Precise positioning and tracing method for embankment piping hidden danger in distributed optical fiber online monitoring

Abstract

This invention provides a method for accurately locating and tracing the source of piping hazards in dams through distributed optical fiber online monitoring, belonging to the field of dam safety monitoring technology. The invention scientifically divides nodes based on optical fiber deployment density and constructs a dynamic sampling mechanism, making data collection more targeted and efficient, avoiding redundant and irrelevant data and missing key information. By comprehensively collecting data through an intelligent sensing system and performing multi-step analysis and processing, it can not only accurately pinpoint the specific location of piping hazards but also deeply analyze the causes of the hazards to achieve source tracing. Finally, it accurately determines the risk level through a preset threshold range. This method forms a complete, scientific, and efficient dam piping hazard monitoring system, providing comprehensive and accurate data support for dam safety status assessment, helping to promptly detect and address potential piping risks, greatly improving dam safety assurance capabilities, and effectively preventing dam disasters caused by piping.

Description

Technical Field

[0001] This invention relates to the field of dam safety monitoring technology, and in particular to a method for accurately locating and tracing the source of dam piping hazards using distributed optical fiber online monitoring. Background Technology

[0002] As important water conservancy engineering facilities, dams play a crucial role in flood control, irrigation, and water supply. However, due to long-term erosion by water flow, geological changes, and their own aging, dams are prone to various safety hazards, among which piping is a relatively common and highly dangerous one. Piping occurs when fine particles in the soil are carried away by water flow, leading to soil structural damage and the formation of seepage channels. If not detected and addressed in time, it can cause dam collapse, resulting in severe economic losses and casualties.

[0003] Currently, traditional methods for monitoring dam safety mainly rely on manual patrols and data collection from a limited number of fixed monitoring points. Manual patrols are not only labor-intensive and inefficient, but also struggle to detect hidden piping hazards, especially at night or in inclement weather, where their effectiveness is significantly reduced. Fixed monitoring points suffer from limited monitoring range and untimely data updates, failing to comprehensively and accurately reflect the overall safety status of the dam. Furthermore, traditional methods have low accuracy in locating piping hazards, making it difficult to determine the specific location and extent of the hazard, which greatly hinders subsequent emergency rescue operations.

[0004] With the development of fiber optic sensing technology, distributed fiber optic sensing technology has been applied to some extent in dam safety monitoring. However, most existing dam monitoring methods based on distributed fiber optics do not fully consider the impact of fiber optic deployment density on monitoring effectiveness. They use a uniform sampling frequency to collect data, resulting in excessive data volume or missing key information, making it difficult to effectively identify piping hazards and accurately locate and trace their source. Furthermore, these methods lack a scientific assessment of hazard risk levels, making it difficult to provide targeted decision support for dam safety management and maintenance.

[0005] Therefore, it is necessary to provide a method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring. This method solves the problems of low location accuracy of piping hazards in traditional methods and the difficulty in quickly and effectively identifying hazards due to the fixed sampling frequency of optical fiber sensors.

[0007] The present invention provides a method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring, comprising the following steps: S1. Based on the distribution of optical fibers, identify the fiber optic deployment density in the dam, and according to the deployment density, divide the fiber optic distribution density nodes into different levels through a preset density level threshold range. S2. Based on the distribution density nodes at each level, a dynamic sampling frequency mechanism is constructed for fiber optic sensing data and environmental monitoring data. S3. Utilize distributed optical fiber sensing technology to coordinate multiple sensors to build an intelligent sensing system, and collect optical fiber sensing data and environmental monitoring data respectively through a dynamic sampling frequency mechanism, and then process them to obtain a dataset. S4. By collecting fiber optic sensing data, analyze the initial potential hazards of each distribution density node and identify all distribution density nodes with initial potential hazards. S5. Based on the structural assessment of the dam, assess the importance of all nodes with potential hazards in terms of distribution density, and introduce corresponding environmental monitoring data as constraints according to importance to trace the source and obtain the source tracing results. S6. Based on the source tracing results, the risk level is determined through the preset hidden danger risk threshold range.

[0008] Preferably, the specific steps of S1 include: S101. Collect information on the specific deployment of optical fibers in the dam, including the laying path and direction of the optical fibers. S102. Based on the actual laying of optical fibers in the dam, the fiber laying density in the dam is identified by calculating the length of optical fibers per unit area or unit length. S103. Pre-set threshold ranges for different density levels, including: high density level threshold range, medium density level threshold range and low density level threshold range; S104. Compare the calculated deployment density with the preset density level threshold range, and divide the distribution density nodes of different fiber optic levels according to the comparison results, and clarify the density level to which each node belongs.

[0009] Preferably, the specific steps of S2 include: S201. Establish the relationship between node level and sampling frequency, including that the higher the distribution density node level, the lower the environmental monitoring sampling frequency and the lower the fiber optic sensing data collection frequency within the node range; the lower the distribution density node level, the higher the environmental monitoring sampling frequency and the higher the fiber optic sensing data collection frequency within the node range. S202. Based on the relationship between the constructed node level and the sampling frequency, formulate corresponding environmental monitoring sampling frequency and fiber optic sensing data collection frequency rules for nodes with different distribution densities, and construct a dynamic sampling frequency mechanism.

[0010] Preferably, the specific steps of S3 include: S301. By utilizing distributed optical fiber sensing technology and combining multiple types of sensors, a collaborative intelligent sensing system is constructed. S302. According to the dynamic sampling frequency mechanism, set the corresponding sampling frequency for each sensor in the intelligent sensing system, and collect fiber optic sensing data and environmental monitoring data respectively according to the set sampling frequency. S303. Organize the collected fiber optic sensing data and environmental monitoring data, remove invalid and abnormal data, and store them according to a certain format and structure to obtain a dataset.

[0011] Preferably, the specific steps of S4 include: S401. Using the sliding window average difference method or direct difference method in data analysis, analyze the fiber optic sensing data in the dataset and extract feature information related to dam hazards. S402. Based on the extracted feature information related to dam hazards, determine the initial hazard status of each distribution density node by setting a multi-feature comprehensive threshold. S403. Select all distribution density nodes with initial hidden dangers and record their location information and related characteristic data.

[0012] Preferably, the specific steps of S5 include: S501. Based on the structural characteristics of the dam and historical data, the importance of all distribution density nodes identified as having initial hidden dangers is assessed to obtain the importance of each node in the overall structure of the dam. S502. Based on the importance of each node in the overall structure of the dam, introduce corresponding environmental monitoring data as constraints for the initial hidden danger distribution density nodes of different importance levels. S503. Utilizing the high spatial resolution characteristics of distributed optical fibers, after introducing environmental constraints, the source of hidden danger signals is traced to obtain the source tracing results.

[0013] Preferably, the specific steps of S6 include: S601. The preset multi-level hidden danger risk threshold range includes: Level 1 risk, Level 2 risk, Level 3 risk and Level 4 risk; S602. Based on the preset risk threshold range of the hazard tracing results, the final risk level is automatically determined.

[0014] Compared with related technologies, the method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring provided by this invention has the following beneficial effects: This invention scientifically divides nodes based on fiber optic deployment density and constructs a dynamic sampling mechanism, making data collection more targeted and efficient, avoiding invalid data redundancy and the omission of key information. By comprehensively collecting data through an intelligent sensing system and performing multi-step big data analysis, it can not only accurately pinpoint the specific location of piping hazards but also deeply analyze the causes of the hazards to achieve source tracing. Finally, it accurately determines the risk level through preset threshold ranges. This method forms a complete, scientific, and efficient monitoring system for dam piping hazards, providing comprehensive and accurate data support for dam safety assessment, helping to promptly detect and address potential piping risks, greatly improving dam safety assurance capabilities, and effectively preventing dam disasters caused by piping. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring, as described in this invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Example In the specific implementation process, such as Figure 1 As shown, the method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring includes the following steps: S1. Based on the distribution of optical fibers, identify the fiber optic deployment density in the dam, and according to the deployment density, divide the fiber optic distribution density nodes into different levels through a preset density level threshold range. S2. Based on the distribution density nodes at each level, a dynamic sampling frequency mechanism is constructed for fiber optic sensing data and environmental monitoring data. S3. Utilize distributed optical fiber sensing technology to coordinate multiple sensors to build an intelligent sensing system, and collect optical fiber sensing data and environmental monitoring data respectively through a dynamic sampling frequency mechanism, and then process them to obtain a dataset. S4. By collecting fiber optic sensing data, analyze the initial potential hazards of each distribution density node and identify all distribution density nodes with initial potential hazards. S5. Based on the structural assessment of the dam, assess the importance of all nodes with potential hazards in terms of distribution density, and introduce corresponding environmental monitoring data as constraints according to importance to trace the source and obtain the source tracing results. S6. Based on the source tracing results, the risk level is determined through the preset hidden danger risk threshold range.

[0018] In the specific implementation process, step S1 includes the following steps: S101. Collect information on the specific deployment of optical fibers in the dam, including the laying path and direction of the optical fibers.

[0019] Specifically, by reviewing design drawings and construction records from the dam's construction, key information such as where the fiber optic cable starts to be laid, along what route it extends, and its final destination can be obtained.

[0020] S102. Based on the actual laying of optical fibers in the dam, the fiber laying density in the dam is identified by calculating the length of optical fibers per unit area or unit length.

[0021] Specifically, after obtaining the fiber optic laying path and direction information, the dam is first divided into areas. Based on the characteristics of fiber optic laying, the dam is divided into several relatively regular areas. Then, the method of calculating the fiber optic length per unit area is used.

[0022] S103. Pre-set threshold ranges for different density levels, including: high density level threshold range, medium density level threshold range and low density level threshold range.

[0023] Specifically, in this embodiment, the threshold range for high density level is set to a fiber length greater than 0.1 meters per square meter, i.e., a deployment density > 0.1 meters / square meter; the threshold range for medium density level is a fiber length between 0.05 meters and 0.1 meters per square meter, i.e., 0.05 meters / square meter ≤ deployment density ≤ 0.1 meters / square meter; and the threshold range for low density level is a fiber length less than 0.05 meters per square meter, i.e., a deployment density < 0.05 meters / square meter.

[0024] S104. Compare the calculated deployment density with the preset density level threshold range, and divide the distribution density nodes of different fiber optic levels according to the comparison results, and clarify the density level to which each node belongs.

[0025] Specifically, after calculating the fiber optic deployment density and determining the preset density level threshold range, the calculation results for each region are compared with the preset threshold range one by one. Based on the comparison, the fiber optic deployment density of that region is determined, thereby classifying the distribution density nodes of different levels. For each classified node, its location information and density level are recorded in detail to provide basic data for subsequent monitoring and analysis.

[0026] In the specific implementation process, step S2 includes the following steps: S201. Establish the relationship between node level and sampling frequency, including that the higher the distribution density node level, the lower the environmental monitoring sampling frequency and the lower the fiber optic sensing data collection frequency within the node range; the lower the distribution density node level, the higher the environmental monitoring sampling frequency and the higher the fiber optic sensing data collection frequency within the node range.

[0027] Specifically, high-density nodes, due to their dense fiber optic deployment, can acquire a large amount of fiber optic sensing information about the dam's condition within a unit of time and space. Even with a relatively low sampling frequency, they can capture enough key data to reflect the situation in the area. Simultaneously, the abundance of fiber optic data in this area reduces the reliance on environmental monitoring data, allowing for a more appropriate reduction in the environmental monitoring sampling frequency. Low-density nodes, on the other hand, have sparse fiber optic deployments, resulting in less fiber optic sensing information. To more accurately grasp the dam's condition in this area, the collection frequency of fiber optic sensing data needs to be increased. Furthermore, due to the limited fiber optic data, more environmental monitoring data is required as a supplement, necessitating a corresponding increase in the environmental monitoring sampling frequency. Medium-density nodes fall between these two levels, balancing the sampling frequencies of fiber optic sensing data and environmental monitoring data based on actual conditions.

[0028] S202. Based on the relationship between the constructed node level and the sampling frequency, formulate corresponding environmental monitoring sampling frequency and fiber optic sensing data collection frequency rules for nodes with different distribution densities, and construct a dynamic sampling frequency mechanism.

[0029] Specifically, in this embodiment, for high-density nodes (such as areas with fiber optic cable density greater than 0.1 m / m²), the environmental monitoring sampling frequency is set to once per hour, and the fiber optic sensor data collection frequency within the node range is set to once every 10 minutes. Because the area has dense fiber optic cables, it can quickly acquire a large amount of information about the dam's interior. The lower sampling frequency meets the monitoring needs while reducing data storage and processing pressure. For low-density nodes (such as areas with fiber optic cable density less than 0.03 m / m²), the environmental monitoring sampling frequency is set to once every 10 minutes, and the fiber optic sensor data collection frequency is set to once per minute. Because the area has fewer fibers, the information acquired is limited, requiring a higher sampling frequency to capture more details. Simultaneously, more reliance is placed on environmental monitoring data to assist in judging the dam's condition, so the environmental monitoring frequency needs to be increased. For medium-density nodes (areas with fiber optic cable density between 0.03 and 0.1 m / m²), the environmental monitoring sampling frequency is set to once every 30 minutes, and the fiber optic sensor data collection frequency is set to once every 5 minutes, balancing the acquisition needs of both types of data.

[0030] In the specific implementation process, step S3 includes the following steps: S301. By utilizing distributed fiber optic sensing technology and combining various types of sensors, a collaborative intelligent sensing system is constructed.

[0031] Specifically, based on the need for monitoring piping hazards in dams, the physical quantities that need to be monitored, such as temperature, strain, and seepage, are clearly defined. Based on this, other types of sensors are selected to supplement the monitoring. For example, piezometers are chosen to accurately measure the seepage pressure inside the dam, and displacement sensors are selected to monitor changes in dam displacement. The communication methods between sensors and the data transmission paths to the data processing center are planned, employing a combination of wired and wireless communication to ensure the stability and real-time performance of data transmission.

[0032] S302. According to the dynamic sampling frequency mechanism, set the corresponding sampling frequency for each sensor in the intelligent sensing system, and collect fiber optic sensing data and environmental monitoring data respectively according to the set sampling frequency.

[0033] Specifically, based on the dynamic sampling frequency mechanism, corresponding sampling frequencies are set for each sensor in the intelligent sensing system, activating the system's data collection function. Simultaneously, the data collection status of each sensor is monitored in real time to ensure accurate data collection according to the set sampling frequency.

[0034] S303. Organize the collected fiber optic sensing data and environmental monitoring data, remove invalid and abnormal data, and store them according to a certain format and structure to obtain a dataset.

[0035] Specifically, a preliminary check is performed on the collected fiber optic sensing data and environmental monitoring data to examine for obvious errors or anomalies. For example, the data is checked to ensure it is within a reasonable range and to identify any missing or duplicate values. Based on the data characteristics and monitoring requirements, criteria for identifying invalid and abnormal data are established. Invalid data, such as data missing due to sensor malfunction or communication interruption, can be directly deleted or interpolated. Abnormal data, such as data deviating from the normal range due to external interference or sensor errors, can be identified and removed using statistical methods. For example, the 3σ principle can be used to determine if data is outlier, and data exceeding three standard deviations from the normal range is discarded. The processed data is then stored according to a specific format and structure for subsequent data analysis and processing.

[0036] In the specific implementation process, step S4 includes the following steps: S401. Using the sliding window average difference method or direct difference method in data analysis, analyze the fiber optic sensing data in the dataset and extract feature information related to dam hazards.

[0037] Specifically, in this embodiment, the sliding window average difference method is used. First, a suitable window size is determined. This window size can be set according to the actual needs of dam monitoring and the characteristics of the data. For example, if monitoring the temperature changes of the dam, the window size can be set to the number of data points within a certain period, such as 10 consecutive temperature data points as a window. Then, the average value of the data within the window is calculated. Next, the window is gradually slid along the data sequence, sliding one data point at a time, calculating the average value within the new window, and calculating the difference between the average values ​​of adjacent windows. If the difference exceeds a pre-set reasonable range, then there may be characteristic changes related to potential dam hazards at this location. For example, an abnormally high temperature indicates potential hazards such as seepage inside the dam.

[0038] In this embodiment, fiber optic sensing data of a section of dam is being monitored, including temperature data. A sliding window averaging method is used, with the window size set to 5 data points. The initial temperature data within the window is [20℃, 21℃, 20℃, 22℃, 21℃], with an average value of 20.8℃. After sliding the window by one data point, the new window data is [21℃, 20℃, 22℃, 21℃, 23℃], with an average value of 21.4℃. The difference between these two window averages is 0.6℃. The pre-set reasonable difference range is 0.2℃, so this 0.6℃ difference exceeds the range, indicating an abnormal temperature change in the dam during the window sliding process. This abnormal change area may be the location of characteristic information related to potential dam hazards.

[0039] S402. Based on the extracted feature information related to dam hazards, the initial hazard status of each distribution density node is determined by setting a multi-feature comprehensive threshold.

[0040] Specifically, multiple characteristics related to potential dam hazards are comprehensively considered, such as abnormal temperature changes, abnormal strain changes, and abnormal vibration. For each characteristic, a reasonable threshold range is set based on historical data, dam design standards, and actual monitoring experience. For example, for temperature characteristics, the normal temperature range is set to 15℃-25℃; when the temperature exceeds this range, the temperature characteristic is considered abnormal. For strain characteristics, the normal strain range is set to 0-100 microstrains; exceeding this range is considered an abnormal strain characteristic. Then, a multi-feature comprehensive judgment method is adopted. Instead of judging the existence of initial hazards at a distribution density node based solely on the abnormality of a single characteristic, a rule is set that an initial hazard is determined to exist at a distribution density node only when both temperature and strain characteristics are abnormal, or when three or more characteristics are abnormal.

[0041] S403. Select all distribution density nodes with initial hidden dangers and record their location information and related characteristic data.

[0042] Specifically, after determining the initial potential hazard status of each distribution density node in step S402, all distribution density nodes are traversed, and those nodes identified as having initial potential hazards are selected. For each selected node, its location information needs to be accurately recorded. This location information can be determined through methods such as the fiber optic cable's deployment coordinates within the dam or the node number. For example, if the fiber optic cable is deployed in a certain grid pattern within the dam, and each node has a unique coordinate number, then this coordinate number can be recorded as the location information. Simultaneously, characteristic data related to the initial potential hazard of that node must also be recorded. This characteristic data includes various abnormal characteristic information extracted in step S401, such as abnormal temperature values, abnormal strain values, and abnormal vibration frequencies.

[0043] In the specific implementation process, step S5 includes the following steps: S501. Based on the structural characteristics of the dam and historical data, the importance of all distribution density nodes with initial hidden dangers is assessed to obtain the importance of each node in the overall structure of the dam.

[0044] Specifically, the structural form of the dam is clearly defined, such as its height, slope, and whether it has a seepage barrier. Then, historical data on the dam is collected and analyzed, covering its operation under various working conditions, including whether piping, leakage, or other potential hazards have occurred, as well as the location, scale, and repair status of such hazards. Through analysis of historical data, areas and key parts of the dam prone to problems are identified. Pre-set importance assessment indicators, each with corresponding multi-level threshold ranges, are used. Each node with initial potential hazards is independently scored based on the assessment indicators, and the scores are aggregated to calculate the average score. The importance level of the node is then determined based on the average score; for example, an average score of 4-5 indicates high importance, 2-3 indicates medium importance, and 1 or below indicates low importance.

[0045] S502. Based on the importance of each node in the overall structure of the dam, introduce corresponding environmental monitoring data as constraints for the initial hazard distribution density nodes of different importance levels.

[0046] Specifically, for nodes of high importance, more comprehensive environmental monitoring data needs to be introduced as constraints to ensure accurate capture of various environmental factors that may affect the node's safety. In this embodiment, for nodes of high importance, in addition to conventional rainfall and water level data, data such as soil moisture and groundwater level changes are also introduced. For nodes of medium importance, the types of environmental monitoring data introduced can be reduced, but it is still necessary to ensure coverage of the main influencing factors. For example, only rainfall and water level data are introduced. For nodes of low importance, only some basic environmental monitoring data, such as rainfall, are introduced.

[0047] S503. Utilizing the high spatial resolution characteristics of distributed optical fibers, after introducing environmental constraints, the source of hidden danger signals is traced to obtain the source tracing results.

[0048] Specifically, distributed optical fibers possess high spatial resolution, enabling precise sensing of changes in various physical quantities along the fiber's length, such as temperature and strain. After introducing environmental constraints, the fiber optic route is first divided into several small spatial regions (e.g., regions are divided at regular intervals). The statistical characteristics (mean, variance) of each spatial region are then subjected to cluster analysis with the environmental monitoring data from the introduced environmental constraints (using existing clustering algorithms such as K-means clustering and hierarchical clustering). Regions with similar statistical characteristics and environmental factors are grouped into the same cluster. The clustering results identify clusters that are significantly different from normal regions, indicating the location of the anomaly source. If a region within a cluster experiences significant data fluctuations, and that region has low-lying terrain and high permeability, then the potential for piping failure exists in that region. Finally, data such as the piping failure outflow rate in that region are obtained.

[0049] In the specific implementation process, step S6 includes the following steps: S601, The preset multi-level hidden danger risk threshold range includes: Level 1 risk, Level 2 risk, Level 3 risk and Level 4 risk.

[0050] S602. Based on the preset risk threshold range of the hazard tracing results, the final risk level is automatically determined.

[0051] Specifically, after obtaining the source tracing results, key information such as piping outflow data is automatically extracted from the source tracing results. Then, it is compared with the preset risk threshold ranges of each level in step S601 one by one to match the preset hidden danger risk threshold range to automatically determine the final risk level and notify.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0054] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for precise location and source tracing of dam piping hazards using distributed optical fiber online monitoring, characterized in that, Includes the following steps: S1. Based on the distribution of optical fibers, identify the fiber optic deployment density in the dam, and according to the deployment density, divide the fiber optic distribution density nodes into different levels through a preset density level threshold range. S2. Based on the distribution density nodes at each level, a dynamic sampling frequency mechanism is constructed for fiber optic sensing data and environmental monitoring data. S3. Utilize distributed optical fiber sensing technology to coordinate multiple sensors to build an intelligent sensing system, and collect optical fiber sensing data and environmental monitoring data respectively through a dynamic sampling frequency mechanism, and then process them to obtain a dataset. S4. By collecting fiber optic sensing data, analyze the initial potential hazards of each distribution density node and identify all distribution density nodes with initial potential hazards. S5. Based on the structural assessment of the dam, assess the importance of all distribution density nodes with potential hazards, and introduce corresponding environmental monitoring data as constraints according to importance to trace the source and obtain the source tracing results. S6. Based on the source tracing results, the risk level is determined through the preset hidden danger risk threshold range.

2. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring as described in claim 1, characterized in that, The specific steps of S1 include: S101. Collect information on the specific deployment of optical fibers in the dam, including the laying path and direction of the optical fibers; S102. Based on the actual laying of optical fibers in the dam, the fiber laying density in the dam is identified by calculating the length of optical fibers per unit area or unit length. S103. Pre-set threshold ranges for different density levels, including: high density level threshold range, medium density level threshold range and low density level threshold range; S104. Compare the calculated deployment density with the preset density level threshold range, and divide the distribution density nodes of different fiber optic levels according to the comparison results, and clarify the density level to which each node belongs.

3. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring as described in claim 1, characterized in that, The specific steps of S2 include: S201. Establish the relationship between node level and sampling frequency, including that the higher the distribution density node level, the lower the environmental monitoring sampling frequency and the lower the fiber optic sensing data collection frequency within the node range; the lower the distribution density node level, the higher the environmental monitoring sampling frequency and the higher the fiber optic sensing data collection frequency within the node range. S202. Based on the relationship between the constructed node level and the sampling frequency, formulate corresponding environmental monitoring sampling frequency and fiber optic sensing data collection frequency rules for nodes with different distribution densities, and construct a dynamic sampling frequency mechanism.

4. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring as described in claim 1, characterized in that, The specific steps of S3 include: S301. By utilizing distributed optical fiber sensing technology and combining multiple types of sensors, a collaborative intelligent sensing system is constructed. S302. According to the dynamic sampling frequency mechanism, set the corresponding sampling frequency for each sensor in the intelligent sensing system, and collect fiber optic sensing data and environmental monitoring data respectively according to the set sampling frequency. S303. Organize the collected fiber optic sensing data and environmental monitoring data, remove invalid and abnormal data, and store them according to a certain format and structure to obtain a dataset.

5. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring according to claim 1, characterized in that, The specific steps of S4 include: S401. Using the sliding window average difference method or direct difference method in data analysis, analyze the fiber optic sensing data in the dataset and extract feature information related to dam hazards. S402. Based on the extracted feature information related to dam hazards, determine the initial hazard status of each distribution density node by setting a multi-feature comprehensive threshold. S403. Select all distribution density nodes with initial hidden dangers and record their location information and related characteristic data.

6. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring according to claim 1, characterized in that, The specific steps of S5 include: S501. Based on the structural characteristics of the dam and historical data, the importance of all distribution density nodes identified as having initial hidden dangers is assessed to obtain the importance of each node in the overall structure of the dam. S502. Based on the importance of each node in the overall structure of the dam, introduce corresponding environmental monitoring data as constraints for the initial hidden danger distribution density nodes of different importance levels. S503. Utilizing the high spatial resolution characteristics of distributed optical fibers, after introducing environmental constraints, the source of hidden danger signals is traced to obtain the source tracing results.

7. The method for accurately locating and tracing the source of piping hazards in dams using distributed optical fiber online monitoring as described in claim 1, characterized in that, The specific steps of S6 include: S601. The preset multi-level hidden danger risk threshold range includes: Level 1 risk, Level 2 risk, Level 3 risk and Level 4 risk; S602. Based on the preset risk threshold range of the hazard tracing results, the final risk level is automatically determined.

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