A coal mine pressure early warning analysis method and system based on big data processing

Abstract

The present application belongs to the technical field of coal mine pressure early warning, and discloses a coal mine pressure early warning analysis method and system based on big data processing. The method constructs a goaf upper three-zone edge mine pressure identification network model; according to the goaf upper three-zone edge mine pressure identification network model, a goaf upper three-zone edge mine pressure deformation node identification based on a mine pressure early warning threshold adaptive method; according to the identification result, a dynamic goaf upper three-zone edge mine pressure deformation node sorting based on a mine pressure early warning threshold adaptive method; and according to the sorting result, a mine pressure identification confirmation strategy based on a mine pressure early warning threshold adaptive method is realized. The present application greatly improves the accuracy of different geological mine pressure early warning in the deformation process of the goaf upper three-zone (curved subsidence zone, fissure zone and caving zone).

Description

Technical Field

[0001] This invention belongs to the field of coal mine pressure early warning technology, and in particular relates to a coal mine pressure early warning analysis method and system based on big data processing. Background Technology

[0002] Mine pressure refers to the deformation and failure of rock masses under the action of gravity and geostress. Its suddenness and unpredictability bring huge risks and losses to coal mine safety production, and may lead to support subsidence, roadway deformation, and induce dynamic disasters such as rockburst, gas inrush, and water inrush. Therefore, accurate prediction and timely early warning of changes in mine pressure are of great significance to ensuring safe coal mine production.

[0003] Currently, mine pressure monitoring methods mainly cover direct monitoring of rock mass stress and displacement, and indirect monitoring of physical quantities such as microseismic activity, ground sounds, and electromagnetic fields generated around the rock mass. Direct monitoring provides the most intuitive response to the danger of the surrounding rock, while indirect monitoring has good causal correlation, but it is costly and data analysis is complex.

[0004] Current direct monitoring of mine pressure suffers from several problems: each sensor operates in isolation, leading to frequent confirmation conflicts; early warning criteria are simplistic, relying on basic threshold and trend warnings; early warning models lack robustness and adaptability, making them unsuitable for different mine conditions; and existing technologies fail to effectively monitor dynamic data.

[0005] To address this issue, the invention patent "Mine Pressure Early Warning Tree System Based on Deep Learning Temporal Prediction and Multidimensional Dynamic Verification" (Publication No. CN117077057A, Publication Date 2023.11.17) discloses a system including sensors and a central server. The sensors include a support-type leaf-level sensor for support body monitoring and a surrounding rock-type leaf-level sensor for surrounding rock monitoring. These sensors collect multi-source data from various monitoring points in the mine, align the data according to time, preprocess all time-series data at the edge using a computational bar, regress outliers, fill in missing values, and group and fuse the time-series data according to two main monitoring types: support body monitoring and surrounding rock monitoring. The obtained one-dimensional, multi-dimensional, and high-dimensional time-series data are then used as leaf-level, multi-dimensional, and high-dimensional data for the central server, respectively. Branch-level and trunk-level time-series data are transmitted to a central server. The central server, acting as a trunk-level monitoring station, connects to the leaf-level sensors. The central server contains an LSTM network model, a verification module, and an early warning module. The LSTM network model predicts the time-series data processed by the leaf-level sensors at each level, obtaining leaf-level, branch-level, and trunk-level predicted data. The verification module compares the measured and predicted leaf-level data based on the 3σ criterion for one-dimensional data to obtain leaf-level verification results, and also compares the combined measured and predicted branch-level and trunk-level data based on the 3σ criterion for multi-dimensional or high-dimensional data to obtain branch-level and trunk-level verification results. The early warning module provides tiered early warnings based on the leaf-level, branch-level, and trunk-level verification results, starting with the lowest-level sensor / leaf end and proceeding sequentially upwards to the monitoring station / trunk end. This patent compares real-time measured data with real-time predicted data, quantifies verification according to the 3σ criterion, and provides tiered early warnings for leaf, branch, and trunk levels, achieving comprehensive, multi-angle early warning of mine pressure anomalies. This overcomes the shortcomings of traditional threshold-based early warning methods that rely on comparing measured and predicted data, thus improving the accuracy of early warnings.

[0006] However, the accuracy of early warnings for different geological and mineral pressures varies considerably during the deformation process of the three zones (bending and subsidence zone, fracture zone, and caving zone) in the goaf. Summary of the Invention

[0007] To overcome the problems existing in related technologies, the present invention discloses a coal mine pressure early warning analysis system and method based on big data processing.

[0008] The technical solution is as follows: A method and system for early warning and analysis of coal mine pressure based on big data processing, the method comprising:

[0009] S1, Construct a network model for identifying edge mining pressure in the upper three zones of the goaf;

[0010] S2, Identification of mining pressure deformation nodes at the edge of the three zones above the goaf using the adaptive method for mining pressure early warning threshold based on the network model;

[0011] S3, based on the identification results, realizes the dynamic sorting of mining pressure deformation nodes at the edge of the three zones in the goaf, which is adaptive to the mining pressure early warning threshold;

[0012] S4. Based on the sorting results, implement a mine pressure identification and confirmation strategy that is adaptive to the mine pressure early warning threshold.

[0013] Furthermore, in step S1, the mining pressure identification network model at the edge of the three zones above the goaf consists of a lidar station, a lidar station server, and a mining pressure early warning monitoring terminal. The method for obtaining the response is as follows:

[0014] (1) If the probe is the content of the user request for mining pressure identification performed by the lidar station server, the lidar station server will directly respond to the user request; otherwise, proceed to step (2) to check whether the neighboring servers of the lidar station server are performing mining pressure identification.

[0015] (2) If the neighboring server performs mining pressure identification of the user's requested content, the neighboring server sends the content to the basic site server that the user probes and responds to the user's request; otherwise, proceed to step (3) to determine whether the lidar station server performs mining pressure identification of the content.

[0016] (3) If none of the three edge mineral pressure deformation nodes in the goaf area in the search process have mineral pressure identification content, the exploration request will be forwarded to the cloud network.

[0017] (4) User requests are responded to in the cloud network. The cloud network sends the request content to the main lidar station server, to the neighboring servers explored by the user, and finally to the user.

[0018] In step S2, the identification of mining pressure deformation nodes at the edge of the three zones above the goaf using the adaptive method for mining pressure early warning threshold includes:

[0019] The identification network for edge mining pressure in the upper three zones of the goaf is F = (C, D), where C = {1, 2…h} is the set of nodes in the network representing deformation due to mining pressure in the upper three zones of the goaf, and D = {1, 2…C} is the set of edges in the network. ij Let D be the edge between node i and node j of the upper three-zone edge of the goaf, where D is the deformation node of the upper three-zone edge of the goaf. ij The bandwidth is used by y ij This indicates that when there is an edge between node i and node j on the edge of the upper three zones of the goaf, then y ij >0, otherwise y ij =0; specifically includes:

[0020] (a) Mining pressure identification information of the edge mining pressure deformation node in the upper three zones of the goaf;

[0021] (b) Adjacent bandwidth of the edge mineral pressure deformation nodes in the upper three zones of the goaf;

[0022] (c) Number of times the mining pressure deformation nodes at the edge of the upper three zones of the goaf were investigated;

[0023] (d) Investigate the center of deformation criticality;

[0024] (e) Investigate the balance rate.

[0025] In step (a), the mineral pressure identification information of the three edge mineral pressure deformation nodes in the goaf is represented as ca. i The more information on mining pressure identification, the more important the mining pressure deformation nodes at the edges of the three zones above the goaf;

[0026] In step (b), the adjacency bandwidth and ho of the three edge mineral pressure deformation nodes in the goaf are... i The expression is:

[0027]

[0028] In the formula, H(i) is the set of neighboring nodes of the mining pressure deformation node i at the edge of the three zones above the goaf in the network, and oy ij The adjacency bandwidth between node i, the edge of the upper three zones of the goaf, and node j, the neighboring edge of the upper three zones of the goaf, is defined as follows:

[0029] The greater the sum of the adjacent bandwidths of the deformation nodes at the edges of the three zones above the goaf, the more important the deformation nodes at the edges of the three zones above the goaf are; the deformation nodes at the edges of the three zones above the goaf include the deformation points at the intersection of the edges of the bending subsidence zone, the fracture zone, and the caving zone.

[0030] In step (c), the number of times the deformation nodes of the three edges of the goaf are explored depends on the type and degree of attention of the mining pressure identification content. That is, the more types there are, the more times the deformation nodes of the three edges of the goaf are explored; the more attention the content receives, the more times the deformation nodes of the three edges of the goaf are explored. The mining pressure identification content includes deformation information at the boundary lines of the edges of the bending subsidence zone, fracture zone, and caving zone.

[0031] In step (d), the exploration deformation criticality centrality is constructed by introducing the number of explorations of the mining pressure deformation nodes at the edges of the three zones above the goaf, and this is used as the mining pressure identification confirmation standard to determine the network mining pressure identification strategy. The mining pressure identification location is determined by the location and exploration status of the mining pressure deformation nodes at the edges of the three zones above the goaf. The exploration deformation criticality centrality of the mining pressure deformation nodes at the edges of the three zones above the goaf is the exploration deformation criticality of all neighboring mining pressure deformation nodes at the edges of the three zones above the goaf and the number of explorations of the mining pressure deformation nodes at the edges of the three zones above the goaf. Deformation criticality includes the mining pressure deformation value that affects safe coal mining.

[0032] In step (e), the exploration balance of the three edge mineral pressure deformation nodes in the goaf is as follows:

[0033]

[0034] In the formula, , where is the exploration equilibrium value of the deformation nodes at the edge of the three zones above the goaf, 'a' is the identification weight of the deformation nodes at the edge of the three zones above the goaf, and 'u' is the value of the deformation nodes at the edge of the three zones above the goaf. g The keyness of the deformation at the edge of the mining pressure deformation node g in the neighboring goaf is η, where η is the Boltzmann constant and u is the keyness of the deformation at the edge of the mining pressure deformation node g in the upper three zones. j Let j be the keyity of the deformation at the edge of the three zones of the neighboring goaf, where j and g are both deformation nodes at the edge of the three zones of the neighboring goaf, and j, g∈H(i). The larger the value, the more balanced the exploration of the deformation nodes at the edge of the three zones above the goaf, the easier it is to meet the user's exploration requests, and the greater the importance of the deformation nodes at the edge of the three zones above the goaf.

[0035] In step (c), the number of times the edge mineral pressure deformation nodes of the three zones above the goaf are investigated includes:

[0036] (c.1) Frequency of content distortion;

[0037] Assuming that the attention received by content follows a frequency of exploration distribution, then the frequency of deformation of content K ranked χ is:

[0038]

[0039] In the formula, χ represents the frequency of deformation, K represents the content, num represents the total number of content, and λ represents the skewness coefficient of the detection frequency distribution. The larger λ is, the easier it is to detect content with high deformation frequency.

[0040] (c.2) User attention to the edge of the three zones of the goaf and the deformation node of the mining pressure.

[0041] By analyzing users' long-term exploration records, we can obtain stable user attention. We define users' long-term attention to the deformation node i at the edge of the three zones above the goaf as... :

[0042]

[0043] In the formula, This represents the statistical flow rate at node i, the edge of the upper three zones of the current goaf, where mining pressure deformation occurs. long (ΔT long This represents the current statistical exploration volume of all users within the edge mining pressure identification set;

[0044] The user's recent focus on the edge mineral pressure deformation node i of the three zones above the goaf is defined as short-term focus. :

[0045]

[0046] In the formula, This represents the statistical exploration data of the deformation node i at the edge of the upper three zones of the goaf over a recent period. short (ΔT short The statistical flow of all users exploring edge mining pressure deformation nodes i in the edge mining pressure identification set in the recent period is defined as follows: A user's current attention depends on their long-term and short-term attention. Therefore, the potential attention of a user to exploring edge mining pressure deformation nodes i in the three zones above the goaf is defined as:

[0047]

[0048] In the formula, The potential attention values ​​are ζ1 and ζ2, which represent the proportion of long-term and short-term attention on the user's current attention, respectively. Considering that the short-term impact is greater, ζ2 should be greater than ζ1.

[0049] (c.3) The user's potential probing behavior;

[0050] Because user content exploration trends are not only influenced by their attention but also closely related to the level of attention a content receives, users tend to request frequently deformed content that they like; the probability of requesting content k in node i of the edge of the three-zone upper goaf under pressure deformation. for:

[0051]

[0052] In the formula, Let b be the frequency of deformation, and b be the number of the b-th content.

[0053] The total number of user probes in the previous mining pressure identification period was u ave Then, the potential exploration volume of content k in node i of the mining pressure deformation node i on the edge of the three zones above the goaf is determined by the user. for

[0054] In step (d), the method for determining the criticality of deformation includes:

[0055] (A) Delete all deformation nodes and edges of the upper three zones of the goaf with a connectivity of 1, and record the number of times the deformation nodes of the upper three zones of the goaf are explored; if there are still deformation nodes of the upper three zones of the goaf with a connectivity of 1, continue the above process, and mark the deformation criticality of these deleted deformation nodes of the upper three zones of the goaf as 1. The exploration deformation criticality is the number of explorations of the deformation node of the upper three zones of the goaf plus 1. If the number of explorations of deformation node j of the upper three zones of the goaf is u j The keyness of the deformation of the exploration node j at the edge of the three zones of the goaf is au. j =u j +1;

[0056] (B) For the mining pressure deformation nodes at the edge of the upper three zones of the goaf with a connectivity degree of 2, mark the exploration deformation criticality of these mining pressure deformation nodes at the edge of the upper three zones of the goaf as exploration number + 2.

[0057] (C) Repeat the above process until all the edge mineral pressure deformation nodes of the three goaf areas are deleted and the corresponding exploration deformation criticality is obtained.

[0058] In step S3, the method for sorting the deformation nodes of the three edges of the goaf based on the adaptive dynamic goaf early warning threshold includes:

[0059] S301, Obtain the confirmation matrix and its evaluation indicators;

[0060] Suppose there are h deformation nodes at the edge of the three zones above the goaf to be sorted, and each deformation node has m evaluation indicators. The value of the l-th evaluation indicator of the i-th deformation node at the edge of the three zones above the goaf is w. il (i=1,2…h,l=1,2…m), the confirmation matrix and its evaluation index, composed of the edge mineral pressure deformation nodes of all three zones in the network goaf, are shown in the following formula:

[0061]

[0062] In the formula, W is the evaluation index value, w hm is the value of the m-th evaluation index of h, the edge mineral pressure deformation node h in the upper three zones of the goaf.

[0063] S302, Normalized confirmation matrix.

[0064] Because the dimensions of the various indicators differ, resulting in orders of magnitude differences, direct comparison is inconvenient. To eliminate the dimensionality differences between the indicators, the index should be standardized, as shown in the following formula:

[0065]

[0066] In the formula, v il The standardized value index of the l-th evaluation index of the deformation node i at the edge of the three zones of the goaf is w. il The value of the l-th evaluation index of the edge mineral pressure deformation node i in the upper three zones of the goaf;

[0067] Therefore, the standard normalized matrix is:

[0068]

[0069] In the formula, V is the exponentially normalized value, v hm The m-th standardized value index of h, the edge mineral pressure deformation node h in the upper three zones of the goaf;

[0070] S303, the mine pressure early warning threshold calculated based on exponential entropy;

[0071] The formula for calculating exponential entropy is:

[0072]

[0073] In the formula, zz l Let there be l exponential entropy values, and let ζ be the coefficients for solving the exponential entropy;

[0074] The information entropy redundancy is calculated as follows:

[0075] dd l =1-z l

[0076] In the formula, dd l Let there be l information entropy values, z l This is the basic value of the l-th information entropy;

[0077] The calculation of the index-based mine pressure early warning threshold is shown in the following formula:

[0078]

[0079] In the formula, y l Let be the l-th indexed mining pressure early warning threshold, m be the m-th node, and l be the l-th node;

[0080] The index-based mineral pressure early warning threshold matrix Y is obtained, as shown in the following formula:

[0081] Y = [y1 y2 y3 y m ]

[0082] In the formula, Y is the value of the exponential mining pressure early warning threshold matrix, y m The threshold for early warning of the m-th index of mining pressure;

[0083] The weighted normalized confirmation matrix can be expressed as:

[0084]

[0085] In the formula, E is the weighted normalized confirmation matrix value, V is the exponential standard normalized value, and v hm y is the m-th standardized index of the deformation node h at the edge of the upper three zones of the goaf; m The threshold for early warning of the m-th index of mining pressure;

[0086] The weighted attribute value of node i, which represents the deformation of the mineral pressure at the edge of the three zones above the goaf, can be expressed as:

[0087]

[0088] In the formula, A i E represents the weighted attribute value of node i, which is the deformation node i at the edge of the three zones above the goaf due to mining pressure. il The l-th weighted normalized confirmation matrix value is the i-th weighted normalized confirmation matrix value of the deformation node i at the edge of the upper three zones of the goaf.

[0089] Since collaborative mining pressure identification can be achieved among the mining pressure deformation nodes at the edges of the three zones above the goaf in the mining pressure identification network, and adjacent mining pressure deformation nodes at the edges of the three zones above the goaf also contribute to the importance of mining pressure identification of the target mining pressure deformation node, an importance evaluation matrix is ​​constructed:

[0090]

[0091] In the formula, B is the importance evaluation matrix value, and A h ε is the weighted attribute value of the deformation node h at the edge of the three zones above the goaf, ψ1 is the degree of the deformation node 1 at the edge of the three zones above the goaf, ψ is the average degree of the deformation nodes at the edge of the three zones above the goaf, and ε is the weighted attribute value of the deformation node h at the edge of the three zones above the goaf. ij The contribution allocation parameter is set to 1 if the three edge mineral pressure deformation nodes of the two goaf areas are connected, and 0 otherwise.

[0092] S304, Importance Calculation;

[0093] Based on the importance evaluation matrix, the importance of the mining pressure deformation node i at the edge of the three zones above the goaf is obtained by summing the attribute value of i and the importance contribution of all adjacent mining pressure deformation nodes at the edge of the three zones above the goaf.

[0094]

[0095] In the formula, For the value of node i, which is the edge of the mining pressure deformation zone in the upper three zones of the goaf, A i A is the weighted attribute value of node i, which is the edge deformation node of the three zones above the goaf due to mining pressure. jThe weighted attribute value of node j, which is the edge of the mining pressure deformation node in the upper three zones of the goaf;

[0096] The importance evaluation matrix fully considers the location of the mining pressure deformation nodes at the edge of the three zones above the goaf, the exploration frequency, the available information for mining pressure identification, and the available bandwidth. It more comprehensively reflects the importance of the mining pressure deformation nodes at the edge of the three zones above the goaf, focusing on the goal of mining pressure identification value.

[0097] In step S4, the implementation of the mine pressure identification and confirmation strategy based on the mine pressure early warning threshold includes:

[0098] S401, update rate;

[0099] The mine pressure identification update rate d(i) is:

[0100]

[0101] In the formula, A(du) m ) represents the number of updates made from the mining pressure deformation node i at the edge of the three upper zones of the goaf per unit time, and e(i) represents the size of the available mining pressure identification information at the mining pressure deformation node i at the edge of the three upper zones of the goaf.

[0102] standardization:

[0103]

[0104] In the formula, f(i) is the standardized value of the mine pressure identification update rate, d(i) is the mine pressure identification update rate, and d(h) is the mine pressure identification update rate of the edge mine pressure deformation node h in the upper three zones of the goaf during deformation.

[0105] S402, Effective early warning measurement for identifying mining pressure deformation nodes at the edge of the three zones in the goaf.

[0106] In step S402, a new effective early warning metric K(i) is designed, including the detection equilibrium deformation criticality centrality rate and the mine pressure identification update rate:

[0107]

[0108] i a =argmax{K(i)}

[0109] In the formula, i a Let K(i) be the inverse value of the set of maximum effective early warning metrics, and v be the effective early warning metric value. iTo comprehensively evaluate the importance of the mining pressure deformation node i at the edge of the three zones above the goaf, f(i) is the mining pressure identification update rate of the mining pressure deformation node at the edge of the three zones above the goaf. If f(i) = 0, it means that the mining pressure identification information of the mining pressure deformation node at the edge of the three zones above the goaf is not full, or no new content has arrived. In order to make the effective early warning measurement expression of the mining pressure deformation node at the edge of the three zones above the goaf consistent, let f(i) = θ, where θ is a very small positive number.

[0110] Another objective of this invention is to provide a coal mine pressure early warning and analysis system based on big data processing, the system comprising:

[0111] The module for constructing a network model for identifying edge mining pressure in the upper three zones of a goaf is used to construct such a model.

[0112] The mine pressure deformation node identification module is used to identify the mine pressure deformation nodes at the edges of the three zones above the goaf based on the mine pressure identification network model of the goaf and the mine pressure early warning threshold adaptive method.

[0113] The mine pressure deformation node sorting module is used to sort the mine pressure deformation nodes on the edge of the three zones of the goaf dynamically and adaptively based on the mine pressure early warning threshold according to the above identification results.

[0114] The mine pressure identification module is used to implement an adaptive mine pressure identification and confirmation strategy based on the above sorting results and mine pressure early warning threshold.

[0115] Combining all the above technical solutions, the beneficial effects of this invention are: this invention greatly improves the accuracy of early warning for different geological and mineral pressures during the deformation process of the three zones (bending and subsidence zone, fracture zone and caving zone) in the goaf. Attached Figure Description

[0116] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0117] Figure 1 This is a flowchart of the coal mine pressure early warning analysis method based on big data processing provided in this embodiment of the invention;

[0118] Figure 2 This is a schematic diagram of the coal mine pressure early warning analysis method based on big data processing provided in this embodiment of the invention;

[0119] Figure 3 This is a schematic diagram of a coal mine pressure early warning and analysis system based on big data processing provided in an embodiment of the present invention;

[0120] In the diagram: 1. Module for constructing a network model for identifying mining pressure at the edge of the three zones above the goaf; 2. Module for identifying mining pressure deformation nodes; 3. Module for sorting mining pressure deformation nodes; 4. Module for identifying mining pressure. Detailed Implementation

[0121] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention; therefore, the present invention is not limited to the specific embodiments disclosed below. Three zones above the goaf (bending subsidence zone, fracture zone, and caving zone).

[0122] Example 1, as Figure 1 As shown, the coal mine pressure early warning analysis method based on big data processing provided in this embodiment of the invention includes:

[0123] S1, Construct a network model for identifying edge mining pressure in the upper three zones of the goaf;

[0124] S2, Based on the mining pressure identification network model of the three-zone edge of the goaf, the deformation node of the mining pressure at the edge of the three-zone edge of the goaf is identified using the mining pressure early warning threshold adaptive method.

[0125] S3, Based on the above identification results, sort the dynamic mining pressure deformation nodes at the edge of the three zones in the goaf based on the adaptive mining pressure early warning threshold.

[0126] S4. Based on the above sorting results, implement a mine pressure identification and confirmation strategy that is adaptive based on the mine pressure early warning threshold.

[0127] For example, in step S1, the three-zone edge mining pressure identification network in the goaf consists of a lidar station, a lidar station server, and a mining pressure early warning monitoring terminal; the lidar station is wirelessly connected and connects to the cloud network through the lidar station; the lidar station server has mining pressure identification function and is used to provide services to users; the mining pressure early warning monitoring terminal is connected to the lidar station server in the cluster and is used to collect information from all lidar station servers;

[0128] For example, a user can randomly probe any three-zone edge mining pressure deformation node in a goaf to request any content. Assuming the transmission time between any three-zone edge mining pressure deformation nodes in the cluster is less than the transmission time to the cloud network, the user obtains the response as follows:

[0129] (1) If the probe is the content of the user request for mining pressure identification performed by the lidar station server, the lidar station server will directly respond to the user request; otherwise, proceed to step (2) to check whether the neighboring servers of the lidar station server are performing mining pressure identification.

[0130] (2) If the neighboring server performs mining pressure identification of the user's requested content, the neighboring server sends the content to the user's probe and responds to the user's request from the basic site server; otherwise, proceed to step (3) to determine whether other lidar station servers perform mining pressure identification of the content.

[0131] (3) If none of the three edge mineral pressure deformation nodes in the goaf area in the search process have mineral pressure identification content, the exploration request will be forwarded to the cloud network.

[0132] (4) User requests are responded to in the cloud network. The cloud network sends the request content to the main lidar station server, then to the neighbor server that the user is exploring, and finally to the user.

[0133] For example, in step S2, the identification of mining pressure deformation nodes at the edge of the three zones above the goaf based on the adaptive method for mining pressure early warning threshold includes:

[0134] The identification network for edge mining pressure in the upper three zones of the goaf is F = (C, D), where C = {1, 2…h} is the set of nodes in the network representing deformation due to mining pressure in the upper three zones of the goaf, and D = {1, 2…C} is the set of edges in the network. ij Let D be the edge between node i and node j of the upper three-zone edge of the goaf, where D is the deformation node of the upper three-zone edge of the goaf. ij The bandwidth is used by y ij This indicates that when there is an edge between node i and node j on the edge of the upper three zones of the goaf, then y ij >0, otherwise y ij =0;

[0135] (a) Mining pressure identification information of the edge mining pressure deformation node in the upper three zones of the goaf;

[0136] The mineral pressure identification information of the edge mineral pressure deformation node in the upper three zones of the goaf is represented as ca i The more available information on mining pressure identification, the more important the mining pressure deformation nodes at the edges of the three zones above the goaf; available information includes bending subsidence, fractures, and collapses;

[0137] (b) Adjacent bandwidth of the edge mineral pressure deformation nodes in the upper three zones of the goaf;

[0138] The expression for the sum of the adjacent bandwidths of the three edge mineral pressure deformation nodes in the goaf is:

[0139]

[0140] In the formula, H(i) is the set of neighboring nodes of the mining pressure deformation node i at the edge of the three zones above the goaf in the network, and oy ij The adjacency bandwidth between node i, the edge of the upper three zones of the goaf, and node j, the neighboring edge of the upper three zones of the goaf, is defined as follows:

[0141] The greater the sum of the adjacent bandwidths of the deformation nodes at the edges of the three zones above the goaf, the more important the deformation nodes at the edges of the three zones above the goaf are; the deformation nodes at the edges of the three zones above the goaf include the deformation points at the intersection of the edges of the bending subsidence zone, the fracture zone, and the caving zone.

[0142] (c) Number of times the edge of the three zones of the goaf was explored for mineral pressure deformation nodes.

[0143] The number of times the deformation nodes at the edges of the three zones above the goaf are explored depends on the type and level of attention given to the content of the mining pressure identification. That is, the more types there are, the more times the deformation nodes at the edges of the three zones above the goaf are explored; the more attention the content receives, the more times the deformation nodes at the edges of the three zones above the goaf are explored. The content of the mining pressure identification includes deformation information at the boundary lines of the edges of the bending subsidence zone, fracture zone, and caving zone.

[0144] (d) Investigate the center of deformation criticality.

[0145] The exploration deformation criticality centrality is constructed by introducing the number of explorations of the deformation nodes at the edges of the three zones above the goaf, and this is used as the mining pressure identification confirmation standard to determine the network mining pressure identification strategy. The mining pressure identification location is determined by the location and exploration status of the deformation nodes at the edges of the three zones above the goaf. The exploration deformation criticality centrality of the deformation nodes at the edges of the three zones above the goaf is the exploration deformation criticality of all neighboring deformation nodes at the edges of the three zones above the goaf and the number of explorations of the deformation nodes at the edges of the three zones above the goaf. Deformation criticality includes the mining pressure deformation value that affects safe coal mining.

[0146] (e) Investigate the balance rate.

[0147] The exploration balance of the three edge mineral pressure deformation nodes in the goaf is as follows:

[0148]

[0149] In the formula, , where is the exploration equilibrium value of the deformation nodes at the edge of the three zones above the goaf, 'a' is the identification weight of the deformation nodes at the edge of the three zones above the goaf, and 'u' is the value of the deformation nodes at the edge of the three zones above the goaf. g Let g be the keyity of the deformation at the edge of the mining pressure deformation node in the neighboring goaf, where η is the Boltzmann constant, representing the system's inherent properties; u jLet j be the keyity of the deformation at the edge of the three zones of the neighboring goaf, where j and g are both deformation nodes at the edge of the three zones of the neighboring goaf, and j, g∈H(i). The larger the value, the more balanced the exploration of the deformation nodes at the edge of the three zones above the goaf, the easier it is to meet the user's exploration requests, and the greater the importance of the deformation nodes at the edge of the three zones above the goaf.

[0150] For example, in step (c), the number of times the edge mineral pressure deformation nodes of the three zones above the goaf are investigated includes:

[0151] (c.1) Frequency of content distortion;

[0152] Assuming that the attention received by content follows a frequency of exploration distribution, then the frequency of deformation of content K ranked χ is:

[0153]

[0154] In the formula, χ represents the frequency of deformation, K represents the content, num represents the total number of content, and λ represents the skewness coefficient of the detection frequency distribution. The larger λ is, the easier it is to detect content with high deformation frequency.

[0155] (c.2) User attention to the edge of the three zones of the goaf and the deformation node of the mining pressure.

[0156] By analyzing users' long-term exploration records, we can obtain stable user attention. We define users' long-term attention to the deformation node i at the edge of the three zones above the goaf as... :

[0157]

[0158] In the formula, This represents the statistical flow rate at node i, the edge of the upper three zones of the current goaf, where mining pressure deformation occurs. long (ΔT long This represents the current statistical exploration volume of all users within the edge mining pressure identification set;

[0159] The user's recent focus on the edge mineral pressure deformation node i of the three zones above the goaf is defined as short-term focus. :

[0160]

[0161] In the formula, This represents the statistical exploration data of the deformation node i at the edge of the upper three zones of the goaf over a recent period. short (ΔT shortThe statistical flow of all users exploring edge mining pressure deformation nodes i in the edge mining pressure identification set in the recent period is defined as follows: A user's current attention depends on their long-term and short-term attention. Therefore, the potential attention of a user to exploring edge mining pressure deformation nodes i in the three zones above the goaf is defined as:

[0162]

[0163] In the formula, The potential attention values ​​are ζ1 and ζ2, which represent the proportion of long-term and short-term attention on the user's current attention, respectively. Considering that the short-term impact is greater, ζ2 should be greater than ζ1.

[0164] (c.3) The user's potential probing behavior.

[0165] Because user content exploration trends are not only influenced by their attention but also closely related to the level of attention a content receives, users tend to request frequently deformed content that they like; the probability of requesting content k in node i of the edge of the three-zone upper goaf under pressure deformation. for:

[0166]

[0167] In the formula, Let b be the frequency of deformation, and b be the number of the b-th content.

[0168] The total number of user probes in the previous mining pressure identification period was u ave Then, the potential exploration volume of content k in node i of the mining pressure deformation node i on the edge of the three zones above the goaf is determined by the user. for

[0169] For example, in step (d), the method for determining the criticality of deformation includes:

[0170] (A) Delete all deformation nodes and edges of the upper three zones of the goaf with a connectivity of 1, and record the number of times the deformation nodes of the upper three zones of the goaf are explored; if there are still deformation nodes of the upper three zones of the goaf with a connectivity of 1, continue the above process, and mark the deformation criticality of these deleted deformation nodes of the upper three zones of the goaf as 1. The exploration deformation criticality is the number of explorations of the deformation node of the upper three zones of the goaf plus 1. If the number of explorations of deformation node j of the upper three zones of the goaf is u j The keyness of the deformation of the exploration node j at the edge of the three zones of the goaf is au. j =u j +1;

[0171] (B) For the mining pressure deformation nodes at the edge of the upper three zones of the goaf with a connectivity degree of 2, mark the exploration deformation criticality of these mining pressure deformation nodes at the edge of the upper three zones of the goaf as exploration number + 2.

[0172] (C) Repeat the above process until all the edge mineral pressure deformation nodes of the three goaf areas are deleted and the corresponding exploration deformation criticality is obtained.

[0173] For example, the numbers next to the edge mineral pressure deformation nodes of the three zones above the goaf indicate the number of areas that have been explored.

[0174] For example, in step S3, the method for sorting the deformation nodes of the three edges of the goaf based on the adaptive dynamic goaf early warning threshold includes:

[0175] Using the information entropy theory, each indicator is adaptively weighted according to the changes in network indicator parameters. According to the information entropy theory, the higher the disorder of the indicator set, the greater the amount of information provided by the indicator, and the higher the mine pressure early warning threshold of the comprehensive evaluation indicator.

[0176] S301, Obtain the confirmation matrix and its evaluation indicators;

[0177] Suppose there are h deformation nodes at the edge of the three zones above the goaf to be sorted, and each deformation node has m evaluation indicators. The value of the l-th evaluation indicator of the i-th deformation node at the edge of the three zones above the goaf is w. il (i=1,2…h,l=1,2…m), the confirmation matrix and its evaluation index, composed of the edge mineral pressure deformation nodes of all three zones in the network goaf, are shown in the following formula:

[0178]

[0179] In the formula, W is the evaluation index value, w hm is the value of the m-th evaluation index of h, the edge mineral pressure deformation node h in the upper three zones of the goaf.

[0180] S302, Normalized confirmation matrix.

[0181] Because the dimensions of the various indicators differ, resulting in orders of magnitude differences, direct comparison is inconvenient. To eliminate the dimensionality differences between the indicators, the index should be standardized, as shown in the following formula:

[0182]

[0183] In the formula, v il The standardized value index of the l-th evaluation index of the deformation node i at the edge of the three zones of the goaf is w. il The value of the l-th evaluation index of the edge mineral pressure deformation node i in the upper three zones of the goaf;

[0184] Therefore, the standard normalized matrix is:

[0185]

[0186] In the formula, V is the exponentially normalized value, v hm The m-th standardized value index of h, the edge mineral pressure deformation node h in the upper three zones of the goaf;

[0187] S303, the mine pressure early warning threshold calculated based on exponential entropy;

[0188] The formula for calculating exponential entropy is:

[0189]

[0190] In the formula, zz l Let there be l exponential entropy values, and let ζ be the coefficients for solving the exponential entropy;

[0191] The information entropy redundancy is calculated as follows:

[0192] dd l =1-z l

[0193] In the formula, dd l Let there be l information entropy values, z l This is the basic value of the l-th information entropy;

[0194] The calculation of the index-based mine pressure early warning threshold is shown in the following formula:

[0195]

[0196] In the formula, y l Let be the l-th indexed mining pressure early warning threshold, m be the m-th node, and l be the l-th node;

[0197] The index-based mineral pressure early warning threshold matrix Y is obtained, as shown in the following formula:

[0198] Y = [y1 y2 y3 y m ]

[0199] In the formula, Y is the value of the exponential mining pressure early warning threshold matrix, y m The threshold for early warning of the m-th index of mining pressure;

[0200] The weighted normalized confirmation matrix can be expressed as:

[0201]

[0202] In the formula, E is the weighted normalized confirmation matrix value, V is the exponential standard normalized value, and v hm y is the m-th standardized index of the deformation node h at the edge of the upper three zones of the goaf;m The threshold for early warning of the m-th index of mining pressure;

[0203] The weighted attribute value of node i, which represents the deformation of the mineral pressure at the edge of the three zones above the goaf, can be expressed as:

[0204]

[0205] In the formula, A i E represents the weighted attribute value of node i, which is the deformation node i at the edge of the three zones above the goaf due to mining pressure. il The l-th weighted normalized confirmation matrix value is the i-th weighted normalized confirmation matrix value of the deformation node i at the edge of the upper three zones of the goaf.

[0206] Since collaborative mining pressure identification can be achieved among the mining pressure deformation nodes at the edges of the three zones above the goaf in the mining pressure identification network, and adjacent mining pressure deformation nodes at the edges of the three zones above the goaf also contribute to the importance of mining pressure identification of the target mining pressure deformation node, an importance evaluation matrix is ​​constructed:

[0207]

[0208] In the formula, B is the importance evaluation matrix value, and A h ε is the weighted attribute value of the deformation node h at the edge of the three zones above the goaf, ψ1 is the degree of the deformation node 1 at the edge of the three zones above the goaf, ψ is the average degree of the deformation nodes at the edge of the three zones above the goaf, and ε is the weighted attribute value of the deformation node h at the edge of the three zones above the goaf. ij The contribution allocation parameter is set to 1 if the three edge mineral pressure deformation nodes of the two goaf areas are connected, and 0 otherwise.

[0209] S304, Importance Calculation.

[0210] Based on the importance evaluation matrix, the importance of the mining pressure deformation node i at the edge of the three zones above the goaf is obtained by summing the attribute value of i and the importance contribution of all adjacent mining pressure deformation nodes at the edge of the three zones above the goaf.

[0211]

[0212] In the formula, For the value of node i, which is the edge of the mining pressure deformation zone in the upper three zones of the goaf, A i A is the weighted attribute value of node i, which is the edge deformation node of the three zones above the goaf due to mining pressure. j The weighted attribute value of node j, which is the edge of the mining pressure deformation node in the upper three zones of the goaf;

[0213] The importance evaluation matrix fully considers the location of the mining pressure deformation nodes at the edge of the three zones above the goaf, the exploration frequency, the available information for mining pressure identification, and the available bandwidth. It more comprehensively reflects the importance of the mining pressure deformation nodes at the edge of the three zones above the goaf, focusing on the goal of mining pressure identification value.

[0214] For example, in step S4, the implementation of the mine pressure identification and confirmation strategy based on the mine pressure early warning threshold includes:

[0215] S401, update rate;

[0216] The mine pressure identification update rate d(i) is:

[0217]

[0218] In the formula, A(du) m ) represents the number of updates made from the mining pressure deformation node i at the edge of the three upper zones of the goaf per unit time, and e(i) represents the size of the available mining pressure identification information at the mining pressure deformation node i at the edge of the three upper zones of the goaf.

[0219] standardization:

[0220]

[0221] In the formula, f(i) is the standardized value of the mine pressure identification update rate, d(i) is the mine pressure identification update rate, and d(h) is the mine pressure identification update rate of the edge mine pressure deformation node h in the upper three zones of the goaf during deformation.

[0222] S402, Effective early warning measurement for identifying mining pressure deformation nodes at the edge of the three zones in the goaf.

[0223] In step S402, a new effective early warning metric K(i) is designed, including the detection equilibrium deformation criticality centrality rate and the mine pressure identification update rate:

[0224]

[0225] i a =agrmax{K(i)}

[0226] In the formula, i a Let K(i) be the inverse value of the set of maximum effective early warning metrics, and v be the effective early warning metric value. i To comprehensively evaluate the importance of the mining pressure deformation node i at the edge of the three zones above the goaf, f(i) is the mining pressure identification update rate of the mining pressure deformation node at the edge of the three zones above the goaf. If f(i) = 0, it means that the mining pressure identification information of the mining pressure deformation node at the edge of the three zones above the goaf is not full, or no new content has arrived. In order to make the effective early warning measurement expression of the mining pressure deformation node at the edge of the three zones above the goaf consistent, let f(i) = θ, where θ is a very small positive number.

[0227] Example 2, as another embodiment of the present invention, such as Figure 2 As shown, the coal mine pressure early warning analysis method based on big data processing provided in this embodiment of the invention further includes:

[0228] S501, establish a network topology based on the attributes of the edge mineral pressure deformation nodes in the upper three zones of the goaf, and calculate the number of detectable edge mineral pressure deformation nodes in the upper three zones of the goaf.

[0229] S502, calculate the corresponding mining pressure early warning threshold by the attributes of the mining pressure deformation nodes at the edge of the three zones above the goaf and the number of times the mining pressure deformation nodes at the edge of the three zones above the goaf are explored;

[0230] S503, calculate the exploration deformation criticality centrality of the edge mineral pressure deformation nodes in the upper three zones of the goaf according to the deformation criticality centrality rule;

[0231] S504, calculate the access balance of the edge mineral pressure deformation nodes in the three zones above the goaf;

[0232] S505, construct a multi-index confirmation matrix, calculate the index mine pressure early warning threshold, and obtain the attribute values ​​of the mine pressure deformation node at the edge of the three zones above the goaf.

[0233] S506, construct an importance evaluation matrix to calculate the importance of the edge mineral pressure deformation nodes in the three zones above the goaf;

[0234] S507, calculate the short-term update rate of the mining pressure deformation nodes at the edge of the three zones above the goaf and the mining pressure identification value of the mining pressure deformation nodes at the edge of the three zones above the goaf, and find the mining pressure deformation node at the edge of the three zones above the goaf with the largest mining pressure identification value.

[0235] As demonstrated by the above embodiments, this invention, through adaptive weighting of various features, more accurately selects edge mining pressure deformation nodes in the three zones above the goaf, improving the edge mining pressure identification hit rate and the response efficiency of user requests. This invention employs multiple network models and simulates the algorithm from multiple perspectives. The results show that, compared with existing mechanisms, this scheme can achieve better mining pressure identification efficiency in more complex geological environments.

[0236] This study focuses on a distributed network cluster for identifying mining pressure at the edges of three zones above a goaf. The location of the mining pressure identified by the deformation nodes at the edges of these zones is determined by factors such as the magnitude of the mining pressure identification, adjacent bandwidth, the centrality of the exploration deformation criticality, and exploration balance. This method utilizes information entropy to allocate an adaptive mining pressure early warning threshold. Combined with the update rate of the deformation nodes at the edges of the three zones above a goaf, it obtains the deformation node at the edges of the three zones above a goaf with the largest mining pressure identification value. Compared with other related solutions, this method has significant advantages in terms of identification accuracy and efficiency for identifying important deformation nodes at the edges of the three zones above a goaf. The main contributions of this invention are as follows:

[0237] (1) The network structure for identifying mining pressure at the edge of the three zones above the goaf was analyzed. Considering the characteristics of the deformation nodes of the mining pressure at the edge of the three zones above the goaf from the lidar station being able to detect and forward detection requests, the characteristics related to the identification of mining pressure at the deformation nodes of the three zones above the goaf were calculated, including the mining pressure identification space, adjacent bandwidth, detection deformation criticality centrality and detection balance, which provides a basis for considering the importance of identifying mining pressure at the deformation nodes of the three zones above the goaf.

[0238] (2) Regarding the impact of multiple feature values ​​on the mining pressure identification values ​​of the mining pressure deformation nodes at the edges of the three zones above the goaf. This invention utilizes information entropy to adaptively allocate the mining pressure early warning threshold for each feature. This helps to flexibly and accurately determine the importance of the mining pressure deformation nodes at the edges of the three zones above the goaf; by calculating the update rate, frequent updates to the content of the mining pressure deformation nodes at the edges of the three zones above the goaf are avoided, thus improving the efficiency of the system.

[0239] (3) This invention has been applied from multiple perspectives under a multi-network model. The application results show that, compared with existing algorithms, this algorithm can more accurately identify the importance of the mining pressure deformation nodes at the edge of the three zones in the goaf, and can more effectively improve the mining pressure identification efficiency and exploration response rate.

[0240] Example 3, as Figure 3 As shown, the coal mine pressure early warning and analysis system based on big data processing provided in this embodiment of the invention includes:

[0241] Module 1, which is used to construct a network model for identifying edge mining pressure in the upper three zones of the goaf, is used to construct such a network model.

[0242] The mine pressure deformation node identification module 2 is used to identify the mine pressure deformation nodes at the edge of the three zones above the goaf based on the mine pressure identification network model of the goaf and the mine pressure early warning threshold adaptive method.

[0243] The mine pressure deformation node sorting module 3 is used to sort the mine pressure deformation nodes on the edge of the three zones of the goaf dynamically and adaptively based on the mine pressure early warning threshold according to the above identification results.

[0244] The mine pressure identification module 4 is used to implement an adaptive mine pressure identification and confirmation strategy based on the above sorting results and mine pressure early warning threshold.

[0245] This invention conducts early warning analysis of coal mine pressure in the goaf of the 5-20303 working face of Qingmousi Coal Mine, covering a range of 350m wide and 1000m long. It leverages the spatiotemporal complementarity and process synergy of multiple methods to achieve dynamic coupled monitoring and early warning of coal mine pressure in the three upper zones (bending subsidence zone, fracture zone, and caving zone) of the Qinglongsi goaf.

[0246] This invention determines the location of mining pressure identification by considering the importance and update frequency of the mining pressure deformation nodes at the edges of the three mining zones above the goaf, thereby making the load on each mining pressure deformation node at the edges of the three mining zones above the goaf more balanced and effective. Ben's invention is guided by meeting user needs and adaptively allocates mining pressure early warning thresholds to various factors affecting the importance of mining pressure identification at the mining pressure deformation nodes at the edges of the three mining zones above the goaf, thus more effectively meeting user requests than other mining pressure identification and confirmation methods.

[0247] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent updates, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A coal mine pressure early warning analysis method based on big data processing, characterized in that, The method includes the following steps: S1, Construct a network model for identifying edge mining pressure in the upper three zones of the goaf; S2, Identification of mining pressure deformation nodes at the edge of the three zones above the goaf using the adaptive method for mining pressure early warning threshold based on the network model; S3, based on the identification results, realizes the dynamic sorting of mining pressure deformation nodes at the edge of the three zones in the goaf, which is adaptive to the mining pressure early warning threshold; S4, Based on the sorting results, implement a mine pressure identification and confirmation strategy that is adaptive based on the mine pressure early warning threshold; In step S2, the identification of mining pressure deformation nodes at the edge of the three zones above the goaf using the adaptive method for mining pressure early warning threshold includes: The mining pressure identification network at the edge of the three zones in the goaf is as follows: , This is a set of nodes representing the deformation due to mining pressure at the edge of the three zones in the network goaf. For the edge set in the network, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. and the edge of the three zones of the goaf, the mining pressure deformation node The edge between, bandwidth used This indicates that the deformation nodes at the edges of the three zones above the goaf are subject to mineral pressure. With the edge of the three zones of the goaf, the mining pressure deformation node If there are edges, then ,otherwise The specific steps are as follows: (a) Mining pressure identification information of the edge of the three zones above the goaf; (b) Adjacent bandwidth of the edge mineral pressure deformation nodes in the upper three zones of the goaf; (c) The number of times the edge of the three zones above the goaf was investigated for mineral pressure deformation nodes; (d) Investigate the center of deformation criticality; (e) Investigate the balance rate; In step (a), the mineral pressure identification information of the three edge mineral pressure deformation nodes in the goaf is represented as follows: The more information on mining pressure identification, the more important the mining pressure deformation nodes at the edges of the three zones above the goaf; In step (b), the adjacency bandwidth of the three edge mineral pressure deformation nodes in the goaf and The expression is: ； In the formula, For the edge of the three zones of the goaf in the network, the nodes are the nodes of the mining pressure deformation. The set of mining pressure deformation nodes on the edge of the neighboring goaf area. For the edge of the three zones of the goaf, the deformation node of the mining pressure is located. The edge of the three-zone mining pressure deformation node in the neighboring goaf area Adjacent bandwidth between; The greater the sum of the adjacent bandwidths of the deformation nodes at the edges of the three zones above the goaf, the more important the deformation nodes at the edges of the three zones above the goaf are; the deformation nodes at the edges of the three zones above the goaf include the deformation points at the intersection of the edges of the bending subsidence zone, the fracture zone, and the caving zone. In step (c), the number of times the deformation nodes of the three edges of the goaf are explored depends on the type and degree of attention of the mining pressure identification content; the more types there are, the more times the deformation nodes of the three edges of the goaf are explored, and the more attention the content receives, the more times the deformation nodes of the three edges of the goaf are explored; the mining pressure identification content includes deformation information at the boundary lines of the edges of the bending subsidence zone, fracture zone, and caving zone; In step (d), the exploration deformation criticality centrality is constructed by introducing the number of explorations of the mining pressure deformation nodes at the edges of the three zones above the goaf, and this is used as the mining pressure identification confirmation standard to determine the network mining pressure identification strategy. The mining pressure identification location is determined by the location and exploration status of the mining pressure deformation nodes at the edges of the three zones above the goaf. The exploration deformation criticality centrality of the mining pressure deformation nodes at the edges of the three zones above the goaf is the exploration deformation criticality of all neighboring mining pressure deformation nodes at the edges of the three zones above the goaf and the number of explorations of the mining pressure deformation nodes at the edges of the three zones above the goaf. In step (e), the exploration balance of the three edge mineral pressure deformation nodes in the goaf is as follows: ； In the formula, The exploration equilibrium value is the value of the edge mineral pressure deformation node in the upper three zones of the goaf. Weights are assigned to identify the mineral pressure deformation nodes at the edges of the three zones above the goaf. For the edge of the neighboring goaf, the mining pressure deformation node. The key to detecting deformation Boltzmann's constant, For the edge of the neighboring goaf, the mining pressure deformation node. The key to detecting deformation All of these are edge mineral pressure deformation nodes in the upper three zones of neighboring goaf areas. , The larger the value, the more balanced the exploration of the deformation nodes of the three edges of the goaf, the easier it is to meet the user's exploration request, and the greater the importance of the deformation nodes of the three edges of the goaf. In step S3, the method for sorting the deformation nodes of the three edges of the goaf based on the adaptive dynamic goaf early warning threshold includes: S301, Obtain the confirmation matrix and its evaluation indicators; It has There are three edge mineral pressure deformation nodes in the upper three zones of the goaf that need to be sorted. Each edge mineral pressure deformation node in the upper three zones of the goaf has... One evaluation indicator, the edge of the three zones in the goaf, and the deformation node of the mining pressure. The The value of each evaluation indicator The confirmation matrix and its evaluation index, composed of the edge mineral pressure deformation nodes of all three zones in the network goaf, are shown in the following formula: ； In the formula, To evaluate the indicator values, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The The values ​​of each evaluation indicator; S302, Normalized confirmation matrix; To eliminate dimensionality differences among indicators, a standardized index is defined as follows: ； In the formula, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The Each evaluation indicator is standardized as an index. For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The The values ​​of each evaluation indicator; Therefore, the standard normalized matrix is: ； In the formula, The standard normalized value of the index. For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The A standardized value index; S303, the mine pressure early warning threshold calculated based on exponential entropy; The formula for calculating exponential entropy is: ； In the formula, for An exponential entropy value, Solve for the coefficients of exponential entropy; The information entropy redundancy is calculated as follows: ； In the formula, for One information entropy value, For the first A basic value of information entropy; The index-based mine pressure early warning threshold is calculated as follows: ； In the formula, For the first Each index has a mining pressure warning threshold. For the first 1 node For the first One node; Index-based mining pressure early warning threshold matrix The result is shown in the following formula: ； In the formula, This represents the values ​​of the index-based mine pressure early warning threshold matrix. For the first Individual index mining pressure early warning thresholds; The weighted normalized confirmation matrix is ​​expressed as: ； In the formula, To weighted normalize the values ​​of the confirmation matrix, The standard normalized value of the index. For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The A standardized value index; For the first Individual index mining pressure early warning thresholds; Mining pressure deformation nodes at the edge of the upper three zones of the goaf The weighted attribute values ​​are represented as follows: ； In the formula, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The weighted attribute values, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The A weighted normalized confirmation matrix value; Because the mining pressure identification network at the edge of the three zones above the goaf achieves collaborative mining pressure identification, the mining pressure identification importance of adjacent mining pressure deformation nodes at the edge of the three zones above the target goaf is contributed to the identification of the mining pressure at the edge of the three zones above the target goaf. An importance evaluation matrix is ​​constructed as follows: ； In the formula, For the importance evaluation matrix values, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The weighted attribute values, The degree of deformation at node 1, the edge of the upper three zones of the goaf. This represents the average degree of deformation at the edge of the three zones in the goaf due to mineral pressure. The contribution allocation parameter is set to 1 if the three edge mineral pressure deformation nodes of the two goaf areas are connected, and 0 otherwise. S304, Importance Calculation; Based on the importance evaluation matrix, the deformation nodes of the three edge zones of the goaf are analyzed. The attribute values ​​and the importance contributions of all adjacent edge mineral pressure deformation nodes in the upper three zones of the goaf are summed to obtain the edge mineral pressure deformation nodes in the upper three zones of the goaf. The importance of; ； In the formula, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. Its value in the network For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The weighted attribute values, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The weighted attribute value.

2. The coal mine pressure early warning analysis method based on big data processing according to claim 1, characterized in that, In step S1, the mining pressure identification network model at the edge of the three zones above the goaf consists of a lidar station, a lidar station server, and a mining pressure early warning monitoring terminal. The method for obtaining the response is as follows: (1) If the probe is the content of the user request for mining pressure identification performed by the lidar station server, the lidar station server will directly respond to the user request; otherwise, proceed to step (2) to check whether the neighboring servers of the lidar station server are performing mining pressure identification. (2) If the neighboring server performs mining pressure identification of the user's request content, the neighboring server will send the content to the basic site server that the user probes and responds to the user's request; Otherwise, proceed to step (3) to determine whether the lidar station server is identifying the content of the mining pressure identification; (3) If none of the three edge mineral pressure deformation nodes in the goaf area in the search process have mineral pressure identification content, the exploration request will be forwarded to the cloud network; (4) User requests are responded to in the cloud network. The cloud network sends the request content to the main lidar station server, to the neighboring servers explored by the user, and finally to the user.

3. The coal mine pressure early warning analysis method based on big data processing according to claim 1, characterized in that, In step (c), the number of times the edge mineral pressure deformation nodes of the three zones above the goaf are investigated includes: (c.1) Frequency of content distortion; If the content's popularity matches the exploration frequency distribution, then the ranking is... Content The deformation frequency is: ； In the formula, For the frequency of deformation, For ranking, For content, For the total number of contents, To investigate the skewness coefficient of the frequency distribution; (c.2) User attention to the deformation nodes of the three edges of the goaf; By analyzing users' long-term exploration records, we can obtain stable user attention and define users' focus on the mineral pressure deformation nodes at the edge of the three zones above the goaf. Long-term focus for The expression is: ； In the formula, This refers to the deformation node of the upper three zones of the current goaf due to mineral pressure. Statistical flow; The current statistical exploration volume of all users within the edge mining pressure identification set; Users will be able to explore the mineral pressure deformation nodes at the edge of the upper three zones of the goaf in the shortest possible time. The focus is defined as short-term focus : ； In the formula, For recent analysis of the deformation nodes of the upper three zones of the goaf. Statistical exploration volume, This represents the statistical traffic explored by all users in the edge mining pressure identification set within a recent period. A user's current focus depends on their long-term and short-term focus; therefore, users explore the edge mining pressure deformation nodes in the three zones above the goaf. Potential concern is defined as: ； In the formula, Potential attention value, and These represent the relative weight of long-term and short-term concerns on the user's current focus, respectively. (c.3) The user's potential probing behavior; Mining pressure deformation nodes at the edge of the upper three zones of the goaf Chinese content Request probability for: ； In the formula, For the frequency of deformation, For the first Number of contents; The total number of user probes in the previous mining pressure identification cycle was Then the user can explore the edge of the three zones of the goaf and the deformation nodes of the mining pressure. Chinese content Potential exploration volume for .

4. The coal mine pressure early warning analysis method based on big data processing according to claim 1, characterized in that, In step (d), the method for determining the criticality of deformation includes: (A) Delete all deformation nodes and edges of the upper three zones of the goaf with a connectivity of 1, and record the number of times the deformation nodes of the upper three zones of the goaf are explored; if there are still deformation nodes of the upper three zones of the goaf with a connectivity of 1, continue the above process, and mark the deformation criticality of these deleted deformation nodes of the upper three zones of the goaf as 1. The exploration deformation criticality is the number of explorations of the deformation node of the upper three zones of the goaf plus 1. If the number of explorations of deformation node j of the upper three zones of the goaf is The keyness of the deformation of the exploration node j at the edge of the three zones of the goaf is: ; (B) For the mining pressure deformation nodes at the edge of the three zones of the goaf with a connectivity degree of 2, mark the exploration deformation criticality of these mining pressure deformation nodes at the edge of the three zones of the goaf as exploration number + 2. (C) Repeat the above process until all the edge mineral pressure deformation nodes of the three goaf areas are deleted and the corresponding exploration deformation criticality is obtained.

5. The coal mine pressure early warning analysis method based on big data processing according to claim 1, characterized in that, In step S4, a mine pressure identification and confirmation strategy based on an adaptive mine pressure early warning threshold is implemented, including: S401, Update Rate; Mining pressure identification update rate for: ； In the formula, The deformation node of the mining pressure at the edge of the upper three zones of the goaf per unit time. The number of updates in the middle, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. The size of available information for identifying mine pressure; standardization: ； In the formula, The value is the standardized value for the mine pressure identification update rate. To improve the mine pressure identification update rate, For the edge of the upper three zones of the goaf, the deformation node is the mining pressure. Update rate of mine pressure identification during deformation; S402, Effective early warning measurement for identifying mining pressure deformation nodes at the edge of the three zones in the goaf.

6. The coal mine pressure early warning analysis method based on big data processing according to claim 5, characterized in that, In step S402, a new effective early warning metric is designed. This includes exploring the criticality centrality of equilibrium deformation and the update rate of mine pressure identification: ； In the formula, The inverse value of the set of maximum values ​​for effective early warning measurement. For effective early warning metrics, To comprehensively evaluate the deformation nodes of the three marginal zones of the goaf after mining pressure. The importance of The update rate for identifying and updating the mineral pressure at the edge of the three zones in the goaf.

7. A coal mine pressure early warning and analysis system based on big data processing, characterized in that, The system implements the coal mine pressure early warning analysis method based on big data processing as described in any one of claims 1-6, and the system includes: The module for constructing the edge mining pressure identification network model of the upper three zones of the goaf (1) is used to construct the edge mining pressure identification network model of the upper three zones of the goaf. The mining pressure deformation node identification module (2) is used to identify the mining pressure deformation nodes of the three-zone edge of the goaf based on the mining pressure early warning threshold adaptive method according to the mining pressure identification network model of the three-zone edge of the goaf. The mine pressure deformation node sorting module (3) is used to sort the mine pressure deformation nodes on the edge of the three zones of the goaf dynamically based on the mine pressure early warning threshold according to the above identification results. The mine pressure identification module (4) is used to implement an adaptive mine pressure identification and confirmation strategy based on the above sorting results and the mine pressure early warning threshold.

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