CN113982687A

CN113982687A - Construction method of negative feedback cloud simulation monitoring and early warning system for rock burst

Info

Publication number: CN113982687A
Application number: CN202111127679.7A
Authority: CN
Inventors: 许文松; 黄尚钦; 赵光明; 孟祥瑞
Original assignee: Anhui University of Science and Technology
Current assignee: Anhui University of Science and Technology
Priority date: 2021-09-26
Filing date: 2021-09-26
Publication date: 2022-01-28
Anticipated expiration: 2041-09-26
Also published as: CN113982687B

Abstract

The invention discloses a construction method of a rock burst negative feedback cloud simulation monitoring and early warning system, which comprises a step-by-step optical fiber and a vibration optical fiber for obtaining passive feedback signals, and a singlechip for sweeping frequency signal characteristics; the signal characteristic peak value judges whether the static correction threshold value responds to the early warning system or not, and the reporting cloud simulation system takes the characteristic signal as initial data; constructing a simulation element model by the initial data through a fusion algorithm and negative feedback data correction, and constructing a coal stratum system model; according to the invention, cloud simulation is carried out through an existing cloud model to obtain the energy accumulation rock burst rule cloud, and the monitoring and early warning function is realized by planning an energy accumulation interval and correcting a pre-estimation threshold value; in the elimination method, the static blasting energy release pressure relief of the drill hole is guided by accurate positioning. According to the invention, cloud simulation is carried out through passive feedback and active negative feedback, the accumulated energy evolution trend of rock burst generation is obtained, multi-parameter unified coupling is realized, self-adaptive accurate monitoring and early warning are realized, and rock burst generation can be effectively prevented.

Description

Construction method of negative feedback cloud simulation monitoring and early warning system for rock burst

Technical Field

The invention relates to the technical field of coal mine rock burst safety monitoring and early warning systems, in particular to a construction method of a rock burst negative feedback cloud simulation monitoring and early warning system.

Background

With the continuous enhancement of coal mining depth and mining intensity, roadway engineering and hydrogeological conditions are more and more complex, the probability of rock burst is increased along with the mining depth, the rock burst has great destructiveness, is one of major disasters of a coal mine, and poses great threat to safety production of the coal mine and life health safety of coal mine workers. Rock burst is a dynamic phenomenon caused by the sudden release of a large amount of elastic energy accumulated in a coal rock mass. Essentially, rock burst is a non-linear dynamic instability process that induces catastrophe due to progressive deterioration of coal and rock mass under non-equilibrium conditions. In the case of the instability theory, not only the magnitude of the accumulated energy value but also the position relation of the accumulated energy are considered, namely the actual conditions of the coal strata, such as the thickness and the properties of the loose circle, are determined; in recent years, related researches on the aspects of rock burst generation mechanism, monitoring early warning, prevention and treatment measures and the like are continuously and deeply conducted in China, the rock burst generation condition is that accumulated energy is larger than minimum damage energy, in actual operation, the accumulated energy has the characteristics of dissipation and non-directional transferability to the field, meanwhile, the coal rock mass has certain elastoplasticity, the accumulated energy with chaotic change cannot be directly measured, how to realize simulation of the accumulated energy through stress change and coal rock stratum conditions before the rock burst occurs needs to be further researched, and meanwhile, the positions of the accumulated energy need to be further researched from the stress distribution characteristics and the energy distribution characteristics.

For monitoring rock burst, the existing early warning methods for the dangerousness of rock burst mainly include a microvibration monitoring method, a drilling cutting method, an electromagnetic radiation method and the like, wherein microvibration monitoring can directly and accurately monitor and calculate microvibration signals, including occurrence time, position and intensity, and the dangerousness is judged by analyzing the occurrence distribution rule of microvibration events; the drilling cutting method is to judge impact danger according to the coal dust amount and the power effect of drilling per meter recorded in the drilling process, and the principle is that the coal dust amount and the power effect discharged during drilling have a certain corresponding relation with the stress state of a coal bed; the electromagnetic radiation method is to determine the stress change of the coal seam according to the electromagnetic radiation intensity change of the coal seam so as to judge the impact risk; the drilling cutting method and the electromagnetic radiation method are local indirect stress monitoring.

The early warning method needs sensors, the traditional sensors cannot go deep into coal rock layers near a roadway along with anchor cables to monitor stress of each point, the mining depth is large, the complexity is large, large errors are caused when the received data cannot be subjected to transverse contrastive analysis, in addition, in the aspect of signal transmission processing, the influence of factors such as magnetic fields on electric signals is easily caused, and the optical fiber sensors have the advantages of small environmental influence factors, good durability, high sensitivity and long-term stable monitoring. At present, the research on early warning precursor information identification processing and early warning simulation judgment is still in a search stage, and how to monitor and early warn the randomness and the fuzziness of rock burst generation under the influence of different conditions is one of the important research subjects for preventing and controlling rock burst of coal mines at present.

Disclosure of Invention

The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, one purpose of the invention is to provide a construction method of a rock burst negative feedback cloud simulation monitoring and early warning system, the method is based on a long-term data set of rock burst event occurrence and negative feedback correction data, data are converted into simulation elements through a fusion algorithm, a cloud model is fed back to perform cloud simulation of the rock burst event occurrence, a response early warning value of each specific position is planned according to estimated simulation and real-time accumulated energy, the response early warning value is negatively fed back to a single chip microcomputer response threshold value, meanwhile, the effectiveness of each early warning index is clear, the early warning accuracy of the system on the rock burst is improved, specific positioning is provided for eliminating the rock burst, and an effective rock burst elimination support method is provided for pressure relief and energy release.

According to the construction method of the rock burst negative feedback cloud simulation monitoring and early warning system provided by the invention, the method comprises the following steps:

s1: arranging the distributed optical fiber and the vibration optical fiber, acquiring a signal of the arranged distributed optical fiber, and adjusting and measuring the information of strain, pressure, displacement, temperature, sound field and bending physical quantity of each point of the anchor cable penetrating into the coal rock mass;

s2: processing the light waves changed in the optical fiber through a singlechip sweep frequency demodulator to obtain initial data, preprocessing the peak value of the light waves, and determining the highest peak and the continuous change time of time sequence change;

s3: converting the peak value change of the initial data into an energy change value through a single chip microcomputer, judging whether the accumulated energy of the event reaches a pre-estimated threshold value, directly responding to an early warning system, and reporting the initial data;

s4: processing the initial data by using a characteristic signal data fusion algorithm, and feeding the processed initial data back to a negative feedback module;

s5: applying the processed initial data in the step S4 to a negative feedback module, the negative feedback module correcting the processed initial data, and performing data coupling on the corrected initial data through cloud computing;

s6: establishing simulation elements according to the cloud computing result, namely establishing a coal-rock mass simulation model for the stress distribution characteristics and the energy distribution characteristics of the coal-rock mass system to determine the stress state of the coal-rock mass, determining the boundaries of a crushing ring, a plastic ring, an elastic ring and a raw rock stress ring in the coal-rock mass system through the stress distribution characteristics, and determining the spatial distribution characteristics of energy accumulated at each point in the coal-rock mass system through the energy distribution characteristics;

the method comprises the steps that main space accumulation energy of space distribution characteristics positions the distribution position of an energy core of a coal rock mass, stress distribution characteristics can further determine the radius of a loose circle, a coal rock mass system model is constructed by combining factors such as strain and the like, the time-sequence analysis of the loose circle property state of the position of the accumulated energy is carried out by using the coal rock mass system, further, the position of neighborhood accumulation energy and the value of the movement vector change of the coal rock mass are positioned, so that the factors influencing cloud simulation are determined, and the real-time change of the corresponding variable of the coal rock mass system is used as a data correction cloud model;

s7: carrying out cloud simulation on the accumulated energy of the rock burst occurrence event in a time sequence manner based on a cloud model, and evolving the iteration rule cloud of the rock burst occurrence accumulated energy;

s8: applying an iteration rule cloud of accumulated energy generated by rock burst in S7, simulating and predicting whether rock burst can occur in the accumulated energy of the day in a time sequence manner, iteratively simulating minimum energy peak values of the rock burst at the coal and rock body positions of each space, and taking different early warning percentages of the minimum energy peak values as threshold values of a correction single chip microcomputer;

s9: dividing a plurality of groups of position pre-estimation simulation intervals for generating impact events without impact ground pressure dangerous energy response early warning values according to the actual energy storage dangerous levels of the positions, planning and determining the response early warning value of each specific position according to pre-estimation simulation and real-time energy accumulation, and obtaining the relative dangerous level of each position according to the condition that the response early warning value of each specific position falls into each position pre-estimation simulation interval;

s10: and monitoring the actual energy storage of the roadway to reach an early warning value of a system, responding to a main monitoring cloud platform to perform simulation prediction judgment and judgment in combination with manual work, and sending out sound and warning light by a singlechip early warning device in the dangerous area.

Preferably, the specific method for processing the initial data by using the feature signal data fusion algorithm in step S4 includes the following steps:

s41: setting distributed optical fiber multiple different positioning position data as a group of passive monitoring data f_iEvent record passive monitoring data set D₁＝[f₁,f₂,f₃,…f_i]Passive feedback data F monitored by different positioning positions of vibration optical fiber_iEvent recording passive dynamic feedback data set D₂＝[F₁,F₂,F₃,…F_i]Wherein i is the value of the optical fiber at different passive feedback positioning positions, and i is a positive integer;

s42: initial data sets were subjected to respective D with Min-max normalization₁，D₂Normalized to [0,1]Interval, conversion function formula is

In, max represents the maximum value of the signal, min represents the minimum value of the signal;

s43: de-noising the wavelet of the measured data set to obtain a new assignment D₁₁＝[f₁,f₂.f₃,…f_i]，D₂₁＝[F₁,F₂,F₃,…F_i]D is₁₁And D₂₁Performing three-term Jacobian matrix one by one, and positioning the non-zero termRecording, namely, a nonlinear correlation difference item;

s44: the above basis may determine D separately₁₁、D₂₁Brillouin frequency shift quantity V_B，

Va is the speed of the acoustic wave field, lambdap is the wavelength of the pump light, n is the effective refractive index of the optical fiber, the data set is swept to determine the temperature and the strain, and deltaT is deltaV_B/C_vT，δε＝δV_B/C_VEWhere Δ T, δ ε are temperature resolution and strain resolution, V, respectively_BFor the smallest amount of variation of the frequency shift that can be obtained, C_VT，C_VERespectively is a temperature coefficient of change of Brillouin frequency shift and a strain coefficient of Brillouin frequency shift;

s45: determining an elastic modulus E, converting stress strain information into main space unit accumulated energy by an elastic mechanics generalized Hooke law, and positioning the coal rock volume energy accumulation position;

s46: with the Hoek-Brown strength criterion,

wherein mb, s, ci, a are rock mass parameters, pi is radial supporting force, r0 is tunnel radius, the radius of the loose coil can be further calculated, and the property state of the loose coil is judged;

s47: feeding back the initial data subjected to the fusion algorithm to a negative feedback module for correction;

preferably, the specific method for performing negative feedback correction by using the negative feedback module in step S5 includes:

s51: positioning dangerous area by using historical accumulated energy position fed back by analog element, and positioning nearby ultrasonic generators to generate sound in sequence to form interference ultrasonic fieldThe interference ultrasonic wave field transmits and reflects signals to change the vibration optical fibers at different coal rock mass space positions, and the time sequence frequency sweeping is carried out to obtain a data set D₃＝[g₁,g₂,g₃…g_i]Wherein i is an active negative feedback vibration optical fiber at different positions;

s52: will D₁₁、D₂₁Non-zero item positioning recording and denoised D₃Wavelet comparison correction, wherein the wavelet denoising x (t) is equal to s (t) and n (t) is carried out on the measured data set;

wherein x (t) is a monitor value; s (t) is useful information; n (t) is noise information following normal distribution, i.e. N (t) N (0, sigma)²)；

S53: using the data set D in S51₃The method comprises the steps of adjusting the phase difference of interference light in a vibrating optical fiber to obtain an ultrasonic field, feeding back information carried by the ultrasonic field, wherein the information is the propagation speed of the ultrasonic field in an incomplete or complete rock stratum, feeding back and determining an accumulated energy position and actual energy storage energy, performing negative feedback to correct a coal rock mass model of simulation elements, and monitoring the conditions of convergence, nucleation and stable expansion energy dissipation of internal cracks of the coal rock mass;

s54: the cloud computing couples the time sequence feedback and negative feedback data arrangement multi-parameters to form a simulation element;

preferably, in step S7, performing time-sequential cloud simulation of rock burst occurrence events accumulated energy based on the cloud model, to obtain an iterative regular cloud of rock burst occurring due to accumulated energy, the specific method includes the following steps:

s71: determining the integral weighted value by applying an entropy weight method, and determining the ith index entropy value E_iAnd weight value W_i，

Wherein X_ijFor data set data of the ith index in the j time sequence time, the weight matrix W ═ W can be obtained₁,W₂…W_n}；

According to the aboveObtaining a fusion algorithm data set and a negative feedback data set, and obtaining a coal stratum energy accumulation evaluation matrix X (X) through dimensionless processing_ij) Determining the i-th index entropy E_iAnd weight value W_iObtaining a weight value matrix;

s72: establishing a cloud model system U of the coal-rock mass system, determining a geological factor mean value, a local energy factor mean value and a support factor of a local area by using feedback and negative feedback data, establishing a coal-rock mass rock burst danger evaluation model, uploading real-time feedback data to the cloud model system, evaluating the danger level of different coal-rock mass space points by taking the real-time feedback data and historical data as evaluation bases and taking the ratio of energy accumulation amount and actual limit stored energy in the area of different coal-rock mass space points as a variable, and correcting the energy factor of the cloud model;

s73: determining cloud simulation constraint conditions, wherein the constraint conditions comprise the data of rock burst generated by historical accumulated energy and the value of the allowed accumulated energy of the self strength of the coal rock mass, and excluding abnormal values and reporting to a cloud model system;

s74: the maximum entropy method is used for research, the maximum entropy spectrum method is used for calculating the rock burst period T generated by different energy accumulations, and the maximum entropy method is used for research

Where j is the frequency

Δ tt is the time interval of a discrete sequence (generally 1 for Δ t in equally spaced sequences), i is an imaginary number,_σm ²is the variance of the prediction error;

s75: performing time sequence analysis based on a cloud model, and for a rock burst generation period T, determining that an integer k and a time value at belong to [0, T ∈ [ ]]If a is at + kT, the historical impact ground pressure generation cumulative energy data set HD is { (a)_i，b_i)|a₀≤a_ikT ≦ and the current accumulated energy trend dataset CD { (a)_i，b_i)|kT≤a_iNot more than at }, and entering the data after division intoCarrying out reverse clouding to obtain a plurality of quasi-periodic clouds and a current trend cloud, respectively representing the rock burst rule of historical accumulated energy and the rock burst trend of current accumulated energy, and then overlapping the plurality of quasi-periodic clouds and the current trend cloud through the arithmetic operation rule of the clouds to generate a rock burst rule cloud of accumulated energy, so as to achieve the purpose of simulating and predicting the occurrence of rock burst;

s76: applying the rock burst generation rule cloud of the accumulated energy in the S75, taking the historical event of the rock burst generation of the accumulated energy as a feedback corrected data set, carrying out N times of simulated iterative evolution on the distribution of the accumulated energy of the coal rock mass, and determining the accumulated energy u of the same spatial region of the coal rock mass_iDetermining weight x by using coal rock mass accumulation energy evaluation matrix in S71_iThen, average weighting is performed

Obtaining iteration rule cloud r of rock burst generated by accumulated energy_N；

S77: according to a combined limit theory, r is carried out under the coal rock limit energy storage states of different local mean value areas_NSimulating and judging r_NWhether the analog value of (a) is caused by vulnerability to generate rock burst, and then r_NChecking;

preferably, the intervals classified according to the danger level of the actual stored energy of each location in step S9 include:

s91: setting the estimation simulation interval of each position of the impact event without the impact ground pressure danger response early warning value as | -R_qi│；

S92: setting the estimation simulation interval of each position of the impact event with the weak rock burst danger occurrence ability response early warning value as-R_zi│；

S93: setting the estimation simulation interval of each position of the impact event with the pre-warning value responded to the rock burst danger in occurrence as-R_yi│；

S94: setting the estimation simulation interval of each position of the impact event with the strong rock burst danger occurrence ability response early warning value as | -R_xi│；

Wherein i is i cumulative energy values for each spatial position, and q, z, y and x represent no, weak, medium and strong danger grades.

Preferably, each specific location interval is further provided with an early warning performance index, including a probability Gain (GA), an error of response early warning average absolute percentage, and an earthquake occurrence probability, and the performance index is checked and calculated as follows:

the probability gain G (A) is:

wherein, P (E | A) is the earthquake probability, P (E) is the background frequency, G (A) >1 shows that the model is effective, and the larger G (A), the higher the early warning efficiency;

mean Absolute Percentage Error (MAPE):

where yi represents the actual value of the signal,

the predicted value is represented, n represents the number of values, and the smaller the three indexes are, the better the accuracy of the prediction model is;

the origin probability calculation formula is as follows: the earthquake probability is the prediction correct times/(prediction correct times + prediction error times)

The early warning performance index of each specific interval of the position simultaneously meets the condition that the probability gain is larger than one or larger, the explanation error is smaller, if the earthquake probability is larger than the random prediction probability, the performance index is applied, otherwise, the performance index is not reserved;

preferably, the method also comprises an early warning system response elimination method, and the method comprises the following steps: s101: cloud simulation positioning is carried out on an energy accumulation dangerous area in a certain range, and spatial arrangement is carried out on a drill hole according to rock stratum fractures and deformation simulated by the cloud in the energy accumulation dangerous area, so that the angle and the path of the drill hole and the depth of the drill hole are guided to be selected, and the rotary hole can optimally reach the spatial position of an energy core;

s102: : analyzing the crushing range and size of static blasting according to a loose ring theory, determining the depth and the aperture of a charging hole, and controlling the crushing size and the deformation of the rock block according to the depth and the aperture of the charging hole;

s103: drilling and cleaning drill cuttings, performing secondary drilling on the space position of an energy core, wherein the depth and the aperture of a charging hole are performed, the drill cuttings in the charging hole are cleaned by dust absorption of a long pipe of a high-pressure fan, then the charging hole is subjected to water injection and cooling, and residual water is sucked out by the long pipe of the high-pressure fan;

s104: after the temperature of the charge drill hole is reduced, determining the charge amount, wherein an empirical formula of the charge amount of the medicament is W (1+ b) Sigma L.w, wherein b is the loss rate of 0.05-0.10, Sigma L is the total length of the drill hole, and W is the broken charge amount of the drill hole in the unit length of the medicament;

selecting a static blasting agent blending proportion and a charging assembly scheme, determining an addition inhibiting agent amount and a medicament-to-water ratio according to the environmental temperature of the coal-rock mass, adjusting the static blasting time, injecting a pasty medicament into a charging drill hole below the horizontal direction by using a gray pump pipeline according to the assembly scheme of different coal-rock mass spatial positions, compacting yellow mud for sealing, charging a fiber bag smaller than the drill hole in an area above a roadway, pushing a drill rod into the charging drill hole, compacting and pushing the yellow mud for sealing, waiting for a medicament reaction, and implementing static blasting;

s105: after static blasting, negatively feeding back coal rock stratum condition information by using an ultrasonic field, checking whether the dosage is residual, monitoring whether stress energy is transferred to surrounding rock in a deep elastic region, and monitoring the thickness of a loosening ring after blasting;

s106: and the thickness of the loose ring is reinforced and supported by adopting an anchor rod and cable combination corresponding to the thickness range of the loose ring, and simultaneously, the drilling hole after static blasting is subjected to grouting and anchor cable supporting, so that rock burst is eliminated and an effective supporting effect is achieved.

The beneficial effects of the invention are as follows:

(1) the method has the advantages that the initial data is feedback, the ultrasonic field is negative feedback to correct simulation elements, source micro-mine earthquake of a monitoring part which cannot be accurately matched in precision is compensated, meanwhile, whether energy accumulation is caused by other factors or not and the stability of a coal rock body is influenced is eliminated, the ultrasonic field is not required to be started in real time, the change of accumulated energy is monitored, estimated and simulated through real-time stress change and simulated stress change trend, a dangerous area is positioned, and then the real-time monitoring is achieved through negative feedback, so that the on-site monitoring is more accurate, meanwhile, the static threshold value of the single chip microcomputer can be used for correcting the negative feedback, the stress change before the rock burst event occurs is directly estimated and early-warned, the time for uploading data processing feedback again is shortened, and the functions of advanced positioning and early-warning and quick response early warning are achieved;

(2) cloud simulation is carried out based on a cloud model, the problem of recurrence of randomness and ambiguity of rock burst accumulated energy is solved to a certain extent, the implied periodic characteristics and trend characteristics of the rock burst accumulated energy are extracted and converted into quantitative digital representations, then a cloud reverse generation algorithm is used for comprehensively forming a rock burst accumulated energy rule cloud on the basis of generating the periodic cloud and the trend cloud, and a new thought and method are provided for simulation and early warning of energy accumulation caused by rock burst;

(3) the energy accumulation position is located through feedback and negative feedback correction cloud simulation, the actual stress concentration area of the coal rock stratum is accurately located, drilling is conducted in a locating mode, according to the thickness of a loosening ring monitored through simulation, holes are reasonably arranged to release static blasting agents, the property of the rock mass is effectively controlled, the static blasting has controllable occurrence time, the property of a crushing body is controlled, the drilling can be effectively conducted through locating to ensure high accuracy, the energy release and pressure release are conducted on the energy accumulation area of the rock burst accurately, the support is effectively strengthened by controlling the loosening ring while the rock burst is accurately eliminated, and after the crushing, the stress is transferred to the surrounding rock from the deep elastic area.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is a schematic flow principle diagram of a method for constructing a rock burst negative feedback cloud simulation monitoring and early warning system provided by the invention;

FIG. 2 is a negative feedback flow chart according to the present invention;

FIG. 3 is a schematic diagram of a cloud simulation flow scheme according to the present invention;

fig. 4 is a structural view of a functional optical fiber arrangement roadway according to the present invention;

fig. 5 is a schematic view of a rotary hole of the elimination method and a rotary hole for charging provided by the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

Referring to fig. 1 to 5, a method for constructing a rock burst negative feedback cloud simulation monitoring and early warning system includes:

(1) step-by-step optical fiber and vibration optical fiber

Specifically, a dangerous area with accumulated energy is set as an early warning area for monitoring rock burst, optical fibers are installed and arranged, the distance between the arranged optical fibers is based on the spatial radius of the optical fiber monitoring, step-by-step optical fibers and vibrating optical fibers are arranged at intervals of 50 meters, if special conditions such as folds and faults occur, the optical fibers are placed on two sides near the optical fibers to measure the characteristic change of surrounding rocks, meanwhile, the space monitored by the optical fibers is positioned and debugged to ensure that the optical fibers can work normally and reduce errors, signals are obtained for the arranged optical fibers, and primary information of physical quantities such as strain, pressure, displacement, temperature, sound field and bending of each point of the coal-rock mass to be measured is obtained through signal demodulation, so that real-time information of the change of the coal-rock mass is obtained.

(2) Sweep frequency acquisition initial data

Specifically, the optical fiber changes along with the stress field and the elastic wave field, the time sequence signal of the optical wave in the optical fiber changes due to the photoelastic effect, the sweep frequency demodulator of the single chip microcomputer processes the signal of the optical wave, simultaneously acquires the optical wave as initial data, and preprocesses the peak value of the optical wave, so that the monitoring of the highest peak and the duration of the time sequence is realized.

On the basis, the distributed optical fiber obtains the value of strain of each point on the monitoring optical fiber, the temperature borne by the monitoring optical fiber in a coal rock body can be approximately regarded as a constant quantity, the vibration optical fiber obtains the information transmitted by elastic waves, the distribution characteristic of energy transmission is determined, the two functional optical fibers are combined, the synchronous monitoring of the dynamic change of factors such as static stress change and disturbance is realized, and the initial data with the measuring standard is obtained.

(3) Judging whether the pre-estimated threshold value responds to the early warning system or not, and reporting data

Specifically, the singlechip demodulator demodulates the frequency and the wave peak, the frequency and the wave peak are converted into accumulated energy, whether the accumulated energy reaches a static threshold value is judged, if the accumulated energy meets the static threshold value, the early warning system is directly responded, and a monitoring event is reported to a cloud model of the coal rock mass;

in the above example, the value of the static threshold can be corrected and changed by the negative feedback module of the analog element, so as to realize rapid early warning for processing the sudden accumulated energy event. Reporting cloud simulation data of a monitoring event, namely storing and recording different points on a monitoring optical fiber into a matrix set in a time sequence manner for a data set obtained by monitoring an accumulated energy event of rock burst as initial data of a simulation element, and then comparing and correcting by a negative feedback data set of the event;

(4) fusion algorithm

Specifically, initial data can be fed back according to the event that energy is accumulated due to rock burst, the data are mediated to obtain light wave signals, the light wave signals are uploaded and processed through cloud computing, the information of the processed initial data comprises the strain and the temperature monitored on a coal rock mass, and then multi-parameters of the data are considered in a coupling mode through combination of positioning on the space such as mining depth and joint conditions, optical fibers in the coal rock mass are positioned in different spaces to form a monitoring network, so that stability monitoring on surrounding rocks and the coal rock mass is realized, the optical fibers and the cloud computing are used, in terms of safety, the optical fibers can be distributed and buried at the bottom of a roadway without considering the generation of electricity leakage danger, the performance of the optical fibers is faster than that of electric signals in terms of signal transmission, the adaptability under complex conditions is stronger, the interference resistance to factors such as electromagnetism is higher, the direct influence of an electric circuit is avoided, and the long-distance ultra-short-time large-capacity transmission to the ground can be realized in terms of transmission information, furthermore, cloud computing can realize rapid response processing on rapidly transmitted optical fiber signals, information contained in the signals can be extracted in a short time, the cost of configuring a high-power, large-computation-capacity and powerful computer and maintaining the signals in real time is reduced by utilizing the cloud technology, and only the fed-back initial data is processed through a fusion algorithm of the cloud computing.

Specifically, on the signal of the initial data obtained by the accumulated energy generated by the rock burst event, a plurality of distributed optical fiber recording data sets at different positioning positions, which are separated by 50m, are arranged into a group in time sequence, and the passive monitoring data f is recorded_iScoring step-by-step fiber-optic passive monitoring data set D₁＝[f₁,f₂,f₃,…f_i]Vibrating the passive feedback data F monitored by different positioning positions of the optical fiber at a plurality of different positioning positions which are separated by 50m_iEvent recording passive feedback data set D₂＝[F₁,F₂,F₃,…F_i]Where i is the value of the fiber at the different location (i ═ 1,2,3 …); and respectively taking the data fed back by different measured optical fibers as a group, numbering the data, forming a data set by utilizing the data fed back in a time sequence manner, and establishing a database of the accumulated energy event generated by rock burst for the recorded data set.

On the basis, before denoising the optical wave signal, normalization processing is usually required, and the main purpose is to facilitate data processing, improve the precision and convergence speed of a classification model, and reduce the processing time delay of a system. Normalization is to make the features in different dimensions have certain comparability on numerical values, so that the accuracy of the classifier can be greatly improved. Data sets were subjected to respective D with Min-max normalization₁，D₂Normalized to [0,1]The interval, the conversion function formulation, also known as dispersion normalization, is scaled to a given interval by calculating the maximum and minimum values of the data, typically by normalizing the data to 0,1]the working principle is to carry out linear transformation on the data and respectively carry out D on the data sets by utilizing Min-max standardization₁，D₂Normalized to [0,1]Interval, conversion function formula is

on the basis, screening effective signals, otherwise, rejecting noise energy to effectively inhibit noise and reduce reconstruction distortion, and not affecting the original signal peak value, and performing wavelet denoising on the measured data set, wherein x (t) is a monitoring value; s (t) is useful information; n (t) is noise information following normal distribution, i.e. N (t) N (0, sigma)²) It follows that for the monitored values, useful information and noise information from a normal distribution are independent of each other and can be accumulated in the frequency domain. Newly assigned data number set value D after denoising₁₁＝[f₁,f₂.f₃,…f_i],D₂₁＝[F₁,F₂,F₃,…F_i]D is₁₁And D₂₁And (3) carrying out three-term Jacobian determinant one by one, and carrying out positioning recording on non-zero terms of the Jacobian determinant, namely detecting whether the characteristic information of the two measured optical fibers is linearly related or not, and carrying out comparison feedback on accumulated energy in the process of stress field and elastic wave transmission.

On the basis of the above, for D₁₁、D₂₁The accumulated energy data set generated by rock burst complies with energy conservation and matched accumulated energy change, so that the frequency shift amount of Brillouin backscattered light under different conditions

Va is the speed of an acoustic wave field, lambdap is the wavelength of pump light, n is the effective refractive index of the optical fiber, the temperature change and the strain are positioned by frequency sweeping, and then delta T is delta V_B/C_vT，δε＝δV_B/C_VEWhere Δ T, δ ε are temperature resolution and strain resolution, V, respectively_BTo be availableMinimum amount of change in frequency shift, C_VT，C_VEThe monitoring space of the distributed optical fiber and the vibrating optical fiber realizes the monitoring of the strain and the vibration of the coal rock mass, the corresponding temperature information is converted into unit energy accumulation and is fed back to a simulation element, and the composite monitoring of the accumulated energy is realized.

On the basis, the strain is obtained on the distributed optical fiber and the vibrating optical fiber, the generalized Hooke's law of the elastic modulus E is determined, the stress-strain temperature information is converted into the energy accumulation of the main space unit,

wherein sigma₁、ε₁Axial term stress and strain, respectively, σ₂、σ₃Are all lateral stresses,. epsilon₃、ε₃Are both laterally strained. The stress of the main space is considered, so that the monitoring precision can be improved, and small changes of the stress can be directly detected, so that the stress can be converted into an energy judgment index.

On the basis, the radius and the definition property of the loosening ring are determined, and the Hoek-Brown strength criterion is adopted to calculate the rock mass state which has stronger adaptability and better accords with the actual engineering. The radius of the loose circle is calculated in such a way that under the supporting condition, the supporting resistance directly influences the radius of the loose circle of the surrounding rock, so that the tangential stress on the boundary of the loose circle is equal to the initial stress, namely the initial stress

Wherein mb, s, ci, a are rock mass parameters, pi is radial supporting force, and r0 is the radius of the tunnel, and the radius of the loosening ring can be further calculated.

Based on the above example, it can be seen that the fed-back initial data is cloud computing using a fusion algorithmAnd then the ultrasonic wave enters a negative feedback module for correction, and the ultrasonic wave has the characteristics of different propagation speeds in different media according to the attenuation rule of the ultrasonic wave propagating in different media, so that the attenuation in a homogeneous rock body is reduced, the speed is high, the attenuation of the ultrasonic wave capability is high, the speed is correspondingly reduced due to the increase of cracks in a broken rock body, and the damage condition of the surrounding rock is predicted according to the wave propagation condition of an ultrasonic wave field. According to the elastic theory, the relationship between the wave velocity of the longitudinal wave of the ultrasonic wave and the elastic parameters of the medium can be obtained by deducing the wave equation of the elastic wave through the static equation of the elastomechanics space problem,

wherein Vp is the longitudinal wave velocity of the coal body, Vs is the velocity of the transverse wave, E is the elastic modulus of the coal body, mu is the Poisson's ratio of the coal body, and rho is the density of the coal body.

On the basis, the loosening circle range of the surrounding rock of the roadway can be calculated, and specifically, the Hoek-Brown strength criterion is adopted.

(5) Negative feedback module

Specifically, vibration optical fibers which are positioned in the coal rock mass and used for monitoring accumulated energy realize the positioning of dangerous accumulated energy positions in a negative feedback module and judge whether destructive influence is caused on the coal rock mass in a monitoring range, ultrasonic wave is generated by an ultrasonic transmitter to form a mutual interference ultrasonic wave field, the ultrasonic wave field is monitored by a damping value in the process of transmitting through an inner path of the ultrasonic wave field, reflected ultrasonic waves are transmitted to the vibration optical fibers nearby to receive negative feedback signals in the aspect of receiving information to form a negative feedback data set, and the data set D is obtained through time-sequence frequency sweeping₃＝[g₁,g₂,g₃…g_i]And i is an active negative feedback vibration optical fiber at different positions, judges whether destructive influence of the coal rock mass is caused in a monitoring range or not, and negatively feeds back a data set to the simulation element.

On the basis of the above, a negative feedback data number set D is formed₃After wavelet transform denoising is performed on the light waves of the measured vibration, a data set D is provided₃₁＝[g₁₁,g₂₁,g₃₁…g_i1]Wherein i is different position active negative feedback vibration optical fiber (i ═ 1,2,3 …), D₃₁Positioning the data set D after the occurrence of the percussion earth pressure₁₁、D₂₁Respectively comparing the Jacobian non-zero positioning records of the data set, comparing and correcting the Jacobian non-zero positioning records, and comparing D₃Substituting the value of (b) into the nonlinear term;

on the basis of the above, for D₃₁Modifying the phase difference caused by interfering ultrasonic field by regulating the information carried by external ultrasonic waves

In the formula, delta n is the change amount of the refractive index of the optical fiber, delta D is the change amount of the diameter of the optical fiber, the difference value of the light propagation paths of the Lm sensing light path and the reference light path, beta is the propagation constant of the light wave in the optical fiber, and epsilon is the strain of the sensing optical fiber; the information is the propagation speed of an ultrasonic field in an incomplete rock stratum, the energy aggregation position and the actual energy storage energy are determined through feedback, the information is negatively fed back to a simulation element, and energy dissipation conditions such as convergence, nucleation, stable expansion and the like of internal cracks of the coal rock stratum are negatively fed back to correct a coal rock stratum model of the simulation element, so that negative feedback adjustment of adaptability of the coal rock stratum is realized.

On the basis of the above example, cloud computing couples multiple parameters, so that comprehensive computation of accumulated energy generated by rock burst is realized, and a coal-rock mass model with negative feedback correction is formed.

Compared with the prior art, the method has the advantages that the position of the energy accumulated when the rock burst event is fed back by using the initial data as the feedback is used as historical positioning, the ultrasonic generator near the energy accumulated is positioned and fed back to emit ultrasonic waves to form a phase interference ultrasonic field, the vibration optical fibers at different positions of the coal rock mass receive ultrasonic wave reflection or transmit information, the information is subjected to negative feedback correction simulation elements, the initial data is applied to carry out combined correction while the vibration event in a dangerous area is monitored, source micro-mineral vibration which cannot be accurately matched by a monitoring part is compensated, whether the energy accumulation is caused by other factors or not and the stability of the coal rock mass is influenced is eliminated, an ultrasonic field does not need to be started in real time, and the coal rock mass model is more accurate.

(6) Simulation element

Specifically, simulation elements are constructed according to the cloud computing result, namely a coal-rock mass simulation model is established for the stress distribution characteristics and the energy distribution characteristics of the coal-rock mass system, the stress state of a coal rock mass is determined, the stress distribution characteristics determine the boundaries of a crushing ring, a plastic ring, an elastic ring and a raw rock stress ring in the coal-rock mass system, and the energy distribution characteristics determine the spatial distribution characteristics of energy accumulated at each point in the coal-rock mass system;

specifically, the main space accumulation energy of the space distribution characteristics positions the distribution position of the energy core of the coal rock mass, the stress distribution characteristics can further determine the radius of the loose circle, then a coal rock mass system model is constructed by combining factors such as strain and the like, the loose circle property state of the accumulated energy position is analyzed in a time sequence manner by using the coal rock mass system, further, the position of neighborhood accumulated energy and the value of the movement vector change of the coal rock mass are positioned, so that the factors influencing cloud simulation are determined, and the real-time change of the corresponding variable of the coal rock mass system is used as data to correct the cloud model

On the basis, the simulation element before the rock burst event occurs, the simulation element when the rock burst event occurs and the function of monitoring the rock stratum system after the rock burst event occurs and the function of self-iterative updating of the rock stratum system which cannot be updated due to real-time change caused by uncertain factor disturbance are realized, and the simulation element is corrected to the cloud model and provides implementation data through initial data feedback and active negative feedback data.

(7) Performing time-sequence cloud simulation based on existing cloud model

Specifically, an entropy weight method is used for determining an overall weighted value, and an energy accumulation evaluation matrix X (X) is obtained by dimensionless processing according to the obtained data set_ij) Determining the i-th index entropy E_iAnd weight value W_i，

Wherein X_ijFor data set data of the ith index in the j time sequence time, the weight matrix W ═ W can be obtained₁,W₂…W_n}。

Specifically, a cloud model of the coal rock mass is constructed, and the cloud model is a proposed comprehensive evaluation method. According to the establishment of a coal rock stratum cloud model U, feedback and negative feedback data are utilized to determine local area mean value geological factors, local mean value accumulation energy factors and support factors, overall weighting is conducted to establish a coal mine rock burst danger evaluation model, the cloud model has three digital characteristics to express mathematical attributes of the cloud model, namely expected Ex, entropy En and super entropy He, and real-time data are uploaded to conduct simulation. Weighting to determine an evaluation grade, starting from the occurrence of rock burst, based on an adaptive cloud model, calling a feedback initial data set and a negative feedback data set by taking data from an accumulated energy event database of the occurrence of the rock burst in the example, using the accumulated energy event of the recent occurrence of the rock burst as a predicted value, determining a cloud simulation constraint condition, wherein the constraint condition comprises the data of the rock burst of the historical accumulated energy and the value of the allowed accumulated energy of the self strength of the coal rock mass, and excluding an abnormal value and reporting to a cloud model system;

based on the above embodiments, the research method in this example is to apply the maximum entropy method to research, and the principle of maximum entropy to perform spectrum analysis is to select a time series corresponding to the most random or most unpredictable, and the autocorrelation function is the same as the known value, which is a nonlinear spectrum analysis method. According to the law of inertia of physics, any object has the tendency of keeping the current state of the object, has certain tendency on occurrence and accumulated energy of rock burst events and subsequent accumulated positions, preprocesses data by applying the maximum entropy principle to the tendency, calculates rock burst period T of different accumulated energy, and applies the maximum entropy method to research

Where j is the frequency

Δ t is the time interval of a discrete sequence (in equispaced sequences Δ t is typically 1), i is an imaginary number, σ_m ²Is the variance of the prediction error. Calculating the rock burst period T of different accumulated energy generation every day by using a maximum entropy spectrum method;

on the basis of the above example, time sequence analysis is carried out based on a cloud model, and for a rock burst occurrence period T, an integer k and a time value at ∈ [0, T ∈ [ T ∈ ] ]]If a is at + kT, the historical rock burst generation energy accumulation data set HD is { (a) } is_i，b_i)|a₀≤a_ikT ≦ and the current energy accumulation trend dataset CD { (a)_i，b_i)|kT≤a_iAt or below), reversely clouding the segmented data to obtain a plurality of quasi-periodic clouds and a current trend cloud, respectively representing the rock burst rule caused by historical accumulated energy and the rock burst trend caused by current accumulated energy, and overlapping the plurality of quasi-periodic clouds and the current trend cloud through the arithmetic operation rule of the clouds to generate a rock burst rule cloud caused by accumulated energy, so as to achieve the purpose of simulating and predicting the occurrence of the rock burst

On the basis of the above example, in order to accurately predict the accumulated energy generated by rock burst, feedback correction is carried out on the historical event data set of the rock burst accumulated energy, and the energy u in different accumulated small regions is determined_iDetermining the weight x according to the coal rock mass accumulation energy evaluation matrix_iNamely, N times of operation are carried out on the clouds of rock burst caused by the energy accumulated under different conditions, then weighted average is carried out,

and obtaining an accumulated energy iteration rule cloud according to the result, and realizing the appearance of the characteristic rule of the accumulated energy implicit data generated by rock burst.

Compared with the prior art, the technology has higher practicability and adaptability, has smaller error than the traditional BP network system prediction value, can realize multidirectional real-time prediction through cloud simulation, can predict and determine a prediction simulation interval through iteration, can correct a singlechip static threshold value through a negative feedback module in real time, enables the direct response to the prediction of the impact ground pressure of accumulated energy to reduce the time from uploading data to processing and then feeding back the data, has quicker response, and has certain guiding significance for evaluating the risk level in the impact ground pressure region of the accumulated energy.

(8) Rock burst is predicted according to energy accumulation and rock burst generation rules

Specifically, cloud simulation is carried out by applying a cloud law of rock burst caused by accumulated energy, simulation prediction of rock burst caused by accumulated energy in a dangerous area is realized, the trend of the accumulated energy is judged, and whether rock burst occurs is judged according to the trend;

specifically, the principle of judging the grade of rock burst generation according to energy is that k is U/U according to the ratio of accumulated energy to limit stored energy₀Where U is the accumulated energy, U₀For the ultimate energy storage, the conditions for generating rock burst are as follows: u shape>U_minIn the formula of U_minThe minimum damage energy when the rock stratum is damaged;

(9) determining response early warning values for each specific location based on predictive modeling and real-time accumulated energy planning

Specifically, the estimation simulation interval of each position of the impact event with the non-impact ground pressure danger response early warning value is set as-Rqi-, and the cloud simulation value display result range is selected, wherein k is the time of day<0.2,U<U_minAnd the simulated energy interval of the cloud simulated accumulated energy change is | Rqi |.

Specifically, each position estimation simulation interval of the impact event with the weak rock burst danger response early warning value is set as Rzi-and the accumulated energy change is changed from U within the range of the cloud simulation value display result, wherein k is 0.2 in the day<U_minTo U ═ U_minThe estimated simulation interval | Rzi | of the change in the previous percent accumulated energy.

In particular, the risk of rock burst in the event of a crash is setIn the cloud simulation value display result range selected by the position estimation simulation intervals of the impact events capable of responding to the early warning value as-Ryi-in the day, k is 0.3, and the accumulated energy changes from U<Umin to U ═ U_minThe estimated simulation interval of the change in the previous percent accumulated energy is Ryi.

Specifically, the estimation simulation intervals of each position of the impact event with the occurrence of the high impact ground pressure danger response early warning value are set as Rxi-selected cloud simulation value display result range, and k is within the day>0.4 accumulated energy change from U<U_minTo U ═ U_minThe estimated simulation interval | Rxi | of the change of the previous percentage accumulated energy.

In addition, i is the i cumulative energy values of each spatial position, and q, z, y and x represent the risk levels of no, weak, medium and strong. Carrying out response grade division on the accumulated energy at each position;

(10) performance index test

Specifically, the method for testing the monitoring and early warning effectiveness of the rock burst before the rock burst event occurs determines that early warning performance indexes of specific intervals at each position of a dangerous range comprise probability Gain (GA), errors of response early warning average absolute percentage and earthquake occurrence probability.

On the basis of the above example, the early warning performance index of each specific interval at each position simultaneously satisfies the condition that the probability gain is greater than one or larger, the error is smaller, the performance index is applied when the earthquake probability is greater than the random prediction probability, otherwise, the performance index is not reserved;

(11) feedback response monitoring and early warning system

Specifically, the actual energy storage monitoring system responds to the section of dangerous area roadway, the lamplight changes from green, blue, yellow and red to represent the levels of no danger, weak danger, medium danger and strong danger, and responds to the total monitoring cloud platform and the section of dangerous area generates early warning sound;

(12) response cancellation method

The elimination method adopts optical fiber feedback and ultrasonic field negative feedback positioning, combines the theory of a loosening ring, firstly adopts advanced drilling, is loaded with static blasting agents which are silent, vibration-free, toxic residue-free, flying stone-free and shock-wave-free, releases stress in advance by static blasting, ensures that the energy accumulation amount is always less than the minimum failure energy, changes the plasticity of coal bodies and the like to reduce the accumulation of stress, and then adopts a corresponding combined supporting method to eliminate or reduce the impact threat of local high-risk areas, wherein the method comprises the following steps:

cloud simulation positioning is carried out on an energy accumulation amount dangerous area in a certain range, drilling holes are spatially arranged according to rock stratum fractures and deformation simulated by the cloud in the energy accumulation dangerous area, drilling hole angles and paths are guided to be selected, factors such as rock joints are considered in the angles, the diameter of a small drilling hole is 36-42 mm, the diameter of a large drilling hole is 70-115 mm, and the drilling depth is the depth from the cloud simulation energy accumulation dangerous position to the outer wall of a roadway.

Analyzing the crushing range and size of the static blasting agent according to a loose ring theory, determining the depth and the aperture of a charging hole, determining the depth of the charging hole by adopting an empirical formula L which is C multiplied by H, wherein H is the crushing thickness, C is the hole depth coefficient, the charging aperture is determined by the required unit loading, and the charging hole depth and the aperture control the crushing size and the deformation of the rock;

drilling and cleaning drill cuttings, performing secondary drilling on the accumulated energy dangerous position according to the hole depth and the hole diameter of the charge drilling hole, cleaning the charge drilling cuttings in a rotating hole by using a long pipe of a high-pressure fan for dust absorption, performing water injection and cooling on the charge drilling hole, and sucking out residual water by using the long pipe of the high-pressure fan;

fourthly, after cooling, selecting a model stirring static blasting agent configuration, adding an inhibitor amount and a ratio of the agent to water according to the underground environment temperature, and adjusting the static blasting time, wherein an empirical formula of the dosage is W (1+ b) Sigma L.w, wherein b is a loss rate of 0.05-0.10, Sigma L is the total length of a drill hole, W is the dosage for breaking the drill hole in the unit length of the model agent, injecting a pasty agent into the charge drill hole by using an ash pump pipeline below the horizontal direction, compacting yellow mud for sealing, charging the area above the roadway by using a fiber bag smaller than the drill hole, pushing a drill rod into the charge drill hole, compacting and then pushing the yellow mud for sealing.

After static blasting, negative feedback coal rock stratum condition information is fed back by using an ultrasonic field, whether the dosage is residual or not is checked, whether stress energy is transferred to surrounding rock in a deep elastic region or not is monitored, and the thickness of a loosening ring after blasting is monitored;

reinforcing support by adopting the anchor rod and cable combination corresponding to the thickness range of the loosening ring, and performing grouting and anchor cable support in the drilled hole after static blasting to eliminate rock burst and achieve effective support effect;

compared with the prior art, specifically, the energy accumulation position is located through negative feedback correction cloud simulation, the position is also an actual stress concentration area of the coal rock layer, drilling is carried out through location, holes are reasonably arranged according to the thickness of the loose ring to place static blasting agents, the static blasting agents are inorganic matters, and are silent, free of vibration, free of flying stones, free of toxic gas, free of shock waves and free of harmful residues in the using process, gas explosion in the coal layer caused by improper control or softening and instability of the coal layer caused by flushing cannot be caused like traditional gunpowder blasting, the generation time of static blasting can be controlled, the properties of a broken body are controlled, drilling can be effectively carried out through location, the accuracy is high, and the energy accumulation area of the impact ground pressure can be discharged and pressure can be released accurately.

The further improvement of the invention is that the performance index is tested and calculated in step 9 as follows:

the probability gain G (A) is:

p (E-A) is r/n; wherein P (E | A) is the earthquake probability, P (E) is the background frequency, G (A)>The time 1 indicates that the model is effective, and the larger the G (A), the higher the early warning efficiency;

mean Absolute Percent Error (MAPE):

where yi represents the actual value of the signal,

and (3) a predicted value is represented, n represents the number of values, and the smaller the three indexes are, the better the accuracy of the prediction model is.

Thirdly, the earthquake probability calculation formula is as follows: if the probability of occurrence of an index is greater than the probability of random prediction (i.e., background probability), the index has a predictive significance and a precursor thereof is determined to have a relationship with the impact ground pressure.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims

1. The construction method of the rock burst negative feedback cloud simulation monitoring and early warning system is characterized by comprising the following steps:

s4: processing the initial data by using a fusion algorithm, and feeding the processed initial data back to a negative feedback module;

the method comprises the steps that main space accumulation energy of space distribution characteristics is used for positioning the distribution position of an energy core of a coal rock body, the radius of a loose circle can be further determined through stress distribution characteristics, a coal rock body system model is constructed by combining factors such as strain and the like, the property state of the loose circle of the accumulated energy position is analyzed by using a coal rock body system, further, the position of neighborhood accumulated energy and the value of the change of the movement vector of the coal rock body are positioned, so that the factors influencing cloud simulation are determined, and the real-time change of the corresponding variable of the coal rock body system is used as a data correction cloud model;

s9: according to the coal-rock mass simulation model, dividing multiple groups of dangerous levels according to actual energy storage of different coal-rock mass positions, simulating predicted accumulated energy and real-time accumulated energy, planning a response early warning value of each specific coal-rock mass space position, and performing efficiency test on each response early warning value so as to determine a simulation prediction interval;

2. The method for constructing the rock burst negative feedback cloud simulation monitoring and early warning system according to claim 1, wherein the specific method for processing the initial data by using the characteristic signal data fusion algorithm in the step S4 comprises the following steps:

s41: will distribute the lightA plurality of different positioning position data are taken as a group of passive monitoring data f_iEvent record passive monitoring data set D₁＝[f₁,f₂,f₃,…f_i]Passive feedback data F monitored by different positioning positions of vibration optical fiber_iEvent recording passive dynamic feedback data set D₂＝[F₁,F₂,F₃,…F_i]Wherein i is the value of the optical fiber at different passive feedback positioning positions, and i is a positive integer;

s42: initial data sets were separately D using Min-max normalization₁，D₂Normalized to [0,1]Interval, conversion function formula is

s43: de-noising the wavelet of the measured data set to obtain a new assignment D₁₁＝[f₁,f₂.f₃,…f_i]，D₂₁＝[F₁,F₂,F₃,…F_i]D is₁₁And D₂₁Carrying out three-term Jacobian determinant one by one, and carrying out positioning record on non-zero terms of the Jacobian determinant, namely non-linear correlation difference terms;

s44: the above basis may determine D separately₁₁、D₂₁Brillouin frequency shift quantity V_BDetermining the strain and temperature as described above;

s46: determining the radius and the property state of the thickness of the loose ring by adopting a Hoek-Brown strength criterion;

s47: and feeding back the initial data subjected to the fusion algorithm to a negative feedback module for correction.

3. The method for constructing the rock burst negative feedback cloud simulation monitoring and early warning system according to claim 2, wherein the concrete method for performing negative feedback correction by using the negative feedback module in the step S5 comprises the following steps:

s51: the method comprises the steps of using historical accumulated energy positions fed back by simulation elements to position a dangerous area, positioning nearby ultrasonic generators to sequentially sound to form a phase interference ultrasonic field, transmitting and reflecting signals by the phase interference ultrasonic field to enable vibration optical fibers at different coal-rock mass space positions to change, and obtaining a data set D by time-sequence frequency sweeping₃＝[g₁,g₂,g₃…g_i]Wherein i is an active negative feedback vibration optical fiber at different positions;

s52: will D₁₁、D₂₁Non-zero item positioning recording and denoised D₃Wavelet comparison and correction;

s54: and the cloud computing couples the time sequence feedback and the negative feedback data sorting multi-parameter to form a simulation element.

4. The construction method of the rock burst negative feedback cloud simulation monitoring and early warning system according to claim 3, characterized by comprising the following steps: in step S7, performing time-sequential rock burst occurrence event accumulated energy cloud simulation based on the cloud model, and performing an iterative rule cloud of evolving rock burst occurrence accumulated energy as follows:

s71: determining the integral weighted value by using an entropy weight method, and obtaining a coal and rock energy accumulation evaluation matrix X (X) through dimensionless processing according to the obtained fusion algorithm data set and negative feedback data set_ij) Determining the i-th index entropy E_iAnd weight value W_iObtaining a weight value matrix;

s73: determining cloud simulation constraint conditions, eliminating abnormal values and reporting a cloud model system;

s74: researching by using a maximum entropy method, and calculating the rock burst period T of different accumulated energy by using a maximum entropy spectrum method;

s75: performing time sequence analysis based on a cloud model, and for a rock burst generation period T, determining that an integer k and a time value at belong to [0, T ∈ [ ]]If a is at + kT, the historical impact ground pressure generation cumulative energy data set HD is { (a)_i，b_i)|a₀≤a_ikT ≦ and the current accumulated energy trend dataset CD { (a)_i，b_i)|kT≤a_iThe segmented data is reversely clouded to obtain a plurality of quasi-periodic clouds and a current trend cloud, the quasi-periodic clouds and the current trend cloud respectively represent the rock burst rule caused by historical accumulated energy and the rock burst trend caused by current accumulated energy, and the quasi-periodic clouds and the current trend cloud are superposed through an arithmetic operation rule of the clouds to generate a rock burst rule cloud caused by accumulated energy, so that the aim of simulating and predicting the occurrence of rock burst is fulfilled;

s76: applying the rock burst generation rule cloud of the accumulated energy in S75, taking the historical event of the rock burst generation of the accumulated energy as a feedback corrected data set, carrying out N times of simulated iterative evolution on the distribution of the accumulated energy of the coal rock mass, and determining the accumulated energy u of the same spatial region of the coal rock mass_iDetermining weight x by using coal rock mass accumulation energy evaluation matrix in S71_iThen, average weighting is performed

S77: according to a combined limit theory, r is carried out under the coal rock limit energy storage states of different local mean value areas_NSimulating and judging r_NWhether the analog value of (a) is caused by vulnerability to generate rock burst, and then r_NAnd (6) checking.

5. The method for constructing the rock burst negative feedback cloud simulation monitoring and early warning system as claimed in claim 4, wherein the intervals classified according to the actual energy storage danger levels of the energy at each position in the step S9 comprise:

6. The method for constructing a rock burst negative feedback cloud simulation monitoring and early warning system according to claim 5, wherein early warning performance indexes are further set in specific intervals of each position, the performance indexes comprise probability Gain (GA), error responding to early warning average absolute percentage and earthquake occurrence probability, and the performance indexes are tested and calculated as follows:

the probability gain G (A) is:

mean Absolute Percentage Error (MAPE):

where yi represents the actual value of the signal,

And simultaneously satisfying that the probability gain is larger than one or larger for the early warning performance index of each specific interval of the position, the explanation error is smaller, if the earthquake probability is larger than the random prediction probability, the performance index is applied, otherwise, the performance index is not reserved.

7. The method for constructing the rock burst negative feedback cloud simulation monitoring and early warning system according to claim 1, further comprising a method for eliminating the response early warning system, wherein the method comprises the following steps:

s101: cloud simulation positioning is carried out on an energy accumulation dangerous area in a certain range, and spatial arrangement is carried out on a drill hole according to rock stratum fractures and deformation simulated by the cloud in the energy accumulation dangerous area, so that the angle and the path of the drill hole and the depth of the drill hole are guided to be selected, and the rotary hole can optimally reach the spatial position of an energy core;

s102: analyzing the crushing range and size of static blasting according to a loose ring theory, determining the depth and the aperture of a charging hole, and controlling the crushing size and the deformation of the rock block according to the depth and the aperture of the charging hole;

s104: after the temperature of the charge drilling is reduced, determining the charge amount, selecting the proportion of static blasting agent blending and the charge assembly scheme, blending and assembling, and implementing static blasting;