Reservoir safety life cycle management system based on digital twinning

Abstract

The invention discloses a reservoir safety life cycle management system based on digital twinning, and relates to the technical field of safety cycle management, and the system comprises a reservoir information acquisition unit, a reservoir safety life cycle prediction unit, a reservoir visualization and early warning processing unit and a local database. The monitoring data of each polling position of the reservoir are comprehensively analyzed, the change value of the pre-estimated safety life cycle of the reservoir due to environment change is intelligently calculated, a reservoir responsible person can conveniently master each weak position of the reservoir, and the weak position of the reservoir can be quickly and accurately determined through digital modeling and color identification on the digital modeling. Therefore, each polling position of the reservoir is more visually displayed to a reservoir principal, the reservoir principal can conveniently process each weak position of the reservoir, the descending speed of the safe life cycle of the reservoir is slowed down, and the safe life cycle of the reservoir can be prolonged when the weak position is properly processed.

Description

Technical Field

[0001] The present invention relates to the technical field of safety cycle management, and in particular to a reservoir safety life cycle management system based on digital twins. Background Art

[0002] During the use of a reservoir, its safety life cycle will continue to decrease as the environment changes. In order to increase the safety life cycle of a reservoir, it is necessary to optimize the management of the reservoir. The traditional method relies on manual inspections to inspect the reservoir, which is inefficient and cannot intuitively show the deterioration of various locations of the reservoir. Digital twins can create a digital virtual entity that is completely consistent with the physical object. Therefore, it is necessary to use digital twin technology to predict and visualize the safety life cycle of the reservoir.

[0003] The prior art, such as the invention patent application with announcement number: CN116362689A, discloses a reservoir safety lifecycle management system based on digital twins, which includes: the data acquisition module is used to obtain monitoring data in real time; the data support module provides software and hardware support for transmitting, storing and using data; the data processing module is used to process data and establish a mapping between data and models; the application support module provides operation support for applications; the application module is used to provide various business application services; the visualization module is used to display models, data and charts in the database. This invention can collect, analyze and process various reservoir data to achieve full-factor monitoring and management of the reservoir.

[0004] Prior art, such as the invention patent application with announcement number: CN116436949A, discloses a reservoir inspection system and method based on virtual reality, and the system includes: in this invention, when there is no data transmission, the line is in a dormant state, and when there is data transmission, the corresponding line performs data transmission accordingly, thereby reducing the loss of the system CPU, and avoiding line crashes caused by processing multiple data at one time, data loss, and ensuring data integrity. Multiple groups of data transmission lines can also effectively increase the data transmission rate, thereby improving the real-time performance of the inspection system.

[0005] From the above scheme, it can be seen that the current reservoir safety life cycle management system lacks certain attention to the deterioration analysis of the outer wall of the reservoir and the connection between the wall and the mountain. The outer wall of the reservoir is prone to corrosion and damage due to environmental changes during long-term use. In addition, the connection between the wall of the reservoir and the mountain will continue to degrade towards the two ends of the wall of the reservoir due to rain and insufficient mountain reinforcement, thereby reducing the safety life cycle of the reservoir. At the same time, there is a lack of attention to the intelligent analysis of the silt accumulation in the water storage area of ​​the reservoir. The silt accumulation in the reservoir will lead to a reduction in the effective storage capacity of the reservoir, reduce the water storage capacity of the reservoir, thereby reducing the regulation capacity of the reservoir and reducing the safety life cycle of the reservoir. Summary of the invention

[0006] The purpose of the present invention is to provide a reservoir safety lifecycle management system based on digital twins, which solves the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a reservoir safety lifecycle management system based on digital twins, including: a reservoir information acquisition unit, which is used to control the drone to inspect each inspection location of the reservoir at each target monitoring time point of the reservoir, and obtain the characteristic values ​​of various data at each inspection location of the reservoir.

[0008] The reservoir information acquisition unit includes: a data update time determination module and a reservoir information acquisition module.

[0009] The data update time determination module is used to analyze each target monitoring time point of the reservoir.

[0010] The reservoir information acquisition module is used to control the drone to inspect each inspection position of the reservoir at each target monitoring time point of the reservoir, obtain the appearance image of each area of ​​the outer wall of the reservoir, the silt height value of each feature point of the water storage area, obtain the drone's inspection route on the designated mountain of the reservoir and each monitoring feature point on the inspection route from the local database, control the drone to inspect along the inspection route of the designated mountain of the reservoir, and perform parallel data monitoring at each monitoring feature point of the inspection route of the designated mountain of the reservoir, so as to obtain the offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point, and similarly obtain the offset distance of the connection between the wall of the reservoir and another designated mountain from the inspection route at each monitoring feature point.

[0011] The reservoir safety life cycle prediction unit is used to screen the abnormal inspection locations of the reservoir, analyze the estimated safety life cycle of the reservoir based on historical data, and send the estimated safety life cycle of the reservoir to the person in charge of the reservoir.

[0012] The reservoir safety life cycle prediction unit includes: a reservoir wall appearance damage analysis module, a reservoir and mountain connection damage analysis module, a reservoir silt damage analysis module, and a reservoir safety life cycle estimation module.

[0013] The reservoir wall appearance damage analysis module is used to evaluate the appearance damage level of each area of ​​the reservoir's outer wall.

[0014] The reservoir-mountain connection damage analysis module is used to evaluate the connection deterioration level of the connection between the reservoir wall and the designated mountain at each monitoring feature point, and to evaluate the connection deterioration level of the connection between the reservoir wall and another designated mountain at each monitoring feature point.

[0015] The reservoir silt damage analysis module is used to analyze the accumulation hazard coefficient of the bottom silt of the reservoir.

[0016] The reservoir safety life cycle estimation module is used to analyze the estimated safety life cycle of the reservoir.

[0017] The reservoir visualization and early warning processing unit is used to digitally model the reservoir after the drone inspection, visualize each inspection location of the reservoir, and send the digital information of the reservoir to the person in charge of the reservoir.

[0018] The reservoir visualization and optimization processing unit includes: a visualization processing module and an early warning processing module.

[0019] The visualization processing module is used to digitally model the reservoir, thereby obtaining a digital model of the reservoir, and to distinguish and identify each inspection position in the digital model of the reservoir by color, thereby obtaining a color identification of each inspection position in the digital model of the reservoir.

[0020] The early warning processing module is used to send the estimated safety life cycle of the reservoir and the color identification of each inspection position of the digital model to the person in charge of the reservoir.

[0021] The beneficial effects of the present invention are as follows: (1) The reservoir information acquisition unit of the present invention intelligently analyzes each target monitoring time point of the reservoir by distinguishing between rainfall and non-rainfall situations, thereby acquiring various data of each inspection position of the reservoir at each target monitoring time point of the reservoir, thereby facilitating subsequent analysis.

[0022] (2) The reservoir safety life cycle prediction unit of the present invention performs a comprehensive analysis of the appearance damage level of each area of ​​the reservoir's outer wall, the connection deterioration level of the connection between the wall and a designated mountain at each monitoring feature point, the connection deterioration level of the connection between the wall and another designated mountain at each monitoring feature point, and the accumulation hazard coefficient of the underwater silt at each target monitoring time point of the reservoir, and intelligently calculates the change value of the estimated safety life cycle of the reservoir due to environmental changes, so as to facilitate the person in charge of the reservoir to grasp the various weak points of the reservoir.

[0023] (3) The reservoir visualization and early warning processing unit of the present invention can more intuitively display the inspection positions of the reservoir to the person in charge of the reservoir through digital modeling and color marking on the digital modeling, so that the person in charge of the reservoir can handle the weak positions of the reservoir conveniently, thereby slowing down the decline rate of the safety life cycle of the reservoir. At the same time, if handled properly, it can increase the safety life cycle of the reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the present invention provides a reservoir safety lifecycle management system based on digital twins, including: a reservoir information acquisition unit, a reservoir safety lifecycle prediction unit, a reservoir visualization and early warning processing unit and a local database.

[0028] It should be noted that the reservoir information acquisition unit is connected to the reservoir safety life cycle prediction unit, the reservoir safety life cycle prediction unit is connected to the reservoir visualization and early warning processing unit, and the local database is connected to the reservoir information acquisition unit, the reservoir safety life cycle prediction unit, and the reservoir visualization and early warning processing unit.

[0029] It should also be noted that the local database is used to store the inspection route of the drone on the designated mountain of the reservoir and the monitoring feature points on the inspection route, the waiting time for the end of rainfall, the inspection interval time when there is no rainfall, the initial appearance image of each area of ​​the outer wall of the reservoir, the appearance change coefficient of each area of ​​the outer wall of the reservoir at each historical target monitoring time point, the appearance damage level corresponding to each appearance damage deterioration coefficient interval, the allowable offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point in each interval time interval, the interval time between the current target monitoring time point and the initial use time point of the reservoir, and the offset of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point and at each historical target monitoring time point. Hazard coefficient, connection deterioration level corresponding to each damage deterioration coefficient interval, initial silt height of each characteristic point in the water storage area of ​​the reservoir, appearance damage life cycle coefficient adjustment parameter value corresponding to each appearance damage level, estimated safety life cycle reduction value due to external wall corresponding to each external wall damage life cycle reduction coefficient interval, connection damage life cycle coefficient adjustment parameter value corresponding to each connection deterioration level, estimated safety life cycle reduction value due to external wall corresponding to each external wall damage life cycle reduction coefficient interval, estimated safety life cycle reduction value due to silt accumulation corresponding to each accumulation hazard coefficient interval, ideal safety life cycle of the reservoir, color marking corresponding to each appearance damage level, color marking corresponding to each connection deterioration level, color marking corresponding to each silt accumulation hazard coefficient interval.

[0030] The reservoir information acquisition unit is used to control the drone to inspect each inspection position of the reservoir at each target monitoring time point of the reservoir, and obtain the characteristic values ​​of various data at each inspection position of the reservoir.

[0031] It should be noted that the inspection locations of the reservoir include: various areas of the outer wall of the reservoir, various monitoring feature points at the connection between the outer wall and a designated mountain, various monitoring feature points at the connection between the outer wall and another designated mountain, and various feature points of the water storage area.

[0032] It should also be noted that the designated mountain and the other designated mountain are divided on the central axis of the reservoir, and one end is randomly selected as the designated mountain, and the other end is selected as the other designated mountain.

[0033] In a specific embodiment of the present invention, the reservoir information acquisition unit includes: a data update time determination module and a reservoir information acquisition module.

[0034] The data update time determination module is used to analyze each target monitoring time point of the reservoir.

[0035] The reservoir information acquisition module is used to control the drone to inspect each inspection position of the reservoir at each target monitoring time point of the reservoir, obtain the appearance image of each area of ​​the outer wall of the reservoir, the silt height value of each feature point of the water storage area, obtain the drone's inspection route on the designated mountain of the reservoir and each monitoring feature point on the inspection route from the local database, control the drone to inspect along the inspection route of the designated mountain of the reservoir, and perform parallel data monitoring at each monitoring feature point of the inspection route of the designated mountain of the reservoir, so as to obtain the offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point, and similarly obtain the offset distance of the connection between the wall of the reservoir and another designated mountain from the inspection route at each monitoring feature point.

[0036] It should be noted that when the construction of the reservoir is completed, the initial connection trajectory between the wall of the reservoir and the designated mountain is the inspection route of the drone on the designated mountain of the reservoir.

[0037] In a specific embodiment of the present invention, the specific analysis method for analyzing each target monitoring time point of the reservoir is as follows: obtaining the waiting time for the end of rainfall from a local database, and after the rainfall in the reservoir ends, if there is no rainfall in the reservoir within the waiting time for the end of rainfall, then the rainfall end time point plus the rainfall end waiting time is used as the rainfall monitoring time point of the reservoir.

[0038] Obtain the no-rainfall inspection interval duration from the local database. If there is no rainfall in the reservoir after the rainfall end time point for the no-rainfall inspection interval duration, then the rainfall end time point plus the no-rainfall inspection interval duration is used as the no-rainfall monitoring time point of the reservoir. If there is no rainfall in the reservoir after the no-rainfall inspection interval duration at the no-rainfall monitoring time point, then the no-rainfall monitoring time point plus the no-rainfall inspection interval duration is used as the no-rainfall monitoring time point of the reservoir, and so on, thereby obtaining each no-rainfall monitoring time point of the reservoir.

[0039] Combine the rainfall monitoring time points and non-rainfall monitoring time points of the reservoir and summarize them as the target monitoring time points of the reservoir.

[0040] In a specific embodiment, the appearance images of each area of ​​the outer wall of the reservoir and the silt height values ​​of each feature point of the water storage area are obtained by a specific method: the appearance images of each area of ​​the outer wall of the reservoir are obtained by a camera carried by a drone, and the silt height values ​​of each feature point of the water storage area of ​​the outer wall of the reservoir can be obtained by an existing irregular triangulated network calculation method.

[0041] In a specific embodiment, the offset distance between the connection point between the wall of the reservoir and the designated mountain and the inspection route at each monitoring feature point is obtained by a laser ranging sensor carried by an unmanned aerial vehicle (UAV) to obtain the offset distance between the connection point between the wall of the reservoir and the designated mountain and the inspection route at each monitoring feature point.

[0042] The reservoir information acquisition unit of the present invention intelligently analyzes each target monitoring time point of the reservoir by distinguishing between rainfall and non-rainfall situations, thereby acquiring various data of each inspection position of the reservoir at each target monitoring time point of the reservoir, thereby facilitating subsequent analysis.

[0043] The reservoir safety life cycle prediction unit is used to screen various abnormal inspection locations of the reservoir, analyze the estimated safety life cycle of the reservoir based on historical data, and send the estimated safety life cycle of the reservoir to the person in charge of the reservoir.

[0044] In a specific embodiment of the present invention, the reservoir safety life cycle prediction unit includes: a reservoir wall appearance damage analysis module, a reservoir and mountain connection damage analysis module, a reservoir silt damage analysis module, and a reservoir safety life cycle estimation module.

[0045] The reservoir wall appearance damage analysis module is used to evaluate the appearance damage level of each area of ​​the reservoir's outer wall.

[0046] The reservoir-mountain connection damage analysis module is used to evaluate the connection deterioration level of the connection between the reservoir wall and the designated mountain at each monitoring feature point, and to evaluate the connection deterioration level of the connection between the reservoir wall and another designated mountain at each monitoring feature point.

[0047] The reservoir silt damage analysis module is used to analyze the accumulation hazard coefficient of the bottom silt of the reservoir.

[0048] The reservoir safety life cycle estimation module is used to analyze the estimated safety life cycle of the reservoir.

[0049] In a specific embodiment of the present invention, the appearance damage level of each area of ​​the outer wall of the reservoir is evaluated by a specific evaluation method: an initial appearance image of each area of ​​the outer wall of the reservoir is obtained from a local database, and the grayscale value of each pixel point of the initial appearance image of each area of ​​the outer wall of the reservoir is obtained by grayscale transformation. , where x represents the number of each region, , y is a positive integer greater than 2, n represents the number of each pixel in the initial appearance image, , m is a positive integer greater than 2.

[0050] According to the appearance images of each area of ​​the outer wall of the reservoir, the grayscale value of each pixel point of the appearance image of each area of ​​the outer wall of the reservoir is obtained through grayscale transformation. , analyze the appearance change coefficient of each area of ​​the reservoir's outer wall at the current monitoring time point , where e is represented by a natural constant.

[0051] Obtain the appearance change coefficient of each area of ​​the reservoir's outer wall at each historical target monitoring time point from the local database , where i represents the number of each historical target monitoring time point, , j is a positive integer greater than 2, and the appearance change coefficient of each area of ​​the reservoir's outer wall at the most recent historical target monitoring time point is extracted .

[0052] Calculate the appearance damage deterioration coefficient of each area of ​​the reservoir's outer wall at the current target monitoring time point , where j represents the number of historical target monitoring time points.

[0053] The appearance damage level corresponding to each appearance damage deterioration coefficient interval is obtained from the local database, and the appearance damage level of each area of ​​the outer wall of the reservoir is mapped.

[0054] It should be noted that the appearance damage levels include: Level 1, Level 2, Level 3 and Level 4. The larger the number, the higher the appearance damage level.

[0055] In a specific embodiment of the present invention, the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is evaluated by the following evaluation method: the allowable offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point in each interval time interval, the interval time between the current target monitoring time point and the initial use time point of the reservoir are obtained from the local database, and the allowable offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point at the current monitoring time point is mapped. , where r represents the number of each monitoring feature point, , s is a positive integer greater than 2.

[0056] Based on the offset distance between the connection point between the reservoir wall and the designated mountain and the inspection route at each monitoring feature point , calculate the deviation hazard coefficient of the connection between the reservoir wall and the designated mountain at each monitoring feature point on the inspection route .

[0057] Obtain the deviation hazard coefficient of the connection between the reservoir wall and the designated mountain from the local database at each monitoring feature point and at each historical target monitoring time point along the inspection route , calculate the damage deterioration coefficient of the connection between the reservoir wall and the designated mountain at each monitoring feature point .

[0058] The connection deterioration level corresponding to each damage deterioration coefficient interval is obtained from the local database, and the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is mapped.

[0059] It should be noted that the connection degradation levels include: level 1, level 2, level 3 and level 4. The larger the number, the higher the connection degradation level.

[0060] In a specific embodiment, the connection deterioration level of the connection between the wall of the reservoir and another designated mountain at each monitoring feature point is evaluated by a specific method: based on the method of evaluating the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point, the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is evaluated similarly.

[0061] In a specific embodiment of the present invention, the accumulation hazard coefficient of the bottom silt of the reservoir is analyzed by a specific analysis method as follows: the initial silt height value of each characteristic point in the water storage area of ​​the reservoir is obtained from the local database. , where p represents the number of each feature point, , q is a positive integer greater than 2.

[0062] According to the silt height value of each characteristic point in the reservoir's water storage area , calculate the silt accumulation hazard coefficient of each characteristic point in the reservoir's water storage area .

[0063] Calculation of the accumulation hazard coefficient of the bottom silt in the reservoir .

[0064] In a specific embodiment of the present invention, the estimated safety life cycle of the reservoir is analyzed, and the specific analysis method is: obtaining the appearance damage life cycle coefficient adjustment parameter value corresponding to each appearance damage level from the local database, and mapping the appearance damage life cycle coefficient adjustment parameter value of each area of ​​the outer wall of the reservoir according to the appearance damage level of each area of ​​the outer wall of the reservoir .

[0065] Calculation of the life cycle reduction coefficient of the external wall damage of the reservoir .

[0066] The estimated safety life cycle reduction value due to the external wall corresponding to each external wall damage life cycle reduction coefficient interval is obtained from the local database, and the estimated safety life cycle reduction value G of the reservoir due to the external wall is mapped.

[0067] The connection damage life cycle coefficient adjustment parameter value corresponding to each connection deterioration level is obtained from the local database. According to the connection deterioration level of the connection between the reservoir wall and the designated mountain at each monitoring feature point, the connection damage life cycle coefficient adjustment parameter value of the connection between the reservoir wall and the designated mountain at each monitoring feature point is mapped. .

[0068] Calculate the damage life cycle reduction coefficient of the connection between the reservoir wall and the specified mountain .

[0069] The estimated safety life cycle reduction value due to the external wall corresponding to each external wall damage life cycle reduction coefficient interval is obtained from the local database, and the estimated safety life cycle reduction value E due to the connection between the wall of the reservoir and the specified mountain is mapped.

[0070] Similarly, the estimated safety life cycle reduction value F due to the connection between the reservoir wall and another specified mountain is obtained by mapping.

[0071] The estimated reduction value of the safe life cycle due to silt accumulation corresponding to each accumulation hazard coefficient interval is obtained from the local database. According to the accumulation hazard coefficient of the bottom silt of the reservoir, the estimated reduction value D of the safe life cycle of the reservoir due to silt accumulation is mapped.

[0072] Obtain the ideal safety life cycle C of the reservoir from the local database and calculate the estimated safety life cycle of the reservoir .

[0073] It should be noted that the higher the appearance damage level, the larger the corresponding appearance damage life cycle coefficient adjustment parameter value, such as the appearance damage life cycle coefficient adjustment parameter value of level 1 appearance damage level is 1.

[0074] It should be noted that, the higher the connection deterioration level, the larger the corresponding connection damage life cycle coefficient adjustment parameter value, such as: the connection damage life cycle coefficient adjustment parameter value of level 1 connection deterioration level is 1.

[0075] The reservoir safety life cycle prediction unit of the present invention performs a comprehensive analysis of the appearance damage level of each area of ​​the outer wall of the reservoir, the connection deterioration level of the connection between the wall and the designated mountain at each monitoring feature point, the connection deterioration level of the connection between the wall and another designated mountain at each monitoring feature point, and the accumulation hazard coefficient of the bottom silt at each target monitoring time point of the reservoir, and intelligently calculates the change value of the estimated safety life cycle of the reservoir due to environmental changes, so as to facilitate the person in charge of the reservoir to grasp the weak positions of the reservoir.

[0076] The reservoir visualization and early warning processing unit is used to digitally model the reservoir after the drone inspection, visualize each inspection location of the reservoir, and send various digital information of the reservoir to the person in charge of the reservoir.

[0077] It should be noted that the digital information of the reservoir includes: the estimated safe life cycle of the reservoir and the color identification of each inspection position of the digital model.

[0078] In a specific embodiment of the present invention, the reservoir visualization and optimization processing unit includes: a visualization processing module and an early warning processing module.

[0079] The visualization processing module is used to digitally model the reservoir, thereby obtaining a digital model of the reservoir, and to distinguish and identify each inspection position in the digital model of the reservoir by color, thereby obtaining a color identification of each inspection position in the digital model of the reservoir.

[0080] It should be noted that the color identification includes: red, orange, yellow and blue.

[0081] The early warning processing module is used to send the estimated safety life cycle of the reservoir and the color identification of each inspection position of the digital model to the person in charge of the reservoir.

[0082] In a specific embodiment, the digital model of the reservoir is obtained by the following specific method: based on the appearance images of each area of ​​the outer wall of the reservoir, the silt height value of each feature point of the water storage area, the offset distance of the connection between the wall and the designated mountain from the inspection route at each monitoring feature point, and the offset distance of the connection between the wall and another designated mountain from the inspection route at each monitoring feature point, and combined with the existing digital modeling technology, the digital model of the reservoir is obtained.

[0083] In a specific embodiment of the present invention, the digital model of the reservoir performs color differentiation and identification on each inspection position, and the specific method is: obtaining the color identification corresponding to each appearance damage level from the local database, and mapping the color identification of each area of ​​the outer wall of the reservoir according to the appearance damage level of each area of ​​the outer wall of the reservoir.

[0084] The color identification corresponding to each connection deterioration level is obtained from the local database, and the color identification of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is mapped according to the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point.

[0085] Similarly, the color identification of the connection between the wall of the reservoir and another designated mountain at each monitoring feature point is obtained by mapping.

[0086] The color identification corresponding to each silt accumulation hazard coefficient interval is obtained from the local database, and the color identification of each characteristic point in the water storage area of ​​the reservoir is mapped according to the silt accumulation hazard coefficient of each characteristic point in the water storage area of ​​the reservoir.

[0087] The reservoir visualization and early warning processing unit of the present invention uses digital modeling and color marking on the digital modeling to more intuitively display the various inspection positions of the reservoir to the reservoir manager, making it convenient for the reservoir manager to handle the weak positions of the reservoir, thereby slowing down the decline rate of the safety life cycle of the reservoir. At the same time, if properly handled, the safety life cycle of the reservoir can be increased.

[0088] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A reservoir safety lifecycle management system based on digital twins, characterized in that: include: A reservoir information acquisition unit is used to control the UAV to inspect each inspection position of the reservoir at each target monitoring time point of the reservoir, and obtain the characteristic values ​​of various data at each inspection position of the reservoir; The reservoir safety life cycle prediction unit is used to screen the abnormal inspection locations of the reservoir, analyze the estimated safety life cycle of the reservoir based on historical data, and send the estimated safety life cycle of the reservoir to the person in charge of the reservoir; The reservoir visualization and early warning processing unit is used to digitally model the reservoir after the drone inspection, visualize each inspection location of the reservoir, and send the digital information of the reservoir to the person in charge of the reservoir.

2. According to claim 1, a reservoir safety lifecycle management system based on digital twins is characterized in that: The reservoir information acquisition unit includes: a data update time determination module and a reservoir information acquisition module; The data update time determination module is used to analyze each target monitoring time point of the reservoir; The reservoir information acquisition module is used to control the drone to inspect each inspection position of the reservoir at each target monitoring time point of the reservoir, obtain the appearance image of each area of ​​the outer wall of the reservoir, the silt height value of each feature point of the water storage area, obtain the drone's inspection route on the designated mountain of the reservoir and each monitoring feature point on the inspection route from the local database, control the drone to inspect along the inspection route of the designated mountain of the reservoir, and perform parallel data monitoring at each monitoring feature point of the inspection route of the designated mountain of the reservoir, so as to obtain the offset distance of the connection between the wall of the reservoir and the designated mountain from the inspection route at each monitoring feature point, and similarly obtain the offset distance of the connection between the wall of the reservoir and another designated mountain from the inspection route at each monitoring feature point.

3. A reservoir safety lifecycle management system based on digital twin according to claim 2, characterized in that: The specific analysis method for each target monitoring time point of the analysis reservoir is as follows: Obtain the waiting time for the end of rainfall from the local database. After the rainfall in the reservoir ends, if there is no rainfall in the reservoir within the waiting time for the end of rainfall, the time point of the end of rainfall plus the waiting time for the end of rainfall is used as the rainfall monitoring time point of the reservoir; Obtain the duration of the no-rainfall inspection interval from the local database. If, after the rainfall end time point of the reservoir, no rainfall occurs in the reservoir after the no-rainfall inspection interval, the rainfall end time point plus the no-rainfall inspection interval time point is used as the no-rainfall monitoring time point of the reservoir. If, at the no-rainfall monitoring time point, no rainfall occurs in the reservoir after the no-rainfall inspection interval, the no-rainfall monitoring time point plus the no-rainfall inspection interval time point is used as the no-rainfall monitoring time point of the reservoir, and so on, thereby obtaining various no-rainfall monitoring time points of the reservoir; Combine the rainfall monitoring time points and non-rainfall monitoring time points of the reservoir and summarize them as the target monitoring time points of the reservoir.

4. A reservoir safety lifecycle management system based on digital twin according to claim 2, characterized in that: The reservoir safety life cycle prediction unit includes: a reservoir wall appearance damage analysis module, a reservoir and mountain connection damage analysis module, a reservoir silt damage analysis module, and a reservoir safety life cycle estimation module; The reservoir wall appearance damage analysis module is used to evaluate the appearance damage level of each area of ​​the reservoir's outer wall; The damage analysis module for the connection between the reservoir and the mountain is used to evaluate the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point, and to evaluate the connection deterioration level of the connection between the wall of the reservoir and another designated mountain at each monitoring feature point; The reservoir sludge damage analysis module is used to analyze the accumulation hazard coefficient of the bottom sludge of the reservoir; The reservoir safety life cycle estimation module is used to analyze the estimated safety life cycle of the reservoir.

5. A reservoir safety lifecycle management system based on digital twin according to claim 4, characterized in that: The specific evaluation method for evaluating the appearance damage level of each area of ​​the outer wall of the reservoir is as follows: The initial appearance images of each area of ​​the outer wall of the reservoir are obtained from the local database, and the grayscale value of each pixel point of the initial appearance images of each area of ​​the outer wall of the reservoir is obtained through grayscale transformation. , where x represents the number of each region, , y is a positive integer greater than 2, n represents the number of each pixel in the initial appearance image, , m is a positive integer greater than 2; According to the appearance images of each area of ​​the outer wall of the reservoir, the grayscale value of each pixel point of the appearance image of each area of ​​the outer wall of the reservoir is obtained through grayscale transformation. , analyze the appearance change coefficient of each area of ​​the reservoir's outer wall at the current monitoring time point , where e is represented as a natural constant; Obtain the appearance change coefficient of each area of ​​the reservoir's outer wall at each historical target monitoring time point from the local database , where i represents the number of each historical target monitoring time point, , j is a positive integer greater than 2, and the appearance change coefficient of each area of ​​the reservoir's outer wall at the most recent historical target monitoring time point is extracted ; Calculate the appearance damage deterioration coefficient of each area of ​​the reservoir's outer wall at the current target monitoring time point , where j represents the number of historical target monitoring time points; The appearance damage level corresponding to each appearance damage deterioration coefficient interval is obtained from the local database, and the appearance damage level of each area of ​​the outer wall of the reservoir is mapped.

6. A reservoir safety lifecycle management system based on digital twins according to claim 5, characterized in that: The evaluation method for evaluating the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is as follows: Obtain the allowable offset distance of the connection between the reservoir wall and the designated mountain at each monitoring feature point on the inspection route at each interval time interval from the local database, and the interval time between the current target monitoring time point and the initial use time point of the reservoir, and map the allowable offset distance of the connection between the reservoir wall and the designated mountain at each monitoring feature point on the inspection route at the current monitoring time point , where r represents the number of each monitoring feature point, , s is a positive integer greater than 2; Based on the offset distance between the connection point between the reservoir wall and the designated mountain and the inspection route at each monitoring feature point , calculate the deviation hazard coefficient of the connection between the reservoir wall and the designated mountain at each monitoring feature point on the inspection route ; Obtain the deviation hazard coefficient of the connection between the reservoir wall and the designated mountain from the local database at each monitoring feature point and at each historical target monitoring time point along the inspection route , calculate the damage deterioration coefficient of the connection between the reservoir wall and the designated mountain at each monitoring feature point ; The connection deterioration level corresponding to each damage deterioration coefficient interval is obtained from the local database, and the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point is mapped.

7. A reservoir safety lifecycle management system based on digital twin according to claim 4, characterized in that: The specific analysis method for analyzing the accumulation hazard coefficient of the bottom silt of the reservoir is as follows: Obtain the initial silt height value of each characteristic point in the reservoir's water storage area from the local database , where p represents the number of each feature point, , q is a positive integer greater than 2; According to the silt height value of each characteristic point in the reservoir's water storage area , calculate the silt accumulation hazard coefficient of each characteristic point in the reservoir's water storage area ; Calculation of the accumulation hazard coefficient of the bottom silt in the reservoir .

8. A reservoir safety lifecycle management system based on digital twin according to claim 4, characterized in that: The specific analysis method for the estimated safety life cycle of the reservoir is as follows: The appearance damage life cycle coefficient adjustment parameter value corresponding to each appearance damage level is obtained from the local database, and the appearance damage life cycle coefficient adjustment parameter value of each area of ​​the outer wall of the reservoir is mapped according to the appearance damage level of each area of ​​the outer wall of the reservoir. ; Calculation of the life cycle reduction coefficient of the external wall damage of the reservoir ; Obtain the estimated safety life cycle reduction value due to the external wall corresponding to each external wall damage life cycle reduction coefficient interval from the local database, and map it to obtain the estimated safety life cycle reduction value G of the reservoir due to the external wall; The connection damage life cycle coefficient adjustment parameter value corresponding to each connection deterioration level is obtained from the local database. According to the connection deterioration level of the connection between the reservoir wall and the designated mountain at each monitoring feature point, the connection damage life cycle coefficient adjustment parameter value of the connection between the reservoir wall and the designated mountain at each monitoring feature point is mapped. ; Calculate the damage life cycle reduction coefficient of the connection between the reservoir wall and the specified mountain ; Obtain the estimated safety life cycle reduction value due to the external wall corresponding to each external wall damage life cycle reduction coefficient interval from the local database, and map it to obtain the estimated safety life cycle reduction value E due to the connection between the wall of the reservoir and the designated mountain; Similarly, the estimated safety life cycle reduction value F of the wall of the reservoir and another designated mountain due to connection is obtained by mapping; Obtain the estimated safety life cycle reduction value due to silt accumulation corresponding to each accumulation hazard coefficient interval from the local database, and map the estimated safety life cycle reduction value D of the reservoir due to silt accumulation according to the accumulation hazard coefficient of the bottom silt of the reservoir; Obtain the ideal safety life cycle C of the reservoir from the local database and calculate the estimated safety life cycle of the reservoir .

9. The reservoir safety lifecycle management system based on digital twin according to claim 1 is characterized in that: The reservoir visualization and optimization processing unit includes: a visualization processing module and an early warning processing module; The visualization processing module is used to perform digital modeling on the reservoir, thereby obtaining a digital model of the reservoir, and to distinguish and identify each inspection position in the digital model of the reservoir by color, thereby obtaining a color identification of each inspection position in the digital model of the reservoir; The early warning processing module is used to send the estimated safety life cycle of the reservoir and the color identification of each inspection position of the digital model to the person in charge of the reservoir.

10. A reservoir safety lifecycle management system based on digital twins according to claim 9, characterized in that: The digital model of the reservoir is used to distinguish and mark each inspection position with a color, and the specific method is as follows: Obtaining color identification corresponding to each appearance damage level from a local database, and mapping the color identification of each area of ​​the outer wall of the reservoir according to the appearance damage level of each area of ​​the outer wall of the reservoir; Obtain the color identification corresponding to each connection deterioration level from the local database, and map the color identification of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point according to the connection deterioration level of the connection between the wall of the reservoir and the designated mountain at each monitoring feature point; Similarly, the color identification of the connection between the wall of the reservoir and another designated mountain at each monitoring feature point is obtained by mapping; The color identification corresponding to each silt accumulation hazard coefficient interval is obtained from the local database, and the color identification of each characteristic point in the water storage area of ​​the reservoir is mapped according to the silt accumulation hazard coefficient of each characteristic point in the water storage area of ​​the reservoir.

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