Rock mass instability classification and early warning method and system based on microseismic feature perception and classification enhancement algorithm

Abstract

The present invention discloses a method for grading and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm, including obtaining the data information of rock mass instability cases of different geotechnical engineering projects; selecting several microseismic parameters as candidate evaluation indicators for rock mass instability risk and conducting correlation analysis to obtain the grading and warning evaluation indicators for rock mass instability; constructing a training data set; constructing a primary model for grading and warning of rock mass instability based on the classification enhancement algorithm and genetic algorithm and training to obtain a grading and warning model for rock mass instability; using the obtained grading and warning model for rock mass instability to complete the grading and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm. The present invention also discloses a system for implementing the method for grading and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm. The present invention can not only realize the grading and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm, but also has higher reliability and better accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of civil engineering, and particularly relates to a method and system for classifying and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm. Background Art

[0002] Rock mass instability is a common disaster phenomenon in deep mining and geological engineering. It is mainly caused by the sudden release of stored energy in rocks under the action of high in-situ stress. Rock mass instability has the characteristics of great destructiveness, sudden occurrence and strong unpredictability. With the development of deep mineral resources, the frequency of rock mass instability disasters continues to rise, posing a severe challenge to the safety of miners' lives and engineering operations. Therefore, the prediction of rock mass instability is of great significance.

[0003] Microseismic monitoring technology has been widely used in rock mass instability monitoring due to its high sensitivity to small-scale seismic activities. By real-time monitoring of microseismic signals in rock strata, researchers can obtain the dynamic response of rocks under stress, and then analyze the fracture characteristics and energy release of rocks. In practical applications, multi-parameter characteristics of microseismic signals (such as magnitude, frequency, waveform characteristics, etc.) can provide important information support for the mechanism of rock mass instability.

[0004] At present, traditional methods for warning of rock mass instability based on microseismic monitoring mainly include methods based on numerical monitoring indicators, mathematical monitoring methods, probability methods, empirical theories, etc. However, these warning schemes for rock mass instability all have problems of low reliability and poor accuracy. Machine learning is a rapidly developing technical field in recent years, and thus some researchers have proposed warning schemes for rock mass instability based on machine learning schemes. However, such warning schemes for rock mass instability based on machine learning still have problems such as insufficient model generalization ability and easy to fall into local optimal solutions in application, resulting in poor reliability and accuracy of the final prediction results. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a method for classifying and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm with high reliability and good accuracy.

[0006] Another purpose of the present invention is to provide a system for realizing the method for classifying and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm.

[0007] The method for classifying and warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm provided by the present invention includes the following steps:

[0008] S1. Obtain the data information of rock mass instability case in different geotechnical engineering projects;

[0009] S2. Select several microseismic parameters as candidate evaluation indicators for rock mass instability risk according to the data information obtained in step S1;

[0010] S3. Conduct a correlation analysis between the several microseismic parameters selected in step S2 and the rock mass instability level to obtain the rock mass instability classification early warning evaluation indicators;

[0011] S4. Based on the rock mass instability classification early warning evaluation indicators obtained in step S3 and the data information obtained in step S1, construct a training data set;

[0012] S5. Based on the classification enhancement algorithm and the genetic algorithm, construct a primary model for rock mass instability classification early warning;

[0013] S6. Use the training data set obtained in step S4 to train the primary model for rock mass instability classification early warning constructed in step S5 to obtain the rock mass instability classification early warning model;

[0014] S7. Use the rock mass instability classification early warning model obtained in step S6 to complete the rock mass instability classification early warning based on microseismic feature perception and the classification enhancement algorithm.

[0015] The step of selecting several microseismic parameters as candidate evaluation indicators for rock mass instability according to the data information obtained in step S1 described in step S2 specifically includes the following steps:

[0016] Determine that the rock mass instability levels include no instability, slight instability, moderate instability, and strong instability;

[0017] The selected candidate evaluation indicators for rock mass instability include the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate, apparent volume rate, apparent stress, moment magnitude, source radius, lithology, initial in-situ stress, etc.

[0018] The step of conducting a correlation analysis between the several microseismic parameters selected in step S2 and the rock mass instability level to obtain the rock mass instability classification early warning evaluation indicators described in step S3 specifically includes the following steps:

[0019] Conduct a correlation analysis between the several microseismic parameters selected in step S2 and the rock mass instability level, and select the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate, and apparent volume rate as the rock mass instability classification early warning evaluation indicators;

[0020] Among them, the calculation formula for microseismic energy E is where ρ is the rock density, c is the wave velocity, R is the microseismic signal propagation distance, V(f) is the source signal velocity spectrum, and f is the microseismic signal frequency;

[0021] The calculation formula for apparent volume V is where M0 is the seismic moment and μ is the rock mass shear modulus;

[0022] The calculation formula for the event rate NR is where N is the number of microseismic events occurring within a specific time, A is the area or volume of the monitoring area, and T is the length of the specific time;

[0023] The calculation formula for the energy rate ER is where EE is the total energy release within a specific time;

[0024] The calculation formula for the apparent volume rate VR is where V is the apparent volume of the monitoring area.

[0025] Based on the classification enhancement algorithm and the genetic algorithm, construct a primary model for rock mass instability classification and early warning in step S5, including the following steps:

[0026] Use the genetic algorithm to optimize the hyperparameters of the classification enhancement algorithm;

[0027] Take the optimized hyperparameters as the hyperparameters of the classification enhancement algorithm;

[0028] Construct a primary model for rock mass instability classification and early warning according to the obtained classification enhancement algorithm.

[0029] The described step S5 specifically includes the following steps:

[0030] Create an initial population: The initial population is a set of chromosomes, and each chromosome contains a set of hyperparameters of the classification enhancement algorithm;

[0031] Calculate the fitness: Calculate the fitness of the current population;

[0032] Selection, crossover, and mutation: According to the fitness of the current population, select several individuals with the top rankings for selection, crossover, and mutation;

[0033] Judge termination: If the number of iterations reaches the set value, or the difference in fitness calculated twice in succession is less than the set threshold, then stop the iteration; Take the hyperparameters corresponding to the individual with the optimal fitness obtained as the hyperparameters of the classification enhancement algorithm;

[0034] For the dataset of microseismic events, where (N, E, V, NR, ER, VR) is the feature vector, y i is the target vector;

[0035] The initial classification enhancement algorithm model with optimal hyperparameters obtained by using the genetic algorithm is expressed as

[0036] F0(x) = argmin c ∑ i L(y i , c)

[0037] In the formula, L(yi , c) is the loss function of the classification boosting algorithm; y i is the target vector, representing the rock mass instability level; c is the parameter of the initialized model;

[0038] Iterate the classification boosting algorithm model. In the t-th iteration, calculate the residual of each seismic parameter:

[0039]

[0040] In the formula, r it is the residual of each seismic parameter in the t-th round; F(x i ) is the predicted value of the model for the input x i ; is the partial derivative of the loss function with respect to the predicted value of the model;

[0041] Train a weak learner to fit the residual, expressed as

[0042] h t (x) = argmin h ∑ i (r it - h(x i )) 2

[0043] In the formula, h t (x) is the weak learner trained in the t-th round; h(x i ) is the predicted value of the weak learner for the input x i ; h is the parameter of the weak learner;

[0044] Then update the classification boosting algorithm model, expressed as

[0045] F i (x) = F i-1 (x) + αh t (x)

[0046] In the formula, F i (x) is the classification boosting algorithm model updated in the t-th round; α is the learning rate;

[0047] Finally, obtain a classification boosting algorithm model with the selected rock mass instability classification warning evaluation index as the input and the rock mass instability classification prediction result as the output, as the primary model for rock mass instability classification warning.

[0048] The present invention also provides a system for implementing the rock mass instability grading and early warning method based on microseismic feature perception and classification enhancement algorithm, including a data acquisition module, an index selection module, an index evaluation module, a training set construction module, a model construction module, a model training module, and a risk prediction module; the data acquisition module, the index selection module, the index evaluation module, the training set construction module, the model construction module, the model training module, and the risk prediction module are connected in series in sequence; the data acquisition module is used to obtain the rock mass instability case data information of different geotechnical engineering projects and upload the data information to the index selection module; the index selection module is used to select several microseismic parameters as candidate evaluation indicators for rock mass instability risk according to the received data information and upload the data information to the index evaluation module; the index evaluation module is used to perform a correlation analysis on the selected several microseismic parameters and the rock mass instability level according to the received data information to obtain the rock mass instability grading and early warning evaluation indicators and upload the data information to the training set construction module; the training set construction module is used to construct a training data set based on the obtained rock mass instability grading and early warning evaluation indicators according to the received data information and upload the data information to the model construction module; the model construction module is used to construct a primary rock mass instability grading and early warning model based on the classification enhancement algorithm and the genetic algorithm according to the received data information and upload the data information to the model training module; the model training module is used to train the constructed primary rock mass instability grading and early warning model with the obtained training data set according to the received data information to obtain the rock mass instability grading and early warning model and upload the data information to the risk prediction module; the risk prediction module is used to complete the rock mass instability grading and early warning based on microseismic feature perception and classification enhancement algorithm according to the received data information and the obtained rock mass instability grading and early warning model.

[0049] The rock mass instability grading and early warning method and system based on microseismic feature perception and classification enhancement algorithm provided by the present invention select evaluation indicators through rock mass instability case data, and train and use the rock mass instability grading and early warning model based on the classification enhancement algorithm optimized by the genetic algorithm. Therefore, the present invention can not only realize the rock mass instability grading and early warning based on microseismic feature perception and classification enhancement algorithm, but also has higher reliability and better accuracy. Brief Description of the Drawings

[0050] Figure 1 It is a schematic flow chart of the method of the present invention.

[0051] Figure 2 It is a schematic diagram of performance indicators of the comparative example of the method of the present invention.

[0052] Figure 3 It is a schematic diagram of the functional modules of the system of the present invention. Detailed Embodiments

[0053] As Figure 1 shown in the following is the schematic flow chart of the method of the present invention: The rock mass instability grading early warning based on microseismic feature perception and classification enhancement algorithm provided by the present invention includes the following steps:

[0054] S1. Obtain the rock mass instability case data information of different geotechnical engineering projects. Some sample data are shown in Table 1;

[0055] Table 1 Schematic table of some sample data

[0056]

[0057] S2. According to the data information obtained in step S1, select several microseismic parameters as candidate evaluation indicators for rock mass instability risk; specifically, it includes the following steps:

[0058] Determine that the rock mass instability levels include no instability, slight instability, moderate instability, and strong instability;

[0059] The selected candidate evaluation indicators for rock mass instability include the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate, apparent volume rate, apparent stress, moment magnitude, source radius, lithology, initial in-situ stress, etc.;

[0060] S3. Conduct a correlation analysis on the several microseismic parameters selected in step S2 and the rock mass instability levels to obtain the rock mass instability grading early warning evaluation indicators; specifically, it includes the following steps:

[0061] Conduct a correlation analysis on the several microseismic parameters selected in step S2 and the rock mass instability levels:

[0062] Except that there is a strong positive correlation between microseismic energy and energy rate, apparent volume and apparent volume rate, and the number of microseismic events and event rate, there is also a certain correlation between other parameters, and there is an obvious positive correlation between each microseismic parameter and the rock mass instability levels. The larger the Spearman coefficient value, the greater the influence of this feature on the rock mass instability levels. Among them, the larger the correlation coefficient between the number of microseismic events and the rock mass instability levels, the strongest positive correlation;

[0063] Therefore, select the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate, and apparent volume rate as the rock mass instability grading early warning evaluation indicators;

[0064] Among them, the calculation formula of microseismic energy E is where ρ is the rock density, c is the wave velocity, R is the microseismic signal propagation distance, V(f) is the source signal velocity spectrum, and f is the microseismic signal frequency;

[0065] The calculation formula of apparent volume V is where M0 is the seismic moment and μ is the rock mass shear modulus;

[0066] The calculation formula for the event rate NR is where N is the number of microseismic events occurring within a specific time, A is the area or volume of the monitoring area, and T is the length of the specific time;

[0067] The calculation formula for the energy rate ER is where EE is the total amount of energy released within a specific time;

[0068] The calculation formula for the apparent volume rate VR is where V is the apparent volume of the monitoring area;

[0069] These several evaluation indicators are closely related to the microcrack activities during the formation process of rock mass instability disasters, comprehensively reflecting the total rupture time, strength, and deformation accumulation of the rock mass, being able to better characterize the development trend of rock mass instability, reflect the stress state and rupture situation of the rock mass, and thus evaluate the development trend and severity of rock mass instability in real time;

[0070] S4. Based on the rock mass instability classification and early warning evaluation indicators obtained in step S3, construct a training data set based on the data information obtained in step S1;

[0071] During specific implementation, random sampling is adopted. The 103 groups of sample data are divided into a training set and a test set. Among them, the training set accounts for 8 parts and the test set accounts for 2 parts. That is, 82 groups are randomly selected from the 103 groups of sample data as the training set samples during the establishment process of the evaluation model to determine the optimization parameters and model structure, and the remaining 21 groups are used as the validation set samples to evaluate the model performance. The two groups of data are independent of each other, have no intersection, and are both representative;

[0072] S5. Based on the classification enhancement algorithm and genetic algorithm, construct a primary model for rock mass instability classification and early warning; including the following steps:

[0073] Adopt the genetic algorithm to optimize the hyperparameters of the classification enhancement algorithm;

[0074] Take the optimized hyperparameters as the hyperparameters of the classification enhancement algorithm;

[0075] According to the obtained classification enhancement algorithm, construct a primary model for rock mass instability classification and early warning;

[0076] During specific implementation, it includes the following steps:

[0077] Create an initial population: The initial population is a group of chromosomes, and each chromosome contains a group of hyperparameters of the classification enhancement algorithm;

[0078] Calculate the fitness: Calculate the fitness of the current population;

[0079] Selection, crossover, and mutation: Select the top several individuals according to the fitness of the current population for selection, crossover, and mutation;

[0080] Termination judgment: If the number of iterations reaches the set value, or the difference in fitness calculated between two adjacent times is less than the set threshold, stop the iteration; Use the hyperparameters corresponding to the individual with the optimal fitness obtained as the hyperparameters of the classification enhancement algorithm;

[0081] For the microseismic event dataset, where (N, E, V, NR, ER, VR) is the feature vector, y i is the target vector (i.e., the predicted rock mass instability level);

[0082] The initial classification enhancement algorithm model with optimal hyperparameters obtained by the genetic algorithm is denoted as

[0083] F0(x) = argmin c ∑ i L(y i , c)

[0084] where L(y i , c) is the loss function of the classification enhancement algorithm; y i is the target vector, representing the rock mass instability level; c is the parameter of the initialized model;

[0085] Iterate the classification enhancement algorithm model. In the t-th iteration, calculate the residual of each seismic parameter:

[0086]

[0087] where r it is the residual of each seismic parameter in the t-th round; F(x i ) is the predicted value of the model for the input x i ; is the partial derivative of the loss function with respect to the predicted value of the model;

[0088] Train a weak learner to fit the residual, denoted as

[0089] h t (x) = argmin h ∑ i (r it - h(x i )) 2

[0090] where h t (x) is the weak learner trained in the t-th round; h(x i ) is the predicted value of the weak learner for the input x i ; h is the parameter of the weak learner;

[0091] Thereby, update the classification enhancement algorithm model, expressed as

[0092] F i (x) = F i-1 (x) + αh t (x)

[0093] In the formula, F i (x) is the classification enhancement algorithm model after the t-th round of update; α is the learning rate;

[0094] Finally, obtain a classification enhancement algorithm model with the selected rock mass instability classification warning evaluation index as the input and the rock mass instability classification prediction result as the output, which is used as the primary rock mass instability classification warning model;

[0095] S6. Use the training data set obtained in step S4 to train the primary rock mass instability classification warning model constructed in step S5 to obtain a rock mass instability classification warning model;

[0096] S7. Use the rock mass instability classification warning model obtained in step S6 to complete the rock mass instability classification warning based on microseismic feature perception and classification enhancement algorithm.

[0097] Compare the method of the present invention with existing schemes (several classification models such as XGBoost (Gradient Boosting Decision Tree), RF (Random Forest), Logistics Regression, BP neural network, etc.) on the test set, and use several indicators such as accuracy, precision, recall rate, F1 value, and AUC value for comprehensive performance comparison. The comparison data is as Figure 2 and shown in Table 2:

[0098] Table 2 Schematic table of performance comparison

[0099]

[0100] Through Figure 2 and Table 2, it can be seen that the accuracy of the method of the present invention reaches 90%, which is 9% higher than that of the CatBoost model (classification enhancement model), the precision is increased by 5%, the recall rate is increased by 1%, and the F1 value is increased by 4%; therefore, the method of the present invention has good accuracy and evaluation effect;

[0101] Compare the method of the present invention with machine learning algorithms such as CatBoost, Random Forest (RF), XGBoost, Logistics Regression, and BP neural network. The accuracy is increased by 9%, 4%, 9%, 19%, and 14% respectively. Its precision, recall rate, and F1 value are all higher than those of other models, and the performance is excellent.

[0102] Such asFigure 3 The following is a schematic diagram of the functional modules of the system of the present invention: The system for implementing the method for grading and early warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm disclosed in the present invention includes a data acquisition module, an index selection module, an index evaluation module, a training set construction module, a model construction module, a model training module, and a risk prediction module; the data acquisition module, the index selection module, the index evaluation module, the training set construction module, the model construction module, the model training module, and the risk prediction module are connected in series in sequence; the data acquisition module is used to acquire the data information of rock mass instability cases of different geotechnical engineering projects and upload the data information to the index selection module; the index selection module is used to select several microseismic parameters as candidate evaluation indexes for rock mass instability risk according to the received data information and upload the data information to the index evaluation module; the index evaluation module is used to perform a correlation analysis on the selected several microseismic parameters and the rock mass instability level according to the received data information to obtain the grading and early warning evaluation indexes for rock mass instability and upload the data information to the training set construction module; the training set construction module is used to construct a training data set based on the received data information according to the obtained grading and early warning evaluation indexes for rock mass instability and upload the data information to the model construction module; the model construction module is used to construct a primary model for grading and early warning of rock mass instability based on the received data information, the classification enhancement algorithm, and the genetic algorithm and upload the data information to the model training module; the model training module is used to train the constructed primary model for grading and early warning of rock mass instability by using the obtained training data set to obtain a model for grading and early warning of rock mass instability and upload the data information to the risk prediction module; the risk prediction module is used to complete the grading and early warning of rock mass instability based on microseismic feature perception and classification enhancement algorithm according to the received data information by using the obtained model for grading and early warning of rock mass instability.

Claims

1. A rock mass instability classification early warning method based on microseismic feature perception and classification enhancement algorithm, comprising the following steps: S1. Obtain rock mass instability case data information for different geotechnical engineering projects; S2. Based on the data information obtained in step S1, several microseismic parameters are selected as candidate assessment indicators for rock mass instability risk; S3. Perform correlation analysis on the several microseismic parameters selected in step S2 and the rock mass instability level to obtain the rock mass instability classification warning evaluation index; S4. According to the rock mass instability classification warning evaluation index obtained in step S3, based on the data information obtained in step S1, a training data set is constructed; S5. Based on the classification enhancement algorithm and genetic algorithm, a primary model for rock mass instability classification warning is constructed; the steps include: Genetic algorithm is used to optimize the hyperparameters of the classification enhancement algorithm; The optimized hyperparameters are used as the hyperparameters of the classification enhancement algorithm; According to the obtained classification enhancement algorithm, a primary model for rock mass instability classification warning is constructed; S6. Using the training data set obtained in step S4, the primary model for rock mass instability classification warning constructed in step S5 is trained to obtain a rock mass instability classification warning model; S7. Using the rock mass instability classification warning model obtained in step S6, complete the rock mass instability classification warning based on microseismic feature perception and classification enhancement algorithm.

2. The rock mass instability classification early warning method based on microseismic feature perception and classification enhancement algorithm according to claim 1 is characterized in that Step S2, according to the data information obtained in step S1, selects several microseismic parameters as candidate evaluation indicators for rock mass instability, which specifically includes the following steps: Determine the rock mass instability level including no instability, slight instability, moderate instability and severe instability; The selected candidate evaluation indicators for rock mass instability include the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate, apparent volume rate, apparent stress, moment magnitude, focal radius, lithology, and initial geostress.

3. The rock mass instability classification early warning method based on microseismic feature perception and classification enhancement algorithm according to claim 2 is characterized in that Step S3 performs correlation analysis on the several microseismic parameters selected in step S2 and the rock mass instability grade to obtain the rock mass instability graded early warning assessment index, which specifically includes the following steps: Conduct correlation analysis on the several microseismic parameters selected in step S2 and the rock mass instability grade, and select the number of microseismic events, microseismic energy, apparent volume, event rate, energy rate and apparent volume rate as rock mass instability graded early warning assessment indicators; Among them, the calculation formula of microseismic energy E is: Where ρ is rock density, c is wave velocity, R is the propagation distance of microseismic signal, V(f) is the velocity spectrum of source signal, and f is the frequency of microseismic signal; The calculation formula for the apparent volume V is: Where M0 is the seismic moment and μ is the rock mass shear modulus; The calculation formula of event rate NR is: Where N is the number of microseismic events occurring within a specific time, A is the area or volume of the monitoring area, and T is the length of a specific time; The calculation formula of energy rate ER is: Where EE is the total amount of energy released within a specific time; The calculation formula of apparent volume rate VR is: Where V is the apparent volume of the monitoring area.

4. The rock mass instability classification early warning method based on microseismic feature perception and classification enhancement algorithm according to claim 3 is characterized in that The step S5 specifically includes the following steps: Create an initial population: The initial population is a set of chromosomes, each of which contains a set of hyperparameters of the classification enhancement algorithm; Calculate fitness: calculate the fitness of the current population; Selection, crossover and mutation: According to the fitness of the current population, select the top individuals and perform selection, crossover and mutation; Judgment termination: If the number of iterations reaches the set value, or the fitness difference between two consecutive calculations is less than the set threshold, the iteration is stopped; the hyperparameters corresponding to the individual with the best fitness are used as the hyperparameters of the classification enhancement algorithm; For the microseismic event data set, (N, E, V, NR, ER, VR) is the feature vector, y i is the target vector; The initial classification enhancement algorithm model with optimal hyperparameters obtained by genetic algorithm is expressed as F0(x)=argmin c ∑ i L(y i ,c) Where L(y i ,c) is the loss function of the classification enhancement algorithm; y i is the target vector, indicating the instability level of the rock mass; c is the parameter for initializing the model; The classification enhancement algorithm model is iterated, and in the tth iteration, the residual of each earthquake parameter is calculated: Where r it is the residual of each earthquake parameter in round t; F(x i ) is the model input x i The predicted value of is the partial derivative of the loss function with respect to the model prediction value; Train a weak learner to fit the residual, expressed as h t (x)=argmin h ∑ i (r it -h(x i )) 2 Where h t (x) is the weak learner in the tth round of training; h(x i ) is a weak learner for input x i The predicted value of; h is the parameter of the weak learner; Thus, the classification enhancement algorithm model is updated and expressed as F i (x)=F i-1 (x)+αh t (x) Where F i (x) is the classification enhancement algorithm model after the tth round of update; α is the learning rate; Finally, a classification enhancement algorithm model with the selected rock instability classification early warning evaluation index as input and the rock instability classification prediction result as output is obtained, which serves as the primary model of rock instability classification early warning.

5. A system for implementing the rock mass instability classification early warning method based on microseismic feature perception and classification enhancement algorithm as described in any one of claims 1 to 4, characterized in that It includes a data acquisition module, an indicator selection module, an indicator evaluation module, a training set construction module, a model construction module, a model training module and a risk prediction module; the data acquisition module, the indicator selection module, the indicator evaluation module, the training set construction module, the model construction module, the model training module and the risk prediction module are connected in series in sequence; the data acquisition module is used to obtain data information on rock mass instability cases of different geotechnical engineering projects, and upload the data information to the indicator selection module; The indicator selection module is used to select several microseismic parameters as candidate assessment indicators of rock mass instability risk based on the received data information and the acquired data information, and upload the data information to the indicator assessment module; The index evaluation module is used to perform correlation analysis on several selected microseismic parameters and rock mass instability levels based on the received data information, obtain rock mass instability classification warning evaluation indicators, and upload the data information to the training set construction module; The training set construction module is used to construct a training data set based on the received data information, the obtained rock mass instability classification warning evaluation indicators, and the acquired data information, and upload the data information to the model construction module; The model building module is used to build a primary model for rock mass instability classification warning based on the received data information, classification enhancement algorithm and genetic algorithm, and upload the data information to the model training module; The model training module is used to train the constructed rock mass instability graded early warning primary model based on the received data information and the obtained training data set, obtain the rock mass instability graded early warning model, and upload the data information to the risk prediction module; The risk prediction module is used to complete the rock mass instability classification warning based on microseismic feature perception and classification enhancement algorithm according to the received data information and the obtained rock mass instability classification warning model.

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