Seepage monitoring system based on hydraulic engineering project

Abstract

The invention relates to the technical field of seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

Description

Technical Field

[0001] The invention relates to the technical field of seepage monitoring, and in particular to a seepage monitoring system based on a water conservancy project. Background Art

[0002] The main purpose of water conservancy projects is to manage and utilize water resources to meet the needs of human life, agricultural production, industrial development, and ecological and environmental protection. These projects cover multiple links such as water collection, storage, transportation, treatment, and discharge, involving a wide range of technical and management measures. Water conservancy projects achieve the rational allocation and use of water resources through measures such as water storage, irrigation, and water supply, ensuring that the water needs of agriculture, industry, and towns are met. Dams, reservoirs, drainage facilities, etc. have built a complete flood control system, reducing the threat of floods to people's lives and property, and protecting infrastructure and the natural environment. Therefore, seepage monitoring is indispensable. Seepage monitoring helps to understand the changes in groundwater levels and grasp the utilization of water resources to achieve effective management and scheduling. Monitoring seepage can help identify and evaluate the impact on the surrounding ecological environment, such as groundwater pollution and soil erosion, so as to take corresponding measures for repair. At the same time, it can promptly detect the precursors of possible natural disasters such as floods and mudslides, and buy time for early warning and emergency response.

[0003] Many traditional seepage monitoring technologies still require a lot of manpower for maintenance and inspection, which increases operating costs and may lead to a waste of human resources. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a seepage monitoring system based on a water conservancy project, which is capable of collecting water level, flow velocity, seepage volume, soil moisture, soil temperature and soil pressure data through sensors in relevant collection units in a data collection module. The data analysis module performs intelligent analysis on the above collected data, calculates standard soil moisture Btsd, standard water flow velocity Slbs, standard soil pressure Btyz, standard soil temperature Bwdz and standard seepage volume Szsl, and establishes a mathematical model between seepage and soil moisture, soil pressure and soil temperature influencing factors based on the above numerical values, and calculates the seepage anomaly index Pybz and transmits it to the risk assessment and early warning module. In the block, the risk assessment and early warning module makes a threshold judgment on the abnormal seepage index Pybz, and sends out an over-limit signal Cx when an over-limit situation occurs. At this time, the moving average method is used to predict the future seepage trend, and a future seepage trend line graph and a seepage risk assessment report are generated, which are uniformly transmitted to the emergency response module. When the emergency response module receives the over-limit signal Cx, it activates the emergency plan, and notifies the relevant management personnel of the emergency plan, the future seepage trend line graph and the seepage risk assessment report via text messages. The seepage situation of the water conservancy project is monitored automatically by the system, and there is no need for large-scale manpower for maintenance and inspection. It has the advantages of reducing operation costs and waste of human resources, and solves the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a seepage monitoring system based on a water conservancy project, comprising a data acquisition module, a data analysis module, a risk assessment and early warning module and an emergency response module;

[0008] The data acquisition module includes a water level acquisition unit, a flow velocity acquisition unit, a seepage volume acquisition unit, a soil moisture acquisition unit, a soil temperature acquisition unit and a soil pressure acquisition unit;

[0009] The water level acquisition unit collects water level height data of the water body through an ultrasonic water level sensor. The flow velocity acquisition unit collects water velocity data through an electromagnetic flowmeter to obtain the flow rate Llsz and the cross-sectional area Lthm of the fluid flow. The seepage volume acquisition unit collects the seepage volume per unit time through a seepage meter to obtain the permeability coefficient k, the seepage cross-sectional area Sljm, the head difference Δh and the seepage path length Ljcd. The soil moisture acquisition unit collects the moisture content data in the soil through a soil moisture sensor to obtain the water mass M w , soil quality M t and the density of water ρ w The soil temperature acquisition unit collects soil temperature data through a temperature sensor to obtain the surface soil temperature T s, surface heat flux density Rlmd and thermal conductivity Trrd of soil, the soil pressure acquisition unit collects water pressure data inside the soil through a pore water pressure sensor to obtain total stress Zylz and pore water pressure Kxyl, and the above-collected data is transmitted to the data analysis module through a wireless network;

[0010] The data analysis module performs intelligent analysis on the transmitted data, calculates the standard soil moisture Btsd, the standard water flow velocity Slbs, the standard soil pressure Btyz, the standard soil temperature Bwdz and the standard seepage volume Szsl, and establishes a mathematical model between the seepage and the influencing factors of soil moisture, soil pressure and soil temperature based on the above values, and calculates the seepage anomaly index Pybz and transmits it to the risk assessment and early warning module;

[0011] The risk assessment and early warning module performs threshold judgment on the abnormal seepage index Pybz, and sends out an over-limit signal Cx when an over-limit situation occurs. At this time, the moving average method is used to predict the future seepage trend, generate a future seepage trend line chart and a seepage risk assessment report, and uniformly transmit them to the emergency response module;

[0012] When the emergency response module receives the over-limit signal Cx, it activates the emergency plan and notifies the relevant management personnel of the emergency plan, the future seepage trend line chart and the seepage risk assessment report via SMS.

[0013] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil moisture Btsd. The calculation formula is as follows:

[0014]

[0015] In the formula, Btsd represents standard soil moisture, M w Indicates the quality of water, M t represents the mass of the soil, ρ w Represents the density of water.

[0016] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the standard water flow rate Slbs. The calculation formula is as follows:

[0017]

[0018] In the formula, Slbs represents the standard water flow velocity, Llsz represents the flow rate, and Lthm represents the cross-sectional area of ​​the fluid flow.

[0019] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil pressure Btyz. The calculation formula is as follows:

[0020] Btyz=Zylz-Kxyl

[0021] In the formula, Btyz represents the standard soil pressure, Zylz represents the total stress, that is, the weight of the soil column, and Kxyl represents the pore water pressure.

[0022] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil temperature Bwdz. The calculation formula is as follows:

[0023]

[0024] In the formula, Bwdz(d) represents the temperature at depth d, Ts represents the surface soil temperature, Rlmd represents the surface heat flux density, and Trrd represents the thermal conductivity of the soil.

[0025] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the standard seepage flow Szsl. The calculation formula is as follows:

[0026]

[0027] In the formula, Szsl represents the standard seepage rate, k represents the permeability coefficient, Sljm represents the seepage cross-sectional area, Δh represents the head difference, and Ljcd represents the seepage path length.

[0028] Preferably, the data analysis module establishes a mathematical model between seepage and soil moisture, soil pressure and soil temperature influencing factors based on Darcy's law, as shown below:

[0029] QS=f+f1*Btsd+f2*Btyz+f3*Bwdz+∈

[0030] In the formula, QS represents the seepage rate, Btsd represents the standard soil moisture, Btyz represents the standard soil pressure, Bwdz represents the standard soil temperature, ∈ represents the error term, and f, f1, f2, and f3 are model parameters obtained through historical data training.

[0031] Preferably, the data analysis module performs intelligent analysis on the transmitted data and calculates the abnormal seepage index Pybz, and the calculation formula is as follows:

[0032]

[0033] In the formula, Pybz represents the abnormal seepage index, QS actual represents the measured seepage rate, QS predicted represents the seepage rate predicted based on the model, σ predicted Represents the standard deviation of the predicted values.

[0034] Preferably, the risk assessment and early warning module performs a threshold judgment on the abnormal seepage index Pybz, and sends an over-limit signal Cx when an over-limit situation occurs. The judgment method is as follows:

[0035] When the seepage abnormality index Pybz is greater than 3, it indicates a serious abnormality;

[0036] When the percolation anomaly index Pybz is less than -3, it indicates a negative anomaly;

[0037] When the above two situations are detected, it is an over-limit situation, and an over-limit signal Cx is issued.

[0038] Preferably, the risk assessment and early warning module uses a moving average method to predict future seepage trends by calculating the moving average of past data. The calculation formula is as follows:

[0039]

[0040] In the formula, MA t represents the moving average of the seepage rate data at time t, QS t-n represents the seepage rate value at the past tn moments, QS t-n+1 represents the seepage rate value at the past t-n+1 time, QS t-1 represents the seepage rate value at the past t-1 moments, and n represents the past n moments.

[0041] Compared with the prior art, the present invention provides a seepage monitoring system based on water conservancy projects, which has the following beneficial effects:

[0042] The present invention uses sensors to collect water level, flow rate, seepage, soil moisture, soil temperature and soil pressure data through relevant collection units in the data collection module. The data analysis module performs intelligent analysis on the above collected data, calculates standard soil moisture Btsd, standard water flow rate Slbs, standard soil pressure Btyz, standard soil temperature Bwdz and standard seepage Szsl, and establishes a mathematical model between seepage and soil moisture, soil pressure and soil temperature influencing factors based on the above values, and calculates the seepage anomaly index Pybz and transmits it to the risk assessment and early warning module. The risk assessment and early warning module The block makes a threshold judgment on the abnormal seepage index Pybz, and sends out an over-limit signal Cx when an over-limit situation occurs. At this time, the moving average method is used to predict the future seepage trend, and a future seepage trend line graph and a seepage risk assessment report are generated, which are uniformly transmitted to the emergency response module. When the emergency response module receives the over-limit signal Cx, it activates the emergency plan, and notifies the relevant management personnel of the emergency plan, the future seepage trend line graph and the seepage risk assessment report via text messages. The seepage situation of the water conservancy project is monitored automatically by the system, and there is no need for large-scale manpower for maintenance and inspection, which reduces the control cost and waste of human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Many traditional seepage monitoring technologies still require a lot of manpower for maintenance and inspection, which increases the operation cost and may lead to a waste of human resources. Therefore, a seepage monitoring system based on water conservancy projects is proposed. Figure 1 ,The system includes a data acquisition module, a data analysis module, a risk assessment and early warning module, and an emergency response module;

[0046] The data acquisition module includes a water level acquisition unit, a flow velocity acquisition unit, a seepage volume acquisition unit, a soil moisture acquisition unit, a soil temperature acquisition unit, and a soil pressure acquisition unit;

[0047] The water level acquisition unit uses an ultrasonic water level sensor to accurately measure the water level data of the water body by emitting ultrasonic waves and receiving their reflected signals. This technology can monitor water level changes in real time to ensure the timeliness and accuracy of water resource management. The flow rate acquisition unit uses an electromagnetic flowmeter to measure the velocity data of water flow using the Faraday electromagnetic induction principle. This device can maintain high accuracy under different flow conditions and is suitable for various water flow environments. The seepage volume acquisition unit uses a seepage meter to monitor the seepage volume per unit time, provide key soil moisture dynamic change information, and support hydrological and soil research;

[0048] In terms of soil monitoring, the soil moisture collection unit uses a soil moisture sensor and a capacitive or resistive measurement principle to obtain real-time soil moisture content data. This information is crucial for agricultural irrigation and soil management. The soil temperature collection unit uses a temperature sensor to accurately measure soil temperature data to help analyze the impact of temperature on plant growth and soil microbial activity. The soil pressure collection unit uses a pore water pressure sensor to monitor the water pressure data inside the soil and provide important information on soil moisture status and permeability characteristics. The above collected data is transmitted to the data analysis module via a wireless network.

[0049] The data analysis module performs intelligent analysis on the transmitted data, calculates the standard soil moisture Btsd, standard water flow velocity Slbs, standard soil pressure Btyz, standard soil temperature Bwdz and standard seepage Szsl, and establishes a mathematical model between seepage and soil moisture, soil pressure and soil temperature influencing factors based on the above values, and calculates the seepage anomaly index Pybz, where:

[0050] The standard soil moisture calculation formula is as follows:

[0051]

[0052] By accurately calculating soil moisture, a reasonable irrigation plan can be formulated to avoid over-irrigation or water shortage, save water resources and increase crop yields. In the formula, Btsd represents the standard soil moisture, M w Indicates the quality of water, M t represents the mass of the soil, ρ w Indicates water density. Long-term monitoring of soil moisture changes helps assess soil health and serves as one of the abnormal assessment indicators for excessive water infiltration.

[0053] The standard water flow velocity calculation formula is as follows:

[0054]

[0055] Understanding water flow velocity can optimize the release and storage strategies of reservoirs and ensure the efficient use of water resources. In the formula, Slbs represents the standard water flow velocity, Llsz represents the flow rate, and Lthm represents the cross-sectional area of ​​fluid flow. Flow velocity data can be used to predict soil erosion and sedimentation, providing a reference for river management and soil protection measures.

[0056] The standard soil pressure calculation formula is as follows:

[0057] Btyz=Zylz-Kxyl

[0058] Soil pressure is an important indicator of soil moisture status. Monitoring its changes can determine soil drainage and seepage characteristics. In the formula, Btyz represents standard soil pressure, Zlyz represents total stress, that is, the weight of the soil column, and Kxyl represents pore water pressure. Standard soil pressure calculation can evaluate the stability of soil under different moisture conditions, help predict the risks of geological disasters such as landslides and subsidence, provide a basis for the study of groundwater level, flow direction and water quality, and contribute to hydrogeological analysis.

[0059] The standard soil temperature calculation formula is as follows:

[0060]

[0061] Soil temperature directly affects the activity of plant roots and microorganisms. Monitoring temperature changes helps manage crop growth cycles. In the formula, Bwdz(d) represents the temperature at depth d, T s represents the surface soil temperature, Rlmd represents the surface heat flux density, and Trrd represents the thermal conductivity of the soil. By accumulating long-term soil temperature data, we can study its response to climate change and promote adaptive management of water conservancy projects.

[0062] The standard seepage rate calculation formula is as follows:

[0063]

[0064] The calculation of seepage flow can monitor the migration process of water in the soil in real time and provide important parameters for the hydrological model. In the formula, Szsl represents the standard seepage flow, k represents the permeability coefficient, Sljm represents the seepage cross-sectional area, Δh represents the head difference, and Ljcd represents the length of the seepage path. With accurate seepage flow data, the design of flood control and drainage systems can be optimized to reduce disaster risks.

[0065] Based on Darcy's law, a mathematical model between seepage and soil moisture, soil pressure and soil temperature is established as follows:

[0066] QS=f+f1*Btsd+f2*Btyz+f3*Bwdz+∈

[0067] The mathematical model can help systematically analyze the relationship between seepage and soil moisture, soil pressure and soil temperature. By establishing equations, the specific impact of various factors on seepage can be quantified and data support can be provided. In the formula, QS represents the seepage rate, Btsd represents the standard soil moisture, Btyz represents the standard soil pressure, Bwdz represents the standard soil temperature, ∈ represents the error term, and f, f1, f2 and f3 are model parameters. The model is obtained through historical data training. It can be used to predict the seepage under different environmental conditions, provide a scientific basis for decision-making on water resources management, irrigation scheduling and geological disaster prevention and control, and reduce risks;

[0068] The model parameter training steps are as follows:

[0069] S1. Collect historical monitoring data of infiltration rate, soil moisture, soil pressure and soil temperature. These data can be obtained from sensors and pre-processed, including cleaning, removing outliers and filling missing data, to ensure data quality and integrity;

[0070] S2. Based on the original data, time series features of various soil properties are constructed to better capture the complex relationship between soil conditions and seepage;

[0071] S3, select a linear regression model to determine parameters;

[0072] S4, divide the data into a training set and a test set, use 70% of the data for training and 30% of the data for model validation, use the training set to train the linear regression model, minimize the square error, and estimate the values ​​of parameters f, f1, f2, and f3;

[0073] The calculation formula of seepage anomaly index is as follows:

[0074]

[0075] By monitoring the abnormal seepage index, noise and interference factors can be effectively identified and eliminated, the quality and reliability of monitoring data can be improved, and a more accurate basis for decision-making can be provided. In the formula, Pydz represents the abnormal seepage index, QS actual represents the measured seepage rate, QS predicted represents the seepage rate predicted based on the model, σ predicted Representing the standard deviation of the predicted value, the anomaly index helps identify the cause of seepage changes, so that targeted management strategies can be formulated to optimize seepage management, especially when it may lead to water waste or soil erosion;

[0076] The risk assessment and early warning module makes a threshold judgment on the abnormal seepage index Pybz, and issues an over-limit signal Cx when an over-limit situation occurs. At this time, the moving average method is used to predict the future seepage trend, and a future seepage trend line chart and a seepage risk assessment report are generated, including:

[0077] The threshold determination method is as follows:

[0078] When the seepage abnormality index Pybz is greater than 3, it indicates a serious abnormality;

[0079] When the percolation anomaly index Pybz is less than -3, it indicates a negative anomaly;

[0080] The above thresholds are usually determined based on statistical analysis of historical monitoring data. Generally speaking, according to the statistical principle of normal distribution, three times the standard deviation can cover about 99.7% of the data points. Therefore, values ​​greater than 3 or less than -3 can be regarded as statistical anomalies. These thresholds can effectively help identify abnormal patterns and then issue early warnings. When the above two situations are monitored, it is an over-limit situation, and an over-limit signal Cx is issued;

[0081] The prediction is made by calculating the moving average of past data. The calculation formula is as follows:

[0082]

[0083] Moving average method is a commonly used time series smoothing and forecasting method. It can predict future trends by calculating the average value of past data. In the formula, MA t represents the moving average of the seepage rate data at time t, QS t-n represents the seepage rate value at the past tn moments, QS t-n+1 represents the seepage rate value at the past t-n+1 time, QS t-1 represents the seepage rate value at the past t-1 moments, and n represents the past n moments. By moving the moving average result forward, the estimation of the future seepage rate can be obtained. For some projects that do not provide a large amount of data, the moving average method has relatively low data requirements, especially when there is only limited historical data. The moving average can still provide effective predictions. At the same time, the moving average method has been widely studied and applied in the field of time series analysis, and its effectiveness has been verified in many actual engineering cases, especially in the field of seepage monitoring. Simple time series prediction models can often provide sufficiently accurate trend predictions.

[0084] When the emergency response module receives the over-limit signal Cx, it activates the emergency plan, plots the historical seepage data and the moving average results on the same chart to form a trend line chart, and generates a seepage risk assessment report. The report content includes the current infiltration rate, moving average trend analysis, comparison results of abnormal seepage indicators and thresholds, risk assessment conclusions and seepage forecast values ​​for the next few weeks. The above information will be sent to relevant managers in real time via SMS notifications to ensure timely and accurate acquisition of important data, facilitate rapid response and take necessary measures to minimize potential risks.

[0085] By automatically monitoring the seepage conditions of water conservancy projects through the system, there is no need for large-scale manpower for maintenance and inspection, which reduces operation costs and waste of human resources.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A seepage monitoring system based on a water conservancy project, characterized in that: It includes data collection module, data analysis module, risk assessment and early warning module and emergency response module; The data acquisition module includes a water level acquisition unit, a flow velocity acquisition unit, a seepage volume acquisition unit, a soil moisture acquisition unit, a soil temperature acquisition unit and a soil pressure acquisition unit; The water level acquisition unit collects water level height data of the water body through an ultrasonic water level sensor. The flow velocity acquisition unit collects water velocity data through an electromagnetic flowmeter to obtain the flow rate Llsz and the cross-sectional area Lthm of the fluid flow. The seepage volume acquisition unit collects the seepage volume per unit time through a seepage meter to obtain the permeability coefficient k, the seepage cross-sectional area Sljm, the head difference Δh and the seepage path length Ljcd. The soil moisture acquisition unit collects the moisture content data in the soil through a soil moisture sensor to obtain the water mass M w , soil quality M t and the density of water ρ w The soil temperature acquisition unit collects soil temperature data through a temperature sensor to obtain the surface soil temperature T s , surface heat flux density Rlmd and thermal conductivity Trrd of soil, the soil pressure acquisition unit collects water pressure data inside the soil through a pore water pressure sensor to obtain total stress Ztlz and pore water pressure Kxyl, and the above-collected data is transmitted to the data analysis module through a wireless network; The data analysis module performs intelligent analysis on the transmitted data, calculates the standard soil moisture Btsd, the standard water flow velocity Slbs, the standard soil pressure Btyz, the standard soil temperature Bwdz and the standard seepage volume Szsl, and establishes a mathematical model between the seepage and the influencing factors of soil moisture, soil pressure and soil temperature based on the above values, and calculates the seepage anomaly index Pybz and transmits it to the risk assessment and early warning module; The risk assessment and early warning module performs threshold judgment on the abnormal seepage index Pybz, and sends out an over-limit signal Cx when an over-limit situation occurs. At this time, the moving average method is used to predict the future seepage trend, generate a future seepage trend line chart and a seepage risk assessment report, and uniformly transmit them to the emergency response module; When the emergency response module receives the over-limit signal Cx, it activates the emergency plan and notifies the relevant management personnel of the emergency plan, the future seepage trend line chart and the seepage risk assessment report via SMS.

2. A seepage monitoring system based on a water conservancy project according to claim 1, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil moisture Btsd. The calculation formula is as follows: In the formula, Btsd represents standard soil moisture, M w Indicates the quality of water, M t represents the mass of the soil, ρ w Represents the density of water.

3. A seepage monitoring system based on a water conservancy project according to claim 2, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the standard water flow rate Slbs. The calculation formula is as follows: In the formula, Slbs represents the standard water flow velocity, Llsz represents the flow rate, and Lthm represents the cross-sectional area of ​​the fluid flow.

4. A seepage monitoring system based on a water conservancy project according to claim 3, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil pressure Btyz. The calculation formula is as follows: Btyz=Zylz-Kxyl In the formula, Btyz represents the standard soil pressure, Zylz represents the total stress, that is, the weight of the soil column, and Kxyl represents the pore water pressure.

5. A seepage monitoring system based on a water conservancy project according to claim 4, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the standard soil temperature Bwdz. The calculation formula is as follows: In the formula, Bwdz(d) represents the temperature at depth d, T s represents the surface soil temperature, Rlmd represents the surface heat flux density, and Trrd represents the thermal conductivity of the soil.

6. A seepage monitoring system based on a water conservancy project according to claim 5, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the standard seepage flow Szsl. The calculation formula is as follows: In the formula, Szsl represents the standard seepage rate, k represents the permeability coefficient, Sljm represents the seepage cross-sectional area, Δh represents the head difference, and Ljcd represents the seepage path length.

7. A seepage monitoring system based on a water conservancy project according to claim 6, characterized in that: The data analysis module establishes a mathematical model between seepage and soil moisture, soil pressure and soil temperature influencing factors based on Darcy's law, as shown below: QS=f+f1*Btsd+f2*Btyz+f3*Bwdz+∈ In the formula, QS represents the seepage rate, Btsd represents the standard soil moisture, Btyz represents the standard soil pressure, Bwdz represents the standard soil temperature, ∈ represents the error term, and f, f1, f2, and f3 are model parameters obtained through historical data training.

8. A seepage monitoring system based on a water conservancy project according to claim 7, characterized in that: The data analysis module performs intelligent analysis on the transmitted data and calculates the abnormal seepage index Pybz. The calculation formula is as follows: In the formula, Pybz represents the abnormal seepage index, QS actual represents the measured seepage rate, QS predicted represents the seepage rate predicted based on the model, σ predicted Represents the standard deviation of the predicted values.

9. A seepage monitoring system based on a water conservancy project according to claim 8, characterized in that: The risk assessment and early warning module performs threshold judgment on the abnormal seepage index Pybz, and issues an over-limit signal Cx when an over-limit situation occurs. The judgment method is as follows: When the seepage abnormality index Pybz is greater than 3, it indicates a serious abnormality; When the percolation anomaly index Pybz is less than -3, it indicates a negative anomaly; When the above two situations are detected, it is an over-limit situation, and an over-limit signal Cx is issued.

10. A seepage monitoring system based on a water conservancy project according to claim 9, characterized in that: The risk assessment and early warning module uses the moving average method to predict future seepage trends by calculating the moving average of past data. The calculation formula is as follows: In the formula, MA t represents the moving average of the seepage rate data at time t, QS t-n represents the seepage rate value at the past tn moments, QS t-n+1 represents the seepage rate value at the past t-n+1 time, QS t-1 represents the seepage rate value at the past t-1 moments, and n represents the past n moments.

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