Single-point Evaluation Method for Dam Safety Monitoring Based on Multiple Correlated Sequences

Abstract

The present invention discloses a single measurement point evaluation method for dam safety monitoring based on multiple correlation sequences, which relates to the technical field of safety monitoring and is used to improve problems such as untimely evaluation and update when replacing monitoring points during dam maintenance. It includes collecting monitoring data, determining abnormal points in the dam's history, screening out abnormal points as monitoring points for monitoring, evaluating the monitoring interference of each monitoring point using fuzzy inference, calculating the safety factor and safety threshold according to the evaluation results, taking remedial measures when the safety factor is low, and predicting the safety factors of all monitoring points of the dam using multiple correlation sequences, collecting the actual data of the corresponding points to calculate the actual value of the safety factor of the monitoring points, comparing the prediction results with the actual values, screening out the monitoring points with high monitoring accuracy and recommending them to the management interface, receiving the input monitoring points from the management interface, predicting and adjusting the input monitoring points, and outputting the adjusted predicted value of the safety factor and the safety threshold.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring, and more specifically, to a method for evaluating a single monitoring point of dam safety based on multiple correlation sequences. Background Art

[0002] Safety monitoring technology refers to a series of technologies and methods for detecting, monitoring, and evaluating the safety of structures, equipment, or the environment. These technologies are widely applied to various infrastructures. When safety monitoring technology is applied to the evaluation of a single monitoring point of a dam, it can comprehensively analyze the single monitoring point of the dam and improve the accuracy of dam safety assessment.

[0003] The prior art has the following deficiencies:

[0004] In the past, when quantitatively evaluating a single monitoring point of a dam, the safety factors of multiple monitoring points were calculated by collecting data around the monitoring points of the dam at a specified time to determine the safety of the dam. When the dam is maintained, the monitoring positions of the single monitoring points that hinder the maintenance will be adjusted, and it is necessary to reselect the time to calculate the safety factors of the adjusted monitoring points after the dam maintenance. However, when the dam is maintained, a large amount of data around the monitoring points will change, which is likely to cause sudden accident problems before the safety is determined. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for evaluating a single monitoring point of dam safety based on multiple correlation sequences. By screening the monitoring positions, the overall safety of the dam is analyzed and evaluated at each monitoring position, and corresponding data is given for the monitoring positions that the user needs to determine to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating a single monitoring point of dam safety based on multiple correlation sequences includes the following steps:

[0008] Step S1: Collect monitoring data, screen out abnormal positions in the dam history as monitoring positions for monitoring according to the monitoring data, collect the distances from the monitoring positions to the upstream and downstream and the changes in the day and night water levels to evaluate the interference items of the monitoring positions, and calculate the safety factor using the monitoring positions with low monitoring interference.

[0009] Step S2: When the safety factor is low, take remedial measures and use multiple correlation sequences to predict the safety factors of the surrounding positions of the monitoring positions with low interference. Use the predicted surrounding positions for cyclic prediction until all the monitoring positions of the dam are predicted, and obtain the predicted values of the safety factors of all the monitoring positions of the dam.

[0010] Step S3: After obtaining the predicted safety factor values, collect the actual data at the corresponding points, calculate the actual safety factor values at the corresponding points, compare the predicted values with the actual values, calculate the adjustment ratio, and select the monitoring points with high monitoring accuracy as the maintenance and adjustment monitoring points for recommendation to the management interface;

[0011] Step S4: Receive the monitoring points input from the management interface, adjust the predicted safety factor values of the input monitoring points according to the adjustment ratio, and output the adjusted predicted safety factor values and safety thresholds to conduct a safety assessment of the overall situation of the dam.

[0012] In a preferred embodiment, in step S1, the monitoring data is the change in the day-night pressure difference at the positions on both sides of the dam, and the monitoring points are initially screened based on the day-night pressure levels at the positions on both sides of the dam.

[0013] In a preferred embodiment, in step S1, the specific steps for initially screening the monitoring points are as follows:

[0014] Divide the dam into N positions with the same area, detect the change in the day-night pressure difference at each of the N positions respectively, arrange the divided positions from largest to smallest according to the magnitude of their values, select the median value of the arranged values of the day-night pressure difference change as the screening threshold, and use the positions exceeding the screening threshold as the monitoring points for monitoring.

[0015] In a preferred embodiment, in step S1, the specific steps for evaluating interference items through fuzzy inference and calculating and screening out the monitoring points with low monitoring interference to calculate the safety factor are as follows:

[0016] Take the distances from the monitoring points to the upstream and downstream and the day-night water level changes as inputs, and the interference intensity of the monitoring points as the output, formulate fuzzy rules, determine the interference intensity of each monitoring point through the fuzzy rules, determine the level of monitoring interference of the monitoring points based on the output results, screen out the monitoring points with low monitoring interference, and sum the normalized distances from the screened monitoring points to the upstream and downstream and the day-night water level changes to obtain the safety factor of the corresponding monitoring points.

[0017] In a preferred embodiment, in step S2, the remedial measure is to set up a small diversion device at the monitoring point to divert and regulate the water at the monitoring point at specific time points, reduce the day-night water level change at the corresponding position, and thus improve the safety factor of the monitoring position.

[0018] In a preferred embodiment, in step S2, the specific steps for using multi-correlated sequences to predict the safety factor of the points around the monitoring points with low interference are as follows:

[0019] Set the point coordinates: Record the coordinates of all points to be predicted;

[0020] Establish an autoregressive model: Mark the monitoring points and the surrounding points of the monitoring points respectively, and construct an autoregressive model by combining the safety coefficients of the two points;

[0021] Calculate the estimation parameters: Calculate the estimation parameters through the safety coefficients of the monitoring point and another monitoring point closest to the monitoring point, and then use the estimation parameters to predict the safety parameters of the surrounding points of the monitoring point;

[0022] Loop prediction: Use the surrounding points of the monitoring point as the new monitoring points for loop prediction;

[0023] Deduplication: Set the deduplication rule, and adjust the safety coefficients of the monitoring points with the same point coordinates during the prediction period according to the point coordinates;

[0024] Set the stop condition: Set the stop condition, and the prediction behavior stops after reaching the stop condition.

[0025] In a preferred embodiment, in step S3, after obtaining the predicted value of the safety coefficient of the monitoring point and the true value of the safety coefficient of the corresponding point, calculate the ratio of the true value of the safety coefficient of each point to the predicted value of the safety coefficient, and take the average value of the calculated ratios as the adjustment ratio.

[0026] In a preferred embodiment, in step S3, when the system recommends monitoring points with high accuracy, compare the ratio of the true value of the safety coefficient of each point to the predicted value of the safety coefficient, set the recommended range, and when the ratio of the true value of the safety coefficient of the point to the predicted value of the safety coefficient is within the recommended range, the system recommends the corresponding point as a monitoring point with high accuracy to the management interface.

[0027] In a preferred embodiment, in step S4, take the product result of the safety coefficient of the monitoring point and the adjustment ratio as the adjusted safety coefficient of the monitoring point. After the system calculates the safety coefficients of the secondary-screened monitoring points, use the percentile method to set the safety threshold, arrange the calculated safety coefficients of each monitoring point from small to large, set the screening ratio, and take the safety coefficients exceeding the number of the screening ratio among all the safety coefficients as the safety threshold.

[0028] The technical effects and advantages of the single-measurement point evaluation method for dam safety monitoring based on multiple correlated sequences of the present invention:

[0029] The present invention collects monitoring data, determines abnormal points in the dam history, screens out the abnormal points as monitoring points for monitoring. Monitoring with abnormal points will lead to a decrease in the overall predicted safety, thereby giving an early warning for maintenance work. The monitoring accuracy of each monitoring point is evaluated, and the safety factor and safety threshold are calculated according to the evaluation results. When the safety factor is low, remedial measures are taken and monitoring points with high monitoring accuracy are screened and recommended to the management interface. The input monitoring points from the management interface are received, predicted and adjusted, and the adjusted safety factor prediction value and safety threshold are output to conduct a safety assessment of the overall situation of the dam, reasonably avoiding the chain problems caused by the change of monitoring points during maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the single-point evaluation method for dam safety monitoring based on multiple correlation sequences of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] The present invention collects monitoring data, determines abnormal points in the dam history, screens out the abnormal points as monitoring points for monitoring, evaluates the monitoring accuracy of each monitoring point, calculates the safety factor and safety threshold according to the evaluation results. When the safety factor is low, remedial measures are taken and monitoring points with high monitoring accuracy are screened and recommended to the management interface. The input monitoring points from the management interface are received, predicted and adjusted, and the adjusted safety factor prediction value and safety threshold are output to conduct a safety assessment of the overall situation of the dam.

[0033] Embodiment, a single-point evaluation method for dam safety monitoring based on multiple correlation sequences, as Figure 1 shown, includes the following steps:

[0034] Step S1: Collect monitoring data, screen out abnormal points in the dam history as monitoring points for monitoring according to the monitoring data, collect the distances from the monitoring points to the upstream and downstream and the daily and nightly water level changes to evaluate the interference items of the monitoring points, and calculate the safety factor using the monitoring points with low monitoring interference.

[0035] Step S2: When the safety factor is low, take remedial measures and use multiple correlation sequences to predict the safety factors of the surrounding points around the monitoring points with low interference. Use the predicted surrounding points for cyclic prediction until the safety factor prediction values of all the monitoring points of the dam are obtained;

[0036] Step S3: After obtaining the safety factor prediction values, collect the actual data of the corresponding points and calculate the actual safety factor values of the corresponding points. Compare the prediction values with the actual values to calculate the adjustment ratio, and select the monitoring points with high monitoring accuracy as the maintenance and adjustment monitoring points and recommend them to the management interface;

[0037] Step S4: Receive the monitoring points input by the management interface, adjust the safety factor prediction values of the input monitoring points according to the adjustment ratio, and output the adjusted safety factor prediction values and safety thresholds to conduct a safety assessment of the overall situation of the dam.

[0038] The specific implementation is as follows:

[0039] In step S1, the monitoring data is the change in the day-night pressure difference at both sides of the dam. When the change in the day-night pressure difference at both sides of the dam is large, it is easy to cause structural weakening problems at the corresponding positions of the dam, increasing the safety hazards of the dam. Selecting the positions with large day-night pressure differences as the average level for monitoring and analysis will reduce the overall safety assessment of the dam, thereby increasing the tolerance of the dam safety assessment.

[0040] When the system determines the pressure difference at both sides of the dam, it first divides the day and night of a day into time periods. For the position to be monitored, randomly select a time point during the day to detect the pressure difference on both sides of the position to be monitored, and then randomly select a time point during the night to detect the pressure difference on both sides of the position to be monitored. Take the absolute value of the difference between the pressure difference changes detected at the two time points at the position to be monitored to obtain the day-night pressure difference change at the corresponding position.

[0041] Divide the dam into N positions, detect the day-night pressure difference changes for each of the N positions respectively, and arrange the divided positions from large to small according to the numerical values. Select the median value of the arranged day-night pressure difference changes as the screening threshold, and use the positions exceeding the screening threshold as the monitoring points for monitoring.

[0042] Differences in the distances of the monitoring points to the upstream and downstream will lead to differences in the soil moisture conditions and pore water pressures, affecting the stability of the safety factor calculation of the monitoring points. Large day-night water level changes at the monitoring points will cause dynamic strain at the monitoring points, resulting in unstable monitoring data. Therefore, both the distances of the monitoring points to the upstream and downstream and the day-night water level changes are interference items for safety assessment.

[0043] When the system evaluates interference items, it measures the upstream and downstream distances and the changes in water levels during day and night for the selected monitoring points. It should be noted that when measuring the changes in water levels at the monitoring points during day and night, it is necessary to ensure that the water level measurements at each monitoring point are taken at the same time point to avoid differences in water levels caused by different time periods and other environmental factors.

[0044] Taking the upstream and downstream distance differences and the changes in water levels during day and night of each monitoring point as inputs, using fuzzy inference to evaluate the interference items of the monitoring points and analyze the interference intensity of the safety factor of the dam for the monitoring points. The specific steps are as follows:

[0045] Define the upstream and downstream distance differences and the changes in water levels during day and night of the monitoring points as inputs, and define the interference intensity as the output. Divide them into fuzzy sets. For example, "Big" and "Small" for the upstream and downstream distance differences of the monitoring points, "High" and "Low" for the changes in water levels during day and night of the monitoring points, and "Strong" and "Weak" for the interference intensity of the monitoring points.

[0046] Formulate fuzzy rules to describe the relationship between the inputs and the output. For example, mark the upstream and downstream distance of the monitoring point as S, mark the change in water level during day and night of the monitoring point as B, and mark the interference intensity of the monitoring point as G. Then it can be defined as:

[0047] Rule 1: IF (S is Big) AND (B is High) THEN (G is Strong)

[0048] Rule 1: IF (S is Small) AND (B is Low) THEN (G is Weak) ...

[0050] Perform fuzzy inference according to the fuzzy rules. When the output result is "Strong", judge that the interference intensity of the monitoring point is strong; when the output result is "Weak", judge that the interference intensity of the monitoring point is weak.

[0051] The system conducts a secondary screening and calculates the safety factor of the monitoring points with weak interference intensity. When calculating the safety factor of the monitoring points, the sum result after normalizing the upstream and downstream distance differences and the changes in water levels during day and night of the monitoring points can be used as the safety factor of the monitoring points.

[0052] When normalizing the upstream and downstream distance differences or the changes in water levels during day and night of the monitoring points, the Max-Min normalization method can be used for processing: x nore = x - x min / x max - x min , where x is the upstream and downstream distance difference or the change in water level during day and night of the monitoring point, xnore is the result after normalization of the upstream and downstream distance difference or the day-night water level change corresponding to the monitoring point, x min is the minimum value of the upstream and downstream distance difference or the day-night water level change of each monitoring point, x max is the maximum value of the upstream and downstream distance difference or the day-night water level change of each monitoring point.

[0053] It should be noted that when collecting monitoring data, pressure sensors can be installed on both sides of the position to be monitored. The pressures on both sides of the position to be monitored are obtained through the pressure sensors and the pressure difference is calculated. When dividing the day and night, it can be divided according to the actual situation. For example, from 8 am to 8 pm is regarded as the daytime period, and from 8 pm to 8 am is regarded as the nighttime period. Then, the detection is carried out at randomly selected time points during the corresponding period at the position to be monitored. The division of the fuzzy set can be adjusted according to the actual situation. For the judgment of the size and height of the upstream and downstream distance difference and the day-night water level change of the monitoring point, the threshold can be set by itself according to the accuracy requirement. For example, the average value is used as the threshold, and the monitoring points with the upstream and downstream distance difference or the day-night water level change exceeding the average value among each monitoring point are set as "Big" or "High", etc., which will not be elaborated here.

[0054] In step S2, after the system calculates the safety factor for the secondarily screened monitoring points, the safety threshold is set by using the percentile method. The safety factors calculated for each monitoring point are arranged from small to large, and the screening ratio is set as M%. The safety factors exceeding M% of all the safety factors are used as the safety threshold. When the safety factor calculated for a monitoring point exceeds the safety threshold, it is judged that the safety factor of the monitoring point is relatively high; otherwise, it is judged that the safety factor of the monitoring point is relatively low and a remedial measure is required. For example, when the screening ratio is set as 60%, the safety factors whose numerical values exceed 60% of all the safety factors are used as the safety threshold, and then the safety factors of each monitoring position are compared with the safety threshold for judgment.

[0055] When the safety factor is relatively low, the remedial measure is to set up a small diversion device at the monitoring point to divert and adjust the water at the monitoring point at a specific time point, reduce the day-night water level change at the corresponding position, thereby improving the safety factor of the monitoring position. After the monitoring points with relatively low safety factors are processed by the remedial measure, the safety factors of the surrounding points of the monitoring points with low interference are predicted by using the multi-correlation sequence. The specific steps are as follows:

[0056] Set the point coordinates: Record the coordinates of all points to be predicted;

[0057] Establish an autoregressive model: Mark the safety factor of the surrounding points as Q new , mark the monitoring point with low interference as Q old , then the autoregressive model can be constructed as: Q new= β0 + β1 × Q old , where β0 and β1 are two estimated parameters.

[0058] Calculate the estimated parameters: When calculating the estimated parameters β0 and β1, let Y be the safety factor of the surrounding points and X be the safety factor of the monitoring points. Randomly select multiple monitoring points, compare the squared differences of the safety factors of the monitoring points pairwise, select the combination with the smallest squared difference for recording, then randomly select the same number of monitoring points, and similarly select the combination with the smallest squared difference for recording to obtain two sets of safety factors. Take one safety factor in each set as the safety factor of the surrounding points and substitute it back into the autoregressive model. Calculate the two evaluation parameters β0 and β1 through the two sets of safety factors. It should be added that when comparing the monitoring points pairwise, select the monitoring point to compare with the nearest monitoring point because the surrounding points of the monitoring point should be the nearest monitoring point or be between the monitoring point and the nearest monitoring point. Therefore, after calculating the estimated parameters by comparing the monitoring point with the nearest monitoring point, predict step by step by shrinking.

[0059] Loop prediction: Regard the estimated parameters of the monitoring point and the surrounding points as the estimated parameters calculated from the monitoring point and the nearest monitoring point to calculate the safety factor of the surrounding points. After calculating the safety factor of the surrounding points, use the corresponding point as the new monitoring point for loop prediction.

[0060] Deduplication: Set the deduplication rule. If the point coordinates of the surrounding points corresponding to two monitoring points are the same, then take the average of the safety factors of the surrounding points calculated using the two monitoring points as the safety factor of the points with the same point coordinates. The system records the point coordinates each time calculating the safety factor of the surrounding points and compares the recorded coordinates with the already recorded coordinates. If there is a repetition, take the average of the safety factors calculated twice as the safety factor of the corresponding point.

[0061] Set the stop condition: Stop the prediction when all the point coordinates to be predicted are recorded. The system determines whether to stop the prediction by comparing the number of point coordinates set in advance with the number of point coordinates recorded during the prediction process. When the number of point coordinates set in advance is the same as the number of point coordinates recorded during the prediction process, stop the prediction.

[0062] Predict all the monitoring points of the dam through multiple correlation sequences, and take the safety factor calculated for each monitoring point as the predicted value of the safety factor of the corresponding monitoring point.

[0063] It should be noted that when there are repeated point coordinates during the prediction process, the quantity will not increase. The remedial measures in this example only deal with the distance. The specific remedial methods are not unique and are mostly existing means, which will not be analyzed here.

[0064] In step S3, after obtaining the predicted value of the safety factor, the actual data of the corresponding point is collected. The actual data is the upstream and downstream distance difference and the day-night water level change at the corresponding point. Similarly, for the method of obtaining the monitored points selected for the second time, after obtaining the upstream and downstream distance difference and the day-night water level change at the corresponding point, they are normalized respectively and the safety factor is calculated as the actual value of the safety factor. It should be noted that when normalizing the upstream and downstream distance difference and the day-night water level change at the corresponding point, the same normalization method used when calculating the safety factor for the monitored points selected for the second time should be used, otherwise comparison cannot be carried out.

[0065] After obtaining the true value of the safety factor for the corresponding point, calculate the ratio of the true value of the safety factor to the predicted value of the safety factor for each point, and take the average of the calculated ratios as the adjustment ratio.

[0066] When the system recommends monitored points with high monitoring accuracy, it compares the ratio of the true value of the safety factor to the predicted value of the safety factor for each point, sets the recommended range. When the ratio of the true value of the safety factor to the predicted value of the safety factor at a point is within the recommended range, the system recommends the corresponding point as a monitored point with high accuracy to the management interface.

[0067] It should be noted that the closer the true value of the safety factor is to the predicted value of the safety factor, that is, the closer the ratio of the true value of the safety factor to the predicted value of the safety factor is to 1, the more accurate the system's prediction of the point is. The recommended range can be adjusted according to the accuracy requirements or set by consulting relevant personnel. For example, the recommended range can be set to [0.95, 1.05], etc.

[0068] In step S4, after the system calculates and obtains the adjustment ratio, it updates and adjusts the predicted value of the safety factor for each monitored point. The product of the safety factor of the monitored point and the adjustment ratio is used as the adjusted safety factor of the monitored point. Since the adjustment ratio is the average ratio of the true value of the safety factor to the predicted value of the safety factor for each point, all adjustment ratios reflect the overall deviation trend between the predicted value of the safety factor and the true value of the safety factor. Using the adjustment ratio to adjust the predicted value of the safety factor for all monitored points can improve the prediction efficiency and accuracy, and also avoid the cost waste of adjusting the predicted value of the safety factor for each monitored point.

[0069] After the system recommends to the management interface, the user can input to the system by selecting the recommended monitored points or selecting monitored points by themselves. The system gives the adjusted safety factor of the corresponding monitored point according to the monitored points selected by the user, and attaches the safety threshold beside it to conduct a safety assessment of the overall situation of the dam, which is convenient for the comparison and analysis of the dam administrator and also reasonably avoids the chain problems caused by changing the monitored points when the dam is maintained.

[0070] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0071] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application of the technical solution and the constraints of the invention. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0072] In addition, each functional module in the various embodiments of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0073] As described above, this is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0074] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating a single measurement point in dam safety monitoring based on multiple correlated sequences, characterized in that Including the following steps: Step S1: Collect monitoring data, screen out abnormal points in the dam history as monitoring points for monitoring according to the monitoring data, collect the distances from the monitoring points to the upstream and downstream and the changes in the day-night water levels to evaluate the interference items of the monitoring points, and calculate the safety factor using the monitoring points with low monitoring interference. Step S2: When the safety factor is low, take remedial measures and use multiple correlation sequences to predict the safety factors of the points around the monitoring points with low interference. Use the predicted surrounding points for cyclic prediction until all the monitoring points of the dam are predicted, and obtain the predicted values of the safety factors of all the monitoring points of the dam. Step S3: After obtaining the predicted values of the safety factors, collect the actual data of the corresponding points to calculate the actual values of the safety factors of the corresponding points. Compare the predicted values and the actual values to calculate the adjustment ratio, and screen out the monitoring points with high monitoring accuracy as the maintenance adjustment monitoring points and recommend them to the management interface. Step S4: Receive the monitoring points input from the management interface, adjust the predicted values of the safety factors of the input monitoring points according to the adjustment ratio, and output the adjusted predicted values of the safety factors and the safety threshold to conduct a safety assessment of the overall situation of the dam.

2. The single-measurement point evaluation method for dam safety monitoring based on multiple correlation sequences according to claim 1, characterized in that: In step S1, the monitoring data is the change in the day-night pressure difference at both sides of the dam, and the monitoring points are initially screened according to the day-night pressure levels at both sides of the dam.

3. The single-measurement point evaluation method for dam safety monitoring based on multiple correlation sequences according to claim 2, characterized in that: In step S1, the specific steps for initially screening the monitoring points are as follows: Divide the dam into N positions with the same area, detect the changes in the day-night pressure differences at the N positions respectively, arrange the divided positions from large to small according to the numerical values, select the median value of the arranged numerical values of the day-night pressure difference changes as the screening threshold, and use the positions exceeding the screening threshold as the monitoring points for monitoring.

4. The single-measurement point evaluation method for dam safety monitoring based on multiple correlation sequences according to claim 1, characterized in that: In step S1, the specific steps for evaluating the interference items through fuzzy inference and calculating and screening out the monitoring points with low monitoring interference to calculate the safety factor are as follows: Take the distances from the monitoring points to the upstream and downstream and the changes in the day-night water levels as inputs, and the interference intensity of the monitoring points as the output. Formulate fuzzy rules, determine the interference intensity of each monitoring point through the fuzzy rules, determine the high or low monitoring interference of the monitoring points through the output results, screen out the monitoring points with low monitoring interference, and normalize and sum the distances from the screened monitoring points to the upstream and downstream and the changes in the day-night water levels to obtain the safety factor of the corresponding monitoring points.

5. The single-measurement point evaluation method for dam safety monitoring based on multiple correlation sequences according to claim 1, characterized in that: In step S2, the remedial measure is to set up a small diversion device at the monitoring point, divert and adjust the water at the monitoring point at specific time points, reduce the day-night water level change at the corresponding position, and thus improve the safety factor of the monitoring position.

6. The single - point evaluation method for dam safety monitoring based on multi - related sequences according to claim 1 is characterized in that: In step S2, the specific steps of using multi - related sequences to predict the safety factors of the points around the monitoring points with low interference are as follows: Set point coordinates: Record the coordinates of all points to be predicted. Establish an autoregressive model: Mark the monitoring points and the surrounding points of the monitoring points respectively, and construct an autoregressive model by combining the safety factors of the two points. Calculate the estimation parameters: Calculate the estimation parameters through the safety factors of the monitoring point and another monitoring point closest to the monitoring point, and then use the estimation parameters to predict the safety parameters of the surrounding points of the monitoring point. Recursive prediction: Take the surrounding points of the monitoring point as the new monitoring points for recursive prediction. Duplicate removal: Set the duplicate - removal rule, and adjust the safety factors of the monitoring points with the same point coordinates during the prediction period according to the point coordinates. Set the stop condition: Set the stop condition, and the prediction behavior stops after reaching the stop condition.

7. The single - point evaluation method for dam safety monitoring based on multi - related sequences according to claim 1 is characterized in that: In step S3, after obtaining the predicted value of the safety factor of the monitoring point and the true value of the safety factor of the corresponding point, calculate the ratio of the true value of the safety factor to the predicted value of the safety factor for each point, and take the average value of the calculated ratios as the adjustment ratio.

8. The single - point evaluation method for dam safety monitoring based on multi - related sequences according to claim 7 is characterized in that: In step S3, when the system recommends the monitoring points with high monitoring accuracy, compare the ratios of the true value of the safety factor to the predicted value of the safety factor for each point, set the recommendation range, and when the ratio of the true value of the safety factor to the predicted value of the safety factor of the point is within the recommendation range, the system recommends the corresponding point as a monitoring point with high accuracy to the management interface.

9. The single - point evaluation method for dam safety monitoring based on multi - related sequences according to claim 1 is characterized in that: In step S4, take the product result of the safety factor of the monitoring point and the adjustment ratio as the adjusted safety factor of the monitoring point. After the system calculates the safety factors of the monitoring points after secondary screening, use the percentile method to set the safety threshold. Arrange the calculated safety factors of each monitoring point from small to large, set the screening ratio, and take the safety factors that exceed the number of the screening ratio among all safety factors as the safety threshold.

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