A slope repair method for water conservancy projects

Abstract

The present invention discloses a slope trimming method for water conservancy projects, relating to the technical field of water conservancy project slope trimming. The method comprises: collecting slope data sequences and environmental data sequences for each slope location on a target slope; determining, based on the changing trend relationship between each slope data sequence and each environmental data sequence, the influence factor of each environmental data sequence on each slope data sequence, as well as the expected sliding factor of each slope location at each time; determining, based on each influence factor and each expected sliding factor, an environmental compensation coefficient for each slope location at each time; correcting the slope data sequence for each slope location using each environmental compensation coefficient to obtain a corrected data sequence for each slope location; and, when the corrected data sequence meets preset trimming conditions, trimming the corresponding slope location to obtain a trimmed target slope. The present invention can improve the accuracy of the collected slope data sequence and improve the accuracy of slope trimming.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy project slope repair, and in particular to a slope repair method for water conservancy projects. Background Art

[0002] In water conservancy projects, slope modification is a key engineering activity aimed at ensuring the safety and durability of the project structure while improving environmental quality and functionality. Slope modification is of great significance because it can prevent soil erosion and slope degradation, thereby extending the service life of the project.

[0003] In the existing method, sensors are used to collect various slope data, and the slopes that need to be repaired are determined based on the changes in the various slope data for maintenance and repair.

[0004] However, in the process of collecting various slope data, sensors are affected by environmental factors such as wind, temperature, and humidity. This makes the collected slope data less accurate, and thus leads to lower accuracy in slope modification. Summary of the Invention

[0005] An embodiment of the present invention provides a slope trimming method for a water conservancy project, which can improve the accuracy of a collected slope data sequence, thereby improving the accuracy of slope trimming.

[0006] A first aspect of an embodiment of the present invention provides a slope trimming method for a water conservancy project, comprising:

[0007] Collect slope data sequences and environmental data sequences at each slope position on the target slope;

[0008] Based on the relationship between the changing trends of each slope data series and each environmental data series, the influence factors of each environmental data series on each slope data series and the potential landslide factors of each slope position at each time are determined. The potential landslide factors are used to characterize the potential landslide trends of the slope positions.

[0009] According to various influencing factors and various factors to be landslided, the environmental compensation coefficient of each slope position at each time is determined;

[0010] By using each environmental compensation coefficient, the slope data sequence of each slope position is corrected to obtain a corrected data sequence of each slope position;

[0011] When the corrected data sequence meets the preset trimming conditions, the corresponding slope position is trimmed to obtain the trimmed target slope.

[0012] A second aspect of an embodiment of the present invention provides a slope trimming device for a water conservancy project, comprising:

[0013] A data acquisition module is used to collect slope data sequences and environmental data sequences at various slope positions on the target slope;

[0014] The data analysis module is used to determine the influence factor of each environmental data sequence on each slope data sequence based on the change trend relationship between each slope data sequence and each environmental data sequence, as well as the potential landslide factor of each slope location at each time. The potential landslide factor is used to characterize the potential landslide trend of the slope location;

[0015] The data analysis module is also used to determine the environmental compensation coefficient of each slope position at each time based on each influencing factor and each potential sliding factor;

[0016] An environmental compensation module is used to correct the slope data sequence of each slope position by using each environmental compensation coefficient to obtain a corrected data sequence of each slope position;

[0017] The slope trimming module is used to trim the corresponding slope position when the corrected data sequence meets the preset trimming conditions to obtain the trimmed target slope.

[0018] In the slope correction method for water conservancy projects provided by an embodiment of the present invention, the influence factor of the environmental data sequence on the slope data sequence, as well as the expected landslide factor of the slope position at each moment, are determined based on the change trend relationship between the slope data sequence and the environmental data sequence. This can improve the accuracy of the analysis of the impact of the environmental data sequence on the slope data sequence. Then, based on the influence factor and the expected landslide factor, the environmental compensation coefficient of the slope position at each moment is determined. In this way, the embodiment of the present invention obtains the environmental compensation coefficient of the slope position at each moment by considering the influence of environmental factors on the slope data sequence. The environmental compensation coefficient is then used to correct the corresponding slope data sequence, thereby obtaining a corrected data sequence for the slope position. In this way, the accuracy of the collected slope data sequence can be improved, thereby improving the accuracy of slope correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A schematic flow chart of a first slope trimming method for a water conservancy project provided by one embodiment of the present invention;

[0021] Figure 2A schematic flow chart of a second method for slope modification for a water conservancy project provided by one embodiment of the present invention;

[0022] Figure 3 This is a schematic structural diagram of a slope trimming device for water conservancy projects provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0023] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a slope modification method for hydraulic engineering projects, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0024] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0025] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with the relevant provisions of laws and regulations.

[0026] It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary and their purpose is only to illustrate the feasibility of implementing the technical solution of the present invention, but it does not mean that the applicant has or will necessarily use the solution.

[0027] In water conservancy projects, slope modification is a key engineering activity aimed at ensuring the safety and durability of the project structure while improving environmental quality and functionality. Slope modification is of great significance because it can prevent soil erosion and slope degradation, thereby extending the service life of the project.

[0028] Existing methods use sensors to collect various slope data. Changes in these data are then used to identify slopes requiring repair and maintenance. However, during the process of collecting slope data, sensors are affected by environmental factors such as wind, temperature, and humidity. This results in low accuracy of the collected slope data, and consequently, low accuracy in slope repair.

[0029] The purpose of the present invention is to provide a slope correction method for water conservancy projects. In the slope correction method for water conservancy projects provided by an embodiment of the present invention, based on the change trend relationship between the slope data sequence and the environmental data sequence, the influence factor of the environmental data sequence on the slope data sequence and the pending landslide factor of the slope position at each moment are determined, which can improve the accuracy of the analysis of the influence of the environmental data sequence on the slope data sequence. Then, based on the influence factor and the pending landslide factor, the environmental compensation coefficient of the slope position at each moment is determined. In this way, the embodiment of the present invention obtains the environmental compensation coefficient of the slope position at each moment by considering the influence of environmental factors on the slope data sequence. Then, using the environmental compensation coefficient, the corresponding slope data sequence is corrected to obtain a corrected data sequence of the slope position. In this way, the accuracy of the collected slope data sequence can be improved, thereby improving the accuracy of slope correction.

[0030] The following describes a specific embodiment of a slope trimming method for a water conservancy project provided by an embodiment of the present invention.

[0031] Figure 1 A schematic flow chart of a slope trimming method for a water conservancy project is provided. The slope trimming method for a water conservancy project can be applied to a service end and can include the following steps S101 to S105.

[0032] S101, collecting slope data sequences and environmental data sequences at each slope position on the target slope;

[0033] In this embodiment, the slope position is used to represent a position feature point on the target slope. For example, the server can pre-set multiple slope positions on the target slope.

[0034] The slope data sequence is used to characterize the sequence of slope parameters at the slope location in chronological order, and the environmental data sequence is used to characterize the sequence of environmental parameters at the slope location in chronological order.

[0035] As an example, the server uses inclinometers and displacement sensors to monitor slope parameters such as displacement, tilt angle, and crack width. This data is recorded in a time series to form a slope data sequence. Simultaneously, soil moisture sensors and groundwater monitoring equipment collect environmental parameters such as rainfall, soil moisture, groundwater level, and temperature at the slope location. These are also recorded in a time series to form an environmental data sequence.

[0036] S102, based on the relationship between the change trends of the slope data sequences and the environmental data sequences, determining the influence factors of the environmental data sequences on the slope data sequences, and the potential landslide factors of the slope locations at each time, wherein the potential landslide factors are used to characterize the potential landslide trends of the slope locations;

[0037] In this embodiment, when collecting slope parameters such as displacement and tilt angle, the corresponding sensors are placed on the slope surface. This means that wind will affect the collected displacement and tilt angle to some extent. Furthermore, changes in temperature and humidity can cause changes in soil volume, thus affecting the readings of the corresponding displacement and tilt angle sensors. Therefore, the impact factor of the environmental data sequence on the slope data sequence is used to characterize the impact of the environmental data sequence on the slope data sequence.

[0038] Due to the varying soil properties, geological structures, and hydrological conditions within the target slope, when a landslide occurs, the target slope typically does not slide all at once. Instead, it slides in a cluster, leading to a gradual increase in the number of clusters. Specifically, when a slope is about to begin sliding, the slope parameters of the target slope are all centered on a single slope location. Other adjacent slope locations are affected by this location, causing the slope data series to show an increase in the size of the slope. Therefore, the potential landslide factor is used to characterize the potential landslide tendency or risk level of a slope location.

[0039] As an example, the server evaluates the changing trend relationship between the slope data sequence and the environmental data sequence through statistical analysis (such as correlation analysis and regression analysis) to determine the degree of influence of each environmental parameter on the slope position stability, that is, the influence factor of each environmental data sequence on each slope data sequence.

[0040] Then, based on the changing trend of the slope data series, combined with the geomechanical model or machine learning algorithm, the potential sliding factor of each slope position at each moment is calculated.

[0041] S103, determining the environmental compensation coefficient at each slope position at each time according to each influencing factor and each potential sliding factor;

[0042] In this embodiment, the environmental compensation coefficient is used to quantify the influence of environmental factors on slope stability, that is, it is a correction coefficient for compensating the influence of the environmental data sequence on the slope data sequence.

[0043] As an example, based on the magnitude of the impact factor and the value of the pending landslide factor, the environmental compensation coefficient for each slope location at each moment is calculated using methods such as weighted summation, fuzzy logic, and neural networks. A larger impact factor indicates a greater degree of interference with the slope data sequence collected at that time, i.e., a lower reliability and a larger environmental compensation coefficient. A larger pending landslide factor indicates a more pronounced pending landslide trend at that slope location, a lesser degree of interference with the slope data sequence collected at that time, i.e., a higher reliability and a smaller environmental compensation coefficient.

[0044] S104, correcting the slope data sequence at each slope position using each environmental compensation coefficient to obtain a corrected data sequence at each slope position;

[0045] In this embodiment, the corrected data sequence is used to represent a data sequence with higher accuracy obtained by correcting the slope data sequence. The slope data sequence includes slope evaluation data collected at multiple moments, and the corrected data sequence includes slope evaluation data corrected at multiple moments.

[0046] For example, when there are no interference from environmental factors like wind, temperature, and humidity, the credibility of the slope data sequence collected at each slope location is relatively high. However, when there are interference from environmental factors like wind, temperature, and humidity, the credibility of the slope data sequence collected at each slope location is relatively low. Therefore, through the above analysis, slope evaluation data can be obtained from slope data sequences with less interference. This less-interfered slope evaluation data can then be used to predict slope evaluation data from slope data sequences with greater interference. This data can then be corrected using the environmental compensation coefficient to obtain a more accurate slope data sequence.

[0047] Specifically, the server can filter out the slope evaluation data at the corresponding time when the impact factor is less than a preset impact threshold in the slope data sequence. The slope evaluation data at the missing time is then interpolated using linear interpolation. The filled slope evaluation data are then sorted in chronological order to form the filled slope data sequence.

[0048] Then, the filled slope data sequence is input into the ARIMA model to predict the slope evaluation data at the current moment. Based on the difference between the predicted value and the actual collected value of the slope evaluation data at the current moment, the actual collected value at the current moment is corrected using the following formula 1 to obtain the corrected slope evaluation data of the slope position at the current moment:

[0049] Formula 1

[0050] In formula 1, To characterize the The first slope position The slope data series is The slope evaluation data after correction at each moment, To characterize the The first slope position The slope data series is Slope evaluation data predicted at each moment, To characterize the The first slope position The slope data series is The slope evaluation data actually collected at each moment, To characterize the The first slope position The slope data series is Environmental compensation coefficient at a certain moment.

[0051] Finally, a correction data sequence of the slope position is constructed based on the corrected slope evaluation data of the slope position at each moment.

[0052] S105 , when the corrected data sequence meets the preset trimming conditions, trimming the corresponding slope position to obtain a trimmed target slope.

[0053] In this embodiment, the preset trimming condition is used to represent the condition that the correction data sequence should meet when trimming the slope position.

[0054] As an example, the server sets a normal value range for each type of slope evaluation data at the slope location, and then compares the slope evaluation data at the current moment in various correction data sequences of the slope location with the normal value range to determine whether it exceeds the normal value range.

[0055] If only one type of slope evaluation data exceeds the normal value range, the slope location will be inspected and maintained; if both the groundwater level and soil moisture evaluation data exceed the normal value range, drainage pipes or intercepting ditches will be added to the slope location to maintain moderate soil moisture; if both the displacement and inclination angle evaluation data exceed the normal value range, some geonets and other devices will be used to improve soil stability at the slope location.

[0056] This embodiment, based on the changing trend relationship between the slope data sequence and the environmental data sequence, determines the impact factor of the environmental data sequence on the slope data sequence, as well as the expected landslide factor of the slope location at each moment. This improves the accuracy of the analysis of the impact of the environmental data sequence on the slope data sequence. Then, based on the impact factor and the expected landslide factor, the environmental compensation coefficient of the slope location at each moment is determined. Thus, by considering the impact of environmental factors on the slope data sequence, the embodiment of the present invention obtains the environmental compensation coefficient of the slope location at each moment. The environmental compensation coefficient is then used to correct the corresponding slope data sequence, thereby obtaining a corrected data sequence for the slope location. This improves the accuracy of the collected slope data sequence, thereby improving the accuracy of slope correction.

[0057] As an optional embodiment, S101 may specifically include:

[0058] Divide the target slope into multiple slope areas according to the preset division size;

[0059] The center point of each slope area is determined as the slope position;

[0060] According to the preset collection frequency, the slope evaluation data and the environmental evaluation data of each slope position are collected respectively to form a slope data sequence and an environmental data sequence of each slope position.

[0061] In this embodiment, the slope evaluation data is used to represent the numerical value for evaluating the slope parameters at the slope location, and the environmental evaluation data is used to represent the numerical value for evaluating the environmental factors at the slope location.

[0062] As an example, the server first sets a preset division size of a slope area, for example, the preset division size can be Then, using tools such as mapping software or geographic information systems, the boundaries of multiple slope areas are drawn on the target slope according to the preset division size, thereby obtaining multiple slope areas.

[0063] Then, within each slope region, the center point of the slope region is set as the slope location. Various slope evaluation data and environmental evaluation data are collected regularly at each slope location at a preset collection frequency (e.g., once per minute). Finally, the various slope evaluation data and environmental evaluation data are arranged in chronological order to form a slope data sequence and an environmental data sequence for each slope location.

[0064] In this embodiment, the target slope is divided into multiple slope regions according to preset dimensions. The center point of each slope region is then determined as the slope location, and data is collected for each slope location. This ensures that data from all regions of the target slope is fully collected, enabling complete monitoring of the target slope.

[0065] As an optional embodiment, Figure 2 As shown, S102 may specifically include the following S201 to S205.

[0066] S201, determining the influence of each environmental data sequence on each slope data sequence based on the instantaneous slope of the slope evaluation data in each slope data sequence and the instantaneous slope of the environmental evaluation data in each environmental data sequence;

[0067] S202, determining the influence factor of each environmental data sequence on each slope data sequence based on the data change amplitude and each influence degree of each slope data sequence and each environmental data sequence in each time window;

[0068] S203, based on the data volatility of each slope data sequence in each time window, evaluating the landslide possibility of each slope position at each time;

[0069] S204, determining the positional similarity between the positions of the slopes based on the landslide potential of the positions of the slopes at each time;

[0070] S205 , determining the potential sliding factor of each slope position at each moment according to the positional similarity between the slope positions.

[0071] In this embodiment, the data variation amplitude is used to represent the difference between the maximum value and the minimum value of the data within the time window, and the data volatility is used to represent the degree of fluctuation of the data within the time window.

[0072] Landslide possibility is used to characterize the possibility of landslide occurring at a slope location at a corresponding time, and position similarity is used to characterize the similarity of characteristics between two slope locations.

[0073] As an example, the server first preprocesses the slope and environmental data series, including denoising and standardization, to ensure consistency and comparability. For each slope and environmental data series, the server calculates its instantaneous slope, or rate of change, over the time series using a differencing method (i.e., the difference between the previous and next data points divided by the time interval). Using correlation coefficients or regression analysis, the server calculates the correlation between the instantaneous slopes of each environmental data series and those of each slope data series, thereby determining the degree of influence of the environmental data series on the slope data series.

[0074] Then, within each time window, the maximum value is subtracted from the minimum value to calculate the magnitude of change in the slope and environmental data series. Based on the magnitude of change and the degree of influence, a weighted approach or a pre-set mathematical model (e.g., multivariate linear regression) is used to calculate the influence factor of each environmental data series on each slope data series.

[0075] The stability or instability of the slope data is then assessed based on the data volatility (e.g., standard deviation, coefficient of variation) of each slope data series within each time window. Based on the data volatility, the landslide probability of each slope location at each time is calculated using thresholding, machine learning models (e.g., logistic regression, support vector machine, etc.), or statistical models (e.g., Poisson distribution, normal distribution, etc.).

[0076] Then, based on the landslide possibility of each slope position at each moment, the positional similarity between each slope position is calculated using similarity measurement methods (such as Euclidean distance, cosine similarity, Pearson correlation coefficient, etc.).

[0077] Finally, combining the landslide possibility and location similarity, a weighted method or other comprehensive evaluation models is used to calculate the potential landslide factor of each slope location at each time.

[0078] This embodiment analyzes the changing trends between each slope data sequence and each environmental data sequence, thereby determining the impact factor of each environmental data sequence on each slope data sequence, as well as the potential landslide factor for each slope location at each time. This facilitates the subsequent determination of environmental compensation coefficients based on the impact factors and landslide factors, thereby correcting the slope data sequence to obtain a more accurate corrected data sequence, improving the accuracy of the collected slope data sequence and, consequently, the accuracy of slope correction.

[0079] As an optional embodiment, S201 may specifically include:

[0080] The instantaneous slope of the slope evaluation data from the r-1th moment to the rth moment in the target slope data sequence is added to the instantaneous slope of the slope evaluation data from the rth moment to the r+1th moment to obtain a first instantaneous slope sum value, where the target slope data sequence is any slope data sequence and r is a positive integer;

[0081] The instantaneous slope of the environmental evaluation data from the r-1th moment to the rth moment in the target environmental data sequence is added to the instantaneous slope of the environmental evaluation data from the rth moment to the r+1th moment to obtain a second instantaneous slope sum value, where the target environmental data sequence is any environmental data sequence;

[0082] Obtain the target Pearson correlation coefficient between the target slope data sequence and the target environment data sequence;

[0083] The first instantaneous slope sum value, the second instantaneous slope sum value and the target Pearson correlation coefficient are used to determine the influence of the target environment data sequence on the target slope data sequence at the rth moment.

[0084] In this embodiment, the influence of the target environment data sequence on the target slope data sequence at the rth moment can be specifically determined by the following formula 2:

[0085] Formula 2

[0086] In formula 2, To characterize the Time The environmental data series The influence degree of a slope data series. To characterize the The instantaneous slope from the slope evaluation data at the r-1th moment to the slope evaluation data at the rth moment in the slope data sequence, To characterize the The instantaneous slope from the slope evaluation data at the rth moment to the slope evaluation data at the r+1th moment in a slope data sequence. To characterize the The instantaneous slope from the environmental evaluation data at the r-1th moment to the environmental evaluation data at the rth moment in the environmental data sequence, To characterize the The instantaneous slope of the environmental evaluation data from the rth moment to the r+1th moment in an environmental data sequence. To characterize the The environmental data series and The Pearson correlation coefficient of the slope data series is Used to represent exponential functions with natural constants as base.

[0087] Among them, the stronger the positive and negative correlation of the Pearson correlation coefficient between the environmental data sequence and the slope data sequence, the greater the influence of the environmental data sequence on the slope data sequence, and the greater the influence degree; It is used to characterize the difference between the instantaneous trend change characteristics of the data at the same time in the environmental data sequence and the slope data sequence. The closer the instantaneous trend change characteristics of the data at the same time in the environmental data sequence are to those in the slope data sequence, the greater the influence of the environmental data sequence on the slope data sequence, and the greater the degree of influence.

[0088] This embodiment analyzes the influence of each environmental data sequence on each slope data sequence based on the instantaneous slope of the slope evaluation data in each slope data sequence and the instantaneous slope of the environmental evaluation data in each environmental data sequence. This allows for accurate assessment of the correlation between the environmental and slope data sequences, facilitating the subsequent accurate calculation of the environmental compensation coefficient for the slope location at each moment. This improves the accuracy of the collected slope data sequences and, consequently, the accuracy of slope correction.

[0089] As an optional embodiment, S202 may specifically include:

[0090] Taking the slope evaluation data at the rth moment in the target slope data sequence and the environmental evaluation data at the rth moment in the target environmental data sequence as the center, a first time window of the slope evaluation data at the rth moment and a second time window of the environmental evaluation data at the rth moment are constructed according to a preset window size. The target slope data sequence is any slope data sequence, the target environmental data sequence is any environmental data sequence, and r is a positive integer.

[0091] The difference between the maximum value and the minimum value of the data in the first time window is determined as the data change amplitude of the first time window, and the difference between the maximum value and the minimum value of the data in the second time window is determined as the data change amplitude of the second time window;

[0092] The influence factor of the target environment data sequence on the target slope data sequence at the rth moment is determined by using the data change amplitude of the first time window, the data change amplitude of the second time window and the influence of the target environment data sequence on the target slope data sequence at the rth moment.

[0093] In this embodiment, the first time window is used to represent the time window of the slope data series, and the second time window is used to represent the time window of the environmental data series. The window sizes of the first and second time windows are equal. That is, if the window size of the first time window is 10 seconds before and after, the window size of the second time window is also 10 seconds before and after.

[0094] As an example, the impact factor of the target environment data sequence on the target slope data sequence at the rth moment can be specifically determined by the following formula 3:

[0095] Formula 3

[0096] In formula 3, To characterize the Time The environmental data series The influencing factors of the slope data series. To characterize the The maximum value of the data in the first time window at the rth moment in the slope data sequence, To characterize the The minimum value of the data in the first time window at the rth moment in the slope data sequence. To characterize the The maximum value of the data in the second time window at the rth moment in the environmental data sequence, To characterize the The minimum value of the data in the second time window at the rth moment in the environmental data sequence. To characterize the Time The environmental data series The influence degree of a slope data series, Used to represent exponential functions with natural constants as base.

[0097] Among them, the greater the influence of the environmental data sequence on the slope data sequence, the greater the influence factor of the environmental data sequence on the slope data sequence; when the data change amplitudes in the time window corresponding to the same moment in the environmental data sequence and the slope data sequence are more similar, that is, the smaller the difference in the data change amplitudes, the greater the influence of the environmental data sequence on the slope data sequence at the current moment, and the greater the influence factor of the environmental data sequence on the slope data sequence.

[0098] This embodiment determines the influence factor of each environmental data sequence on each slope data sequence based on the magnitude of change in each time window and the respective influence degrees. By further modifying the influence degrees to obtain the influence factor, it facilitates the subsequent accurate calculation of the environmental compensation coefficient for the slope location at each moment. This improves the accuracy of the collected slope data sequences and, consequently, the accuracy of slope correction.

[0099] As an optional embodiment, the slope data sequence at each slope position includes a plurality of slope sub-data sequences of different types;

[0100] S203 may specifically include:

[0101] Perform linear fitting on each data in the time window at the rth moment in each slope sub-data sequence at the target slope position to obtain multiple baselines, where the target slope position is any slope position and r is a positive integer;

[0102] Compare each data in the time window at the rth moment in each slope sub-data sequence with the corresponding benchmark data on the baseline to obtain the data volatility of each slope sub-data sequence in the time window at the rth moment;

[0103] The local landslide possibility corresponding to each slope sub-data sequence is determined by using the data standard deviation of each slope sub-data sequence in the time window at the rth moment and the data volatility of each slope sub-data sequence in the time window at the rth moment;

[0104] The local landslide possibility corresponding to each slope sub-data sequence is averaged and the landslide possibility of the target slope position at the rth moment is obtained.

[0105] In this embodiment, there are multiple slope parameters, so the slope data sequence may include multiple different types of slope sub-data sequences. For example, it may include a slope sub-data sequence corresponding to displacement, a slope sub-data sequence corresponding to inclination angle, and a slope sub-data sequence corresponding to crack width.

[0106] Perform straight line fitting on the data in the time window at the rth moment in the slope sub-data sequence, and the fitted straight line is the baseline.

[0107] Local landslide possibility is used to characterize the possibility of landslide occurrence at the slope location obtained based on a single slope sub-data series.

[0108] As an example, the landslide possibility of the target slope position at time r can be determined by the following formula 4:

[0109] Formula 4

[0110] In formula 4, Used to characterize the landslide possibility of the target slope location at time r. It is used to characterize the data standard deviation in the time window of the rth moment in the sth slope sub-data sequence at the target slope location, n is used to characterize the number of slope sub-data sequences at the target slope location, and norm is used to characterize the linear normalization function. The kth data in the time window at the rth moment in the sth slope sub-data sequence used to represent the target slope position, It is used to represent the data on the baseline corresponding to the kth data in the time window at the rth moment in the sth slope sub-data sequence at the target slope location. m is used to represent the number of data in the time window. Used to represent exponential functions with natural constants as base.

[0111] Among them, when the standard deviation of the data in the time window is larger, it means that the possibility of slope anomaly is greater, that is, the possibility of landslide is greater; It is used to characterize the difference between the data within the time window and the data on the corresponding baseline. The smaller the difference, the greater the possibility of landslide.

[0112] This embodiment comprehensively evaluates the landslide potential of each slope location at each moment based on the data volatility of each slope data sequence within each time window. Accurately determining the landslide potential of each slope location at each moment facilitates the subsequent accurate calculation of the environmental compensation coefficient for each slope location at that moment. This improves the accuracy of the collected slope data sequence and, consequently, the accuracy of slope modification.

[0113] As an optional embodiment, S204 may specifically include:

[0114] The average landslide possibility of the first slope position at each moment is calculated to obtain the average landslide possibility of the first slope position;

[0115] The average landslide possibility of the second slope position at each moment is calculated to obtain the average landslide possibility of the second slope position;

[0116] The positional similarity between the first side slope position and the second side slope position is determined using the average landslide potential of the first side slope position, the average landslide potential of the second side slope position, and the distance between the first side slope position and the second side slope position.

[0117] In this embodiment, the position similarity between the first side slope position and the second side slope position can be determined by the following formula 5:

[0118] Formula 5

[0119] In formula 5, It is used to characterize the positional similarity between the c-th slope position and the v-th slope position, Used to characterize the average landslide possibility of the c-th slope position, Used to characterize the average landslide possibility of the vth slope location. Used to represent the distance between the cth slope position and the vth slope position, Used to represent exponential functions with natural constants as base.

[0120] Among them, the smaller the difference between the possibilities of abnormal landslides occurring at two locations, the greater the similarity between the two locations, that is, the greater the positional similarity between the two locations; the smaller the distance between the two locations, the greater the positional similarity between the two locations.

[0121] This embodiment comprehensively evaluates the positional similarity between the first and second slope locations based on the average landslide potential at the first and second slope locations, as well as the distance between them. This facilitates subsequent determination of the potential landslide factor for each slope location based on the positional similarity between the slope locations, thereby improving the accuracy of the determination of the potential landslide factor.

[0122] As an optional embodiment, S205 may specifically include:

[0123] The slope position whose landslide possibility at the rth moment is greater than the first preset threshold is determined as the suspected landslide position at the rth moment, where r is a positive integer;

[0124] Taking the suspected landslide location as the seed point, based on the positional similarity between the slope locations, the region growing algorithm is used to grow the area to be landslide at the suspected landslide location at the rth time.

[0125] Based on the area to be landslided at the suspected landslide position at the rth time and the distance between the target slope position and the center position of the area to be landslided, the landslide factor to be landslided at the rth time is determined. The target slope position is any slope position.

[0126] In this embodiment, the server compares the landslide possibility of each slope position at the rth moment with the first preset threshold, and then determines the slope position whose landslide possibility at the rth moment is greater than the first preset threshold as the suspected landslide position at the rth moment.

[0127] Then, the positional similarity between the suspected landslide location and each adjacent slope location is determined according to Formula 5 above. Each positional similarity is compared with a second preset threshold. Adjacent slope locations with positional similarities greater than the second preset threshold are then assigned to the area to be landslided corresponding to the suspected landslide location. This calculation is then repeated in a loop, centering on other slope locations in the area to be landslided, until the positional similarity between all slope locations in the area to be landslided and their corresponding adjacent slope locations is less than or equal to the second preset threshold. This results in the complete area to be landslided for the suspected landslide location at time r.

[0128] Then, the number of slope locations in the area to be landslided at time r and before is chronologically organized into a slope quantity sequence. The sequence is analyzed from back to front, determining whether the last data point is greater than the second-to-last data point. If so, the sequence continues to determine whether the second-to-last data point is greater than the third-to-last data point. If so, the sequence continues until it stops and the number of data points that meet the criteria is obtained.

[0129] When the number of data that meet the conditions is greater than or equal to the preset threshold, the pending sliding factor of the target slope position at the rth moment is determined by the following formula 6:

[0130] Formula 6

[0131] In formula 6, It is used to characterize the potential sliding factor of the c-th slope position at the r-th time. Used to represent the number of data that meet the conditions. Used to represent the preset quantity threshold. It is used to represent the distance between the cth slope position and the center position of the area to be landslide at the rth time, and norm is used to represent the linear normalization function.

[0132] When the number of data that meets the conditions is less than the preset threshold, the waiting sliding factor of the target slope position at the rth moment is determined to be 0.

[0133] Through this embodiment, the suspected landslide location at time r is screened from each slope location. Then, using the suspected landslide location as a seed point, a region growing algorithm is used based on the positional similarity between the various slope locations to obtain the area to be landed at the suspected landslide location at time r. Based on the area to be landed at the suspected landslide location at time r and the distance between the target slope location and the center of the area to be landed, the expected landslide factor at the target slope location at time r is determined. In this way, the expected landslide factor of the slope location can be accurately determined, thereby improving the accuracy of slope shaping.

[0134] As an optional embodiment, S103 may specifically include:

[0135] The influence factors of each environmental data sequence on the target slope data sequence are averaged to obtain the average influence factor of the target slope position. The target slope data sequence corresponds to the target slope position.

[0136] The average impact factor is divided by the value of the pending landslide factor at the target slope position at the rth moment, and a linear normalization calculation is performed to obtain the environmental compensation coefficient at the target slope position at the rth moment, where r is a positive integer.

[0137] In this embodiment, the environmental compensation coefficient of the target slope position at time r can be determined by the following formula 7:

[0138] Formula 7

[0139] In this embodiment, To characterize the The first slope position The slope data series is in The environmental compensation coefficient of the slope evaluation data at a certain moment, To characterize the The slope position is The waiting slip factor at a moment, It is used to represent the quantity of environmental data series, and norm is used to represent the linear normalization function. To characterize the At this moment, The jth environmental data sequence at the slope position is The influencing factors of the slope data series.

[0140] The smaller the slip factor, the higher the data credibility, and the smaller the environmental compensation. Conversely, the larger the environmental compensation coefficient. The larger the impact factor, the lower the data credibility, and the larger the environmental compensation. Conversely, the smaller the environmental compensation coefficient.

[0141] This embodiment determines the environmental compensation coefficient for each slope location at each moment based on the various influencing factors and the factors to be landslided. This allows the slope data sequence for that location to be corrected based on the environmental compensation coefficient, resulting in a corrected data sequence for that location. This improves the accuracy of the collected slope data sequence, and thus the accuracy of slope correction.

[0142] Based on the slope trimming method for water conservancy projects, the present invention also provides a specific embodiment of a slope trimming device for water conservancy projects.

[0143] Figure 3 A schematic structural diagram of a slope trimming device for a water conservancy project provided by an embodiment of the present invention is shown. The slope trimming device 300 for a water conservancy project may include a data acquisition module 310 , a data analysis module 320 , an environmental compensation module 330 and a slope trimming module 340 .

[0144] The data acquisition module 310 is used to acquire slope data sequences and environmental data sequences at various slope positions on the target slope;

[0145] The data analysis module 320 is used to determine the influence factor of each environmental data sequence on each slope data sequence and the potential landslide factor of each slope location at each time based on the change trend relationship between each slope data sequence and each environmental data sequence. The potential landslide factor is used to represent the potential landslide tendency of the slope location;

[0146] The data analysis module 320 is further used to determine the environmental compensation coefficient of each slope position at each time according to each influencing factor and each potential sliding factor;

[0147] The environmental compensation module 330 is used to correct the slope data sequence of each slope position using each environmental compensation coefficient to obtain a corrected data sequence of each slope position;

[0148] The slope trimming module 340 is used to trim the corresponding slope position when the corrected data sequence meets the preset trimming conditions to obtain a trimmed target slope.

[0149] In the slope correction device for water conservancy projects provided by an embodiment of the present invention, the influence factor of the environmental data sequence on the slope data sequence, as well as the expected landslide factor of the slope position at each moment, are determined based on the change trend relationship between the slope data sequence and the environmental data sequence. This can improve the accuracy of the analysis of the impact of the environmental data sequence on the slope data sequence. Then, based on the influence factor and the expected landslide factor, the environmental compensation coefficient of the slope position at each moment is determined. In this way, the embodiment of the present invention obtains the environmental compensation coefficient of the slope position at each moment by considering the influence of environmental factors on the slope data sequence. The environmental compensation coefficient is then used to correct the corresponding slope data sequence, thereby obtaining a corrected data sequence for the slope position. In this way, the accuracy of the collected slope data sequence can be improved, thereby improving the accuracy of slope correction.

[0150] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0151] It should also be noted that the exemplary embodiments described herein describe methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the steps described above. In other words, the steps may be performed in the order described in the embodiments, or in a different order, or several steps may be performed simultaneously.

[0152] The above description is only a specific embodiment of the present invention. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention.

Claims

1. A slope trimming method for a water conservancy project, characterized in that: The method comprises: Collect slope data sequences and environmental data sequences at each slope position on the target slope; Determining, based on the relationship between the changing trends of the slope data sequences and the environmental data sequences, the influence factors of the environmental data sequences on the slope data sequences, and the potential sliding factors of the slope positions at each moment, wherein the potential sliding factors are used to characterize the potential sliding trends of the slope positions; Determining the environmental compensation coefficient of each slope position at each time according to each of the influencing factors and each of the pending sliding factors; Correcting the slope data sequence at each of the slope positions using the environmental compensation coefficients to obtain a corrected data sequence at each of the slope positions; When the corrected data sequence meets the preset trimming condition, trimming the corresponding slope position to obtain the trimmed target slope; The method for obtaining the influencing factor includes: determining the degree of influence of each environmental data sequence on each slope data sequence based on the instantaneous slope of the slope evaluation data in each slope data sequence and the instantaneous slope of the environmental evaluation data in each environmental data sequence; determining the influencing factor of each environmental data sequence on each slope data sequence based on the data change amplitude of each slope data sequence and each environmental data sequence in each time window and the respective influence degrees; The method for obtaining the to-be-slip factor includes: Based on the data volatility of each slope data sequence in each time window, evaluating the landslide possibility of each slope position at each time; determining the position similarity between each slope position according to the landslide possibility of each slope position at each time; The slope position whose landslide possibility at the rth moment is greater than a first preset threshold is determined as the suspected landslide position at the rth moment, where r is a positive integer; using the suspected landslide position as a seed point, a region growing algorithm is performed based on the positional similarity between the slope positions to obtain the area to be landslided at the suspected landslide position at the rth moment; Determining a to-be-slip factor of the target slope position at the rth moment based on the to-be-slip area of ​​the suspected landslide position at the rth moment and the distance between the target slope position and the center position of the to-be-slip area, wherein the target slope position is any one of the slope positions; The determining of the influence of each environmental data sequence on each slope data sequence based on the instantaneous slope of the slope evaluation data in each slope data sequence and the instantaneous slope of the environmental evaluation data in each environmental data sequence comprises: Adding the instantaneous slope of the slope evaluation data from the r-1th moment to the rth moment in the target slope data sequence to the instantaneous slope of the slope evaluation data from the rth moment to the r+1th moment to obtain a first instantaneous slope sum value, wherein the target slope data sequence is any one of the slope data sequences, and r is a positive integer; Adding the instantaneous slope of the environmental evaluation data from the r-1th moment to the environmental evaluation data at the rth moment in the target environmental data sequence to the instantaneous slope of the environmental evaluation data from the rth moment to the environmental evaluation data at the r+1th moment in the target environmental data sequence to obtain a second instantaneous slope sum value, wherein the target environmental data sequence is any one of the environmental data sequences; Obtaining a target Pearson correlation coefficient between the target slope data sequence and the target environment data sequence; The first instantaneous slope sum value, the second instantaneous slope sum value, and the target Pearson correlation coefficient are used to determine the influence of the target environment data sequence on the target slope data sequence at the rth moment.

2. The slope repair method for water conservancy projects according to claim 1, characterized in that: The step of collecting slope data sequences and environmental data sequences at various slope positions on the target slope includes: Dividing the target slope into multiple slope areas according to a preset division size; Determine the center point of each slope area as the slope position; According to the preset collection frequency, the slope evaluation data and the environmental evaluation data of each of the slope positions are collected respectively to form a slope data sequence and an environmental data sequence of each of the slope positions.

3. The slope repair method for water conservancy projects according to claim 1, characterized in that: Determining the influence factor of each environmental data sequence on each slope data sequence according to the data change amplitude of each slope data sequence and each environmental data sequence in each time window and each influence degree includes: Taking the slope evaluation data at the rth moment in the target slope data sequence and the environmental evaluation data at the rth moment in the target environmental data sequence as the center, constructing a first time window of the slope evaluation data at the rth moment and a second time window of the environmental evaluation data at the rth moment according to a preset window size, the target slope data sequence is any one of the slope data sequences, the target environmental data sequence is any one of the environmental data sequences, and r is a positive integer; Determine the difference between the maximum value and the minimum value of the data in the first time window as the data change amplitude of the first time window, and determine the difference between the maximum value and the minimum value of the data in the second time window as the data change amplitude of the second time window; The influence factor of the target environment data sequence on the target slope data sequence at the rth moment is determined by using the data change amplitude of the first time window, the data change amplitude of the second time window and the influence of the target environment data sequence on the target slope data sequence at the rth moment.

4. The slope repair method for water conservancy projects according to claim 1, characterized in that: The side slope data sequence of each side slope position includes a plurality of side slope sub-data sequences of different types; The evaluating the landslide possibility of each slope position at each moment based on the data volatility of each slope data sequence in each time window includes: Performing linear fitting on each data in the time window at the rth moment in each of the slope sub-data sequences at the target slope position to obtain a plurality of baselines, wherein the target slope position is any one of the slope positions, and r is a positive integer; Comparing each data in the time window at the rth moment in each of the slope sub-data sequences with the corresponding benchmark data on the benchmark line, to obtain the data volatility of each of the slope sub-data sequences in the time window at the rth moment; Determining the local landslide possibility corresponding to each of the slope sub-data sequences by using the data standard deviation of each of the slope sub-data sequences in the time window at the r-th moment and the data volatility of each of the slope sub-data sequences in the time window at the r-th moment; The local landslide possibility corresponding to each of the slope sub-data sequences is averaged to obtain the landslide possibility of the target slope position at the rth moment.

5. The slope repair method for water conservancy projects according to claim 1, characterized in that: Determining the positional similarity between the slope positions according to the landslide possibility of each slope position at each time, including: Calculating the average of the landslide probability at the first slope position at each time to obtain the average landslide probability at the first slope position; Calculating the average of the landslide probability at the second slope position at each time to obtain the average landslide probability at the second slope position; The positional similarity between the first side slope position and the second side slope position is determined using the average landslide potential of the first side slope position, the average landslide potential of the second side slope position, and the distance between the first side slope position and the second side slope position.

6. The slope repair method for water conservancy projects according to claim 1, characterized in that: The calculation formula of the waiting slip factor includes: in, It is used to characterize the potential sliding factor of the c-th slope position at the r-th time. Used to represent the number of data that meet the conditions. Used to represent the preset quantity threshold, It is used to represent the distance between the cth slope position and the center position of the area to be landslide at the rth time, and norm is used to represent the linear normalization function.

7. The slope repair method for water conservancy projects according to any one of claims 1 to 6, characterized in that: Determining the environmental compensation coefficient of each slope position at each time according to each of the influencing factors and each of the pending sliding factors includes: Calculating the average of the impact factors of each of the environmental data sequences on the target slope data sequence to obtain the average impact factor of the target slope position, wherein the target slope data sequence corresponds to the target slope position; The average impact factor is divided by the value of the pending sliding factor of the target slope position at the rth moment, and a linear normalization calculation is performed to obtain the environmental compensation coefficient of the target slope position at the rth moment, where r is a positive integer.

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