Intelligent Reservoir Water Management Method, Device and Equipment Based on Digital Twin

Abstract

The present invention discloses an intelligent reservoir water management method, device and equipment based on digital twin, belonging to the technical field of digital management. The present invention obtains reservoir data of a target reservoir, and establishes a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; determines model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and a preset scheduling strategy, wherein the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located; determines a monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data, achieving the effect of improving the effectiveness and representativeness of data.

Description

Technical Field

[0001] The present invention relates to the field of digital management, and particularly to an intelligent reservoir water management method, device and equipment based on digital twins. Background Technique

[0002] Reservoirs are important water conservancy facilities. Since multiple monitoring devices and control devices are generally installed in reservoirs, reservoirs have become important water resource monitoring units in China. Currently, compared with the method of setting sensors to determine physical parameters for monitoring, the detection of plankton in water generally requires regular monitoring. Depending on conditions and regions, the detection of plankton is generally carried out according to the annual change cycle of runoff or seasonally. Currently, since it is required to ensure that time-representative samples can be obtained in the standards of the Technical Guidelines for Aquatic Ecology Monitoring - Aquatic Organism Monitoring and Evaluation of Lakes and Reservoirs, relatively high monitoring frequencies and sampling frequencies are set in actual reservoir sampling. And because the detection of plankton in water requires stratified sampling and sensors cannot be set to reduce costs, this actual reservoir sampling method results in a large amount of resources being consumed for the detection of aquatic organisms.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide an intelligent reservoir water management method, device, equipment and storage medium based on digital twins, aiming to improve the effectiveness and representativeness of data.

[0005] To achieve the above object, the present invention provides an intelligent reservoir water management method based on digital twins. The intelligent reservoir water management method based on digital twins includes the following steps:

[0006] Obtain the reservoir data of the target reservoir, and establish a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model;

[0007] Determine the model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and a preset scheduling strategy. The forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located;

[0008] Determine the monitoring plan for aquatic organism monitoring of the target reservoir according to the model prediction data.

[0009] Optionally, the model prediction data includes: a predicted water storage change curve and a water quality index change curve. The monitoring plan includes a monitoring time. The step of determining the monitoring plan for aquatic organism monitoring of the target reservoir according to the model prediction data includes:

[0010] Obtain a first preset time interval associated with each moment on the predicted water storage change curve and a second preset time interval associated with each moment on the water quality index change curve;

[0011] Calculate the water storage standard deviation corresponding to each moment based on the predicted water storage data in the first preset time interval to obtain the water storage standard deviation corresponding to each moment, and calculate the water quality index change standard deviation corresponding to each moment based on the water quality index data in the second preset time interval to obtain the water quality index standard deviation corresponding to each moment;

[0012] Determine a water storage change standard deviation curve based on the water storage standard deviations corresponding to all moments, and determine a water quality index change standard deviation curve based on the water quality index standard deviations corresponding to all moments;

[0013] Determine the monitoring time according to the water storage change standard deviation curve and the water quality index change standard deviation curve.

[0014] Optionally, the number of water quality index change standard deviation curves is more than one. The step of determining the monitoring time according to the water storage change standard deviation curve and the water quality index change standard deviation curve includes:

[0015] Determine a first monitoring time interval according to the water storage change standard deviation curve;

[0016] Superimpose the more than one water quality index change standard deviation curves to obtain a water quality comprehensive index change standard deviation curve;

[0017] Obtain the moment corresponding to the minimum water quality comprehensive index change standard deviation within the first monitoring time interval according to the water quality comprehensive index change standard deviation curve as the monitoring time.

[0018] Optionally, the step of determining the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data, and the preset scheduling strategy includes:

[0019] Determine the predicted inflow and the corresponding predicted inflow water quality prediction data according to the forecast data;

[0020] Determine the predicted outflow according to the forecast data and the preset scheduling strategy;

[0021] Determine the model prediction data based on the reservoir twin model, the predicted inflow, the predicted inflow water quality data, and the predicted outflow.

[0022] Optionally, the step of determining the model prediction data based on the reservoir twin model, the predicted inflow, the predicted inflow water quality data, and the predicted outflow includes:

[0023] Determine the predicted water storage change curve of the target reservoir based on the reservoir twin model, the predicted inflow, and the predicted outflow;

[0024] Determine the water quality index change curves corresponding to the respective water quality indices of the target reservoir based on the reservoir water quality data of the reservoir twin model, the current water storage of the reservoir twin model, the predicted inflow, the predicted inflow water quality data, and the predicted outflow;

[0025] Use the predicted water storage change curve and the water quality index change curves as the model prediction data.

[0026] Optionally, the water quality indices include at least one of: acidity and alkalinity index, dissolved oxygen index, turbidity index, conductivity index, temperature index, salinity index, residual chlorine index, ammonia nitrogen concentration index.

[0027] Optionally, the step of establishing the reservoir twin model of the target reservoir based on the reservoir data and the preset reservoir model includes:

[0028] Normalize the reservoir data according to the data type of the reservoir data;

[0029] Map the reservoir data to the parameters corresponding to the preset reservoir model according to the preset mapping relationship, and generate the reservoir twin model.

[0030] In addition, to achieve the above object, the present invention also provides an intelligent reservoir water service management device based on digital twin, and the intelligent reservoir water service management device based on digital twin includes:

[0031] A twin module, configured to obtain the reservoir data of the target reservoir, and establish the reservoir twin model of the target reservoir based on the reservoir data and the preset reservoir model;

[0032] A prediction module, configured to determine the model prediction data of the reservoir twin model based on the reservoir twin model, the forecast data, and the preset scheduling strategy, where the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located;

[0033] An operation module, configured to determine a monitoring plan for aquatic organism monitoring of the target reservoir according to the model prediction data.

[0034] In addition, to achieve the above object, the present invention further provides an intelligent reservoir water management device based on digital twin. The intelligent reservoir water management device based on digital twin includes: a memory, a processor, and a digital twin-based intelligent reservoir water management program stored on the memory and executable on the processor. The digital twin-based intelligent reservoir water management program is configured to implement the steps of the digital twin-based intelligent reservoir water management method described in any one of the above.

[0035] In addition, to achieve the above object, the present invention further provides a storage medium. A digital twin-based intelligent reservoir water management program is stored on the storage medium. When the digital twin-based intelligent reservoir water management program is executed by a processor, it implements the steps of the digital twin-based intelligent reservoir water management method described in any one of the above.

[0036] The present invention proposes a digital twin-based intelligent reservoir water management method. The method obtains reservoir data of a target reservoir, establishes a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model, and determines model prediction data of the reservoir twin model according to the reservoir twin model, forecast data, and a preset scheduling strategy. The forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located. Compared with traditional aquatic organism monitoring methods, the data monitored by determining the monitoring plan based on the model prediction data is more representative, thereby improving the effectiveness of the data. Description of the Drawings

[0037] Figure 1 is a schematic structural diagram of an intelligent reservoir water management device based on digital twin in the hardware operating environment related to the embodiment solution of the present invention;

[0038] Figure 2 is a schematic flowchart of the first embodiment of the digital twin-based intelligent reservoir water management method of the present invention;

[0039] Figure 3 is a schematic flowchart of the second embodiment of the digital twin-based intelligent reservoir water management method of the present invention;

[0040] Figure 4 is a schematic flowchart of the third embodiment of the digital twin-based intelligent reservoir water management method of the present invention;

[0041] Figure 5This is a schematic flowchart of the fourth embodiment of the intelligent reservoir water management method based on digital twin of the present invention.

[0042] The realization of the purpose, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0043] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] Refer to Figure 1 , Figure 1 This is a schematic structural diagram of the intelligent reservoir water management device based on digital twin for the hardware operating environment involved in the embodiment solution of the present invention.

[0045] As Figure 1 shown, the intelligent reservoir water management device based on digital twin may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The interaction device 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard). Optionally, the interaction device 1003 may also be connected to the communication bus through a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0046] Those skilled in the art can understand that Figure 1 the structure shown in

[0047] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an intelligent reservoir water management program based on digital twin.

[0048] In Figure 1In the intelligent reservoir water management device based on digital twin shown, the network interface 1004 is mainly used for data communication with other devices; the interaction device 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the intelligent reservoir water management device based on digital twin of the present invention can be arranged in the intelligent reservoir water management device based on digital twin. The intelligent reservoir water management device based on digital twin calls the intelligent reservoir water management program stored in the memory 1005 through the processor 1001 and executes the intelligent reservoir water management method provided by the embodiment of the present invention.

[0049] The embodiment of the present invention provides an intelligent reservoir water management method based on digital twin. Refer to Figure 2 , Figure 2 which is a schematic flow chart of the first embodiment of an intelligent reservoir water management method based on digital twin of the present invention.

[0050] In this embodiment, the intelligent reservoir water management method based on digital twin includes:

[0051] Step S1, obtaining the reservoir data of the target reservoir and establishing a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model;

[0052] The reservoir data here includes: the water quality data of the target reservoir. For the data monitored in real time by sensors, preferably, it is the data at the current moment. For the non-real-time data detected regularly, it is the data collected before the current moment and closest to the current moment. The reservoir twin model of the target reservoir is actually a digital twin model generated by mapping the reservoir data based on the architecture of a preset reservoir. Water management includes: formulating a monitoring plan for aquatic biological monitoring, protecting the reservoir water quality, and scheduling the reservoir water resources, etc.

[0053] Step S2, determining the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data, and a preset scheduling strategy. The forecast data includes: the meteorological forecast data of the area where the target reservoir is located, the hydrological forecast data of the area where the target reservoir is located, and the hydropower demand data of the area where the target reservoir is located;

[0054] The forecast data here are predictions provided by other units outside the target reservoir. Generally, the meteorological forecast data here can be provided by the meteorological bureau, and the hydrological forecast data here can be provided by the water conservancy department. The hydropower demand data here include electricity demand and water demand. The electricity demand data are very important for reservoirs equipped with generating units. The water demand here can also be provided by the water conservancy department. The preset scheduling plan here can include: the level of emergency response, the corresponding response operations, the water volume allocation plan, and the scheduling requirements, etc. The scheduling requirements here can include: cross-section ecological flow indicators and minimum downstream discharge, etc. Optionally, determine the operating conditions of the target reservoir in a future period according to the forecast data and the preset scheduling strategy, and map the corresponding operating conditions to the reservoir twin model, so as to determine the model prediction data in the reservoir twin model. For example: during the flood season, when the forecast data and the preset scheduling strategy determine to enter the first-level flood control response and execute the corresponding response operations, determine the implementation plan for reservoir operations such as water retaining, discharging, and releasing water, and simulate in the reservoir twin model to calculate the model prediction data. In addition, preferably, the level of flood control response can be determined according to the data of the reservoir twin model, the forecast data, and the preset scheduling strategy, so as to further improve the accuracy of the model prediction data.

[0055] Step S3, determine the monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data.

[0056] Preferably, the time and monitoring sites for aquatic biological monitoring of the target reservoir can be determined according to the model prediction data. For example: conduct aquatic biological monitoring during a period with relatively small water volume changes. This is because during the monitoring process, if the inflow and outflow volumes are large, the data fluctuations are large, and the representativeness of the monitored aquatic biological data is low. In addition, the water volume of the reservoir in different periods often affects the difficulty of the monitoring process. For example: a period with a relatively high water volume change in the model prediction data often indicates the presence of bad weather in this area. In addition, in the case of an imbalance in the proportion of the reservoir's inflow runoff, that is, a change occurs in the originally fixed proportion of the runoff water source. At this time, the sampling sites should be adjusted to ensure that the data can be representative.

[0057] In this embodiment, by obtaining the reservoir data of the target reservoir, establishing a reservoir digital twin model of the target reservoir according to the reservoir data and a preset reservoir model, and determining the model prediction data of the reservoir digital twin model according to the reservoir digital twin model, forecast data, and a preset scheduling strategy, the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located. Compared with the traditional aquatic biological monitoring method, the data monitored by determining the monitoring plan based on the model prediction data is more representative, thereby improving the effectiveness of the data.

[0058] Further, based on the first embodiment, a second embodiment of the intelligent reservoir water management method based on digital twin of the present invention is proposed. In this embodiment, referring to Figure 3 , the model prediction data includes: a predicted water storage change curve and a water quality index change curve, and the monitoring plan includes a monitoring time. The step of determining the monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data includes:

[0059] Step S31, obtaining a first preset time interval associated with each moment on the predicted water storage change curve and a second preset time interval associated with each moment on the water quality index change curve;

[0060] For the start moment on the predicted water storage change curve, the associated first preset time interval is a time interval starting from the start moment and adjacent after the start moment; for the end moment on the predicted water storage change curve, the associated first preset time interval is a time interval starting from the end moment and adjacent before the end moment; for the intermediate moments other than the start moment and the end moment on the predicted water storage change curve, the associated first preset time interval is a continuous period of time including the intermediate moment. Optionally, the intermediate moment is at the center of the continuous period of time including the intermediate moment.

[0061] For the start moment on the water quality index change curve, the associated second preset time interval is a time interval starting from the start moment and adjacent after the start moment; for the end moment on the predicted water storage change curve, the associated second preset time interval is a time interval starting from the end moment and adjacent before the end moment; for the intermediate moments other than the start moment and the end moment on the predicted water storage change curve, the associated second preset time interval is a continuous period of time including the intermediate moment. Optionally, the intermediate moment is at the center of the continuous period of time including the intermediate moment.

[0062] Step S32: Calculate the standard deviation of the water storage volume at the corresponding moment based on the predicted water storage volume data in the first preset time interval, obtaining the standard deviation of the water storage volume corresponding to each moment. And calculate the standard deviation of the change in water quality index at the corresponding moment based on the water quality index data in the second preset time interval, obtaining the standard deviation of the water quality index corresponding to each moment.

[0063] In this embodiment, when calculating the standard deviation of the water storage volume at the corresponding moment based on the predicted water storage volume data in the first preset time interval, since there is predicted water storage volume data in the first preset time interval corresponding to each moment, the standard deviation of the water storage volume corresponding to each moment can be calculated. When calculating the standard deviation of the change in water quality index at the corresponding moment based on the water quality index data in the second preset time interval, since there is water quality index data in the second preset time interval corresponding to each moment, the standard deviation of the water quality index corresponding to each moment can be calculated.

[0064] Step S33: Determine the standard deviation curve of the water storage volume change based on the standard deviation of the water storage volume corresponding to all moments, and determine the standard deviation curve of the water quality index change based on the standard deviation of the water quality index corresponding to all moments.

[0065] Based on sorting all the standard deviations of the water storage volume in chronological order and drawing the standard deviation curve of the water storage volume change; based on sorting all the standard deviations of the water quality index in chronological order and drawing the standard deviation curve of the water quality index change. It should be noted that there are often multiple specific different water quality indexes, and the water quality indexes include at least one of the following: acidity and alkalinity index, dissolved oxygen content index, turbidity index, conductivity index, temperature index, salinity index, residual chlorine index, ammonia nitrogen concentration index. Here, specifically, the standard deviation curve of the water quality index change is drawn according to the same index type.

[0066] Step S34: Determine the monitoring time based on the standard deviation curve of the water storage volume change and the standard deviation curve of the water quality index change.

[0067] Optionally, jointly determine the monitoring time based on the standard deviation curve of the water storage volume change and the standard deviation curve of the water quality index change. In other embodiments, based on the standard deviation curve of the water storage volume change, determine whether the standard deviation curve of the water quality index change matches the standard deviation curve of the water storage volume change. When the standard deviation curve of the water quality index change matches the standard deviation curve of the water storage volume change, jointly determine the monitoring time based on the standard deviation curve of the water storage volume change and the standard deviation curve of the water quality index change; when the standard deviation curve of the water quality index change does not match the standard deviation curve of the water storage volume change, determine the monitoring time from the standard deviation curve of the water storage volume change.

[0068] In this embodiment, the standard deviation of the water storage volume at the corresponding moment is calculated through the predicted water storage volume data in the first preset time interval, and the standard deviation of the water storage volume corresponding to each moment is obtained. And the standard deviation of the change of the water quality index at the corresponding moment is calculated according to the water quality index data in the second preset time interval, and the standard deviation of the water quality index corresponding to each moment is obtained. It can determine whether there are large changes in a variety of different indicators. During the process of large changes, it will cause an increase in the standard deviation. By plotting curves, and then the monitoring time is determined according to the standard deviation curve of the change of the water storage volume and the standard deviation curve of the change of the water quality index, so that the most representative monitoring time can be determined.

[0069] Further, the number of the standard deviation curves of the change of the water quality index is more than one. The step of determining the monitoring time according to the standard deviation curve of the change of the water storage volume and the standard deviation curve of the change of the water quality index includes:

[0070] Determine the first monitoring time interval according to the standard deviation curve of the change of the water storage volume;

[0071] Superimpose more than one of the standard deviation curves of the change of the water quality index to obtain a standard deviation curve of the change of the comprehensive water quality index;

[0072] Obtain the moment corresponding to the minimum standard deviation of the change of the comprehensive water quality index in the first monitoring time interval according to the standard deviation curve of the change of the comprehensive water quality index as the monitoring time.

[0073] In this embodiment, the time corresponding to the standard deviation of the change of the water storage volume below the threshold of the change of the water storage volume in the standard deviation curve of the change of the water storage volume is determined as the first monitoring time interval. In other embodiments, the standard deviation curves of the change of the water quality index matching the standard deviation curve of the change of the water storage volume are superimposed to obtain a standard deviation curve of the change of the comprehensive water quality index.

[0074] In this embodiment, the first monitoring time interval is determined through the standard deviation curve of the change of the water storage volume; more than one of the standard deviation curves of the change of the water quality index are superimposed to obtain a standard deviation curve of the change of the comprehensive water quality index; the moment corresponding to the minimum standard deviation of the change of the comprehensive water quality index in the first monitoring time interval is obtained according to the standard deviation curve of the change of the comprehensive water quality index as the monitoring time, so as to improve the representative ability of the monitoring data at the monitoring time.

[0075] Further, based on the first embodiment or the second embodiment, the third embodiment of the intelligent reservoir water service management method based on digital twin of the present invention is proposed. In this embodiment, referring to Figure 4 , the step of determining the model prediction data of the reservoir digital twin model according to the reservoir digital twin model, the forecast data and the preset scheduling strategy includes:

[0076] Step S21: Determine the predicted inflow and the corresponding predicted inflow water quality prediction data according to the said forecast data;

[0077] Determine the water flow rate and the corresponding water quality of the channels through which water enters the target reservoir according to the said forecast data. The channels through which water enters the target reservoir include: the inflowing river, the diversion canal, the drainage pipeline, etc. It should be noted that according to the flow velocity and the upstream monitoring data, the accuracy of the said forecast data can be improved.

[0078] Step S22: Determine the predicted outflow according to the said forecast data and the preset scheduling strategy;

[0079] It should be noted that since the preset scheduling strategy is generally based on the water resources situation and the power situation of the reservoir and the upstream and downstream of the reservoir. Therefore, it should be noted that generally, in this embodiment, the execution of Step S22 does not need to be based on the water quality situation. Of course, in other embodiments, in special cases, for example: before the activity of swimming across the Pearl River in Guangzhou, it is necessary to control the reservoirs with different water qualities to adjust the corresponding outflow. Of course, this also includes various water sports competitions. Therefore, the water quality can also be used as a factor to determine the predicted outflow. Determine the predicted water quality indicators according to the said forecast data, and determine the predicted outflow according to the predicted water quality indicators and the event scheduling strategy in the preset scheduling strategy. Of course, this preset scheduling strategy will also affect the inflow and the inflow water quality data of the downstream reservoir.

[0080] Step S23: Determine the model prediction data according to the reservoir twin model, the predicted inflow, the predicted inflow water quality prediction data, and the predicted outflow;

[0081] In this embodiment, determine the predicted inflow and the corresponding predicted inflow water quality prediction data according to the said forecast data, and determine the predicted outflow through the said forecast data and the preset scheduling strategy, so as to improve the accuracy of the model prediction data.

[0082] Further, the step of determining the model prediction data according to the reservoir twin model, the predicted inflow, the predicted inflow water quality prediction data, and the predicted outflow includes:

[0083] Determine the predicted water storage change curve of the target reservoir according to the reservoir twin model, the predicted inflow, and the predicted outflow;

[0084] Determine the water quality index change curve corresponding to each water quality index of the target reservoir according to the reservoir water quality data of the reservoir twin model, the current water storage of the reservoir twin model, the predicted inflow, the predicted inflow water quality prediction data, and the predicted outflow;

[0085] Using the predicted water storage change curve and the water quality index change curve as the model prediction data.

[0086] Specifically, the predicted inflow and the predicted outflow calculate the water resource change values of the target reservoir at each time. According to the water resource change values and the reservoir twin model, the predicted water storage change curve of the target reservoir is determined. According to the reservoir water quality data of the reservoir twin model, the current water storage of the reservoir twin model, the predicted inflow, the predicted inflow water quality data, and the predicted outflow, the change curve of the content of the physical data corresponding to each index data is determined. According to the change curve of the content of the corresponding physical data and the predicted water storage change curve, the water quality index change curve corresponding to each water quality index is determined.

[0087] Furthermore, based on any of the above embodiments, a fourth embodiment of the intelligent reservoir water management method based on digital twin according to the present invention is proposed. In this embodiment, the step of establishing the reservoir twin model of the target reservoir according to the reservoir data and the preset reservoir model includes:

[0088] Step S11, normalizing the reservoir data according to the data type of the reservoir data;

[0089] In this embodiment, since the sources of the reservoir data may not be the same, it is necessary to normalize the reservoir data according to the data type of the reservoir data, so as to standardize the reservoir data and facilitate subsequent data mapping.

[0090] Step S12, mapping the reservoir data to the parameters corresponding to the preset reservoir model according to the preset mapping relationship, and generating the reservoir twin model.

[0091] In this embodiment, by normalizing the reservoir data according to the data type of the reservoir data, the accuracy of mapping the reservoir data to the parameters corresponding to the preset reservoir model according to the preset mapping relationship and generating the reservoir twin model is improved.

[0092] In addition, an embodiment of the present invention also proposes an intelligent reservoir water management device based on digital twin. The intelligent reservoir water management device based on digital twin includes:

[0093] A twin module, configured to obtain the reservoir data of the target reservoir, and establish the reservoir twin model of the target reservoir according to the reservoir data and the preset reservoir model;

[0094] A prediction module, configured to determine the model prediction data of the reservoir digital twin model according to the reservoir digital twin model, the forecast data, and a preset scheduling strategy, where the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located;

[0095] An operation module, configured to determine a monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data.

[0096] In addition, an embodiment of the present invention further provides an intelligent reservoir water management device based on digital twin, which is characterized in that the intelligent reservoir water management device based on digital twin includes: a memory, a processor, and an intelligent reservoir water management program based on digital twin stored on the memory and executable on the processor, and the intelligent reservoir water management program based on digital twin is configured to implement the steps of the intelligent reservoir water management method based on digital twin described in any one of the above.

[0097] In addition, an embodiment of the present invention further provides a storage medium, on which an intelligent reservoir water management program based on digital twin is stored, and when the intelligent reservoir water management program based on digital twin is executed by a processor, the steps of the intelligent reservoir water management method based on digital twin described in any one of the above are implemented.

[0098] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or system including the element.

[0099] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0100] Through the description of the above embodiments, those skilled in the art can clearly understand that the above method of the embodiment can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented through hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0101] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An intelligent reservoir water management method based on digital twin, characterized in that, The intelligent reservoir water management method based on digital twin includes the following steps: Obtain the reservoir data of the target reservoir, and establish a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; Determine the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data, and a preset scheduling strategy, where the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located; Determine a monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data; The model prediction data includes: a predicted water storage change curve and a water quality index change curve, the monitoring plan includes a monitoring time, and the step of determining the monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data includes: Obtain a first preset time interval associated with each moment on the predicted water storage change curve and a second preset time interval associated with each moment on the water quality index change curve; Calculate the water storage standard deviation corresponding to each moment according to the predicted water storage data in the first preset time interval to obtain the water storage standard deviation corresponding to each moment, and calculate the water quality index change standard deviation corresponding to each moment according to the water quality index data in the second preset time interval to obtain the water quality index standard deviation corresponding to each moment; Determine a water storage change standard deviation curve according to the water storage standard deviations corresponding to all moments, and determine a water quality index change standard deviation curve according to the water quality index standard deviations corresponding to all moments; Determine the monitoring time according to the water storage change standard deviation curve and the water quality index change standard deviation curve.

2. The intelligent reservoir water management method based on digital twin according to claim 1, characterized in that, The number of water quality index change standard deviation curves is more than one, and the step of determining the monitoring time according to the water storage change standard deviation curve and the water quality index change standard deviation curve includes: Determine a first monitoring time interval according to the water storage change standard deviation curve; Superimpose the more than one water quality index change standard deviation curves to obtain a water quality comprehensive index change standard deviation curve; Obtain the moment corresponding to the minimum water quality comprehensive index change standard deviation within the first monitoring time interval according to the water quality comprehensive index change standard deviation curve as the monitoring time.

3. The intelligent reservoir water management method based on digital twin according to claim 1, characterized in that, The step of determining the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data, and a preset scheduling strategy includes: Determine the predicted inflow and the corresponding predicted inflow water quality prediction data according to the forecast data; Determine the predicted outflow according to the forecast data and the preset scheduling strategy; Determine the model prediction data according to the reservoir twin model, the predicted inflow, the predicted inflow water quality prediction data, and the predicted outflow.

4. The intelligent reservoir water management method based on digital twin according to claim 3, characterized in that, The step of determining the model prediction data according to the reservoir twin model, the predicted inflow, the predicted inflow water quality prediction data, and the predicted outflow includes: Determine the predicted water storage change curve of the target reservoir according to the reservoir twin model, the predicted inflow, and the predicted outflow; Determine the water quality index change curves corresponding to each water quality index of the target reservoir based on the water quality data of the reservoir twin model, the current water storage volume of the reservoir twin model, the predicted inflow, the predicted inflow water quality data, and the predicted outflow; Use the predicted water storage volume change curve and the water quality index change curve as the model prediction data.

5. The intelligent reservoir water management method based on digital twin according to claim 4, characterized in that, The water quality indexes include at least one of the following: acidity and alkalinity index, dissolved oxygen amount index, turbidity index, conductivity index, temperature index, salinity index, residual chlorine index, ammonia nitrogen concentration index.

6. The intelligent reservoir water management method based on digital twin according to any one of claims 1 to 5, characterized in that, The step of establishing the reservoir twin model of the target reservoir according to the reservoir data and the preset reservoir model includes: Normalize the reservoir data according to the data type of the reservoir data; Map the reservoir data to the parameters corresponding to the preset reservoir model according to the preset mapping relationship, and generate the reservoir twin model.

7. An intelligent reservoir water management device based on digital twin, characterized in that, The intelligent reservoir water management device based on digital twin includes: A twin module, configured to obtain the reservoir data of the target reservoir, and establish the reservoir twin model of the target reservoir according to the reservoir data and the preset reservoir model; A prediction module, configured to determine the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data, and the preset scheduling strategy, where the forecast data includes: meteorological forecast data of the area where the target reservoir is located, hydrological forecast data of the area where the target reservoir is located, and hydropower demand data of the area where the target reservoir is located; An operation module, configured to determine the monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data; the model prediction data includes: a predicted water storage volume change curve and a water quality index change curve, the monitoring plan includes the monitoring time, and the step of determining the monitoring plan for aquatic biological monitoring of the target reservoir according to the model prediction data includes: Obtain the first preset time interval associated with each moment on the predicted water storage volume change curve and the second preset time interval associated with each moment on the water quality index change curve; Calculate the water storage volume standard deviation at the corresponding moment according to the predicted water storage volume data in the first preset time interval to obtain the water storage volume standard deviation corresponding to each moment, and calculate the water quality index change standard deviation at the corresponding moment according to the water quality index data in the second preset time interval to obtain the water quality index standard deviation corresponding to each moment; Determine the water storage volume change standard deviation curve according to the water storage volume standard deviations corresponding to all moments, and determine the water quality index change standard deviation curve according to the water quality index standard deviations corresponding to all moments; Determine the monitoring time according to the water storage volume change standard deviation curve and the water quality index change standard deviation curve.

8. An intelligent reservoir water management device based on digital twin, characterized in that, The intelligent reservoir water management device based on digital twin includes: a memory, a processor, and an intelligent reservoir water management program based on digital twin stored on the memory and executable on the processor, and the intelligent reservoir water management program based on digital twin is configured to implement the steps of the intelligent reservoir water management method based on digital twin according to any one of claims 1 to 6.

9. A storage medium, characterized in that, A digital twin-based intelligent reservoir water management program is stored on the storage medium. When the digital twin-based intelligent reservoir water management program is executed by a processor, it implements the steps of the digital twin-based intelligent reservoir water management method according to any one of claims 1 to 6.

Images