Method and system for reorganizing and analyzing manual comparison and measurement data for dam safety monitoring

Abstract

The invention discloses a dam safety monitoring manual comparison data reorganization and analysis method and system. The method comprises the steps that automatic measurement and manual measurement are conducted on dam measurement points; respectively and correspondingly forming an automatic value measurement sequence and a manual value measurement sequence according to data of each automatic measurement and manual measurement; performing smoothing processing on the automatic value measurement sequence and the manual value measurement sequence by using empirical mode decomposition, and reconstructing the processed data; performing deviation calculation on the reconstructed automatic value measurement sequence and the artificial value measurement sequence, and obtaining a dynamic control limit value according to the deviation; and comparing the dynamic control limit value with the deviated data value to identify abnormal data points of the dam according to a comparison result. According to the invention, the calculated dynamic control limit value is utilized to carry out reorganization analysis, real-time detection and judgment on the monitoring data, and a basis is provided for verifying and evaluating the accuracy and reliability of automatic value measurement.

Description

Technical Field

[0001] The invention relates to the technical field of hydraulic engineering safety monitoring, and in particular to a method and system for compiling and analyzing artificial comparative measurement data for dam safety monitoring. Background Art

[0002] A dam is a hydraulic engineering structure that is mainly used to intercept the flow of water in rivers and channels to raise the water level or regulate the flow. Its main functions include flood control, water supply, irrigation, hydroelectric power generation, and improved navigation. Dams can regulate the flow of rivers and protect the shore, which is crucial to the safety of life and property of people along the upstream and downstream coasts. Dams are built with a variety of materials, including concrete, earth and stone, mortar masonry, etc., and the design theory and application are constantly being improved and developed.

[0003] At present, the traditional manual comparison data compilation method is used for dam monitoring. This traditional method relies on the experience and subjective judgment of the monitoring personnel, and performs data analysis by comparing process lines and variances through experience. It lacks a systematic denoising and dynamic adjustment mechanism. The noise in the monitoring data will increase the uncertainty of the monitoring results and affect the measurement accuracy. The limit value in the traditional comparison method is static and cannot adapt to the dynamic changes of the data. It is easy to miss or misjudge abnormal values. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the present invention proposes a method for compiling and analyzing manual comparative measurement data for dam safety monitoring, mainly to solve the problems of insufficient measurement accuracy, easy data omission or misjudgment of abnormal values ​​caused by the lack of systematic denoising and dynamic adjustment mechanism in the prior art.

[0006] Another object of the present invention is to provide a dam safety monitoring manual comparison data compilation and analysis system.

[0007] To achieve the above-mentioned purpose, the present invention provides a method for compiling and analyzing dam safety monitoring manual comparative measurement data, comprising:

[0008] Performing automated measurement and manual measurement on the dam measurement points; wherein the number of measurements and the measurement time of the automated measurement and the manual measurement are the same;

[0009] According to the data of each automated measurement and manual measurement, an automated measurement value sequence and a manual measurement value sequence are respectively formed;

[0010] The automated measurement sequence and the manual measurement sequence are smoothed by using empirical mode decomposition, and the processed data are reconstructed.

[0011] Deviation calculation is performed on the reconstructed automatic measurement sequence and manual measurement sequence, and dynamic control limits are obtained based on the deviation;

[0012] The dynamic control limit and the data value of the deviation are compared to identify abnormal data points of the dam based on the comparison results.

[0013] The dam safety monitoring manual comparative measurement data compilation and analysis method of the embodiment of the present invention may also have the following additional technical features:

[0014] In one embodiment of the present invention, the dam measuring points include the dam abutment, the dam body, and the dam foundation; the automated measurement uses automated monitoring equipment to perform continuous measurement at preset time intervals; and the manual measurement uses a measuring instrument to perform manual measurement at the same time points as the automated measurement.

[0015] In one embodiment of the present invention, the data of the automated measurement and manual measurement include measurement time, measurement point, measurement value and environmental parameters; and the automated measurement value sequence and the manual measurement value sequence are formed according to the data of each automated measurement and manual measurement, respectively, including:

[0016] According to the measurement values ​​of the extracted automated measurement data, an automated measurement sequence is formed. t};

[0017] According to the measurement values ​​of the extracted manual measurement data, a manual measurement value sequence {M t}.

[0018] In one embodiment of the present invention, the automated measurement sequence and the manual measurement sequence are smoothed using empirical mode decomposition, including:

[0019] Set the initial signal to the automated measurement sequence x(t) = {A t};

[0020] Find all local maxima and local minima in the initial signal x(t);

[0021] The upper envelope is constructed through the local maximum points, the lower envelope is constructed through the local minimum points, and the spline interpolation method is used for interpolation;

[0022] Calculate the mean m(t) of the upper and lower envelopes;

[0023] Calculate the difference between the initial signal and the mean value h(t) = x(t) - m(t). If h(t) meets the IMF condition, then h(t) is an IMF component.

[0024] Extract the IMF from the signal to obtain IMF1(t), and use the residual signal r(t)=x(t)-IMF1(t) as the new signal until the signal cannot be further decomposed;

[0025] The automated measurement sequence x(t) = {A t} is decomposed into multiple IMF components and a residual:

[0026]

[0027] The obtained artificial measurement sequence y(t) = {M t} is decomposed into multiple IMF components and a residual:

[0028]

[0029] Where n represents the total number of IMF components obtained by decomposition, and i represents the index as a positive integer.

[0030] In one embodiment of the present invention, reconstructing the processed data includes:

[0031] According to the frequency characteristics and amplitude characteristics, the IMF components are selected and the selected IMF components are reconstructed to obtain the denoised signal. For the automatic measurement sequence {A t}:

[0032]

[0033] For the artificial measurement sequence {M t}:

[0034]

[0035] In one embodiment of the present invention, the deviation of the reconstructed automatic measurement sequence and the manual measurement sequence is calculated, and the dynamic control limit is obtained according to the deviation, including:

[0036] For each time point t, calculate the deviation between the reconstructed automated measurement and the manual measurement

[0037]

[0038] The mean μ of the deviation sequence is calculated based on the deviation δ(t) δ and standard deviation σ δ ;

[0039] Compute dynamic control limits using either an exponentially weighted moving average or a generalized autoregressive conditional heteroskedasticity model.

[0040] In one embodiment of the present invention, the mean μ of the deviation sequence is calculated based on the deviation δ(t). δ and standard deviation σ δ ,include:

[0041]

[0042]

[0043] Where N represents the total number of measured time points.

[0044] In one embodiment of the present invention, the dynamic control limit is calculated using an exponentially weighted moving average or a generalized autoregressive conditional heteroskedasticity model, including:

[0045] Using the exponentially weighted moving average method:

[0046] The data is smoothed by weighted averaging, and the control limits are calculated as follows:

[0047] EWMA t =λδ(t)+(1-λ)EWMA t-1

[0048] Among them, λ represents the smoothing parameter (0<λ<<1), EWMA t-1 Represents the EWMA value at the previous moment, initially EWMA0 = δ(1);

[0049] The upper and lower control limits are:

[0050] UCL t =EWMA t +Lσ δ

[0051] LCL t =EWMA t -Lσ δ

[0052] Where L represents the control limit factor;

[0053] Or, using the generalized autoregressive conditional heteroskedasticity model approach:

[0054] For the deviation sequence δ(t), the following GARCH model is established:

[0055] δ(t)=μ δ +∈ t

[0056]

[0057]

[0058] Among them, α0, α1 and β1 are model parameters. Through parameter estimation, the conditional variance is calculated And get the dynamic control limit:

[0059] UCL t =μ δ+Lσ δ

[0060] LCL t =μ δ -Lσ δ .

[0061] In one embodiment of the present invention, comparing the data values ​​of the dynamic control limit and the deviation to identify abnormal data points of the dam according to the comparison result includes:

[0062] Using the calculated dynamic control limits, for each time point t, the deviation δ(t) is compared with the upper and lower control limits UCL t and LCL t Compare and get the comparison result, if δ(t)>UCL t or δ(t) <LCL t , then there is an abnormal value at time t, otherwise the measured value at time t is normal.

[0063] To achieve the above-mentioned purpose, the present invention provides a dam safety monitoring manual comparison data compilation and analysis system, comprising:

[0064] A dam measuring point measurement module is used to perform automatic measurement and manual measurement of the dam measuring points; wherein the number of measurements and the measurement time of the automatic measurement and the manual measurement are the same;

[0065] A measurement sequence generation module is used to generate an automatic measurement sequence and a manual measurement sequence according to the data of each automatic measurement and manual measurement respectively;

[0066] An empirical mode decomposition module is used to use empirical mode decomposition to smooth the automatic measurement sequence and the manual measurement sequence respectively, and reconstruct the processed data;

[0067] The deviation calculation module is used to calculate the deviation of the reconstructed automatic measurement sequence and the manual measurement sequence, and obtain the dynamic control limit according to the deviation;

[0068] The abnormal data identification module is used to compare the data values ​​of the dynamic control limit and the deviation to identify abnormal data points of the dam according to the comparison result.

[0069] The dam safety monitoring manual comparison measurement data compilation and analysis method and system of the embodiment of the present invention utilizes the calculated dynamic control limit to compile and analyze the monitoring data, perform real-time detection and judgment, and provide a basis for verifying and evaluating the accuracy and reliability of automated measurement values.

[0070] The beneficial effects of the present invention are:

[0071] 1) The present invention adopts empirical mode decomposition (EMD) technology to smooth the measured value sequence, which can effectively remove noise in the data, improve data quality, and provide a more reliable basis for subsequent analysis.

[0072] 2) The present invention adopts dynamic control limits rather than static control limits, which can better adapt to the volatility and variability of monitoring data and enhance the ability to identify abnormal situations.

[0073] 3) The present invention selects and reconstructs multiple groups of IMF components and reconstructs signals using frequency and amplitude characteristics, thereby being able to extract effective monitoring information and improve the precision and effectiveness of data analysis.

[0074] 4) The present invention utilizes the calculated dynamic control limit to perform real-time detection and judgment on the monitoring data, ensuring timely response and processing when abnormal data occurs, thereby improving the reliability of dam safety monitoring.

[0075] 5) The present invention is not only applicable to dam monitoring, but can also be extended to other engineering fields that require high-precision and high-reliability monitoring, such as structural health monitoring of bridges, tunnels, etc.

[0076] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0078] Figure 1 is a flow chart of a method for compiling and analyzing manual comparative measurement data for dam safety monitoring according to an embodiment of the present invention;

[0079] Figure 2 is a structural diagram of a dam safety monitoring manual comparative measurement data compilation and analysis system according to an embodiment of the present invention;

[0080] Figure 3 is a schematic diagram of a terminal device according to an embodiment of the present invention;

[0081] Figure 4 is a schematic diagram of a readable storage medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0083] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0084] The following describes the method, system, terminal device and readable storage medium for compiling and analyzing manual comparative measurement data for dam safety monitoring according to embodiments of the present invention with reference to the accompanying drawings.

[0085] Figure 1 FIG. 1 is a flow chart of a method for compiling and analyzing manual comparative measurement data for dam safety monitoring according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0086] S1, automatic measurement and manual measurement are performed on the dam measuring points; the number of measurements and the measurement time of the automatic measurement and the manual measurement are the same.

[0087] Specifically, interval measurements are performed on specific measuring points of the dam, and the measurements include automated measurements and manual measurements, and the number of measurements and the measurement time of the automated measurements and the manual measurements are the same.

[0088] The specific measuring points include the dam shoulder, dam body and dam foundation. In this embodiment, the measuring points of the dam shoulder are set in the dam shoulder area on both sides of the dam to monitor the relative displacement between the dam and the mountains on both sides and check whether there are signs of shear or sliding.

[0089] The measuring points of the dam body are set at different height layers of the dam body and evenly arranged along the height direction of the dam body. They are used to monitor the displacement changes inside the dam body, especially whether there is horizontal or vertical displacement in the middle part.

[0090] The measuring point of the dam foundation is set near the dam foot to monitor the stability of the dam foundation and detect whether there is foundation settlement or displacement.

[0091] It can be understood that, in this embodiment, only the most important measuring points in the dam monitoring process are recorded, and the remaining measuring points are also applicable to the monitoring method.

[0092] The automated measurement in this embodiment uses the dam safety monitoring automation system for continuous monitoring, while the manual measurement uses instruments for manual measurement, specifically including: vertical displacement data, i.e., the vertical displacement of the measuring point relative to the initial position, mainly monitoring the settlement or uplift situation, and the automated measurement uses the dam safety monitoring automation system for data collection, such as a static level. Manual measurement uses instruments to connect the monitoring instrument cable core wire for data collection.

[0093] Horizontal displacement data refers to the deformation of the dam structure, including shear deformation, tensile deformation, etc. Automated measurement uses the dam safety monitoring automation system to collect data, such as strain gauges, multi-point displacement meters, etc. Manual measurement uses instruments to connect the monitoring instrument cable core wires for data collection.

[0094] The automated measurement uses automated monitoring equipment to perform continuous measurement at preset time intervals, and the manual measurement uses a measuring instrument to perform manual measurement at the same time point as the automated measurement.

[0095] In this embodiment, it is necessary to determine a measurement schedule, and the manual and automated measurement times on the measurement day should be the same; in addition, it is also necessary to determine the number of measurements, such as measuring each measuring point 10 times. At each predetermined measurement time point, automated measurement and manual measurement are performed respectively to ensure that the two measurement time points are consistent to avoid data errors caused by time differences.

[0096] S2, forming an automated measurement sequence and a manual measurement sequence respectively according to the data of each automated measurement and manual measurement.

[0097] Specifically, the data of each automated measurement and manual measurement are recorded to form an automated measurement sequence and a manual measurement sequence, respectively.

[0098] Among them, the data of automated measurement and manual measurement include measurement time, measurement point, measurement value and environmental parameters.

[0099] Extract the measured values ​​of the automated measurement data to form an automated measurement sequence t}.

[0100] Extract the measured values ​​of the artificial measurement data to form an artificial measurement sequence {M t}.

[0101] Among them, the measurement time provides a timestamp, that is, the timestamp of each measurement can fix the measurement value on the time axis to ensure the time series of the data. With the timestamp, the measurement sequence can be analyzed in time series to identify trends, cycles and mutations. Therefore, the measurement time can make the automatic measurement sequence and the manual measurement sequence be compared and analyzed at the same time point to evaluate the consistency and accuracy of the data. By analyzing the measurements at different time points, sudden abnormal events and long-term changes can be identified.

[0102] The measurement points provide the specific locations of the measurement values ​​on the dam, which facilitates spatial analysis and management. Different measurement points cover different parts of the dam to ensure comprehensive monitoring. By analyzing the data at specific measurement points, we can understand the specific conditions and potential risks of the area. By comparing the measurement sequences of different measurement points, we can discover local anomalies and overall change trends.

[0103] Measurement values are the core of monitoring data, directly reflecting the status and changes of the dam. Measurement values provide a quantitative data basis for statistical analysis and mathematical modeling. By analyzing the changes in measurement values, abnormal events and structural changes can be identified. The continuous recording of measurement values can be used to evaluate the long-term trends and health conditions of the dam.

[0104] It can be understood that environmental parameters (such as temperature, humidity, rainfall, etc.) are external factors affecting the measured values of the dam and must be taken into account to explain the changes in measurement values. Through environmental parameters, the measured values can be corrected to eliminate non-structural changes caused by environmental changes. By combining environmental parameters and measurement values, a more comprehensive comprehensive analysis can be carried out to understand the performance of the dam under different environmental conditions.

[0105] S3. Use empirical mode decomposition to smooth the automated measurement value sequence and the manual measurement value sequence respectively, and reconstruct the processed data.

[0106] Specifically, use empirical mode decomposition to smooth the automated measurement value sequence and the manual measurement value sequence respectively, including:

[0107] S301. Set the initial signal as the automated measurement value sequence x(t) = {A t};

[0108] S302. Find all local maxima and local minima in the initial signal x(t). Specifically, define an array or list to store the indices of local maxima and local minima. Traverse each data point in the signal, excluding the first and last points because these two points cannot be compared. For each data point x(i), check whether this point is greater than the previous point and the next point, that is, x(i)>x(i - 1) and x(i)>x(i + 1), then x(i) is a local maximum. Similarly, for each data point x(i), check whether this point is less than the previous point and the next point, that is, x(i)<x(i - 1) and x(i)<x(i + 1), then x(i) is a local minimum. Record the indices of the detected local maxima and local minima and store them in the maximum index list and the minimum index list respectively.

[0109] S303. Construct the upper envelope line through local maximum points and the lower envelope line through local minimum points, and use spline interpolation for interpolation. The spline interpolation method used in this embodiment is a commonly used interpolation method, suitable for smoothly connecting a series of data points. The cubic spline interpolation adopted in this implementation ensures that at each data point, the second derivative of the interpolation function is continuous, obtaining a smooth curve to form the envelope line of the signal.

[0110] S304, calculate the mean m(t) of the upper envelope and the lower envelope, obtain the upper envelope u(t) and the lower envelope l(t) through the above steps, and for each time point t, calculate the mean

[0111] S305, calculate the difference between the initial signal and the mean h(t)=x(t)-m(t), if h(t) meets the IMF condition, i.e., zero mean and unipolar change, then h(t) is an IMF component, otherwise repeat steps S301-S305;

[0112] S306, extracting IMF from the signal to obtain IMF1(t), taking the residual signal r(t)=x(t)-IMF1(t) as a new signal, and repeating the above steps until the signal cannot be further decomposed;

[0113] Get the automatic measurement sequence x(t) = {A t} is decomposed into multiple IMF components and a residual:

[0114]

[0115] Repeat steps S301-S306 to obtain the artificial measurement sequence y(t)={M t} is decomposed into multiple IMF components and a residual:

[0116]

[0117] Where n represents the total number of IMF components obtained by decomposition, and i represents the index as a positive integer.

[0118] In this embodiment, in dam monitoring, the main purpose of selecting appropriate IMF (intrinsic mode function) components for reconstruction is to remove noise and retain the main features of the signal, thereby improving the reliability and accuracy of the monitoring data. According to the main characteristics of the signal, the low-frequency components usually contain the main trends and change laws of the signal. These components represent the overall deformation and long-term change trend of the dam.

[0119] High-frequency components usually include noise and short-term fluctuations in the signal. These components may include equipment errors, environmental noise, etc.

[0120] Therefore, noise removal is the main feature of retaining the signal, removing random noise and high-frequency interference, and ensuring that the reconstructed signal still contains the key information in the original signal, such as the displacement trend and change law of the dam.

[0121] Furthermore, the IMF components are selected according to the frequency characteristics and amplitude characteristics, and the selected IMF components are reconstructed to obtain the denoised signal. t}:

[0122]

[0123] For the artificial measurement sequence {M t}:

[0124]

[0125] Among them, IMF i (t) represents the i-th intrinsic mode function component, and k represents the total number of IMF components used to reconstruct the signal.

[0126] S4, calculate the deviation of the reconstructed automatic measurement sequence and the manual measurement sequence, and obtain the dynamic control limit according to the deviation.

[0127] For each time point t, the deviation δ(t) between the reconstructed automated measurement and the manual measurement is calculated.

[0128] The mean μ of the deviation sequence is calculated based on the deviation δ(t) δ and standard deviation σ δ , specifically:

[0129]

[0130]

[0131] Where N represents the total number of measured time points.

[0132] Dynamic control limits are calculated using either an exponentially weighted moving average or a generalized autoregressive conditional heteroskedasticity model.

[0133] Specifically, the exponentially weighted moving average (EWMA) method is used in this embodiment:

[0134] The data is smoothed by weighted averaging, giving more weight to recent data. The control limits are calculated as follows:

[0135] EWMA t =λδ(t)+(1-λ)EWMA t-1

[0136] Among them, λ represents the smoothing parameter (0<λ<<1), EWMA t-1 Represents the EWMA value at the previous moment, initially EWMA0 = δ(1).

[0137] The upper and lower control limits are:

[0138] UCL t =EWMA t +Lσ δ

[0139] LCL t =EWMA t -Lσ δ

[0140] Where L represents the control limit factor, which usually takes a value between 2 and 3.

[0141] In this example, the generalized autoregressive conditional heteroskedasticity model (GARCH) method is used:

[0142] The GARCH model can handle the volatility of time series and is suitable for situations with conditional heteroskedasticity. For the deviation series δ(t), the following GARCH model can be established:

[0143] δ(t)=μ δ +∈ t

[0144]

[0145]

[0146] Among them, α0, α1 and β1 are model parameters. By estimating these parameters, the conditional variance can be calculated And get the dynamic control limit:

[0147] UCL t =μ δ +Lσ δ

[0148] LCL t =μ δ -Lσ δ

[0149] S5, comparing the data values ​​of the dynamic control limit and the deviation to identify abnormal data points of the dam based on the comparison results.

[0150] Specifically, using the calculated dynamic control limits, for each time point t, the deviation δ(t) is compared with the upper and lower control limits UCL t and LCL t For comparison, if δ(t)>UCL t or δ(t) <LCL t , then there is an abnormal value at time t, otherwise the measured value at time t is normal. In this embodiment, the deviation at each time point is compared to monitor the state of the dam in real time and detect abnormal situations in time.

[0151] Furthermore, detecting whether the deviation exceeds the control limit can effectively identify abnormal data points and warn of possible structural problems or environmental impacts.

[0152] According to the dam safety monitoring manual comparative measurement data compilation and analysis method of the embodiment of the present invention, the present invention uses the calculated dynamic control limit to compile and analyze the monitoring data, and conducts real-time detection and judgment to provide a basis for verifying and evaluating the accuracy and reliability of the automated measurement. By detecting abnormal values, early risk assessment can be performed to avoid potential safety hazards.

[0153] In order to implement the above embodiment, Figure 2 As shown, this embodiment also provides a dam safety monitoring manual comparison data compilation and analysis system 10, including:

[0154] The dam measuring point measurement module 20 is used to perform automatic measurement and manual measurement on the dam measuring points; wherein the number of measurement times and the measurement time of the automatic measurement and the manual measurement are the same;

[0155] A measurement sequence generation module 30, used to generate an automatic measurement sequence and a manual measurement sequence according to the data of each automatic measurement and manual measurement respectively;

[0156] An empirical mode decomposition module 40 is used to use empirical mode decomposition to smooth the automatic measurement sequence and the manual measurement sequence respectively, and reconstruct the processed data;

[0157] The deviation calculation module 50 is used to calculate the deviation of the reconstructed automatic measurement sequence and the manual measurement sequence, and obtain the dynamic control limit according to the deviation;

[0158] The abnormal data identification module 60 is used to compare the data values ​​of the dynamic control limit and the deviation to identify abnormal data points of the dam according to the comparison result.

[0159] According to the dam safety monitoring manual comparative measurement data compilation and analysis system of the embodiment of the present invention, the present invention uses the calculated dynamic control limit to compile and analyze the monitoring data, and conduct real-time detection and judgment to provide a basis for verifying and evaluating the accuracy and reliability of the automated measurement. By detecting abnormal values, early risk assessment can be performed to avoid potential safety hazards.

[0160] like Figure 3 As shown, this embodiment proposes a terminal device for compiling and analyzing a method for manual comparative measurement data for dam safety monitoring. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0161] The memory 210 may include a readable medium in the form of a volatile memory, such as a RAM 211 and / or a cache memory 212 , and may further include a ROM 213 .

[0162] Among them, the memory 210 also stores a computer program, and the computer program can be executed by the processor 220, so that the processor 220 executes the application of any of the above-mentioned methods for compiling and analyzing dam safety monitoring manual comparison data in the embodiments of the present application, and its specific implementation method is consistent with the implementation method and the technical effect achieved in the above-mentioned application embodiments, and some contents are not repeated. The memory 210 can also include a program / utility 214 having a group (at least one) of program modules 215, such program modules include but are not limited to: an operating system, one or more application programs, other program modules and program data, each of these examples or some combination may include the implementation of a network environment.

[0163] Accordingly, the processor 220 may execute the above-mentioned computer programs, and may execute the program / utility 214 .

[0164] The bus 230 may be a bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0165] The terminal device 200 may also communicate with one or more external devices 240 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the terminal device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the terminal device 200 to communicate with one or more other computing devices. Such communication may be performed through an I / O interface 250. In addition, the terminal device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the terminal device 200 through a bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0166] like Figure 4 As shown, this embodiment proposes a computer-readable storage medium for a method for compiling and analyzing manual comparative measurement data for dam safety monitoring. Instructions are stored on the computer-readable storage medium. When the instructions are executed by the processor, any one of the above-mentioned methods for compiling and analyzing manual comparative measurement data for dam safety monitoring is implemented. The specific implementation method is consistent with the implementation method and the technical effect achieved in the above-mentioned application embodiments, and some contents will not be repeated here.

[0167] Figure 4The program product 300 provided in this embodiment for implementing the above-mentioned application is shown, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited to this. In this embodiment, the readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0168] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which may send, propagate, or transmit a program used by or in combination with an instruction execution system, an apparatus, or a device. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0169] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0170] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A method for compiling and analyzing artificial comparative measurement data for dam safety monitoring, characterized in that: include: Performing automated measurement and manual measurement on the dam measurement points; wherein the number of measurements and the measurement time of the automated measurement and the manual measurement are the same; According to the data of each automated measurement and manual measurement, an automated measurement value sequence and a manual measurement value sequence are respectively formed; The automated measurement sequence and the manual measurement sequence are smoothed by using empirical mode decomposition, and the processed data are reconstructed. Deviation calculation is performed on the reconstructed automatic measurement sequence and manual measurement sequence, and dynamic control limits are obtained based on the deviation; The dynamic control limit and the data value of the deviation are compared to identify abnormal data points of the dam based on the comparison results.

2. The method according to claim 1, characterized in that The dam measuring points include the dam shoulder, dam body and dam foundation; the automated measurement uses automated monitoring equipment to perform continuous measurement at preset time intervals; the manual measurement uses a measuring instrument to perform manual measurement at the same time points as the automated measurement.

3. The method according to claim 2, characterized in that The data of the automated measurement and manual measurement include measurement time, measurement point, measurement value and environmental parameters; the automated measurement value sequence and the manual measurement value sequence are respectively formed according to the data of each automated measurement and manual measurement, including: According to the measurement values ​​of the extracted automated measurement data, an automated measurement sequence is formed. t }; According to the measurement values ​​of the extracted manual measurement data, a manual measurement value sequence {M t }.

4. The method according to claim 3, characterized in that Empirical mode decomposition is used to smooth the automatic measurement sequence and the manual measurement sequence, including: Set the initial signal to the automated measurement sequence x(t) = {A t }; Find all local maxima and local minima in the initial signal x(t); The upper envelope is constructed through the local maximum points, the lower envelope is constructed through the local minimum points, and the spline interpolation method is used for interpolation; Calculate the mean m(t) of the upper and lower envelopes; Calculate the difference between the initial signal and the mean value h(t) = x(t) - m(t). If h(t) meets the IMF condition, then h(t) is an IMF component. Extract the IMF from the signal to obtain IMF1(t), and use the residual signal r(t)=x(t)-IMF1(t) as the new signal until the signal cannot be further decomposed; The automated measurement sequence x(t) = {A t } is decomposed into multiple IMF components and a residual: The obtained artificial measurement sequence y(t) = {M t } is decomposed into multiple IMF components and a residual: Where n represents the total number of IMF components obtained by decomposition, and i represents the index as a positive integer.

5. The method according to claim 4, characterized in that Reconstruct the processed data, including: According to the frequency characteristics and amplitude characteristics, the IMF components are selected and the selected IMF components are reconstructed to obtain the denoised signal. For the automatic measurement sequence {A t }: For the artificial measurement sequence {M t }:

6. The method according to claim 5, characterized in that Deviation calculation is performed on the reconstructed automated measurement sequence and manual measurement sequence, and dynamic control limits are obtained based on the deviations, including: For each time point t, the deviation δ(t) between the reconstructed automated measurement and the manual measurement is calculated. The mean μ of the deviation sequence is calculated based on the deviation δ(t) δ and standard deviation σ δ ; Compute dynamic control limits using either an exponentially weighted moving average or a generalized autoregressive conditional heteroskedasticity model.

7. The method according to claim 6, characterized in that The mean μ of the deviation sequence is calculated based on the deviation δ(t) δ and standard deviation σ δ , include: Where N represents the total number of time points measured.

8. The method according to claim 7, characterized in that Calculate dynamic control limits using exponentially weighted moving average or generalized autoregressive conditional heteroskedasticity models, including: Using the exponentially weighted moving average method: The data is smoothed by weighted averaging, and the control limits are calculated as follows: EWMA t =λδ(t)+(1-λ)EWMA t-1 Among them, λ represents the smoothing parameter (0<λ<<1), EWMA t-1 Represents the EWMA value at the previous moment, initially EWMA0 = δ(1); The upper and lower control limits are: UCL t =EWMA t +Lσ δ LCL t =EWMA t -Lσ δ Where L represents the control limit factor; Or, using the generalized autoregressive conditional heteroskedasticity model approach: For the deviation sequence δ(t), the following GARCH model is established: δ(t)=μ δ +∈ t Among them, α0, α1 and β1 are model parameters. Through parameter estimation, the conditional variance is calculated And get the dynamic control limit: UCL t =μ δ +Lσ δ LCL t =μ δ -Lσ δ 。 9. The method according to claim 8, characterized in that The step of comparing the dynamic control limit and the deviation data value to identify abnormal dam data points according to the comparison result includes: Using the calculated dynamic control limits, for each time point t, the deviation δ(t) is compared with the upper and lower control limits UCL t and LCL t Compare and get the comparison result, if δ(t)>UCL t or δ(t) <LCL t , then there is an abnormal value at time t, otherwise the measured value at time t is normal.

10. A dam safety monitoring manual comparison data compilation and analysis system, characterized in that: include: A dam measuring point measurement module is used to perform automatic measurement and manual measurement of the dam measuring points; wherein the number of measurements and the measurement time of the automatic measurement and the manual measurement are the same; A measurement sequence generation module is used to generate an automatic measurement sequence and a manual measurement sequence according to the data of each automatic measurement and manual measurement respectively; An empirical mode decomposition module is used to use empirical mode decomposition to smooth the automatic measurement sequence and the manual measurement sequence respectively, and reconstruct the processed data; The deviation calculation module is used to calculate the deviation of the reconstructed automatic measurement sequence and the manual measurement sequence, and obtain the dynamic control limit according to the deviation; The abnormal data identification module is used to compare the data values ​​of the dynamic control limit and the deviation to identify abnormal data points of the dam according to the comparison result.

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