Multi-coal seam roof monitoring and floor damage prediction method

Abstract

The invention discloses a multi-coal seam roof monitoring and floor damage prediction method, which belongs to the technical field of coal mining, and comprises the following steps: acquiring coal seam roof state data, and generating a roof state anomaly index; judging whether the state of the top plate is abnormal or not according to the top plate state abnormal index, and generating a top plate state normal signal and a top plate state abnormal signal; based on the top plate state abnormal signal, obtaining bottom plate state data and mining state data, establishing a bottom plate damage prediction model, and generating a bottom plate damage prediction index; according to the floor damage prediction index, the risk of floor damage is judged; according to the invention, by constructing the bottom plate damage prediction model based on weight distribution and dynamically calculating the bottom plate damage prediction index, collaborative analysis and graded early warning of the top plate stability and the bottom plate damage risk are realized, the problem of lagging of traditional single parameter early warning is solved, and the real-time performance and accuracy of mine safety monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coal mining, and particularly relates to a method for monitoring the roof of multiple coal seams and predicting the floor failure. Background Art

[0002] In coal mining, floor failure is one of the main risks leading to mine disasters. Floor instability may cause roadway collapse, water inrush accidents, and surface subsidence, seriously threatening personnel safety, equipment integrity, and production efficiency. Especially under the condition of combined mining of multiple coal seams, the mutual disturbance between the upper and lower coal seams will exacerbate the stress concentration and crack propagation of the floor, further increasing the risk of failure.

[0003] Currently, the prediction methods for floor failure mainly include threshold alarm based on support pressure, empirical judgment through the change trend of subsidence, or estimation of floor bearing capacity using a simplified mechanical model.

[0004] However, the existing floor failure prediction methods mostly rely on a single parameter (such as support pressure or subsidence) for judgment, lacking comprehensive analysis of multi-source data, resulting in delayed early warning and insufficient model accuracy. In addition, the traditional methods do not fully consider the influence of mining conditions (such as mining depth, advancing speed) and the mining intensity of adjacent coal seams, making it difficult to dynamically evaluate the risk of floor failure. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a method for monitoring the roof of multiple coal seams and predicting the floor failure, which solves the above problems.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A method for monitoring the roof of multiple coal seams and predicting the floor failure, comprising the following steps:

[0007] Obtain the roof state data of the coal seam and generate a roof state anomaly index; wherein, the roof state data of the coal seam includes the support pressure of the coal seam roof and the subsidence of the coal seam roof;

[0008] According to the roof state anomaly index, judge whether the state of the roof is abnormal and generate a roof state signal; wherein, the roof state signal includes a normal roof state signal and an abnormal roof state signal;

[0009] Based on the abnormal roof state signal, obtain the floor state data and the mining state data, establish a floor failure prediction model, and generate a floor failure prediction index; wherein, the floor state data includes the floor crack density and the floor crack growth rate, and the mining state data includes the current mining depth and the advancing speed of the working face;

[0010] According to the floor failure prediction index, judge the risk of floor failure;

[0011] Among them, based on the abnormal roof state signal, obtaining the floor state data and mining state data, establishing a floor failure prediction model, and generating a floor failure prediction index specifically includes the following steps:

[0012] Obtain the floor state data and mining state data;

[0013] Generate a floor state evaluation value according to the floor state data;

[0014] Generate a mining state evaluation value according to the mining state data;

[0015] Establish a floor failure prediction model;

[0016] Substitute the floor state evaluation value, mining state evaluation value, and roof state abnormal index into the floor failure prediction model to generate a floor failure prediction index;

[0017] Among them, the expression of the floor failure prediction model is:

[0018]

[0019] In the expression, A top represents the roof state abnormal index, C mining represents the mining state evaluation value, R floor represents the roof state abnormal index, S represents the support anchor chain state evaluation value, K represents the adjacent coal seam mining intensity evaluation coefficient, and α, β, and γ are all weight ratios, and α + β + γ = 1.

[0020] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0021] Further technical solution: The obtaining of the coal seam roof state data and generating the roof state abnormal index specifically includes the following steps:

[0022] Obtain the coal seam roof support pressure and the coal seam roof subsidence amount;

[0023] Generate a support pressure evaluation index according to the coal seam roof support pressure;

[0024] Generate a subsidence amount evaluation index according to the coal seam roof subsidence amount;

[0025] Perform weighted processing on the support pressure evaluation index and the subsidence evaluation index to generate a roof state abnormal index.

[0026] Further technical solution: The specific generation method of the support pressure evaluation index is:

[0027] Perform a difference processing on the coal seam roof support pressure and the rated support bearing capacity to generate a pressure difference;

[0028] Perform a ratio process on the pressure difference and the rated support bearing capacity to generate a support pressure difference degree value;

[0029] Set a detection period, evenly divide the detection period into several detection time segments, and obtain the support pressure values within the detection time segments;

[0030] Perform a difference process on the support pressure values within the detection time segments and the support pressure values within adjacent detection time segments in time sequence to generate a support pressure change value;

[0031] Perform a ratio process on the support pressure change value and the duration of the detection time segment to generate a support pressure change speed;

[0032] Perform a ratio process on the support pressure change speed and the support pressure change speed warning value to generate a support pressure change speed evaluation value;

[0033] Perform a weighted process on the support pressure difference degree value and the support pressure change speed evaluation value to generate a support pressure evaluation index.

[0034] Further technical solution: The specific generation method of the subsidence evaluation index is as follows:

[0035] Perform a difference process on the coal seam roof subsidence amount and the subsidence distance warning value to generate a subsidence distance difference;

[0036] Perform a ratio process on the subsidence distance difference and the subsidence distance warning value to generate a subsidence amount difference degree value;

[0037] Set a detection period, evenly divide the detection period into several detection time segments, and obtain the subsidence amount within the detection time segments;

[0038] Perform a difference process on the subsidence amount values within the detection time segments and the subsidence amount values within adjacent detection time segments in time sequence to generate a subsidence amount change value;

[0039] Perform a ratio process on the subsidence amount change value and the duration of the detection time segment to generate a subsidence amount change speed;

[0040] Perform a difference process on the subsidence amount change speed and the subsidence amount change speed warning value to generate a change speed difference;

[0041] Perform a ratio process on the change speed difference and the subsidence amount change speed warning value to generate a subsidence amount change speed evaluation value;

[0042] Perform a weighted process on the subsidence amount difference degree value and the subsidence amount change speed evaluation value to generate a subsidence amount evaluation index.

[0043] Further technical solution: The specific judgment method for determining whether the state of the roof is abnormal is as follows:

[0044] Compare the roof state anomaly index with the roof state anomaly index threshold value;

[0045] When the roof state anomaly index is greater than the roof state anomaly index threshold value, generate a roof state anomaly signal.

[0046] Further technical solution: The specific generation method of the floor state evaluation value is as follows:

[0047] Perform a difference processing on the floor crack density and the floor crack density warning value to generate a density difference;

[0048] Perform a ratio processing on the density difference and the floor crack density warning value to generate a crack density anomaly degree value;

[0049] Perform a difference processing on the floor crack growth rate and the growth rate threshold value to generate a growth rate difference;

[0050] Perform a ratio processing on the growth rate difference and the growth rate threshold value to generate a growth rate anomaly degree value;

[0051] Perform a weighted processing on the crack density anomaly degree value and the growth rate anomaly degree value to generate a floor state evaluation value.

[0052] Further technical solution: The specific generation method of the mining state evaluation value is as follows:

[0053] Perform a difference processing on the mining depth and the exploitable depth to generate a mining depth difference;

[0054] Perform a ratio processing on the mining depth difference and the exploitable depth to generate a mining depth evaluation value;

[0055] Perform a difference processing on the working face advancing speed and the working face advancing speed threshold value to generate a working face advancing speed difference;

[0056] Perform a ratio processing on the working face advancing speed difference and the working face advancing speed threshold value to generate a working face advancing speed anomaly degree value;

[0057] Perform a weighted processing on the mining depth evaluation value and the working face advancing speed anomaly degree value to generate a mining state evaluation value.

[0058] Further technical solution: The specific acquisition method of the support anchor chain state evaluation value is as follows:

[0059] Obtain the state data of the anchor chain; among them, the state data of the anchor chain includes the set number of anchor chains, the number of broken anchor chains, and the force of the unbroken anchor chains;

[0060] Perform a difference processing on the set number of anchor chains and the standard set number to generate a set number difference of anchor chains;

[0061] The difference between the set number of anchor chains is processed as a ratio with the standard set number of anchor chains to generate an evaluation value of the set number of anchor chains;

[0062] The difference between the number of broken anchor chains and the set number of anchor chains is processed to generate the remaining number of anchor chains;

[0063] The remaining number of anchor chains is processed as a ratio with the set number of anchor chains to generate an influence coefficient of anchor chain breakage;

[0064] The difference between the force on the unbroken anchor chain and the rated force is processed to generate a force difference;

[0065] All the force differences are averaged to generate an average force difference;

[0066] The average force difference is processed as a ratio with the rated force to generate an evaluation value of the force;

[0067] Through the formula:

[0068] S=(M set *Q Splitting )*a1+F Tension *a2;

[0069] Generate an evaluation value S of the support anchor chain state;

[0070] In the formula, M set represents the evaluation value of the set number of anchor chains, Q Splitting represents the influence coefficient of anchor chain breakage, F Tension represents the evaluation value of the force, and a1 and a2 are both proportionality coefficients, and a1 + a2 = 1.

[0071] Further technical solution: The specific method for obtaining the adjacent coal seam mining intensity evaluation coefficient is as follows:

[0072] Obtain the adjacent coal seam mining intensity evaluation value;

[0073] Through the formula:

[0074]

[0075] Generate the adjacent coal seam mining intensity evaluation coefficient K;

[0076] In the formula, Q i represents the mining intensity evaluation value of the i-th adjacent coal seam, and Q0 represents the adjacent coal seam mining intensity evaluation threshold, is the weight coefficient, and

[0077] The present invention provides a multi-coal seam roof monitoring and floor failure prediction method, which has the following beneficial effects compared with the prior art:

[0078] The present invention collects data on the support pressure and subsidence amount of the coal seam roof in real time, generates a support pressure evaluation index and a subsidence amount evaluation index respectively, and obtains a roof state anomaly index after weighted fusion; if this index exceeds the threshold, an anomaly signal is triggered, and multi-source data such as the floor crack density, growth rate, mining depth, and working face advance speed are synchronously obtained. Combining the state of the support anchor chain and the evaluation coefficient of the mining intensity of the adjacent coal seam, a floor failure prediction model based on weight distribution is constructed, and the floor failure prediction index is dynamically calculated to realize the collaborative analysis and hierarchical early warning of roof stability and floor failure risk, solve the problem of lag in traditional single-parameter early warning, and improve the real-time performance and accuracy of mine safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flowchart of a multi-coal seam roof monitoring and floor failure prediction method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0080] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0081] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0082] Please refer to Figure 1 , which is a multi-coal seam roof monitoring and floor failure prediction method provided by an embodiment of the present invention, including the following steps:

[0083] Step 1: Obtain the coal seam roof state data and generate a roof state anomaly index; wherein, the coal seam roof state data includes the coal seam roof support pressure and the coal seam roof subsidence amount;

[0084] Step 2: According to the roof state anomaly index, determine whether the state of the roof is abnormal and generate a roof state signal; wherein, the roof state signal includes a roof state normal signal and a roof state abnormal signal;

[0085] Step 3: Based on the roof state abnormal signal, obtain the floor state data and the mining state data, establish a floor failure prediction model, and generate a floor failure prediction index; wherein, the floor state data includes the floor crack density and the floor crack growth rate, and the mining state data includes the current mining depth and the working face advance speed;

[0086] Step 4: According to the floor failure prediction index, judge the risk of floor failure.

[0087] As a preferred embodiment of the present invention, step one specifically includes the following steps:

[0088] S10: Obtain the support pressure of the coal seam roof and the subsidence amount of the coal seam roof;

[0089] S11: Generate a support pressure evaluation index according to the support pressure of the coal seam roof;

[0090] S12: Generate a subsidence amount evaluation index according to the subsidence amount of the coal seam roof;

[0091] S13: Perform weighted processing on the support pressure evaluation index and the subsidence evaluation index to generate a roof state abnormality index

[0092] As a preferred embodiment of the present invention, the specific method for generating the support pressure evaluation index is as follows:

[0093] Perform a difference operation on the support pressure of the coal seam roof and the rated support bearing capacity to generate a pressure difference;

[0094] Perform a ratio operation on the pressure difference and the rated support bearing capacity to generate a support pressure difference degree value;

[0095] Set a detection period, divide the detection period into several detection time segments evenly, and obtain the support pressure values within the detection time segments;

[0096] Perform a difference operation on the support pressure values within the detection time segment and the support pressure values within the adjacent detection time segments in time sequence to generate a support pressure change value;

[0097] Perform a ratio operation on the support pressure change value and the duration of the detection time segment to generate a support pressure change speed;

[0098] Perform a ratio operation on the support pressure change speed and the support pressure change speed warning value to generate a support pressure change speed evaluation value;

[0099] Perform weighted processing on the support pressure difference degree value and the support pressure change speed evaluation value to generate a support pressure evaluation index;

[0100] It should be noted that the support pressure change speed warning value is set by relevant personnel in the field.

[0101] As a preferred embodiment of the present invention, the specific method for generating the subsidence evaluation index is as follows:

[0102] Perform a difference operation on the subsidence amount of the coal seam roof and the subsidence distance warning value to generate a subsidence distance difference;

[0103] Perform a ratio operation on the subsidence distance difference and the subsidence distance warning value to generate a subsidence amount difference degree value;

[0104] Set a detection period, evenly divide the detection period into several detection time segments, and obtain the subsidence amount within the detection time segment;

[0105] Perform a difference operation on the subsidence amount value within the detection time segment and the subsidence amount value within the adjacent detection time segment in time sequence to generate a subsidence amount change value;

[0106] Perform a ratio operation on the subsidence amount change value and the duration of the detection time segment to generate a subsidence amount change speed;

[0107] Perform a difference operation on the subsidence amount change speed and the subsidence amount change speed warning value to generate a change speed difference;

[0108] Perform a ratio operation on the change speed difference and the subsidence amount change speed warning value to generate a subsidence amount change speed evaluation value;

[0109] Perform a weighted operation on the subsidence amount difference degree value and the subsidence amount change speed evaluation value to generate a subsidence amount evaluation index;

[0110] It should be noted that the subsidence distance warning value and the subsidence amount change speed warning value are set by relevant personnel in the field.

[0111] As a preferred embodiment of the present invention, the judgment method in step two is specifically as follows:

[0112] Compare the roof state abnormal index with the roof state abnormal index threshold;

[0113] It should be noted that the value of the roof state abnormal index threshold is set by relevant personnel in the field; for example, the roof state abnormal index threshold can be calculated by setting the values of the support pressure evaluation index and the subsidence evaluation index;

[0114] When the roof state abnormal index is less than or equal to the roof state abnormal index threshold, generate a roof state normal signal; the smaller the roof state abnormal index, the more normal the roof state;

[0115] When the roof state abnormal index is greater than the roof state abnormal index threshold, generate a roof state abnormal signal; the larger the roof state abnormal index, the more abnormal the roof state.

[0116] As a preferred embodiment of the present invention, step three specifically includes the following steps:

[0117] S30: Obtain the floor state data and the mining state data;

[0118] S31: Generate a floor state evaluation value according to the floor state data;

[0119] S32: Generate a mining state evaluation value according to the mining state data;

[0120] S33: Establishing a floor damage prediction model;

[0121] S34: Substituting the floor state evaluation value, the mining state evaluation value and the roof state abnormality index into the floor damage prediction model to generate a floor damage prediction index;

[0122] The expression of the base plate damage prediction model is:

[0123]

[0124] In the expression, A top It represents the roof abnormality index, C mining It represents the mining status assessment value, R floor It represents the roof abnormality index, S represents the support anchor chain status assessment value, K represents the adjacent coal seam mining intensity assessment coefficient, α, β, γ are all weight ratios, and α+β+γ=1;

[0125] It should be explained that the values ​​of α, β, and γ are set by relevant personnel in this field, and the methods for setting the values ​​include but are not limited to expert consultation method, hierarchical analysis method, etc.

[0126] As a preferred embodiment of the present invention, the method for generating the floor condition evaluation value is specifically as follows:

[0127] Performing difference processing on the floor crack density and the floor crack density warning value to generate a density difference;

[0128] The density difference is processed by ratioing the density warning value of the bottom plate crack to generate the abnormal degree value of the crack density;

[0129] Performing a difference processing on the growth rate of the bottom plate crack and the growth rate threshold to generate a growth rate difference;

[0130] The growth rate difference is processed by ratio with the growth rate threshold to generate a growth rate abnormality degree value;

[0131] The abnormal degree value of crack density and the abnormal degree value of growth rate are weighted to generate the floor condition assessment value;

[0132] It should be explained that the bottom plate crack density warning value and growth rate threshold are preset values, and their values ​​are set by relevant personnel in this field.

[0133] As a preferred embodiment of the present invention, the mining status evaluation value is generated in the following manner:

[0134] Performing difference processing on the mining depth and the exploitable depth to generate a mining depth difference;

[0135] Process the difference in mining depth and the exploitable depth to generate a mining depth evaluation value;

[0136] Process the difference between the face advancing speed and the face advancing speed threshold to generate a face advancing speed difference;

[0137] Process the ratio of the face advancing speed difference to the face advancing speed threshold to generate a face advancing speed abnormality degree value;

[0138] Perform weighted processing on the mining depth evaluation value and the face advancing speed abnormality degree value to generate a mining state evaluation value;

[0139] It should be noted that the face advancing speed threshold is the maximum speed of coal seam mining, and this speed is the ideal speed for safe coal seam mining; in addition, the exploitable depth refers to the maximum depth of the coal seam.

[0140] As a preferred embodiment of the present invention, the method for obtaining the support anchor chain state evaluation value is specifically as follows:

[0141] Obtain the state data of the anchor chain; among them, the state data of the anchor chain includes the set number of anchor chains, the number of broken anchor chains, and the force on the unbroken anchor chains;

[0142] Process the difference between the set number of anchor chains and the standard set number to generate a difference in the set number of anchor chains;

[0143] Process the ratio of the difference in the set number of anchor chains to the standard set number of anchor chains to generate an evaluation value of the set number of anchor chains;

[0144] Process the difference between the number of broken anchor chains and the set number of anchor chains to generate the remaining number of anchor chains;

[0145] Process the ratio of the remaining number of anchor chains to the set number of anchor chains to generate an influence coefficient of anchor chain breakage;

[0146] Process the difference between the force on the unbroken anchor chains and the rated force to generate a force difference;

[0147] It should be noted that the force on the unbroken anchor chains is the force on a single anchor chain;

[0148] Perform average processing on all the force differences to generate an average force difference;

[0149] Process the ratio of the average force difference to the rated force to generate a force evaluation value;

[0150] Through the formula:

[0151] S=(M set *QSplitting )*a1 + F Tension *a2;

[0152] Generate the support anchor chain status evaluation value S;

[0153] In the formula, M set represents the evaluation value of the number of anchor chains set, Q Splitting represents the influence coefficient of anchor chain fracture, F Tension represents the evaluation value of the force intensity, a1 and a2 are both proportionality coefficients, and a1 + a2 = 1;

[0154] It should be explained that the values of a1 and a2 are set by relevant personnel in this field themselves, and the way of taking values can be through the expert consultation method; the expert consultation method is an existing algorithm and will not be elaborated here.

[0155] As a preferred embodiment of the present invention, the method for obtaining the adjacent coal seam mining intensity evaluation coefficient is specifically as follows:

[0156] Obtain the adjacent coal seam mining intensity evaluation value;

[0157] It should be explained that the method for obtaining the adjacent coal seam mining intensity evaluation value is the same as the method for obtaining the mining state evaluation value; for example, by processing the mining depth and the working face advancing speed of the adjacent coal seam, the adjacent coal seam mining intensity evaluation value is generated, and its processing method is the same as the processing method of the mining state evaluation value;

[0158] Through the formula:

[0159]

[0160] Generate the adjacent coal seam mining intensity evaluation coefficient K;

[0161] In the formula, Q i represents the mining intensity evaluation value of the i-th adjacent coal seam, Q0 represents the adjacent coal seam mining intensity evaluation threshold, is the weight coefficient, and

[0162] It should be explained that it can be obtained by the analytic hierarchy process; in addition, the adjacent coal seam mining intensity evaluation threshold is a set value and is set by relevant personnel in this field themselves.

[0163] As a preferred embodiment of the present invention, the method for judging the risk of floor failure is specifically as follows:

[0164] Compare the floor failure prediction index with the floor failure prediction index threshold;

[0165] It should be noted that the threshold value of the floor failure prediction index is a set value, and its value is set by relevant personnel in the field themselves;

[0166] When the floor failure prediction index is less than or equal to the threshold value of the floor failure prediction index, it indicates that the current risk of floor failure is relatively low; the smaller the floor failure prediction index, the lower the current risk of floor failure;

[0167] When the floor failure prediction index is greater than the threshold value of the floor failure prediction index, it indicates that the current risk of floor failure is relatively high; the larger the floor failure prediction index, the higher the current risk of floor failure.

[0168] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the roof of multiple coal seams and predicting the floor failure, characterized in that It includes the following steps: Obtain the data of the coal seam roof state and generate the roof state anomaly index; among them, the data of the coal seam roof state includes the support pressure of the coal seam roof and the subsidence amount of the coal seam roof; According to the roof state anomaly index, judge whether the state of the roof is abnormal and generate the roof state signal; among them, the roof state signal includes the roof state normal signal and the roof state abnormal signal; Based on the roof state abnormal signal, obtain the floor state data and the mining state data, establish a floor failure prediction model, and generate the floor failure prediction index; among them, the floor state data includes the floor crack density and the floor crack growth rate, and the mining state data includes the current mining depth and the working face advance speed; Judge the risk of floor failure according to the floor failure prediction index; Among them, the step of obtaining the floor state data and the mining state data based on the roof state abnormal signal, establishing a floor failure prediction model, and generating the floor failure prediction index specifically includes the following steps: Obtain the floor state data and the mining state data; Generate the floor state evaluation value according to the floor state data; Generate the mining state evaluation value according to the mining state data; Establish a floor failure prediction model; Substitute the floor state evaluation value, the mining state evaluation value and the roof state anomaly index into the floor failure prediction model to generate the floor failure prediction index; Among them, the expression of the floor failure prediction model is: In the expression, A top represents the abnormal index of the roof state, C mining represents the evaluation value of the mining state, R floor represents the abnormal index of the roof state, S represents the evaluation value of the support anchor chain state, K represents the evaluation coefficient of the adjacent coal seam mining intensity, and α, β, and γ are all weight ratios, and α + β + γ = 1.

2. A method for monitoring the multi - coal - seam roof and predicting the floor failure according to claim 1, characterized in that, The step of obtaining the data of the coal seam roof state and generating the roof state anomaly index specifically includes the following steps: Obtain the support pressure of the coal seam roof and the subsidence amount of the coal seam roof; Generate the support pressure evaluation index according to the support pressure of the coal seam roof; Generate the subsidence amount evaluation index according to the subsidence amount of the coal seam roof; Perform weighted processing on the support pressure evaluation index and the subsidence evaluation index to generate the roof state anomaly index.

3. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 2, characterized in that, The specific generation method of the support pressure evaluation index is: Perform a difference processing on the support pressure of the coal seam roof and the rated support bearing capacity to generate a pressure difference; Perform a ratio processing on the pressure difference and the rated support bearing capacity to generate the support pressure difference degree value; Set the detection period, divide the detection period into several detection time periods on average, and obtain the support pressure values within the detection time periods; Perform a difference processing on the support pressure value within the detection time period and the support pressure value within the adjacent detection time period in time sequence to generate the support pressure change value; Perform a ratio processing on the support pressure change value and the duration of the detection time period to generate the support pressure change speed; Perform a ratio processing on the support pressure change speed and the support pressure change speed warning value to generate the support pressure change speed evaluation value; Perform weighted processing on the support pressure difference degree value and the support pressure change speed evaluation value to generate the support pressure evaluation index.

4. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 2, characterized in that, The specific generation method of the subsidence evaluation index is: Perform a difference processing on the subsidence amount of the coal seam roof and the subsidence distance warning value to generate the subsidence distance difference; Perform a ratio processing on the subsidence distance difference and the subsidence distance warning value to generate the subsidence amount difference degree value; Set the detection period, divide the detection period into several detection time periods on average, and obtain the subsidence amount within the detection time periods; The subsidence values within the detection period are processed by taking the difference from the subsidence values in adjacent detection periods in terms of time sequence to generate subsidence change values; The subsidence change values are processed by taking the ratio to the duration of the detection period to generate subsidence change speeds; The subsidence change speeds are processed by taking the difference from the subsidence change speed warning values to generate change speed differences; The change speed differences are processed by taking the ratio to the subsidence change speed warning values to generate subsidence change speed evaluation values; The subsidence difference degree values and the subsidence change speed evaluation values are processed by weighting to generate subsidence evaluation indices.

5. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 2, characterized in that, The specific way to judge whether the state of the roof is abnormal is as follows: The roof state abnormal index is compared with the roof state abnormal index threshold; When the roof state abnormal index is greater than the roof state abnormal index threshold, a roof state abnormal signal is generated.

6. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 1, characterized in that, The specific way to generate the floor state evaluation value is as follows: The floor crack density is processed by taking the difference from the floor crack density warning value to generate density differences; The density differences are processed by taking the ratio to the floor crack density warning value to generate crack density abnormal degree values; The floor crack growth speed is processed by taking the difference from the growth speed threshold to generate growth speed differences; The growth speed differences are processed by taking the ratio to the growth speed threshold to generate growth speed abnormal degree values; The crack density abnormal degree values and the growth speed abnormal degree values are processed by weighting to generate floor state evaluation values.

7. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 1, characterized in that, The specific way to generate the mining state evaluation value is as follows: The mining depth is processed by taking the difference from the exploitable depth to generate mining depth differences; The mining depth differences are processed by taking the ratio to the exploitable depth to generate mining depth evaluation values; The working face advancing speed is processed by taking the difference from the working face advancing speed threshold to generate working face advancing speed differences; The working face advancing speed differences are processed by taking the ratio to the working face advancing speed threshold to generate working face advancing speed abnormal degree values; The mining depth evaluation values and the working face advancing speed abnormal degree values are processed by weighting to generate mining state evaluation values.

8. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 1, characterized in that, The specific way to obtain the support anchor chain state evaluation value is as follows: Obtain the state data of the anchor chain; among them, the state data of the anchor chain includes the set number of anchor chains, the number of broken anchor chains, and the force on the unbroken anchor chains; The set number of anchor chains is processed by taking the difference from the standard set number to generate anchor chain set number differences; The anchor chain set number differences are processed by taking the ratio to the anchor chain standard set number to generate anchor chain set number evaluation values; The number of broken anchor chains is processed by taking the difference from the set number of anchor chains to generate the remaining number of anchor chains; The remaining number of anchor chains is processed by taking the ratio to the set number of anchor chains to generate an anchor chain breakage influence coefficient; The force on the unbroken anchor chains is processed by taking the difference from the rated force to generate force difference values; All the force difference values are processed by taking the average to generate an average force difference value; The average force difference value is processed by taking the ratio to the rated force to generate a force evaluation value; Through the formula: S = (M set * Q Splitting ) * a1 + F Tension * a2; Generate the support anchor chain state evaluation value S; In the formula, M set represents the evaluation value of the number of anchor chain settings, Q Splitting represents the influence coefficient of anchor chain fracture, F Tension represents the evaluation value of the force intensity, and a1 and a2 are both proportionality coefficients, and a1 + a2 = 1.

9. A method for monitoring the roof of multiple coal seams and predicting the floor failure according to claim 1, characterized in that, The specific way to obtain the adjacent coal seam mining intensity evaluation coefficient is as follows: Obtain the adjacent coal seam mining intensity evaluation value; Through the formula: Generate the evaluation coefficient K for the mining intensity of adjacent coal seams; In the formula, Q i represents the evaluation value of the mining intensity of the i-th adjacent coal seam, and Q0 represents the evaluation threshold of the mining intensity of the adjacent coal seam. is the weight coefficient, and

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