A reservoir dam risk pre-play method, device, equipment and storage medium

Abstract

The application discloses a reservoir dam risk pre-rehearsal method, device, equipment and storage medium. The method comprises the following steps: obtaining historical hydrological data in a target historical period and generating a corresponding hydrological data simulation table; for each time node in the hydrological data simulation table, determining a target area corresponding to the simulation hydrological data on the time node on the dam water surface in the constructed reservoir area three-dimensional model and performing grid division on the target area; for each grid obtained by division, analyzing stress data on the grid in the reservoir area three-dimensional model in combination with the simulation hydrological data, and determining corresponding seepage data based on the stress data and the seepage data mapping table; based on the stress data and the seepage data, pre-rehearsing the risk status of the reservoir dam under each simulation hydrological data; the scheme is beneficial to improving the problems of poor timeliness and lagging reaction of related technologies.

Description

Technical Field

[0001] One or more embodiments of the present invention relate to the field of water conservancy early warning technology, and in particular to a method, apparatus, equipment and storage medium for risk prediction of reservoir dams. Background Technology

[0002] The ability to promptly identify potential risks to dams has always been a key focus of water conservancy early warning systems.

[0003] However, most of the current technologies rely on manual methods and real-time data to assess the current risk status of reservoirs and dams, which suffers from problems such as low efficiency and delayed response. Summary of the Invention

[0004] In view of this, one or more embodiments of the present invention provide a method, apparatus, equipment and storage medium for risk simulation of reservoir dams.

[0005] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0006] According to a first aspect of one or more embodiments of the present invention, a risk simulation method for reservoir dams is proposed, the method comprising:

[0007] Historical hydrological data for a target historical period is acquired, and a corresponding hydrological data simulation table is generated based on the historical hydrological data; wherein the hydrological data simulation table contains simulated hydrological data at several time points within the target historical period;

[0008] For each time node in the hydrological data simulation table, the target area corresponding to the simulated hydrological data at the time node is determined on the upstream surface of the dam in the constructed 3D model of the reservoir area, and the target area is divided into grids.

[0009] For each grid obtained by division, the stress data on the grid is analyzed in the three-dimensional model of the reservoir area in combination with the simulated hydrological data. Based on the stress data obtained by analysis, the corresponding seepage data is determined by querying the generated seepage data mapping table, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data.

[0010] Based on the stress data and seepage data, the risk status of the reservoir dam under various simulated hydrological data is simulated.

[0011] In one alternative implementation, generating the corresponding hydrological data simulation table based on the historical hydrological data includes:

[0012] Based on the time period of the rainy season and flood season, the distribution of several time nodes in the hydrological data simulation table and the type of simulated hydrological data at the several time nodes are determined. Then, combined with the time nodes to which the historical hydrological data belongs, the hydrological data simulation table is generated.

[0013] In one alternative implementation, determining the target area corresponding to the simulated hydrological data at the specified time point on the upstream face of the dam within the constructed 3D reservoir model, and then rasterizing the target area, includes:

[0014] The structural feature data of the reservoir dam and the elevation data corresponding to the preset reservoir range are obtained from the three-dimensional model of the reservoir area; wherein, the preset reservoir range is the range formed by expanding the riverbank of the reservoir outward according to a preset value;

[0015] Based on the elevation data and the current simulated hydrological data, the reservoir water area is determined, and then based on the elevation data within the reservoir water area, the reservoir bottom baseline is determined.

[0016] The lower boundary of the target area is determined by the structural feature data and the reservoir bottom baseline, and the upper boundary of the target area is determined by the structural feature data and the simulated hydrological data. The target area is then determined and divided according to a preset grid size.

[0017] In one alternative implementation, the step of performing a risk simulation of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data includes:

[0018] If the difference between the stress data and the design bearing capacity is less than the preset bearing capacity threshold, and / or the difference between the seepage data and the design seepage data is less than the preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

[0019] In one alternative implementation, the step of performing a risk simulation of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data includes:

[0020] If the difference between the stress data and the stress data obtained from historical hydrological data that has caused actual risks is less than a preset pressure threshold, and / or the difference between the seepage data and the seepage data obtained from historical hydrological data that has caused actual risks is less than a preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

[0021] In one alternative implementation, the method further includes:

[0022] Using the simulated hydrological data that presents risks during the rehearsal as a reference, the real-time hydrological data is used to determine whether the reservoir dam is at risk, and if risks are present, the solutions corresponding to the prepared simulated hydrological data are obtained.

[0023] In one alternative implementation, the construction process of the three-dimensional model of the reservoir area includes:

[0024] The BIM design model of the reservoir dam and the modeling feature data uploaded by various IoT sensing devices are obtained; wherein, the modeling feature data includes at least the elevation data corresponding to the reservoir area.

[0025] Based on the BIM design model and the modeling feature data, a three-dimensional simulation of each component in the reservoir area is performed to obtain a three-dimensional model of the reservoir area.

[0026] In one alternative implementation, the process of generating the seepage data mapping table includes:

[0027] The structural feature data of the reservoir dam are obtained from the three-dimensional model of the reservoir area;

[0028] Based on the structural feature data, the seepage status of the reservoir dam under different stress data is analyzed and a corresponding seepage data mapping table is generated.

[0029] According to a second aspect of one or more embodiments of the present invention, a risk prediction device for a reservoir dam is provided, the device comprising a simulation table generation unit, a grid division unit, a stress and seepage analysis unit, and a risk prediction unit; wherein:

[0030] The simulation table generation is used to obtain historical hydrological data within the target historical period and generate a corresponding hydrological data simulation table based on the historical hydrological data; wherein, the hydrological data simulation table contains simulated hydrological data at several time nodes within the target historical period;

[0031] The grid division unit is used to determine the target area corresponding to the simulated hydrological data at each time point in the hydrological data simulation table, on the water-facing surface of the dam in the constructed three-dimensional model of the reservoir area, and to perform grid division on the target area.

[0032] The stress and seepage analysis unit is used to analyze the stress data on each grid in the three-dimensional model of the reservoir area in combination with the simulated hydrological data, and to query the generated seepage data mapping table based on the stress data to determine the corresponding seepage data, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data.

[0033] The risk simulation unit is used to simulate the risk status of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data.

[0034] According to a third aspect of one or more embodiments of the present invention, an electronic device is provided, comprising:

[0035] The processor, and memory for storing processor-executable instructions;

[0036] The processor implements the steps in the method described in the first aspect by running the executable instructions.

[0037] According to a fourth aspect of one or more embodiments of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0038] As described above, this invention utilizes historical hydrological data to perform corresponding stress and seepage analysis in a three-dimensional model of the reservoir area. The stress and seepage conditions obtained from this analysis are used to simulate the risk status of the reservoir dam under various historical hydrological data. This scheme not only provides an accurate method for assessing dam risk based on hydrological data, but also offers effective references and corresponding solutions for subsequent processing of real-time hydrological data. Furthermore, relying on the seasonal patterns of hydrological data, future risks can be directly predicted, thus helping to improve the problems of poor timeliness and delayed response in related technologies. Attached Figure Description

[0039] Figure 1 A flowchart of a risk simulation method for a reservoir dam, provided as an exemplary embodiment.

[0040] Figure 2 A flowchart illustrating a method for generating a hydrological data simulation table, as shown in an exemplary embodiment.

[0041] Figure 3 A flowchart illustrating a method for determining a target region and dividing it into grids, as shown in an exemplary embodiment.

[0042] Figure 4 This is a schematic diagram illustrating the stress and seepage analysis of a dam as an exemplary embodiment.

[0043] Figure 5 A flowchart illustrating a method for generating a seepage data mapping table, as shown in an exemplary embodiment.

[0044] Figure 6 A flowchart illustrating a method for anticipating dam risks, as shown in an exemplary embodiment.

[0045] Figure 7 A flowchart illustrating a method for anticipating dam risks is shown as another exemplary embodiment.

[0046] Figure 8 A flowchart illustrating a method for constructing a 3D model of a library area, as shown in an exemplary embodiment.

[0047] Figure 9 A flowchart illustrating a method for assessing dam risk as an exemplary embodiment.

[0048] Figure 10 A schematic diagram of the structure of an electronic device for a risk prediction device for a reservoir dam, provided as an exemplary embodiment.

[0049] Figure 11 A block diagram of a risk simulation device for a reservoir dam, provided as an exemplary embodiment. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0051] It should be noted that the steps of the corresponding methods in other embodiments are not necessarily performed in the order shown and described in this invention. In some other embodiments, the methods may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0052] How to promptly identify potential safety risks of dams has always been a key focus of water conservancy early warning. However, most related technologies still rely on manual methods and real-time data to assess dam risks, which results in low efficiency and delayed response.

[0053] In view of this, the present invention proposes a risk prediction method for reservoir dams. This method uses historical hydrological data to perform stress and seepage analysis in a three-dimensional model of the reservoir area to predict the risk status of the dam under historical hydrological data. This method can provide a reference and corresponding solution file for the subsequent processing of real-time hydrological data, or be used to predict risks after predicting hydrological data for future periods. This helps to improve the problems of poor timeliness and delayed response of related technologies.

[0054] The risk assessment method can be executed in various electronic devices. Specifically, the method can be executed by a server or server cluster with strong computing power, or by a cloud platform that can schedule the server or server cluster to complete various tasks. In addition, the electronic device executing the method should be able to interact with databases or related devices that provide data such as historical hydrological data, BIM design models, and elevation data to obtain the aforementioned data.

[0055] Please refer to Figure 1 , Figure 1 The diagram shows a flowchart of a risk simulation method for a reservoir dam provided by an exemplary embodiment of the present invention.

[0056] The risk assessment method for reservoir dams may include the following specific steps:

[0057] Step 102: Obtain historical hydrological data within the target historical period, and generate a corresponding hydrological data simulation table based on the historical hydrological data; wherein, the hydrological data simulation table contains simulated hydrological data at several time nodes within the target historical period.

[0058] In this embodiment, the cloud platform or central server used to manage reservoir affairs can first acquire various historical hydrological data within a certain target historical period. The types of historical hydrological data include, but are not limited to, water quality, water level, water flow, and rainfall. After filtering, cleaning, fitting, and screening the historical hydrological data, a corresponding hydrological data simulation table can be generated. The data structure of the hydrological data simulation table is not specifically limited. The table can contain various simulated hydrological data at multiple time nodes within the target historical period. The simulated hydrological data is data obtained by processing the historical hydrological data, and its specific data values ​​may be consistent with the original data values.

[0059] There are several ways to generate hydrological data simulation tables based on historical hydrological data.

[0060] Please refer to Figure 2 , Figure 2 The diagram shown is a flowchart illustrating a method for generating a hydrological data simulation table in an exemplary embodiment.

[0061] In one alternative implementation, step 102, which involves generating a corresponding hydrological data simulation table based on the historical hydrological data, may include the following specific steps:

[0062] Step 1022: Based on the time period of the rainy season and flood season, determine the distribution of several time nodes in the hydrological data simulation table and the type of simulated hydrological data at the several time nodes, and then generate the hydrological data simulation table by combining the time nodes to which the historical hydrological data belongs.

[0063] Specifically, the distribution of several time nodes in the hydrological data simulation table can be adjusted based on the timing of the rainy season and flood season. Firstly, when acquiring historical hydrological data, more data can be obtained specifically for the rainy season and flood season. For example, quarterly water levels can be obtained for non-rainy season and flood season, while monthly water levels can be obtained specifically for the rainy season and flood season. Secondly, when processing historical hydrological data, more data can be selected specifically for the rainy season and flood season. Both methods can increase the data density of the generated hydrological data simulation table within the rainy season and flood season. Conversely, the data density can be reduced during the dry season. Furthermore, it is understood that the rainy season and flood season should correspond to the specific geographical location of the reservoir area.

[0064] The type of simulated hydrological data at each time point in the hydrological data simulation table can also be related to whether the time point belongs to the rainy season or flood season. For example, assuming that the hydrological data simulation table contains simulated hydrological data for the first quarter, the fourth quarter, and each month from May to October of a certain year, for the rainy season from May to October, the historical hydrological data can be processed to extract data such as the maximum water level and rainfall in each month from May to October. For the non-rainy season of the first quarter and the fourth quarter, data such as water quality and water level in a certain month of the quarter can be obtained without performing the above operations.

[0065] Step 104: For each time node in the hydrological data simulation table, determine the target area corresponding to the simulated hydrological data at the time node on the upstream surface of the dam in the constructed 3D reservoir model, and divide the target area into grids.

[0066] In this embodiment, after obtaining the simulated hydrological data table for the target historical period, the cloud platform / central server can use the simulated hydrological data at each time node in the simulated hydrological data table to simulate the water model in the constructed three-dimensional reservoir model, so as to determine the target area on the upstream surface of the dam corresponding to the simulated hydrological data in the three-dimensional reservoir model, and then perform grid division on the target area.

[0067] The target area is affected by multiple factors such as dam structure, water level, and topography. There are various ways to determine the target area and divide it into grids.

[0068] Please refer to Figure 3 , Figure 3 The diagram shown is a flowchart illustrating a method for determining a target region and dividing it into grids, as presented in an exemplary embodiment.

[0069] In one alternative implementation, step 104, which involves determining the target area corresponding to the simulated hydrological data at the time node on the upstream surface of the dam in the constructed 3D reservoir model and dividing the target area into grids, may include the following specific steps:

[0070] Step 1042: Obtain structural feature data of the reservoir dam and elevation data corresponding to the preset reservoir range from the three-dimensional model of the reservoir area; wherein, the preset reservoir range is the range formed by expanding the riverbank of the reservoir outward according to a preset value;

[0071] Step 1044: Based on the elevation data and the current simulated hydrological data, determine the reservoir water area, and then based on the elevation data within the reservoir water area, determine the reservoir bottom baseline;

[0072] Step 1046: Determine the lower boundary of the target area using the structural feature data and the reservoir bottom baseline, determine the upper boundary of the target area using the structural feature data and the simulated hydrological data, then determine the target area, and divide the target area according to a preset grid size.

[0073] Specifically, firstly, the elevation data corresponding to the preset reservoir range can be obtained from the three-dimensional model of the reservoir area. The preset reservoir range is the range formed by expanding the reservoir riverbank outward according to a preset value. For example, it can be the area enclosed by expanding the reservoir riverbank outward by 1 km, in order to adapt to changes in the landform within the reservoir area.

[0074] The location information of the reservoir bottom can be determined by the elevation data corresponding to the preset reservoir range. Combined with the current simulated hydrological data such as water level, the range of the reservoir water area can be determined. Considering that the reservoir bottom is not a regular plane, the reservoir bottom baseline can be determined by performing operations such as outlier removal and averaging on the location information of the reservoir bottom within the water area.

[0075] The structural feature data of the dam can be obtained from the 3D model of the reservoir area or the BIM design model of the dam. Combined with the baseline of the reservoir bottom, the lower boundary of the target area can be determined. Combined with the current simulated hydrological data such as water level, the upper boundary of the target area can be determined. Then, the target area corresponding to the current simulated hydrological data can be determined from the upper and lower boundary and structural feature data. Then, the target area is divided into grids according to the preset grid size, such as 0.5m*0.5m.

[0076] Step 106: For each grid obtained by division, the stress data on the grid is analyzed in the three-dimensional model of the reservoir area in combination with the simulated hydrological data. Based on the stress data obtained by analysis, the corresponding seepage data is determined by querying the generated seepage data mapping table, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data.

[0077] In this embodiment, after the grid division of the dam's upstream surface is completed, the cloud platform / central server can perform stress and seepage analysis on a grid-by-grid basis. For example, the water quality in the current simulated hydrological data can be used as the density, and the distance from the grid vertex to the water surface in the reservoir's three-dimensional model can be used as the water depth. Substituting these values ​​into the water pressure formula, the water pressure on the grid can be obtained. Then, based on the water pressure of the grid, the generated seepage data mapping table can be queried to obtain the seepage data on the grid, thereby obtaining the stress and seepage conditions at various locations on the dam surface under different hydrological data.

[0078] Please refer to Figure 4 , Figure 4 The diagram shown is a schematic representation of the stress and seepage analysis of a dam, illustrating an exemplary embodiment.

[0079] like Figure 4 As shown, the stress and seepage conditions at various locations along the slope on the upstream side of the dam, from the reservoir bottom baseline to the current water level, can be analyzed.

[0080] Please refer to Figure 5 , Figure 5 The diagram shown is a flowchart illustrating a method for generating a seepage data mapping table in an exemplary embodiment.

[0081] In one alternative implementation, the generation process of the seepage data mapping table may include the following specific steps:

[0082] Step 502: Obtain the structural feature data of the reservoir dam from the three-dimensional model of the reservoir area;

[0083] Step 504: Based on the structural feature data, analyze the seepage status of the reservoir dam under different stress data and generate a corresponding seepage data mapping table.

[0084] Specifically, after the establishment of the three-dimensional model of the reservoir area is completed, the structural feature data of each component of the dam in the three-dimensional model of the reservoir area, or the structural feature data of the reservoir dam such as materials, dimensions, and composition can be obtained from the BIM design model of the reservoir dam. Then, based on the structural feature data such as materials, dimensions, and composition, the seepage status of each location of the reservoir dam under different stress data can be analyzed by retrieving relevant data or simulation, and a mapping table that can be used to query the corresponding seepage data based on the stress data can be generated for subsequent use.

[0085] It should be noted that steps 502 to 504 can be regarded as steps 102 to 108 that are independent of each other in execution. After the seepage data mapping table is generated for the first time, the table can be read every time step 106 is executed, without the need to create the table again.

[0086] Step 108: Based on the stress data and seepage data, conduct a pre-simulation of the risk status of the reservoir dam under various simulated hydrological data.

[0087] In this embodiment, after obtaining the stress and seepage conditions on each grid in the target area under each simulated hydrological data, the cloud platform / central server can perform a pre-simulation of the risk status of the reservoir dam under each simulated hydrological data based on the stress data and the seepage data. For example, it can pre-simulate whether there are potential risk points of the reservoir dam at each water level.

[0088] There are several possible ways to simulate dam risks based on stress and seepage data.

[0089] Please refer to Figure 6 , Figure 6 The diagram shown is a flowchart illustrating a method for pre-simulating dam risks in an exemplary embodiment.

[0090] In one alternative implementation, step 108, which involves performing a risk simulation of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data, may include the following specific steps:

[0091] Step 1082: If the difference between the stress data and the design pressure limit is less than the preset pressure threshold, and / or the difference between the seepage data and the design seepage data is less than the preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

[0092] Please refer to Figure 7 , Figure 6 The diagram shown is a flowchart illustrating a method for pre-simulating dam risks, as illustrated in another exemplary embodiment.

[0093] In another alternative implementation, step 108, which involves performing a pre-simulation of the risk status of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data, may include the following specific steps: Step 1084, if the difference between the stress data and the stress data obtained from historical hydrological data that has caused actual risks is less than a preset pressure threshold, and / or the difference between the seepage data and the seepage data obtained from historical hydrological data that has caused actual risks is less than a preset seepage threshold, then it is determined that the reservoir dam has risks under the current simulated hydrological data during the pre-simulation process.

[0094] Specifically, in one example, the design bearing capacity and design seepage data of the reservoir dam can be obtained first. By comparing the stress data on each grid in the target area under each simulated hydrological data with the design bearing capacity, and comparing the seepage data on each grid with the design seepage data, it can be determined whether the reservoir dam has potential risks under each simulated hydrological data.

[0095] In another example, stress and seepage data obtained from historical hydrological data that have already caused actual risks can also be used as risk assessment criteria.

[0096] When the difference between the stress data obtained from the analysis and the design pressure limit, or the water pressure that has caused actual risk, is less than the preset pressure threshold, and / or the difference between the seepage data obtained from the analysis and the design seepage data, or the seepage data that has caused actual risk, is less than the preset seepage threshold, it can be determined that there is a potential risk to the reservoir dam under the current simulated hydrological data, and the grid positions that exceed the threshold can be regarded as risk points.

[0097] To ensure the completeness of the plan, the risk simulation method for the reservoir dam may further include a method for constructing a three-dimensional model of the reservoir area.

[0098] Please refer to Figure 8 , Figure 8 The diagram shown is a flowchart illustrating a method for constructing a three-dimensional model of a library area, as demonstrated in an exemplary embodiment.

[0099] In one alternative implementation, the construction process of the three-dimensional model of the reservoir area may include the following specific steps:

[0100] Step 802: Obtain the BIM design model of the reservoir dam and the modeling feature data uploaded by various IoT sensing devices; wherein, the modeling feature data includes at least the elevation data corresponding to the reservoir area;

[0101] Step 804: Based on the BIM design model and the modeling feature data, perform a three-dimensional simulation of each component in the reservoir area to obtain a three-dimensional model of the reservoir area.

[0102] Specifically, before executing the above-mentioned risk simulation method, the cloud platform / central server can first request or directly read various types of modeling feature data stored locally from relevant databases. These various types of modeling feature data include at least the BIM (Building Information Modeling) design model of the reservoir dam at the beginning of its design, as well as the elevation data corresponding to the reservoir area scanned and uploaded by drones.

[0103] After acquiring the modeling feature data, the cloud platform / central server can perform a 3D simulation of various components within the reservoir area, including the dam body and dam foundation, based on the modeling feature data. That is, the size, location, and other information of each component are calculated from the modeling feature data to achieve a 3D mapping of the reservoir area from a computer perspective, resulting in a 3D model of the reservoir area. When the modeling feature data is sufficient, the constructed model can reflect information about various components within the reservoir area, including those in the air, on the surface, underwater, and inside the dam body.

[0104] Even better, to ensure the model is timely and accurate, after the initial modeling is completed, the cloud platform / central server can also acquire real-time situational data from various IoT sensing devices, and perform data fusion and dynamic updates on the constructed model based on the real-time situational data.

[0105] It should be noted that steps 802 to 804 can be regarded as steps that are independent of each other in execution from the previous steps. After the initial construction of the 3D model of the reservoir area is completed, the currently stored model can be read each time step 104 or 106 is executed, without the need for repeated modeling, and the model update and model call do not conflict.

[0106] Furthermore, the risk prediction method for reservoir dams may also include a method for subsequent dam risk assessment based on the results of the aforementioned risk prediction, in order to solve the problems of poor timeliness and delayed response in real-time monitoring and analysis.

[0107] Please refer to Figure 9 , Figure 9 The diagram shown is a flowchart illustrating a method for determining dam risk in an exemplary embodiment.

[0108] In one alternative implementation, the risk simulation method for the reservoir dam may further include the following specific steps:

[0109] Step 110: Using the simulated hydrological data that indicate potential risks as a reference, determine whether the reservoir dam is at risk based on real-time hydrological data, and if a risk exists, obtain the solution corresponding to the prepared simulated hydrological data.

[0110] Specifically, after completing the risk simulation of the reservoir dam under various simulated hydrological data within the target historical period, several simulated hydrological data with potential risks can be obtained. By searching the dam safety knowledge graph or conducting expert analysis, solutions corresponding to the simulated hydrological data with potential risks can be obtained. Subsequently, during real-time monitoring and analysis, if the difference between the real-time hydrological data and the simulated hydrological data with potential risks is less than a threshold, the real-time hydrological data can be considered to also have safety hazards. The solutions corresponding to the previously prepared simulated hydrological data can be obtained, and timely processing can be carried out according to the measures indicated by the solutions.

[0111] In addition, the changing patterns of hydrological data can be analyzed, and historical hydrological data can be used to predict hydrological data for future periods. Then, the above methods can be used to simulate the risk status of the dam under the predicted hydrological data, so as to achieve risk prediction of reservoir dams.

[0112] As described above, this invention utilizes historical hydrological data to perform corresponding stress and seepage analysis in a three-dimensional model of the reservoir area. The stress and seepage conditions obtained from this analysis are used to simulate the risk status of the reservoir dam under various historical hydrological data. This scheme not only provides an accurate method for assessing dam risk based on hydrological data, but also offers effective references and corresponding solutions for subsequent processing of real-time hydrological data. Furthermore, relying on the seasonal patterns of hydrological data, future risks can be directly predicted, thus helping to improve the problems of poor timeliness and delayed response in related technologies.

[0113] Please refer to Figure 10 , Figure 10 The diagram shown is a schematic diagram of the structure of an electronic device for a risk prediction device for a reservoir dam, provided in an exemplary embodiment of the present invention.

[0114] At the hardware level, the electronic device includes a processor 1002, an internal bus 1004, a network interface 1006, a memory 1008, and a non-volatile memory 1010, and may also include other hardware required for services. One or more embodiments of the present invention can be implemented in software, for example, the processor 1002 reads the corresponding computer program from the non-volatile memory 1010 into the memory 1008 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0115] Please refer to Figure 11 , Figure 11 The image shows a risk prediction device for a reservoir dam provided in an exemplary embodiment of the present invention. The device can be applied to applications such as... Figure 10 The present invention is implemented in the electronic device shown.

[0116] The risk simulation device for the reservoir dam includes a simulation table generation unit 1110, a grid division unit 1120, a stress and seepage analysis unit 1130, and a risk simulation unit 1140; wherein:

[0117] The simulation table generation unit 1110 is used to acquire historical hydrological data within the target historical period and generate a corresponding hydrological data simulation table based on the historical hydrological data; wherein, the hydrological data simulation table contains simulated hydrological data at several time nodes within the target historical period.

[0118] The grid division unit 1120 is used to determine the target area corresponding to the simulated hydrological data at each time node in the hydrological data simulation table, in the constructed reservoir area three-dimensional model on the water-facing surface of the dam, and to perform grid division on the target area.

[0119] The water pressure and seepage analysis unit 1130 is used to analyze the stress data on each grid in the three-dimensional model of the reservoir area in combination with the simulated hydrological data for each grid obtained by division, and to query the generated seepage data mapping table based on the stress data obtained by analysis to determine the corresponding seepage data, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data.

[0120] The risk simulation unit 1140 is used to simulate the risk status of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data.

[0121] Optionally, the simulation table generation unit 1110, when generating a corresponding hydrological data simulation table based on the historical hydrological data, is specifically used for:

[0122] Based on the timing of the rainy season and flood season, the distribution of several time nodes in the hydrological data simulation table and the types of simulated hydrological data at those time nodes are determined. Then, combined with the time nodes to which the historical hydrological data belongs, the hydrological data simulation table is generated. Optionally, the raster division unit 1120, when determining the target area corresponding to the simulated hydrological data at the time nodes on the upstream surface of the dam in the constructed 3D reservoir model and performing raster division on the target area, is specifically used for:

[0123] The structural feature data of the reservoir dam and the elevation data corresponding to the preset reservoir range are obtained from the three-dimensional model of the reservoir area; wherein, the preset reservoir range is the range formed by expanding the riverbank of the reservoir outward according to a preset value;

[0124] Based on the elevation data and the current simulated hydrological data, the reservoir water area is determined, and then based on the elevation data within the reservoir water area, the reservoir bottom baseline is determined.

[0125] The lower boundary of the target area is determined by the structural feature data and the reservoir bottom baseline, and the upper boundary of the target area is determined by the structural feature data and the simulated hydrological data. The target area is then determined and divided according to a preset grid size.

[0126] Optionally, the risk simulation unit 1140, when performing a risk simulation of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data, is specifically used for:

[0127] If the difference between the stress data and the design bearing capacity is less than the preset bearing capacity threshold, and / or the difference between the seepage data and the design seepage data is less than the preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

[0128] Optionally, the risk simulation unit 1140, when performing a risk simulation of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data, is specifically used for:

[0129] If the difference between the stress data and the stress data obtained from historical hydrological data that has caused actual risks is less than a preset pressure threshold, and / or the difference between the seepage data and the seepage data obtained from historical hydrological data that has caused actual risks is less than a preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

[0130] Alternatively, the device may further include a risk assessment unit 1150:

[0131] The risk judgment unit 1150 is used to determine whether the reservoir dam is at risk by referring to the simulated hydrological data that are at risk during the pre-simulation process and by using real-time hydrological data. If there is a risk, it can obtain the solution corresponding to the prepared simulated hydrological data.

[0132] Alternatively, the device may further include a model building unit 1160:

[0133] The model building unit 1160 is used to acquire the BIM design model of the reservoir dam and the modeling feature data uploaded by various IoT sensing devices; wherein, the modeling feature data includes at least the elevation data corresponding to the reservoir area.

[0134] Based on the BIM design model and the modeling feature data, a three-dimensional simulation of each component in the reservoir area is performed to obtain a three-dimensional model of the reservoir area.

[0135] Alternatively, the device may further include a seepage table generation unit 1170:

[0136] The seepage table generation unit 1170 is used to obtain the structural feature data of the reservoir dam from the three-dimensional model of the reservoir area.

[0137] Based on the structural feature data, the seepage status of the reservoir dam under different stress data is analyzed and a corresponding seepage data mapping table is generated.

[0138] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0139] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0141] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0143] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0144] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0145] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0146] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A risk prediction method for reservoir dams, characterized in that, The method includes: Historical hydrological data for a target historical period is acquired, and a corresponding hydrological data simulation table is generated based on the historical hydrological data; wherein the hydrological data simulation table contains simulated hydrological data at several time points within the target historical period; For each time node in the hydrological data simulation table, the target area corresponding to the simulated hydrological data at the time node is determined on the upstream surface of the dam in the constructed 3D model of the reservoir area, and the target area is divided into grids. For each grid obtained by division, the stress data on the grid is analyzed in the three-dimensional model of the reservoir area in combination with the simulated hydrological data. Based on the stress data obtained by analysis, the corresponding seepage data is determined by querying the generated seepage data mapping table, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data. Based on the stress data and seepage data, the risk status of the reservoir dam under various simulated hydrological data is simulated.

2. The method according to claim 1, characterized in that, The process of generating a corresponding hydrological data simulation table based on the historical hydrological data includes: Based on the time period of the rainy season and flood season, the distribution of several time nodes in the hydrological data simulation table and the type of simulated hydrological data at the several time nodes are determined. Then, combined with the time nodes to which the historical hydrological data belongs, the hydrological data simulation table is generated.

3. The method according to claim 1, characterized in that, The step of determining the target area corresponding to the simulated hydrological data at the specified time point on the upstream surface of the dam in the constructed 3D model of the reservoir area, and dividing the target area into grids, includes: The structural feature data of the reservoir dam and the elevation data corresponding to the preset reservoir range are obtained from the three-dimensional model of the reservoir area; wherein, the preset reservoir range is the range formed by expanding the riverbank of the reservoir outward according to a preset value; Based on the elevation data and the current simulated hydrological data, the reservoir water area is determined, and then based on the elevation data within the reservoir water area, the reservoir bottom baseline is determined. The lower boundary of the target area is determined by the structural feature data and the reservoir bottom baseline, and the upper boundary of the target area is determined by the structural feature data and the simulated hydrological data. The target area is then determined and divided according to a preset grid size.

4. The method according to claim 1, characterized in that, The process of pre-simulating the risk status of the reservoir dam under various simulated hydrological data based on the stress data and seepage data includes: If the difference between the stress data and the design bearing capacity is less than the preset bearing capacity threshold, and / or the difference between the seepage data and the design seepage data is less than the preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

5. The method according to claim 1, characterized in that, The process of pre-simulating the risk status of the reservoir dam under various simulated hydrological data based on the stress data and seepage data includes: If the difference between the stress data and the stress data obtained from historical hydrological data that has caused actual risks is less than a preset pressure threshold, and / or the difference between the seepage data and the seepage data obtained from historical hydrological data that has caused actual risks is less than a preset seepage threshold, it is determined that the reservoir dam is at risk under the current simulated hydrological data during the simulation process.

6. The method according to claim 5, characterized in that, The method further includes: Using the simulated hydrological data that presents risks during the rehearsal as a reference, the real-time hydrological data is used to determine whether the reservoir dam is at risk, and if risks are present, the solutions corresponding to the prepared simulated hydrological data are obtained.

7. The method according to claim 1, characterized in that, The process of constructing the three-dimensional model of the reservoir area includes: The BIM design model of the reservoir dam and the modeling feature data uploaded by various IoT sensing devices are obtained; wherein, the modeling feature data includes at least the elevation data corresponding to the reservoir area. Based on the BIM design model and the modeling feature data, a three-dimensional simulation of each component in the reservoir area is performed to obtain a three-dimensional model of the reservoir area.

8. The method according to claim 7, characterized in that, The process of generating the seepage data mapping table includes: The structural feature data of the reservoir dam are obtained from the three-dimensional model of the reservoir area; Based on the structural feature data, the seepage status of the reservoir dam under different stress data is analyzed and a corresponding seepage data mapping table is generated.

9. A risk prediction device for a reservoir dam, characterized in that, The device includes a simulation table generation unit, a grid division unit, a stress and seepage analysis unit, and a risk simulation unit; wherein: The simulation table generation unit is used to acquire historical hydrological data within the target historical period and generate a corresponding hydrological data simulation table based on the historical hydrological data; wherein, the hydrological data simulation table contains simulated hydrological data at several time nodes within the target historical period; The grid division unit is used to determine the target area corresponding to the simulated hydrological data at each time point in the hydrological data simulation table, on the water-facing surface of the dam in the constructed three-dimensional model of the reservoir area, and to perform grid division on the target area. The stress and seepage analysis unit is used to analyze the stress data on each grid in the three-dimensional model of the reservoir area in combination with the simulated hydrological data, and to query the generated seepage data mapping table based on the stress data to determine the corresponding seepage data, thereby obtaining the stress data and seepage data on each grid in the target area corresponding to each simulated hydrological data. The risk simulation unit is used to simulate the risk status of the reservoir dam under various simulated hydrological data based on the stress data and the seepage data.

10. An electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method according to any one of claims 1-8 by running the executable instructions.

11. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

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