Tunnel geology monitoring management system based on big data

Abstract

The invention specifically relates to a tunnel geology monitoring management system based on big data. The system comprises the following parts: a data acquisition module; the data analysis module is used for analyzing the state information data related to the tunnel geology to obtain a corresponding displacement change coefficient, a stress change coefficient and an osmotic pressure coefficient; an evaluation module; and an early warning module. According to the invention, by means of various professional sensors, a comprehensive data acquisition system is constructed, and key elements influencing the geological stability of the tunnel are covered; on the basis, by calculating the displacement change coefficient, the stress change coefficient, the osmotic pressure coefficient and the evaluation coefficient, the tunnel geological condition can be deeply analyzed, the safety state of the tunnel geology can be accurately evaluated, and a firm and reliable data support is provided for subsequent decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and in particular to a tunnel geological monitoring and management system based on big data. Background Art

[0002] During the construction and operation of tunnel projects, the geological conditions of the tunnel play a decisive role in the safety and stability of the project.

[0003] Factors such as displacement and stress changes in the tunnel's surrounding rock, as well as groundwater seepage pressure, can directly impact tunnel structural stability and may even lead to serious accidents such as collapse and water seepage. Traditional tunnel geological monitoring methods often suffer from incomplete data collection, limited analysis and processing capabilities, and untimely early warnings, making them unable to meet the geological safety monitoring needs of modern tunnel projects.

[0004] Therefore, the development of an efficient and accurate tunnel geological monitoring and management system has important practical significance. Therefore, a tunnel geological monitoring and management system based on big data is needed to deal with the above-mentioned problems. Summary of the Invention

[0005] The purpose of the present invention is to propose a tunnel geological monitoring and management system based on big data in order to solve the above problems.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A tunnel geological monitoring and management system based on big data, including the following parts:

[0008] Data acquisition module: collects tunnel geological related data, including displacement change data of tunnel surrounding rock, stress change data of tunnel surrounding rock, and seepage pressure data of groundwater in the tunnel;

[0009] Data analysis module: After analyzing the tunnel geological status information data, the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient are obtained;

[0010] Evaluation module: After comprehensive analysis of the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the evaluation coefficient is obtained, and the early warning level of the tunnel geology is evaluated based on the evaluation coefficient;

[0011] Early warning module: Carry out corresponding early warning processing according to the early warning level corresponding to the evaluation coefficient.

[0012] Preferably, the displacement change of the tunnel surrounding rock is measured by a displacement sensor; the stress change of the tunnel surrounding rock is measured by a stress sensor; and the seepage pressure of the groundwater in the tunnel is measured by a seepage pressure sensor.

[0013] Preferably, the analysis of the tunnel geology-related data to obtain the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient specifically includes the following parts:

[0014] Preset the standard parameter value of the displacement change of the tunnel surrounding rock in the tunnel state information, obtain the displacement change value of the tunnel surrounding rock within the preset time period, and perform difference calculation between the displacement change value and the standard parameter value corresponding to the parameter to obtain the standard deviation value corresponding to the parameter;

[0015] A threshold value of the standard deviation is preset, and the standard deviation values greater than the threshold value are counted to obtain the abnormal standard deviation value;

[0016] Preset the allowable fluctuation range of the standard deviation value of the displacement change parameter of the surrounding rock, match the standard deviation value of the displacement change parameter of the surrounding rock with the allowable fluctuation range, and if the standard deviation value is not within its corresponding allowable floating range, mark the standard deviation value as deviating from the standard deviation value;

[0017] Obtain the maximum deviation standard deviation value and the minimum deviation standard deviation value within a preset time period, and extract the time difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value, and record it as the extreme deviation timing;

[0018] Record any generation time that deviates from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; preset the allowable fluctuation range of the adjacent time difference value, and record the adjacent time difference value that is not within the allowable fluctuation range of the adjacent time difference value as the adjacent time anomaly value;

[0019] The standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient;

[0020] According to the above-mentioned process of analyzing the displacement change of the tunnel surrounding rock and obtaining the displacement change coefficient, the stress change coefficient and the seepage pressure coefficient are obtained after analyzing the stress change data of the tunnel surrounding rock and the seepage pressure data of the groundwater in the tunnel.

[0021] Preferably, the standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient, which specifically includes the following parts:

[0022] The weight factors of the preset standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value are respectively multiplied by the standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value with their corresponding weight factors and then summed to obtain the displacement variation coefficient.

[0023] Preferably, the evaluation coefficient obtained by comprehensively analyzing the displacement variation coefficient, the stress variation coefficient, and the seepage pressure coefficient specifically includes the following parts:

[0024] After normalizing the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the displacement variation coefficient and stress variation coefficient are used as the two right-angled sides of a right triangle, respectively. The remaining side is connected to form a complete right triangle. The seepage pressure coefficient is used as the height of the right triangle to construct a triangular pyramid model, and the surface area of the triangular pyramid is calculated. The surface area of the triangular pyramid is recorded as the evaluation coefficient.

[0025] Preferably, the evaluation of the early warning level of tunnel geology by using the evaluation coefficient specifically includes the following parts:

[0026] Three groups of threshold value ranges are preset, and the value range of each group of threshold values corresponds to a warning level. The evaluation coefficient is matched with the value range of the three groups of threshold values to obtain the warning level corresponding to the evaluation coefficient; the warning levels include level one warning, level two warning and level three warning.

[0027] Preferably, the corresponding warning processing according to the warning level corresponding to the evaluation coefficient includes the following parts:

[0028] When the warning level corresponding to the evaluation coefficient is level one, a low-level alarm is issued to alert relevant personnel, and the displacement change coefficient, stress change coefficient, seepage pressure coefficient, and evaluation coefficient are sent to the relevant personnel's smart terminal. Personnel are arranged to conduct daily inspections of the tunnel to check for obvious signs of geological changes.

[0029] When the warning level corresponding to the evaluation coefficient is level 2: Based on the level 1 warning, an intermediate alarm is issued to notify the tunnel management department and relevant technical personnel; the frequency of tunnel geological data collection is increased, and changes in parameters such as displacement, stress, and seepage pressure are closely monitored;

[0030] When the warning level corresponding to the evaluation coefficient is level three: based on the warning level being level two, the highest level alarm is immediately issued, and relevant personnel, including on-site staff, heads of tunnel management departments, geological experts, etc., are notified through various means; at the same time, emergency plans are automatically activated, such as stopping all construction or operation activities in the tunnel and evacuating on-site personnel to a safe area.

[0031] Preferably, the low-level alarm emits a slight prompt sound and a flashing blue indicator light in the tunnel monitoring center to alert the on-duty personnel to the abnormality of the tunnel geological data;

[0032] Medium alarm: A relatively mild alarm sound and flashing yellow warning light will be emitted inside the tunnel and management area to attract the attention of relevant personnel;

[0033] Highest level alarm: Inside the tunnel and related management areas, a high-decibel sirens emit continuous and sharp sound signals, accompanied by flashing red warning lights, to alert on-site personnel.

[0034] Preferably, the system further comprises a monitoring and management module: monitoring the working status of each sensor in real time, and evaluating the status of the sensor by calculating indicators such as the transmission frequency of sensor data and the continuity of data;

[0035] Real-time monitoring of the performance of each module, including the transmission rate of the data transmission module, the storage capacity and read and write speed of the data storage module, and the computing efficiency of the data analysis module;

[0036] The collected tunnel geological data and analysis results are presented to users in the form of intuitive charts and reports;

[0037] Data storage module: stores the tunnel geological data obtained from the data acquisition module, as well as the displacement variation coefficient, stress variation coefficient, seepage pressure coefficient, and evaluation coefficient generated by the data analysis module; adopts a distributed storage architecture to store data files on a preset number of nodes;

[0038] Provide the data required by the assessment module and early warning module to support their real-time assessment and early warning operations;

[0039] Provide the monitoring and management module with the operating status information of the data storage module, such as storage space usage and data backup status.

[0040] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0041] 1. The present invention uses a variety of professional sensors to build a comprehensive data acquisition system that covers the key factors affecting the geological stability of the tunnel. On this basis, by calculating the displacement variation coefficient, stress variation coefficient, seepage pressure coefficient, and evaluation coefficient, it can deeply analyze the tunnel geological conditions and accurately assess the safety status of the tunnel geology, providing solid and reliable data support for subsequent decision-making.

[0042] 2. This invention clearly divides warning levels into primary, secondary, and tertiary levels based on assessment coefficients, each matched with corresponding warning measures. Low-level, intermediate, and highest-level alerts promptly convey geological anomaly information through different means, ensuring rapid awareness among relevant personnel. Furthermore, subsequent actions are automatically linked to each warning level. Furthermore, a monitoring and management module monitors the status of sensors and modules in real time, while a data storage module utilizes a distributed architecture and comprehensive backup mechanisms to ensure data security. These measures comprehensively guarantee the safety and efficiency of tunnel projects from construction to operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0045] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0046] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0047] See also Figure 1 As shown, the present invention provides a technical solution:

[0048] A tunnel geological monitoring and management system based on big data, including the following parts:

[0049] Data acquisition module: collects tunnel geological related data, including displacement change data of tunnel surrounding rock, stress change data of tunnel surrounding rock, and seepage pressure data of groundwater in the tunnel;

[0050] Displacement sensors are used to measure the displacement of tunnel surrounding rock. Displacement is one of the important indicators reflecting the stability of tunnel surrounding rock, and its changes can directly reflect the deformation degree and trend of the surrounding rock. In tunnel engineering, commonly used displacement sensors include total stations, levels, and convergence meters.

[0051] The stress changes in the tunnel surrounding rock are measured by stress sensors. The stress changes can reflect the mechanical state inside the surrounding rock. In tunnel engineering, commonly used stress sensors include strain gauges and pressure cells.

[0052] A strain gauge is a sensor based on the resistance strain effect. It reflects the strain of a metal wire or semiconductor material by measuring the resistance change. When the strain gauge is attached to the surface or inside the tunnel surrounding rock, when the surrounding rock is deformed by force, the strain gauge will also deform, and its resistance value will change. By measuring the change in resistance value, the strain of the surrounding rock can be calculated, and then the stress change can be obtained. It is suitable for measuring local stress changes on the surface or inside the tunnel surrounding rock, and is often used to monitor stress concentration areas in the tunnel surrounding rock.

[0053] The seepage pressure of groundwater in tunnels is measured using a pressure sensor. The magnitude of the seepage pressure is closely related to the groundwater head height. The relationship between the seepage pressure p and the head height h is p = ρgh, where ρ is the density of water and g is the acceleration due to gravity. By measuring the seepage pressure, changes in the groundwater level can be indirectly understood and the impact of groundwater on the stability of the tunnel surrounding rock can be assessed. In tunnel engineering, commonly used seepage pressure sensors include vibrating wire piezometers and resistance piezometers.

[0054] A vibrating-wire osmometer is a sensor that measures osmotic pressure based on the relationship between the frequency of the vibrating wire and the pressure it experiences. It primarily consists of a vibrating wire, a diaphragm, and a coil. When osmotic pressure acts on the diaphragm, the diaphragm deforms, causing the tension in the vibrating wire to change, which in turn changes the wire's vibration frequency. By measuring the wire's vibration frequency, the osmotic pressure can be calculated. The sensor is suitable for long-term monitoring of groundwater osmotic pressure changes in tunnels, particularly in harsh environments.

[0055] Data analysis module: After analyzing the tunnel geological status information data, the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient are obtained;

[0056] After analyzing the tunnel geological data, the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient are obtained, which specifically include the following parts:

[0057] Preset the standard parameter value of the displacement change of the tunnel surrounding rock in the tunnel state information, obtain the displacement change value of the tunnel surrounding rock within the preset time period, and perform difference calculation between the displacement change value and the standard parameter value corresponding to the parameter to obtain the standard deviation value corresponding to the parameter;

[0058] A threshold value of the standard deviation is preset, and the standard deviation values greater than the threshold value are counted to obtain the abnormal standard deviation value;

[0059] Preset the allowable fluctuation range of the standard deviation value of the displacement change parameter of the surrounding rock, match the standard deviation value of the displacement change parameter of the surrounding rock with the allowable fluctuation range, and if the standard deviation value is not within its corresponding allowable floating range, mark the standard deviation value as deviating from the standard deviation value;

[0060] Obtain the maximum deviation standard deviation value and the minimum deviation standard deviation value within a preset time period, and extract the time difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value, and record it as the extreme deviation timing;

[0061] Record any generation time that deviates from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; preset the allowable fluctuation range of the adjacent time difference value, and record the adjacent time difference value that is not within the allowable fluctuation range of the adjacent time difference value as the adjacent time anomaly value;

[0062] The standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient;

[0063] The standard deviation, abnormal standard deviation, deviation from standard deviation, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient, which specifically includes the following parts:

[0064] The weight factors of the preset standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value are respectively multiplied by the standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value with their corresponding weight factors, and then summed to obtain the displacement variation coefficient;

[0065] Based on the above-mentioned process of analyzing the displacement change of the tunnel surrounding rock and obtaining the displacement change coefficient, the stress change data of the tunnel surrounding rock and the seepage pressure data of the groundwater in the tunnel are analyzed to obtain the stress change coefficient and the seepage pressure coefficient respectively;

[0066] The evaluation coefficient is obtained by comprehensively analyzing the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, which specifically includes the following parts:

[0067] After normalizing the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the displacement variation coefficient and stress variation coefficient are used as the two right-angled sides of a right triangle, and the remaining side is connected to form a complete right triangle. The seepage pressure coefficient is used as the height of the right triangle to construct a triangular pyramid model, and the surface area of the triangular pyramid is calculated. The surface area of the triangular pyramid is recorded as the evaluation coefficient;

[0068] Evaluation module: After comprehensive analysis of the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the evaluation coefficient is obtained, and the early warning level of the tunnel geology is evaluated based on the evaluation coefficient;

[0069] The early warning level of tunnel geology is assessed through the evaluation coefficient, which specifically includes the following parts:

[0070] Three groups of threshold value ranges are preset, and the value range of each group of threshold values corresponds to an early warning level. The evaluation coefficient is matched with the value range of the three groups of threshold values to obtain the early warning level corresponding to the evaluation coefficient; the early warning levels include level one, level two, and level three.

[0071] Early warning module: Carry out corresponding early warning processing according to the early warning level corresponding to the evaluation coefficient;

[0072] According to the warning level corresponding to the evaluation coefficient, corresponding warning processing is carried out, including the following parts:

[0073] When the warning level corresponding to the evaluation coefficient is level one, a low-level alarm is issued to alert relevant personnel, and the displacement change coefficient, stress change coefficient, seepage pressure coefficient, and evaluation coefficient are sent to the relevant personnel's smart terminal. Routine inspections of the tunnel are arranged to check for obvious signs of geological changes. The causes of the abnormal data are analyzed to determine whether further measures are needed. At the same time, geological data is continuously monitored to ensure safe operation of the tunnel.

[0074] When the warning level corresponding to the evaluation coefficient is Level 2: Based on the Level 1 warning level, an intermediate alert is issued to notify the tunnel management department and relevant technical personnel; the frequency of tunnel geological data collection is increased, and changes in parameters such as displacement, stress, and seepage pressure are closely monitored; professional personnel are organized to conduct a comprehensive inspection of the tunnel to assess possible safety hazards; and appropriate response measures are formulated, such as strengthening tunnel support and adjusting the construction schedule, to reduce the risk of disasters.

[0075] When the evaluation coefficient corresponds to a Level 3 warning level, the highest level alert is immediately issued, based on the Level 2 warning level. Relevant personnel, including on-site staff, tunnel management department heads, and geological experts, are notified through various means. At the same time, emergency plans are automatically activated, such as halting all construction or operational activities within the tunnel and evacuating on-site personnel to a safe area. A professional geological exploration team is immediately dispatched to conduct a detailed survey of the geological conditions surrounding the tunnel to analyze the likelihood and scope of the disaster. Real-time monitoring of various geological data within the tunnel, such as displacement, stress, and seepage pressure, is performed to keep abreast of geological changes.

[0076] Low-level alarm: A slight beep and a flashing blue indicator light are emitted in the tunnel monitoring center to alert the on-duty personnel to abnormalities in the tunnel geological data;

[0077] Medium alarm: A relatively mild alarm sound and flashing yellow warning light will be emitted inside the tunnel and management area to attract the attention of relevant personnel;

[0078] Highest level alarm: Inside the tunnel and related management areas, a high-decibel siren will emit a continuous and sharp sound signal, accompanied by a flashing red warning light, to alert on-site personnel.

[0079] Monitoring and management module: monitors the working status of each sensor in real time and evaluates the status of the sensor by calculating indicators such as the transmission frequency of sensor data and the continuity of data;

[0080] For example, if the data transmission frequency of a sensor is lower than the set threshold, it is considered that the sensor may be faulty. At the same time, by analyzing the continuity of the data, if there is a long interruption or abnormal fluctuation in the data, it can also be determined that there is a problem with the sensor;

[0081] Real-time monitoring of the performance of each module, including the transmission rate of the data transmission module, the storage capacity and read and write speed of the data storage module, and the computing efficiency of the data analysis module;

[0082] When the transmission rate is lower than the set threshold, the data transmission module needs to be checked and optimized;

[0083] The collected tunnel geological data and analysis results are presented to users in the form of intuitive charts and reports. Line charts can be used to show the changing trends of displacement, stress and other data over time; bar charts can be used to compare seepage pressure data at different locations; and pie charts can be used to show the distribution of different warning levels. Through data visualization, users can more intuitively understand the tunnel geological conditions.

[0084] Data storage module: stores the tunnel geological data obtained from the data acquisition module, as well as the displacement variation coefficient, stress variation coefficient, seepage pressure coefficient, and evaluation coefficient generated by the data analysis module; adopts a distributed storage architecture to store data files on a preset number of nodes;

[0085] Provide the data required by the assessment module and early warning module to support their real-time assessment and early warning operations;

[0086] Provide the monitoring and management module with the operating status information of the data storage module, such as storage space usage and data backup status;

[0087] Full backup of database data is performed daily, and full backup of HDFS data files is performed weekly. Backup data is stored in an off-site data center to prevent data loss due to local disasters.

[0088] Perform incremental backups of updated data between full backups to reduce backup time and storage space usage.

[0089] When a failure occurs, restore to the last backup state through the recovery tool;

[0090] Encrypt sensitive data stored in databases and file systems, such as using the AES encryption algorithm; assign different access rights to different user roles, such as administrators can perform full data operations, while ordinary users can only query data.

[0091] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0092] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tunnel geological monitoring and management system based on big data, characterized in that: Includes the following sections: Data acquisition module: collects tunnel geological related data, including displacement change data of tunnel surrounding rock, stress change data of tunnel surrounding rock, and seepage pressure data of groundwater in the tunnel; Data analysis module: After analyzing the tunnel geological status information data, the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient are obtained; Evaluation module: After comprehensive analysis of the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the evaluation coefficient is obtained, and the early warning level of the tunnel geology is evaluated based on the evaluation coefficient; Early warning module: Carry out corresponding early warning processing according to the early warning level corresponding to the evaluation coefficient.

2. The tunnel geological monitoring and management system based on big data according to claim 1 is characterized in that: The displacement change of the tunnel surrounding rock is measured by the displacement sensor; the stress change of the tunnel surrounding rock is measured by the stress sensor; and the seepage pressure of the groundwater in the tunnel is measured by the seepage pressure sensor.

3. The tunnel geological monitoring and management system based on big data according to claim 1 is characterized in that: The above analysis of the tunnel geological data yields the corresponding displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, which specifically include the following parts: Preset the standard parameter value of the displacement change of the tunnel surrounding rock in the tunnel state information, obtain the displacement change value of the tunnel surrounding rock within the preset time period, and perform difference calculation between the displacement change value and the standard parameter value corresponding to the parameter to obtain the standard deviation value corresponding to the parameter; A threshold value of the standard deviation is preset, and the standard deviation values greater than the threshold value are counted to obtain the abnormal standard deviation value; Preset the allowable fluctuation range of the standard deviation value of the displacement change parameter of the surrounding rock, match the standard deviation value of the displacement change parameter of the surrounding rock with the allowable fluctuation range, and if the standard deviation value is not within its corresponding allowable floating range, mark the standard deviation value as deviating from the standard deviation value; Obtain the maximum deviation standard deviation value and the minimum deviation standard deviation value within a preset time period, and extract the time difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value, and record it as the extreme deviation timing; Record any generation time that deviates from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; The allowed fluctuation range of the adjacent time difference is preset, and the adjacent time difference values that are not within the allowed fluctuation range of the adjacent time difference are recorded as adjacent time abnormal values; The standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient; According to the above-mentioned process of analyzing the displacement change of the tunnel surrounding rock and obtaining the displacement change coefficient, the stress change coefficient and the seepage pressure coefficient are obtained after analyzing the stress change data of the tunnel surrounding rock and the seepage pressure data of the groundwater in the tunnel.

4. The tunnel geological monitoring and management system based on big data according to claim 3 is characterized in that: The standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time abnormal value are comprehensively processed to obtain the displacement variation coefficient, which specifically includes the following parts: The weight factors of the preset standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value are respectively multiplied by the standard deviation value, abnormal standard deviation value, deviation standard deviation value, extreme deviation timing, and adjacent time anomaly value with their corresponding weight factors and then summed to obtain the displacement variation coefficient.

5. The tunnel geological monitoring and management system based on big data according to claim 4 is characterized in that: The evaluation coefficient is obtained by comprehensively analyzing the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, which specifically includes the following parts: After normalizing the displacement variation coefficient, stress variation coefficient, and seepage pressure coefficient, the displacement variation coefficient and stress variation coefficient are used as the two right-angled sides of a right triangle, respectively. The remaining side is connected to form a complete right triangle. The seepage pressure coefficient is used as the height of the right triangle to construct a triangular pyramid model, and the surface area of the triangular pyramid is calculated. The surface area of the triangular pyramid is recorded as the evaluation coefficient.

6. The tunnel geological monitoring and management system based on big data according to claim 5, characterized in that: The evaluation of the early warning level of tunnel geology by the evaluation coefficient specifically includes the following parts: Three groups of threshold value ranges are preset, and the value range of each group of threshold values corresponds to an early warning level. The evaluation coefficient is matched with the value range of the three groups of threshold values to obtain the early warning level corresponding to the evaluation coefficient; The warning levels include level one, level two and level three.

7. The tunnel geological monitoring and management system based on big data according to claim 6, characterized in that: The corresponding warning level according to the evaluation coefficient is used for the corresponding warning processing, including the following parts: When the warning level corresponding to the evaluation coefficient is level one, a low-level alarm is issued to alert relevant personnel, and the displacement change coefficient, stress change coefficient, seepage pressure coefficient, and evaluation coefficient are sent to the relevant personnel's smart terminal. Personnel are arranged to conduct daily inspections of the tunnel to check for obvious signs of geological changes. When the warning level corresponding to the evaluation coefficient is level 2: Based on the level 1 warning, an intermediate alarm is issued to notify the tunnel management department and relevant technical personnel; the frequency of tunnel geological data collection is increased, and changes in displacement, stress, and seepage pressure parameters are closely monitored; When the warning level corresponding to the evaluation coefficient is the third level warning: on the basis of the second level warning, the highest level alarm will be issued immediately, and relevant personnel will be notified through various means, including on-site staff, heads of tunnel management departments, and geological experts.

8. The tunnel geological monitoring and management system based on big data according to claim 7, characterized in that: Low-level alarm: A slight beep and a flashing blue indicator light are emitted in the tunnel monitoring center to alert the on-duty personnel to abnormalities in the tunnel geological data; Medium alarm: A relatively mild alarm sound and flashing yellow warning light will be emitted inside the tunnel and management area to attract the attention of relevant personnel; Highest level alarm: Inside the tunnel and related management areas, a high-decibel sirens emit continuous and sharp sound signals, accompanied by flashing red warning lights, to alert on-site personnel.

9. The tunnel geological monitoring and management system based on big data according to claim 1, characterized in that: The system also includes: Monitoring and management module: monitors the working status of each sensor in real time and evaluates the status of the sensor by calculating the transmission frequency of sensor data and the continuity index of the data; Real-time monitoring of the performance of each module, including the transmission rate of the data transmission module, the storage capacity and read and write speed of the data storage module, and the computing efficiency of the data analysis module; Data storage module: stores the tunnel geological data obtained from the data acquisition module, as well as the displacement variation coefficient, stress variation coefficient, seepage pressure coefficient, and evaluation coefficient generated by the data analysis module; adopts a distributed storage architecture to store data files on a preset number of nodes; Provide the data required by the evaluation module and the early warning module to support their real-time evaluation and early warning operations, and provide the monitoring and management module with the operating status information of the data storage module.

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