Block chain credible evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock

Abstract

The invention discloses a block chain credible evaluation and dynamic parameter adjustment method for roadway surrounding rock intelligent toughness support, and relates to the technical field of mineral engineering and information. A block chain double-chain collaborative evidence storage layer of a data evidence storage sub-chain and a rule management sub-chain is constructed, a sensor array is installed to collect data of surrounding rock of a roadway, edge node configuration and preprocessing are carried out on the data, a trapezoidal membership function is adopted to carry out membership division on the processed data, and a division result is sent to an intelligent contract to carry out dynamic fuzzy evaluation. Processing the evaluation rule, performing weight calculation according to the membership degree to obtain a risk score, dividing the risk score by means of a digital twin platform to provide different support decisions, sending different information to nodes of all parties, and making measures corresponding to the node according to the risk score and the support decisions; according to the block chain credible evaluation and dynamic parameter adjustment method for the intelligent toughness support of the roadway surrounding rock, evaluation rule dynamic optimization is realized, and a multi-party cooperative intelligent response system is established.

Description

Technical Field

[0001] This invention relates to the fields of mining engineering and information technology, and in particular to a blockchain-based reliable evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock. Background Technology

[0002] Currently, raw coal accounts for two-thirds of my country's total primary energy production. As my country's main energy source, coal plays a crucial role in ensuring national energy security. In mines, the stability of roadways is of paramount importance, directly affecting the safety, efficiency, and cost of the project. Roadway stability is the "lifeline" of underground engineering and a core objective that must be given priority consideration throughout the design, construction, and operation processes. Deep mines are affected by high ground pressure and strong disturbances, and there are four major challenges in stability assessment that urgently need to be addressed.

[0003] Traditional tunnel data mostly relies on sensors (such as roof separation meters and stress gauges) for measurement. However, data monitoring depends on manual recording, and paper data is easily tampered with, leading to questions about the reliability of the measured data. Existing fuzzy mathematical evaluation models have flaws: weight determination depends on expert experience, rule base updates are lagging, they cannot adapt to tunnel deformation in real time, and support adjustment schemes are severely delayed. In the face of emergencies, collaborative response to problems is inefficient, traditional evaluation methods have limitations, and evaluation weights are prone to becoming fixed. Summary of the Invention

[0004] The purpose of this invention is to provide a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock. It adopts a "four-layer dual-chain" intelligent architecture, including a physical perception layer, a blockchain evidence storage layer, an intelligent decision-making layer, a collaborative response layer, a data evidence storage sub-chain, and a rule management sub-chain, as well as a security enhancement system that runs through each layer. This ensures the trustworthiness of the evaluation data throughout the entire process, enables dynamic optimization of evaluation rules, and establishes a multi-party collaborative intelligent response system.

[0005] To achieve the above objectives, this invention provides a blockchain-based reliable evaluation and dynamic parameter adjustment method for intelligent toughness support of tunnel surrounding rock, comprising the following steps: S1. Construct a dual-chain collaborative notarization layer of blockchain, consisting of a data notarization subchain and a rule management subchain, and set up security and disaster recovery mechanisms; S2. Install a sensor array to collect data on the surrounding rock of the tunnel, configure and preprocess its edge nodes, and store the processed data in the data storage subchain. S3. Use trapezoidal membership functions to partition the data processed by S2 and calculate historical weighted values, and store them in the data storage subchain. The partitioning results of S4 and S3 are sent to the "smart contract" to perform dynamic fuzzy evaluation according to the evaluation rules in the sub-chain managed by the S1 rules. S5. Process the evaluation rules of S4 and calculate the risk score based on the membership degree of S3. S6. Relying on the digital twin platform to classify the risk scores of S5 to provide different support decisions; S7. Based on the support decision in S6, send different information to the corresponding nodes in the coordination process. Each node then takes corresponding measures based on its risk score and the support decision.

[0006] Preferably, the data storage subchain in S1 includes the storage content of the delegated proof-of-stake consensus mechanism, e-data structure, sensor raw data, edge computing membership degree, and evaluation structure hash; The rule management subchain includes a consensus mechanism based on Practical Byzantine Fault Tolerance-Rule (PBFT-Rule), version control, and an evaluation rule update mechanism where expert node voting accounts for no less than 75%.

[0007] Preferably, the sensor array of S2 includes fiber optic displacement gauges, microseismic sensors, and anchor stress gauges; edge node configuration and preprocessing include data spatiotemporal alignment and outlier filtering based on PTP clock synchronization.

[0008] Preferably, the specific process of S3 for membership division using the top plate displacement as an example is as follows: S31. Determine the stable interval, warning interval, and danger interval, as well as the membership values ​​of the three intervals, using the trapezoidal function parameters; S32. Then, the average of the 10 most recent displacement monitoring values ​​is used as a historical data reference. This average is multiplied by a weighting coefficient of 0.3 to obtain the historical weighted value. S33. Calculate and adjust the initial membership values ​​based on the calculation results of S31 and S32; S34. Finally, output the membership values ​​of the three intervals: "stable", "warning", and "dangerous", to realize the handling and judgment of the top plate displacement state.

[0009] Preferably, the initial membership value calculation and formula in S33 are as follows: ; In the formula, This is the initial value of the membership degree. For standard membership degree, This refers to the degree of historical affiliation.

[0010] Preferably, the triggering mechanism for the dynamic fuzzy evaluation of the subchain by the smart contract in S4 according to the rule management evaluation rules includes the following two cases: S41. One method is that when new data is added to the chain, the smart contract's triggering mechanism will immediately start the evaluation. There is also a timed triggering under normal circumstances, which is set to automatically evaluate every 8 hours to check whether there is an accumulated risk of data changes within 8 hours. S42. Another type is sudden change triggering, which immediately initiates risk assessment when the sensor detects a displacement change exceeding a set threshold.

[0011] Preferably, the specific process of S5 is as follows: S51. When the evaluation rules do not need to be optimized, the smart contract automatically verifies whether the rule version currently participating in this membership calculation is consistent with the latest valid rule version. If they are consistent, the rule version is locked directly as the sole basis for this evaluation. Then, the smart contract uses this rule to calculate the risk score by inputting the membership calculated by the edge node into the rule. It also automatically stores a report containing "evaluation time, rule version used, scores of each indicator, comprehensive risk value, and risk level" in the data storage subchain and associates it with the corresponding rule version hash. Finally, it outputs the risk score. S52. When the evaluation rules need to be optimized, experts submit a weight adjustment plan through expert nodes. At this time, with the consent of no less than three-quarters of the mining party nodes and the safety supervision bureau nodes, the new rules will be written into the smart contract and replace the original rules. Then, the subsequent steps of S51 can be executed to obtain the risk score. S53. If the risk score calculated by S52 or S53 is not significantly different from the actual risk score, the result will be output. If the calculated risk score is significantly different from the actual risk score, a "correction proposal" will be automatically submitted to request an adjustment of the weights. Finally, the decision will be made by the mining node and the safety supervision bureau node, and the risk score will be output again after the adjustment.

[0012] Preferably, the support decisions corresponding to different risk scores in S6 are as follows: When the risk value is no greater than 60, the digital twin platform maintains basic monitoring and model synchronization, receives physical perception layer data in real time, updates the tunnel BIM model, performs lightweight simulation, and does not start deep calculation. When the risk value is in the range of 60 to 75, initiate rapid diagnosis and contingency plan simulation, accurately locate the risk source, analyze the high-risk coordinates, highlight the high-risk markers in BIM, input real-time data, call the simplified model to predict the deformation trend, and simulate 1 to 2 support strategies.

[0013] When the risk value is in the range of 75 to 85, deep simulation and scheme optimization are performed. The specific lithology, current support, and hydrological conditions are used as input parameters. The corresponding simulation models are FLAC3D mechanical calculation, ABAQUS transient analysis, and COMSOL seepage coupling. The output results are displacement cloud map and stress concentration zone coordinates, safety factor of different schemes, and water inrush risk distribution map. Based on the above deep simulation, 3 to 5 support strategies are simulated.

[0014] Preferably, the security and disaster recovery mechanism in S1 includes zero-knowledge proof verification and a network outage emergency mode, the specific contents of which are as follows: Zero-knowledge proof verification: When a user needs certain data, they send a data request instruction to the system. After receiving the request, the system verifies the user's identity through the verification contract. At this time, the system generates proof materials for the user but does not disclose the specific data content. The zero-knowledge prover zkProver generates a zero-knowledge proof according to the rules of the rule management subchain and delivers the proof to the verification contract. After verification, the data is stored in the data storage subchain. Finally, the data storage subchain returns the encrypted corresponding data to the user.

[0015] Network outage emergency mode: In network outage emergency mode, edge nodes will cache data for more than 72 hours, and the local rule base will use the most recently effective version. When the network is restored, the difference blocks will be automatically synchronized.

[0016] Preferably, the nodes involved in the collaborative response in S7 include the mining node, the design institute node, and the safety supervision bureau node. When the risk value is no greater than 60, no operation is required from any of the nodes. When the risk value is greater than 60 but not greater than 75, the mining node receives a warning SMS or audible and visual alarm and remotely starts and stops the support robot and monitors the status of the equipment. Other nodes only need to respond to the needs of the mining node. When the risk value is no less than 75, in addition to the mining node implementing the support decision, the design institute node must obtain the digital twin simulation report, analyze the effect of the support decision, mark the geologically sensitive parameters, and share information with the mining node. When the risk value is greater than 75, the safety supervision bureau node forcibly stops the operation.

[0017] Preferably, the specific process of S4 is as follows: The cross-attention mechanism module integrates two types of information: the global dependency relationship of Transformer and the front-to-back dependency relationship extracted by BiGRU. It uses a query-key-value mechanism, taking the output of BiGRU as the query and the output of Transformer as the key and value, and calculates the attention weight between the two.

[0018] Therefore, the present invention adopts the above-mentioned blockchain-based reliable evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock, which has the following advantages compared with the prior art: 1. To ensure the credibility of evaluation data throughout the entire process, a blockchain distributed storage ledger is used to store the sensor's raw data, fuzzy membership values, rule version hashes, and evaluation results, ultimately forming a traceable chain of evidence that cannot be tampered with manually. The aim is to solve the problems of data distortion and data silos.

[0019] 2. Achieve dynamic optimization of evaluation rules. Based on the smart contract consensus mechanism, update the weight of the fuzzy rule base in real time, so that the evaluation model can adapt to the dynamic characteristics of tunnel deformation and eliminate the technical bottleneck of rule lag in traditional methods.

[0020] 3. Establish a multi-party collaborative intelligent response system, and realize automatic hierarchical push of risk warnings and collaborative support decision-making through a decentralized node network, thereby reducing the accident response time from hours to minutes.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] Figure 1 This is a system architecture diagram of a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock according to the present invention. Figure 2 This is a flowchart of the full life-cycle data on-chain process of a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock according to the present invention. Figure 3 This is a flowchart of the dynamic rule update process for a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock, as described in this invention. Figure 4 This is a flowchart of the fuzzy evaluation engine for a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock, as described in this invention. Detailed Implementation

[0023] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0024] Example like Figures 1-4 As shown, the present invention provides a blockchain-based trusted evaluation and dynamic parameter adjustment method for intelligent toughness support of tunnel surrounding rock, comprising the following steps: S1. Construct a dual-chain collaborative notarization layer of blockchain, consisting of a data notarization subchain and a rule management subchain, and set up security and disaster recovery mechanisms; The data storage subchain includes a consensus mechanism using Delegated Proof-of-Stake (DPoS-Data), a data structure using Merkle Patricia Tree, raw sensor data, edge computing membership, and storage content using evaluation structure hashes. The rule-managed subchain includes a consensus mechanism that adopts the Practical Byzantine Fault Tolerance-Rule (PBFT-Rule), version control that adopts the Version Control-Rule, and an evaluation rule update mechanism where expert node voting accounts for no less than 75%. The security and disaster recovery mechanism includes zero-knowledge proof verification and a network outage emergency mode, the details of which are as follows: Zero-knowledge proof verification: When a user needs certain data, they send a data request instruction to the system. After receiving the request, the system verifies the user's identity through the verification contract. At this time, the system generates proof materials for the user but does not disclose the specific data content. The zero-knowledge prover zkProver generates a zero-knowledge proof according to the rules of the rule management subchain and delivers the proof to the verification contract. After verification, the data is stored in the data storage subchain. Finally, the data storage subchain returns the encrypted corresponding data to the user.

[0025] Emergency mode for network outages: In emergency mode for network outages, edge nodes will cache data older than 72 hours, and the local rule base will use the most recently effective version. Once the network is restored, the difference blocks will be automatically synchronized. S2. Install a sensor array to collect data on the surrounding rock of the tunnel, configure and preprocess its edge nodes, and store the processed data in the data storage subchain. The sensor array includes fiber optic displacement gauges, microseismic sensors, and anchor stress gauges; edge node configuration and preprocessing include data spatiotemporal alignment and outlier filtering based on PTP clock synchronization; S3. Use trapezoidal membership functions to partition the data processed by S2 and calculate historical weighted values, and store them in the data storage subchain. S31. Determine the stable interval, warning interval, and danger interval, as well as the membership values ​​of the three intervals, using the trapezoidal function parameters; Stable interval: The corresponding trapezoidal function parameters are {0, 2, 4, 6}, and the membership values ​​that satisfy this condition are "stable". Warning interval: The corresponding trapezoidal function parameters are {4, 6, 8, 10}, and the members that meet this condition are "warning" members; Dangerous region: The corresponding trapezoidal function parameters are {4, 6, 8, 10}. Member values ​​that satisfy this condition are considered "dangerous". S32. Then, the average of the 10 most recent displacement monitoring values ​​is used as a historical data reference. This average is multiplied by a weighting coefficient of 0.3 to obtain the historical weighted value. S33. Calculate and adjust the initial membership values ​​based on the calculation results of S31 and S32; The initial membership degree calculation and formula are as follows: ; In the formula, This is the initial value of the membership degree. For standard membership degree, Historical affiliation degree; S34. Finally, output the membership values ​​of the three intervals "stable, warning, and danger" to realize the handling judgment of the top plate displacement state. Stable interval membership degree: Subtract the historical weighted value from the initial stable membership degree. If the result is negative, take 0 (i.e., not lower than 0). Membership degree of the warning interval: calculated directly using the trapezoidal function; Danger zone membership degree: The initial stable membership degree is added to the historical weighted value. If the result exceeds 1, it is taken as 1. The membership degree value is a number between 0 and 1. The standard membership degree is calculated based on the pre-set scientific standards, models or expert experience. The historical membership degree is obtained based on the actual situation that has occurred in this specific tunnel or specific area in the past. The partitioning results of S4 and S3 are sent to the "smart contract" to perform dynamic fuzzy evaluation according to the evaluation rules in the sub-chain managed by the S1 rules. The triggering mechanism for smart contracts to perform dynamic fuzzy evaluations based on the evaluation rules of subchains includes the following two cases: S41. One method is that when new data is added to the chain, the smart contract's triggering mechanism will immediately start the evaluation. There is also a timed triggering under normal circumstances, which is set to automatically evaluate every 8 hours to check whether there is an accumulated risk of data changes within 8 hours. S42. Another type is sudden change triggering, where a risk assessment is immediately initiated when the sensor detects a displacement change exceeding a set threshold. S5. Process the evaluation rules of S4 and calculate the risk score based on the membership degree of S3. S51. When the evaluation rules do not need to be optimized, the smart contract automatically verifies whether the rule version currently participating in this membership calculation is consistent with the latest valid rule version. If they are consistent, the rule version is locked directly as the sole basis for this evaluation. Then, the smart contract uses this rule to calculate the risk score by inputting the membership calculated by the edge node into the rule. It also automatically stores a report containing "evaluation time, rule version used, scores of each indicator, comprehensive risk value, and risk level" in the data storage subchain and associates it with the corresponding rule version hash. Finally, it outputs the risk score. S52. When the evaluation rules need to be optimized, experts submit a weight adjustment plan through expert nodes. At this time, with the consent of no less than three-quarters of the mining party nodes and the safety supervision bureau nodes, the new rules will be written into the smart contract and replace the original rules. Then, the subsequent steps of S51 can be executed to obtain the risk score. S53. If the risk score calculated by S52 or S53 is not much different from the actual value, the result is output. If the risk score calculated above is much different from the actual value, an "correction proposal" is automatically submitted to apply for weight adjustment. Finally, the decision is made by the mining node and the safety supervision bureau node. After adjustment, the risk score is output again. S6. Using a digital twin platform, the risk scores of S5 are categorized to provide different support decisions; the support decisions corresponding to different risk scores are shown below: When the risk value is no greater than 60, the digital twin platform maintains basic monitoring and model synchronization, receives physical perception layer data in real time, updates the tunnel BIM model, performs lightweight simulation, and does not start deep calculation. When the risk value is in the range of 60 to 75, initiate rapid diagnosis and contingency plan simulation, accurately locate the risk source, analyze the high-risk coordinates, highlight the high-risk markers in BIM, input real-time data, call the simplified model to predict the deformation trend, and simulate 1 to 2 support strategies.

[0026] When the risk value is in the range of 75 to 85, deep simulation and scheme optimization are performed. The specific lithology, current support, and hydrological conditions are used as input parameters. The corresponding simulation models are FLAC3D mechanical calculation, ABAQUS transient analysis, and COMSOL seepage coupling. The output results are displacement cloud map and stress concentration zone coordinates, safety factor of different schemes, and water inrush risk distribution map. Based on the above deep simulation, 3 to 5 support strategies are simulated. S7. Based on the support decision in S6, send different information to the corresponding nodes of the coordination, and each node takes corresponding measures according to the risk score and support decision. The collaborative response involves nodes from the mining company, the design institute, and the safety supervision bureau. When the risk value is no greater than 60, no action is required from any of the nodes. When the risk value is greater than 60 but not greater than 75, the mining company node receives a warning SMS or audible / visual alarm and remotely starts / stops the support robot and monitors the equipment status. The risk coordinates obtained by the support robot are high-risk coordinates calculated through a smart contract assessment report. When the support robot needs to be activated, it will go to the high-risk coordinate area to perform operations. For example, if coordinates (123, 456) are a high-risk area, the action performed is: sprayed concrete, thickness: 50±5mm, material ratio: C30 concrete. Other nodes simply respond to the mining company node's requests. When the risk value is no less than 75, in addition to the mining company node implementing support decisions, the design institute node must obtain a digital twin simulation report, analyze the effectiveness of the support decisions, mark geologically sensitive parameters, and share information with the mining company node. When the risk value is greater than 75, the safety supervision bureau node forcibly halts the operation.

[0027] The specific implementation process is as follows: 1. Building the Physical Sensing Layer—Hardware Deployment and Initialization Sensor arrays are deployed in soft rock tunnels with a burial depth greater than 800m. The sensor arrays are deployed in the roof, sidewalls, and floor heave areas of the tunnel. The main functions are: monitoring of surrounding rock deformation (such as fiber optic displacement gauges with an accuracy of ±0.1mm), monitoring of stress state (such as microseismic sensors with a frequency response of 0.1-1000Hz), monitoring of hydrogeology (such as piezometers with a range of 0-5MPa), and monitoring of support structure (anchor bolt stress gauges with 150% overload protection).

[0028] Fiber optic displacement gauges on the top plate The system is installed with a range of 0-50mm and an accuracy of ±0.1mm; the micro-vibration sensor is installed inside the rock mass on both sides, with a frequency response of 0.1-1000Hz and a sensitivity of >5V / g; the anchor stress gauge is installed at the end of the support anchor, with overload protection of 150% and temperature compensation of -30~80℃.

[0029] The hardware platform uses NVIDIA Jetson AGX Orin (200 TOPS hashrate) and is intrinsically safe for mining. 2. Construct a dual-chain collaborative evidence storage system The Hyperledger Fabric 2.5 chain type is adopted, with two sorting nodes for transaction sorting; three mining party endorsement nodes for data verification; and one safety supervision bureau audit node for supervision and traceability.

[0030] The specific process of on-chaining the entire lifecycle is as follows: When the edge node collects and processes the basic data of the tunnel, it outputs the membership degree and sends it to the "smart contract"; the smart contract verifies the authenticity and legality of the data and sends the verification result (whether it is valid) to the "mining endorsement node"; the "mining endorsement node" sends the valid data to the "sorting node" and packages these transactions into a new block for broadcast. Upon receiving the new block broadcast by the "sorting node", the ledger is updated synchronously to ensure that all data is consistent; at this time, the safety supervision bureau node stores the data related to the transaction in IPFS and uploads the unique identifier "hash value" to the blockchain.

[0031] 3. Perform dynamic fuzzy evaluation There are two main triggering mechanisms for smart contracts to assess risks using evaluation rules. The first is that when new data is added to the chain, the smart contract's triggering mechanism will immediately start the assessment. There is also a timed triggering under normal circumstances, which is set to automatically assess every 8 hours (to check whether there is an accumulated risk of data changes within 8 hours). The second is a sudden change triggering mechanism, such as when a sensor detects a displacement change that exceeds a set threshold, the risk assessment will be started immediately.

[0032] When the evaluation rules do not require optimization, the smart contract automatically verifies whether the rule version currently participating in the membership calculation is consistent with the latest valid rule version. If they are consistent, the rule version is locked and used as the sole basis for this evaluation. Then, the smart contract uses this rule to calculate the risk score by inputting the membership calculated by the edge node into the rule. It also automatically stores a report containing "evaluation time, rule version used, scores of each indicator, comprehensive risk value, and risk level" in the data storage chain and associates it with the corresponding rule version hash. Finally, it outputs the risk score.

[0033] When the evaluation rules need to be optimized, experts submit a weight adjustment plan through expert nodes. At this time, with the consent of ≥3 / 4 of the mining party nodes and the safety supervision bureau nodes, the new rules will be written into the smart contract and replace the original rules. Then, the steps that do not need to be optimized in the evaluation rules will be executed.

[0034] The membership degree calculated from the edge nodes is combined with the weights in the aforementioned evaluation rules (e.g., displacement weight 0.4, stress weight 0.3, hydrology weight 0.3) to obtain a risk score. If the calculated risk score is not significantly different from the actual risk score, the result is output. If the calculated risk score is significantly different from the actual risk score (e.g., the calculated risk is high, but the manually confirmed risk is not high), the system will automatically submit a "correction proposal" to apply for weight adjustment. Finally, the decision is made by the mining node and the safety supervision bureau node, and the risk score is output again after adjustment.

[0035] 4. Support strategy Support strategies are primarily implemented using digital twin platforms, which will perform different operations depending on the risk level.

[0036] Step 1: When the risk value is ≤60, the digital twin platform maintains basic monitoring and model synchronization, receives physical perception layer data in real time, updates the tunnel BIM model, performs lightweight simulation, and does not start deep calculation.

[0037] Step Two: For risks between 60 and 75, initiate rapid diagnosis and contingency plan simulation to accurately locate risk sources, analyze high-risk coordinates, and highlight them in the BIM. Input real-time data, call the simplified model to predict deformation trends, and simulate 1-2 support strategies.

[0038] Step 3: For a risk value of 75 < risk value ≤ 85, perform in-depth simulation and scheme optimization, using specific lithology, current support, and hydrological conditions as input parameters. The corresponding simulation models are FLAC3D mechanical calculation, ABAQUS transient analysis, and COMSOL seepage coupling, respectively. The output results are displacement cloud maps and stress concentration zone coordinates, safety factors for different schemes, and water inrush risk distribution maps. Based on the above in-depth simulation, simulate 3 to 5 support strategies.

[0039] 5. Multi-party collaboration The smart contract collaborative response mechanism outputs the risk value as a risk level and executes the corresponding operation, as follows: If the risk value is ≤60, no action is taken; if the risk value is ≤75, the mine is notified via SMS; if the risk value is ≤85, the support robot is activated; if the risk value is >85, the safety supervision bureau will force a work stoppage.

[0040] Based on different risk scores, the system will send different information to each node, and the nodes will then conduct multi-party collaboration accordingly, as shown in the table below.

[0041] Table 1 Multi-party Node Function Table Specific Implementation The geology of a certain coal mine consists of sandy mudstone roof. An anomaly appeared in the roof 20m behind the tunneling face.

[0043] 1. Physical sensing layer captures anomalies. The roof displacement sensor detected a deformation rate of 1.8 mm / h (normal value <0.5 mm / h); the micro-vibration sensor recorded a low-frequency vibration of 35 Hz; the anchor stress gauge showed a sudden increase of 22% in support pressure. At this time, the edge nodes synchronized their clocks, aligning the timestamps of the three sensor sets to 14:00:05.327, eliminating an anomaly in displacement due to dust interference, and performing initial membership calculations. Deformation rate 1.8 mm / h risk membership. =0.85, the actual risk of similar data in the same region last month was 0.7, therefore: ; This value is initially judged to be high risk.

[0044] 2. Blockchain-based evidence storage and dual-link collaboration The original data (cryptographic hash a1b2c3d4) is stored in the data storage subchain, with an initial membership value of 0.805, and is bound to the current rule tag (e.g., v2.3-RockDeform-2025). The rule subchain records the weight parameters used this time. At this point, the displacement change rate of 1.8mm / h is greater than the contract threshold of 0.5mm / h, automatically activating the evaluation engine.

[0045] 3. Dynamic Analysis of Intelligent Decision-Making Layer In the TEE environment, the following parameters were input: displacement 1.8 mm / h + vibration 35 Hz + stress increase 22%. Using 128 fuzzy rules from rule chain version v2.3, the overall risk value was calculated to be 78.6. Since the risk value of 78.6 > the optimization threshold of 75, the rule optimizer was activated. Analysis revealed that the recent mudstone roof is more sensitive to this, leading to a proposal to increase the vibration weight from 0.1 to 0.15.

[0046] Digital twin simulation, loading a BIM model, simulates the current "anchor bolt + metal mesh" support scheme. Finite element analysis shows that the stress concentration factor kt = 2.4 > the safety threshold of 2.0, and finally outputs the optimal solution.

[0047] 4. Coordinate with multiple parties at the corresponding level Risk value 78.6 > 75, activate support decision contract, begin sending instructions to support robot, coordinates: (X:2835, Y:-810, Z:107) action: install anchor bolts. .

[0048] The mine's design center received an early warning text message and is remotely monitoring the robot's operations. Design Institute Platform: Obtain simulation reports, mark the area as a mudstone-sensitive area, and update the support standard library.

[0049] Safety Supervision Bureau Terminal: Initiate on-chain tracing to verify the complete record from data collection to execution, and finally generate an audit report.

[0050] Therefore, this invention adopts a blockchain-based credible evaluation and dynamic parameter adjustment method for intelligent toughness support of roadway surrounding rock, as described above. It employs a "four-layer dual-chain" intelligent architecture, consisting of a physical perception layer, a blockchain evidence storage layer, an intelligent decision-making layer, a collaborative response layer, a data evidence storage sub-chain, and a rule management sub-chain, as well as a security enhancement system that runs through each layer. This ensures the credibility of the evaluation data throughout the entire process, enables dynamic optimization of evaluation rules, and establishes a multi-party collaborative intelligent response system.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-based reliable evaluation and dynamic parameter adjustment method for intelligent toughness support of tunnel surrounding rock, characterized in that: The method comprises the following steps: S1, constructing a blockchain double-chain collaborative evidence layer of data storage sub-chain and rule management sub-chain and setting up security and disaster recovery mechanism; S2, installing sensor array to collect data of roadway surrounding rock, configuring and preprocessing edge node and storing processed data into data storage sub-chain; S3, using trapezoidal membership function to divide and calculate membership degree of data processed by S2 and storing into data storage sub-chain; S4, sending division result of S3 to "smart contract" to conduct dynamic fuzzy evaluation according to evaluation rules in rule management sub-chain of S1; S5, processing evaluation rules of S4 and calculating weight according to membership degree of S3 to obtain risk score; S6, dividing risk score of S5 by digital twin platform to provide different support decisions; S7, sending different information to corresponding nodes according to support decisions of S6, and each node makes corresponding measures according to risk score and support decisions.

2. The method of claim 1, wherein the method is characterized by: The data storage sub-chain in S1 comprises consensus mechanism of delegated proof of stake, e data structure, sensor raw data, edge computing membership degree and evaluation structure hash evidence content; The rule management sub-chain comprises consensus mechanism of practical byzantine fault tolerance-rule management PBFT-Rule, version control and expert node voting evaluation rule updating mechanism not less than 75%.

3. The method according to claim 2, wherein the sensor array of S2 comprises a fiber displacement meter, a microseismic sensor and an anchor stress meter; and the edge node configuration and preprocessing comprises data space-time alignment based on PTP clock synchronization and abnormal value filtering.

4. The method according to claim 3, wherein the specific process of membership degree division of S3 taking roof displacement as an example is as follows: S31, dividing stable interval, warning interval and dangerous interval and corresponding membership values of the intervals by trapezoidal function parameters; S32, taking the mean value of the last 10 displacement monitoring values as historical data reference, multiplying the mean value by weight coefficient 0.3 to obtain historical weighted value; S33, calculating membership degree initial value based on calculation results of S31 and S32 and adjusting the initial value; S34, finally outputting membership degree values of "stable, warning and dangerous" three intervals to realize disposal judgment of roof displacement state.

5. The method according to claim 4, wherein the membership degree initial value calculation in S33 is as follows:

6. The method according to claim 5, wherein the trigger mechanism of the smart contract in S4 for dynamic fuzzy evaluation according to evaluation rules of the rule management sub-chain comprises the following two cases: ​ ​ ； wherein is the initial membership value, is the standard membership, is the historical membership. ​ ​ S41, when new data is chained, the trigger mechanism of the smart contract will immediately start the evaluation, and the normal timing trigger will also be set to automatically evaluate every 8 hours to check whether there is a cumulative risk within 8 hours; S42, another is mutation trigger, when the sensor monitors that the displacement change exceeds the set threshold, it will immediately start to evaluate the risk.

7. The blockchain-based intelligent evaluation and dynamic parameter adjustment method for roadway surrounding rock intelligent toughness support according to claim 6, wherein: The specific process of S5 is as follows: S51, when the evaluation rule does not need to be optimized, the smart contract automatically checks whether the rule version currently participating in the membership calculation is consistent with the current latest effective rule version, and if it is consistent, the version of the rule is directly locked as the only basis for this evaluation, then the smart contract brings the membership calculated by the edge node into the rule to calculate the risk score, and automatically stores the report containing "evaluation time, used rule version, each index score, comprehensive risk value, risk level" into the data storage sub-chain, and associates the corresponding rule version hash, and finally outputs the risk score; S52, when the evaluation rule needs to be optimized, the expert submits the weight adjustment scheme through the expert node, and the new rule will be written into the smart contract and replace the original rule with the consent of no less than three-quarters of the mining node and the safety supervision bureau node, then the subsequent steps of S51 can be executed to obtain the risk score; S53, when the risk score calculated by S52 or S53 is not much different from the actual value, the result is output, and when the risk score calculated above is greatly different from the actual value, a "correction proposal" is automatically submitted to apply for adjusting the weight, and finally the result is output after the decision of the mining node and the safety supervision bureau node.

8. The method of claim 7, wherein the method is characterized by: The support decision corresponding to different risk scores in S6 is shown as follows: When the risk value is not greater than 60, the digital twin platform maintains basic monitoring and model synchronization, receives physical perception layer data in real time, updates the roadway BIM model, and performs lightweight simulation without starting deep calculation; When the risk value is in the range of 60 to 75, rapid diagnosis and pre-plan rehearsal are started, the risk source is accurately located, the high-risk coordinates are analyzed, and are highlighted in the BIM, real-time data is input, a simplified model is called to predict the deformation trend, and 1-2 support strategies are preformed; When the risk value is in the range of 75 to 85, deep simulation and scheme optimization are performed, with specific lithology, current support, and hydrological conditions as input parameters, the corresponding simulation models are FLAC3D mechanical calculation, ABAQUS transient analysis, and COMSOL seepage coupling, and the output results are displacement cloud map and stress concentration area coordinates, safety factor of different schemes, and water gushing risk distribution map, and 3-5 support strategies are simulated according to the above deep simulation.

9. The blockchain-based intelligent evaluation and dynamic parameter adjustment method for roadway surrounding rock intelligent toughness support according to claim 8, wherein: The specific process of the safety and disaster recovery mechanism in S1 includes zero-knowledge proof verification and network interruption emergency mode, and the specific contents of the two are as follows: Zero-knowledge proof verification: When the user needs certain data, send the system a request for data instructions, the system after getting the request, verify the identity of the contract to the user, at this time the system generates proof materials for the user but does not disclose the specific data content, zero-knowledge proof zkProver will generate a zero-knowledge proof according to the rules of the rule management sub-chain and deliver the proof to the verification contract, after verification, the data is stored in the data storage sub-chain, and finally the data storage sub-chain returns the encrypted corresponding data to the user; Network emergency mode: In network emergency mode, the edge node will cache data greater than 72 hours, and the local rule library will use the latest effective version. When the network is restored, it will automatically synchronize the difference block.

10. The method according to claim 9, wherein the method is characterized in that: The nodes responding in cooperation in S7 include the mine node, the design institute node and the safety supervision bureau node; when the risk value is not greater than 60, the nodes do not need to operate; when the risk value is greater than 60 but not greater than 75, the mine node receives the warning message or the sound and light alarm and remotely starts and stops the supporting robot and the monitoring equipment state, and the other nodes respond to the demand of the mine node; When the risk value is not less than 75, in addition to the implementation of the supporting decision of the mine node, the design institute node needs to obtain the digital twin simulation report, analyze the supporting decision effect, mark the geological sensitive parameters and share the information of both parties with the mine node; When the risk value is greater than 75, the safety supervision bureau node is forced to stop.

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