A cascade reservoir visual scheduling system and method based on real-time dynamic reservoir capacity

Abstract

The application discloses a kind of based on real-time dynamic reservoir capacity cascade reservoir visual scheduling system and method, system includes visual integrated modeling platform, provides drainage basin data management, one-dimensional and two-dimensional hydrodynamic coupling model construction, grid subdivision and achievement visualization function;Dynamic reservoir capacity real-time calculation module, realizes the fast accurate calculation of cascade reservoir dynamic reservoir capacity using double-factor linear interpolation algorithm;Standardized model adapter, define uniform interface encapsulation heterogeneous hydrology hydrodynamic model, realize the plug and play of model;Calculation API service module, encapsulate core hydraulics calculation function as RESTful API interface;Unified security authentication module, through call, verify, render three authentication processes realize the security single sign-on of main platform and subsystem;Solve the problem that traditional scheduling system is low in integration, dynamic reservoir capacity calculation precision is poor, model access is complex and cross-system security is weak, provide efficient, accurate, safe integrated decision support platform for the optimization of drainage basin cascade reservoir scheduling.

Description

Technical Field

[0001] This invention belongs to the field of reservoir scheduling technology, specifically relating to a cascade reservoir visualization scheduling system and method based on real-time dynamic reservoir capacity. Background Technology

[0002] Reservoir scheduling is the core of comprehensive water resource utilization and flood control and disaster reduction. With the development of information technology, traditional scheduling models relying on independent software, static charts, and manual calculations are no longer sufficient to meet the demands of refined and intelligent scheduling of modern reservoir groups. The main problems currently include: 1. System fragmentation: Different software is often used in hydrological forecasting, hydrodynamic simulation, and scheduling decision-making, making data exchange difficult, forming "information silos," and resulting in a lengthy decision-making chain. 2. Coarse calculation of key parameters: Dynamic storage capacity is a key parameter reflecting the actual water storage capacity of a reservoir under non-steady flow conditions. Traditional methods often use static storage capacity curves or empirical formulas for estimation, which cannot accurately consider the coupling effect of water level and inflow, leading to insufficient scheduling accuracy. 3. Complex model integration: Professional hydrological and hydrodynamic models usually have specific input and output formats. Integrating them into a unified business platform requires a lot of customized development, which is labor-intensive and difficult to maintain. 4. Weak security of multi-system collaboration: The integrated scheduling platform is often composed of multiple business subsystems such as forecasting, calculation, and display. It lacks a unified and secure identity authentication and single sign-on mechanism, resulting in decentralized management and security risks.

[0003] Although some existing platforms have introduced visualization technology, they have not yet formed an effective overall solution in terms of the accuracy of the core computing engine, the standardization of model management, and the security of the system architecture. Therefore, it is necessary to design a cascade reservoir visualization scheduling system and method based on real-time dynamic reservoir capacity to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a visualized scheduling system and method for cascade reservoirs based on real-time dynamic reservoir capacity. It aims to overcome the problems of low integration, poor accuracy of dynamic reservoir capacity calculation, complex model access, and weak cross-system security in traditional scheduling systems. By constructing a unified visualized modeling platform, it integrates a high-precision real-time dynamic reservoir capacity calculation engine, a standardized model adapter architecture, lightweight computing API services, and a unified security authentication framework, thereby achieving full-process integration, standardization, and security from data to model, from calculation to decision-making, and from login to application.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity includes a display layer, a service layer, a core layer, a data layer, and a security layer; The presentation layer includes a visualization integrated modeling platform, which is built on a geographic information system and is used to provide functions such as watershed basic data management, visualization configuration of one-dimensional and two-dimensional hydrodynamic coupling models, model meshing, submission of computation tasks, and multi-dimensional display of results. The service layer includes a computing API service module, which encapsulates the core layer's computing functions into RESTful web API interfaces for the presentation layer's front end or other applications to call. The core layer includes a real-time dynamic reservoir capacity calculation module, a standardized model adapter, a hydrological model adapter, and a hydrodynamic model adapter. The real-time dynamic reservoir capacity calculation module receives downstream water level and upstream inflow parameters, calculates and outputs the corresponding dynamic reservoir capacity value based on a pre-stored water level-flow-dynamic reservoir capacity relationship data table using a two-factor linear interpolation algorithm, and supports visualization of the calculation results in data tables and curve clusters. The hydrological model adapter and the hydrodynamic model adapter inherit from a unified standardized model adapter parent class, and are used to encapsulate the corresponding hydrological model and hydrodynamic model, respectively, to realize the functions of reading parameters from standardized input files, driving native model calculations, and writing results to standardized output files. The data layer includes databases and file storage systems, which are used to store the basic data required for computation and the results generated by computation; The security layer includes a unified security authentication module, which is used to enable secure single sign-on based on encrypted tokens and one-time login codes by interacting between the main platform and various application subsystems through three authentication interfaces: call, verify, and render. The visualization integrated modeling platform in the presentation layer triggers the real-time dynamic reservoir capacity calculation module or hydrological model adapter / hydrodynamic model adapter in the core layer to perform calculations by calling the interface of the calculation API service module in the service layer. Each module in the core layer connects to the database and file storage system in the data layer to read input data and write calculation results. The unified security authentication module in the security layer interacts with the presentation layer, service layer and core layer respectively to ensure that the user access process is secure and controllable.

[0006] Preferably, the real-time dynamic storage capacity calculation module includes: The parameter receiving unit is used to receive the downstream water level value and the upstream inflow rate value transmitted through the front-end interface or API of the visual integrated modeling platform. The data query unit is used to query the discrete water level-flow-dynamic reservoir capacity relationship data table that is pre-placed in the data layer and generated by calibration through a one-dimensional and two-dimensional hydrodynamic coupling model; The interpolation calculation unit is used to perform bilinear interpolation calculations on a relational data table with the received water level value and flow rate value as two variables to obtain an accurate dynamic reservoir capacity value. The result feedback unit is used to return the calculated dynamic reservoir capacity value and drive the front-end interface of the visualization integrated modeling platform to update the data table and the dynamic reservoir capacity relationship curve cluster with water level and flow rate as the horizontal axes.

[0007] Preferably, the parent class of the standardized model adapter defines standardized input and output interface methods; the standardized input file is param.json, whose data structure includes basic model parameters basicParams and model running dataset runDataSet; the standardized output file is result.json, used to store various result data generated by model calculation; the interface methods include: the handler() method for executing input file parsing and model driving, and the getResult() method for reading and returning the contents of the output file; the hydrological model adapter and the hydrodynamic model adapter implement the handler() method and the getResult() method respectively to drive the corresponding native hydrological model or hydrodynamic model to perform calculations, and store the standardized results in the data layer.

[0008] Preferably, the API interfaces provided by the computing API service module include at least: The dynamic reservoir capacity calculation interface is / api / volume / sg. The request parameters include WaterLevel and Discharge, and the returned parameter is volume. The flood wave propagation time calculation interface is / api / runtime / sg. The request parameters include WaterLevel and Discharge, and the return parameter is PropagationTime. The interface uses the POST request method, and both the request and response bodies are in JSON format.

[0009] Preferably, the three-stage authentication process of the unified security authentication module is as follows: The main platform backend calls the subsystem's call interface, sending a message containing the login ID, timestamp, and greetings generated from the timestamp and pre-shared information salt. After the subsystem verifies the validity of the timestamp and greetings, it generates a reply token and returns it. The main platform backend calls the subsystem's verify interface, sending the login ID, call message, timestamp, and user information encrypted with dynamic encryption salt. After the subsystem verifies the information, it decrypts the user information, generates a one-time login code (oncecode), returns it, and stores the user information accordingly. The main platform frontend loads the subsystem page address along with the one-time login code, accesses the subsystem's render interface, and after the subsystem verifies the validity of the login code, it completes the user login session initialization and renders the protected subsystem page, while simultaneously invalidating the one-time login code.

[0010] Preferably, a scheduling method for a cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity includes the following steps: S1, users complete identity verification through the unified security authentication module and log in to the visual integrated modeling platform; S2, on the dynamic reservoir capacity calculation function page of the visualization integrated modeling platform, input or select the target downstream water level and upstream inflow parameters; S3, the front end of the visualization integrated modeling platform calls the dynamic storage capacity calculation API in the calculation API service module to send parameters to the back end service; S4, the backend service calls the dynamic storage capacity real-time calculation module, queries the pre-set relational data table in the data layer according to the received parameters, performs two-factor linear interpolation calculation, and obtains the dynamic storage capacity value; S5 returns the calculated dynamic storage capacity value to the front end through the dynamic storage capacity calculation API. The front end interface updates the data table synchronously and highlights the corresponding data points and relationship curves in the dynamic curve cluster chart. S6, based on the calculated dynamic reservoir capacity and other hydraulic parameters, uses the optimization scheduling calculation module in the visualization integrated modeling platform to perform simulation and optimization analysis of multi-objective scheduling schemes.

[0011] Preferably, the two-factor linear interpolation calculation in step S4 is specifically as follows: Using the input water level Z and flow rate Q as interpolation points, find four grid points (Z1, Q1, V11), (Z1, Q2, V12), (Z2, Q1, V21), and (Z2, Q2, V22) that enclose (Z,Q) in the discrete data table (Zi, Qj, Vij). First, fix the water levels Z1 and Z2 and perform linear interpolation on the flow rate Q to obtain V1 and V2 respectively. Then, perform linear interpolation on the water level Z to obtain the final dynamic reservoir capacity V. Alternatively, fix the flow rate first and then perform interpolation on the water level.

[0012] Preferably, when simulating hydrological and hydrodynamic processes, the method further includes: Configure model parameters and computation cycle on the visual integrated modeling platform; The visualization integrated modeling platform can be accessed through the computation API service module or by directly calling the corresponding hydrological model adapter or hydrodynamic model adapter. The adapter's handler() method reads or generates the param.json input file, drives the encapsulated native model to perform calculations, and writes the results to the result.json file for storage in the data layer; The visualization integrated modeling platform obtains the calculation results from the data layer through the adapter's getResult() method and performs visualization rendering of process lines, contour maps, and 3D scenes.

[0013] Preferably, a computer device includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to run the aforementioned method for visual scheduling of cascade reservoirs based on real-time dynamic reservoir capacity.

[0014] Preferably, a computer-readable storage medium is provided, on which computer instructions are stored, the computer instructions being used to cause a computer to run the described method for visual scheduling of cascade reservoirs based on real-time dynamic reservoir capacity.

[0015] The beneficial effects of the cascade reservoir visualization scheduling system and method based on real-time dynamic reservoir capacity provided by this invention are as follows: 1. Significantly improved calculation accuracy and timeliness: By generating a high-precision relation table through coupling the hydrodynamic model and combining it with real-time bilinear interpolation, the calculation of key parameters such as dynamic reservoir capacity is achieved at the second level with high precision, overcoming the coarseness and lag of traditional methods.

[0016] 2. High system integration and interactivity: The entire process of modeling, simulation, calculation, analysis and display is integrated into a unified visualization platform, providing an intuitive graphical operation interface, which greatly improves the efficiency of scheduling and user experience.

[0017] 3. Strong standardization and scalability: The model adapter architecture and API service design make the access of professional models and the expansion of system functions standardized and convenient, reducing the cost of system maintenance and upgrades.

[0018] 4. Safe, reliable and meets domestic requirements: The multi-layered security authentication mechanism ensures system and data security; the technology stack supports the domestic software and hardware ecosystem and is in line with the national information technology application innovation strategy. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall architecture of the cascade reservoir scheduling system in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the workflow of the real-time dynamic storage capacity calculation module in this embodiment of the invention. Figure 3 This is a flowchart illustrating the execution of the standardized model adapter in an embodiment of the present invention. Figure 4 This is a visualization example of the calculation results of the hydrological and hydrodynamic model in an embodiment of the present invention; Figure 5 This is a timing diagram of the three-way handshake interaction of the unified security authentication module in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a computer device in an embodiment of the present invention. Detailed Implementation

[0020] Example 1: like Figure 1 As shown, a cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity includes a display layer, a service layer, a core layer, a data layer, and a security layer. The presentation layer includes a visualization integrated modeling platform, which is built on a geographic information system and is used to provide functions such as watershed basic data management, visualization configuration of one-dimensional and two-dimensional hydrodynamic coupling models, model meshing, submission of computation tasks, and multi-dimensional display of results. The service layer includes a computing API service module, which encapsulates the core layer's computing functions into RESTful web API interfaces for the presentation layer's front end or other applications to call. The core layer includes a real-time dynamic reservoir capacity calculation module, a standardized model adapter, a hydrological model adapter, and a hydrodynamic model adapter. The real-time dynamic reservoir capacity calculation module receives downstream water level and upstream inflow parameters, calculates and outputs the corresponding dynamic reservoir capacity value based on a pre-stored water level-flow-dynamic reservoir capacity relationship data table using a two-factor linear interpolation algorithm, and supports visualization of the calculation results in data tables and curve clusters. The hydrological model adapter and the hydrodynamic model adapter inherit from a unified standardized model adapter parent class, and are used to encapsulate the corresponding hydrological model and hydrodynamic model, respectively, to realize the functions of reading parameters from standardized input files, driving native model calculations, and writing results to standardized output files. The data layer includes databases and file storage systems, which are used to store the basic data required for computation and the results generated by computation; The security layer includes a unified security authentication module, which is used to enable secure single sign-on based on encrypted tokens and one-time login codes by interacting between the main platform and various application subsystems through three authentication interfaces: call, verify, and render. The visualization integrated modeling platform in the presentation layer triggers the real-time dynamic reservoir capacity calculation module or hydrological model adapter / hydrodynamic model adapter in the core layer to perform calculations by calling the interface of the calculation API service module in the service layer. Each module in the core layer connects to the database and file storage system in the data layer to read input data and write calculation results. The unified security authentication module in the security layer interacts with the presentation layer, service layer and core layer respectively to ensure that the user access process is secure and controllable.

[0021] Preferably, the real-time dynamic storage capacity calculation module includes: The parameter receiving unit is used to receive the downstream water level value and the upstream inflow rate value transmitted through the front-end interface or API of the visual integrated modeling platform. The data query unit is used to query the discrete water level-flow-dynamic reservoir capacity relationship data table that is pre-placed in the data layer and generated by calibration through a one-dimensional and two-dimensional hydrodynamic coupling model; The interpolation calculation unit is used to perform bilinear interpolation calculations on a relational data table with the received water level value and flow rate value as two variables to obtain an accurate dynamic reservoir capacity value. The result feedback unit is used to return the calculated dynamic reservoir capacity value and drive the front-end interface of the visualization integrated modeling platform to update the data table and the dynamic reservoir capacity relationship curve cluster with water level and flow rate as the horizontal axes.

[0022] Preferably, the parent class of the standardized model adapter defines standardized input and output interface methods; the standardized input file is param.json, whose data structure includes basic model parameters basicParams and model running dataset runDataSet; the standardized output file is result.json, used to store various result data generated by model calculation; the interface methods include: the handler() method for executing input file parsing and model driving, and the getResult() method for reading and returning the contents of the output file; the hydrological model adapter and the hydrodynamic model adapter implement the handler() method and the getResult() method respectively to drive the corresponding native hydrological model or hydrodynamic model to perform calculations, and store the standardized results in the data layer.

[0023] Preferably, the API interfaces provided by the computing API service module include at least: The dynamic reservoir capacity calculation interface is / api / volume / sg. The request parameters include WaterLevel and Discharge, and the returned parameter is volume. The flood wave propagation time calculation interface is / api / runtime / sg. The request parameters include WaterLevel and Discharge, and the return parameter is PropagationTime. The interface uses the POST request method, and both the request and response bodies are in JSON format.

[0024] Furthermore, the API service module also includes the model execution interface / api / model / run and API route management.

[0025] Preferably, the three-stage authentication process of the unified security authentication module is as follows: The main platform backend calls the subsystem's call interface, sending a message containing the login ID, timestamp, and greetings generated from the timestamp and pre-shared information salt. After the subsystem verifies the validity of the timestamp and greetings, it generates a reply token and returns it. The main platform backend calls the subsystem's verify interface, sending the login ID, call message, timestamp, and user information encrypted with dynamic encryption salt. After the subsystem verifies the information, it decrypts the user information, generates a one-time login code (oncecode), returns it, and stores the user information accordingly. The main platform frontend loads the subsystem page address along with the one-time login code, accesses the subsystem's render interface, and after the subsystem verifies the validity of the login code, it completes the user login session initialization and renders the protected subsystem page, while simultaneously invalidating the one-time login code.

[0026] Preferably, a scheduling method for a cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity includes the following steps: S1, users complete identity verification through the unified security authentication module and log in to the visual integrated modeling platform; S2, on the dynamic reservoir capacity calculation function page of the visualization integrated modeling platform, input or select the target downstream water level and upstream inflow parameters; S3, the front end of the visualization integrated modeling platform calls the dynamic storage capacity calculation API in the calculation API service module to send parameters to the back end service; S4, the backend service calls the dynamic storage capacity real-time calculation module, queries the pre-set relational data table in the data layer according to the received parameters, performs two-factor linear interpolation calculation, and obtains the dynamic storage capacity value; S5 returns the calculated dynamic storage capacity value to the front end through the dynamic storage capacity calculation API. The front end interface updates the data table synchronously and highlights the corresponding data points and relationship curves in the dynamic curve cluster chart. S6, based on the calculated dynamic reservoir capacity and other hydraulic parameters, uses the optimization scheduling calculation module in the visualization integrated modeling platform to perform simulation and optimization analysis of multi-objective scheduling schemes.

[0027] Preferably, the two-factor linear interpolation calculation in step S4 is specifically as follows: Using the input water level Z and flow rate Q as interpolation points, find four grid points (Z1, Q1, V11), (Z1, Q2, V12), (Z2, Q1, V21), and (Z2, Q2, V22) that enclose (Z,Q) in the discrete data table (Zi, Qj, Vij). First, fix the water levels Z1 and Z2 and perform linear interpolation on the flow rate Q to obtain V1 and V2 respectively. Then, perform linear interpolation on the water level Z to obtain the final dynamic reservoir capacity V. Alternatively, fix the flow rate first and then perform interpolation on the water level.

[0028] Preferably, when simulating hydrological and hydrodynamic processes, the method further includes: Configure model parameters and computation cycle on the visual integrated modeling platform; The visualization integrated modeling platform can be accessed through the computation API service module or by directly calling the corresponding hydrological model adapter or hydrodynamic model adapter. The adapter's handler() method reads or generates the param.json input file, drives the encapsulated native model to perform calculations, and writes the results to the result.json file for storage in the data layer; The visualization integrated modeling platform obtains the calculation results from the data layer through the adapter's getResult() method and performs visualization rendering of process lines, contour maps, and 3D scenes.

[0029] Preferably, a computer device includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to run the aforementioned method for visual scheduling of cascade reservoirs based on real-time dynamic reservoir capacity.

[0030] Preferably, a computer-readable storage medium is provided, on which computer instructions are stored, the computer instructions being used to cause a computer to run the described method for visual scheduling of cascade reservoirs based on real-time dynamic reservoir capacity.

[0031] Example 2: As attached Figure 1 As shown, the system of this invention adopts a layered architecture design. Users access the visual integrated modeling platform (presentation layer) through a browser. The platform front-end triggers the back-end dynamic database real-time calculation module or standardized model adapter (core layer) to perform specific calculations by calling various interfaces of the calculation API service module (service layer). All data required and generated by the calculations are managed by the database and file storage system (data layer). A unified security authentication module (security layer) runs through the entire access process, ensuring security and controllability at each stage.

[0032] The implementation example of real-time dynamic storage capacity calculation provided in this embodiment is as follows: like Figure 2As shown, taking the calculation of the dynamic reservoir capacity at a certain moment in front of the Three Gorges Dam as an example: 1. After logging in securely, users will enter the "Dynamic Storage Capacity Calculation" page.

[0033] 2. In the input area of ​​the page, enter (or select from the real-time database) the current upstream water level Z=64.0m and the inflow rate Q=20000 m³ / s.

[0034] 3. Click the "Calculate" button. The front end calls the / api / volume / sg API and sends the parameters {"Parameters": {"WaterLevel": "64.0", "Discharge": "20000"}}.

[0035] 4. The backend computing engine receives the request. In the pre-stored "Three Gorges-Gezhouba Dynamic Reservoir Capacity Relationship Table", find the four grid points surrounding the point (64.0, 20000): (63.5, 19000, V11), (63.5, 21000, V12), (64.5, 19000, V21), (64.5, 21000, V22).

[0036] 5. Perform bilinear interpolation: First, interpolate the flow rates of 19000 and 21000 at a water level of 63.5m to obtain V1; then, obtain V2 at a water level of 64.5m; finally, interpolate between V1 and V2 at a water level of 64.0m to obtain the final dynamic reservoir capacity value V.

[0037] 6. The result {"volume": V} is returned to the front end. A new record (64.0, 20000, V) is added to the "Dynamic Storage Capacity Data Table" on the page. At the same time, in the "Water Level-Dynamic Storage Capacity Curve Cluster", the point (64.0, V) will be highlighted on the curve with Q=20000; and in the "Flow Rate-Dynamic Storage Capacity Curve Cluster", the point (20000, V) will be highlighted on the curve with Z=64.0.

[0038] The standardized model adapter workflow is as follows: like Figure 3 As shown, when it is necessary to forecast hydrological and hydrodynamic processes for the next 72 hours: 1. On the "Hydrological and Hydrodynamic Model Calculation" page, users set the start and end times, output step size, and other basicParams, and select the runDataSet data source such as flow rate, water level, and rainfall.

[0039] 2. The platform backend calls the corresponding architectureAdapter.

[0040] 3. The adapter's handler() method automatically generates a param.json file containing basicParams and runDataSet based on the configuration, and stores it in the specified directory / archigh / input / .

[0041] 4. The `handler()` method starts the hydrodynamic model it encapsulates, written in native Fortran / C++. This model reads input from `param.json` and performs numerical calculations.

[0042] 5. After the calculation is completed, the model will write the predicted water level process, flow process, reservoir capacity change and other results into the / archigh / input / result.json file in a predetermined format.

[0043] 6. The platform then calls the adapter's getResult() method. This method reads result.json, parses the data into the platform's internal data structure, and returns it.

[0044] 7. The platform frontend receives the data and uses charting libraries such as ECharts to draw water level and flow rate curves for each station, displaying the reservoir capacity for each time period in tabular form, as shown in the attached table. Figure 4 As shown.

[0045] The unified security authentication process is as follows: like Figure 5 As shown, when the subsystem connects to the main platform: 1. The main platform backend sends a call request to the subsystem backend, which includes the login ID, the current timestamp, and the call message generated by MD5 using "timestamp + reverse information salt".

[0046] 2. The subsystem verifies the timestamp within ±2 seconds and uses the same algorithm to locally generate a call phrase for comparison. After successful verification, a reply token (generated by MD5 using "subsystem timestamp + reverse information salt + main platform call phrase") is generated and returned.

[0047] 3. The main platform backend uses user information and dynamic salt to encrypt and generate incantation, calls the subsystem verify interface, and transmits login ID, call message, timestamp and incantation.

[0048] 4. The subsystem verifies the call phrase and uses "scent code + call phrase" to form a decryption salt to decrypt the incantation to obtain user information. After verifying permissions, it generates a 36-bit random one-time login code (oncecode) and returns it. The oncecode and user information are then stored together in memory.

[0049] 5. The main platform frontend guides the user's browser to the subsystem frontend address, such as https: / / subsystem.com / # / login / {oncecode}.

[0050] 6. The subsystem frontend submits the original code to its own backend render service. The backend verifies the validity of the original code, obtains the associated user information, completes the login session creation, and then the original code immediately expires. The frontend renders the personalized page for the logged-in user.

[0051] Through the above implementation methods, the present invention realizes a fully integrated, safe and controllable intelligent application of cascade reservoir scheduling, encompassing the entire chain from data perception, model calculation, simulation to decision support.

[0052] Example 3: like Figure 6 As shown, embodiments of the present invention also provide a computer device. Figure 6 Taking a single processor 10 as an example, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices, such as display devices coupled to the interface. In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations, for example, as a server array, a group of blade servers, or a multiprocessor system.

[0053] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0054] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0055] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0056] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0057] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0058] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

Claims

1. A cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity, characterized in that, It includes a presentation layer, a service layer, a core layer, a data layer, and a security layer; The presentation layer includes a visualization integrated modeling platform, which is built on a geographic information system and is used to provide functions such as watershed basic data management, visualization configuration of one-dimensional and two-dimensional hydrodynamic coupling models, model meshing, submission of computation tasks, and multi-dimensional display of results. The service layer includes a computing API service module, which encapsulates the computing functions of the core layer into network API interfaces for the front end of the presentation layer or other applications to call. The core layer includes a real-time dynamic reservoir capacity calculation module, a standardized model adapter, a hydrological model adapter, and a hydrodynamic model adapter. The real-time dynamic reservoir capacity calculation module receives downstream water level and upstream inflow parameters, calculates and outputs the corresponding dynamic reservoir capacity value based on a pre-stored water level-flow-dynamic reservoir capacity relationship data table using a two-factor linear interpolation algorithm, and then outputs the corresponding dynamic reservoir capacity value. The hydrological model adapter and the hydrodynamic model adapter inherit from a unified standardized model adapter parent class, and are used to encapsulate the corresponding hydrological model and hydrodynamic model, respectively, to realize the functions of reading parameters from the standardized input file, driving the native model calculation, and writing the results to the standardized output file. The data layer includes databases and file storage systems, which are used to store the basic data required for computation and the results generated by computation; The security layer includes a unified security authentication module, which is used to interact between the main platform and various application subsystems through a three-way authentication interface to achieve secure single sign-on based on encrypted tokens and one-time login codes; The visualization integrated modeling platform in the presentation layer triggers the real-time dynamic reservoir capacity calculation module or hydrological model adapter / hydrodynamic model adapter in the core layer to perform calculations by calling the interface of the calculation API service module in the service layer. Each module in the core layer connects to the database and file storage system in the data layer to read input data and write calculation results. The unified security authentication module in the security layer interacts with the presentation layer, service layer and core layer respectively to ensure that the user access process is secure and controllable.

2. The cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity as described in claim 1, characterized in that, The real-time dynamic storage capacity calculation module includes: The parameter receiving unit is used to receive the downstream water level value and the upstream inflow rate value transmitted through the front-end interface or API of the visual integrated modeling platform. The data query unit is used to query the discrete water level-flow-dynamic reservoir capacity relationship data table that is pre-placed in the data layer and generated by calibration through a one-dimensional and two-dimensional hydrodynamic coupling model; The interpolation calculation unit is used to perform bilinear interpolation calculations on a relational data table with the received water level value and flow rate value as two variables to obtain an accurate dynamic reservoir capacity value. The result feedback unit is used to return the calculated dynamic reservoir capacity value and drive the front-end interface of the visualization integrated modeling platform to update the data table and the dynamic reservoir capacity relationship curve cluster with water level and flow rate as the horizontal axes.

3. The cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity according to claim 1, characterized in that, The standardized model adapter parent class defines standardized input / output interface methods; The standardized input file is param.json, whose data structure includes the basic model parameters basicParams and the model runtime dataset runDataSet; The standardized output file is result.json, which is used to store various result data generated by the model calculation; The interface methods include: the handler() method for performing input file parsing and model-driven operations, and the getResult() method for reading and returning the contents of the output file; The hydrological model adapter and the hydrodynamic model adapter implement the handler() method and the getResult() method, respectively, to drive the corresponding native hydrological model or hydrodynamic model to perform calculations and store the standardized results in the data layer.

4. A cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity as described in claim 1, characterized in that, The API interfaces provided by the computing API service module include at least: The dynamic reservoir capacity calculation interface requests parameters including WaterLevel and Discharge, and returns a volume parameter. The flood wave propagation time calculation interface requests parameters including WaterLevel and Discharge, and returns PropagationTime as the parameter. The interface uses the POST request method, and both the request and response bodies are in JSON format.

5. A cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity as described in claim 1, characterized in that, The three-stage authentication process of the unified security authentication module is as follows: The main platform backend calls the subsystem's call interface, sending a call message containing the login ID, timestamp, and a message generated from the timestamp and pre-shared information salt. After the subsystem verifies the validity of the timestamp and the call message, it generates an answer password and returns it. The main platform backend calls the subsystem's verify interface, sending the login ID, call message, timestamp, and user information encrypted with a dynamic encryption salt. After the subsystem verifies the information, it decrypts the user information, generates a one-time login code, returns it, and stores the user information accordingly. The main platform frontend loads the subsystem page address along with the one-time login code, accesses the subsystem's render interface, and after the subsystem verifies the validity of the login code, it completes the user login session initialization and renders the protected subsystem page, while simultaneously invalidating the one-time login code.

6. A scheduling method for a cascade reservoir visualization scheduling system based on real-time dynamic reservoir capacity according to any one of claims 1-5, characterized in that, Includes the following steps: S1, users complete identity verification through the unified security authentication module and log in to the visual integrated modeling platform; S2, on the dynamic reservoir capacity calculation function page of the visualization integrated modeling platform, input or select the target downstream water level and upstream inflow parameters; S3, the front end of the visualization integrated modeling platform calls the dynamic storage capacity calculation API in the calculation API service module to send parameters to the back end service; S4, the backend service calls the dynamic storage capacity real-time calculation module, queries the pre-set relational data table in the data layer according to the received parameters, performs two-factor linear interpolation calculation, and obtains the dynamic storage capacity value; S5 returns the calculated dynamic storage capacity value to the front end through the dynamic storage capacity calculation API. The front end interface updates the data table synchronously and highlights the corresponding data points and relationship curves in the dynamic curve cluster chart. S6, based on the calculated dynamic reservoir capacity and other hydraulic parameters, uses the optimization scheduling calculation module in the visualization integrated modeling platform to perform simulation and optimization analysis of multi-objective scheduling schemes.

7. A method for visually scheduling cascade reservoirs based on real-time dynamic reservoir capacity according to claim 6, characterized in that, The two-factor linear interpolation calculation in step S4 is as follows: Using the input water level Z and flow rate Q as interpolation points, find four grid points (Z1, Q1, V11), (Z1, Q2, V12), (Z2, Q1, V21), and (Z2, Q2, V22) that enclose (Z, Q) in the discrete data table (Zi, Qj, Vij). First, fix the water levels Z1 and Z2 and perform linear interpolation on the flow rate Q to obtain V1 and V2 respectively. Then, perform linear interpolation on the water level Z to obtain the final dynamic reservoir capacity V. Alternatively, fix the flow rate first and then perform interpolation on the water level.

8. A method for visual scheduling of cascade reservoirs based on real-time dynamic reservoir capacity according to claim 6, characterized in that, When simulating hydrodynamic processes, the methods also include: Configure model parameters and computation cycle on the visual integrated modeling platform; The visualization integrated modeling platform can be accessed through the computation API service module or by directly calling the corresponding hydrological model adapter or hydrodynamic model adapter. The adapter's handler() method reads or generates the param.json input file, drives the encapsulated native model to perform calculations, and writes the results to the result.json file for storage in the data layer; The visualization integrated modeling platform obtains the calculation results from the data layer through the adapter's getResult() method and performs visualization rendering of process lines, contour maps, and 3D scenes.

9. A computer device, characterized in that, It includes a memory and a processor, which are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to run the cascade reservoir visualization scheduling method based on real-time dynamic reservoir capacity as described in any one of claims 6-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the cascade reservoir visualization scheduling method based on real-time dynamic reservoir capacity as described in any one of claims 6-8.

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