A soft rock creep test system and method based on the action of periodic dynamic loads

Abstract

The present invention discloses a soft rock creep test system and method based on the action of periodic dynamic loads. The system includes a test data acquisition and normalization unit, a downhole formation physical and mechanical property parameter feature extraction unit, a periodic dynamic load action clustering unit, a soft rock creep safety test unit, and a soft rock creep parameter update unit, which can achieve the integration of test data at different temperatures in the downhole formation of a drilling platform, the analysis of the physical and mechanical property parameters and mining difficulty of the downhole formation, so as to complete the test of soft rock creep. The present invention can form a feature extraction scheme for the physical and mechanical property parameters of the downhole formation of a general drilling platform, realize the management of the physical and mechanical property parameters of the downhole formation under the action of periodic dynamic loads at different mining depths, the soft rock creep test and evaluation capabilities of the system, ensure the soft rock creep test efficiency of the high drilling platform downhole formation, and improve the safety of downhole mining of the drilling platform.

Description

Technical Field

[0001] The present invention relates to the field of testing at different mining depths, and particularly to a soft rock creep testing system and method based on the action of periodic dynamic loads. Background Art

[0002] In the fields such as oil and gas field exploitation engineering, the creep characteristics of soft rock have an important impact on the long-term stability and safety of engineering. Creep refers to the phenomenon that soft rock gradually deforms over time under the action of continuous stress. The existing soft rock creep testing methods mainly include laboratory testing and field monitoring. Although these methods have revealed the creep behavior of soft rock to a certain extent, there are still many deficiencies when facing the action of complex periodic dynamic loads.

[0003] Currently, laboratory testing methods such as uniaxial compression creep test and triaxial creep test usually apply a constant stress for loading. Although this method can simulate the creep behavior of soft rock under constant stress conditions, in actual engineering, soft rock is often subjected to the action of periodic dynamic loads, and this dynamic stress environment cannot be accurately reflected by the constant stress test. Therefore, the laboratory testing method has certain limitations in simulating the creep behavior under real engineering conditions. In addition, laboratory testing equipment is usually relatively complex, with cumbersome operations, long testing cycles, and high costs, and these factors limit its wide application in actual engineering.

[0004] On the other hand, the field monitoring method installs monitoring equipment in actual engineering to record the deformation and stress changes of soft rock in real time. Although this method can obtain the creep data of soft rock in a real environment, the field monitoring also has some disadvantages. First, the field monitoring equipment is easily damaged in a complex underground environment, with great maintenance difficulty and difficult to guarantee data reliability. Second, the action of periodic dynamic loads is often accompanied by environmental changes and construction interferences, resulting in the monitoring data being affected by multiple factors and difficult to isolate the creep effect of a single factor. In addition, the field monitoring needs to be carried out continuously for a long time, with a long cycle and high cost, which is not conducive to rapid evaluation and decision-making.

[0005] In summary, the existing soft rock creep testing methods have significant deficiencies when dealing with the action of periodic dynamic loads and cannot comprehensively and accurately reflect the creep behavior of soft rock in a complex stress environment. Therefore, there is an urgent need for a soft rock creep testing system and method that can simulate the action of periodic dynamic loads and has efficient and accurate testing capabilities to meet the actual engineering requirements and improve the scientificity and safety of engineering design and oil and gas field exploitation. Summary of the Invention

[0006] The embodiments of the present invention aim to provide a soft rock creep test system and method based on the action of periodic dynamic loads, and to build a soft rock creep monitoring system for the underground formation of a drilling platform, so as to solve the technical problems of poor versatility, low accuracy and efficiency in the existing soft rock creep test methods for the underground formation of a drilling platform.

[0007] To this end, the present invention provides a soft rock creep test system based on the action of periodic dynamic loads, including:

[0008] A test data acquisition and normalization unit, configured to acquire the test data at different temperatures of the underground formation of the drilling platform, and perform normalization processing on the test data to form data with consistent format and integrity;

[0009] An underground formation physical and mechanical property parameter feature extraction unit, configured to use the data with consistent format and integrity and different underground formation physical and mechanical property parameter feature extraction methods in the drilling platform control center to complete the soft rock creep test work in the underground formation of the drilling platform and extract the physical and mechanical property parameters of the underground formation at different mining depths and form abnormal features;

[0010] A periodic dynamic load action clustering unit, configured to analyze and cluster the soft rock creep test work of the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths according to the abnormal features per unit period, and at the same time analyze the mechanical vibration frequencies of different equipment within the periodic dynamic load action at different mining depths to form the analysis and clustering parameters of the periodic dynamic load action at different mining depths;

[0011] A soft rock creep safety test unit, configured to build a soft rock creep model for different mining depths in the underground formation of the drilling platform, and the soft rock creep model includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model and a soft rock creep numerical simulation model; perform soft rock creep tests on the corresponding viscous, elastic, plastic deformations, damage variables and creep behaviors according to the soft rock creep model and the data with consistent format and integrity, and conduct a division of soft rock creep influencing factors, so as to complete the soft rock creep safety test of the underground formation of the drilling platform;

[0012] A soft rock creep parameter update unit, configured to perform modeling and determination of the soft rock creep safety of the underground formation of the drilling platform by using the soft rock creep safety test parameters of the underground formation of the drilling platform with the Burgers algorithm, and update the parameters of the Burgers algorithm by using a multi-layer attention mechanism and convolutional neural network optimization.

[0013] The parameter update of the Burgers algorithm by using a multi-layer attention mechanism and convolutional neural network optimization is expressed as:

[0014]

[0015] Among them, ε(t) represents the updated creep strain, ε0 represents the safety limit value of soft rock creep, σ0 represents the stress periodic dynamic load, W1 represents the mechanical vibration frequency, η1 represents the viscosity coefficient, W2 represents the environmental temperature change variance, e represents the natural constant, Q, K, and V respectively represent the query vector, key vector, and value vector of the attention mechanism, P represents the soft rock creep influencing factor matrix, η2 represents the viscosity coefficient, t represents the unit period, f represents the parameter feature vector of the convolution input, g represents the convolution kernel, and n represents the feature dimension.

[0016] Preferably, for the above-mentioned soft rock creep test system based on periodic dynamic load action, the test data acquisition and normalization unit includes:

[0017] The to-be-tested data acquisition module is used to acquire the to-be-tested data at different temperatures generated by different mining depths of the underground formation of the drilling platform per unit period;

[0018] The data normalization module is used to normalize the to-be-tested data at different temperatures into corresponding physical parameter variables according to the predetermined Log transformation;

[0019] The data missing value processing module is used to process the missing values of the physical parameter variables obtained by normalization to obtain noise-free test data; the missing value processing includes but is not limited to deletion method, interpolation method, and machine learning method processing;

[0020] The periodic dynamic load source tracking module is used to track the data of the fatigue influence degree of different devices from the noise-free test data, and normalize the data of the fatigue influence degree of different devices according to the periodic dynamic load source of the test depth to obtain the device periodic dynamic load source information matching the periodic dynamic load source of the test depth;

[0021] The environmental influence periodic dynamic load information analysis module is used to perform environmental influence analysis on the device periodic dynamic load source information to form different environmental periodic dynamic load source parameter variables as the format-consistent and complete data.

[0022] Preferably, for the above-mentioned soft rock creep test system based on periodic dynamic load action, the underground formation physical and mechanical property parameter feature extraction unit includes:

[0023] The big data edge computing gateway adaptation module is used to preset and adapt the big data edge computing gateway to the mining volume and mining difficulty, and establish an adaptation statistical table;

[0024] The drilling platform control center module is used to adjust the extraction steps and preset the extraction parameters of different underground formation physical and mechanical property parameter feature extraction methods in the drilling platform control center;

[0025] The underground formation physical and mechanical property parameter feature extraction module for different mining depths is used to extract the periodic underground formation physical and mechanical property parameter features for different mining depths according to the underground formation physical and mechanical property parameter feature extraction method, the data with consistent format and integrity, and the adaptation statistical table, and generate the abnormal features of the underground formation physical and mechanical property parameters for different mining depths;

[0026] The underground formation physical and mechanical property parameter feature extraction module is used to analyze the execution status of the soft rock creep test work by the big data edge computing gateway, judge and analyze the soft rock creep test work of the underground formation physical and mechanical property parameters, and generate the abnormal features of the underground formation physical and mechanical property parameters in the soft rock creep test. The abnormal features of the underground formation physical and mechanical property parameters in the soft rock creep test and the abnormal features of the underground formation physical and mechanical property parameters for different mining depths form the abnormal features.

[0027] Preferably, for a soft rock creep test system based on periodic dynamic load action, the periodic dynamic load action clustering unit includes:

[0028] The underground formation physical and mechanical property parameter analysis module is used to locate the rock strata for the abnormal features of the underground formation physical and mechanical property parameters in the soft rock creep test and the abnormal features of the underground formation physical and mechanical property parameters for different mining depths, including but not limited to the stress analysis of the underground formation physical and mechanical property parameters, the temperature analysis of the underground formation physical and mechanical property parameters, and the abnormal location of the underground formation physical and mechanical property parameters;

[0029] The physical and mechanical property parameter classification and processing flow module is used to classify and form the underground formation physical and mechanical property parameter information in the abnormal features of the underground formation physical and mechanical property parameters and the abnormal features of the underground formation physical and mechanical property parameters for different mining depths, and obtain the data processing flow corresponding to the underground formation physical and mechanical property parameter information, and form the reference benchmark of the underground formation physical and mechanical property parameters, the coefficient to be optimized of the underground formation physical and mechanical property parameter calculation model, and the types of noise;

[0030] The periodic dynamic load source module for different mining depths is used to detect the mechanical vibration of the entire periodic dynamic load action for different mining depths, and statistically analyze the periodic dynamic load sources of the equipment for different mining depths to form the analysis clustering parameters of the periodic dynamic load action for different mining depths.

[0031] Preferably, for a soft rock creep test system based on periodic dynamic load action, the soft rock creep safety test unit includes:

[0032] The soft rock creep model management module is used to construct and manage the viscoelastic-plastic model of soft rock creep, the damage creep model of soft rock, and the numerical simulation model of soft rock creep;

[0033] The soft rock creep identification module is used to identify the soft rock creep safety of different mining depths, systems and ground stations according to the test data to be tested at different temperatures, the viscoelastic-plastic model of soft rock creep, the damage creep model of soft rock and the numerical simulation model of soft rock creep, and adopt a preset attention mechanism and identification algorithm to obtain soft rock creep identification parameters;

[0034] The mining difficulty identification module is used to quantitatively evaluate the mining difficulty of the underground strata at different mining depths according to the soft rock creep safety identification parameters and the source of the periodic dynamic load of the equipment mining difficulty, and obtain the mining difficulty identification parameters;

[0035] The soft rock creep influencing factor division module is used to automatically complete the soft rock creep safety identification of different mining depths, systems and ground stations according to the soft rock creep identification parameters and the mining difficulty identification parameters, and form a record of the influencing factors of the soft rock creep test;

[0036] The 3D visualization module of the soft rock creep process is used to 3D visualize the soft rock creep process of the system.

[0037] Preferably, in the above soft rock creep test system based on the action of periodic dynamic load, the soft rock creep parameter update unit includes:

[0038] The record generation module is used to continuously test the working state at different mining depths and the source of the periodic dynamic load of the equipment mining difficulty of the system, and form records of the state at different mining depths and the mining difficulty of the drilling platform, providing a reference basis for subsequent mining and soft rock creep testing;

[0039] The soft rock creep test execution module for the underground strata of the drilling platform is used to determine the soft rock creep safety at different mining depths to obtain soft rock creep safety test parameters, evaluate the duration of different mining depths or soft rock creep grades, and at the same time evaluate the test accuracy of different mining depths for future soft rock creep test work;

[0040] The multi-layer attention mechanism and convolutional neural network optimization module is used to update the parameters of the Burgers algorithm according to the soft rock creep safety at different mining depths, combined with the test accuracy of different mining depths for future soft rock creep test work, and using the multi-layer attention mechanism and convolutional neural network optimization.

[0041] The present invention also provides a soft rock creep test method based on the action of periodic dynamic load, including the following steps:

[0042] Step S100, collect the test data to be tested at different temperatures of the underground strata of the drilling platform, and normalize the test data to form format-consistent and complete data;

[0043] Step S200: Use the format-consistent and complete data and the extraction methods for physical and mechanical property parameters of different underground formations in the drilling platform control center to complete the soft rock creep test work in the underground formation of the drilling platform and extract the physical and mechanical property parameter characteristics of the underground formation at different mining depths, and form abnormal characteristics.

[0044] Step S300: Analytically cluster the soft rock creep test work in the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths based on the abnormal characteristics per unit cycle. At the same time, analyze the mechanical vibration frequencies of different equipment within the periodic dynamic load at different mining depths to form the analytic clustering parameters for the periodic dynamic load at different mining depths.

[0045] Step S400: Model the soft rock creep test work in the underground formation of the drilling platform and the soft rock creep safety at different mining depths to form a soft rock creep model. The soft rock creep model includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model, and a soft rock creep numerical simulation model. According to the soft rock creep model and the format-consistent and complete data, conduct soft rock creep tests on the corresponding viscous, elastic, plastic deformations, damage variables, and creep behaviors, and divide the influencing factors of soft rock creep, thereby completing the soft rock creep safety test of the underground formation of the drilling platform.

[0046] Step S500: Use the soft rock creep safety test parameters of the underground formation of the drilling platform to model the soft rock creep safety of the underground formation of the drilling platform using the Burgers algorithm and identify the soft rock creep safety. And use the multi-layer attention mechanism and convolutional neural network optimization to update the parameters of the Burgers algorithm.

[0047] Preferably, the above-mentioned soft rock creep test method based on periodic dynamic load action:

[0048] The steps of collecting the test data to be measured at different temperatures in the underground formation of the drilling platform and normalizing the test data to form format-consistent and complete data specifically include: collecting the test data to be measured at different temperatures generated at different mining depths in the underground formation of the drilling platform per unit cycle; normalizing the test data to be measured at different temperatures into corresponding physical parameter variables according to the predetermined Log transformation; performing missing value processing on the normalized physical parameter variables to obtain noise-free test data; the missing value processing includes but is not limited to deletion method, interpolation method, and machine learning method processing; tracking the data on the fatigue influence degree of different equipment from the noise-free test data, and normalizing the data on the fatigue influence degree of different equipment according to the source of the periodic dynamic load at the test depth to obtain the equipment periodic dynamic load source information matching the source of the periodic dynamic load at the test depth; performing environmental impact analysis on the equipment periodic dynamic load source information to form different environmental periodic dynamic load source parameter variables as the format-consistent and complete data.

[0049] Using the format-consistent integrity data and the extraction methods for the physical and mechanical property parameters of different underground formations in the drilling platform control center to complete the soft rock creep test work in the underground formations of the drilling platform and the extraction of the physical and mechanical property parameters of the underground formations at different mining depths and form the abnormal features, the specific steps include: presetting the big data edge computing gateway, adapting to the mining volume and mining difficulty, and establishing an adaptation statistical table; realizing the adjustment of the extraction steps and the presetting of extraction parameters for the extraction methods of the physical and mechanical property parameters of different underground formations in the drilling platform control center; according to the extraction methods of the physical and mechanical property parameters of the underground formations, the format-consistent integrity data, and the adaptation statistical table, extracting the physical and mechanical property parameters of the underground formations periodically at different mining depths, and generating the abnormal features of the physical and mechanical property parameters of the underground formations at different mining depths; the big data edge computing gateway analyzes the execution status of the soft rock creep test work, judges and analyzes the soft rock creep test work of the physical and mechanical property parameters of the underground formations, and generates the abnormal features of the physical and mechanical property parameters of the underground formations in the soft rock creep test. The abnormal features of the physical and mechanical property parameters of the underground formations in the soft rock creep test and the abnormal features of the physical and mechanical property parameters of the underground formations at different mining depths form the abnormal features.

[0050] Preferably, a soft rock creep test method based on periodic dynamic load action is as follows:

[0051] The steps of analyzing and clustering the soft rock creep test work of the underground formations of the drilling platform and the physical and mechanical property parameters of the underground formations at different mining depths according to the abnormal features of each unit cycle, and at the same time analyzing the mechanical vibration frequencies of different equipment within the periodic dynamic load action at different mining depths to form the analysis and clustering parameters of the periodic dynamic load action at different mining depths specifically include: positioning the rock formations for the abnormal features of the physical and mechanical property parameters of the underground formations in the soft rock creep test and the abnormal features of the physical and mechanical property parameters of the underground formations at different mining depths, including but not limited to stress analysis of the physical and mechanical property parameters of the underground formations, temperature analysis of the physical and mechanical property parameters of the underground formations, and abnormal positioning of the physical and mechanical property parameters of the underground formations; classifying and forming the physical and mechanical property parameter information in the abnormal features of the physical and mechanical property parameters of the underground formations and the abnormal features of the physical and mechanical property parameters of the underground formations at different mining depths, and obtaining the data processing flow corresponding to the physical and mechanical property parameter information, forming the reference benchmark of the physical and mechanical property parameters of the underground formations, the undetermined coefficients of the calculation model of the physical and mechanical property parameters of the underground formations, and the types of noise; detecting the mechanical vibration of the entire periodic dynamic load action at different mining depths, and statistically analyzing the sources of the periodic dynamic load of the equipment at different mining depths to form the analysis and clustering parameters of the periodic dynamic load action at different mining depths.

[0052] A soft rock creep model is formed by modeling different mining depths in the underground formation of a drilling platform. The soft rock creep model includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model, and a soft rock creep numerical simulation model. According to the soft rock creep model and the format-consistent and complete data, soft rock creep tests are carried out on the corresponding viscous, elastic, plastic deformations, damage variables, and creep behaviors, and the influencing factors of soft rock creep are divided. Furthermore, the steps to complete the soft rock creep safety test of the underground formation of the drilling platform specifically include: constructing and managing the soft rock creep viscoelastic-plastic model, the soft rock damage creep model, and the soft rock creep numerical simulation model; according to the data to be tested at different temperatures, the soft rock creep viscoelastic-plastic model, the soft rock damage creep model, and the soft rock creep numerical simulation model, using a preset attention mechanism and a recognition algorithm to recognize the soft rock creep safety of different mining depths, systems, and ground stations to obtain soft rock creep recognition parameters; according to the soft rock creep safety recognition parameters and the periodic dynamic load sources of equipment mining difficulty, quantitatively evaluate the mining difficulty of the underground formation at different mining depths to obtain mining difficulty recognition parameters; regularly or automatically complete the soft rock creep safety recognition of different mining depths, systems, and ground stations according to the soft rock creep recognition parameters and the mining difficulty recognition parameters, and form a record of the influencing factors of soft rock creep tests; 3D visualize the soft rock creep process of the system.

[0053] Preferably, a soft rock creep test method based on the action of periodic dynamic loads is as follows:

[0054] The steps of modeling the soft rock creep safety of the underground formation of the drilling platform by using the Burgers algorithm and recognizing the soft rock creep safety by using the soft rock creep safety test parameters of the underground formation of the drilling platform, and updating the parameters of the Burgers algorithm by using a multi-layer attention mechanism and convolutional neural network optimization specifically include:

[0055] Continuously test the working states at different mining depths and the periodic dynamic load sources of the system equipment mining difficulty, form records of the states at different mining depths and the mining difficulty of the drilling platform, and provide a reference basis for subsequent mining and soft rock creep tests;

[0056] Determine the soft rock creep safety at different mining depths to obtain soft rock creep safety test parameters, evaluate the duration of different mining depths or soft rock creep grades, and at the same time evaluate the test accuracy of different mining depths for future soft rock creep test work;

[0057] According to the soft rock creep safety at different mining depths, combined with the test accuracy of different mining depths for future soft rock creep test work, and use a multi-layer attention mechanism and convolutional neural network optimization to update the parameters of the Burgers algorithm.

[0058] Beneficial effects:

[0059] A soft rock creep test system and method based on the action of periodic dynamic loads proposed by the present invention can form a scheme for extracting the physical and mechanical property parameter characteristics of the underground formation of a general drilling platform, realizing the management of the physical and mechanical property parameters of the underground formation under periodic dynamic loads at different mining depths, systematically extracting the physical and mechanical property parameter characteristics of different underground formations, and testing the soft rock creep during the formation change process, ensuring the soft rock creep test efficiency of the high drilling platform underground formation, and then achieving the effective analysis of the drilling platform underground formation. At the same time, the present invention uses a multi-layer attention mechanism and a convolutional neural network to optimize the model, ensuring the accuracy of the Burgers algorithm for soft rock creep testing and improving the safety of underground mining of the drilling platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the composition of a soft rock creep test system based on the action of periodic dynamic loads according to the present invention;

[0061] Figure 2 It is a schematic flow diagram of a soft rock creep test method based on the action of periodic dynamic loads according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0063] An embodiment of the present invention provides a soft rock creep test system based on the action of periodic dynamic loads, as Figure 1 shown, including:

[0064] A test data acquisition and normalization unit, which is used to integrate, normalize and process the data to be tested at different temperatures, form complete data with a consistent format for use by the soft rock creep test system, and is the information layer of the entire soft rock creep test system.

[0065] An underground formation physical and mechanical property parameter feature extraction unit, which is used to complete the soft rock creep test work and extract the physical and mechanical property parameter characteristics of the underground formation at different mining depths based on the complete data with a consistent format and different customizable data processing flows of the underground formation physical and mechanical property parameters in the drilling platform control center. This unit realizes the flexible identification of the physical and mechanical property parameter characteristics of the underground formation by using the complete data with a consistent format and a formation change judgment algorithm on the basis of the traditional underground formation physical and mechanical property parameter feature extraction technology.

[0066] The periodic dynamic load acting clustering unit is used for soft rock creep testing work, analyzing and clustering the physical and mechanical property parameters of underground strata at different mining depths and the periodic dynamic loads acting at different mining depths. Through manual input and automatic integration, the periodic dynamic load acting tests at different mining depths are completed. At the same time, a platform for analyzing the physical and mechanical property parameters of underground strata is provided to complete the analysis and processing of the physical and mechanical property parameters of underground strata.

[0067] The soft rock creep safety testing unit is used to form a soft rock creep model for soft rock creep modeling of different mining depths and systems. The soft rock creep model includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model, and a soft rock creep numerical simulation model. Based on the soft rock creep model and the format-consistent and complete data, soft rock creep tests are carried out on the corresponding viscous, elastic, plastic deformations, damage variables, and creep behaviors, and the influencing factors of soft rock creep are divided to complete the soft rock creep safety testing of the system.

[0068] The soft rock creep parameter updating unit is used to complete the system record generation and the soft rock creep testing execution of the underground strata of the drilling platform through the soft rock creep safety of the system. And aiming at the soft rock creep problems of the system, the parameters of the Burgers algorithm are updated by using a multi-layer attention mechanism and a convolutional neural network optimization, which is the application layer of soft rock creep information.

[0069] The parameters of the Burgers algorithm are updated by using a multi-layer attention mechanism and a convolutional neural network optimization, and the expression is:

[0070]

[0071] Among them, ε(t) represents the updated creep strain, ε0 represents the soft rock creep safety limit value, σ0 represents the stress periodic dynamic load, W1 represents the mechanical vibration frequency, η1 represents the viscous coefficient, W2 represents the environmental temperature change variance, e represents the natural constant, Q, K, and V respectively represent the query vector, key vector, and value vector of the attention mechanism, P represents the soft rock creep influencing factor matrix, η2 represents the viscous coefficient, t represents the unit period, f represents the parameter feature vector of the convolutional input, g represents the convolutional kernel, and n represents the feature dimension.

[0072] Among them, the physical and mechanical property parameters include: density, the mass density of the soft rock material, usually expressed in kg / m 3 It is represented. Porosity, the ratio of the pore volume inside the soft rock to the total volume, usually expressed as a percentage. Water content, the ratio of the mass of water in the soft rock to the dry mass, usually expressed as a percentage. Compressive strength, the maximum stress when the soft rock reaches failure in a uniaxial compression test. Elastic modulus, the stress-strain relationship of the soft rock in the elastic deformation stage, indicating the stiffness of the material. Poisson's ratio, the ratio of the lateral strain to the axial strain when the soft rock is compressed.

[0073] The above solution provided by this embodiment can form a solution for extracting the physical and mechanical property parameter characteristics of the underground formation of a general drilling platform, realizing the management of the physical and mechanical property parameters of the underground formation under periodic dynamic loads at different mining depths, the system's soft rock creep test and evaluation capabilities, and ensuring the soft rock creep test efficiency of the underground formation of the high drilling platform.

[0074] Preferably, the test data acquisition and normalization unit includes a to-be-tested data acquisition module, a data normalization module, a data missing value processing module, a periodic dynamic load source tracking module, and an environmental impact periodic dynamic load information analysis module.

[0075] Specifically:

[0076] The to-be-tested data acquisition module is used to collect the to-be-tested data at different temperatures generated by each system of the underground formation of the drilling platform per unit cycle. In some solutions of the present invention example, the to-be-tested data acquisition module integrates the status information of different mining depths through wireless signals, and the status information is encapsulated using a lossless compression information structure protocol.

[0077] The data normalization module is used to deformat the to-be-tested data at different temperatures integrated by the to-be-tested data acquisition module according to the Log transformation and normalize it into corresponding physical variables. In the present invention example, the data normalization module normalizes the lossless compression information structure, and corresponds the normalized parameters to different mining depth monitoring protocols one by one according to different mining depth types to obtain their physical quantities; at the same time, the data normalization module uses the cloud to complete the normalization of the soft rock creep test work plan information, soft rock creep test work process information, etc. in JSON format.

[0078] The data missing value processing module is used to perform missing value processing on the normalized physical variables by methods such as deletion method, interpolation method, and machine learning method to obtain standardized test data; in the present invention example, the deletion method includes deleting records: directly deleting the records (rows) containing missing values, which is applicable to the case where the proportion of missing values is very small and randomly distributed. Deleting features: deleting features (columns) containing a large number of missing values, which is applicable to the case where the proportion of missing values of a certain feature is very high and has little impact on parsing.

[0079] The interpolation method includes mean interpolation: replacing the missing value with the mean value of this feature, which is applicable to numerical data. Median interpolation: replacing the missing value with the median value of this feature, which is applicable to numerical data, especially in the case of outliers. Mode interpolation: replacing the missing value with the mode of this feature, which is applicable to categorical data. Constant interpolation: replacing the missing value with a specified constant value, which is applicable to filling special values in specific scenarios.

[0080] Machine learning methods include predictive model imputation: constructing a predictive model (such as linear regression, decision tree, random forest, etc.) to predict missing values using other features. Autoencoders: using a neural network model for missing value imputation by training an autoencoder to reconstruct the data..

[0081] The periodic dynamic load source tracking module is used to select the equipment periodic dynamic load source information from the noise-free test data, normalize multiple types of parameter variables as needed, and calculate the equipment periodic dynamic load source information.

[0082] The environmental impact periodic dynamic load information analysis module is used to analyze the environmental impact of the tracked periodic dynamic load source information to form standardized periodic dynamic load source parameter variables; in the example of the present invention, different state parameter variables at the same moment are formed into different dimension coefficients according to the dimension.

[0083] Preferably, the downhole formation physical and mechanical property parameter feature extraction unit includes a big data edge computing gateway adaptation module, a drilling platform control center module, a downhole formation physical and mechanical property parameter feature extraction module for different mining depths, and a downhole formation physical and mechanical property parameter feature extraction module. Among them:

[0084] The big data edge computing gateway adaptation module is used for big data edge computing gateway configuration and scheduling to complete the supervision of the mining volume and mining difficulty, and establish an adaptation statistical table.

[0085] The drilling platform control center module is used to implement the extraction step adjustment and extraction parameter presetting of different downhole formation physical and mechanical property parameter feature extraction methods in the drilling platform control center, such as the preset values required for downhole formation physical and mechanical property parameter feature extraction, the coefficients to be optimized in the downhole formation physical and mechanical property parameter calculation model, and the types of noise, etc.

[0086] The downhole formation physical and mechanical property parameter feature extraction module for different mining depths is used to extract the periodic downhole formation physical and mechanical property parameter features for different mining depths according to the preset values, the coefficients to be optimized in the downhole formation physical and mechanical property parameter calculation model, the types of noise, and the adaptation statistical table, and generate the downhole formation physical and mechanical property parameter feature extraction records for different mining depths; when it exceeds the preset value range, it is determined as the downhole formation physical and mechanical property parameters for different mining depths; in necessary cases, it can also initiate a big data edge computing gateway supervision application, and further combine the adaptation statistical table to complete the downhole formation physical and mechanical property parameter feature extraction for different mining depths.

[0087] The module for extracting characteristics of physical and mechanical property parameters of underground strata is used to analyze the execution status of soft rock creep test work by the big data edge computing gateway. For the soft rock creep test work of discovering physical and mechanical property parameters of underground strata, further judgment and analysis are carried out through supervision by the big data edge computing gateway, etc., and a record of extracting characteristics of physical and mechanical property parameters of underground strata for soft rock creep test is generated. In the example of the present invention, for the extraction of characteristics of physical and mechanical property parameters of underground strata in soft rock creep test, first, different inspection items such as the execution steps of soft rock creep test work, rock stratum positioning, reception situation, test point alarm situation, data recording, etc. in the soft rock creep test work are analyzed and inspected by the type of noise, and a reception situation record is generated; for the soft rock creep test work with abnormal reception, the coefficient to be optimized of the calculation model of physical and mechanical property parameters of underground strata is calculated, and the reason for the abnormality is further analyzed and judged. In necessary cases, supervision by the big data edge computing gateway is initiated, and the reason for physical and mechanical property parameters of underground strata is located and analyzed through the adaptation statistical table, and a record of extracting characteristics of physical and mechanical property parameters of underground strata for soft rock creep test is generated.

[0088] Preferably, the periodic dynamic load action clustering unit includes a module for analyzing physical and mechanical property parameters of underground strata, a module for classifying and processing physical and mechanical property parameters and their processes, and a module for periodic dynamic load sources at different mining depths. Specifically:

[0089] The module for analyzing physical and mechanical property parameters of underground strata is used for the interaction between soft rock creep test work and information on physical and mechanical property parameters of underground strata at different mining depths, including information such as stress analysis of physical and mechanical property parameters of underground strata, temperature analysis of physical and mechanical property parameters of underground strata, and abnormal positioning of physical and mechanical property parameters of underground strata.

[0090] The module for classifying and processing physical and mechanical property parameters and their processes is used to count, analyze, and refine information on physical and mechanical property parameters of underground strata and data processing processes, and form a reference benchmark for physical and mechanical property parameters of underground strata, a coefficient to be optimized of the calculation model of physical and mechanical property parameters of underground strata, and the type of noise. In the example of the present invention, the module for classifying and processing physical and mechanical property parameters and their processes counts and analyzes the disposal information of physical and mechanical property parameters of underground strata recorded in the module for analyzing physical and mechanical property parameters of underground strata, and forms a knowledge set that is closely related to specific operations and can be used for overall system analysis and judgment. For typical physical and mechanical property parameters of underground strata, their knowledge set can also be summarized into a reference benchmark for physical and mechanical property parameters of underground strata and a coefficient to be optimized of the calculation model of physical and mechanical property parameters of underground strata for use by the unit for extracting characteristics of physical and mechanical property parameters of underground strata.

[0091] The module for periodic dynamic load sources at different mining depths is used for mechanical vibration detection of the entire periodic dynamic load action from commissioning, upgrading, multi-layer attention mechanism and convolutional neural network to scrapping at different mining depths. Meanwhile, it statistically analyzes the formation change state, mining difficulty, mutation time nodes of underground formation physical and mechanical property parameters, etc. of different mining depths, which are the sources of the equipment's periodic dynamic load. In the example of the present invention, the module for periodic dynamic load sources at different mining depths automatically processes commissioning, upgrading, multi-layer attention mechanism and convolutional neural network at different mining depths, and meanwhile statistically analyzes information such as the formation change time at different mining depths, the frequency of underground formation physical and mechanical property parameters, and the reasons for underground formation physical and mechanical property parameters, and regularly generates records of periodic dynamic load sources at different mining depths.

[0092] Preferably, the soft rock creep safety test unit includes a soft rock creep model management module, a soft rock creep identification module, a mining difficulty identification module, a soft rock creep influencing factor division module, and a 3D visualization module for the soft rock creep process. Specifically:

[0093] The soft rock creep model management module is used to construct and manage the viscoelastic-plastic model of soft rock creep, the damage creep model of soft rock, and the numerical simulation model of soft rock creep. In the example of the present invention, the test points at different mining depths are used to construct a model in the form of coefficients to be optimized for the calculation model of underground formation physical and mechanical property parameters by category and module. The relationship between each node value and the theoretically preset value is calculated to obtain the transfer node to the upper-level node, which is the soft rock creep model at different mining depths. The viscous, elastic, plastic deformation, damage variable, and numerical simulation model of soft rock creep are based on the soft rock creep model at different mining depths, and through comprehensive system and in-station architecture design, the coefficients to be optimized for the calculation model of underground formation physical and mechanical property parameters are their soft rock creep models.

[0094] The soft rock creep identification module is used for comprehensive analysis based on the data to be tested at different temperatures and the soft rock creep model, etc., and adopts a suitable attention mechanism and identification algorithm to evaluate the soft rock creep safety of different mining depths, systems, and underground formations of the drilling platform. In the example of the present invention, the soft rock creep model constructed by the soft rock creep model management module is combined with the data to be tested at different temperatures to complete the evaluation of soft rock creep safety. The soft rock creep safety of the system is divided into primary creep, secondary creep, tertiary creep, and quaternary creep, where: Characteristics of primary creep (Grade I creep): Under low stress conditions, the creep deformation rate of soft rock is extremely slow and can basically be ignored. Applicable conditions: Applicable to those soft rock types with relatively high strength and stable structure. Mining impact: Has little impact on long-term stability and usually does not require special treatment measures.

[0095] Characteristics of secondary creep (Level II creep): Under medium stress conditions, the creep deformation rate of soft rock is relatively slow, and the deformation mainly occurs in the initial stage and then tends to be stable. Applicable conditions: Applicable to those types of soft rock that exhibit a certain degree of creep under medium stress but do not quickly become unstable. Mining impact: It has a certain impact on long-term stability, and appropriate design and support measures need to be considered to control deformation.

[0096] Characteristics of tertiary creep (Level III creep): Under high stress conditions, the creep deformation rate of soft rock is relatively fast, and the amount of deformation is large. It may tend to be stable for a period of time, but may still continue to deform in the long term. Applicable conditions: Applicable to those types of soft rock that will undergo obvious creep deformation under high stress. Mining impact: It has a significant impact on long-term stability. Detailed creep analysis is required, and effective support and reinforcement measures need to be taken.

[0097] Characteristics of quaternary creep (Level IV creep): Under very high stress conditions, the creep deformation rate of soft rock is extremely fast, and the amount of deformation is large. It is easy to enter the accelerated creep stage and may ultimately lead to the failure of the rock mass. Applicable conditions: Applicable to those types of soft rock that will quickly become unstable under very high stress. Mining impact: It has a great impact on long-term stability. Strong support and reinforcement measures must be taken, and continuous monitoring and maintenance are required.

[0098] The mining difficulty determination module is used to quantitatively evaluate the mining difficulty of a drilling platform at different mining depths, systems, and downhole formations of the drilling platform according to the periodic dynamic load sources of the mining difficulty of the system equipment.

[0099] The soft rock creep influence factor division module is used to automatically organize and complete system supervision according to the soft rock creep evaluation and mining difficulty determination parameters, and form a record of the influencing factors of the soft rock creep test. In the example of the present invention, when the soft rock creep influence factor division module detects a change in the soft rock creep safety of the system or the mining difficulty of the drilling platform, it will automatically initiate corresponding supervision and form a soft rock creep test record in combination with the soft rock creep model.

[0100] The 3D visualization module of the soft rock creep process is used to 3D visualize the soft rock creep process of the system in the form of graphs and tables.

[0101] Preferably, the soft rock creep parameter update unit includes a record generation module, a soft rock creep test execution module for the downhole formation of the drilling platform, and a multi-layer attention mechanism and convolutional neural network optimization module. More specifically:

[0102] The record generation module is used to continuously test the working status at different mining depths and the periodic dynamic load sources of the mining difficulty of the system equipment, and form a longitudinal record generation of the status at different mining depths and the mining difficulty of the drilling platform.

[0103] The downhole formation soft rock creep test execution module of the drilling platform is used to determine the soft rock creep safety at different mining depths, evaluate the duration at different mining depths or soft rock creep grades, and at the same time is used to evaluate the test accuracy of future soft rock creep test work. In the example of the present invention, an information-driven downhole formation soft rock creep test execution technology for the drilling platform is adopted, which does not require precise physical models and prior knowledge of different mining depths and systems. Based on the integrated historical information, implicit information is mined through information analysis and processing methods such as machine learning and neural networks for evaluation.

[0104] The multi-layer attention mechanism and convolutional neural network optimization module is used to determine parameters according to the system soft rock creep and mining difficulty, and at the same time, combined with the execution situation of the downhole formation soft rock creep test of the drilling platform, comprehensively judge and give the optimization of the multi-layer attention mechanism and convolutional neural network. In the example of the present invention, the multi-layer attention mechanism and convolutional neural network optimization module collects the system soft rock creep information and the execution parameters of the downhole formation soft rock creep test of the drilling platform generated by the soft rock creep safety test unit per unit cycle, combines the preset noise types, gives decision suggestions for the multi-layer attention mechanism and convolutional neural network, and at the same time, according to the recorded generation and the parameters of the downhole formation soft rock creep test execution, gives early warnings about the possible physical and mechanical property parameters of the downhole formation in advance and provides maintenance suggestions.

[0105] In some embodiments of the present invention, a soft rock creep test method based on periodic dynamic load action is also provided, as Figure 2 shown, which may include the following steps:

[0106] Step S100: Collect the test data at different temperatures of the downhole formation of the drilling platform, and normalize the test data to form data with consistent format and integrity;

[0107] The data with consistent format and integrity formed is used by the soft rock creep test system.

[0108] Step S200: Use the data with consistent format and integrity and the feature extraction methods of different downhole formation physical and mechanical property parameters in the drilling platform control center to complete the soft rock creep test work in the downhole formation of the drilling platform and the feature extraction of the physical and mechanical property parameters of the downhole formation at different mining depths, and form abnormal features;

[0109] Based on the traditional feature extraction technology of downhole formation physical and mechanical property parameters, the flexible identification of downhole formation physical and mechanical property parameter features is realized by using the data with consistent format and integrity and the formation change judgment algorithm.

[0110] Step S300: Analytically cluster the soft rock creep test work of the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths based on the abnormal characteristics per unit cycle. At the same time, analyze the mechanical vibration frequencies of different equipment within the periodic dynamic loads at different mining depths to form the analytic clustering parameters for the periodic dynamic loads at different mining depths.

[0111] Through manual input and automatic integration, complete the tests on the periodic dynamic loads at different mining depths. At the same time, provide an analysis platform for the physical and mechanical property parameters of the underground formation to complete the analysis and processing of the physical and mechanical property parameters of the underground formation.

[0112] Step S400: Model the soft rock creep test work in the underground formation of the drilling platform and the soft rock creep safety at different mining depths to form a soft rock creep model, which includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model, and a soft rock creep numerical simulation model.

[0113] Based on the soft rock creep model and the data with consistent format and integrity, conduct soft rock creep tests on the corresponding viscous, elastic, plastic deformations, damage variables, and creep behaviors, and divide the influencing factors of soft rock creep, thereby completing the soft rock creep safety test of the underground formation of the drilling platform.

[0114] Step S500: Use the soft rock creep safety test parameters of the underground formation of the drilling platform to model the soft rock creep safety of the underground formation of the drilling platform using the Burgers algorithm and identify the soft rock creep safety. Use the multi-layer attention mechanism and convolutional neural network optimization to update the parameters of the Burgers algorithm.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical periodic dynamic load sources; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A soft rock creep test system based on periodic dynamic load, characterized in that: include: A test data acquisition and normalization unit, used to acquire the test data at different temperatures of the underground formation of the drilling platform, and normalize the test data to form data with consistent format and integrity; A downhole formation physical and mechanical property parameter feature extraction unit is used to complete the soft rock creep test work in the downhole formation of the drilling platform and the physical and mechanical property parameter feature extraction of the downhole formation at different mining depths by using the format consistent integrity data and the physical and mechanical property parameter feature extraction method of different downhole formations in the drilling platform control center and form abnormal features, wherein the physical and mechanical property parameters include: density, porosity, water content, compressive strength, elastic modulus, Poisson's ratio; The periodic dynamic load clustering unit is used to analyze and cluster the soft rock creep test work of the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths based on the abnormal characteristics of the unit period. At the same time, it analyzes the mechanical vibration frequencies of different equipment within the periodic dynamic load at different mining depths to form analytical clustering parameters of periodic dynamic load at different mining depths; The soft rock creep safety test unit is used to model different mining depths in the underground formation of the drilling platform to form a soft rock creep model, wherein the soft rock creep model includes a soft rock creep viscoelastic-plastic model, a soft rock damage creep model and a soft rock creep numerical simulation model; according to the soft rock creep model and the format consistent integrity data, the corresponding viscosity, elasticity, plastic deformation, damage variables, and creep behavior are tested for soft rock creep, and the factors affecting soft rock creep are divided, thereby completing the soft rock creep safety test of the underground formation of the drilling platform; The soft rock creep parameter updating unit is used to model the soft rock creep safety of the underground formation of the drilling platform using the Burgers algorithm and identify the soft rock creep safety through the soft rock creep safety test parameters of the underground formation of the drilling platform, and to update the parameters of the Burgers algorithm using the multi-layer attention mechanism and convolutional neural network optimization; The multi-layer attention mechanism and convolutional neural network optimization are used to update the parameters of the Burgers algorithm, and the expression is: in, represents the updated creep strain, Indicates the safety limit value of soft rock creep, represents stress cyclic dynamic load, Indicates the mechanical vibration frequency, represents the viscosity coefficient, represents the variance of ambient temperature change, e represents the natural constant, Q , K , V They represent the query vector, key vector, and value vector of the attention mechanism respectively. P represents the soft rock creep influencing factor matrix. Represents the viscosity coefficient, t represents the unit period, f represents the parameter feature vector of the convolution input, g represents the convolution kernel, and n represents the feature dimension.

2. A soft rock creep testing system based on periodic dynamic load according to claim 1, characterized in that: The test data acquisition and normalization unit comprises: The test data acquisition module is used to collect the test data at different temperatures generated by different mining depths of the underground formation of the drilling platform in a unit period; A data normalization module, used for normalizing the test data at different temperatures into corresponding physical parameter variables according to a predetermined Log transformation; Data missing value processing module, used to process the missing values ​​of normalized physical parameters to obtain noise-free test data; missing value processing includes deletion method, interpolation method and machine learning method processing; A periodic dynamic load source tracking module is used to track the fatigue impact degree data of different equipment from the noise-free test data, and normalize the fatigue impact degree data of different equipment according to the test depth periodic dynamic load source to obtain the equipment periodic dynamic load source information matching the test depth periodic dynamic load source; The environment impact periodic dynamic load information analysis module is used to perform environmental impact analysis on the equipment periodic dynamic load source information to form different environmental periodic dynamic load source parameter variables as the format consistent integrity data.

3. The soft rock creep testing system based on periodic dynamic load according to claim 1 is characterized in that: The underground formation physical and mechanical property parameter feature extraction unit includes: a big data edge computing gateway adaptation module, which is used for the big data edge computing gateway to preset and adapt the mining volume and mining difficulty, and establish an adaptation statistical table; The drilling platform control center module is used to adjust the extraction steps and preset the extraction parameters of the physical and mechanical property parameter feature extraction method of different downhole formations in the drilling platform control center; A module for extracting physical and mechanical property parameters of underground formations at different mining depths, which is used to extract physical and mechanical property parameters of underground formations at different mining depths periodically according to the method for extracting physical and mechanical property parameters of underground formations, the format-consistent integrity data and the adaptation statistical table, and to generate abnormal characteristics of physical and mechanical property parameters of underground formations at different mining depths; The module for extracting the physical and mechanical property parameters characteristics of underground formations is used for the big data edge computing gateway to analyze the execution status of the soft rock creep test work, to judge and analyze the soft rock creep test work for discovering the physical and mechanical property parameters of underground formations, and to generate abnormal characteristics of the physical and mechanical property parameters of underground formations in the soft rock creep test. The abnormal characteristics of the physical and mechanical property parameters of underground formations in the soft rock creep test and the abnormal characteristics of the physical and mechanical property parameters of underground formations at different mining depths form the abnormal characteristics.

4. The soft rock creep testing system based on periodic dynamic load according to claim 1 is characterized in that: The periodic dynamic load action clustering unit comprises: The downhole formation physical and mechanical property parameter analysis module is used to locate the rock formation based on the abnormal characteristics of the downhole formation physical and mechanical property parameters in the soft rock creep test and the abnormal characteristics of the downhole formation physical and mechanical property parameters at different mining depths, including stress analysis of downhole formation physical and mechanical property parameters, temperature analysis of downhole formation physical and mechanical property parameters, and abnormal location of downhole formation physical and mechanical property parameters; The physical and mechanical property parameter classification and processing flow module is used to classify and form the physical and mechanical property parameter abnormal characteristics of the underground formation and the physical and mechanical property parameter information of the underground formation in the physical and mechanical property parameter abnormal characteristics of the underground formation at different mining depths, and obtain the data processing flow corresponding to the physical and mechanical property parameter information of the underground formation, and form the physical and mechanical property parameter reference benchmark of the underground formation, the coefficients to be optimized of the calculation model of the physical and mechanical property parameter of the underground formation, and the noise types; The module of periodic dynamic load sources at different mining depths is used to detect the mechanical vibration of the entire periodic dynamic load at different mining depths, and statistically analyze the periodic dynamic load sources of equipment at different mining depths to form analytical clustering parameters of periodic dynamic load effects at different mining depths.

5. The soft rock creep testing system based on periodic dynamic load according to claim 1 is characterized in that: The soft rock creep safety test unit includes: a soft rock creep model management module, which is used to construct and manage soft rock creep viscoelastic-plastic models, soft rock damage creep models and soft rock creep numerical simulation models; The soft rock creep identification module is used to identify the soft rock creep safety of different mining depths, soft rock creep test systems and ground stations based on the test data at different temperatures, soft rock creep viscoelastic-plastic model, soft rock damage creep model and soft rock creep numerical simulation model, using a preset attention mechanism and identification algorithm to obtain soft rock creep identification parameters; A mining difficulty identification module is used to quantitatively evaluate the mining difficulty of underground formations at different mining depths and obtain mining difficulty identification parameters based on the soft rock creep safety identification parameters and the equipment mining difficulty periodic dynamic load source; The module for classifying influencing factors of soft rock creep is used to automatically complete the soft rock creep safety identification of different mining depths, soft rock creep test systems and ground stations according to the soft rock creep identification parameters and mining difficulty identification parameters, and form a record of influencing factors of soft rock creep test; the module for 3D visualization of soft rock creep process is used to 3D visualize the soft rock creep process of the soft rock creep test system.

6. The soft rock creep testing system based on periodic dynamic load according to claim 1 is characterized in that: The soft rock creep parameter updating unit includes: a record generation module, which is used to continuously test the working status of different mining depths and the periodic dynamic load source of the mining difficulty of the soft rock creep test system equipment, and form records of different mining depths and the mining difficulty of the drilling platform, so as to provide a reference basis for subsequent mining and soft rock creep testing; The soft rock creep test execution module of the underground formation of the drilling platform is used to determine the soft rock creep safety at different mining depths to obtain the soft rock creep safety test parameters, evaluate the duration of different mining depths or soft rock creep levels, and evaluate the test accuracy of different mining depths for future soft rock creep tests; The multi-layer attention mechanism and convolutional neural network optimization module are used to improve the creep safety of soft rock according to different mining depths, and the test accuracy of future soft rock creep testing work combined with different mining depths. The multi-layer attention mechanism and convolutional neural network optimization are used to update the parameters of the Burgers algorithm.

7. A soft rock creep testing system based on periodic dynamic load according to any one of claims 1 to 6, characterized in that: The operation process of the system is realized through a soft rock creep test method based on periodic dynamic load, which includes the following steps: Step S100, collecting data to be tested at different temperatures of the underground formation of the drilling platform, and normalizing the test data to form data with consistent format and integrity; Step S200, using the format consistent integrity data and the method for extracting the physical and mechanical property parameters of different downhole formations in the drilling platform control center to complete the soft rock creep test in the downhole formation of the drilling platform and the physical and mechanical property parameter feature extraction of the downhole formations at different mining depths and form abnormal features; Step S300: Analyze and cluster the soft rock creep test work of the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths according to the abnormal characteristics of the unit cycle, and analyze the mechanical vibration frequencies of different equipment under the periodic dynamic load at different mining depths to form analytical clustering parameters of the periodic dynamic load at different mining depths; Step S400, modeling the soft rock creep test work in the underground formation of the drilling platform and the soft rock creep safety at different mining depths to form a soft rock creep model, wherein the soft rock creep model includes a soft rock creep viscoelastic plastic model, a soft rock damage creep model and a soft rock creep numerical simulation model; Step S500: Perform soft rock creep test on corresponding viscosity, elasticity, plastic deformation, damage variable and creep behavior according to the soft rock creep model and the format consistent integrity data, and divide the influencing factors of soft rock creep, so as to complete the soft rock creep safety test of the downhole formation of the drilling platform; use the Burgers algorithm to model the soft rock creep safety of the downhole formation of the drilling platform and identify the soft rock creep safety through the soft rock creep safety test parameters of the downhole formation of the drilling platform, and use the multi-layer attention mechanism and convolutional neural network optimization to update the parameters of the Burgers algorithm.

8. The method of a soft rock creep testing system based on periodic dynamic load according to claim 7, characterized in that: The steps of collecting the test data at different temperatures of the underground formation of the drilling platform and normalizing the test data to form data with consistent format and integrity specifically include: collecting the test data at different temperatures generated by different mining depths of the underground formation of the drilling platform in a unit period; Normalizing the test data at different temperatures into corresponding physical parameter variables according to a predetermined Log transformation; performing missing value processing on the normalized physical parameter variables to obtain noise-free test data; Missing value processing includes deletion, interpolation and machine learning methods; tracking the fatigue influence data of different equipment from the noise-free test data, normalizing the fatigue influence data of different equipment according to the test depth periodic dynamic load source to obtain the equipment periodic dynamic load source information matching the test depth periodic dynamic load source; Performing environmental impact analysis on the periodic dynamic load source information of the equipment to form periodic dynamic load source parameter variables in different environments as the format-consistent integrity data; The steps of using the format consistent integrity data and the physical and mechanical property parameter feature extraction method of different downhole formations in the drilling platform control center to complete the soft rock creep test work in the downhole formation of the drilling platform and the physical and mechanical property parameter feature extraction of the downhole formations at different mining depths and form abnormal features specifically include: presetting the big data edge computing gateway, adapting the mining volume and mining difficulty, and establishing an adaptation statistical table; Realize the adjustment of extraction steps and preset extraction parameters of the feature extraction method of physical and mechanical property parameters of different downhole formations in the drilling platform control center; According to the method for extracting physical and mechanical property parameters of underground formations, the format consistent integrity data and the adaptation statistical table, periodic physical and mechanical property parameter feature extraction of underground formations at different mining depths is performed, and abnormal features of physical and mechanical property parameters of underground formations at different mining depths are generated; The big data edge computing gateway analyzes the execution status of the soft rock creep test, judges and analyzes the soft rock creep test work for discovering the physical and mechanical property parameters of the downhole formation, and generates abnormal characteristics of the physical and mechanical property parameters of the downhole formation of the soft rock creep test. The abnormal characteristics of the physical and mechanical property parameters of the downhole formation of the soft rock creep test and the abnormal characteristics of the physical and mechanical property parameters of the downhole formation of the different mining depths form the abnormal characteristics.

9. The method of a soft rock creep testing system based on periodic dynamic load according to claim 7, characterized in that: According to the abnormal characteristics of the unit cycle, the soft rock creep test work of the underground formation of the drilling platform and the physical and mechanical property parameters of the underground formation at different mining depths are analyzed and clustered, and at the same time, the mechanical vibration frequencies of different equipment under the periodic dynamic load at different mining depths are analyzed to form the analytical clustering parameters of the periodic dynamic load at different mining depths. The steps specifically include: locating the rock formation based on the abnormal characteristics of the physical and mechanical property parameters of the underground formation in the soft rock creep test and the abnormal characteristics of the physical and mechanical property parameters of the underground formation at different mining depths, including stress analysis of the physical and mechanical property parameters of the underground formation, temperature analysis of the physical and mechanical property parameters of the underground formation, and abnormal positioning of the physical and mechanical property parameters of the underground formation; Classify and form the abnormal characteristics of physical and mechanical property parameters of underground formations and the physical and mechanical property parameter information of underground formations in the abnormal characteristics of physical and mechanical property parameters of underground formations at different mining depths, and obtain the data processing flow corresponding to the physical and mechanical property parameter information of underground formations, so as to form the reference benchmark of physical and mechanical property parameters of underground formations, the coefficients to be optimized of the calculation model of physical and mechanical property parameters of underground formations, and the types of noise; detect the mechanical vibration of the entire periodic dynamic load at different mining depths, and statistically analyze the sources of periodic dynamic loads of equipment at different mining depths to form analytical clustering parameters of periodic dynamic loads at different mining depths; Modeling different mining depths in the underground formation of the drilling platform to form a soft rock creep model, wherein the soft rock creep model includes a soft rock creep viscoelastic plastic model, a soft rock damage creep model and a soft rock creep numerical simulation model; According to the soft rock creep model and the format consistent integrity data, the corresponding viscosity, elasticity, plastic deformation, damage variables, and creep behavior are tested for soft rock creep, and the influencing factors of soft rock creep are divided, and the steps of completing the soft rock creep safety test of the underground formation of the drilling platform specifically include: constructing and managing the soft rock creep viscoelastic-plastic model, the soft rock damage creep model, and the soft rock creep numerical simulation model; According to the test data at different temperatures, the soft rock creep viscoelastic-plastic model, the soft rock damage creep model and the soft rock creep numerical simulation model, the preset attention mechanism and identification algorithm are used to identify the soft rock creep safety of different mining depths, soft rock creep test systems and ground stations to obtain soft rock creep identification parameters; according to the soft rock creep safety identification parameters and the periodic dynamic load source of equipment mining difficulty, the mining difficulty of underground formations at different mining depths is quantitatively evaluated and the mining difficulty identification parameters are obtained; Regularly or according to the soft rock creep identification parameters and mining difficulty identification parameters, the soft rock creep safety identification of different mining depths, soft rock creep test systems and ground stations is automatically completed, and a record of influencing factors of soft rock creep testing is formed; the soft rock creep process of the soft rock creep test system is visualized in 3D.

10. The method of a soft rock creep testing system based on periodic dynamic load according to claim 7, characterized in that: The soft rock creep safety of the underground formation of the drilling platform is modeled and identified by the Burgers algorithm through the soft rock creep safety test parameters of the underground formation of the drilling platform, and the parameters of the Burgers algorithm are updated by using the multi-layer attention mechanism and convolutional neural network optimization. The specific steps include: continuously testing the working status of different mining depths and the periodic dynamic load source of the mining difficulty of the soft rock creep test system equipment, forming a record of different mining depths and the mining difficulty of the drilling platform, providing a reference for subsequent mining and soft rock creep testing; Determine the soft rock creep safety at different mining depths to obtain the soft rock creep safety test parameters, evaluate the duration of different mining depths or soft rock creep grades, and evaluate the test accuracy of future soft rock creep tests at different mining depths; based on the soft rock creep safety at different mining depths, combined with the test accuracy of future soft rock creep tests at different mining depths, use the multi-layer attention mechanism and convolutional neural network optimization to update the parameters of the Burgers algorithm.

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