Mining method tunnel stratum response intelligent system and method and block chain storage method

Abstract

The invention relates to the technical field of mining method tunnel construction, and provides a mining method tunnel stratum response intelligent system and method and a block chain storage method.The system comprises a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent prediction module, a result output module, a block chain storage module and a construction control mechanism; the intelligent prediction module adopts a machine learning or deep learning algorithm, such as a support vector machine, a random forest, a neural network and the like, to dynamically predict stratum response, and a block chain storage method ensures safe storage and tracing of construction data. Through accurate prediction, safe storage and intelligent control, the problems that in traditional construction, stratum response prediction is inaccurate, data storage is unsafe, and construction control is not fine are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining-method tunnel construction, and specifically to an intelligent system and method for stratum response in mining-method tunnels, and a blockchain storage method. Background Art

[0002] Tunnel construction using the mining method is a commonly used underground engineering construction method with advantages such as fast construction speed, low cost, and a wide range of applications. However, during tunnel excavation, the prediction and control of stratum response has always been a difficult and key point in construction. Traditional stratum response prediction methods are mainly based on empirical formulas and geological survey data, with limited prediction accuracy and reliability, and it is difficult to meet the needs of modern construction for accurate prediction of stratum response. At the same time, the secure storage and traceability of construction data are also important issues that need to be addressed during construction. Traditional data storage methods have risks such as data loss, tampering, and leakage, which pose hidden dangers to construction safety and quality control. In addition, traditional construction control methods mainly rely on the experience and judgment of operators, making it difficult to achieve refined and intelligent control.

[0003] The rapid development of intelligent algorithms, blockchain technology, and automated control technologies has provided new solutions for mining-based tunnel construction. Intelligent algorithms can dynamically predict ground response based on real-time monitoring and historical data, improving prediction accuracy and reliability. Blockchain technology ensures the secure storage and traceability of construction data, preventing data loss, tampering, and leakage. Automated control technology enables intelligent adjustment of excavation parameters, improving the stability and safety of the construction process. Therefore, applying intelligent algorithms, blockchain technology, and automated control technologies to mining-based tunnel construction holds significant practical significance and application value. Summary of the Invention

[0004] The first aspect of the present invention proposes an intelligent system and method for stratum response of mining-method tunnels, and a blockchain storage method, aiming to solve the problems of inaccurate stratum response prediction, insecure data storage, and imprecise construction control in traditional mining-method tunnel construction. By integrating advanced intelligent algorithms, blockchain technology, and automated control mechanisms, accurate prediction, secure storage, and intelligent control of tunnel stratum response are achieved, thereby improving the efficiency and quality of mining-method tunnel construction and reducing construction risks and costs.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the technical solution of the present invention provides an intelligent system for the response of a mining tunnel stratum, comprising a data acquisition module, a data preprocessing module, a feature extraction module and an intelligent prediction module.

[0006] The data acquisition module is used to collect geological parameters, excavation parameters and construction environment parameters in real time during the tunnel excavation process;

[0007] The data preprocessing module is used to clean, format and normalize the data collected by the data acquisition module to improve data quality and algorithm efficiency;

[0008] The feature extraction module is used to extract key features related to formation response from the data preprocessed by the data preprocessing module;

[0009] The intelligent prediction module is used to dynamically predict the formation response based on machine learning or deep learning algorithms using historical data and real-time data extracted by the feature extraction module.

[0010] Preferably, the system further comprises a model training module and a model evaluation module,

[0011] The model training module is used to train and optimize the algorithm model of the intelligent prediction module;

[0012] The model evaluation module is used to evaluate and verify the performance of the model to ensure the reliability and accuracy of the prediction results.

[0013] Preferably, the system further comprises a result output module for displaying the prediction results of the intelligent prediction module in a graphical or numerical manner.

[0014] Preferably, the system further includes a blockchain storage module for storing the data collected by the data collection module, including a data packaging unit, a timestamp adding unit, a consensus mechanism unit and a data storage unit.

[0015] The data packaging unit is used to package real-time monitoring data, historical data, and predicted data during the tunnel excavation process into data blocks; the timestamp adding unit is used to add a unique timestamp to each data block to ensure the timeliness and non-tamperability of the data; the consensus mechanism unit is used to adopt a consensus mechanism to ensure the correct addition and verification of data blocks on the blockchain, and the consensus mechanism includes proof of work, proof of stake, or practical Byzantine fault tolerance mechanism; the data storage unit is used to store the verified data blocks in the blockchain network to form a distributed ledger to ensure the security and traceability of the data.

[0016] Preferably, the blockchain storage module adopts Bitcoin blockchain or Ethereum blockchain.

[0017] Preferably, the machine learning or deep learning algorithm includes a support vector machine, a random forest or a neural network.

[0018] Preferably, the system further comprises a construction control mechanism, which comprises a control module, an execution module, a monitoring module, an alarm and protection module, a human-computer interaction interface module and a remote monitoring and diagnosis module.

[0019] The control module is used to receive the output results predicted by the intelligent prediction module and adjust the excavation parameters according to the preset control strategy; the execution module is used to control the operating parameters of the tunnel boring machine according to the output results of the control module; the monitoring module is used to monitor the geological parameters, excavation parameters and construction environment parameters during the excavation process in real time; the alarm and protection module is used to issue an alarm signal in time and take corresponding protection measures when an abnormal situation occurs during the excavation process; the human-computer interaction interface module is used to provide a human-computer interaction interface; the remote monitoring and diagnosis module is used to realize the function of remote monitoring of the tunnel excavation process and provide fault diagnosis and remote support services.

[0020] Preferably, the construction control mechanism adopts automated control technology, including PID control, fuzzy control or neural network control, to achieve intelligent adjustment of excavation parameters.

[0021] A second aspect of the present invention provides a method for intelligent response of a mine tunnel stratum, comprising the following steps:

[0022] Real-time collection of geological parameters, excavation parameters and construction environment parameters during tunnel excavation;

[0023] Clean, format and normalize the collected data to complete preprocessing;

[0024] Extract key features related to formation response from preprocessed data;

[0025] Based on machine learning or deep learning algorithms, dynamic prediction of formation response is performed using historical data and real-time data corresponding to key characteristics;

[0026] The predicted results are displayed graphically or numerically.

[0027] A third aspect of the present invention provides a blockchain storage method for mining tunnel stratum data, comprising the following steps:

[0028] S1. Pack real-time monitoring data, historical data, and forecast data into data blocks. Each data block contains multiple records, and each record includes a data type, a timestamp, or a data value.

[0029] S2. Add a unique timestamp to each data block to ensure the time sequence and immutability of the data. The timestamp adopts a unified time standard and is encrypted to prevent tampering.

[0030] S3. A consensus mechanism is used to ensure the correct addition and verification of data blocks on the blockchain. By calculating the workload or verifying the stake, nodes are elected to generate new data blocks and add them to the blockchain. At the same time, other nodes will verify the newly added data blocks to ensure their correctness and legitimacy.

[0031] S4. The verified data blocks are stored in the blockchain network to form a distributed ledger. Each node will save a complete copy of the blockchain to ensure the security and traceability of the data. At the same time, encryption technology and access control mechanisms are used to ensure the privacy and security of the data.

[0032] It can be seen from the above technical solutions that the present invention can realize the accurate prediction, safe storage and intelligent control of tunnel stratum response, so as to improve the efficiency and quality of mining tunnel construction and reduce construction risks and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structure of the intelligent algorithm for tunnel stratum response of the present invention;

[0034] Figure 2 This is a flowchart of the blockchain storage method of the present invention;

[0035] Figure 3 It is a schematic structural diagram of the construction control mechanism of the present invention;

[0036] Figure 4 It is a schematic diagram of the layout of the intelligent sensing device of the present invention;

[0037] Figure 5 This is a diagram showing the formation response prediction results of the present invention;

[0038] Figure 6 This is a control flow chart of tunneling parameter adjustment according to the present invention;

[0039] The symbols in the figure are: intelligent sensing device 1, geological radar 11, stress sensor 12, displacement sensor 13, earth pressure sensor 14, data processing system A, data preprocessing module A1, feature extraction module A2, intelligent prediction module A3, result output module A4, tunnel plane Figure 2 , control module 3, execution module 4, alarm and protection module 5, monitoring module 6, remote monitoring and diagnosis module 7. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings:

[0041] A first aspect of an embodiment of the present invention provides an intelligent system for stratum response in a mining tunnel, comprising a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent prediction module, a model training module, a model evaluation module, a result output module, a blockchain storage module, and a construction control mechanism;

[0042] The data acquisition module is used to collect geological parameters, excavation parameters and construction environment parameters in real time during the tunnel excavation process;

[0043] The data preprocessing module is used to clean, format and normalize the data collected by the data acquisition module to improve data quality and algorithm efficiency;

[0044] The feature extraction module is used to extract key features related to formation response from the data preprocessed by the data preprocessing module;

[0045] The intelligent prediction module is used to dynamically predict the formation response based on machine learning or deep learning algorithms using historical data and real-time data extracted by the feature extraction module;

[0046] The model training module is used to train and optimize the algorithm model of the intelligent prediction module;

[0047] The model evaluation module is used to evaluate and verify the performance of the model to ensure the reliability and accuracy of the prediction results;

[0048] A result output module, used to display the prediction results of the intelligent prediction module in a graphical or numerical manner;

[0049] A blockchain storage module, for storing data collected by the data collection module, comprising a data packaging unit, a timestamp adding unit, a consensus mechanism unit, and a data storage unit. The data packaging unit is used to package real-time monitoring data, historical data, and predicted data during tunnel excavation into data blocks; the timestamp adding unit is used to add a unique timestamp to each data block to ensure the time sequence and immutability of the data; the consensus mechanism unit is used to ensure the correct addition and verification of data blocks on the blockchain using a consensus mechanism, such as proof of work, proof of stake, or practical Byzantine fault tolerance; and the data storage unit is used to store verified data blocks in the blockchain network to form a distributed ledger, ensuring data security and traceability.

[0050] The construction control mechanism includes a control module, an execution module, a monitoring module, an alarm and protection module, a human-computer interaction interface module and a remote monitoring and diagnosis module. The control module is used to receive the output results predicted by the intelligent prediction module and adjust the excavation parameters according to a preset control strategy; the execution module is used to control the operating parameters of the tunnel boring machine according to the output results of the control module; the monitoring module is used to monitor the geological parameters, excavation parameters and construction environment parameters during the excavation process in real time; the alarm and protection module is used to promptly issue an alarm signal and take corresponding protection measures when an abnormal situation occurs during the excavation process; the human-computer interaction interface module is used to provide a human-computer interaction interface; the remote monitoring and diagnosis module is used to realize the function of remote monitoring of the tunnel excavation process and provide fault diagnosis and remote support services.

[0051] For example, the working principle of the mine tunnel stratum response intelligent system is as follows:

[0052] like Figure 1 、 Figure 4 As shown, first of all, during the tunnel excavation process, Figure 2 An intelligent sensing device 1 is arranged on the top, and the intelligent sensing device 1 includes a geological radar 11, a stress sensor 12, a displacement sensor 13 and an earth pressure sensor 14.

[0053] like Figure 4 As shown, the geological radar 11 needs to arrange one survey line at the ground position corresponding to the center line of the left and right tunnels within the specified detection area, one survey line on the left side of the left tunnel and one on the right side of the right tunnel, and the survey line spacing is 4m.

[0054] like Figure 4 As shown, the stress sensors 12 should be arranged in sequence with a front-to-back spacing of about 50m, with 3 sensors on the left, middle and right. The first sensor on the top of the tunnel should be placed forward, about 40m away from the tunnel face. The first sensors on both sides should be placed in the same position, about 50m away from the tunnel face.

[0055] like Figure 4 As shown, when burying the monitoring point of the displacement sensor 13, a hole with a depth of 60 cm and a bottom size of 20 cm × 20 cm should be dug first, and the monitoring sign with a reflective patch should be placed in the hole, with the reflective patch facing the direction of the monitoring station, and then filled with concrete.

[0056] like Figure 4 As shown, the monitoring points of the earth pressure sensor 14 are vertically arranged with a spacing of 2m to 5m between monitoring points, and the spacing at the lower part should be denser. The earth pressure sensor 14 is usually installed on the facing surface of the tunnel lining structure to measure the pressure of the surrounding rock on the lining structure.

[0057] like Figure 1As shown, after the arrangement of the intelligent sensing device 1 is completed, data processing needs to be performed through the data processing system A. The data processing system A includes a data preprocessing module A1, a feature extraction module A2, an intelligent prediction module A3, and a result output module A4.

[0058] like Figure 1 As shown, the data preprocessing module A1 cleans the collected data to remove noise and outliers; performs formatting processing to convert the data into a format that can be recognized by the algorithm; and performs normalization processing to convert the data to the same order of magnitude.

[0059] like Figure 1 As shown in Figure 2, the feature extraction module A2 extracts key features related to the formation response from the preprocessed data, such as the variability of geological parameters, the stability of tunneling parameters, and the mutation of construction environment parameters.

[0060] like Figure 1 As shown, the intelligent prediction module A3 uses a machine learning algorithm (such as a support vector machine (SVM) or a deep learning algorithm (such as a convolutional neural network (CNN)) to dynamically predict the formation response based on historical data and real-time data. Furthermore, the intelligent prediction module A3 trains the model so that it can automatically identify the patterns and features in the data, thereby achieving accurate prediction of the formation response.

[0061] like Figure 1 , Figure 5 As shown, the result output module A4 displays the prediction results to the operator or control system in a graphical (such as formation displacement curve, stress distribution diagram) or numerical (such as predicted displacement, stress value) manner so that corresponding measures can be taken in time.

[0062] like Figure 2 As shown, the working principle of the blockchain storage module is as follows:

[0063] Step 1 Data packaging unit: Pack real-time monitoring data, historical data, and forecast data into data blocks. Each data block contains multiple records, and each record includes information such as data type, timestamp, and data value.

[0064] Step 2: Timestamp Adding Unit: Add a unique timestamp to each data block to ensure the data's time sequence and immutability. The timestamp uses a globally unified time standard (such as UTC) and is encrypted to prevent tampering.

[0065] Step 3: Consensus Mechanism Unit: This unit uses consensus mechanisms such as Proof of Work (PoW) or Proof of Stake (PoS) to ensure the correct addition and verification of data blocks on the blockchain. By calculating the workload or verifying the stake, nodes are elected to generate new data blocks and add them to the blockchain. Simultaneously, other nodes verify the newly added data blocks to ensure their correctness and legitimacy.

[0066] Step 4: Data Storage Unit: Verified data blocks are stored in the blockchain network, forming a distributed ledger. Each node maintains a complete copy of the blockchain to ensure data security and traceability. Encryption technology and access control mechanisms are also used to ensure data privacy and security.

[0067] The working principle of the construction control mechanism is as follows:

[0068] like Figure 3 As shown, the construction control mechanism includes a control module 3, an execution module 4, an alarm and protection module 5, a monitoring module 6, and a remote monitoring and diagnosis module 7.

[0069] The control module 3 receives the output results of the intelligent algorithm module and the blockchain storage module, and adjusts the excavation parameters according to the preset control strategy.

[0070] The execution module 4 controls the operating parameters of the roadheader according to the output results of the control module.

[0071] The monitoring module 6 monitors the geological parameters, excavation parameters and construction environment parameters during the excavation process in real time.

[0072] The alarm and protection module 5 will promptly issue an alarm signal if an abnormality occurs during excavation, such as excessive ground displacement, stress concentration, or groundwater leakage. This alarm signal can be transmitted to the operator or control system via acoustic, optical, or remote communication. Simultaneously, appropriate protective measures are implemented, such as suspending excavation, adjusting excavation parameters, and strengthening support structures, to ensure the safety and stability of the construction process.

[0073] The remote monitoring and diagnosis module 7 enables remote monitoring of the tunnel excavation process. Using remote communication networks and cloud platform technologies, it transmits real-time monitoring data and control information to a remote monitoring center or cloud platform server. It also provides fault diagnosis and remote support services, helping operators identify and resolve problems promptly.

[0074] Furthermore, if Figure 6 As shown in the figure, the specific steps of tunneling parameter adjustment and control are as follows:

[0075] Step 1: Data Packaging Unit: Receives the output from the intelligent algorithm module and blockchain storage module and adjusts tunneling parameters according to pre-set control strategies. These strategies include stratum displacement control, stress control, and groundwater control, and are selected and applied based on specific circumstances.

[0076] Step 2: The execution module controls the operating parameters of the tunnel boring machine based on the output of the control module. By adjusting parameters such as tunneling speed, thrust, and cutter rotation speed, intelligent control of the tunneling process is achieved. Sensors and feedback mechanisms are also used to monitor changes in tunneling parameters in real time and adjust them as needed.

[0077] Step 3: Monitoring Module: This module monitors geological parameters, excavation parameters, and construction environment parameters in real time during excavation. By deploying intelligent sensing devices and a sensor network, comprehensive monitoring and recording of the excavation process are achieved. Simultaneously, monitoring results are transmitted in real time to the intelligent algorithm module and control module for prediction and control.

[0078] Based on the same inventive concept, a second aspect of an embodiment of the present invention provides a method for intelligent response of a mine tunnel stratum, comprising the following steps:

[0079] Real-time collection of geological parameters, excavation parameters and construction environment parameters during tunnel excavation;

[0080] Clean, format and normalize the collected data to complete preprocessing;

[0081] Extract key features related to formation response from preprocessed data;

[0082] Based on machine learning or deep learning algorithms, dynamic prediction of formation response is performed using historical data and real-time data corresponding to key characteristics;

[0083] The predicted results are displayed graphically or numerically.

[0084] Based on the same inventive concept, a third aspect of an embodiment of the present invention provides a blockchain storage method for mining tunnel stratum data, characterized by comprising the following steps:

[0085] S1. Pack real-time monitoring data, historical data, and forecast data into data blocks. Each data block contains multiple records, and each record includes a data type, a timestamp, or a data value.

[0086] S2. Add a unique timestamp to each data block to ensure the time sequence and immutability of the data. The timestamp adopts a unified time standard and is encrypted to prevent tampering.

[0087] S3. A consensus mechanism is used to ensure the correct addition and verification of data blocks on the blockchain. By calculating the workload or verifying the stake, nodes are elected to generate new data blocks and add them to the blockchain. At the same time, other nodes will verify the newly added data blocks to ensure their correctness and legitimacy.

[0088] S4. The verified data blocks are stored in the blockchain network to form a distributed ledger. Each node will save a complete copy of the blockchain to ensure the security and traceability of the data. At the same time, encryption technology and access control mechanisms are used to ensure the privacy and security of the data.

[0089] In summary, the system includes a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent prediction module and a result output module, as well as a blockchain storage method and a construction control mechanism. The intelligent prediction module uses machine learning or deep learning algorithms, such as support vector machines, random forests, neural networks, etc., to dynamically predict the response of the stratum. The blockchain storage method ensures the safe storage and traceability of construction data, and adopts consensus mechanisms such as proof of work, proof of stake or practical Byzantine fault tolerance. The construction control mechanism includes a control module, an execution module, a monitoring module, an alarm and protection module, a human-computer interaction interface module and a remote monitoring and diagnosis module to achieve intelligent adjustment of excavation parameters and timely response to abnormal situations. The present invention solves the problems of inaccurate prediction of stratum response, insecure data storage and imprecise construction control in traditional construction through precise prediction, secure storage and intelligent control.

[0090] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. An intelligent system for responding to strata in mining tunnels, characterized in that: Including data acquisition module, data preprocessing module, feature extraction module and intelligent prediction module, The data acquisition module is used to collect geological parameters, excavation parameters and construction environment parameters in real time during the tunnel excavation process; The data preprocessing module is used to clean, format and normalize the data collected by the data acquisition module to improve data quality and algorithm efficiency; The feature extraction module is used to extract key features related to formation response from the data preprocessed by the data preprocessing module; The intelligent prediction module is used to dynamically predict the formation response based on machine learning or deep learning algorithms using historical data and real-time data extracted by the feature extraction module.

2. The system according to claim 1, wherein: The system also includes a model training module and a model evaluation module. The model training module is used to train and optimize the algorithm model of the intelligent prediction module; The model evaluation module is used to evaluate and verify the performance of the model to ensure the reliability and accuracy of the prediction results.

3. The system according to claim 1, wherein: The system further includes a result output module for displaying the prediction results of the intelligent prediction module in a graphical or numerical manner.

4. The system according to claim 1, wherein: The system also includes a blockchain storage module for storing data collected by the data collection module, including a data packaging unit, a timestamp adding unit, a consensus mechanism unit and a data storage unit. The data packaging unit is used to package real-time monitoring data, historical data and predicted data during the tunnel excavation process into data blocks; the timestamp adding unit is used to add a unique timestamp to each data block to ensure the timeliness and non-tamperability of the data; the consensus mechanism unit is used to adopt a consensus mechanism to ensure the correct addition and verification of data blocks on the blockchain, and the consensus mechanism includes proof of work, proof of stake or practical Byzantine fault tolerance mechanism; the data storage unit is used to store verified data blocks in the blockchain network to form a distributed ledger to ensure the security and traceability of the data.

5. The system according to claim 4, characterized in that The blockchain storage module adopts Bitcoin blockchain or Ethereum blockchain.

6. The system according to claim 1, wherein: The machine learning or deep learning algorithm includes support vector machine, random forest or neural network.

7. The system according to any one of claims 1 to 6, characterized in that: The system also includes a construction control mechanism, which includes a control module, an execution module, a monitoring module, an alarm and protection module, a human-computer interaction interface module and a remote monitoring and diagnosis module. The control module is used to receive the output results predicted by the intelligent prediction module and adjust the excavation parameters according to the preset control strategy; the execution module is used to control the operating parameters of the tunnel boring machine according to the output results of the control module; the monitoring module is used to monitor the geological parameters, excavation parameters and construction environment parameters during the excavation process in real time; the alarm and protection module is used to issue an alarm signal in time and take corresponding protection measures when an abnormal situation occurs during the excavation process; the human-computer interaction interface module is used to provide a human-computer interaction interface; the remote monitoring and diagnosis module is used to realize the function of remote monitoring of the tunnel excavation process and provide fault diagnosis and remote support services.

8. The system according to claim 7, characterized in that The construction control mechanism adopts automatic control technology, including PID control, fuzzy control or neural network control, to achieve intelligent adjustment of excavation parameters.

9. A method for intelligent response of mining tunnel strata, characterized in that: The following steps are involved: Real-time collection of geological parameters, excavation parameters and construction environment parameters during tunnel excavation; Clean, format and normalize the collected data to complete preprocessing; Extract key features related to formation response from preprocessed data; Based on machine learning or deep learning algorithms, dynamic prediction of formation response is performed using historical data and real-time data corresponding to key characteristics; The predicted results are displayed graphically or numerically.

10. A blockchain storage method for mining tunnel stratum data, characterized in that: The following steps are involved: S1. Pack real-time monitoring data, historical data, and forecast data into data blocks. Each data block contains multiple records, and each record includes a data type, a timestamp, or a data value. S2. Add a unique timestamp to each data block to ensure the time sequence and immutability of the data. The timestamp adopts a unified time standard and is encrypted to prevent tampering. S3. A consensus mechanism is used to ensure the correct addition and verification of data blocks on the blockchain. By calculating the workload or verifying the stake, nodes are elected to generate new data blocks and add them to the blockchain. At the same time, other nodes will verify the newly added data blocks to ensure their correctness and legitimacy. S4. The verified data blocks are stored in the blockchain network to form a distributed ledger. Each node will save a complete copy of the blockchain to ensure the security and traceability of the data. At the same time, encryption technology and access control mechanisms are used to ensure the privacy and security of the data.

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