A method, device and storage medium for determining information of a river basin region

Abstract

This invention discloses a method, apparatus, device, and storage medium for determining information in a watershed area, comprising: determining a causal loop diagram with qualitative coupling relationships based on subsystems within the domain; classifying parameters under different domains to obtain type objects, and constructing a dynamic simulation model with quantitative relationships based on type objects, data, and the causal loop diagram; acquiring current information of a specified domain using a scenario combination approach, and inputting the current information into the dynamic simulation model to obtain current information on the dynamic evolution of the target domain over time. Based on the dynamic simulation model and current data of the specified domain, the current information on the dynamic evolution of the target domain is simulated, characterizing the feedback relationships and nonlinear evolution processes between subsystems, thereby improving the overall simulation capability for the interaction and continuous dynamic evolution of the domain. Since the simulation model is constructed based on information from multiple domains, covering the interaction relationships of each domain, the accuracy of the simulation results is improved.

Description

Technical Field

[0001] This invention relates to the field of watershed area management technology, and in particular to a method, apparatus, equipment and storage medium for determining information about watershed areas. Background Technology

[0002] Against the backdrop of increasingly prominent resource constraints and ecological fragility, watershed management, especially in ecologically fragile areas like the Yellow River Basin where human activities are intense, is facing intensified water supply and demand imbalances. This not only affects economic development but also puts continuous pressure on ecosystem stability, involving interactions across multiple fields such as hydrology, sediment, ecological environment, and socio-economics. To address the dual pressures of climate change and human activities, effectively identifying and managing multi-system information in this watershed region is crucial for providing important quantitative support for regional resource optimization and policy formulation, and has become an urgent issue to be resolved.

[0003] Currently, when determining information and managing watershed areas, the commonly used techniques are single subsystems, loosely coupled multi-model techniques based on physical mechanisms, and static assessment of ties. However, when dealing with deeply coupled issues involving water, sediment, ecology, and socio-economic factors that require long-term dynamic simulation, these techniques suffer from fragmented or static analysis dimensions and lack the overall simulation capability for the interaction and continuous dynamic evolution of the domain, thereby reducing the accuracy of information determination. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for determining information about a watershed area, so as to achieve accurate determination of information about the watershed area.

[0005] According to a first aspect of the present invention, a method for determining information about a watershed area is provided. The method includes: collecting historical information from different areas of a target watershed area, wherein the historical information includes parameters and data corresponding to the parameters. Based on the parameters of each domain, corresponding domain subsystems are constructed, and causal loop diagrams with qualitative coupling relationships are determined based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The parameters in different fields are classified to obtain type objects, and a dynamic simulation model with quantitative relationships is constructed based on the type objects, the data, and the causal loop diagram. The current information of a specified domain is obtained by using a scenario combination method, and the current information is input into the dynamic simulation model to obtain the current information of the dynamic evolution of the target domain over time, wherein the target domain is associated with the specified domain.

[0006] According to another aspect of the present invention, an information determination device for a watershed area is provided. The device includes: a historical information acquisition module for acquiring historical information from different areas of a target watershed area, wherein the historical information includes parameters and data corresponding to the parameters; The causal loop diagram determination module is used to construct corresponding domain subsystems based on parameters under each domain, and determine causal loop diagrams with qualitative coupling relationships based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The dynamic simulation model construction module is used to classify the parameters in different fields to obtain type objects, and construct a dynamic simulation model with quantitative relationships based on the type objects, the data and the causal loop diagram. The current information determination module is used to obtain current information of a specified domain by using a scenario combination method, and input the current information into the dynamic simulation model to obtain the current information of the target domain dynamically evolving over time, wherein the target domain is associated with the specified domain.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: one or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any embodiment of the present invention.

[0008] According to another aspect of the present invention, a storage medium for computer-executable instructions is provided, on which a computer program is stored, which, when executed by a processor, implements the method as described in any of the embodiments of the present invention.

[0009] The technical solution of this invention constructs a causal loop diagram with qualitative coupling relationships based on subsystems of each field, which includes information from four fields: hydrology, sediment, ecological environment, and socio-economics. Based on the causal loop diagram and historical data, a dynamic simulation model with quantitative relationships is constructed. Based on the dynamic simulation model and current data of a specified field, the current information of the target field's dynamic evolution is simulated, thereby improving the overall simulation capability of the interaction and continuous dynamic evolution of the fields. Since the simulation model is constructed based on information from multiple fields and covers the interaction relationships of each field, the accuracy of the simulation results is improved.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for determining information about a watershed area according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of a causal loop diagram provided in Embodiment 1 of the present invention; Figure 3 This is a flowchart of another method for determining watershed area information according to Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the structure of a watershed area information determination device according to Embodiment 3 of the present invention; Figure 5 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or terminal device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or terminal devices.

[0015] Example 1 Figure 1This is a flowchart of a method for determining information about a watershed area according to Embodiment 1 of the present invention. This embodiment is applicable to situations where information about a watershed area needs to be determined. The method can be executed by a device for determining watershed area information. This device can be implemented in hardware and / or software, and can be integrated into an electronic device with data processing capabilities. Figure 1 As shown, the method includes: S101, collect historical information from different fields in the target watershed area.

[0016] Specifically, this implementation method pre-determines the target watershed area to be managed. After determining the target watershed area, multi-source data for that area is collected. This multi-source data specifically refers to historical information from different fields, such as hydrological, sediment transport, ecological environment, and socio-economic information from 2010 to 2021. Specifically, for the ecological environment field, meteorological and land use information are included; for the hydrological field, runoff information is included; and for the socio-economic field, population, industrial structure, and water use information are included. Of course, this implementation method is merely illustrative and does not limit the specific content of the historical information collected in each field.

[0017] In addition, the historical information in each field includes both parameters and the specific data corresponding to the parameters. For example, the ecological environment field includes not only meteorological information but also the specific data corresponding to the meteorological information. The hydrology field includes not only runoff information but also the specific values ​​corresponding to the runoff information. Of course, this embodiment is only an example and does not limit the specific content of the historical information obtained in each field.

[0018] S102, construct the corresponding domain subsystem based on the parameters of each domain, and determine the causal loop diagram with qualitative coupling relationship based on the domain subsystem.

[0019] Optionally, the process involves constructing corresponding domain subsystems based on parameters in each domain, and determining causal loop diagrams with qualitative coupling relationships based on the domain subsystems. This includes: acquiring regional characteristics of the target watershed area, and constructing corresponding domain subsystems based on parameters and regional characteristics in each domain; acquiring domain knowledge in each domain, and splicing the domain subsystems together based on the domain knowledge to obtain causal loop diagrams.

[0020] Specifically, this embodiment acquires the regional characteristics of the target watershed area. These regional characteristics may include inherent attributes such as the region's geographical location. Based on parameters and regional characteristics within each domain, corresponding domain subsystems are constructed. These domain subsystems include a hydrological subsystem, a sediment transport subsystem, an ecological environment subsystem, and a socio-economic subsystem. This embodiment only uses these four subsystems as examples; in practical applications, the number of subsystems can be determined based on the needs of the domains involved in the analysis and management. For example, for the ecological environment domain, historical information within the ecological domain is acquired. Since historical information contains both parameters and data, and the subsystem construction primarily references regional characteristics to establish the relationships between parameters, only the parameters from the historical information need to be extracted at this stage; the specific numerical values ​​corresponding to the parameters are not considered.

[0021] In this implementation, after constructing corresponding subsystems based on parameters extracted from historical information in each domain, a causal loop diagram with directional coupling relationships is determined based on the four subsystems constructed above. In specific applications, the causal loop diagram is obtained by splicing together the subsystems from each domain, such as... Figure 2 The diagram shown is a schematic of a causal loop. Since each subsystem is independent, it is necessary to determine the causal relationships between parameters in different domains when splicing them together, and to connect parameters with causal relationships in different domains to achieve the splicing of subsystems in different domains. Specifically, determining the causal relationships between parameters in different domains can be based on domain knowledge. Domain knowledge refers to the known professional knowledge within and between different domains. For example, land use parameters in the ecological environment are directly related to the industrial structure of the socio-economic subsystem. A larger value in the land use parameter indicates a larger proportion of agriculture and forestry in the industrial structure. Of course, this embodiment is merely an example and does not limit the specific content of domain knowledge.

[0022] S103 classifies parameters from different domains to obtain type objects, and constructs a dynamic simulation model with quantitative relationships based on type objects, data, and causal loop diagrams.

[0023] Optionally, the parameters in different domains are classified to obtain type objects, including: receiving the classification instructions input by the user, and classifying the parameters in different domains according to the classification instructions to obtain type objects, wherein the type objects include state variables, rate variables, auxiliary variables and constants.

[0024] Optionally, a dynamic simulation model with quantitative relationships can be constructed based on the type of object, data, and causal loop diagram, including: determining the qualitative coupling relationship of each type of object based on the data and causal loop diagram; and constructing a dynamic simulation model with quantitative relationships based on the qualitative coupling relationship.

[0025] Specifically, in this embodiment, after constructing a causal loop diagram to determine the causal relationships of parameters in each domain, a dynamic simulation model with simulation capabilities is further constructed based on the causal loop diagram. Before model construction, the parameters in the collected historical information are classified. Specifically, according to the user's classification instructions, the parameters are classified into state variables, rate variables, auxiliary variables, and constants, and the parameters with defined types in the causal loop diagram are used as type objects. Of course, this embodiment is only an example and does not specifically limit the types of each parameter. In addition, after obtaining the type objects with known types, this embodiment establishes a set of data equations connecting each type object based on the collected data and the qualitative coupling relationships of the type objects contained in the causal loop diagram, and determines the quantitative relationships between type objects based on the set of mathematical equations. The quantitative relationships determined above are used as the dynamic simulation model, thereby realizing the transformation of qualitative coupling relationships into a computable quantitative simulation model. The core of the dynamic simulation model constructed in this embodiment lies in quantifying the nonlinear feedback mechanism between water, sediment, ecology, and socio-economic factors through equations.

[0026] Optionally, after constructing a dynamic simulation model with quantitative relationships based on type objects, data, and causal loop diagrams, the method further includes: inputting historical information into the dynamic simulation model to obtain simulated values ​​of key variables in each domain; calculating the difference between the simulated values ​​and actual values ​​of the key variables; and determining that the dynamic simulation model has passed verification when the difference is less than the pre-approval threshold.

[0027] Specifically, in this embodiment, after constructing the dynamic simulation model, in order to ensure the accuracy of the model simulation results, the dynamic simulation model will be verified based on the collected historical information. In the verification process, the historical information is input into the dynamic simulation model, the historical time period is simulated, and the simulated values ​​of key variables output by the model, such as runoff, sediment volume, GDP or population, are compared with the actual observed values. By adjusting the parameters, the fitting error index is made to meet the preset accuracy requirements, thus verifying the model's ability to reproduce the past behavior of the system.

[0028] S104 uses a scenario combination method to obtain current information of a specified domain, and inputs the current information into a dynamic simulation model to obtain current information of the dynamic evolution of the target domain over time.

[0029] Optionally, current information in a specified domain can be obtained by using a scenario combination approach, including: obtaining a pre-defined scenario combination approach, wherein the scenario combination approach includes management policy scenarios and climate collaboration scenarios; and generating current information for the dynamic evolution of the specified domain over time based on the scenario combination approach.

[0030] Specifically, this implementation sets a future-oriented simulation period, such as 2022-2050, and designs scenario combinations. These combinations include management policy scenarios and climate coordination scenarios. The management policy scenario characterizes the intensity differences of ecological restoration policies by setting different annual growth rates of forest and grassland area. The climate coordination scenario uses future precipitation data under different shared socio-economic paths and representative concentration paths provided by the Coupling Model Intercomparison Programme-6 (CMIP6) to characterize the combination of climate change and socio-economic development paths. After obtaining current information for the specified domain using the above scenario combinations, this information is input one by one into the dynamic simulation model, driving the model to run to the target year. The model simulates and outputs the year-by-year dynamic evolution trajectories of key state variables and auxiliary variables under various possible future conditions, serving as the current information for the target domain, which is correlated with the specified domain.

[0031] The technical solution of this invention constructs a causal loop diagram with qualitative coupling relationships based on subsystems of each field, which includes information from four fields: hydrology, sediment, ecological environment, and socio-economics. Based on the causal loop diagram and historical data, a dynamic simulation model with quantitative relationships is constructed. Based on the dynamic simulation model and current data of a specified field, the current information of the target field's dynamic evolution is simulated, thereby improving the overall simulation capability of the interaction and continuous dynamic evolution of the fields. Since the simulation model is constructed based on information from multiple fields and covers the interaction relationships of each field, the accuracy of the simulation results is improved.

[0032] Example 2 Figure 3 This is a flowchart of another method for determining watershed area information provided by an embodiment of the present invention. Based on the above embodiment, after inputting current information into a dynamic simulation model to obtain current information of the target area, this embodiment further includes: extracting key variables from the current information and visualizing the key variables; analyzing the key variables to obtain analysis results, and determining management decisions for the target watershed area based on the analysis results, such as... Figure 3 As shown, the method includes: S201, collect historical information from different fields in the target watershed area.

[0033] S202, construct the corresponding domain subsystem based on the parameters of each domain, and determine the causal loop diagram with qualitative coupling relationship based on the domain subsystem.

[0034] Optionally, the process involves constructing corresponding domain subsystems based on parameters in each domain, and determining causal loop diagrams with qualitative coupling relationships based on the domain subsystems. This includes: acquiring regional characteristics of the target watershed area, and constructing corresponding domain subsystems based on parameters and regional characteristics in each domain; acquiring domain knowledge in each domain, and splicing the domain subsystems together based on the domain knowledge to obtain causal loop diagrams.

[0035] S203 classifies parameters from different domains to obtain type objects, and constructs a dynamic simulation model with quantitative relationships based on type objects, data, and causal loop diagrams.

[0036] Optionally, the parameters in different domains are classified to obtain type objects, including: receiving the classification instructions input by the user, and classifying the parameters in different domains according to the classification instructions to obtain type objects, wherein the type objects include state variables, rate variables, auxiliary variables and constants.

[0037] Optionally, a dynamic simulation model with quantitative relationships can be constructed based on the type of object, data, and causal loop diagram, including: determining the qualitative coupling relationship of each type of object based on the data and causal loop diagram; and constructing a dynamic simulation model with quantitative relationships based on the qualitative coupling relationship.

[0038] Optionally, after constructing a dynamic simulation model with quantitative relationships based on type objects, data, and causal loop diagrams, the method further includes: inputting historical information into the dynamic simulation model to obtain simulated values ​​of key variables in each domain; calculating the difference between the simulated values ​​and actual values ​​of the key variables; and determining that the dynamic simulation model has passed verification when the difference is less than the pre-approval threshold.

[0039] S204 uses a scenario combination approach to obtain current information in a specified domain, and inputs this current information into a dynamic simulation model to obtain current information on the dynamic evolution of the target domain over time.

[0040] Optionally, current information in a specified domain can be obtained by using a scenario combination approach, including: obtaining a pre-defined scenario combination approach, wherein the scenario combination approach includes management policy scenarios and climate collaboration scenarios; and generating current information for the dynamic evolution of the specified domain over time based on the scenario combination approach.

[0041] S205: Extract key variables from the current information and visualize them.

[0042] Specifically, in this embodiment, key variables are extracted from the current information of the target area simulated by the clubhouse model. For example, time-series data of key indicators such as runoff, sediment volume, ecological area, and GDP are extracted, and curves showing the changes of key variables over time are generated, or comparison charts of the effects under different policy intensities are generated, as well as multi-dimensional scatter plots reflecting the synergistic relationship of multiple variables. Of course, this embodiment is only an example and does not limit the specific display method of key variables. In addition, users can also pre-specify the display method according to their viewing needs, and display the key variables generated by the simulation according to the user's specified display method, thereby meeting the user's personalized needs.

[0043] S206, Analyze key variables to obtain analysis results, and determine management decisions for the target watershed area based on the analysis results. Specifically, in this embodiment, after obtaining the key variables from the model simulation, multi-dimensional comparative analysis and comprehensive evaluation of the key variables can be performed according to user needs. For example, this can involve comparing and analyzing the improvement effects of water and sediment processes under different management policy scenarios, the impact on water resource utilization, the trade-off with economic development, and assessing the hydrological uncertainties and risk levels faced by the system under different climate-social pathways. Of course, this embodiment is merely illustrative and does not limit the specific content of the analysis results. Furthermore, after obtaining the key variables through the model, the system can receive user analysis requests, such as "Please determine the hydrological risk level faced by the system under the climate-social pathway." When the terminal receives the user's analysis request, it calculates the key variables obtained through simulation according to the pre-configured calculation method to obtain the hydrological risk level. Again, this embodiment is merely illustrative and does not limit the specific content of the analysis results or the calculation method.

[0044] Furthermore, after obtaining the analysis results, this implementation method can determine management decisions for the target watershed area based on the analysis results. Specifically, it identifies optimal scenario combinations that can synergistically improve water resource security, ecological security, and sustainable development. For example, it recommends moderate-intensity forest and grassland restoration policies and avoids high-emission climates. In this implementation method, a management decision table can be pre-set, containing the correspondence between analysis results and management decisions. Therefore, after obtaining the analysis results, the management decision corresponding to the current analysis results can be obtained by looking up the table. Thus, based on the principles of system dynamics, this implementation method constructs a simulation model containing four interacting subsystems: hydrology, sediment, ecological environment, and socio-economic factors. By defining coupling variables and feedback equations between subsystems, its nonlinear interaction relationships are quantified. After validating the model using historical data, the model is driven to simulate the dynamic evolution trajectory of key system indicators from the base year to the target year under different preset management policies and climate change scenarios. Finally, by comparing and analyzing the simulation results of different scenarios, the optimized management path is identified.

[0045] It is worth mentioning that this implementation first achieves deep integration and closed-loop dynamic simulation of multiple subsystems, that is, placing the watershed's hydrological processes, sediment processes, ecological environment status (with forest and grassland area as the key indicator), and socio-economic development within the same system dynamics framework for simulation. A complete causal feedback chain is formed within the model, capable of simulating closed-loop dynamic processes such as "ecological construction → hydrological change → sediment response → economic impact → further ecological investment," overcoming the shortcomings of existing technologies that suffer from fragmented analysis dimensions or only perform static correlation assessments. Secondly, the complex feedback mechanism within the system is explicitly quantified, and the model structure is flexible and transparent, clearly demonstrating the interaction paths between various system elements through causal loop diagrams, and all feedback relationships are explicitly defined through mathematical equations. This structured representation makes the model mechanisms transparent, facilitating understanding and communication. Decision-makers and researchers can easily modify any assumptions, parameters, or structural relationships within the model—for example, adjusting the function of investment on forestry and grassland restoration efficiency—and immediately observe the chain reaction of changes in system behavior. This provides a powerful tool for rapid testing and mechanistic analysis of policy scenarios, overcoming the limitations of existing physically coupled models that are structurally rigid and difficult to modify. Finally, it offers robust long-term, multi-scenario dynamic simulation and forward-looking decision support capabilities. This implementation can simulate the continuous evolution of the system over decades and allows for combined scenario analysis of various management policies and future climate pathways. For example, it can clearly demonstrate the differentiated evolution trajectories of sediment load, runoff, and GDP up to 2050 under different forestry and grassland restoration policies and identify the "thresholds" of policy effectiveness, or quantitatively reveal the uncertainty range of hydrological and sediment responses under different climate pathways. This dynamic, quantitative, and multi-scenario comparative simulation output provides managers with direct, visualized scientific evidence for developing adaptive, forward-looking, and resilient long-term strategies, far superior to static link assessments or single forecast reports.

[0046] The technical solution of this invention constructs a causal loop diagram with qualitative coupling relationships based on subsystems of each field, which includes information from four fields: hydrology, sediment, ecological environment, and socio-economics. Based on the causal loop diagram and historical data, a dynamic simulation model with quantitative relationships is constructed. Based on the dynamic simulation model and current data of a specified field, the current information of the target field's dynamic evolution is simulated, thereby improving the overall simulation capability of the interaction and continuous dynamic evolution of the fields. Since the simulation model is constructed based on information from multiple fields and covers the interaction relationships of each field, the accuracy of the simulation results is improved.

[0047] Example 3 Figure 4 This is a schematic diagram of a watershed area information determination device provided in an embodiment of the present invention. Figure 4As shown, the device includes: a historical information acquisition module 310, a causal loop diagram determination module 320, a dynamic simulation model construction module 330, and a current information determination module 340.

[0048] The historical information acquisition module 310 is used to collect historical information from different areas of the target watershed region. The historical information includes parameters and the data corresponding to the parameters. The causal loop diagram determination module 320 is used to construct corresponding domain subsystems based on parameters under each domain, and determine causal loop diagrams with qualitative coupling relationships based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The dynamic simulation model construction module 330 is used to classify parameters in different fields to obtain type objects, and construct a dynamic simulation model with quantitative relationships based on type objects, data and causal loop diagrams. The current information determination module 340 is used to obtain current information of a specified domain by using a scenario combination method, and input the current information into the dynamic simulation model to obtain the current information of the target domain dynamically evolving over time, wherein the target domain is associated with the specified domain.

[0049] Optionally, a causal loop diagram determination module is used to obtain the regional characteristics of the target watershed area and construct corresponding domain subsystems based on the parameters and regional characteristics of each domain. Acquire domain knowledge from various fields, and then connect the subsystems of each field based on the domain knowledge to obtain a causal loop diagram.

[0050] Optionally, the dynamics simulation model building module includes a classification unit, which receives classification instructions input by the user and classifies parameters in different domains according to the classification instructions to obtain type objects. Among them, type objects include state variables, rate variables, auxiliary variables, and constants.

[0051] Optionally, the dynamic simulation model building module includes a dynamic simulation model building unit, which is used to determine the qualitative coupling relationship of each type of object based on data and causal loop diagrams; A dynamic simulation model with quantitative relationships is constructed based on qualitative coupling relationships.

[0052] Optionally, the device also includes a model verification module, which is used to input historical information into the dynamic simulation model to obtain simulated values ​​of key variables in various fields; Calculate the difference between the simulated and actual values ​​of key variables. If the difference is less than the pre-approval threshold, the dynamic simulation model is deemed to have passed the verification.

[0053] Optionally, the current information determination module is used to obtain a pre-set scenario combination method, wherein the scenario combination method includes management policy scenario and climate collaboration scenario; Generate current information that dynamically evolves over time for a specified domain based on scenario combination.

[0054] Optionally, the device also includes a decision-making module for extracting key variables from current information and visualizing the key variables; Analyze key variables to obtain analysis results, and determine management decisions for the target watershed area based on the analysis results.

[0055] The watershed area information determination device provided in the embodiments of the present invention can execute the watershed area information determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0056] Example 4 Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0057] The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0058] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0059] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other electronic devices through computer networks such as the Internet and / or various telecommunications networks.

[0060] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining information about watershed areas.

[0061] That is, collecting historical information from different areas of the target watershed region, including parameters and the data corresponding to those parameters; Based on the parameters of each domain, corresponding domain subsystems are constructed, and causal loop diagrams with qualitative coupling relationships are determined based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The parameters in different fields are classified to obtain type objects, and a dynamic simulation model with quantitative relationships is constructed based on the type objects, data and causal loop diagrams. The current information of a specified domain is obtained by using a scenario combination method, and the current information is input into a dynamic simulation model to obtain the current information of the target domain. The target domain is related to the specified domain, and the current information evolves dynamically over time.

[0062] In some embodiments, the method for determining watershed area information may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining watershed area information described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining watershed area information by any other suitable means (e.g., by means of firmware).

[0063] Various embodiments of the apparatuses and techniques described above herein can be implemented in digital electronic circuit devices, integrated circuit devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), device-on-a-chip (SoC) devices, complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable device including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage device, at least one input device, and at least one output device, and transmitting data and instructions to the storage device, the at least one input device, and the at least one output device.

[0064] Computer programs used to implement the watershed area information determination method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other non-stop data migration device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0065] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution apparatus, device, or electronic device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage electronics, magnetic storage electronics, or any suitable combination thereof.

[0066] To provide interaction with a user, the devices and techniques described herein can be implemented on an electronic device having: a display device (e.g., a touchscreen) for displaying information to the user; and buttons through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0067] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0068] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of determining information of a river basin area, characterized by, The method includes: Historical information from different areas of the target watershed region is collected, wherein the historical information includes parameters and data corresponding to the parameters; Based on the parameters of each domain, corresponding domain subsystems are constructed, and causal loop diagrams with qualitative coupling relationships are determined based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The parameters in different fields are classified to obtain type objects, and a dynamic simulation model with quantitative relationships is constructed based on the type objects, the data, and the causal loop diagram. The current information of a specified domain is obtained by using a scenario combination method, and the current information is input into the dynamic simulation model to obtain the current information of the dynamic evolution of the target domain over time, wherein the target domain is associated with the specified domain.

2. The method of claim 1, wherein, The step of constructing corresponding domain subsystems based on parameters in each domain, and determining causal loop diagrams with qualitative coupling relationships based on the domain subsystems, includes: Obtain the regional characteristics of the target watershed area, and construct the corresponding domain subsystem based on the parameters under each domain and the regional characteristics; Acquire domain knowledge from each domain, and then stitch together the domain subsystems based on the domain knowledge to obtain the causal loop diagram.

3. The method of claim 1, wherein, The process of classifying the parameters in different domains to obtain type objects includes: The system receives classification instructions input by the user and classifies the parameters under different domains according to the classification instructions to obtain the type objects. The type objects include state variables, rate variables, auxiliary variables, and constants.

4. The method of claim 3, wherein, The construction of a dynamic simulation model with quantitative relationships based on the type of object, the data, and the causal loop diagram includes: Based on the data and the causal loop diagram, determine the qualitative coupling relationships of each type of object; Based on the qualitative coupling relationship, a dynamic simulation model with a quantitative relationship is constructed.

5. The method of claim 1, wherein, After constructing a dynamic simulation model with quantitative relationships based on the type object, the data, and the causal loop diagram, the method further includes: The historical information is input into the dynamic simulation model to obtain the simulated values ​​of key variables in each field; Calculate the difference between the simulated and actual values ​​of the key variables. If the difference is less than the pre-approval threshold, the dynamic simulation model is deemed to have passed the verification.

6. The method of claim 1, wherein, The method of obtaining current information on the dynamic evolution of a specified domain over time using a scenario combination approach includes: Obtain a pre-defined scenario combination method, wherein the scenario combination method includes a management policy scenario and a climate collaboration scenario; Based on the scenario combination method, current information is generated for the specified domain as it dynamically evolves over time.

7. The method according to any one of claims 1 to 6, characterized in that, After inputting the current information into the dynamic simulation model to obtain the current information of the target domain, the method further includes: Extract key variables from the current information and visualize them. The key variables are analyzed to obtain the analysis results, and management decisions for the target watershed area are determined based on the analysis results.

8. An information determining apparatus of a river basin area, characterized by comprising: The device includes: The historical information acquisition module is used to collect historical information from different areas of the target watershed region, wherein the historical information includes parameters and data corresponding to the parameters; The causal loop diagram determination module is used to construct corresponding domain subsystems based on parameters under each domain, and determine causal loop diagrams with qualitative coupling relationships based on the domain subsystems. The domain subsystems include hydrological subsystems, sediment transport subsystems, ecological environment subsystems, and socio-economic subsystems. The dynamic simulation model construction module is used to classify the parameters in different fields to obtain type objects, and construct a dynamic simulation model with quantitative relationships based on the type objects, the data and the causal loop diagram. The current information determination module is used to obtain current information of a specified domain by using a scenario combination method, and input the current information into the dynamic simulation model to obtain the current information of the target domain dynamically evolving over time, wherein the target domain is associated with the specified domain.

9. An electronic device, comprising: The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A storage medium for computer-executable instructions, wherein a computer program is stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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