of microseismic events.

[0031] Optionally, the second microseismic event clustering statistics module includes:

[0032] A calculation module, used to calculate the sum of the squares of the distances from each microseismic event in the monitoring area to the center of the monitoring area;

[0033] The selection module is used to select any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold.

[0034] Optionally, the microseismic event first cluster statistical module includes: a microseismic event first cluster screening module, used to screen valid microseismic event first clusters by comparing the percentage of the total number of microseismic events in the first cluster to the total number of microseismic events in a preset time period with a second percentage threshold.

[0035] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0036] (1) The present invention can comprehensively evaluate the impact risk of mines by referring to microseismic monitoring data, and proposes a comprehensive analysis method based on clustering of microseismic events, thereby improving the scientificity and reliability of mine impact risk assessment.

[0037] (2) The present invention uses a cluster analysis method to delineate and divide all mining areas within a time period. Compared with the cluster analysis of microseismic events only for specific structural areas, the present invention contains a richer amount of microseismic data and provides a more comprehensive analysis of the data.

[0038] (3) The present invention establishes a criterion for distinguishing the overall distribution of microseismic events in mines, which can timely check invalid microseismic events and screen out abnormal production activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a mine impact risk investigation method shown in an embodiment of the present application;

[0040] Figure 2 The first clustering plane analysis diagram of the microseismic events of the A mine shown in the embodiment of the present application;

[0041] Figure 3 This is the first clustering plane analysis diagram of the microseismic events of the B mine shown in the embodiment of the present application;

[0042] Figure 4 This is the first clustering plane analysis diagram of the C mine microseismic events shown in the embodiment of the present application;

[0043] Figure 5 This is the first clustering plane analysis diagram of the microseismic events of the D mine shown in the embodiment of the present application;

[0044] Figure 6 This is the first clustering plane analysis diagram of the E mine microseismic events shown in the embodiment of the present application;

[0045] Figure 7 This is the second clustering plane distribution diagram of the working face 1301-2 of the A mine shown in the embodiment of the present application;

[0046] Figure 8 This is the second clustering plane distribution diagram of the track chute of Mine B 3315 shown in the embodiment of the present application;

[0047] Fig. 9 This is the second clustering plane distribution diagram of the 5310 working face of the B mine shown in the embodiment of the present application;

[0048] Fig.10 This is the second clustering plane distribution diagram of the 6315 working face of the B mine shown in the embodiment of the present application;

[0049] Fig.11This is the second clustering plane distribution diagram of the C mine 3410 working face shown in the embodiment of the present application;

[0050] Fig.12 This is the second clustering plane distribution diagram of the 7317 working face of the D mine shown in the embodiment of the present application;

[0051] Fig.13 This is the second clustering plane distribution diagram of the E mine 22304 working face shown in the embodiment of the present application;

[0052] Fig.14 This is the second clustering plane distribution diagram of the E mine 6305 working face shown in the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0054] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of methods and devices consistent with some aspects of the present application as detailed in the attached claims.

[0055] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0056] Embodiment 1

[0057] This application provides a method for investigating the risk of mine impact. Figure 1 As shown, including:

[0058] Step 1: Count the first cluster of microseismic events within a preset time period;

[0059] During the specific implementation process, the K-means algorithm is used to quantitatively analyze the spatial distribution of microseismic activities, and automatically divide them into different clustering areas according to the distance between the earthquake source locations.

[0060] Specifically, the number of clusters is determined based on the actual mining conditions on site (including excavation and mining conditions), that is, one excavation working face or mining working face corresponds to one class k, and the initial microseismic event cluster center is determined based on the excavation and mining areas within a time period (generally the center position of the working face is selected) for retrieval and division, and then each microseismic event cluster is circled.

[0061] The K-means algorithm is very effective for clustering points in three-dimensional Euclidean space. This algorithm is used to perform location event clustering analysis. This algorithm is a clustering algorithm that classifies based on a function criterion. The clustering criterion function is the sum of the squared distances SSE (Sum of the Squared Error) from each microseismic event in the monitoring area to the center of the area. The criterion function definition formula for all M monitoring areas is shown in formula (1):

[0062]

[0063] Where M is the number of monitoring areas, k is the number of any microseismic event clusters, and Z j is the center point of the jth monitoring area, X i is the i-th microseismic event, N j is the jth monitoring area S j The microseismic events included in S j is the jth monitoring area.

[0064] The clustering criterion of the K-means algorithm is to select a monitoring area center Z P Let the criterion function SSE be minimized, that is, make SSE p The value of is extremely small, so we can get formula (2).

[0065]

[0066] Specifically, the steps for counting the first cluster of microseismic events are as follows:

[0067] Assuming there are N microseismic events, the calculation steps are as follows:

[0068] ① Choose M initial monitoring area centers Z1(1), Z2(1),…, Z M (1), M<N, the serial number in the bracket represents the number of iterations; ② According to the shortest distance principle, the remaining microseismic events are assigned to one of the M monitoring area centers, that is, formula (3);

[0069] min = {‖X i -Z i (j)||j=1,2,…,m}=|X i -Z P (j)‖,X i ∈S P……(3)

[0070] In the formula, m is the order number of the iterative operation, X i is the i-th microseismic event in the monitoring area, j is the j-th iteration operation, S is the monitoring area, i is the number of centers of the initial monitoring area, and P is the p-th monitoring area;

[0071] ③ Calculate the new vector value of each monitoring area center, that is, use the mean vector as the new monitoring area center, see formula (4).

[0072]

[0073] ④If Z P (m+1)≠Z P (m), P = 1, 2, ..., M, then return to step ②, re-divide the monitoring area for microseismic events one by one, and repeat the iterative calculation. Otherwise, the algorithm converges and the calculation is completed.

[0074] After counting the first cluster of microseismic events, the validity of the overall distribution of microseismic events is further evaluated based on the percentage of the total number of microseismic events of each type in the total number of microseismic events of the month;

[0075] Specifically, this embodiment establishes two types of evaluation results, "valid" and "invalid", and determines the overall distribution results of microseismic events in the mining area according to the percentage of the total number of microseismic events in each cluster to the total number of microseismic events in that month (or 1 minus the percentage of the total number of microseismic events at discrete points to the total number of microseismic events in that month).

[0076] The specific criteria are shown in Table 1:

[0077] Table 1: Schematic diagram of the first discrimination result

[0078]

[0079] Abnormal production activities can be screened out through the identification of the first cluster of microseismic events mentioned above.

[0080] Figure 2 to Figure 6 The figure below shows a cluster analysis based on the overall distribution of microseismic events in five mines under Shanneng Group in January 2023. The clustering statistical factors include microseismic frequency, maximum energy, cumulative energy, etc. Different colors represent different types of microseismic events.

[0081] The overall distribution of microseismic events in the five mines under Shanneng Group was identified, as shown in Tables 2 to 6. The identification results are as follows:

[0082] Table 2: Results of overall distribution of microseismic events in coal mine A

[0083]

[0084]

[0085] Table 3: Results of overall distribution of microseismic events in coal mine B

[0086]

[0087] Table 4: Results of overall distribution of microseismic events in C coal mine

[0088]

[0089] Table 5: Overall distribution of microseismic events in D coal mine

[0090]

[0091]

[0092] Table 6: Results of overall distribution of microseismic events in E coal mine

[0093]

[0094] As shown in the above microseismic event cluster distribution diagram and the discrimination results, based on the distribution of microseismic events and the mining conditions of the working face, it can be seen that microseismic events in cluster 3 area will not occur during the normal mining stage of the E mine. Therefore, if this situation occurs, it is necessary to contact the mine for investigation (the investigation result is ground geophysical exploration).

[0095] Step 2: Select and count the second cluster of microseismic events from the first cluster of microseismic events;

[0096] In the specific implementation process, the second cluster number of microseismic events is determined according to the mining conditions and mining influencing factors (the second cluster number of microseismic events can be adjusted). Specifically, on the basis that the first cluster of microseismic events is valid, the second cluster of microseismic events is retrieved and divided;

[0097] In this embodiment, the second cluster of microseismic events is screened from the microseismic events occurring within a preset radius of the microseismic event cluster center, and the specific preset radius is set to 150 m.

[0098] Furthermore, the specific steps for screening the second cluster of microseismic events are the same as those in step 1 and will not be repeated here.

[0099] Furthermore, the validity of the distribution of microseismic events in the second clusters is evaluated based on the percentage of the total number of microseismic events in each second cluster to the total number of microseismic events in the first cluster;

[0100] Two types of evaluation results, "valid" and "invalid", are set up, and the overall distribution results of microseismic events of each working face are judged according to the percentage of the total number of microseismic events of the second cluster to the total number of microseismic events of the first cluster.

[0101] Specifically, by comparing the percentage of the total number of the second cluster of microseismic events to the total number of the first cluster of microseismic events with a first percentage threshold, the validity of the second cluster of microseismic events is evaluated;

[0102] The effectiveness of the microseismic events can be determined by evaluating the second cluster of microseismic events as described above.

[0103] Figure 7 to Figure 14 The figure shows the number of clusters and the plane distribution diagram of the five mines under Shanneng Group in January 2023. According to the actual mining situation during the time period, the 1301-2 working face of Mine A is divided into 5 clusters, the 3 mining working faces of Mine B are divided into 15 clusters, the 3410 working face of Mine C is divided into 4 clusters, the 7317 working face of Mine D is divided into 5 clusters, and the 2 mining working faces of Mine E are divided into 8 clusters.

[0104] Step 3: Compare the percentage of the total number of microseismic events in the second cluster to the total number of microseismic events in the first cluster with the first percentage threshold to evaluate the effectiveness of the second cluster of microseismic events.

[0105] The specific criteria are shown in Table 7:

[0106] Table 7: Schematic diagram of the second discrimination result

[0107]

[0108] Table 8 shows the clustering analysis results of each working face of key mines in January 2023. The clustering analysis results of microseismic events on each working face in January were all qualified.

[0109] Table 8: Statistical table of cluster analysis results of microseismic events in typical mine working faces

[0110]

[0111] Combined with the cluster distribution diagram of microseismic events in the 1301-2 working face of A coal mine in step 2, during the mining period of the working face, microseismic events were mainly concentrated in the two chute areas (clusters 1, 3, and 5), as shown in Table 9, with a cumulative share of about 95.13%. Therefore, the two chute areas need to be focused on in the subsequent mining stage, and corresponding decompression measures should be taken in the mining area when necessary.

[0112] Table 9: Statistical table of cluster analysis results of 1301-2 working face of A coal mine

[0113]

[0114]

[0115] Combined with the cluster distribution map of microseismic events in the 3315 track chute of Coal Mine B in step 2, the microseismic events mainly formed three obvious cluster areas (clusters 1, 4, and 5), as shown in Table 10, with a cumulative share of about 95.08%. The three concentrated areas are biased towards the goaf side, so the goaf area needs to be focused on in the subsequent mining stage.

[0116] Table 10: Cluster analysis results of 3315 track chute in B coal mine

[0117]

[0118] Combined with the cluster distribution map of microseismic events in the 5310 working face of B Coal Mine in step 2, the microseismic events are mainly concentrated in the internal area of ​​the working face (clusters 1, 4, and 5). As shown in Table 11, there are a certain number of microseismic events near the adjacent goaf (cluster 3). Therefore, in the subsequent mining stage, attention should be paid to the area in front of the working face and advance pressure relief measures should be taken.

[0119] Table 11: Cluster analysis results of 5310 working face in B coal mine

[0120]

[0121] Combined with the cluster distribution diagram of microseismic events in the 6315 working face of B Coal Mine in step 3, as shown in Table 12, the distribution of microseismic events is biased towards the solid coal side, and there are fewer microseismic events near the goaf, which is different from the theoretical analysis results and needs to be investigated.

[0122] Table 12: Cluster analysis results of 6315 working face in B coal mine

[0123]

[0124] Combined with the cluster distribution map of microseismic events in the 3410 working face of C Coal Mine in step 2, microseismic events are mainly concentrated in the internal area of ​​the working face, biased towards the goaf side (clusters 1, 2, and 4), as shown in Table 13. Therefore, in the subsequent mining stage, attention should be paid to the stress changes on the goaf side, and pressure relief measures should be taken when necessary.

[0125] Table 13: Statistical table of cluster analysis results of 3410 working face in C coal mine

[0126]

[0127] Combined with the cluster distribution map of microseismic events of the 7317 working face of D Coal Mine in step 2, the microseismic events are mainly concentrated behind the goaf of the working face (clusters 1 and 2). As shown in Table 14, there are fewer microseismic events in front of the working face, which is different from the theoretical analysis results and needs to be investigated in combination with the on-site situation.

[0128] Table 14: Cluster analysis results of 7317 working face in D coal mine

[0129]

[0130]

[0131] Combined with the cluster distribution diagram of microseismic events in the 22304 working face of E Coal Mine in step 2, as shown in Table 15, microseismic events are mainly concentrated in the internal area of ​​the working face. Affected by the Liuhai syncline structure, microseismic events are distributed along one side of the syncline. Therefore, the impact of the syncline structure needs to be focused on during the mining phase.

[0132] Table 15: Cluster analysis results of the 22304 working face of E coal mine

[0133]

[0134] Combined with the cluster distribution diagram of microseismic events in the 6305 working face of the E coal mine in step 3, as shown in Table 16, microseismic events are mainly concentrated in the internal area of ​​the working face. The 6305 connecting tunnel and fault structure in this area form a "triangular coal" area with a high degree of stress concentration and concentrated distribution of microseismic events. Therefore, the impact of syncline structure should be paid special attention to during the mining stage.

[0135] Table 16: Cluster analysis results of 6305 working face in E coal mine

[0136]

[0137] Embodiment 2

[0138] This embodiment provides a mine impact risk investigation device, which is implemented by the mine impact risk investigation method described in the first embodiment, and includes:

[0139] A microseismic event first cluster statistical module, used for counting the first cluster of microseismic events within a preset time period;

[0140] A microseismic event second cluster statistical module, used for screening and statistically analyzing the second microseismic event cluster in the first microseismic event cluster;

[0141] The microseismic event judgment module is used to compare the percentage of the total number of the second cluster of microseismic events to the total number of the first cluster of microseismic events with a first percentage threshold to evaluate the effectiveness of the second cluster of microseismic events.

[0142] In a feasible implementation manner, the second microseismic event clustering statistics module includes:

[0143] A calculation module, used to calculate the sum of the squares of the distances from each microseismic event in the monitoring area to the center of the monitoring area;

[0144] The selection module is used to select any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold.

[0145] In a feasible implementation, the microseismic event first cluster statistical module includes: a microseismic event first cluster screening module, which is used to screen effective microseismic event first clusters by comparing the percentage of the total number of microseismic events in the first cluster to the total number of microseismic events in a preset time period with a second percentage threshold.

[0146] This embodiment has the same technical features as the mine impact risk investigation method provided in Embodiment 1, and therefore can solve the same technical problems and achieve the same technical effects.

[0147] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0148] The applicant of this application has made a detailed explanation and description of the implementation examples of this application in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of this application, and the detailed description is only to help readers better understand the spirit of this application, and it is not a limitation on the scope of protection of this application. On the contrary, any improvements or modifications based on the inventive spirit of this application should fall within the scope of protection of this application.

Claims

1. A mine impact risk investigation method, characterized in that: include: Counting the first cluster of microseismic events within a preset time period; Screening and counting the second cluster of microseismic events from the first cluster of microseismic events; The percentage of the total number of microseismic events in the second cluster to the total number of microseismic events in the first cluster is compared with the first percentage threshold to evaluate the validity of the second cluster of microseismic events.

2. The mine impact risk investigation method according to claim 1, characterized in that: The first clustering of microseismic events is counted within a preset time period, comprising: By determining the cluster center of microseismic events according to the mine excavation and mining work areas within a preset time period, and delineating the first cluster of microseismic events according to the cluster center of microseismic events; Determine whether the first cluster of microseismic events identified is valid.

3. The mine impact risk investigation method according to claim 2 is characterized in that: The method of determining the microseismic event cluster center according to the mine excavation and mining work area includes: determining a driving working face or a mining working face as a microseismic event cluster number, and selecting the center position of the driving working face or the mining working face as the microseismic event cluster center.

4. The mine impact risk investigation method according to claim 2 is characterized in that: The determining whether the delineated first cluster of microseismic events is valid includes: screening a valid first cluster of microseismic events by comparing the percentage of the total number of the first cluster of microseismic events to the total number of microseismic events in a preset time period with a second percentage threshold.

5. The mine impact risk investigation method according to claim 2, characterized in that: The step of screening and counting the second cluster of microseismic events in the first cluster of microseismic events includes screening the second cluster of microseismic events from the microseismic events occurring within a preset radius of the center of the microseismic event cluster.

6. The mine impact risk investigation method according to claim 1 is characterized in that: The method of counting the first cluster of microseismic events within a preset time period or screening the second cluster of microseismic events from the first cluster of microseismic events comprises: Calculate the sum of the squares of the distances from each microseismic event in the monitoring area to the center of the monitoring area; Select any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold.

7. The mine impact risk investigation method according to claim 6, characterized in that: The step of selecting any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold comprises the following steps: Step S1: Select M initial monitoring area centers; Step S2: Allocate the microseismic event to one of the M monitoring area centers according to the shortest distance principle; Step S3: Calculate the new vector value between the microseismic event and the center of each monitoring area and the average value of the new vector value, and take the center of the monitoring area where the vector value between the center of the monitoring area and the microseismic event and the average value of the new vector value are equal as the new center of the monitoring area; Step S4: If the new monitoring area center is not equal to one of the M monitoring area centers, return to step S2 and redivide the monitoring areas for the microseismic events one by one. If they are equal, the microseismic event clustering is determined.

8. A mine impact risk investigation device, implemented by the mine impact risk investigation method according to any one of claims 1 to 7, characterized in that: include: A microseismic event first cluster statistical module, used for counting the first cluster of microseismic events within a preset time period; A microseismic event second cluster statistical module, used for screening and statistically analyzing the second microseismic event cluster in the first microseismic event cluster; The microseismic event judgment module is used to compare the percentage of the total number of the second cluster of microseismic events to the total number of the first cluster of microseismic events with a first percentage threshold to evaluate the effectiveness of the second cluster of microseismic events.

9. The mine impact risk investigation device according to claim 8, characterized in that: The second clustering statistics module of microseismic events includes: A calculation module, used to calculate the sum of the squares of the distances from each microseismic event in the monitoring area to the center of the monitoring area; The selection module is used to select any monitoring area center so that the distance from the microseismic event to the monitoring area center is less than or equal to a preset distance threshold.

10. The mine impact risk investigation device according to claim 8, characterized in that: The microseismic event first cluster statistical module includes: a microseismic event first cluster screening module, which is used to screen effective microseismic event first clusters by comparing the percentage of the total number of microseismic events in the first cluster to the total number of microseismic events in a preset time period with a second percentage threshold.

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