Intelligent reservoir water affair management method, device and equipment based on digital twinning

Abstract

The invention discloses an intelligent reservoir water affair management method, device and equipment based on digital twinning, and belongs to the technical field of digital management. The method comprises the following steps: acquiring reservoir data of a target reservoir, and establishing a reservoir twinborn model of the target reservoir according to the reservoir data and a preset reservoir model; determining model prediction data of the reservoir twinborn model according to the reservoir twinborn model, forecast data and a preset scheduling strategy, the forecast data comprises weather forecast data of an 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; and determining a monitoring scheme for aquatic organism monitoring of the target reservoir according to the model prediction data. According to the invention, the effectiveness and representativeness of the data are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital management, and in particular to a method, device and equipment for intelligent reservoir water management based on digital twins. Background Art

[0002] Reservoirs are important water conservancy facilities. Since reservoirs are generally equipped with multiple monitoring devices and control devices, they have become important water resource monitoring units in my country. At present, compared with physical parameters that can be monitored by setting sensors, the detection of plankton in water bodies generally requires regular monitoring. Depending on the conditions and regions, plankton detection is generally carried out based on the annual variation cycle of runoff or by season. At present, since the standards for monitoring and evaluation of aquatic organisms in lakes and reservoirs in the Technical Guide to Water Ecological Monitoring need to ensure that time-representative samples can be obtained, a higher monitoring frequency and sampling frequency will be set in actual reservoir sampling. In addition, since the detection of plankton in the body requires stratified sampling, it is impossible to reduce costs by setting sensors. This actual reservoir sampling method requires a large amount of resources to be used to detect aquatic organisms.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide an intelligent reservoir water management method, device 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, and the intelligent reservoir water management method based on digital twins comprises the following steps: Acquire reservoir data of a target reservoir, and establish a reservoir twin model of the target reservoir based on the reservoir data and a preset reservoir model; Determining model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and a preset dispatching 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 water and electricity demand data of the area where the target reservoir is located; A monitoring plan for aquatic organism monitoring in the target reservoir is determined based on the model prediction data.

[0006] Optionally, the model prediction data includes: a predicted water storage change curve and a water quality index change curve, the monitoring scheme includes a monitoring time, and the step of determining the monitoring scheme for aquatic biological monitoring of the target reservoir according to the model prediction data includes: Obtaining a first preset time interval associated with each moment on the predicted water storage capacity change curve and a second preset time interval associated with each moment on the water quality index change curve; Calculate the standard deviation of water storage at the corresponding moment according to the predicted water storage data in the first preset time interval to obtain the standard deviation of water storage corresponding to each moment, and calculate the standard deviation of water quality index change at the corresponding moment according to the water quality index data in the second preset time interval to obtain the standard deviation of water quality index corresponding to each moment; Determine the standard deviation curve of water storage change according to the standard deviation of water storage corresponding to all moments, and determine the standard deviation curve of water quality index change according to the standard deviation of water quality index corresponding to all moments; The monitoring time is determined according to the standard deviation curve of water storage change and the standard deviation curve of water quality index change.

[0007] Optionally, the number of the water quality index change standard deviation curves is more than one, and the step of determining the monitoring time according to the water storage capacity change standard deviation curve and the water quality index change standard deviation curve comprises: Determining a first monitoring time interval according to the water storage capacity change standard deviation curve; Superimposing more than one water quality index change standard deviation curve to obtain a water quality comprehensive index change standard deviation curve; The time corresponding to the minimum standard deviation of the comprehensive water quality index change in the first monitoring time interval is obtained according to the comprehensive water quality index change standard deviation curve as the monitoring time.

[0008] Optionally, the step of determining model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and a preset dispatching strategy includes: Determine the predicted inflow flow and the corresponding predicted inflow water quality prediction data according to the forecast data; Determine the outbound forecast flow rate according to the forecast data and the preset dispatch strategy; The model prediction data is determined based on the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow.

[0009] Optionally, the step of determining the model prediction data according to the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow includes: Determine a predicted water storage capacity change curve of the target reservoir according to the reservoir twin model, the predicted inflow flow, and the predicted outflow flow; Determine a 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 capacity of the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow; The predicted water storage capacity change curve and the water quality index change curve are used as the model prediction data.

[0010] Optionally, the water quality index includes at least one of a pH index, a dissolved oxygen content index, a turbidity index, a conductivity index, a temperature index, a salinity index, a residual chlorine index, and an ammonia nitrogen concentration index.

[0011] Optionally, the step of establishing the reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model includes: Normalizing the reservoir data according to the data type of the reservoir data; The reservoir data is mapped to the parameters corresponding to the preset reservoir model according to the preset mapping relationship, and the reservoir twin model is generated.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also provides an intelligent reservoir water management device based on digital twins, and the intelligent reservoir water management device based on digital twins includes: A twin module, used for acquiring reservoir data of a target reservoir, and establishing a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; A prediction module, used to determine the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data and the preset dispatching strategy, wherein 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 water and electricity demand data of the area where the target reservoir is located; The operation module is used to determine a monitoring plan for aquatic organism monitoring in the target reservoir according to the model prediction data.

[0013] In addition, to achieve the above-mentioned objectives, the present invention also provides an intelligent reservoir water management device based on digital twins, and the intelligent reservoir water management device based on digital twins includes: a memory, a processor, and an intelligent reservoir water management program based on digital twins stored on the memory and executable on the processor, and the intelligent reservoir water management program based on digital twins is configured to implement the steps of the intelligent reservoir water management method based on digital twins described in any one of the above-mentioned items.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which is stored a smart reservoir water management program based on digital twins. When the smart reservoir water management program based on digital twins is executed by a processor, the steps of the smart reservoir water management method based on digital twins described in any one of the above-mentioned items are implemented.

[0015] The present invention proposes an intelligent reservoir water management method based on digital twins. The method 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, and determines 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 water and electricity demand data of the area where the target reservoir is located. Compared with traditional aquatic biological monitoring methods, the data monitored by the monitoring scheme determined based on model prediction data can be more representative, thereby improving the effectiveness of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural schematic diagram of an intelligent reservoir water management device based on digital twins in the hardware operating environment involved in the embodiment of the present invention; Figure 2 It is a flow chart of the first embodiment of the intelligent reservoir water management method based on digital twins of the present invention; Figure 3 It is a flow chart of a second embodiment of the intelligent reservoir water management method based on digital twins of the present invention; Figure 4 It is a flow chart of the third embodiment of the intelligent reservoir water management method based on digital twins of the present invention; Figure 5 This is a flow chart of the fourth embodiment of the intelligent reservoir water management method based on digital twins of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] 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.

[0019] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an intelligent reservoir water management device based on digital twins in the hardware operating environment involved in an embodiment of the present invention.

[0020] like Figure 1As shown, the intelligent reservoir water management device based on digital twins may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive 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 interactive device 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional interactive 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. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0021] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation of the smart reservoir water management equipment based on digital twins, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0022] like Figure 1 As 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 a smart reservoir water management program based on digital twins.

[0023] exist Figure 1 In the smart reservoir water management device based on digital twin shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the smart reservoir water management device based on digital twin of the present invention can be set in the smart reservoir water management device based on digital twin, and the smart reservoir water management device based on digital twin calls the smart reservoir water management program based on digital twin stored in the memory 1005 through the processor 1001, and executes the smart reservoir water management method based on digital twin provided in an embodiment of the present invention.

[0024] The embodiment of the present invention provides a smart reservoir water management method based on digital twins, referring to Figure 2 , Figure 2This is a flow chart of the first embodiment of an intelligent reservoir water management method based on digital twins of the present invention.

[0025] In this embodiment, the intelligent reservoir water management method based on digital twins includes: Step S1, obtaining reservoir data of a target reservoir, and establishing a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; The reservoir data here include: water quality data of the target reservoir. For data monitored in real time by sensors, preferably, it is data at the current moment. For non-real-time data detected regularly, it is data collected before the current moment and at the moment 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 the preset reservoir. Water management includes: the formulation of monitoring plans for aquatic biological monitoring, reservoir water quality protection, reservoir water resources scheduling, etc.

[0026] Step S2, determining the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data and the preset dispatching strategy, wherein 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 water and electricity demand data of the area where the target reservoir is located; The forecast data here is a forecast provided by other units other than the target reservoir. Generally, the meteorological forecast data here can be provided by the Meteorological Bureau, the hydrological forecast data here can be provided by the water conservancy department, and the hydropower demand data here includes electricity demand and water demand. The electricity demand data is very important for reservoirs equipped with generators, and the water demand here can also be provided by the water conservancy department. The preset scheduling scheme here may include: the level of emergency response, the corresponding response operation, the water allocation scheme and the scheduling requirements, etc. The scheduling requirements here may include: cross-section ecological flow indicators and minimum downstream flow, etc. Optionally, the operation of the target reservoir in the future period is determined according to the forecast data and the preset scheduling strategy, and the corresponding operation is mapped to the reservoir twin model, so as to determine the model prediction data in the reservoir twin model. For example: in the flood season, when the forecast data and the preset scheduling strategy are used, it is determined to enter the first-level flood control response and execute the corresponding response operation, determine the execution scheme of the reservoir operation such as water retaining, water discharge, and water release, and simulate it in the reservoir twin model to calculate the model prediction data. In addition, preferably, the level of flood control response can be determined based on the data of the reservoir twin model, forecast data and preset scheduling strategy, so as to further improve the accuracy of the model prediction data.

[0027] Step S3, determining a monitoring plan for aquatic organism monitoring of the target reservoir based on the model prediction data.

[0028] Preferably, the time and location for aquatic biological monitoring of the target reservoir can be determined based on the model prediction data. For example: aquatic biological monitoring is carried out during periods when the water volume changes little. This is because during the monitoring process, if the inflow and outflow are large, the data fluctuates greatly, and the representativeness of the monitored aquatic biological data is low. In addition, the amount of water in the reservoir at different times often affects the difficulty of the monitoring process. For example: periods with higher changes in water volume in the model prediction data often represent bad weather in the area. In addition, when the proportion of the inflow runoff of the reservoir is unbalanced, that is, the runoff water source that originally occupied a fixed proportion has changed. At this time, the sampling site should be adjusted to ensure that the data is representative.

[0029] In this embodiment, reservoir data of the target reservoir is obtained, and a reservoir twin model of the target reservoir is established according to the reservoir data and a preset reservoir model, and model prediction data of the reservoir twin model is determined 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 water and electricity demand data of the area where the target reservoir is located. Compared with traditional aquatic biological monitoring methods, the data monitored by the monitoring scheme determined based on model prediction data can be more representative, thereby improving the effectiveness of the data.

[0030] Further, based on the first embodiment, a second embodiment of the intelligent reservoir water management method based on digital twins 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, 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: Step S31, obtaining a first preset time interval associated with each moment on the predicted water storage capacity change curve and a second preset time interval associated with each moment on the water quality index change curve; For the start moment on the predicted water storage capacity change curve, the associated first preset time interval is the start moment and a period of time adjacent to the start moment; for the end moment on the predicted water storage capacity change curve, the associated first preset time interval is the end moment and a period of time adjacent to the end moment; for the middle moment other than the start moment and the end moment on the predicted water storage capacity change curve, the associated first preset time interval is a continuous period of time including the middle moment, and optionally, the middle moment is at the center of the continuous period of time including the middle moment.

[0031] For the start moment on the water quality index change curve, the associated second preset time interval is the start moment and a period of time adjacent to the start moment; for the end moment on the predicted water storage capacity change curve, the associated second preset time interval is the end moment and a period of time adjacent to the end moment; for the middle moment other than the start moment and the end moment on the predicted water storage capacity change curve, the associated second preset time interval is a continuous period of time including the middle moment, and optionally, the middle moment is at the center of the continuous period of time including the middle moment.

[0032] Step S32, calculating the standard deviation of water storage at the corresponding moment according to the predicted water storage data in the first preset time interval, and obtaining the standard deviation of water storage corresponding to each moment, and calculating the standard deviation of water quality index change at the corresponding moment according to the water quality index data in the second preset time interval, and obtaining the standard deviation of water quality index corresponding to each moment; In this embodiment, the standard deviation of the water storage capacity at the corresponding moment is calculated based on the predicted water storage capacity data in the first preset time interval. Since each moment has the corresponding predicted water storage capacity data in the first preset time interval, the standard deviation of the water storage capacity corresponding to each moment can be calculated. The standard deviation of the change of the water quality index at the corresponding moment is calculated based on the water quality index data in the second preset time interval. Since each moment has the corresponding water quality index data in the second preset time interval, the standard deviation of the water quality index corresponding to each moment can be calculated.

[0033] Step S33, determining a water storage capacity change standard deviation curve according to the water storage capacity standard deviation corresponding to all moments, and determining a water quality index change standard deviation curve according to the water quality index standard deviation corresponding to all moments; Based on sorting all the standard deviations of water storage in chronological order and drawing the standard deviation curve of water storage change; based on sorting all the standard deviations of water quality indicators in chronological order and drawing the standard deviation curve of water quality indicator change. It is necessary that water quality indicators often have multiple specific different indicators, and the water quality indicators include: pH index, dissolved oxygen index, turbidity index, conductivity index, temperature index, salinity index, residual chlorine index, ammonia nitrogen concentration index, at least one of the indicators. The specific here is to draw the standard deviation curve of water quality indicator change based on the same indicator type.

[0034] Step S34, determining the monitoring time according to the water storage capacity change standard deviation curve and the water quality index change standard deviation curve.

[0035] Optionally, the monitoring time is jointly determined based on the water storage capacity change standard deviation curve and the water quality index change standard deviation curve. In other embodiments, whether the water quality index change standard deviation curve matches the water storage capacity change standard deviation curve is determined based on the water storage capacity change standard deviation curve. When the water quality index change standard deviation curve matches the water storage capacity change standard deviation curve, the monitoring time is jointly determined based on the water storage capacity change standard deviation curve and the water quality index change standard deviation curve; when the water quality index change standard deviation curve does not match the water storage capacity change standard deviation curve, the monitoring time is determined by the water storage capacity change standard deviation curve.

[0036] In this embodiment, the standard deviation of water storage at the corresponding moment is calculated by using the predicted water storage data in the first preset time interval to obtain the standard deviation of water storage corresponding to each moment, and the standard deviation of water quality index change at the corresponding moment is calculated according to the water quality index data in the second preset time interval to obtain the standard deviation of water quality index corresponding to each moment; it can be determined whether a variety of different indicators have undergone large changes, which will lead to an increase in the standard deviation, and by drawing a curve, the monitoring time can be determined based on the water storage change standard deviation curve and the water quality index change standard deviation curve, thereby determining the most representative monitoring time.

[0037] Furthermore, the number of the water quality index change standard deviation curves is more than one, and the step of determining the monitoring time according to the water storage capacity change standard deviation curve and the water quality index change standard deviation curve comprises: Determining a first monitoring time interval according to the water storage capacity change standard deviation curve; Superimposing more than one water quality index change standard deviation curve to obtain a water quality comprehensive index change standard deviation curve; The time corresponding to the minimum standard deviation of the comprehensive water quality index change in the first monitoring time interval is obtained according to the comprehensive water quality index change standard deviation curve as the monitoring time.

[0038] In this embodiment, the time corresponding to the water storage change standard deviation below the water storage change threshold in the water storage change standard deviation curve is determined as the first monitoring time interval. In other embodiments, the water quality index change standard deviation curve matching the water storage change standard deviation curve is superimposed to obtain the water quality comprehensive index change standard deviation curve.

[0039] In this embodiment, the first monitoring time interval is determined by the water storage capacity change standard deviation curve; more than one water quality index change standard deviation curve are superimposed to obtain a comprehensive water quality index change standard deviation curve; the moment corresponding to the minimum comprehensive water quality index change standard deviation within the first monitoring time interval is obtained according to the comprehensive water quality index change standard deviation curve as the monitoring time, thereby improving the representativeness of the monitoring data at the monitoring time.

[0040] Further, based on the first embodiment or the second embodiment, a third embodiment of the intelligent reservoir water management method based on digital twins of the present invention is proposed. In this embodiment, referring to Figure 4 The step of determining the model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and preset dispatching strategy includes: Step S21, determining the predicted inflow flow and the corresponding predicted inflow water quality prediction data according to the forecast data; The water flow rate and corresponding water quality of the channel through which water enters the target reservoir are determined according to the forecast data, where the channel into the target reservoir includes: inlet rivers, water diversion channels, and drainage pipes, etc. It should be noted that the accuracy of the forecast data here can be improved according to the flow rate and upstream monitoring data.

[0041] Step S22, determining the outbound forecast flow rate according to the forecast data and the preset dispatching strategy; It should be noted that the preset scheduling strategy is generally based on the water resources and power conditions of the reservoir and the upstream and downstream of the reservoir. Therefore, it should be noted that, generally speaking, in this embodiment, the execution of step S22 does not need to be based on the water quality. Of course, in other embodiments, under special circumstances, for example: before the event of crossing the Pearl River in Guangzhou, it is necessary to control the reservoirs with different water quality to adjust the corresponding outbound flow, of course, including various water competitions. Therefore, water quality can also be used as a factor in determining the predicted outbound flow. The predicted water quality index is determined based on the forecast data, and the predicted outbound flow is determined based on the predicted water quality index and the event scheduling strategy in the preset scheduling strategy. Of course, this preset scheduling strategy will also affect the inbound flow and inbound water quality data of the downstream reservoir.

[0042] Step S23, determining the model prediction data according to the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow.

[0043] In this embodiment, the predicted inflow flow and the corresponding predicted inflow water quality data are determined based on the forecast data, and the predicted outflow flow is determined by the forecast data and the preset scheduling strategy, thereby improving the accuracy of the model prediction data.

[0044] Furthermore, the step of determining the model prediction data according to the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow includes: Determine a predicted water storage capacity change curve of the target reservoir according to the reservoir twin model, the predicted inflow flow, and the predicted outflow flow; Determine a 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 capacity of the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow; The predicted water storage capacity change curve and the water quality index change curve are used as the model prediction data.

[0045] Specifically, the predicted inflow flow and the predicted outflow flow calculate the water resource change values ​​of the target reservoir at various times, and determine the predicted water storage capacity change curve of the target reservoir based on the water resource change values ​​and the reservoir twin model. Determine the content change curve of the physical data corresponding to each indicator data based on the reservoir water quality data of the reservoir twin model, the current water storage capacity of the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data, and the predicted outflow flow. Determine the water quality index change curve corresponding to each water quality index based on the corresponding content change curve of the physical data and the predicted water storage capacity change curve.

[0046] Further, based on any of the above embodiments, a fourth embodiment of the intelligent reservoir water management method based on digital twins of 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: Step S11, normalizing the reservoir data according to the data type of the reservoir data; In this embodiment, since the sources of the reservoir data may be different, 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.

[0047] Step S12: 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.

[0048] In this embodiment, the reservoir data is normalized according to the data type of the reservoir data, thereby improving the accuracy of subsequently 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.

[0049] In addition, an embodiment of the present invention further proposes an intelligent reservoir water management device based on digital twins, and the intelligent reservoir water management device based on digital twins includes: A twin module, used for acquiring reservoir data of a target reservoir, and establishing a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; A prediction module, used to determine the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data and the preset dispatching strategy, wherein 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 water and electricity demand data of the area where the target reservoir is located; The operation module is used to determine a monitoring plan for aquatic organism monitoring in the target reservoir according to the model prediction data.

[0050] In addition, an embodiment of the present invention also proposes an intelligent reservoir water management device based on digital twins, characterized in that the intelligent reservoir water management device based on digital twins includes: a memory, a processor, and an intelligent reservoir water management program based on digital twins stored on the memory and executable on the processor, and the intelligent reservoir water management program based on digital twins is configured to implement the steps of the intelligent reservoir water management method based on digital twins described in any one of the above items.

[0051] In addition, an embodiment of the present invention also proposes a storage medium, on which is stored a smart reservoir water management program based on digital twins. When the smart reservoir water management program based on digital twins is executed by a processor, the steps of any of the above-mentioned smart reservoir water management methods based on digital twins are implemented.

[0052] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0053] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0054] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0055] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart reservoir water management method based on digital twins, characterized in that: The digital twin-based intelligent reservoir water management method comprises the following steps: Acquire reservoir data of a target reservoir, and establish a reservoir twin model of the target reservoir based on the reservoir data and a preset reservoir model; Determining model prediction data of the reservoir twin model according to the reservoir twin model, forecast data and a preset dispatching 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 water and electricity demand data of the area where the target reservoir is located; A monitoring plan for aquatic organism monitoring in the target reservoir is determined based on the model prediction data.

2. The intelligent reservoir water management method based on digital twins according to claim 1, characterized in that: The model prediction data includes: a predicted water storage change curve and a water quality index change curve, the monitoring scheme includes a monitoring time, and the step of determining the monitoring scheme for aquatic biological monitoring of the target reservoir according to the model prediction data includes: Obtaining a first preset time interval associated with each moment on the predicted water storage capacity change curve and a second preset time interval associated with each moment on the water quality index change curve; Calculate the standard deviation of water storage at the corresponding moment according to the predicted water storage data in the first preset time interval to obtain the standard deviation of water storage corresponding to each moment, and calculate the standard deviation of water quality index change at the corresponding moment according to the water quality index data in the second preset time interval to obtain the standard deviation of water quality index corresponding to each moment; Determine the standard deviation curve of water storage change according to the standard deviation of water storage corresponding to all moments, and determine the standard deviation curve of water quality index change according to the standard deviation of water quality index corresponding to all moments; The monitoring time is determined according to the standard deviation curve of water storage change and the standard deviation curve of water quality index change.

3. The intelligent reservoir water management method based on digital twins according to claim 2 is characterized in that: The number of the water quality index change standard deviation curves is more than one, and the step of determining the monitoring time according to the water storage capacity change standard deviation curve and the water quality index change standard deviation curve comprises: Determining a first monitoring time interval according to the water storage capacity change standard deviation curve; Superimposing more than one water quality index change standard deviation curve to obtain a water quality comprehensive index change standard deviation curve; The time corresponding to the minimum standard deviation of the comprehensive water quality index change in the first monitoring time interval is obtained according to the comprehensive water quality index change standard deviation curve as the monitoring time.

4. The intelligent reservoir water management method based on digital twins 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, forecast data and a preset dispatching strategy comprises: Determine the predicted inflow flow and the corresponding predicted inflow water quality prediction data according to the forecast data; Determine the outbound forecast flow rate according to the forecast data and the preset dispatch strategy; The model prediction data is determined based on the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow.

5. The intelligent reservoir water management method based on digital twins according to claim 4 is characterized in that: The step of determining the model prediction data according to the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow comprises: Determine a predicted water storage capacity change curve of the target reservoir according to the reservoir twin model, the predicted inflow flow, and the predicted outflow flow; Determine a 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 capacity of the reservoir twin model, the predicted inflow flow, the predicted inflow water quality data and the predicted outflow flow; The predicted water storage capacity change curve and the water quality index change curve are used as the model prediction data.

6. The intelligent reservoir water management method based on digital twins according to claim 5 is characterized in that: The water quality index includes: at least one of a pH index, a dissolved oxygen index, a turbidity index, a conductivity index, a temperature index, a salinity index, a residual chlorine index, and an ammonia nitrogen concentration index.

7. The intelligent reservoir water management method based on digital twin according to any one of claims 1 to 6, 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 comprises: Normalizing the reservoir data according to the data type of the reservoir data; The reservoir data is mapped to the parameters corresponding to the preset reservoir model according to the preset mapping relationship, and the reservoir twin model is generated.

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 twin module, used for acquiring reservoir data of a target reservoir, and establishing a reservoir twin model of the target reservoir according to the reservoir data and a preset reservoir model; A prediction module, used to determine the model prediction data of the reservoir twin model according to the reservoir twin model, the forecast data and the preset dispatching strategy, wherein 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 water and electricity demand data of the area where the target reservoir is located; The operation module is used to determine a monitoring plan for aquatic organism monitoring in the target reservoir according to the model prediction data.

9. An intelligent reservoir water management device based on digital twins, characterized in that: The digital twin-based intelligent reservoir water management device includes: a memory, a processor, and a digital twin-based intelligent reservoir water management program stored in 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 as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a smart reservoir water management program based on digital twins. When the smart reservoir water management program based on digital twins is executed by the processor, the steps of the smart reservoir water management method based on digital twins as described in any one of claims 1 to 7 are implemented.

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