Shield construction digital management method

Abstract

The invention discloses a shield construction digital management method which comprises the following steps: S1, data acquisition and system construction: arranging various sensors on a shield tunneling machine and a construction site to acquire original data in real time, and caching the data through a site acquisition point data acquisition program; s2, data transmission: performing timestamp correction, deduplication processing and incremental transmission through a data preprocessing gateway; s3, data processing: establishing a real-time analysis model and an early warning mechanism, and triggering graded early warning based on a threshold value and a multi-parameter comprehensive score; s4, data backup: adopting a full-amount and incremental combined backup strategy, performing remote encrypted storage, and verifying consistency through a hash algorithm; and S5, system optimization: regularly evaluating system performance and optimizing sensor arrangement, a cache strategy and an algorithm model. According to the method, the data acquisition efficiency of shield construction can be effectively improved, the data processing pressure of a headquarter center is relieved, the data transmission quality and safety are guaranteed, and real-time analysis and effective early warning are achieved.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring machine (TBM) construction technology, specifically to a digital management method for TBM construction. Background Technology

[0002] Tunnel boring machine (TBM) construction is a highly complex underground engineering method with extremely high technical requirements. The process involves numerous intricate procedures and equipment, including TBM excavation, segment assembly, and grouting. Simultaneously, it requires real-time monitoring of changes in geological conditions, equipment operating status, and construction environmental parameters. The harsh construction site environment and complex, variable geological conditions present significant challenges to data management during the construction process.

[0003] Currently, some tunnel boring machine (TBM) construction projects employ a "two-level caching + loose storage" scheme for data acquisition. This scheme addresses stability issues during data acquisition to some extent. For example, in the event of network anomalies or interruptions, the first-level caching mechanism at the on-site acquisition points and the server can temporarily store the acquired data, ensuring no data loss. Simultaneously, the real-time acquisition of raw data and subsequent processing of format issues improves data acquisition efficiency and reduces data acquisition delays caused by format verification.

[0004] However, once the data is transmitted to the headquarters, a comprehensive data format check and cleaning process is required due to the lack of rigorous format verification in the early stages. This process can cause significant delays when dealing with large volumes of data. For example, in large-scale shield tunnel construction projects, massive amounts of sensor data are generated daily, including operating parameters of various components of the tunnel boring machine and geological change data. Performing format checks and cleaning on this data requires substantial computing resources and time, preventing the data from being used for timely construction decisions.

[0005] In the high-real-time requirements of tunnel boring machine (TBM) construction, construction decisions must be based on accurate and timely data. Excessive data processing steps, especially delays in data format checking and cleaning, severely impact decision-making speed. For example, when the TBM encounters complex geological conditions, such as suddenly appearing soft strata or obstacles, it needs to adjust tunneling parameters, such as thrust and cutterhead rotation speed, promptly. However, due to data processing delays, decision-makers cannot obtain accurate geological and equipment operating data in a timely manner, leading to delayed decisions and potentially causing problems such as TBM deviation from the design axis, excessive cutter wear, or even engineering accidents. Summary of the Invention

[0006] This invention aims to at least solve one of the technical problems existing in the prior art. Therefore, one objective of this invention is to propose a digital management method for tunnel boring machine (TBM) construction. This method can effectively improve the data acquisition efficiency of TBM construction, reduce the data processing pressure on the headquarters center, ensure data transmission quality and security, and achieve real-time analysis and effective early warning.

[0007] Therefore, this invention proposes a digital management method for tunnel boring machine (TBM) construction, comprising the following steps:

[0008] S1. Data Acquisition and System Construction: Multiple sensors are deployed on the tunnel boring machine and construction site to collect raw data in real time. Data is cached through the data acquisition program at the on-site acquisition points. Edge computing nodes are added to standardize, verify, and cache the raw data, and then transmit the data after compressing the data volume.

[0009] S2. Data transmission: A ground-level local area network is constructed to connect each collection point. Timestamp correction, deduplication, and incremental transmission are performed through a data preprocessing gateway. Packet transmission, data verification, and CSV format compression are adopted.

[0010] S3. Data Processing: The headquarters center classifies and stores the received data, establishes a real-time analysis model and early warning mechanism, and triggers graded early warnings based on thresholds and multi-parameter comprehensive scoring.

[0011] S4. Data Backup: Employs a backup strategy that combines full and incremental backups, with off-site encrypted storage and consistency verified through a hash algorithm;

[0012] S5. System Optimization: Regularly evaluate system performance and optimize sensor placement, caching strategies, and algorithm models.

[0013] Preferably, the standardization process for edge computing nodes in S1 includes:

[0014] Through the formula:

[0015]

[0016] Normalize data from different units and mark data that exceeds the measurement range.

[0017] Preferably, the incremental transmission strategy in S2 is expressed by the formula:

[0018] D 传输 =D current -D previous ;

[0019] Identify incremental data and set the data packet size to 50kb; trigger retransmission upon verification failure; where: D 传输 Indicates the incremental data that needs to be transmitted; D current Indicates the complete dataset currently collected; Dprevious This indicates the dataset that has been transmitted since the last synchronization.

[0020] Preferably, the multi-parameter comprehensive scoring formula in S3 is:

[0021]

[0022] Among them, T current It is the current cutter head torque, V current It is the current propulsion speed, T min and T max This is the preset normal range for the cutter head torque, V min and V max This is within the normal range of propulsion speed, where α1 and α2 are weighting coefficients; if S combined Exceeding the set threshold S threshold If the threshold is exceeded, a warning will be triggered; if the threshold is exceeded, a tiered warning will be triggered. Level 1 warnings will automatically adjust the construction parameters, while Level 2 and Level 3 warnings will require manual intervention.

[0023] Preferably, in S4, full backups are performed weekly, and incremental backups are performed daily, using a hash algorithm to identify data changes.

[0024] H block =Hash(D block );

[0025] Among them, D block H represents the content of the data block. block It is the hash value of the data block;

[0026] Verification during recovery to ensure integrity:

[0027] ΔH=|H restore -H backup |;

[0028] If ΔH = 0, it means that the data consistency verification has passed and the recovered data is complete and accurate;

[0029] If ΔH≠0, then data loss or corruption may occur during the recovery process, requiring repair or re-recovery.

[0030] The advantages of this invention compared to the prior art are:

[0031] Improve data acquisition efficiency: Raw data is collected in real time by the on-site data acquisition program without real-time format verification, and the edge computing nodes perform preliminary data processing, reducing the format verification step in the data acquisition process and thus improving data acquisition efficiency.

[0032] Reduce the data processing pressure on the headquarters center: The edge computing nodes perform preliminary processing on the collected raw data, such as formatting, unit conversion, timestamp synchronization, and abnormal data verification, and cache the data. Only the cleaned data is sent to the headquarters center, which reduces the amount of data transmitted to the headquarters, reduces the data processing pressure on the headquarters server, improves data transmission efficiency, and reduces the latency caused by too much data.

[0033] Ensuring data transmission quality and security: The construction of the ground-level internal LAN ensures high-speed and stable ground-level data transmission; the data preprocessing gateway performs rapid data correction, format unification, and deduplication to avoid invalid data transmission; the adoption of incremental transmission strategies, reasonable setting of data packet size, establishment of data verification mechanisms, and CSV format compression measures improve transmission efficiency, ensure data transmission quality and security, reduce network load, and shorten data transmission latency.

[0034] Real-time analysis and effective early warning: The headquarters center classifies and stores data for easy querying and analysis, and establishes a real-time analysis model to analyze key data in real time, providing a basis for construction decisions; the data early warning mechanism analyzes abnormal data, and based on preset thresholds and the analysis of multiple parameters, can promptly issue early warning information of different levels, notify management personnel through various means and take corresponding measures, and record early warning events, thereby improving the safety, efficiency and reliability of construction. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the S1 method in this invention;

[0037] Figure 2 This is a flowchart of the S2 method in this invention;

[0038] Figure 3 This is a flowchart of the S3 method in this invention. Detailed Implementation

[0039] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0040] The present invention will now be described in further detail with reference to the accompanying drawings.

[0041] Combination Figures 1-3 This invention discloses a digital management method for tunnel boring machine (TBM) construction, comprising data acquisition, transmission, processing, backup and recovery, and system optimization and updates. For data acquisition, various types of data are collected through multiple sensors. The acquisition program has a first-level caching mechanism, and edge computing nodes are added for preliminary processing. During data transmission, a ground-level internal local area network is constructed. The server has a receiving program and a caching mechanism, and a data preprocessing gateway is set up to perform various processing methods, including incremental transmission, packet processing, data verification, and CSV format compression. After receiving the data, the headquarters center classifies and stores it, establishes a real-time analysis model and an early warning mechanism, and issues different levels of warnings based on abnormal data. Data backup adopts a combined full and incremental strategy, with data stored and encrypted at an off-site disaster recovery center, and verified during recovery. System optimization and updates assess system performance, optimize transmission, adjust sensor placement and caching strategies based on the results, and update system functions as technology advances.

[0042] This method achieves efficient data collection, stable transmission, accurate analysis and processing, and secure backup and recovery during shield tunneling through the coordinated operation of each stage. At the same time, it can continuously optimize the system according to the actual situation, ensuring the smooth progress of construction, improving construction safety, efficiency and reliability, and adapting to the needs of shield tunneling technology development and process updates.

[0043] The present invention provides a digital management method for tunnel boring machine (TBM) construction, comprising:

[0044] S1. Construction of Data Acquisition System

[0045] Multiple sensors, including but not limited to pressure sensors, temperature sensors, displacement sensors, vibration sensors, and electrical sensors, are deployed at key locations on the tunnel boring machine (TBM) and construction site to collect various data such as TBM operating parameters, geological parameters, and construction environment parameters. A data acquisition program is established for each on-site data collection point, equipped with a primary caching mechanism. This caching mechanism can temporarily store collected data in the event of network anomalies or interruptions, ensuring no data loss. The cache capacity is reasonably set based on the data acquisition frequency and the duration of network outages; for example, it can be set to store data collected within the last 30 minutes. The data acquisition program is configured to collect all raw data in real time without real-time format verification to maximize acquisition efficiency. The collected data includes sensor measurements, equipment operating status information, and construction progress information.

[0046] Furthermore, edge computing nodes have been added. Located at the field data acquisition points, these nodes are primarily used for preliminary data processing, including data formatting, unit conversion, timestamp synchronization, and verification of abnormal data. These edge computing nodes operate alongside the data acquisition program; while the program stores raw data during network outages, the edge computing nodes preprocess the raw data, reducing the processing load on the headquarters when dealing with large volumes of data. Firstly, the raw data collected comes from various sensors (such as pressure sensors, temperature sensors, and displacement sensors). Before being transmitted to headquarters, this data undergoes unified processing at the edge computing nodes to ensure data consistency and quality.

[0047] To achieve this goal, the first step is to standardize the sensor data from different sources. Standardization refers to converting data collected by different sensors into a unified timestamp format and physical units. For example, sensors may use different time formats or different units of measurement (e.g., pressure may be expressed in Pascals or atmospheres, and temperature may be expressed in degrees Celsius or Fahrenheit). Standardization is performed using the following formula:

[0048]

[0049] The raw data consists of the collected numerical values, with the minimum and maximum values ​​representing the minimum and maximum values ​​within the sensor's data range, respectively. Standardized data is converted to a standard range, such as 0 to 1, which eliminates unit differences between different sensors, making the data more consistent and easier for subsequent analysis.

[0050] In addition, edge computing nodes perform preliminary verification to check if the data is within a reasonable range, avoiding the transmission of large amounts of abnormal data. If abnormal data is detected, such as exceeding the sensor's measurement range or logical inconsistencies, the data is marked. Common verification methods include setting maximum and minimum value boundaries; if data exceeds these boundaries, it is considered an outlier. For normal data, edge computing nodes format it into standardized data and buffer it before transmission.

[0051] When processing data, edge computing nodes can significantly reduce the amount of data that needs to be transmitted to headquarters by caching formatted and cleaned data. After initial processing at the field collection point, only the cleaned data needs to be sent to the headquarters center, instead of transmitting all the original data. This reduces the data processing pressure on the headquarters server and improves data transmission efficiency. Specifically, if each data packet is D in size, the edge computing node can compress the data before transmission, reducing the amount of data transmitted to R times the original size, where R is the data compression ratio.

[0052] Suppose a sensor collects 10 data points per second, with the raw data transmission rate being 10KB per second. After processing by edge computing nodes, the data can be compressed to 50% of its original size, resulting in a compressed data transmission rate of 5KB. Assuming 100 sensors are operating simultaneously at the construction site, the amount of data to be transmitted per second decreases from 1000KB to 500KB, significantly reducing data transmission bandwidth requirements and latency. This processing method not only improves data transmission efficiency but also effectively reduces latency caused by excessive data processing at headquarters.

[0053] S2, Data Transmission System Setup

[0054] A ground-level internal local area network (LAN) is constructed, connecting various data acquisition points, tunnel boring machine control systems, and construction equipment at the construction site to the LAN via wired or wireless means to ensure high-speed and stable ground-level data transmission. The ground-level server is equipped with a data receiving program and a primary caching mechanism to temporarily store received data and prevent data loss due to subsequent transmission failures.

[0055] A data preprocessing gateway is set up between the ground-level server and the headquarters center. The main function of this gateway is to receive the raw data transmitted from the field or the data after preliminary cleaning, and to perform rapid correction, format unification and deduplication on it, thereby avoiding the transmission of invalid data.

[0056] Specifically, during data transmission, timestamp correction is first applied to ensure the synchronization of time data collected by various sensors. This process eliminates time discrepancies caused by clock asynchrony between different devices. The timestamp correction formula is as follows:

[0057] T 校正 =T 原始 +Δt;

[0058] Among them, T 校正 It is the corrected timestamp; T 原始 Δt is the raw timestamp acquired by the sensor; Δt is the synchronization error or adjustment value, calculated by the system clock synchronization algorithm. This formula can unify the timestamps of different sensors to the same moment, avoiding data errors caused by inconsistent timestamps in subsequent analysis.

[0059] In terms of deduplication, the system automatically identifies and removes duplicate or invalid data based on the sensor's acquisition frequency, preset data range, and real-time data change characteristics. For example, if a sensor's acquired value remains unchanged for a period of time, the data can be considered redundant and will not be transmitted again, thus effectively reducing the size of data packets and the transmission frequency.

[0060] To further improve transmission efficiency, an incremental transmission strategy is adopted. This means that instead of transmitting all data to headquarters each time, only data that has changed or been newly added since the last synchronization is transmitted. This method significantly reduces the amount of data transmitted, especially at construction sites with large data volumes, greatly reducing bandwidth burden. Incremental transmission determines which data is new or updated by comparing version numbers or timestamps. The specific algorithm is as follows:

[0061] D 传输 =D current -D previous ;

[0062] Where: D 传输 Indicates the incremental data that needs to be transmitted; D current Indicates the complete dataset currently collected; D previous This indicates the dataset that has been transmitted since the last synchronization.

[0063] This algorithm can quickly identify and transmit changed data. Based on this, incremental data transmission ensures that only newly added data is transmitted each time, without transmitting redundant historical data, thereby further improving transmission efficiency.

[0064] During transmission, data is processed into packets, and the packet size is set appropriately based on network bandwidth and transmission stability, for example, each packet is 50kb. Simultaneously, a data verification mechanism is established to verify each packet, ensuring data integrity during transmission. If a packet verification error is detected, a retransmission mechanism is automatically triggered.

[0065] To ensure data transmission quality and security, CSV compression is employed. This process not only reduces the size of transmitted data but also lowers bandwidth usage and ensures data integrity and consistency during transmission. For example, suppose there are 100 sensors, each transmitting 10KB of data per second. Without compression and incremental transmission, the total data volume per second would be 1000KB. However, by compressing, the size of each data packet is reduced to 50%, meaning each sensor's data volume becomes 5KB, thus reducing the total transmission volume per second to 500KB. Furthermore, with incremental transmission, only the changed data is transmitted; assuming only 10% of the data is updated daily, the actual transmitted data volume is only 50KB. Through this optimization, data transmission efficiency is significantly improved, greatly reducing network load and shortening data transmission latency.

[0066] S3, Data Processing and Management

[0067] After receiving the data, the headquarters center categorizes and stores it in different database tables based on data type, such as a tunnel boring machine (TBM) operation database, a geological database, and a construction progress database, facilitating subsequent querying and analysis. A real-time data analysis model is established to perform real-time analysis of key data. For example, by analyzing parameters such as TBM cutterhead torque and propulsion speed, the tunneling status of the TBM can be assessed in real time; based on changes in geological data, the geological conditions ahead can be predicted, providing a basis for construction decisions.

[0068] A data early warning mechanism is set up to analyze the abnormal data marked in S1. When the analysis results exceed a preset threshold, an early warning message is automatically issued. The early warning message is sent to relevant managers through various means such as SMS, email, and system pop-ups to ensure timely action.

[0069] Specifically, through the preliminary data cleaning and verification mechanism set in S1, the system can mark abnormal data. Threshold verification is used to detect whether any parameters exceed the normal range, such as the tunnel boring machine cutterhead torque T. current Exceeding the preset maximum value T max Or minimum value T min This data is then marked as anomalous. Furthermore, the system can also flag data anomalies by monitoring trend changes. For example, if the propulsion speed V... current If a sudden, significant fluctuation occurs, the system will identify it as an anomaly and mark it. This marked anomalous data will not be directly transmitted to the headquarters; instead, it will undergo initial cleaning by adding edge computing nodes to reduce unnecessary data transmission burden.

[0070] Next, data alerts are triggered by comparing real-time data with preset thresholds. A simple threshold trigger condition is, for example, the cutter head torque T. current More than T max or below T min When this happens, an early warning is immediately triggered. However, in some cases, exceeding the limit of a single parameter is insufficient to determine whether the system is abnormal; therefore, analysis based on multiple parameters is more accurate. For example, a comprehensive score S can be set. combined The formula for determining the operating status of a tunnel boring machine is as follows:

[0071]

[0072] Among them, T current It is the current cutter head torque, V current It is the current propulsion speed, T min and T max This is the preset normal range for the cutter head torque, V min and V max This is within the normal range of propulsion speed, where α1 and α2 are weighting coefficients. If S combinedExceeding the set threshold S threshold This will trigger an early warning. The purpose of this formula is to comprehensively consider the impact of multiple parameters on the tunnel boring machine's operating status, thereby improving the accuracy of anomaly detection. For example, if the cutterhead torque is close to its upper limit while the propulsion speed is too high, it indicates that the equipment is encountering unstable geological conditions, and the system will use this formula to make a comprehensive judgment and issue an alarm.

[0073] Regarding the warning level settings, multiple levels are categorized based on the severity of the abnormal data. Level 1 warnings (Severe) are used to handle abnormal situations that have a significant impact on safety or construction progress, such as persistently excessive cutterhead torque or abnormally high feed speed, leading to equipment damage or construction halt. When triggered, immediate work stoppage and inspection are required. Level 2 warnings (Medium) are used for relatively serious situations that do not pose an immediate threat, such as occasional excessive cutterhead torque. The system will prompt management personnel to check the equipment and adjust construction parameters. Level 3 warnings (Minor) are used for smaller abnormal fluctuations, such as slight delays in construction progress. These warnings remind management personnel to adjust resources or optimize processes, but will not result in an immediate work stoppage.

[0074] Once an alert is triggered, the system will notify relevant management personnel through various means such as SMS, email, and pop-ups, and respond with preset measures. For example, for a Level 1 alert, the system can automatically adjust construction parameters (such as feed speed or cutterhead speed); for Level 2 and Level 3 alerts, manual intervention is required to check equipment or adjust the schedule. Furthermore, all alert events are recorded and tracked, forming a detailed alert log for subsequent analysis and decision-making.

[0075] Through its early warning mechanism, the system can promptly identify and respond to potential problems during construction, improving safety, efficiency, and reliability. For example, in a particular construction operation, if the cutterhead torque exceeds the limit and the feed speed is too fast, the system will issue a comprehensive score (S). combined A level-two warning was triggered, notifying on-site operators to adjust construction parameters promptly. This efficient early warning and response system prevented equipment malfunctions and significant delays in construction progress, thus ensuring the smooth progress of the project.

[0076] S4, Data Backup and Recovery

[0077] In this step, the first priority is to ensure the efficiency and reliability of the backup. A strategy combining full and incremental backups can reduce the impact on system performance and backup time while ensuring data security. A full backup backs up all important data, while an incremental backup only backs up data that has changed since the last backup. This approach ensures data recovery integrity while reducing storage space usage and backup time.

[0078] Specifically, full backups are scheduled weekly, meaning all data is fully backed up each week, while incremental backups are performed daily, backing up only newly added or updated data. Full backups consume more storage space but ensure data can be restored to its original state under any circumstances. Incremental backups only record changed portions, significantly reducing backup time and storage pressure. To make incremental backups more efficient, timestamps and hash algorithms are used to identify which data has changed. For example, a hash algorithm is used to calculate the hash value H of a data block. block If the hash value of the current data block is different from the hash value at the time of the last backup, it is considered that the data block has changed and an incremental backup must be performed.

[0079] The formula for calculating hash value is as follows:

[0080] H block =Hash(D block );

[0081] Among them, D block H represents the content of the data block. block This is the hash value of the data block. If the current hash value differs from the hash value saved during the last backup, the data block needs to be incrementally backed up. In this way, the system can accurately identify data changes, improve backup efficiency, and ensure data integrity.

[0082] In terms of backup data storage, using an off-site disaster recovery center to store backup data can effectively prevent the risk of local data loss. The disaster recovery center deploys multiple redundant storage devices and provides automatic failover and recovery capabilities. At the disaster recovery center, backup data is stored using encryption to ensure data security. The transmission of backup data also requires encryption algorithms, such as AES encryption, to ensure that data is not leaked during transmission.

[0083] In the event of data loss or corruption, data can be quickly recovered from the disaster recovery center to ensure the continuity of construction management. During the recovery process, data verification is required to ensure data integrity and consistency. Specifically, if the verification value is inconsistent when comparing the recovered data with the original data, the recovery process is considered a failure, requiring re-recovery or supplementary recovery. Therefore, a data consistency verification algorithm is used to compare the hash values ​​of the recovered data with those of the backup data. Assume the hash value of the recovered data block is H. restore The hash value of the backup data block is H. backup Then, the following consistency check formula is used for comparison:

[0084] ΔH=|H restore -H backup |;

[0085] If ΔH = 0, it means the data consistency verification passed and the recovered data is complete and accurate. If ΔH ≠ 0, it means that data loss or corruption occurred during the recovery process, and repair or re-recovery is required.

[0086] For example, suppose a tunnel boring machine (TBM) project discovers an inconsistency in the hash value of a data block during the recovery process. This indicates that the data block was corrupted during backup or recovery. The system will search for the latest version of the data block using incremental backup data and re-recover it until the consistency and integrity of the recovered data meet the requirements. In this way, the system can quickly and accurately recover lost or corrupted data and avoid interruptions in construction management due to data inconsistency.

[0087] S5, System Optimization and Updates

[0088] This step begins with a detailed performance evaluation of the data acquisition and transmission system. Evaluation metrics primarily include data acquisition frequency, transmission latency, and system stability, ensuring the system meets real-time and stability requirements during actual construction. To quantify these metrics, the performance of the data acquisition and transmission system is evaluated using the following formula:

[0089]

[0090] Here, data acquisition latency refers to the time from when data is collected by the sensor to when it is sent to the buffer; transmission latency refers to the time required for data to be transmitted from the field server to the headquarters center; and data loss rate refers to the proportion of data lost during data acquisition and transmission. The purpose of this formula is to comprehensively evaluate the system's response speed and data reliability, and to make adjustments based on the evaluation results.

[0091] Regularly evaluating system performance can accurately identify bottlenecks in the data acquisition and transmission system. For example, if the evaluation results indicate excessively high transmission latency, it may be due to insufficient network bandwidth or aging transmission equipment. In this case, upgrading network equipment or increasing network bandwidth can optimize transmission performance. If the data loss rate is too high, it may be due to sensor malfunction or inappropriate caching strategies, requiring inspection and optimization of sensor placement or improvement of the stability of the caching mechanism.

[0092] The key to adjusting sensor placement is ensuring the comprehensiveness and accuracy of data acquisition. By assessing the deployment density and distribution of sensors, it can be determined whether more sensors need to be added in certain key areas. For example, in areas with significant variations in soil conditions, it may be necessary to add pressure or displacement sensors to acquire more data in real time, aiding in the analysis and prediction of geological conditions.

[0093] Optimizing caching strategies is also an important step in improving system performance. By properly adjusting the caching mechanism, data loss and transmission latency can be reduced. Assume the cache capacity is C. cache And set the cache update frequency to f. update The relationship between cache capacity and update frequency can be expressed by the following formula:

[0094] C cache =f update ×T data ;

[0095] Among them, T data f is the time interval between each data collection. update This refers to the frequency of cache updates. By properly configuring C... cache and f update This ensures that data is not lost during network outages and improves the speed of data collection and transmission.

[0096] Furthermore, with the development of tunnel boring machine (TBM) construction technology and the updating of construction techniques, the system's functions also need to be updated in a timely manner. For example, with the advancement of geological exploration technology, new sensor types (such as high-precision temperature sensors or laser rangefinders) can be added to adapt to new construction needs. At the same time, data analysis models also need to be continuously updated to adapt to new construction requirements. More advanced artificial intelligence and machine learning algorithms can be introduced to predict the working status of the TBM, thereby improving construction efficiency and safety.

[0097] For example, with the application of new geological detection technologies, the system can combine historical data from geological databases and use machine learning algorithms to predict potential geological obstacles encountered during the tunnel boring machine's (TBM) advancement. In this case, the system needs to adjust its tunneling strategy in real time, such as changing the advance speed or replacing the cutterhead, to ensure smooth construction. During this process, the machine learning algorithm makes predictions using the following formula:

[0098]

[0099] in, This represents the predicted operating state of the tunnel boring machine, x1, x2, ..., x n These are the input sensor data (such as feed speed, cutterhead torque, etc.), β0,β1,...,β n These are the regression coefficients of the model. By updating these regression coefficients in real time, the accuracy of predictions is continuously improved, thereby adjusting construction parameters and optimizing the construction process.

[0100] In summary, compared with the prior art, the present invention has the following advantages:

[0101] 1. Data Acquisition and Preprocessing:

[0102] Vibration sensors (range 0-1000Hz) are installed on the cutterhead of the tunnel boring machine. The raw data of the edge nodes is normalized to the range of 0-1 using a formula. When an instantaneous frequency exceeding 800Hz is detected, it is marked as an anomaly, and only the valid data segment is transmitted, reducing the data volume of a single sensor from 10KB / s to 4KB / s.

[0103] 2. Warning Trigger:

[0104] When ground-penetrating radar detects sudden changes in earth pressure (T) current =85MPa, preset T max =80MPa) and propulsion speed V current =40mm / min(V max =35mm / min), taking α1=0.6, α2=0.4, we calculate S combined =0.92, exceeding the threshold of 0.9 triggers a level one warning, and the system automatically reduces the propulsion speed to 30mm / min.

[0105] 3. Data recovery:

[0106] If a cyberattack corrupts the database, incremental backups are restored from the disaster recovery center. By comparing hash values, three data blocks with ΔH = 0 are identified. The system automatically retrieves the previous day's backup data to complete the repair, reducing the total recovery time by 78% compared to traditional full recovery.

[0107] 4. Industrial applicability:

[0108] This method has been applied to a subway tunnel project, achieving real-time data processing delay of less than 2 seconds, reducing the early warning response time to within 10 seconds, and significantly reducing the equipment failure rate, thus verifying the significant technological advancement of this invention.

[0109] Finally, any aspects not fully described in this invention utilize existing mature products and technologies.

[0110] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A digital management method for tunnel boring machine (TBM) construction, characterized in that: Includes the following steps: S1. Data Acquisition and System Construction: Multiple sensors are deployed on the tunnel boring machine and construction site to collect raw data in real time, and the data is cached by the data acquisition program at the on-site acquisition points. Add edge computing nodes to standardize, check for anomalies, and cache the raw data, and then transmit the data after compressing the data volume. S2. Data transmission: A ground-level local area network is constructed to connect each collection point. Timestamp correction, deduplication, and incremental transmission are performed through a data preprocessing gateway. Packet transmission, data verification, and CSV format compression are adopted. S3. Data Processing: The headquarters center classifies and stores the received data, establishes a real-time analysis model and early warning mechanism, and triggers graded early warnings based on thresholds and multi-parameter comprehensive scoring. S4. Data Backup: Employs a backup strategy that combines full and incremental backups, with off-site encrypted storage and consistency verified through a hash algorithm; S5. System Optimization: Regularly evaluate system performance and optimize sensor placement, caching strategies, and algorithm models.

2. The digital management method for tunnel boring machine construction according to claim 1, characterized in that: The standardization process for edge computing nodes in S1 includes: Through the formula: Normalize data from different units and mark data that exceeds the measurement range.

3. The digital management method for tunnel boring machine construction according to claim 1, characterized in that: The incremental transmission strategy in S2 identifies incremental data using the following formula: D 传输 =D current -D previous ; The data packet size is set to 50kb, and a retransmission is triggered when verification fails; where: D 传输 Indicates the incremental data that needs to be transmitted; D current Indicates the complete dataset currently collected; D previous This indicates the dataset that has been transmitted since the last synchronization.

4. The digital management method for tunnel boring machine construction according to claim 1, characterized in that: The multi-parameter comprehensive scoring formula in S3 is as follows: Among them, T current It is the current cutter head torque, V current It is the current propulsion speed, T min and T max This is the preset normal range for the cutter head torque, V min and V max This is within the normal range of propulsion speed, where α1 and α2 are weighting coefficients; if S combined Exceeding the set threshold S threshold If the threshold is exceeded, a warning will be triggered; if the threshold is exceeded, a tiered warning will be triggered. Level 1 warnings will automatically adjust the construction parameters, while Level 2 and Level 3 warnings will require manual intervention.

5. The digital management method for tunnel boring machine construction according to claim 1, characterized in that: In S4, full backups are performed weekly, and incremental backups are performed daily, using a hash algorithm to identify data changes. H block =Hash(D block ); Among them, D block H represents the content of the data block. block It is the hash value of the data block; Verification during recovery to ensure integrity: ΔH=|H restore -H backup |; If ΔH = 0, it means that the data consistency verification has passed and the recovered data is complete and accurate; If ΔH≠0, then data loss or corruption may occur during the recovery process, requiring repair or re-recovery.

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