Dam safety assessment method and system, electronic equipment and storage medium

Abstract

The invention provides a dam safety assessment method and system, electronic equipment and a storage medium, and the method comprises the steps: monitoring the operation state of a dam based on a digital twin sensing network, and obtaining operation state data; the operation state data comprises historical monitoring data in a preset time period and real-time monitoring data at a preset time point; inputting the historical monitoring data into a cloud model constructed based on a multi-step reverse cloud transformation algorithm, and drawing up a safety monitoring index of the dam by using the cloud model; and comparing and analyzing the real-time monitoring data and the safety monitoring indexes to evaluate the safety state of the dam. According to the method, the cloud model of the multi-step reverse cloud transformation algorithm is introduced as a data driving model, and the dam monitoring indexes are drafted in combination with the digital twinborn technology, so that an effective method is provided for the safety assessment of the dam monitoring indexes, the problem that the traditional cloud model is a pure imaginary number when the super entropy value is calculated is solved, and the accuracy and reliability of cloud model application are improved.

Description

Technical Field

[0001] The present application belongs to the field of smart water conservancy technology and relates to a dam safety assessment method, system, electronic device and storage medium. Background Art

[0002] As an important part of water conservancy projects, the safe operation of dams is of great significance to protecting people's lives and property and social and economic development. However, dams are affected by many factors during operation, such as hydrology, geology, climate, etc. The uncertainty of these factors brings challenges to the safety assessment of dams. Traditional dam safety assessment methods mainly rely on experience and deterministic models, which are difficult to accurately handle uncertain factors, resulting in low reliability of assessment results.

[0003] In recent years, with the development of information technology, emerging technologies such as cloud models have been applied to dam safety assessment. Cloud models are an effective tool for dealing with uncertainty problems. They can convert qualitative concepts into quantitative data, thereby better capturing and quantifying uncertainty factors. However, when calculating the hyperentropy value, the traditional cloud model method may have a situation where the hyperentropy value is a pure imaginary number, which will affect the accuracy and reliability of the cloud model. Summary of the invention

[0004] The purpose of this application is to provide a dam safety assessment method, system, electronic device and storage medium to improve the accuracy and reliability of dam safety assessment.

[0005] In a first aspect, the present application provides a dam safety assessment method, comprising: monitoring the operating status of the dam based on a digital twin perception network to obtain operating status data; the operating status data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point; inputting the historical monitoring data into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and using the cloud model to formulate safety monitoring indicators for the dam; comparing and analyzing the real-time monitoring data with the safety monitoring indicators to evaluate the safety status of the dam.

[0006] In an implementation of the first aspect, the operation status of the dam is monitored based on the digital twin perception network, and the operation status data obtained includes:

[0007] Construct an evaluation indicator system including multiple monitoring projects;

[0008] For each monitoring project, a specific number of measuring points are arranged at different parts of the dam;

[0009] The environmental quantity perception data and the effect quantity perception data are monitored by the measuring points to obtain the operating status data.

[0010] In an implementation of the first aspect, the historical monitoring data is input into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and the safety monitoring indicators of the dam are formulated using the cloud model, including:

[0011] A multi-step reverse cloud generator is used to convert the effect size perception data into digital features reflecting qualitative concepts; the set of digital features is represented as (Ex, En, He), where Ex represents expectation, En represents entropy, and He represents hyperentropy;

[0012] Based on the digital features, a cloud droplet group representing the measurement point data is created using a forward cloud generator, and the degree of certainty of each cloud droplet in the cloud droplet group is obtained;

[0013] Based on the probability density of cloud droplet distribution and the certainty of cloud droplets, the contribution of the cloud droplet group to the concept of integrity is calculated;

[0014] Based on the contribution of the cloud droplet group to the concept of integrity, safety monitoring indicators with different monitoring effect quantities for each part are formulated according to the 3En rule.

[0015] In an implementation of the first aspect, using a multi-step reverse cloud generator to convert the effect size perception data into digital features reflecting qualitative concepts includes:

[0016] Performing multiple random sampling on cloud droplets in the cloud droplet group to obtain multiple sample groups accordingly;

[0017] Calculate the average value and variance of all cloud droplets in each sample group, wherein the average value is the expectation;

[0018] Based on the mean value and the variance, the entropy and the super entropy value are calculated.

[0019] In an implementation of the first aspect, based on the digital features, a forward cloud generator is used to create a cloud droplet group representing the measurement point data, and obtaining the certainty of each cloud droplet in the cloud droplet group includes:

[0020] S1, with En as expectation and He as standard deviation, generate a normal random number En'=norm(En,He);

[0021] S2, generate a normal random number with Ex as expectation and |En'| as standard deviation, x is a cloud droplet in the domain space x = norm(Ex, En');

[0022] S3. Calculate and solve the formula for certainty: μ x =exp[-(x-Ex 2 ) / 2(En') 2 ];

[0023] S4, let μ have certainty i x i Become a cloud drop in the number field, where i is any real number;

[0024] S5. Repeat steps S1 to S4 until a preset number of cloud droplets are generated, thereby obtaining the cloud droplet group representing the measurement point data.

[0025] In an implementation of the first aspect, the real-time monitoring data is the maximum index and the minimum value monitored by any measuring point for a specific monitoring item; and comparing and analyzing the real-time monitoring data with the safety monitoring index to evaluate the safety status of the dam includes:

[0026] Determine whether the maximum and minimum values ​​are within the corresponding numerical range of the security monitoring indicator;

[0027] If yes, it means the dam is in good working condition;

[0028] Otherwise it indicates that the dam is not operating normally.

[0029] In an implementation manner of the first aspect, it also includes: preprocessing the operating status data.

[0030] In the second aspect, the present application provides a dam safety assessment system, including: a status monitoring module, which is used to monitor the operating status of the dam based on a digital twin perception network to obtain operating status data; the operating status data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point; an indicator formulation module, which is used to input the historical monitoring data into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and use the cloud model to formulate the safety monitoring indicators of the dam; a safety assessment module, which is used to compare and analyze the real-time monitoring data with the safety monitoring indicators to evaluate the safety status of the dam.

[0031] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device performs any of the dam safety assessment methods described above.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned dam safety assessment methods.

[0033] As described above, the dam safety assessment method, system, electronic device and storage medium described in this application introduces a cloud model of a multi-step inverse cloud transformation algorithm as a data-driven model, and combines digital twin technology to formulate dam monitoring indicators, providing an effective method for dam safety assessment, solving the problem that the traditional cloud model method is a pure imaginary number when calculating the super entropy value, and improving the accuracy and reliability of cloud model application. Based on the preliminarily realized digital twin safety perception system, a more comprehensive monitoring and analysis of the dam structure, function and operating status is achieved; at the same time, the cloud model is used to quantify the variability factors in the dam safety evaluation to improve the accuracy and reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Shown is a schematic structural diagram of a mobile terminal described in the present application in one embodiment.

[0035] Figure 2 Shown is a flow chart of a dam safety assessment method according to an embodiment of the present application.

[0036] Figure 3 A schematic diagram showing an evaluation index system described in the present application in one embodiment.

[0037] Figure 4a A time series diagram showing the temperature and rainfall in one embodiment of the present application.

[0038] Figure 4b A time series diagram showing the water level described in the present application in one embodiment.

[0039] Figure 5a A time series diagram showing the horizontal displacements of the measuring points SP1 to SP5 described in the present application in one embodiment.

[0040] Figure 5b A time series diagram showing the horizontal displacements of the measuring points CZ1 to CZ5 described in the present application in one embodiment.

[0041] Figure 5c A time series diagram showing the horizontal displacements of the measuring points JY1 and JY2 described in the present application in one embodiment.

[0042] Figure 6 A schematic diagram showing a process line of uplift pressure measurement values ​​described in the present application in one embodiment.

[0043] Figure 7 A flow chart showing a method for generating a cloud droplet group according to an embodiment of the present application is shown.

[0044] Figure 8 A schematic diagram showing a normal cloud described in the present application in an embodiment.

[0045] Figures 9a to 9e The cloud droplet diagram showing the horizontal displacement measurement points SP1 to SP5 described in the embodiment of the present application.

[0046] Figures 10a to 10e The cloud droplet diagram of the vertical displacement measurement points SP1 to SP5 described in the embodiment of the present application is displayed.

[0047] Figure 11a to Figure 11b The cloud droplet diagram of the bedrock deformation measuring points JY1 and JY2 described in the embodiment of the present application is shown.

[0048] Figure 12a to Figure 12b The cloud drop diagram of the seepage measurement points SL1 and SL2 described in the embodiments of the present application is shown.

[0049] Fig.13 A schematic diagram showing the structure of a dam safety assessment system according to an embodiment of the present application.

[0050] Fig.14 A diagram showing the architecture of a dam safety assessment system according to an embodiment of the present application.

[0051] Fig.15 A schematic diagram showing the structure of an electronic device described in the present application in an embodiment. DETAILED DESCRIPTION

[0052] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0053] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0054] In addition, in this application, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0055] The establishment of dam safety monitoring indicators is a rather complicated task, which needs to be combined with the structural characteristics of the dam and take into account the demonstration of theories and methods. There are two main types of methods for formulating dam monitoring indicators. One is to use mathematical analysis methods to mine the information contained in the dam based on the existing monitoring effect, such as confidence interval estimation method, typical small probability method, etc.; the other is to obtain extreme values ​​through structural calculation based on the mechanical properties of the dam body and dam foundation. The structural analysis method is to use the finite element method to establish a model to simulate the actual stress conditions of the dam and bedrock. This method can calculate the deformation and stress-strain change laws of the dam body under various working conditions. This method is related to the causes and mechanisms of dam failures, has clear physical concepts, and can simulate some load conditions that have never been encountered. However, the calculation workload is often very huge when it is used, and it is impossible to consider the real-time state changes of the dam. It is more convenient to use mathematical analysis methods to formulate dam deformation monitoring indicators, especially the early warning effect of formulating first-level monitoring indicators based on long-term monitoring data sequences is better. Therefore, formulating monitoring indicators based on monitoring effect quantities through mathematical analysis methods is a more common method in the field of dam safety monitoring.

[0056] The typical small probability method is based on the probability distribution of sample data for analysis. From the principle and algorithm, it can be seen that the accuracy of the monitoring indicators proposed by the typical small probability method is mainly determined by the sample capacity of the monitoring data and the certainty of its sample probability distribution. The typical small probability method can connect the adverse load combination with the strength and stability of the dam body, but it takes a long time to obtain appropriate sample data, and the monitoring data must contain a sufficient number of data under the action of adverse load combinations. It is often difficult to meet this requirement in actual projects. If the number of sub-samples is not enough to represent the characteristics or probability distribution of the parent body and cannot be used to identify the parent body distribution, the results proposed by the typical small probability method will lack certain reliability. Different from the typical small probability method, the confidence interval method uses the residuals between the measured values ​​and the predicted values ​​of each effect as samples, performs a distribution test on the residual sequence, and then determines the confidence interval according to the probability distribution function that conforms to the trend. However, due to the limitation of the length of time that the prediction model can predict, in order to ensure the accuracy of the confidence interval, the model needs to be updated regularly. The main idea of ​​the two traditional methods is to only consider the randomness of the occurrence of abnormal dam monitoring values, which is inconsistent with reality. During the service life of a dam, a probabilistic event is a response to the probability of problems occurring at the dam. Low-probability abnormalities cannot reflect dangerous conditions at the dam. Its uncertainty should also include fuzzy concepts, which are ignored by traditional methods.

[0057] In view of the above limitations, the following embodiments of the present application provide a dam safety assessment method, system, electronic device and storage medium, which can well resolve the relationship between ambiguity and randomness by utilizing a cloud model. The cloud model algorithm formed by the cross-integration of multiple theories can combine the advantages of probability theory, mathematical statistics and fuzzy set theory when studying uncertainty problems. The cloud model algorithm is introduced in the formulation of dam safety monitoring indicators. The cloud characteristic values ​​calculated based on the monitoring data can better link quantitative monitoring values, qualitative safety analysis and quantitative safety monitoring indicators, and scientifically and rationally formulate safety monitoring indicators.

[0058] The dam safety assessment method provided in the embodiment of the present application can be run in a mobile terminal, a computer terminal or similar device. Taking running on the mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure diagram of a mobile terminal of the dam safety assessment method. Figure 1 As shown, the mobile terminal may include: a processor and a memory, the processor may be a central processing unit, and the memory is used to store data. It should be noted that: Figure 1 The mobile terminal in the figure is only used as an example and does not limit the specific structure of the mobile terminal.

[0059] Optionally, the mobile terminal may further include: a communication transmission device and an input and output device.

[0060] Optionally, the memory may be used to store computer programs, such as software programs and modules of application software, and the memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0061] Optionally, the communication transmission device can be used to receive or send data via a network, which may include a wireless network provided by a communication provider of the mobile terminal. The communication transmission device may include a NIC (Network Interface Controller) that can be connected to other network devices through a base station so as to communicate with the Internet.

[0062] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0063] See also Figure 2 , showing a flow chart of a dam safety assessment method described in the present application in one embodiment. Figure 2 As shown, the present application provides a dam safety assessment method, including the following steps S100 to S300.

[0064] In step S100, the operating status of the dam is monitored based on the digital twin perception network to obtain operating status data.

[0065] Digital twin monitoring IoT perception collects various data of physical entities such as dams in real time to achieve real-time monitoring of the dam status. For example, a large-scale water conservancy hub project is located in the central region, and the dam body is a concrete gravity dam. The dam has been under construction for a long time, and a digital twin perception network has been initially built, which can monitor the operation status of the hub building more comprehensively. The automated monitoring system has realized the automatic collection function of multiple internal observation and internal deformation monitoring point data, which can more completely capture the deformation of various parts of the dam.

[0066] In one embodiment of the present application, the operating status data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point.

[0067] Specifically, by continuously recording the changes in all monitoring indicators from the past to the present, a historical monitoring database was constructed. These data can not only reflect the health status of the dam over a period of time in the past, but also provide an important basis for the subsequent formulation of dam safety monitoring indicators.

[0068] Monitoring indicators may be abnormal within the preset time period. In order to make the data more representative, the maximum or minimum value can be selected for analysis. The real-time monitoring data at the preset time point obtained by this application can be the maximum or minimum value, so that the conditions of different parts of the dam that users are concerned about can be evaluated immediately.

[0069] In practical applications, some monitoring indicators may have abnormal values. In order to ensure that the analysis results are more representative and accurate, the extreme values ​​(i.e., maximum and / or minimum values) within a period of time are generally used as reference points for in-depth research. In the embodiment of the present application, the real-time monitoring data can be the maximum and minimum values ​​monitored by any measuring point for a specific monitoring project, which helps to instantly evaluate the status of a specific dam part that the user is concerned about.

[0070] In one embodiment of the present application, step S100 of monitoring the operating status of the dam based on the digital twin perception network and obtaining operating status data may include the following steps S101 to S103.

[0071] In step S101, an evaluation index system including multiple monitoring items is constructed.

[0072] The operating status of the dam is the result of a comprehensive reflection of the operating conditions of various parts of the dam, and the operating conditions of each part are monitored by the deployed monitoring items. Among them, the various parts of the dam include the dam body of each section of the dam, the left and right bank slopes, the dam foundation, etc. The status monitoring of each part includes deformation, seepage, stress strain, temperature and other items.

[0073] See also Figure 3 , showing a schematic diagram of the evaluation index system described in this application in one embodiment. Figure 3 As shown, each monitoring item can be further subdivided, such as deformation monitoring items include horizontal displacement, vertical displacement, inclination, and joint crack monitoring; seepage monitoring items include dam body and dam foundation uplift pressure, pore water pressure, dam body seepage, and bypass dam seepage monitoring; stress and strain monitoring items include dam heel, dam toe and local stress monitoring.

[0074] For each subdivided monitoring project, different dams will adopt different monitoring methods according to the characteristics of the project. For example, horizontal displacement can be measured by vertical line method, tension line method, sight line method, laser alignment method, corner net method, etc. Vertical displacement can be measured by geometric leveling method, energy leveling method, etc. The uplift pressure of each part can be measured by osmometer, pressure tube and other methods.

[0075] In step S102, for each monitoring item, a specific number of measuring points are arranged at different locations of the dam.

[0076] In order to accurately monitor the operating status of all parts and aspects of the dam, each detailed monitoring project has multiple measuring points. Therefore, the evaluation index system of the dam operating status will eventually be refined to the different measuring points of the dam's multiple monitoring projects, and the selected characteristic measuring points are the underlying evaluation indicators of the dam operating status safety assessment.

[0077] In step S103, the environmental quantity perception data and the effect quantity perception data are monitored by the measuring points to obtain the operating status data.

[0078] This application is based on the monitoring data of water level, temperature and rainfall upstream of the dam. Figure 4a and Figure 4b The environmental quantity change process line shown in Figure 4a is a time series graph showing temperature and rainfall. Figure 4b is a time series diagram showing the water level. Figure 4a and Figure 4b It can be seen that in the past five years, the water level, temperature and rainfall upstream of the dam area have changed periodically on an annual basis, with low water levels from June to September and high water levels from November to February of the following year; the temperature and rainfall are the opposite, with high temperatures and sufficient rainfall in summer.

[0079] For the monitoring of deformation effect quantity perception data, the horizontal displacement, vertical displacement and bedrock deformation data of the powerhouse A dam section and the flood discharge B dam section were selected for analysis. The deformation effect quantity time series diagram is shown in Figure 5a to Figure 5c ,in Figure 5a is a time series diagram showing the horizontal displacement of the measuring points SP1 to SP5, Figure 5b 5C is a time series diagram showing the horizontal displacement of the measuring points CZ1 to CZ5, and FIG. 5C is a time series diagram showing the horizontal displacement of the measuring points JY1 and JY2. The extreme value table is shown in Table 1.

[0080] Table 1 Extreme value table of deformation effect

[0081]

[0082]

[0083] After comparing with the temperature and upstream water level data, it can be seen that the main factors affecting the horizontal displacement of the two dam sections are temperature and water level. In summer, due to high temperature and heavy rain, the dam body deforms downstream; in winter, due to low temperature and little rain, the dam body deforms upstream. From a temporal perspective, the horizontal displacement pattern of the dam is relatively obvious, which is consistent with the general law of concrete dam deformation. The time of the maximum displacement each year is relatively close, and the inter-annual variation pattern is the same.

[0084] The vertical displacement trends of different dam sections are highly similar, and the amplitude of each change is within a reasonable range. The vertical displacement of the flood discharge dam section B is higher than that of the powerhouse dam section A, and the subsidence change is more significant. The vertical displacement of the dam body is affected by two factors: temperature and upstream water level.

[0085] The variation characteristics of bedrock deformation vary significantly with the seasons, and have a strong interannual periodic variation regularity. All measuring points deformed downstream, and the corresponding analysis with the monitoring time showed that the measured values ​​of joint deformation were mainly affected by the external temperature.

[0086] This application arranges a piezometer. When monitoring the seepage situation, the measuring points are selected for the flood discharge dam section B to analyze the dynamic performance of its foundation uplift pressure. The relevant measurement process lines are as follows: Figure 6 The key characteristic values ​​of each measuring point are shown in Table 2.

[0087] Table 2 Extreme value table of lift pressure characteristic value at each measuring point

[0088]

[0089] Overall, the foundation uplift pressure of the flood discharge B dam section is relatively stable, without large fluctuations, and its change trend is mainly related to the rise and fall of the river water level and the change of rainfall. Based on the comprehensive evaluation of various monitoring data, the overall operation of the dam project was good and normal during the monitoring period.

[0090] In one embodiment of the present application, the dam safety assessment method described in the present application also includes: preprocessing the operating status data.

[0091] Specifically, due to various equipment failures or environmental interference, there may be missing values, outliers or noise in the raw data of the measurement and monitoring. By using interpolation to fill in missing data, using statistical methods to identify and remove outliers, and applying filtering technology to remove high-frequency noise, the data set can be effectively purified to ensure that its quality meets the requirements for further processing. In addition, data from different measurements may be collected based on different time bases, so all monitoring data must be adjusted according to a unified time standard. This application does not limit the preprocessing method, and other preprocessing methods to ensure data quality are also applicable to this application.

[0092] Furthermore, the present application will incorporate the acquired real-time monitoring data into the historical monitoring database to achieve automatic updating and continuous accumulation of data.

[0093] In step S200, the historical monitoring data is input into a cloud model constructed based on a multi-step reverse cloud transformation (MBCT_SR) algorithm, and the cloud model is used to formulate safety monitoring indicators of the dam.

[0094] The cloud model is a cognitive model that can achieve bidirectional conversion between conceptual connotation and numerical description. It uses a specific construction algorithm to convert between qualitative concepts and quantitative numerical values, revealing the inherent connection between randomness and fuzziness.

[0095] In the embodiment of the present application, the cloud model, as a data-driven core component, can make full use of the large amount of data collected by the IoT sensors, and effectively simulate and predict the performance of the dam under different conditions. This not only enhances the real-time monitoring capability of the health status of the dam structure, but also helps to timely discover potential safety hazards, providing strong data support for dam safety management.

[0096] In the implementation of cloud model, second-order normal cloud is a commonly used method. Cloud generator is the key algorithm to realize the function of cloud model, which can realize the conversion between qualitative concept description and quantitative numerical expression, including reverse cloud generator and forward cloud generator.

[0097] The reverse cloud generator converts quantitative sample data into qualitative concepts represented by cloud model eigenvalues ​​through parameter estimation.

[0098] If the sample sequence is x i (i=1,2,…,n), then the sample mean can be expressed as The absolute central moment of the first-order sample can be expressed as The sample variance can be expressed as Traditional cloud models usually use the single-step reverse cloud (SBCT-1stM) algorithm to calculate the super entropy value. According to the SBCT-1stM algorithm, the calculation formulas for expectation (Ex), entropy (En) and super entropy (He) are as follows:

[0099]

[0100] It is worth noting that when S 2 <En 2 When , there are cases where the super entropy value is a pure imaginary number. This will affect the accuracy and reliability of the cloud model.

[0101] To overcome this limitation, this application introduces an improved multi-step reverse cloud transformation (MBCT_SR) algorithm. The algorithm performs multiple random sampling and grouping of cloud droplet samples, calculates the mean and variance of each group, and thus obtains more accurate entropy and super entropy values. The MBCT_SR algorithm can effectively avoid the problem of super entropy being an imaginary number, and further improves the stability and credibility of the cloud model in practical applications.

[0102] In one embodiment of the present application, the historical monitoring data is input into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and step S200 of formulating the safety monitoring indicators of the dam using the cloud model may include the following steps S201 to S204.

[0103] This application takes into full consideration the various operating conditions and load combinations experienced by the main project of the water conservancy hub, and selects the data of the past five years for analysis. These data will be used as input parameters of the cloud model constructed based on the multi-step inverse cloud transformation algorithm to formulate safety monitoring indicators.

[0104] In step S201, a multi-step reverse cloud generator is used to convert the effect size perception data into digital features reflecting qualitative concepts.

[0105] Specifically, the set of digital features is expressed as (Ex, En, He), where Ex represents expectation, which is the center line of the cloud ladder diagram; En represents entropy, which quantifies the uncertainty of the concept, that is, the degree of discreteness of the cloud droplet diagram; He represents super entropy, which further describes the uncertainty of the entropy value, which is manifested as the thickness of the cloud layer in the cloud droplet diagram.

[0106] In one embodiment of the present application, a multi-step inverse cloud generator is used to convert the effect quantity perception data into digital features reflecting qualitative concepts, including: performing multiple random sampling on the cloud droplets in the cloud droplet group to obtain multiple sample groups; calculating the average value and variance of all cloud droplets in each of the sample groups, the average value being the expectation; and calculating the entropy and the super entropy value based on the average value and the variance.

[0107] Specifically, random sampling groups include:

[0108] FOR(i=1,i<=m,i++)

[0109] FOR(j=1,j<=r,j++)

[0110] Randomly sample n cloud droplet samples;

[0111] ENDFOR

[0112]

[0113] ENDFOR

[0114] The calculation formulas of the entropy and the super entropy are as follows:

[0115]

[0116] In step S202, based on the digital features, a forward cloud generator is used to create a cloud droplet group representing the measurement point data, and the degree of certainty of each cloud droplet in the cloud droplet group is obtained.

[0117] The forward cloud generator is used to realize the mapping between the qualitative description of the concept and the quantitative representation. Taking the second-order forward normal cloud transformation as an example, by inputting three characteristic parameters such as expectation, entropy and super entropy and the required number of cloud droplets n, n cloud droplets x = {x1, x2, ..., xn} representing the concept and their certainty can be obtained.

[0118] See also Figure 7 , showing a flow chart of a cloud droplet group generation method described in the present application in one embodiment. Figure 7 As shown, based on the digital features, a forward cloud generator is used to create a cloud droplet group representing the measurement point data, and the certainty of each cloud droplet in the cloud droplet group is obtained, including:

[0119] S1. With En as expectation and He as standard deviation, generate a normal random number En'=norm(En,He).

[0120] S2. Generate a normal random number with Ex as expectation and |En'| as standard deviation, and x is a cloud droplet in the domain space x = norm(Ex, En').

[0121] S3. Calculate and solve the formula for certainty: μ x =exp[-(x-Ex 2 ) / 2(En') 2 ].

[0122] S4, let μ have certainty i x i Become a cloud drop in the number field, where i is any real number.

[0123] S5. Repeat steps S1 to S4 until a preset number of cloud droplets are generated, thereby obtaining the cloud droplet group representing the measurement point data.

[0124] In step S203, based on the probability density of cloud droplet distribution and the certainty of cloud droplets, the contribution of the cloud droplet group to the concept of integrity is calculated.

[0125] In the embodiment of the present application, the randomness of uncertain events is studied in more than a single consideration. After the cloud model is constructed, the probability density and certainty of the distribution of n cloud droplets are considered. The contribution of the cloud droplet group contained in any small interval to the concept of integrity can be expressed as:

[0126]

[0127] In the constructed cloud model, the total contribution of the overall cloud droplets is 1, which can be expressed as the following formula:

[0128]

[0129] in:

[0130]

[0131] In step S204, based on the contribution of the cloud droplet group to the overall concept, safety monitoring indicators with different monitoring effect quantities for each part are formulated according to the 3En rule.

[0132] See also Figure 8 , showing a schematic diagram of a normal cloud described in the present application in an embodiment. Figure 8 As shown in the figure, in the interval [Ex-3En, Ex+3En], the contribution rate of the cloud droplet group to the qualitative concept is extremely high, reaching 99.74%, while the contribution rate of the cloud droplets outside this interval is only 0.26%, which can be ignored. This classification standard is the "3En criterion" of the normal cloud model. From the contribution formula, it can be seen that the larger the Ex interval, the smaller the contribution of the cloud droplet interval. The super entropy of the cloud droplet reflects the randomness of the data's membership to the qualitative concept in a specific interval. The super entropy indicates the membership of a certain data to the qualitative concept. Based on this criterion, when formulating monitoring indicators, its fuzziness and randomness can be fully displayed. The cloud droplets outside the interval [Ex-3En, Ex+3En] can be characterized as a set of non-qualitative concepts. These cloud droplets are likely to represent abnormal or unexpected data. Therefore, based on the 3En criterion, the threshold of the monitoring indicator of the dam operation status can be set more scientifically and reasonably through the cloud model theory.

[0133] In step S300, the real-time monitoring data is compared and analyzed with the safety monitoring index to evaluate the safety status of the dam.

[0134] In the embodiment of the present application, the real-time monitoring data is the maximum and minimum values ​​monitored by any measuring point for a specific monitoring item.

[0135] In one embodiment of the present application, the real-time monitoring data is compared and analyzed with the safety monitoring index to evaluate the safety status of the dam, including: determining whether the maximum index and the minimum value are within the corresponding numerical range of the safety monitoring index; if so, it indicates that the operation of the dam is good; otherwise, it indicates that the operation of the dam is abnormal.

[0136] Furthermore, if the maximum and minimum values ​​exceed the range of the safety monitoring index, an early warning signal is issued to prompt relevant personnel to take corresponding measures.

[0137] The following will describe the technical solution of the present application and the technical effects achieved in combination with a specific embodiment. Specifically, the monitoring data from January 1, 2019 to December 31, 2023 are selected for the calculation of safety monitoring indicators. The inverse cloud generators of the SBCT-1stM algorithm and the MBCT-SR algorithm are used to convert the original data of the monitoring point effect into expectation (Ex), entropy (En) and hyperentropy (He) reflecting qualitative concepts, and the digital features obtained by the two algorithms are compared, and the advantages and disadvantages of the two algorithms are analyzed. The forward cloud generator is preferably used to create a cloud droplet group representing the measuring point data (x = {x1, x2, ..., xn}, n is 1000 in this calculation), and the degree of certainty of each cloud droplet can be obtained at the same time; according to the probability density of the cloud droplet distribution and the degree of contribution to the whole, the safety monitoring indicators of different monitoring effect quantities of each dam section in the main project of the water conservancy hub are formulated according to the "3En rule".

[0138] In order to formulate the horizontal displacement safety monitoring index, this application uses the SP1, SP2, SP3 of the A dam section of the water conservancy project and the SP4, SP5 of the B dam section as the calculation case of the horizontal displacement monitoring index. Figure 9 is the cloud drop diagram of the horizontal displacement measurement points obtained according to the above calculation process, and Table 3 is the cloud model characteristic value and monitoring index value corresponding to each measurement point. Among them, Ex is the expectation, En is S and He S En and He are the entropy and super entropy values ​​when the SBCT_1stM algorithm is used for calculation; En and He are the entropy and super entropy values ​​when the MBCT_SR algorithm is used for calculation.

[0139] Table 3 Horizontal displacement cloud characteristic values ​​and monitoring indicators

[0140]

[0141] It can be seen from the table that the entropy values ​​of the horizontal displacement of the powerhouse A dam section and the flood discharge B dam section calculated by the SBCT_1stM algorithm are greater than the results calculated by the improved MBCT_SR algorithm, and the super entropy values ​​calculated by the SBCT_1stM algorithm are all pure imaginary numbers. From the horizontal displacement effect quantity perception monitoring data, it can be seen that the maximum and minimum values ​​of the SP4 and SP5 measuring points of the flood discharge B dam section are 4.95mm, 5.65mm and 2.05mm, 2.68mm respectively; the maximum and minimum values ​​of the horizontal displacement monitoring points SP1, SP2, and SP3 of the powerhouse A dam section are 2.13mm, 2.51mm, 3.69mm and 0.73mm, 0.95mm, 1.88mm respectively, and the measured values ​​are all less than the monitoring indicators proposed by the MBCT_SR algorithm method.

[0142] In order to formulate the vertical displacement safety monitoring index, the measuring points CZ1, CZ2, CZ3 of the A dam section of the powerhouse of the water conservancy project and the measuring points CZ4 and CZ5 of the B dam section of the flood discharge are used as calculation cases of the vertical displacement monitoring index. The cloud droplet diagram of the vertical displacement measuring points is also obtained according to the above calculation process, as shown in Figure 10. The cloud model characteristic values ​​and monitoring index values ​​corresponding to each measuring point are shown in Table 4.

[0143] Table 4 Vertical displacement cloud characteristic values ​​and monitoring indicators

[0144]

[0145] It can be seen from the table that, at the monitoring points of the vertical displacement of the powerhouse A dam section, the entropy values ​​calculated by the SBCT_1stM algorithm are all greater than the results calculated by the improved MBCT_SR algorithm, and the super entropy values ​​calculated by the SBCT_1stM algorithm are all pure imaginary numbers; at the monitoring points of the vertical displacement of the flood discharge B dam section, the entropy values ​​calculated by the SBCT_1stM algorithm are all smaller, and the super entropy values ​​are all larger. From the vertical displacement effect quantity perception monitoring data, it can be seen that the maximum and minimum values ​​of the measuring points CZ4 and CZ5 in the flood discharge B dam section are 33.15mm, 32.72mm and 21.4mm, 21.17mm respectively; the maximum and minimum values ​​of the vertical displacement monitoring points CZ1, CZ2, and CZ3 in the powerhouse A dam section are 25.9mm, 21.18mm, 22.4mm and 20.38mm, 15.85mm, 17mm respectively. The measured values ​​are all within the monitoring index range proposed by the MBCT_SR algorithm, and the actual vertical deformation of the dam is good.

[0146] In order to formulate the safety monitoring index of bedrock deformation, the bedrock deformation effect quantity perception data were analyzed by selecting the measuring points JY1 and JY2 of the flood discharge B dam section of the water conservancy project. The cloud drop diagram of the bedrock deformation measuring points was obtained according to the above calculation process, as shown in Figure 11. The cloud model characteristic values ​​and monitoring index values ​​corresponding to each measuring point are shown in Table 5.

[0147] Table 5 Bedrock deformation point cloud characteristic values ​​and monitoring indicators

[0148]

[0149] As can be seen from the table, the entropy and super entropy calculation results of the bedrock deformation monitoring points by the two algorithms are similar to the horizontal displacement and vertical monitoring points of the powerhouse A dam section. The entropy values ​​calculated by the SBCT_1stM algorithm are all larger, and the super entropy values ​​are all pure imaginary numbers. From the bedrock deformation effect quantity perception monitoring data, it can be seen that the maximum and minimum values ​​of the flood discharge B dam section measuring points JY1 and JY2 are -2.47mm, -2.48mm and -2.68mm, -2.63mm respectively. The measured values ​​are within the monitoring index range proposed by the MBCT_SR algorithm, and the actual situation of the dam bedrock deformation is good.

[0150] In order to formulate the seepage monitoring index, the measuring points SL1 and SL2 of the flood discharge dam section B were selected as the calculation cases of the dam foundation uplift pressure monitoring index of the water conservancy project. The cloud drop diagram of the seepage measuring point was obtained according to the above calculation process, as shown in Figure 12. The cloud model characteristic values ​​and monitoring index values ​​corresponding to each measuring point are shown in Table 6.

[0151] Table 6 Seepage measurement point cloud characteristic values ​​and monitoring indexes

[0152]

[0153] As can be seen from the table, similar to most of the measurement points mentioned above, the entropy values ​​of the seepage measurement points calculated by the SBCT_1stM algorithm are all too large, and the super entropy values ​​are all pure imaginary numbers. From the monitoring data of the seepage effect quantity perception, it can be seen that the maximum values ​​of the measurement points SL1 and SL2 of the flood discharge B dam section are 15.21mm and 15.2mm respectively. They are all within the preset monitoring indicators, the seepage of the dam is in a relatively stable state, and the actual seepage of the dam is good.

[0154] This application combines digital twin technology with the cloud model of the improved MBCT_SR algorithm, and takes a certain dam section of a water conservancy hub as an example to formulate and analyze monitoring indicators. The results show:

[0155] (1) The cloud model that introduces the MBCT_SR algorithm can effectively avoid the situation where the super entropy value is a complex number, and the entropy value in most cases is lower than the result calculated by the SBCT_1stM algorithm.

[0156] (2) The verified safety status of the dam section is stable, which is consistent with the actual situation. The cloud model of the improved MBCT_SR algorithm can provide an effective way for dam safety monitoring and evaluation.

[0157] It should be noted that the protection scope of the dam safety assessment method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0158] See also Fig.13 , showing a schematic diagram of the structure of the dam safety assessment system described in this application in one embodiment. Fig.13 As shown, the present application provides a dam safety assessment system, including a status monitoring module, an indicator formulation module and a safety assessment module.

[0159] In one embodiment of the present application, the state monitoring module is used to monitor the operating state of the dam based on the digital twin perception network to obtain operating state data; the operating state data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point;

[0160] In one embodiment of the present application, the indicator formulation module is used to input the historical monitoring data into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and use the cloud model to formulate the safety monitoring indicators of the dam;

[0161] In one embodiment of the present application, the safety assessment module is used to compare and analyze the real-time monitoring data with the safety monitoring indicators to evaluate the safety status of the dam.

[0162] It should be noted that the structures and principles of the status monitoring module, indicator formulation module and safety assessment module described in the embodiments of the present application correspond one-to-one to the steps in the above-mentioned dam safety assessment method, so they will not be repeated here.

[0163] See also Fig.14 , showing the architecture diagram of the dam safety assessment system described in the present application in one embodiment. The dam safety assessment system provided in the embodiment of the present application can implement the dam safety assessment method described in the present application, but the implementation device of the dam safety assessment method described in the present application includes but is not limited to the structure of the dam safety assessment system listed in the present embodiment, and all structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0164] See also Fig.15 , showing a schematic diagram of the structure of an electronic device described in the present application in one embodiment. Fig.15 As shown, the present application provides an electronic device, including: a processor and a memory.

[0165] The memory is used to store computer programs.

[0166] The processor is used to execute the computer program stored in the memory so that the electronic device executes any one of the above-mentioned dam safety assessment methods.

[0167] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0168] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0169] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0170] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the dam safety assessment method described in any of the above items is implemented. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0171] The present application embodiment may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer or data center.

[0172] In summary, based on the analysis of traditional safety monitoring index formulation methods, this application uses digital twin technology to obtain dam monitoring data, processes it through the MBCT_SR algorithm, formulates monitoring indicators according to the "3En criterion", and then analyzes the safety status of the dam. Taking the powerhouse dam section and the spillway dam section of the water conservancy hub as examples, the monitoring data and monitoring indicators are compared and analyzed, and it is found that all monitoring indicators of the dam are within the range of monitoring indicator values, which indicates that the deformation and seepage of the dam are in good condition and within a safe and controllable range. Since only the data-driven model is used without the comprehensive consideration of the mechanism-driven model, the evaluation results may have a certain degree of contingency. Therefore, future research should focus on collecting more comprehensive mechanism data, combining data and mechanism-driven models in the process of formulating monitoring indicators, and providing a more effective method for dam safety assessment.

[0173] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product may be a software installation package, and when the above method is required, the computer program product may be downloaded and executed on a computer.

[0174] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0175] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A dam safety assessment method, characterized in that: include: Based on the digital twin sensing network, the operating status of the dam is monitored to obtain operating status data; the operating status data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point; Inputting the historical monitoring data into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and using the cloud model to formulate safety monitoring indicators for the dam; The real-time monitoring data is compared and analyzed with the safety monitoring indicators to evaluate the safety status of the dam.

2. The method according to claim 1, characterized in that: Based on the digital twin perception network, the operating status of the dam is monitored, and the operating status data obtained include: Construct an evaluation indicator system including multiple monitoring projects; For each monitoring project, a specific number of measuring points are arranged at different parts of the dam; The environmental quantity perception data and the effect quantity perception data are monitored by the measuring points to obtain the operating status data.

3. The method according to claim 2, characterized in that The historical monitoring data is input into a cloud model constructed based on a multi-step inverse cloud transformation algorithm. The cloud model is used to formulate the safety monitoring indicators of the dam, including: A multi-step reverse cloud generator is used to convert the effect size perception data into digital features reflecting qualitative concepts; the set of digital features is represented as (Ex, En, He), where Ex represents expectation, En represents entropy, and He represents hyperentropy; Based on the digital features, a cloud droplet group representing the measurement point data is created using a forward cloud generator, and the degree of certainty of each cloud droplet in the cloud droplet group is obtained; Based on the probability density of cloud droplet distribution and the certainty of cloud droplets, the contribution of the cloud droplet group to the concept of integrity is calculated; Based on the contribution of the cloud droplet group to the concept of integrity, safety monitoring indicators with different monitoring effect quantities for each part are formulated according to the 3En rule.

4. The method according to claim 3, characterized in that The multi-step reverse cloud generator was used to convert the effect size perception data into numerical features reflecting qualitative concepts including: Performing multiple random sampling on cloud droplets in the cloud droplet group to obtain multiple sample groups accordingly; Calculate the average value and variance of all cloud droplets in each sample group, wherein the average value is the expectation; Based on the mean value and the variance, the entropy and the super entropy value are calculated.

5. The method according to claim 3, characterized in that: Based on the digital features, a cloud droplet group representing the measurement point data is created using a forward cloud generator, and the certainty of each cloud droplet in the cloud droplet group is obtained, including: S1, with En as expectation and He as standard deviation, generate a normal random number En'=norm(En,He); S2, generate a normal random number with Ex as expectation and |En'| as standard deviation, x is a cloud droplet in the domain space x = norm(Ex, En'); S3. Calculate and solve the formula for certainty: μ x =exp[-(x-Ex 2 ) / 2(En') 2 ]; S4, let have certainty μ i x i Become a cloud drop in the number field, where i is any real number; S5. Repeat steps S1 to S4 until a preset number of cloud droplets are generated, thereby obtaining the cloud droplet group representing the measurement point data.

6. The method according to claim 1, characterized in that The real-time monitoring data is the maximum and minimum values ​​monitored by any measuring point for a specific monitoring item; Comparing and analyzing the real-time monitoring data with the safety monitoring indicators to evaluate the safety status of the dam includes: Determine whether the maximum and minimum values ​​are within the corresponding numerical range of the security monitoring indicator; If yes, it means the dam is in good working order; Otherwise it indicates that the dam is not operating normally.

7. The method according to claim 1, characterized in that Also includes: The operating status data is preprocessed.

8. A dam safety assessment system, characterized in that: include: The status monitoring module is used to monitor the operating status of the dam based on the digital twin perception network and obtain the operating status data; The operating status data includes historical monitoring data within a preset time period and real-time monitoring data at a preset time point; An indicator formulation module, used for inputting the historical monitoring data into a cloud model constructed based on a multi-step inverse cloud transformation algorithm, and formulating the safety monitoring indicators of the dam using the cloud model; The safety assessment module is used to compare and analyze the real-time monitoring data with the safety monitoring indicators to evaluate the safety status of the dam.

9. An electronic device, characterized in that: include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the dam safety assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the dam safety assessment method according to any one of claims 1 to 7 is implemented.

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