Mine support scheme environment analogy database intelligent recommendation method

Abstract

The invention belongs to the technical field of data processing and mine support, and particularly relates to a mine support scheme environment analogy database intelligent recommendation method, which comprises the following steps: acquiring multi-source time sequence data of a to-be-supported area and performing benchmark preprocessing; according to the micro-seismic event energy and the seismic source distance, a dynamic mining disturbance response index is constructed in combination with geological medium attenuation characteristics; based on a rock rheological damage theory, introducing a forgetting mechanism to carry out time-varying weighted accumulation on the dynamic mining disturbance response index in the historical time window, and calculating a rock rheological damage accumulation factor; and constructing a matching model containing the damage correction dimension, and calculating a comprehensive matching degree score and recommending support parameters according to the static geologic feature difference and the damage accumulation factor difference between the to-be-supported area and the historical case. According to the method, the technical problem that historical accumulated hidden dangers cannot be revealed through single-moment monitoring is solved, and common lag type roof fall accidents in deep mining can be effectively prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and mine support, and particularly relates to a mine support scheme environment analogy database intelligent recommendation method. BACKGROUND

[0002] In the mining operation of deep metal mines, scientific and reasonable roadway and stope support design is the core element to ensure the safety of personnel and equipment. The traditional support design mode usually relies on a case-based reasoning expert system, that is, by collecting the geological parameters of the current working condition, the highest similarity successful case is found in the historical database, and then the support parameters such as anchor spacing and shotcrete thickness are reused.

[0003] However, the existing data recommendation technology has obvious limitations in practical application. First, the existing technology mainly relies on static geological indicators for matching, ignoring the dynamic disturbance caused by frequent blasting and tunneling in deep mining, so that two regions with the same static indicators but different dynamic stress environments are mistakenly considered similar. In the deep mining environment, the frequent action of dynamic stress can significantly change the stability of the surrounding rock, and the static parameters cannot fully reflect this change. In addition, when performing case matching, the existing method usually calculates the difference between static features by using simple Euclidean distance or cosine similarity, without specifically modeling and adjusting the weight for the key dimension of rock mass damage state.

[0004] Secondly, the rock mass material itself has rheological and memory properties, and the existing recommendation algorithm often only focuses on the monitoring value at the current time, without effectively evaluating the cumulative damage effect in the past period of time. This situation leads to the fact that for those regions that appear to be complete on the surface but have serious fatigue damage inside due to historical disturbance, the recommendation system easily underestimates their potential risks and gives support recommendations with insufficient support resistance, thereby burying safety hazards. SUMMARY

[0005] In order to solve the specific technical problems of low matching degree and insufficient safety of support scheme recommendation caused by ignoring the influence of dynamic disturbance caused by frequent blasting in the deep mining process and ignoring the cumulative damage history of rock mass rheological properties in the existing technology, the present application provides a mine support scheme environment analogy database intelligent recommendation method, which comprises: Obtaining multi-source time series data of the to-be-supported region and performing baseline preprocessing, the multi-source time series data comprising microseismic monitoring data, static geological data and historical case library data; According to the energy value and hypocenter distance of the microseismic event in the microseismic monitoring data, combining the attenuation characteristics of the geological medium, a dynamic mining disturbance response index reflecting the instantaneous impact intensity of the current mining activity on the to-be-supported region is calculated; Based on the theory of rock rheological damage, a forgetting mechanism is introduced to perform time-varying weighted accumulation of the dynamic mining disturbance response index within the historical time window, and to calculate the rock mass rheological damage accumulation factor that reflects the historical fatigue state of the rock mass. A similarity matching model including a damage correction dimension is constructed. Based on the differences in static geological characteristics between the area to be supported and historical cases in the historical case database, as well as the differences in the cumulative factor of rock mass rheological damage, a comprehensive matching score is calculated. Target support parameters are then selected and recommended from the historical case database based on the comprehensive matching score.

[0006] This invention, by adopting the above-mentioned technical solution, constructs a dynamic and static combined support recommendation system. It not only considers static geological characteristics but also incorporates dynamic mining disturbances and historical cumulative damage of the rock mass into the evaluation system. It can accurately identify high-risk rock masses that are surface intact but internally fatigued, thereby recommending more targeted and safer redundant support parameters. This effectively prevents delayed roof collapse accidents commonly seen in deep mining and transforms historical microseismic monitoring data into damage state indicators that can be directly used for matching decisions, improving the targeting and safety redundancy of the recommended support schemes.

[0007] In one embodiment, the microseismic monitoring data includes the time of occurrence of the microseismic event, the coordinates of the seismic source, and the energy released; the static geological data includes rock quality indicators, uniaxial compressive strength of the rock, and joint orientation information; and the historical case database data includes geological environment descriptions of historical cases, implemented support parameters, and maintenance records.

[0008] By clarifying the specific composition of various types of data, a foundation was laid for subsequent data processing and feature extraction, ensuring the integrity and traceability of multi-source data.

[0009] In one embodiment, the calculation of the dynamic mining disturbance response index, which reflects the instantaneous impact intensity of the current mining activity on the area to be supported, is specifically expressed as follows:

[0010] In the formula, Indicates time The dynamic mining disturbance response index. This indicates the total number of microseismic events detected within the current time window. Indicates the first The energy value released by this event. Indicates the first The Euclidean distance between the epicenter coordinates of the event and the center point of the area to be supported. Indicates the damping coefficient of the geological medium. Represents the power exponent of geometric decay of energy. This represents the energy normalization coefficient.

[0011] The dynamic mining disturbance response index constructed by the expression can comprehensively evaluate the influence of energy size, seismic source distance and geological medium attenuation on impact intensity, smooth the numerical fluctuation of extreme energy events by using a logarithmic function, and scientifically reflect the instantaneous dynamic disturbance degree of mining activities on the rock mass.

[0012] In one embodiment, the geological medium damping coefficient is inversely derived according to a rock integrity coefficient, and the value thereof is inversely proportional to the rock quality index; and the energy geometric attenuation power index is set according to the wave velocity attenuation law of the geological medium.

[0013] In one embodiment, the rock mass rheological damage cumulative factor reflecting the historical fatigue state of the rock mass is calculated, and the specific expression is as follows:

[0014] In the formula, represents the rock mass rheological damage cumulative factor at time t, represents the effective tracing time window length, represents the dynamic mining disturbance response index at historical time t, represents a rock mass self-repair rate coefficient, represents a rock mass tolerance capacity constant. By introducing the cumulative calculation of the forgetting mechanism, the fatigue evolution process of the rock mass under repeated impacts is reflected, the discrete instantaneous disturbance is converted into a continuous rock mass damage state, and the technical problem that a single time monitoring cannot reveal historical cumulative hidden dangers is solved.

[0015] In one embodiment, the rock mass self-repair rate coefficient is determined according to the rock type, and a smaller value is taken for brittle rock to represent strong memory, and a larger value is taken for soft rock to represent weak memory; and the rock mass tolerance capacity constant is determined by laboratory rock fatigue test.

[0016] By differentiating the parameters according to the rock type, the damage cumulative model can adapt to different geological conditions, and the universality and accuracy of the method are improved.

[0017] By differentiating the parameters according to the rock type, the damage cumulative model can adapt to different geological conditions, and the universality and accuracy of the method are improved.

[0018] In one embodiment, the calculation of the comprehensive matching degree score comprises:

[0019] In the formula, represents the comprehensive matching degree score of the current area to be supported and the i th case in the historical case library, represents the total number of static geological feature indexes, represents the i th static geological feature index, represents the i th dynamic geological feature index.​ The weight of each static geological characteristic index, Indicates the current area to be supported. A static geological characteristic index, Indicates the first The first historical case A static geological characteristic index, This represents the cumulative rheological damage factor of the rock mass in the area to be supported at the current moment. Indicates the first The cumulative factor of rock mass rheological damage during support implementation in a historical case. This represents the penalty weight coefficient for the damage dimension. This represents the numerical stability constant.

[0020] In one embodiment, the weights of the static geological feature indicators are determined by principal component analysis; the penalty weight coefficient of the damage dimension is set to a constant greater than 1.

[0021] In one embodiment, the benchmarking preprocessing includes: The energy data in the microseismic monitoring data are logarithmically processed to eliminate differences in magnitude; The unstructured geological descriptions in the static geological data are mapped to numerical values ​​using an expert scoring method; A unified time sampling window is set, and the continuously collected data is discretized and aligned according to the time sampling window.

[0022] By using systematic benchmarking preprocessing, the differences in dimensions and formats among multi-source heterogeneous data are eliminated, improving data consistency and comparability, and providing a high-quality input foundation for subsequent complex calculations.

[0023] In one embodiment, the recommended target support parameters include: selecting a preset number of historical cases with the highest comprehensive matching score, and calculating a weighted average of the support parameters of the historical cases to obtain the final recommended support parameters.

[0024] By weighted averaging the support parameters of multiple highly matched cases, the randomness of a single case is avoided, thus improving the stability and reliability of the recommendation results.

[0025] The technical solution of the present invention has the following beneficial technical effects: The application constructs a dynamic and static combined support recommendation system, not only considers static geological characteristics, but also takes dynamic mining disturbance and historical cumulative damage of rock mass into the evaluation system, can accurately identify high-risk rock mass which is complete on the surface but fatigued inside, thereby recommending more targeted and safe redundant support parameters, effectively preventing common lagging roof fall accidents in deep mining, and realizing the organic unification of deep mining of historical data value and engineering safety guarantee.

[0026] Further, by introducing a forgetting mechanism to time-varyingly weight and accumulate the dynamic mining disturbance response index in the historical time window, the discrete instantaneous disturbance is converted into continuous rock mass damage state, solving the technical problem that single moment monitoring cannot reveal historical cumulative hidden dangers, and significantly improving the safety and adaptability of deep mining support design. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flowchart of a mine support scheme environment analogy database intelligent recommendation method according to an embodiment of the application; Figure 2 is a schematic diagram of a rock mass damage time-varying evolution process under mining disturbance according to an embodiment of the application; Figure 3 is a support scheme recommendation accuracy comparison diagram under complex working conditions according to an embodiment of the application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments of the application.

[0029] Figure 1 is a flowchart of a mine support scheme environment analogy database intelligent recommendation method according to an embodiment of the application. As shown in Figure 1 , the mine support scheme environment analogy database intelligent recommendation method includes steps S101 to S104, which are described in detail below: S101, obtaining multi-source time series data of the to-be-supported area and performing benchmarking preprocessing, the multi-source time series data including microseismic monitoring data, static geological data and historical case library data.

[0030] In this embodiment, data acquisition is the basic link of the intelligent recommendation method. First, real-time microseismic monitoring data is collected by a microseismic monitoring system deployed in a mine, and a Geophone array sensor array is exemplarily used. The microseismic monitoring data specifically includes the occurrence time of a microseismic event, the three-dimensional coordinates of a seismic source, and the energy value released by the microseismic event. The energy value is in joules. Second, static geological data of the area to be supported is obtained through geological drilling core recording and working face geological recording. The static geological data includes rock quality indicators, rock uniaxial compressive strength, and joint occurrence information. Finally, historical case database data is extracted from the company's past engineering database. The historical case database data includes a geological environment description of a historical case, implemented support parameters, and later maintenance records. The support parameters exemplarily include anchor rod length and anchor rod spacing.

[0031] After obtaining the multi-source time series data, the multi-source time series data needs to be benchmarked and preprocessed. In view of the large span of the energy data in the microseismic monitoring data, the energy data is logarithmically processed to eliminate the order of magnitude difference. The unstructured geological description in the static geological data is mapped to a value in the interval [0, 1] through expert scoring, and the description of a broken rock mass is exemplarily mapped to 0.3. A unified time sampling window is set, exemplarily in days, and the continuously collected data is discretized and aligned according to the time sampling window, so as to facilitate subsequent time series analysis.

[0032] Through the above filtering and standardization of the multi-source time series data, a more accurate and unified data set can be obtained, and the dimensional obstacles between different types of data in the multi-source time series data are eliminated, providing a high-quality input basis for subsequent complex operations.

[0033] S102, according to the energy value and the source distance of the microseismic event in the microseismic monitoring data, and in combination with the attenuation characteristics of the geological medium, a dynamic mining disturbance response index reflecting the instantaneous impact intensity of the current mining activity on the area to be supported is calculated.

[0034] In this embodiment, mining activities such as blasting and tunneling can cause rock mass rupture and generate microseismic events. Therefore, the present application captures these microseismic events through a microseismic monitoring system. Since only the number of microseismic events cannot truly reflect the impact degree, the energy value released by the microseismic event and the source distance must be considered comprehensively. The specific operation method in the embodiment of the present application is as follows: First, the center coordinates of the area to be supported are determined. For each microseismic event occurring in the current time window the instantaneous impact intensity of the event on the area to be supported is calculated. The specific calculation expression is as follows:

[0035] In the formula, representing the time instant , is a dimensionless value; represents the total number of microseismic events monitored within the current time window; represents the energy value released by the th event, in Joule; represents the Euclidean distance between the hypocenter of the th event and the center point of the area to be supported, in meters; represents the geological medium damping coefficient, in , which is obtained by back-calculation through the expression , where is a constant, and the rock mass quality index is lower, the more broken the rock mass is, and the larger the geological medium damping coefficient is; represents the energy geometric attenuation power index, which is set according to the measured wave velocity attenuation law in the mine, and is preferably 2; represents the energy normalization coefficient, which is used to prevent numerical overflow.

[0036] For example, the following parameters are set for calculation demonstration: Assuming that the energy normalization coefficient is , the geological medium damping coefficient is 0.02, and the energy geometric attenuation power index is 2.

[0037] Assuming that microseismic events occur at the current time instant, the energy value released by the microseismic event is 10 7 Joule, and the distance between the microseismic event and the center point of the area to be supported is 50 meters.

[0038] First, the distance attenuation term in the denominator is calculated: Then, the instantaneous impact contribution of the microseismic event to the area to be supported is calculated: Finally, the dynamic mining disturbance response index is calculated: .

[0039] If the microseismic event is farther away from the center point of the area to be supported, for example, when the distance is 500 meters, the denominator will become very large, resulting in a sharp decrease in the effective impact contribution of the microseismic event to the area to be supported, and the calculated dynamic mining disturbance response index will also be very small, possibly close to 0.

[0040] Through the above process, it can be known that by constructing the dynamic mining disturbance response index, the actual impact strength of each vibration event on the rock mass can be scientifically evaluated, the influence of extreme energy values is smoothed by using the logarithmic function, and the distance attenuation parameter and the damping parameter are used to truly reflect the wave attenuation law, so that the external disturbance strength can be more accurately evaluated.

[0041] S103, based on the rock rheological damage theory, a forgetting mechanism is introduced to time-varying weighted accumulation of the dynamic mining disturbance response index in the historical time window, and a rock rheological damage cumulative factor reflecting the historical fatigue state of the rock mass is calculated.

[0042] In the embodiment, since the rock mass has memory, the past disturbance will cause the cumulative damage of the rock mass, therefore, the embodiment constructs a rock rheological damage cumulative factor to represent the cumulative damage state of the rock mass, and the calculation method of the rock rheological damage cumulative factor is as follows:

[0043] In the formula, represents the rock rheological damage cumulative factor at time t, and the value range is [0, 1]; represents the effective tracing time window length, for example, 30 days; represents the dynamic mining disturbance response index at the historical time t i; represents the rock self-repair rate coefficient; represents the rock tolerance capacity constant; represents the historical time variable. Continue the calculation demonstration of the above example: Suppose that in the tracing window

[0044] , only one disturbance occurred yesterday, and its is 3.93, and there is no disturbance at other times. Suppose that the rock self-repair rate coefficient is 0.1, and the rock tolerance capacity constant

[0045] is 10. First, the time attenuation weight is calculated: .

[0046] The weighted disturbance value is calculated: .

[0047] Substitute the main expression:

[0048] . .

[0049] ​This means that although there was no disturbance today, the rock mass still bears about 30% of the accumulated damage due to yesterday's impact. If it is subjected to impact for several consecutive days, this value will continue to approach 1, indicating that the rock mass is close to failure.

[0050] As can be seen from the above process, by calculating the cumulative factor of rock mass rheological damage, the real physical process of rock mass injury, accumulation and partial recovery can be reflected. This can help identify potential danger zones that are currently calm but have been severely damaged in the past, thus avoiding misjudgment of dangerous areas.

[0051] S104. Construct a similarity matching model that includes a damage correction dimension. Calculate the comprehensive matching score based on the differences in static geological characteristics between the area to be supported and historical cases, as well as the differences in the cumulative factor of rock mass rheological damage. Recommend target support parameters based on the comprehensive matching score.

[0052] In this embodiment, the rock mass rheological damage accumulation factor obtained in step S103 is added as a key dimension to the database retrieval.

[0053] The specific calculation expression is as follows:

[0054] In the formula, This indicates the current area awaiting support and the first case in the historical case database. The overall matching score of each case. This represents the total number of static geological characteristic indicators. It is the first The weight of each static geological characteristic index, Indicates the current area to be supported. A static geological characteristic index, Indicates the first The first historical case Examples of static geological characteristic indicators include uniaxial compressive strength of rock and rock quality indicators; This represents the cumulative rheological damage factor of the rock mass in the area to be supported at the current moment. Indicates the first The cumulative factor of rock mass rheological damage during support implementation in a historical case. It is the penalty weight coefficient for the damage dimension; It is a numerical stability constant, exemplarily 1.

[0055] Calculation example: Suppose there are two historical cases, A and B.

[0056] Area to be supported: Static characteristic values The current damage is 0.5. A value of 0.8 indicates a high damage state.

[0057] Case A: static parameters are highly similar but in a low damage state, = 0.5, historical damage = 0.1, Case B: static parameters are slightly worse but in a high damage state, = 0.6, historical damage = 0.8. Let the weight = 1, the penalty coefficient = 10, = 1.

[0058] Calculate the matching denominator of Case A: Static difference square: , damage difference item: , = , score .

[0059] Calculate the matching denominator of Case B: Static difference square: , damage difference item: , = , score .

[0060] The results show that although the static geological features of Case A are completely consistent with the area to be supported, due to the low damage accumulation state of Case A and the high damage accumulation state of the area to be supported, the damage factor difference is large, and the matching degree score of Case A is much lower than that of Case B. Although Case B has slight differences in static geological features from the area to be supported, its damage accumulation state is highly consistent with the area to be supported, and the comprehensive matching degree score is higher. The system recommends the support parameters of Case B accordingly, and since Case B implements greater support resistance in a high damage working condition, it can effectively ensure the safety of the area to be supported. Finally, a number of cases with the highest matching degree scores are selected, and the support parameters are weighted and averaged to output the recommended support parameters.

[0061] Through the above process, it can be known that through the similarity matching containing damage correction, the dynamic and static combined accurate recommendation is realized, the hidden damage state of the rock mass is forced to be paid attention to by the system, the insufficient support resistance caused by only looking at the surface geological parameters is prevented, and the safety of the scheme is improved.

[0062] The effects of the present application are further described with reference to Figure 2 and Figure 3 .

[0063] With reference to Figure 2, the column chart representing the dynamic mining disturbance response index appears a peak with the occurrence of the disturbance event, and the broken line chart representing the rock mass rheological damage cumulative factor gradually rises with the continuous occurrence of the disturbance event. In the weak disturbance stage, the damage factor rises slowly; with the increase of the disturbance frequency and intensity, the damage factor accelerates and continuously accumulates to approach the critical value, and then even if the disturbance intensity fluctuates, the damage factor still maintains at a high level, which embodies the memory effect of irreversible accumulation of rock mass damage. This verifies that the model of the application can effectively capture the historical cumulative damage state of the rock mass under repeated disturbance.

[0064] With reference to Figure 3 The prior art cannot perceive the dynamic damage in the intermediate high disturbance area, so that the recommended value is much lower than the actual demand, forming a safety gap; and the application automatically increases the recommended supporting resistance in the high disturbance area along the actual demand curve, and the deviation between the supporting resistance recommended by the application and the actual demand in the high disturbance area is obviously smaller than that of the prior art, effectively eliminating the safety gap.

[0065] It should be noted that, for those skilled in the art, without departing from the concept of the application, a number of modifications and improvements can be made, which are all within the protection scope of the application. Therefore, the protection scope of the application patent should be subject to the appended claims.

Claims

1. A method for intelligent recommendation of mine support schemes using environmental analogy databases, characterized in that: include: Acquire multi-source time-series data of the area to be supported and perform benchmarking preprocessing. The multi-source time-series data includes microseismic monitoring data, static geological data, and historical case database data. Based on the energy value and source distance of the microseismic events in the microseismic monitoring data, and combined with the attenuation characteristics of the geological medium, a dynamic mining disturbance response index reflecting the instantaneous impact intensity of the current mining activity on the area to be supported is calculated. Based on the theory of rock rheological damage, a forgetting mechanism is introduced to perform time-varying weighted accumulation of the dynamic mining disturbance response index within the historical time window, and to calculate the rock mass rheological damage accumulation factor that reflects the historical fatigue state of the rock mass. A similarity matching model including a damage correction dimension is constructed. Based on the differences in static geological characteristics between the area to be supported and historical cases in the historical case database, as well as the differences in the cumulative factor of rock mass rheological damage, a comprehensive matching score is calculated. Target support parameters are then selected and recommended from the historical case database based on the comprehensive matching score.

2. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 1, characterized in that, The microseismic monitoring data includes the time of occurrence of the microseismic event, the coordinates of the epicenter, and the energy released; the static geological data includes rock quality indicators, uniaxial compressive strength of the rock, and joint orientation information; the historical case database includes geological environment descriptions of historical cases, implemented support parameters, and maintenance records.

3. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 2, characterized in that, The calculation reflects the dynamic mining disturbance response index to the instantaneous impact intensity of the current mining activity on the area to be supported, and the specific expression is as follows: In the formula, Indicates time The dynamic mining disturbance response index. This indicates the total number of microseismic events detected within the current time window. Indicates the first The energy value released by this event. Indicates the first The Euclidean distance between the epicenter coordinates of the event and the center point of the area to be supported. Indicates the damping coefficient of the geological medium. Represents the power exponent of geometric decay of energy. This represents the energy normalization coefficient.

4. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 3, characterized in that, The damping coefficient of the geological medium is derived from the rock integrity coefficient, and its value is inversely proportional to the rock quality index; the energy geometric attenuation power exponent is set according to the wave velocity attenuation law of the geological medium.

5. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 1, characterized in that, The calculation of the rock mass rheological damage accumulation factor, which reflects the historical fatigue state of the rock mass, is specifically expressed as follows: In the formula, Indicates time Rock mass rheological damage accumulation factor Indicates the length of the effective traceability time window. Representing historical moments The dynamic mining disturbance response index. Indicates the rock mass self-healing rate coefficient. This represents the rock mass tolerance constant.

6. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 5, characterized in that, The self-healing rate coefficient of the rock mass is determined according to the rock type. A smaller value is taken for brittle rocks to characterize strong memory, and a larger value is taken for soft rocks to characterize weak memory. The rock mass tolerance capacity constant is determined by laboratory rock fatigue test.

7. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 1, characterized in that, The calculation of the overall matching score includes: In the formula, This indicates the current area awaiting support and the first case in the historical case database. The overall matching score of each case This represents the total number of static geological characteristic indicators. Indicates the first The weight of each static geological characteristic index, Indicates the current area to be supported. A static geological characteristic index, Indicates the first The first historical case A static geological characteristic index, This represents the cumulative rheological damage factor of the rock mass in the area to be supported at the current moment. Indicates the first The cumulative factor of rock mass rheological damage during support implementation in a historical case. This represents the penalty weight coefficient for the damage dimension. This represents the numerical stability constant.

8. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 7, characterized in that, The weights of the static geological feature indicators are determined by principal component analysis; the penalty weight coefficient of the damage dimension is set to a constant greater than 1.

9. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 1, characterized in that, The benchmarking preprocessing includes: The energy data in the microseismic monitoring data are logarithmically processed to eliminate differences in magnitude; The unstructured geological descriptions in the static geological data are mapped to numerical values ​​using an expert scoring method; A unified time sampling window is set, and the continuously collected data is discretized and aligned according to the time sampling window.

10. The intelligent recommendation method for mine support schemes based on environmental analogy databases according to claim 1, characterized in that, The recommended target support parameters include: selecting a preset number of historical cases with the highest comprehensive matching score, and calculating the weighted average of the support parameters of the historical cases to obtain the final recommended support parameters.

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