Water resource scheduling deduction method and system
By obtaining rainfall forecast data and water resource description text, identifying resource elements and inputting water resource scheduling optimization model, generating runoff forecast and scheduling prediction solutions, the problem of backward water resource scheduling methods in the existing technology is solved and the management of water resources is improved and utilization efficiency reduces costs and waste.
Patent Information
- Application Number
- CN202411078147.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-07
- Publication Date
- 2024-12-31
AI Technical Summary
Existing water resource scheduling methods often lag behind changes in water and rain conditions, resulting in low reliability of reservoir waste and water supply, high cost, and difficulty in effectively utilizing limited water resources.
By obtaining rainfall forecast data and case water resource description text, identifying resource elements, inputting water resource scheduling optimization model, generating runoff forecast and scheduling prediction schemes, and optimizing models through network debugging, improving the accuracy and efficiency of scheduling decisions.
Effective management and utilization of water resources has been achieved, water supply reliability has been improved, dispatch costs have been reduced, water resource waste has been reduced, and the dispatch efficiency of reservoirs has been improved.
Smart Images
Figure CN119227929A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the fields of water conservancy and data processing technology, and in particular to a water resources scheduling simulation method and system. Background Art
[0002] Water resource dispatch is to meet the water demand as much as possible according to the weather conditions, water and rainfall conditions, drought conditions, etc., under the premise of ensuring the safety of water conservancy projects in the system, and according to the use plan of water conservancy projects. In northern China, rainfall distribution is uneven throughout the year, with more than 70% of rainfall concentrated in June, July and August. The soil is long-term short of water, resulting in a small effective runoff and less available surface water resources. However, this has formed a strong contradiction with the demand for water resources for economic and social development, and there are huge challenges in promoting the continuous development of water resource dispatch technology.
[0003] The commonly used water resource dispatching methods are to dispatch the existing water resources when the water resources are known. This makes the water resource dispatching plan often lag behind the changes in water and rainfall conditions, which may cause a large amount of water to be abandoned in the reservoir, so that the relatively scarce water resources will flow downstream before they can be stored in time. Therefore, a water resource dispatching simulation method is urgently needed to improve water supply reliability, reduce dispatching costs, and protect the environment. Summary of the invention
[0004] The embodiments of the present application provide a water resource scheduling simulation method and system, which realize the effective management and utilization of water resources through reasonable decision-making and scheduling schemes.
[0005] The present application embodiment provides a water resources scheduling deduction method, including: Acquire rainfall forecast data for a target water area, and determine a description text of a case water resource associated with the rainfall forecast data, wherein the description text includes water resource description information of the case water resource in the target water area; Identifying resource elements based on the description text and the rainfall forecast data; Inputting rainfall forecast data and identified resource elements into a water resource scheduling optimization model to generate a corresponding runoff forecast and a water resource scheduling prediction scheme based on the rainfall forecast data and the description vector of the resource elements, wherein the water resource scheduling optimization model is obtained after network debugging of the water resource scheduling optimization model based on the difference between the water resource scheduling prediction scheme outputted by the optimization model in historical data and the actual water resource scheduling authentication scheme, and the water resource scheduling authentication scheme is a water resource scheduling scheme corresponding to the target water area for characterizing a scheduling decision task of at least one target water conservancy facility; According to the generated water resource scheduling forecast plan, content display is driven by events based on the timeline, and perspective following and broadcasting are performed.
[0006] Optionally, based on the difference between the water resource scheduling prediction scheme outputted by the optimization model and the actual water resource scheduling verification scheme in the historical data, the network debugging of the water resource scheduling optimization model includes: Determining the cost coefficient of the model debugging evaluation index according to the difference between the water resources scheduling prediction scheme and the water resources scheduling certification scheme; The water resource scheduling optimization model is debugged according to the cost coefficient of the model debugging evaluation index.
[0007] Optionally, according to the cost coefficient of the model network debugging evaluation index, debugging the water resources scheduling optimization model includes: According to the cost coefficient of the model debugging evaluation index, the model variables of the water resources scheduling optimization model are adjusted.
[0008] Optionally, according to the difference between the water resources scheduling prediction scheme and the water resources scheduling authentication scheme, determining the cost coefficient of the model debugging evaluation index includes: Determining a cost coefficient of a first local network debugging evaluation indicator of the model debugging evaluation indicator according to a commonality metric value between the water resources scheduling prediction scheme and the water resources scheduling authentication scheme; Determine the cost coefficient of the second local network debugging evaluation index of the first network debugging evaluation index according to the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system determined in the water resources scheduling prediction plan and the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system in the water resources scheduling authentication plan; The cost coefficient of the model debugging evaluation index is determined according to the cost coefficient of the first local network debugging evaluation index and the cost coefficient of the second local network debugging evaluation index.
[0009] Optionally, determining the cost coefficient of the first local network debugging evaluation index of the model network debugging evaluation index according to the commonality metric value of the water resources scheduling prediction scheme and the water resources scheduling authentication scheme includes: Obtaining importance coefficients corresponding to respective scheduling decision tasks in the water resources scheduling forecasting scheme, wherein each scheduling decision task in the water resources scheduling forecasting scheme includes the scheduling decision task of at least one target water conservancy facility system; According to the importance coefficients corresponding to each scheduling decision task in the water resources scheduling prediction scheme, and the commonality measurement values of each scheduling decision task in the water resources scheduling prediction scheme and each scheduling decision task in the water resources scheduling authentication scheme, the cost coefficient of the model debugging evaluation index of the first network debugging evaluation index is determined.
[0010] Optionally, based on the difference between the water resource scheduling prediction scheme outputted by the optimization model in the historical data and the actual water resource scheduling verification scheme, the network debugging of the water resource scheduling optimization model further includes: In the training process, the BP neural network and support vector machine coupling method are used to iteratively obtain the optimal parameters.
[0011] Optionally, according to the generated water resource scheduling forecast plan, the content display is promoted based on the events of the timeline, and the perspective following and broadcasting are performed, including: A timeline is provided, and based on the generated water resources scheduling forecast scheme and the watershed model of the target water area, event simulation is performed through animation, and simulation events are reported during the simulation process.
[0012] An embodiment of the present application further proposes a water resource scheduling deduction system, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the water resource scheduling deduction method as described above are implemented.
[0013] The method of the embodiment of the present application combines rainfall forecast data and a description text of case water resources, outputs a runoff forecast and a water resource scheduling prediction plan through a water resource scheduling optimization model, and realizes effective management and utilization of water resources through reasonable decision-making and scheduling plans.
[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 The following is a schematic diagram of the basic process of the water resources scheduling simulation method of this embodiment. DETAILED DESCRIPTION
[0016] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0017] This application embodiment proposes a water resources scheduling deduction method, such as Figure 1 As shown, the following steps are included: In step S101, rainfall forecast data of a target water area is obtained, and a description text of a case water resource related to the rainfall forecast data is determined, wherein the description text includes water resource description information of the case water resource of the target water area. For example, some examples may perform rainfall forecasting using WRF.
[0018] In step S102, resource elements are identified based on the description text and the rainfall forecast data.
[0019] In step S103, the rainfall forecast data and the identified resource elements are input into the water resource scheduling optimization model to generate the corresponding runoff forecast and the water resource scheduling prediction scheme based on the rainfall forecast data and the description vector of the resource elements, wherein the water resource scheduling optimization model is obtained after network debugging of the water resource scheduling optimization model based on the difference between the water resource scheduling prediction scheme output by the optimization model in the historical data and the actual water resource scheduling certification scheme, and the water resource scheduling certification scheme is the water resource scheduling scheme corresponding to the target water area for characterizing the scheduling decision task of at least one target water conservancy facility. The description vector of the specific resource element can be understood as a feature graph, which is an important component of the water resource scheduling optimization network. The feature graph is a visualization tool for representing data features and relationships. It presents different elements in the data in the form of nodes and represents the relationship between different elements through edge connections.
[0020] Feature maps usually contain the following important elements: Node: Each node represents a resource element in the water source data. For example, a node can represent different water resource elements such as water level, flow, water quality, etc. The properties of the node can be set according to specific needs. For example, the size, color or shape of the node can represent the importance or specific attributes of the element; Edges: Edges represent the relationships and interactions between resource elements. For example, if two resource elements have a dependency or influence relationship, there will be an edge connecting them. The attributes of the edge can represent the strength, direction, or type of the relationship.
[0021] Weight: Weight is used to indicate the strength or importance of an edge. It can reflect the degree of association or influence between resource elements. Larger weights indicate stronger relationships or interactions, while smaller weights indicate weaker relationships. Water resource scheduling optimization models can be used to identify resources, obtain runoff forecasts, and water resource scheduling prediction plans for case water source data (runoff forecasts).
[0022] In step S104, according to the generated water resource scheduling prediction plan, content display is promoted based on events on the timeline, and perspective following and broadcasting are performed.
[0023] The method of the embodiment of the present application combines rainfall forecast data and a description text of case water resources, outputs a runoff forecast and a water resource scheduling prediction plan through a water resource scheduling optimization model, and realizes effective management and utilization of water resources through reasonable decision-making and scheduling plans.
[0024] In some embodiments, based on the difference between the water resource scheduling prediction scheme outputted according to the optimization model in the historical data and the actual water resource scheduling authentication scheme, network debugging of the water resource scheduling optimization model includes: According to the difference between the water resource scheduling prediction scheme and the water resource scheduling authentication scheme, the cost coefficient of the model debugging evaluation index is determined. The debugging evaluation index can be the accuracy or cost-effectiveness of water resource scheduling, which represents one of the goals of network debugging. The cost coefficient is used to measure the importance of the index to the model debugging. If the cost coefficient of a certain index is large, it means that the index has a greater impact on the model.
[0025] The water resource scheduling optimization model is debugged according to the cost coefficient of the model debugging evaluation index. By optimizing the network parameters and the model, the loss function (ie, the debugging evaluation index) can be minimized to achieve the expected scheduling effect.
[0026] In some embodiments, according to the cost coefficient of the model network debugging evaluation index, debugging the water resource scheduling optimization model includes: According to the cost coefficient of the model debugging evaluation index, the model variables of the water resource scheduling optimization model are adjusted. By improving the model variables, the prediction ability and accuracy of the model can be improved, thereby improving the performance of water resource scheduling optimization.
[0027] In some embodiments, according to the difference between the water resources scheduling prediction scheme and the water resources scheduling authentication scheme, determining the cost coefficient of the model debugging evaluation index includes: According to the commonality metric value between the water resource scheduling prediction scheme and the water resource scheduling authentication scheme, a cost coefficient of the first local network debugging evaluation index of the model debugging evaluation index is determined.
[0028] Based on the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system determined in the water resources scheduling prediction plan and the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system in the water resources scheduling authentication plan, the cost coefficient of the second local network debugging evaluation indicator of the first network debugging evaluation indicator is determined.
[0029] The cost coefficient of the model debugging evaluation index is determined according to the cost coefficient of the first local network debugging evaluation index and the cost coefficient of the second local network debugging evaluation index.
[0030] For example, suppose that both the water resource scheduling prediction scheme and the water resource scheduling authentication scheme focus on the water level change of the water source, and the commonality metric value of the two is high. Therefore, the water level change can be used as the first local network debugging evaluation indicator and given a higher cost coefficient. This means that during the network debugging process, more attention will be paid to and optimized as an indicator of water level change to ensure the consistency of the prediction and authentication results.
[0031] In some embodiments, determining the cost coefficient of the first local network debugging evaluation index of the model network debugging evaluation index according to the commonality metric value of the water resource scheduling prediction scheme and the water resource scheduling authentication scheme includes: Obtaining importance coefficients corresponding to respective scheduling decision tasks in the water resource scheduling forecasting scheme, wherein each scheduling decision task in the water resource scheduling forecasting scheme includes the scheduling decision task of at least one target water conservancy facility system.
[0032] According to the importance coefficients corresponding to each scheduling decision task in the water resources scheduling prediction scheme, and the commonality measurement values of each scheduling decision task in the water resources scheduling prediction scheme and each scheduling decision task in the water resources scheduling authentication scheme, the cost coefficient of the model debugging evaluation index of the first network debugging evaluation index is determined.
[0033] In the specific example, it is necessary to consider the two scheme-based metrics of each scheduling decision task and multiply them by the importance coefficient of the task to obtain the cost coefficient. For example, if a scheduling decision task has a high commonality metric value in both the water resource scheduling prediction scheme and the water resource scheduling authentication scheme, and is also very important in the entire water resource scheduling, then its cost coefficient will be high, indicating that this task has a greater impact on network debugging.
[0034] By calculating the cost coefficient in this way, more reasonable decisions can be made during network debugging.
[0035] In some embodiments, based on the difference between the water resource scheduling prediction plan output by the optimization model in historical data and the actual water resource scheduling certification plan, network debugging of the water resource scheduling optimization model also includes: using the BP neural network and support vector machine coupling method to iteratively obtain the optimal parameters during the training process.
[0036] In some embodiments, according to the generated water resource scheduling prediction plan, promoting content display based on events on the timeline, and performing perspective following and broadcasting include: A timeline is provided, and based on the generated water resources scheduling forecast scheme and the watershed model of the target water area, event simulation is performed through animation, and simulation events are reported during the simulation process.
[0037] For example, water resource scheduling forecast plan A is a historical review of flood No. 1 in 2023.
[0038] Time period: 2023 / 6 / 16 0:00~2023 / 6 / 21 03:00 1. (First, the whole area will experience rainfall, then switch to the upper reaches for illustration) From X:00 on ...
[0039] 2. (Diagram of main stream scene) A large water rise has occurred (will occur) in the main stream of the Yangtze River. The water conditions in XX, XX, XX, XX, and XX are stable. It is estimated that a flood will form in the main stream at 3:00 on the 18th.
[0040] 3. (xx detailed scene, water level display) At 6:00 on the 16th, the xx reservoir began to rise; the water level was 986.41m and the flow rate was 1769m³ / s.
[0041] 4. (xx detailed scene, water level display) At 6:00 on the 16th, the xx reservoir began to rise; the water level was 780.1m and the flow rate was 1626m³ / s.
[0042] 5. (Flood peak process perspective, large scene annotation) The flood peak (will) pass through xx at 9:00 on the 19th and 12:00 on the 19th, and the peak flows are 2377m³ / s and 7005m³ / s respectively.
[0043] 6. (xx detailed scene, water level display) At 3:00 on the 8th, the water level of xx reservoir was 93.11m, with an inflow of 2873m³ / s and an outflow of 2770m³ / s.
[0044] 7. (xx Dam, water level changes) It is planned to regulate and control floods through the xx reservoir, reducing the peak flood by 4120m³ / s. The highest water level in front of the dam is expected to be 98.74m, the maximum inflow is 7005m³ / s, the maximum outflow is 2885m³ / s, and the peak reduction rate is 59%.
[0045] 8. The flood peak will pass through XXX\XXX at 9:00 and 12:00 on the 19th respectively.
[0046] (Back to the big scene, the ambient light effect returns to a no-rainfall state) This flood is not expected to exceed the river's flood-carrying capacity.
[0047] An embodiment of the present application further proposes a water resource scheduling deduction system, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the water resource scheduling deduction method as described above are implemented.
[0048] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. It is not limited to the examples described in this specification or during the implementation of this application, and its examples will be interpreted as non-exclusive.
[0049] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description.
[0050] The above embodiments are merely exemplary embodiments of the present disclosure. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present disclosure, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.
Claims
1. A water resources scheduling simulation method, characterized in that: include: Obtaining rainfall forecast data for a target water area, and determining a description text of a case water resource associated with the rainfall forecast data, wherein the description text includes water resource description information of the case water resource in the target water area; Identifying resource elements based on the description text and the rainfall forecast data; Inputting rainfall forecast data and identified resource elements into a water resource scheduling optimization model to generate a corresponding runoff forecast and a water resource scheduling prediction scheme based on the rainfall forecast data and the description vector of the resource elements, wherein the water resource scheduling optimization model is obtained after network debugging of the water resource scheduling optimization model based on the difference between the water resource scheduling prediction scheme outputted by the optimization model in historical data and the actual water resource scheduling authentication scheme, and the water resource scheduling authentication scheme is a water resource scheduling scheme corresponding to the target water area for characterizing a scheduling decision task of at least one target water conservancy facility; According to the generated water resource scheduling forecast plan, content display is driven by events based on the timeline, and perspective following and broadcasting are performed.
2. The water resources scheduling simulation method according to claim 1, characterized in that: Based on the difference between the water resource scheduling prediction scheme output by the optimization model in historical data and the actual water resource scheduling verification scheme, the network debugging of the water resource scheduling optimization model includes: Determining the cost coefficient of the model debugging evaluation index according to the difference between the water resources scheduling prediction scheme and the water resources scheduling certification scheme; The water resource scheduling optimization model is debugged according to the cost coefficient of the model debugging evaluation index.
3. The water resources scheduling simulation method according to claim 2, characterized in that: According to the cost coefficient of the model network debugging evaluation index, debugging the water resources scheduling optimization model includes: According to the cost coefficient of the model debugging evaluation index, the model variables of the water resources scheduling optimization model are adjusted.
4. The water resources scheduling simulation method according to claim 3, characterized in that: According to the difference between the water resources scheduling prediction scheme and the water resources scheduling certification scheme, the cost coefficients of the model debugging evaluation index are determined to include: Determining a cost coefficient of a first local network debugging evaluation indicator of the model debugging evaluation indicator according to a commonality metric value between the water resources scheduling prediction scheme and the water resources scheduling authentication scheme; Determine the cost coefficient of the second local network debugging evaluation index of the first network debugging evaluation index according to the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system determined in the water resources scheduling prediction plan and the distribution characteristics of the scheduling decision tasks of at least one target water conservancy facility system in the water resources scheduling authentication plan; The cost coefficient of the model debugging evaluation index is determined according to the cost coefficient of the first local network debugging evaluation index and the cost coefficient of the second local network debugging evaluation index.
5. The water resources scheduling simulation method according to claim 4, characterized in that: Determining the cost coefficient of the first local network debugging evaluation index of the model network debugging evaluation index according to the commonality metric value of the water resources scheduling prediction scheme and the water resources scheduling authentication scheme includes: Obtaining importance coefficients corresponding to respective scheduling decision tasks in the water resources scheduling forecasting scheme, wherein each scheduling decision task in the water resources scheduling forecasting scheme includes the scheduling decision task of at least one target water conservancy facility system; According to the importance coefficients corresponding to each scheduling decision task in the water resources scheduling prediction scheme, and the commonality measurement values of each scheduling decision task in the water resources scheduling prediction scheme and each scheduling decision task in the water resources scheduling authentication scheme, the cost coefficient of the model debugging evaluation index of the first network debugging evaluation index is determined.
6. The water resources scheduling simulation method according to claim 5, characterized in that: Based on the difference between the water resource scheduling prediction scheme output by the optimization model in historical data and the actual water resource scheduling verification scheme, the network debugging of the water resource scheduling optimization model also includes: In the training process, the BP neural network and support vector machine coupling method are used to iteratively obtain the optimal parameters.
7. The water resources scheduling simulation method according to claim 1, characterized in that: According to the generated water resource scheduling forecast plan, the content display is driven by events based on the timeline, and the perspective following and broadcasting are performed, including: A timeline is provided, and based on the generated water resources scheduling forecast scheme and the watershed model of the target water area, event simulation is performed through animation, and simulation events are reported during the simulation process.
8. A water resources scheduling simulation system, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the water resources scheduling simulation method as described in any one of claims 1 to 7 are implemented.