Water conservancy project safety monitoring system based on data analysis

Abstract

The application discloses a water conservancy project safety monitoring system based on data analysis, and relates to the technical field of water conservancy project safety management, comprising a collection component for acquiring real-time seepage data, structural damage data and temperature-related data of key positions of a water conservancy project, a processing component for inputting the real-time seepage data, structural damage data and temperature-related data acquired by the collection component and performing cleaning processing on the input data, and inputting the cleaned data into the processing component in the analysis component, wherein the application progresses from early anomaly identification to coupling risk assessment and then to dynamic comprehensive early warning, the output early warning index clearly reflects the risk level, the dam seepage monitoring is optimized from "single dimension" to "multi-dimensional coupling" and from "static monitoring" to "dynamic early warning", the accuracy and foresight of seepage risk identification are significantly improved through the collaborative integration of multi-dimensional parameters, and a better solution is provided for water conservancy project safety monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservancy safety management, in particular to a water conservancy safety monitoring system based on data analysis. BACKGROUND

[0002] Water conservancy is an important infrastructure of the national economy, and the dam, as the core component of water conservancy, its safe operation is directly related to the safety of life and property of the downstream people and the stability of the society and economy, and seepage monitoring is the key link of dam safety monitoring, seepage anomaly is an early sign of dam structure damage and stability decline, and timely identification of seepage risk is of great significance to prevent dam break accidents;

[0003] The existing water conservancy safety monitoring system mainly relies on seepage flow, seepage pressure and other conventional parameters for monitoring, these parameters can reflect the macro state of seepage, but it is difficult to capture early hidden risks, for example, the change of seepage path caused by the new water source of dam crack, which may lead to the change of seepage temperature and composition, and the expansion of micro cracks in the dam body, which will also aggravate seepage erosion before the formation of macro cracks, the existing safety monitoring system is difficult to evaluate the coupling effect of micro damage and seepage, and the early warning monitoring is mainly based on static parameters, which is difficult to deal with the sudden risks brought by dynamic environmental factors such as rapid change of reservoir water level and extreme rainfall, resulting in lagging or misjudgment of early warning, therefore, the existing safety monitoring system still needs to be improved in the depth transformation from "state monitoring" to "risk early warning". SUMMARY

[0004] The purpose of the present application is to provide a water conservancy safety monitoring system based on data analysis, which solves the problems mentioned in the background,

[0005] To achieve the above purpose, the present application provides the following technical scheme: a water conservancy safety monitoring system based on data analysis, comprising:

[0006] The acquisition component is used to obtain real-time seepage data, structure damage data and temperature related data of the key position of water conservancy;

[0007] The processing component is used to input the real-time seepage data, structure damage data and temperature related data obtained by the acquisition component, and to clean the input data, and input the cleaned data into the analysis component;

[0008] The analysis component comprises:

[0009] Based on the real-time seepage data, the real-time seepage flow, the historical normal working condition seepage flow threshold, the real-time seepage temperature, the seepage temperature anomaly threshold, the real-time seepage conductivity, the reservoir water conductivity reference value, the real-time seepage pressure, the seepage pressure safety threshold and the seepage weight coefficient output real-time seepage multi-feature anomaly coefficient, and the early implicit seepage anomaly is accurately identified through the real-time seepage multi-feature anomaly coefficient.

[0010] Based on the real-time seepage multi-feature anomaly coefficient, the real-time dam body acoustic emission signal cumulative energy, the acoustic emission energy safety threshold, the real-time seepage turbidity, the seepage turbidity reference value and the coupling amplification coefficient in the structural damage data, the damage erosion coupling risk coefficient is output, and the coupling risk of seepage anomaly and dam micro-damage, seepage erosion is evaluated through the damage erosion coupling risk coefficient.

[0011] Based on the temperature related data, the damage erosion coupling risk coefficient, the dam strain rate, the strain rate safety threshold, the near 3-day seepage temperature daily fluctuation amplitude mean value, the temperature fluctuation safety threshold, the dynamic risk coefficient and the damage weight coefficient output dynamic comprehensive early warning index, which comprehensively reflects the cooperative risk of static anomaly and dynamic trend.

[0012] The monitoring component: the dynamic comprehensive early warning index is input to the monitoring component, and the monitoring component performs control instruction execution based on the input data.

[0013] Optionally, the analysis component includes a seepage calculation module, a damage calculation module and a comprehensive early warning module.

[0014] Optionally, the acquisition process of the acquisition component is as follows:

[0015] The seepage flow sensor is installed at the downstream seepage outlet of the dam to directly collect real-time seepage flow, the seepage temperature sensor is installed on the seepage path to directly collect real-time seepage temperature, the conductivity sensor is installed at the seepage outlet to directly collect real-time seepage conductivity, the osmotic pressure gauge is installed behind the dam foundation cutoff wall or near the dam body drainage hole to directly collect real-time osmotic pressure, the acoustic emission sensor is installed on the dam surface or internal key stress position to directly collect real-time dam body acoustic emission signal cumulative energy, the turbidity sensor is installed at the seepage outlet to directly collect real-time seepage turbidity, and the strain sensor is installed at the key stress position of the dam body to directly collect the strain rate of the dam body.

[0016] Optionally, the processing process of the processing component is as follows:

[0017] The arithmetic mean of the real-time seepage temperature data for 30 consecutive days is taken to obtain the average seepage temperature for nearly 30 days, and the difference between the current strain value and the strain value 5 minutes ago is calculated, and then divided by the time interval to obtain the strain rate change rate of the dam body. The difference between the maximum value and the minimum value of the real-time seepage temperature is taken every day, and the arithmetic mean of the difference for 3 consecutive days is taken to obtain the average seepage temperature fluctuation amplitude for nearly 3 days.

[0018] Optionally, the processing flow of the seepage calculation module is as follows:

[0019] A1: Reflect the macroscopic seepage state by introducing the comparison between the real-time seepage flow and the historical threshold value;

[0020] A2: Capture the temperature deviation caused by the change of the seepage path by combining the real-time seepage temperature with the average value for nearly 30 days and the abnormal threshold value;

[0021] A3: Identify the change of the seepage composition by comparing the real-time electrical conductivity with the reference value of the reservoir water;

[0022] A4: Reflect the pressure influence of the seepage on the dam body by combining the real-time seepage pressure with the safety threshold value.

[0023] Optionally, the processing flow of the damage calculation module is as follows:

[0024] B1: Reflect the extension degree of the micro-cracks in the dam body by combining the acoustic emission energy with the safety threshold value;

[0025] B2: Reflect the erosion intensity of the seepage on the dam body by combining the real-time turbidity with the reference value.

[0026] Optionally, the processing flow of the comprehensive early warning module is as follows:

[0027] C1: Provide a static risk basis by integrating the real-time seepage multi-feature abnormal coefficient and the damage erosion coupling risk coefficient;

[0028] C2: Capture the short-term stress mutation of the dam body by combining the strain rate change rate with the safety threshold value;

[0029] C3: Reflect the long-term stability of the seepage path by combining the average temperature fluctuation amplitude for nearly 3 days with the safety threshold value.

[0030] Optionally, the monitoring component execution process is as follows:

[0031] Based on the range of dynamic comprehensive early warning index, corresponding safety monitoring measures are performed, when the dynamic comprehensive early warning index is less than or equal to 1.0, it is determined that the state is normal, the original period of routine monitoring is maintained, when 1.0 is less than the dynamic comprehensive early warning index and less than or equal to 1.5, it is determined that the state is slightly abnormal, the collection frequency of the acoustic emission sensor and the turbidity sensor is increased, and the monitoring of the dam body micro-damage and seepage erosion is strengthened, when 1.5 is less than the dynamic comprehensive early warning index and less than or equal to 2.0, it is determined that the state is moderately risky, the internal structure detection of the dam body is started, and the hidden damage is investigated, and when the dynamic comprehensive early warning index is greater than 2.0, it is determined that the state is severely risky, and the emergency response mechanism is triggered.

[0032] Compared with the prior art, the beneficial effects of the present application are as follows:

[0033] Firstly, the present application introduces novel parameters such as seepage temperature difference and conductivity deviation, integrates conventional parameters such as seepage flow and seepage pressure, realizes accurate identification of early hidden seepage anomalies, temperature difference reflects seepage path changes, conductivity deviation reflects seepage composition changes, and makes up for the limitations of single conventional parameters, which can capture seepage anomaly signs earlier.

[0034] Secondly, the present application couples acoustic emission energy reflecting the extension of internal micro-cracks of the dam body with seepage turbidity reflecting seepage erosion, comprehensively evaluates the interaction of seepage anomalies, structural micro-damage and erosion, and solves the problem of lack of coupling evaluation in existing systems, which is closer to the essence of the risk formation mechanism.

[0035] Thirdly, the present application integrates dynamic parameters such as strain rate change rate reflecting structural stress mutation and temperature fluctuation amplitude reflecting long-term stability of seepage path, combines real-time seepage multi-element characteristic anomaly coefficient and damage erosion coupling risk coefficient, forms a dynamic comprehensive early warning index, which captures the synergistic effect of static state and dynamic trend, improves the forward-looking nature of early warning, and improves the accuracy of static evaluation of existing models.

[0036] Fourthly, the present application gradually progresses from early anomaly identification to coupling risk evaluation and then to dynamic comprehensive early warning, forms a complete risk evaluation closed loop, and the output early warning index clearly reflects the risk level, providing an operable basis for engineering decision-making, effectively solving the problems of early warning lag and insufficient decision-making basis in existing systems, promoting the optimization of dam seepage monitoring from "single dimension" to "multi-dimensional coupling" and from "static monitoring" to "dynamic early warning", significantly improving the accuracy and forward-looking nature of seepage risk identification through the synergistic integration of multi-dimensional parameters, and providing a better solution for water conservancy engineering safety monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0037] Fig. 1 The flowchart of the analysis component of the present application is shown in the figure;

[0038] Fig. 2The system structure diagram of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0040] Embodiment one, please refer to Figs. 1-2 The present application provides a water conservancy project safety monitoring system based on data analysis, comprising:

[0041] The acquisition component is used to obtain real-time seepage data, structural damage data and temperature-related data of the key position of the water conservancy project;

[0042] The processing component is used to input the real-time seepage data, structural damage data and temperature-related data obtained by the acquisition component, and to clean the input data, and input the cleaned data into the analysis component;

[0043] The analysis component comprises:

[0044] Based on the real-time seepage flow, the historical normal working condition seepage flow threshold value, the real-time seepage temperature, the average seepage temperature in the past 30 days, the seepage temperature anomaly threshold value, the real-time seepage conductivity, the reservoir water conductivity reference value, the real-time seepage pressure, the seepage pressure safety threshold value and the seepage weight coefficient in the real-time seepage data, a real-time seepage multi-feature anomaly coefficient is output, and the early-stage hidden seepage anomaly is accurately identified through the real-time seepage multi-feature anomaly coefficient;

[0045] Based on the real-time seepage multi-feature anomaly coefficient, the real-time dam body acoustic emission signal cumulative energy, the acoustic emission energy safety threshold value, the real-time seepage turbidity, the seepage turbidity reference value and the coupling amplification coefficient in the structural damage data, a damage erosion coupling risk coefficient is output, and the coupling risk of seepage anomaly and dam micro-damage and seepage erosion is evaluated through the damage erosion coupling risk coefficient;

[0046] Based on the damage erosion coupling risk coefficient, the dam body strain rate, the strain rate safety threshold value, the average seepage temperature daily fluctuation amplitude in the past 3 days, the temperature fluctuation safety threshold value, the dynamic risk coefficient and the damage weight coefficient in the temperature-related data, a dynamic comprehensive early warning index is output, and the synergistic risk of static anomaly and dynamic trend is comprehensively reflected;

[0047] The monitoring component inputs the dynamic comprehensive early warning index into the monitoring component, and the monitoring component performs control instruction execution based on the input data;

[0048] The analysis component comprises a seepage calculation module, a damage calculation module and a comprehensive early warning module.

[0049] The collection process of the collection component is as follows: a seepage flow sensor is installed at the seepage outlet downstream of the dam to directly collect real-time seepage flow; a seepage temperature sensor is installed on the seepage path to directly collect real-time seepage temperature; an electrical conductivity sensor is installed at the seepage outlet to directly collect real-time seepage electrical conductivity; a osmotic pressure gauge is installed behind the anti-seepage wall of the dam foundation or near the drainage hole of the dam body to directly collect real-time osmotic pressure; an acoustic emission sensor is installed on the surface or internal key stress position of the dam body to directly collect real-time acoustic emission signal cumulative energy of the dam body; a turbidity sensor is installed at the seepage outlet to directly collect real-time seepage turbidity; and a strain sensor is installed at the key stress position of the dam body to directly collect the strain rate of the dam body.

[0050] The processing flow of the seepage calculation module is as follows:

[0051] The arithmetic mean value of the real-time seepage temperature data of the continuous 30 days is obtained to obtain the average seepage temperature value of the recent 30 days; the difference between the current strain value and the strain value 5 minutes ago is divided by the time interval to obtain the strain rate change rate of the dam body; the difference between the maximum value and the minimum value of the real-time seepage temperature is obtained every day, and the arithmetic mean value of the difference of the continuous 3 days is obtained to obtain the average seepage temperature daily fluctuation amplitude value of the recent 3 days;

[0052] A1: Reflecting the macro seepage state by comparing the real-time seepage flow with the historical threshold value;

[0053] A2: Capturing the temperature deviation caused by the change of the seepage path by combining the real-time seepage temperature with the average value of the recent 30 days and the abnormal threshold value;

[0054] A3: Identifying the change of the seepage component by comparing the real-time electrical conductivity with the reference value of the reservoir water;

[0055] A4: Reflecting the pressure influence of the seepage on the dam body by combining the real-time osmotic pressure with the safety threshold value;

[0056] Specifically, the calculation method of the seepage calculation module is as follows:

[0057] MFAF=(Q / Q ref )×ω1+H×ω2+(EC / EC ref )×ω3+(P / P ref )×ω4;

[0058] H=|T-T avg | / T thH is the seepage temperature anomaly term, which is used to quantify the deviation of real-time seepage temperature from the normal working condition benchmark value. Through normalization processing, the influence of the difference in benchmark temperature of different monitoring points is eliminated, so that the temperature anomaly rate can be directly weighted and summed with the seepage rate and other dimensional anomaly rates, realizing the collaborative analysis of multiple parameters;

[0059] Q is the real-time seepage flow rate, indicating the seepage volume per unit time through the dam body or dam foundation. By using a seepage flow sensor installed at the downstream seepage outlet of the dam, the seepage flow rate is directly collected, reflecting the patency of the seepage channel. A sudden increase in flow rate indicates that new cracks may appear in the dam body or existing cracks may expand.

[0060] Q ref is the historical normal working condition seepage flow rate threshold, indicating the statistical upper limit value of the seepage flow rate during normal operation of the dam. Based on the seepage flow rate data during normal operation in the past 3-5 years, the upper limit of the 95% confidence interval is calculated as the benchmark for determining whether the real-time seepage flow rate is abnormal. If Q exceeds Q ref , it indicates that the seepage deviates from the normal state.

[0061] T is the real-time seepage temperature, which is collected directly by a seepage temperature sensor installed on the seepage path. Changes in temperature reflect changes in the source of seepage. For example, a sudden drop in temperature indicates the mixing of low-temperature groundwater, suggesting that the cracks in the dam foundation are connected.

[0062] T avg is the average seepage temperature in the past 30 days, which is calculated by formula to eliminate seasonal temperature fluctuations. The calculation method is as follows:

[0063] ;

[0064] where Ti is the real-time seepage temperature on the i-th day.

[0065] T th is the seepage temperature anomaly threshold, indicating the maximum range of allowed seepage temperature deviation from the mean value. Based on engineering experience, when |T-T avg |>T th , it indicates that the seepage temperature is abnormal, suggesting a change in the seepage path.

[0066] EC is the real-time seepage conductivity, indicating the concentration of dissolved ions in the seepage water. An electric conductivity sensor is installed at the seepage outlet to directly collect the data. A sudden increase in EC indicates that high-ionic-concentration groundwater has mixed into the seepage or that the dam material has been eroded.

[0067] EC ref is the reference value of the reservoir water conductivity, indicating the average value of the reservoir water conductivity at normal water level. It is obtained by collecting reservoir water samples and measuring them with a conductivity meter. It serves as the benchmark for EC anomalies. If EC exceeds EC ref , it indicates that the composition of the seepage deviates from the reservoir water.

[0068] P is a real-time seepage pressure, indicating the pressure of seepage on the dam body, which is directly collected by a seepage pressure gauge installed behind the anti-seepage wall of the dam foundation or near the drainage hole of the dam body. Excessive seepage pressure can reduce the effective stress of the dam body and may cause piping or flow soil damage;

[0069] P ref is a seepage pressure safety threshold, indicating the maximum seepage pressure allowed by the dam body. If P exceeds P ref , it indicates that the seepage pressure has reached a dangerous level, which is determined based on the design documents;

[0070] ω1, ω2, ω3, ω4 are the contribution ratios of each parameter to the abnormal coefficient, which are determined according to engineering experience, and ω1 + ω2 + ω3 + ω4 = 1;

[0071] MFAF is a real-time seepage multi-feature abnormal coefficient, which is obtained by integrating the real-time seepage flow Q, the historical normal working condition seepage flow threshold Q ref , the real-time seepage temperature T, the average seepage temperature T avg of the past 30 days, the seepage temperature abnormal threshold T th , the real-time seepage conductivity EC, the reference value of reservoir water conductivity EC ref , the real-time seepage pressure P, the seepage pressure safety threshold P ref , and the seepage weight coefficients ω1, ω2, ω3, ω4. It realizes multi-dimensional accurate identification of early implicit anomalies of seepage. Among them, the comparison of real-time seepage flow and historical threshold reflects the macro seepage state, the combination of real-time seepage temperature and the average of the past 30 days and the abnormal threshold captures the temperature deviation caused by the change of seepage path, the comparison of real-time conductivity and reservoir water reference value identifies the change of seepage composition, and the combination of real-time seepage pressure and safety threshold reflects the pressure influence of seepage on the dam body. Through the balance of the contribution ratio of each parameter by the seepage weight coefficient, the comprehensiveness and pertinence of abnormal identification are ensured, thereby breaking through the limitations of conventional single parameter monitoring. Through the collaborative analysis of physical multi-dimensional features, early implicit anomalies of seepage are captured, and accurate basis is provided for subsequent risk assessment;

[0072] The processing flow of the damage calculation module is as follows:

[0073] B1: Reflects the degree of micro-crack propagation in the dam body by combining acoustic emission energy with a safety threshold;

[0074] B2: Reflects the erosion intensity of seepage on the dam body by combining real-time turbidity with a reference value;

[0075] Specifically, the calculation process of the damage calculation module is as follows:

[0076] SDEC = MFAF x [1 + a x (AE energy ) / AE th+ Turb / Turb ref ] ;

[0077] wherein AE energy is the real-time dam acoustic emission signal cumulative energy, representing the total elastic wave energy generated by the micro-crack propagation inside the dam, which is directly collected by the acoustic emission sensor installed on the surface or inside the key parts of the dam, AE energy The sudden increase indicates that the micro-crack inside the dam is rapidly expanding and may develop into a macro-crack;

[0078] AE th is the acoustic emission energy safety threshold, representing the upper limit of the allowed acoustic emission energy, which is determined based on the dam material test, AE energy Exceeding AE th indicates that the micro-crack propagation has reached a dangerous level;

[0079] Turb is the real-time seepage turbidity, representing the concentration index of suspended particles in seepage water, which is directly collected by the turbidity sensor installed at the seepage outlet, and the sudden increase in Turb indicates that the seepage is eroding the dam foundation particles, which is easy to cause piping or erosion damage;

[0080] Turb ref is the seepage turbidity reference value, representing the average value of seepage turbidity during normal operation, which is measured by the turbidimeter through the collection of normal seepage samples, and is used as the reference for the abnormal Turb, exceeding Turb ref indicates that the seepage erosion is intensifying;

[0081] α is the coupling amplification coefficient, which is valued at 0.4-0.6 based on engineering experience, and the aged dam takes a high value to amplify the mutual promotion effect of micro-crack propagation and seepage erosion;

[0082] SDEC is the damage and erosion coupling risk coefficient, which is obtained by integrating the real-time seepage multi-feature abnormality coefficient MFAF, the real-time dam acoustic emission signal cumulative energy AE energy , the acoustic emission energy safety threshold AE th , the real-time seepage turbidity Turb, the seepage turbidity reference value Turb ref and the coupling amplification coefficient α, which realizes the coupling risk assessment of seepage abnormalities, dam micro-damage and seepage erosion, wherein MFAF provides the basic seepage abnormal state, the real-time dam acoustic emission signal cumulative energy AE energy and the acoustic emission energy safety threshold AE th combined, reflecting the degree of micro-crack propagation inside the dam, and the real-time seepage turbidity Turb and the seepage turbidity reference value Turb refThe combination reflects the erosion intensity of seepage on the dam body, and the coupling amplification factor α amplifies the mutual promotion effect of microcrack propagation and seepage erosion. This fills the gap in the existing technology for the coupling assessment of hidden micro-damage and seepage, comprehensively reflects the essential formation mechanism of risk, and improves the depth and accuracy of risk assessment.

[0083] The processing flow of the integrated early warning module is as follows:

[0084] C1: By integrating the real-time seepage multivariate characteristic anomaly coefficient with the damage and erosion coupling risk coefficient, a static risk basis is provided;

[0085] C2: By combining the rate of change of strain rate with a safety threshold, short-term stress abrupt changes in the dam body are captured;

[0086] C3: The long-term stability of the seepage path is reflected by combining the average temperature fluctuation range over nearly 3 days with the safety threshold.

[0087] Specifically, the calculation method of the integrated early warning module is as follows:

[0088] ;

[0089] in The strain rate of the dam body represents the short-term rate of change of the dam body strain. It is continuously collected using strain sensors and analyzed using the formula... Calculations indicate that a sudden increase in stress suggests a change in the stress on the dam body, which could easily lead to structural deformation and instability.

[0090] The strain rate safety threshold represents the upper limit of the allowable strain rate, which is determined based on the design. Exceeding the threshold indicates that the dam body deformation is aggravated.

[0091] This represents the average daily fluctuation range of seepage temperature over the past three days, reflecting the long-term stability of the seepage path. The sudden increase indicates that the path is constantly changing;

[0092] The temperature fluctuation safety threshold represents the upper limit of allowable temperature fluctuations. It is determined based on historical data statistics. Exceeding the threshold indicates a decrease in the stability of the seepage path.

[0093] The dynamic risk coefficient is set at 0.3-0.5 based on engineering experience, with a higher value used during extreme weather events to amplify the risk impact of short-term dynamic factors such as strain rate and temperature fluctuations.

[0094] By integrating the real-time seepage multivariate characteristic anomaly coefficient MFAF, the damage-erosion coupling risk coefficient SDEC, and the dam body strain rate change rate... Strain rate safety threshold Average daily fluctuation range of seepage temperature over the past 3 days Temperature fluctuation safety threshold Dynamic risk coefficient The system incorporates damage weighting coefficients to achieve dynamic comprehensive early warning. Specifically, the real-time seepage multivariate characteristic anomaly coefficient MFAF and the damage-erosion coupling risk coefficient SDEC provide a static risk basis, along with the strain rate change rate. With safety threshold Combined, the short-term stress changes in the dam body were captured, along with the average temperature fluctuation range over the past three days. With safety threshold Combined, it reflects the long-term stability of the seepage path and the dynamic risk coefficient. By amplifying the impact of short-term dynamic factors, the damage weighting coefficient highlights the contribution of the core risk damage erosion coupling risk coefficient SDEC. This approach breaks through the limitations of existing static early warning systems. Through the synergistic analysis of static status and dynamic trends, it enhances the foresight and comprehensiveness of early warning, providing dynamic and quantitative basis for engineering decision-making.

[0095] The monitoring component performs the following process:

[0096] Based on the range of the dynamic comprehensive early warning index, corresponding safety monitoring measures are implemented. When the dynamic comprehensive early warning index is ≤1.0, it is judged as a normal state, and the original cycle of routine monitoring is maintained. When 1.0 < dynamic comprehensive early warning index ≤1.5, it is judged as a slight anomaly, and the acquisition frequency of acoustic emission sensors and turbidity sensors is increased to strengthen the monitoring of micro-damage and seepage erosion of the dam body. When 1.5 < dynamic comprehensive early warning index ≤2.0, it is judged as a moderate risk, and the internal structure inspection of the dam body is initiated. Professional personnel are organized to conduct on-site inspections to investigate hidden damage. When the dynamic comprehensive early warning index >2.0, it is judged as a severe risk, and the emergency response mechanism is triggered. Professional personnel are organized to conduct 24-hour monitoring of key parts of the dam body and issue early warning information to downstream areas.

[0097] By constructing a complete technical chain from early latent anomaly identification to coupled risk assessment and dynamic comprehensive early warning, the problem of conventional monitoring being unable to identify early latent seepage anomalies has been solved. This fills the gap in the coupled assessment of micro-damage and seepage erosion, and achieves comprehensive integration of static and dynamic risks. The dynamic comprehensive early warning index output by the system clearly reflects the risk level, providing accurate and operable basis for water conservancy project safety decisions. It promotes the technological upgrade of dam safety monitoring from "single dimension" to "multi-dimensional coupling" and from "static monitoring" to "dynamic early warning," significantly improving the accuracy and foresight of seepage risk identification and effectively enhancing the safety assurance capability of water conservancy projects.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A water conservancy safety monitoring system based on data analysis, characterized in that: include: Data acquisition components: used to acquire real-time seepage data, structural damage data, and temperature-related data at key locations in water conservancy projects; Processing unit: Used to input real-time seepage data, structural damage data and temperature-related data acquired by the acquisition unit, and to clean the input data before inputting the cleaned data into the analysis unit; Analysis components: Based on the real-time seepage flow rate, historical normal operating condition seepage flow rate threshold, real-time seepage temperature, average seepage temperature over the past 30 days, seepage temperature anomaly threshold, real-time seepage conductivity, reservoir water conductivity benchmark value, real-time seepage pressure, seepage pressure safety threshold, and seepage weight coefficient in the real-time seepage data, the real-time seepage multi-characteristic anomaly coefficient is output, and the early latent seepage anomaly is accurately identified through the real-time seepage multi-characteristic anomaly coefficient. Based on the real-time seepage multivariate characteristic anomaly coefficient, real-time dam body acoustic emission signal cumulative energy, acoustic emission energy safety threshold, real-time seepage turbidity, seepage turbidity benchmark value, and coupling amplification coefficient in the structural damage data, the damage erosion coupling risk coefficient is output. The coupling risk of seepage anomaly with dam body micro-damage and seepage erosion is assessed through the damage erosion coupling risk coefficient. Based on the damage-erosion coupling risk coefficient, dam strain rate, strain rate safety threshold, average daily fluctuation of seepage temperature over the past 3 days, temperature fluctuation safety threshold, dynamic risk coefficient, and damage weight coefficient in temperature-related data, a dynamic comprehensive early warning index is output to comprehensively reflect the synergistic risk of static anomalies and dynamic trends. The analysis components include a seepage calculation module, a damage calculation module, and a comprehensive early warning module; The calculation process of the seepage calculation module is as follows: MFAF = (Q / Q ref ) x ω1 + H x ω2 + (EC / EC ref ) x ω3 + (P / P ref ) x ω4; MFAF is the real-time seepage multi-feature anomaly coefficient; wherein H = |T-T avg | / T th , H is the seepage temperature anomaly term; Q is the real-time seepage flow; Q ref is the historical normal working condition seepage flow threshold; T is the real-time seepage temperature; T avg is the average value of seepage temperature in the past 30 days; T th is the seepage temperature anomaly threshold; EC is the real-time seepage conductivity; EC ref is the reference value of reservoir water conductivity; P is the real-time seepage pressure; P ref is the seepage pressure safety threshold; ω1, ω2, ω3, ω4 are the contribution ratios of each parameter to the anomaly coefficient, which are determined according to engineering experience, and ω1+ω2+ω3+ω4=1; The damage calculation module performs the following calculations: SDEC = MFAF x [1 + a x (AE energy ) / AE th + Turb / Turb ref ]; SDEC is the damage erosion coupling risk coefficient; wherein AE energy is the real-time dam acoustic emission signal cumulative energy; AE th is the acoustic emission energy safety threshold; Turb is the real-time seepage turbidity; Turb ref Turb is the seepage turbidity reference value; α is a coupling amplification coefficient, and is valued at 0.4-0.6 based on engineering experience. The calculation process of the integrated early warning module is as follows: ； DSWI is the dynamic comprehensive early warning index; wherein is the dam strain rate change rate; is the strain rate safety threshold; is the average value of the daily fluctuation range of seepage temperature in the past 3 days; is the temperature fluctuation safety threshold; is the dynamic risk coefficient, which is taken as 0.3-0.5 based on engineering experience; Monitoring Components: The dynamic comprehensive early warning index is input into the monitoring components, which then execute control commands based on the input data.

2. The data analysis based safety monitoring system for water works as claimed in claim 1, wherein: The acquisition process of the acquisition component includes: A seepage flow sensor is installed at the downstream seepage outlet of the dam to directly collect real-time seepage flow. A seepage temperature sensor is installed in the seepage path to directly collect real-time seepage temperature. A conductivity sensor is installed at the seepage outlet to directly collect real-time seepage conductivity. A osmotic pressure gauge is installed behind the dam foundation cutoff wall or near the dam body drainage hole to directly collect real-time seepage pressure.

3. The data analysis based safety monitoring system for water works as claimed in claim 2 wherein: The acquisition process of the acquisition component also includes: using an acoustic emission sensor installed on the surface or key stress-bearing parts of the dam body to directly acquire the cumulative energy of the acoustic emission signal of the dam body in real time; using a turbidity sensor installed at the seepage outlet to directly acquire the turbidity of the seepage in real time; and using a strain sensor installed on the key stress-bearing parts of the dam body to directly acquire the strain rate of the dam body.

4. The data analysis based safety monitoring system for water works as claimed in claim 3 wherein: The processing flow of the seepage calculation module is as follows: The arithmetic mean of the real-time seepage temperature data for 30 consecutive days is used to obtain the average seepage temperature for the past 30 days. The difference between the current strain value and the strain value 5 minutes ago is divided by the time interval to obtain the strain rate change rate of the dam body. The arithmetic mean of the difference between the maximum and minimum real-time seepage temperature values ​​for each day and the difference for 3 consecutive days is used to obtain the average daily fluctuation range of seepage temperature for the past 3 days. A1: Reflect the macroscopic seepage state by comparing the real-time seepage flow with the historical threshold value; A2: Capture the temperature deviation caused by the change of seepage path by combining the real-time seepage temperature with the average value of the past 30 days and the abnormal threshold value; A3: Identify the change of seepage composition by comparing the real-time conductivity with the reference value of reservoir water; A4: Reflect the pressure impact of seepage on the dam body by combining the real-time seepage pressure with the safety threshold value.

5. The data analysis based safety monitoring system for water works as claimed in claim 4 wherein: The processing flow of the damage calculation module is as follows: B1: Reflect the degree of micro-crack propagation in the dam body by combining the acoustic emission energy with the safety threshold value; B2: Reflect the erosion intensity of seepage on the dam body by combining the real-time turbidity with the reference value.

6. The data analysis based safety monitoring system for water projects as claimed in claim 5 wherein: The processing flow of the comprehensive early warning module is as follows: C1: Provide a static risk basis by integrating the real-time seepage multi-feature abnormal coefficient and the damage erosion coupling risk coefficient; C2: Capture the short-term stress mutation of the dam body by combining the strain rate change rate with the safety threshold value; C3: Reflect the long-term stability of the seepage path by combining the average value of the temperature fluctuation amplitude of the past 3 days with the safety threshold value.

7. The data analysis based safety monitoring system for water works as claimed in claim 6 wherein: The monitoring component execution process is as follows: Based on the range of dynamic comprehensive early warning index, the corresponding safety monitoring measures are executed. When the dynamic comprehensive early warning index is ≤1.0, it is determined to be in normal state, and the original period of regular monitoring is maintained. When 1.0< dynamic comprehensive early warning index ≤1.5, it is determined to be in mild abnormality, and the collection frequency of acoustic emission sensors and turbidity sensors is increased to strengthen the monitoring of micro-damage and seepage erosion of the dam body. When 1.5< dynamic comprehensive early warning index ≤2.0, it is determined to be in moderate risk, and the internal structure detection of the dam body is started to check for hidden damage. When the dynamic comprehensive early warning index is >2.0, it is determined to be in severe risk, and the emergency response mechanism is triggered.

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