Integrated system and method, program product and device based on hydrological and hydrodynamic models

Abstract

The present disclosure relates to the technical field of data monitoring, and relates to an integrated system and method based on a hydrological and hydrodynamic model, a program product and equipment. The system comprises: an intention recognition module, configured to perform intention recognition on demand description information, and generate a structured parameter set, the demand description information being used to indicate input information required by a hydrological and hydrodynamic model calculation; a data interaction module, configured to perform data standardization processing on the structured parameter set, and generate a model input file from the processed structured parameter set; a model calling module, configured to call a target model input file and a target hydrological and hydrodynamic model, call the target hydrological and hydrodynamic model based on the target model input file to perform task processing, and obtain a reference result file; a result reading module, configured to read the reference result file and perform time serialization processing, cleaning processing and format conversion, and obtain a target result file; and a visualization module, configured to load the target result file to a BS system for visualization.

Description

Technical Field

[0001] This disclosure relates to the field of data monitoring technology, and more specifically, to an integrated system and method, program product and equipment based on a hydrological and hydrodynamic model. Background Technology

[0002] Current hydrological and hydrodynamic models are mainly applied to scenarios such as rivers, lakes, and watersheds to simulate processes such as rainfall, runoff, and flood evolution. These models have basic numerical calculation capabilities and can generate simulation results by input parameters (such as rainfall intensity, topographic data, boundary conditions, etc.) for use in urban drainage, flood forecasting, and monitoring.

[0003] However, currently, simulation and prediction based on hydrological and hydrodynamic models require operators to have in-depth domain knowledge, the configuration process is cumbersome, resulting in high learning costs and technical barriers, which can easily lead to user errors. Furthermore, the simulation and prediction results based on hydrological and hydrodynamic models require professional operation for interpretation, and integration with BS (Browser-Server Architecture) systems also requires a lot of modification and optimization, further increasing the technical barriers and development difficulty, and to some extent introducing new problems and risks.

[0004] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide an integrated system based on a hydrological and hydrodynamic model, a system integration method based on a hydrological and hydrodynamic model, a computer program product, and an electronic device, thereby providing a standardized and intelligent implementation path for the engineering application of hydrological and hydrodynamic models.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, an integrated system based on a hydrological and hydrodynamic model is provided, comprising: an intent recognition module for acquiring demand description information for the hydrological and hydrodynamic model and performing intent recognition on the demand description information to generate a structured parameter set based on the intent recognition result; the demand description information is natural language description information used to indicate the input information required for the calculation of the hydrological and hydrodynamic model, and the input information includes at least model type, rainfall data, and topographic data; a data interaction module for performing data standardization processing on the structured parameter set and generating a model input file from the standardized structured parameter set using a preset file template; a model invocation module for invoking the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task, and performing task processing on the corresponding target hydrological and hydrodynamic model based on the target model input file through asynchronous processing to obtain a reference result file; a result reading module for reading the reference result file and performing time-series processing, cleaning processing, and format conversion processing sequentially to obtain a target result file; and a visualization module for loading the target result file into a browser / server BS system to visualize the target result file in the visualization interface of the BS system.

[0008] In one exemplary embodiment of this disclosure, the intent recognition module includes: an intent recognition unit, configured to receive requirement description information and perform intent recognition on the requirement description information based on a large language model to obtain an intent recognition result; a parameter acquisition unit, configured to perform named entity recognition on the intent recognition result to obtain an entity recognition result, and perform numerical range parsing on the entity recognition result to obtain a reference parameter set; and a parameter verification unit, configured to interface with a configuration database through a stream processing engine to verify the reference parameter set based on the configuration database, and determine the verified reference parameter set as a structured parameter set.

[0009] In one exemplary embodiment of this disclosure, the data interaction module includes: a data processing stream construction unit for constructing a data processing stream based on a predetermined algorithm; a data processing unit for performing data standardization processing on the structured parameter set through the data processing stream to obtain a standardized structured parameter set; a template invocation unit for invoking a preset file template and rendering the standardized structured parameter set onto the preset file template to obtain a target file template; and a file generation unit for writing the target file template into a model input file.

[0010] In an exemplary embodiment of this disclosure, the data processing unit is further configured to call a verification file and parameter mapping rules, and perform legality verification on the structured parameter set based on the verification file; the template calling unit is further configured to call a preset file template according to the model type, and fill the standardized structured parameter set into the preset file template based on the parameter mapping rules to obtain the target file template.

[0011] In one exemplary embodiment of this disclosure, the model invocation module includes: an interface unit, configured to obtain a model invocation task in response to a model invocation operation, wherein the model invocation operation carries at least a model type, an identifier of input information, and model execution parameters; and a model invocation unit, configured to process the model invocation task in an asynchronous manner to obtain a reference result file.

[0012] In one exemplary embodiment of this disclosure, the model invocation unit includes: a memory unit for recording the task status of the model invocation task; a message feedback unit for feeding back the task identifier of the model invocation task to the front end, the task identifier being used to query the task status in the memory unit; and a task processing unit for running the target hydrological and hydrodynamic model in a container to perform task processing based on the target model input file and obtain a reference result file; wherein different hydrological and hydrodynamic models run in different containers.

[0013] In one exemplary embodiment of this disclosure, the result reading module includes: a parsing unit, used to read and parse a reference result file to obtain a first parsing file, the first parsing file including at least time dimension information, spatial coordinate information, and hydrological variable information; a time series standardization unit, used to standardize the time step of the parsing file to obtain a second parsing file; a data standardization unit, used to process outliers and missing values ​​in the second parsing file to obtain a third parsing file; and a format conversion unit, used to convert the third parsing file into a target file format to obtain a target result file.

[0014] In one exemplary embodiment of this disclosure, the format conversion unit includes: a vector conversion unit, used to filter the third parsed file into data subsets corresponding to different times based on the time dimension; a raster conversion unit, used to perform raster conversion on the data subsets to obtain an image file in the target format, and output the spatial reference, resolution, and compression method of the raster; and a pyramid construction unit, used to construct an image pyramid based on the spatial reference, resolution, and compression method of the raster and the image file to obtain a target result file, wherein the image pyramid supports different levels of scaling display.

[0015] In one exemplary embodiment of this disclosure, the visualization module includes: a loading unit, used to load the target result file into the rendering model corresponding to the BS system, so as to render the target result file through the rendering model to obtain the rendering result; and a display unit, used to visualize the rendering result in the visualization interface of the BS system.

[0016] According to one aspect of this disclosure, a system integration method based on a hydrological and hydrodynamic model is provided, applied to an integrated system based on a hydrological and hydrodynamic model. The method includes: acquiring demand description information for the hydrological and hydrodynamic model through an intent recognition module, and performing intent recognition on the demand description information to generate a structured parameter set based on the intent recognition result. The demand description information is natural language description information used to indicate the input information required for the hydrological and hydrodynamic model calculation. The input information includes at least model type, rainfall data, and topographic data. The structured parameter set is standardized using a data interaction model, and a model input file is generated from the standardized structured parameter set using a preset file template. A model invocation module invokes the target model input file and the corresponding target hydrological and hydrodynamic model based on a model invocation task, and performs task processing on the corresponding target hydrological and hydrodynamic model based on the target model input file using an asynchronous processing method to obtain a reference result file. A result reading module reads the reference result file and sequentially performs time-series processing, cleaning processing, and format conversion processing to obtain a target result file. A visualization module loads the target result file into a browser / server (BS) system to visualize the target result file in the visualization interface of the BS system.

[0017] According to one aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the above methods.

[0018] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above methods by executing the executable instructions.

[0019] The integrated system based on a hydrological and hydrodynamic model in the exemplary embodiments of this disclosure, on the one hand, reduces operational complexity. Through the intent recognition module, users can describe their model invocation needs using natural language, and the system automatically parses and generates structured parameters, greatly reducing the requirements for users' professional backgrounds. Even non-model experts can easily initiate complex simulation calculations, improving the system's usability. Furthermore, it eliminates the need for manually writing scripts or setting parameters item by item through a graphical interface, avoiding time-consuming and error-prone problems, and significantly improving the overall work efficiency from requirement to result. On the other hand, the data interaction module ensures the uniformity of the input data format and standards, and the final generated model input file effectively solves the problem of model calculation errors or incomparable results. Moreover, the result reading module performs a series of standardization processes on the original model output (time serialization, cleaning, format conversion), ensuring the quality and consistency of the final target result file, providing a reliable and well-organized data foundation for subsequent analysis and visualization. The model invocation module uses an asynchronous approach to call models for calculations. This means that after a user submits a task, they do not need to wait for the calculation to complete and can continue with other operations. This is crucial for time-consuming hydrological and hydrodynamic simulations, avoiding front-end interface blocking, improving user experience, and facilitating reasonable scheduling and resource allocation of computational tasks on the server side. Furthermore, the visualization module directly loads the processed standard results into the BS system for display, allowing users to intuitively view simulation results through a browser without the need for other specialized desktop software. Simultaneously, the modular system ensures better compatibility between this system and the BS system, providing a standardized and intelligent implementation path for the engineering application of hydrological and hydrodynamic models.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation.

[0022] Figure 1 An architecture diagram of an integrated system based on a hydrodynamic model according to an exemplary embodiment of the present disclosure is shown.

[0023] Figure 2 A schematic diagram of the composition of an intent recognition module according to an exemplary embodiment of the present disclosure is shown.

[0024] Figure 3 A schematic diagram of the composition of a data interaction module according to an exemplary embodiment of the present disclosure is shown.

[0025] Figure 4 A schematic diagram of the composition of a model invocation unit according to an exemplary embodiment of the present disclosure is shown.

[0026] Figure 5 A schematic diagram of the composition of a result reading module according to an exemplary embodiment of the present disclosure is shown.

[0027] Figure 6 A schematic diagram of the composition of a format conversion unit according to an exemplary embodiment of the present disclosure is shown.

[0028] Figure 7 A flowchart of a system integration method based on a hydrological and hydrodynamic model according to an exemplary embodiment of the present disclosure is shown.

[0029] Figure 8 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown.

[0030] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0031] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0032] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0033] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0034] Currently, when performing simulations and predictions based on hydrological and hydrodynamic models, the following issues hinder the standardization and intelligent implementation of these models in engineering applications.

[0035] First, the operation is highly complex. Currently, simulating and predicting hydrological and hydrodynamic models requires users to have in-depth domain knowledge to correctly set model parameters and interpret output results, posing a high learning cost and technical barrier for non-professional users. For example, they need to be familiar with technical terms such as the Manning coefficient and rainfall distribution patterns, and manually edit complex configuration files.

[0036] Secondly, calling hydrological and hydrodynamic models requires command lines or fixed-format configuration methods, which are difficult to operate and may lead to user errors.

[0037] Furthermore, the calculation results files of current hydrological and hydrodynamic models require specialized tools for parsing and visualization, which limits data accessibility and sharing efficiency. Output files generated by different model systems typically require specific tools, forcing users to master multiple operating procedures, further increasing the technical barrier and usage costs.

[0038] Furthermore, when integrating existing hydrological and hydrodynamic models with BS systems, the format of the output files is difficult to interface with the BS system. This necessitates complex data conversion and processing after obtaining the output results from the hydrological and hydrodynamic models, increasing the difficulty and cost of integration. Moreover, current hydrological and hydrodynamic models are difficult to directly migrate to the cloud servers or distributed architectures upon which the BS system relies. This requires significant modifications and optimizations to both the hydrological and hydrodynamic models and the BS system, and may even necessitate the redevelopment of some functions, which is not only time-consuming and labor-intensive but may also introduce new problems and risks. Additionally, due to the high degree of computational redundancy in hydrological and hydrodynamic models, integration with the BS system may result in a decrease in system response speed and processing capacity. Under conditions of large-scale data and high concurrency, the model may be unable to respond promptly, impacting the user experience.

[0039] Based on one or more of the above-mentioned problems, the exemplary embodiments of this disclosure provide an integrated system based on a hydrological and hydrodynamic model. Through a modular integrated system, the overall system is divided into five major modules, decoupling the stages of intent recognition, data preparation, model calculation, result processing, and visualization. This makes the system have good scalability and maintainability, and provides a standardized and intelligent implementation path for the engineering application of hydrological and hydrodynamic models and system integration, thus solving the aforementioned problems.

[0040] like Figure 1 The diagram shows an integrated system 100 based on a hydrological and hydrodynamic model, an exemplary embodiment of this disclosure. The system includes an intent recognition module 110, a data interaction module 120, a model invocation module 130, a result reading module 140, and a visualization module 150. Specifically:

[0041] The intent recognition module 110 is used to obtain the demand description information for the hydrological and hydrodynamic model and to perform intent recognition on the demand description information in order to generate a structured parameter set based on the intent recognition result. The demand description information is natural language description information used to indicate the input information required for the calculation of the hydrological and hydrodynamic model. The input information includes at least the model type, rainfall data and topographic data.

[0042] The data interaction module 120 is used to perform data standardization processing on the structured parameter set and generate a model input file from the standardized structured parameter set using a preset file template.

[0043] The model invocation module 130 is used to invoke the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task, and to perform task processing by invoking the corresponding target hydrological and hydrodynamic model based on the target model input file in an asynchronous manner to obtain a reference result file.

[0044] The result reading module 140 is used to read the reference result file and perform time serialization, cleaning and format conversion processing in sequence to obtain the target result file.

[0045] The visualization module 150 is used to load the target result file into the browser / server BS system so that the target result file can be visualized in the visualization interface of the BS system.

[0046] The exemplary embodiments of this disclosure achieve seamless integration and intelligent collaboration among various stages through modularization, which is beneficial for constructing an integrated system of hydrological and hydrodynamic models and a business unit (BS) system integration architecture that integrates hardware and software. It should be noted that the integrated system based on hydrological and hydrodynamic models in the exemplary embodiments of this disclosure can be applied to scenarios such as urban flood disaster monitoring and early warning in smart cities. For example, the method of this disclosure can achieve monitoring and prediction of such problems through deep integration of hydrological and hydrodynamic models with urban BS management platforms.

[0047] The following section provides a more detailed explanation of each module.

[0048] The intent recognition module 110 is used to automatically read the requirement description information and perform intent recognition.

[0049] The requirement description information refers to the natural language instructions issued by the user to request hydrological and hydrodynamic model calculations. These instructions can be sentences, keyword combinations, or descriptive text containing multiple conditions. It can be understood that the requirement description information is unstructured, rather than a fixed program command or form; non-professional users can provide this information as needed. The requirement description information may include the input information required for the hydrological and hydrodynamic model calculations, such as the required configuration and rainfall, and may include at least the model type, rainfall data, and topographic data. The model type refers to the specific type of hydrological or hydrodynamic calculation engine, i.e., the specific category of the hydrological and hydrodynamic model. Rainfall data is one of the core inputs driving the hydrological and hydrodynamic model and can be historical measured data, including rainfall amount, rainfall duration, and peak rainfall coefficient. Topographic data describes the surface elevation and is the basis for the hydrological and hydrodynamic model to calculate the direction, velocity, and inundation extent of water flow. It can exist in the form of a DEM (Digital Elevation Model), which is not limited in the exemplary embodiments of this disclosure.

[0050] In one exemplary embodiment, such as Figure 2 As shown, the intent recognition module includes an intent recognition unit 210, a parameter acquisition unit 220, and a parameter verification unit 230, specifically:

[0051] The intent recognition unit 210 is used to receive the requirement description information and perform intent recognition on the requirement description information based on the large language model to obtain the intent recognition result.

[0052] The intent recognition unit 210 can be obtained by fine-tuning a large language model using specialized corpora in the hydrological field. For example, it can use a specialized corpus in the hydrological field containing over 2000 professional terms and their mapping relationships with parameters for fine-tuning. The large language model could be, for example, the Wenxin Yiyan ERNIE-4.0 or the Tongyi Qianwen Qwen-72B model. Alternatively, other large language models with certain natural language processing capabilities, such as the Xinghuo large model, can be used. By fine-tuning the large language model, key parameters can be extracted and a structured parameter set can be generated. The structured parameter set is a set of data that, after being organized and standardized, can be directly understood and processed by computer programs.

[0053] The parameter acquisition unit 220 is used to perform named entity recognition on the intent recognition result, obtain the entity recognition result, and perform numerical range analysis on the entity recognition result to obtain a reference parameter set.

[0054] Named entity recognition (NAME) identifies entities with specific meanings in text. The resulting entity recognition results include, but are not limited to, model names, geographic entities, rainfall standards, data source types, and output indicators, such as "2023," "summer," and "rainfall." Numerical range parsing refers to identifying and structurally representing numerical values ​​and their ranges, units, or frequency information from text descriptions. It can combine entity recognition results with regular expressions to parse numerical ranges. For example, if a user inputs "simulating rainfall between 50 and 100 millimeters," combining entity recognition results with regular expressions will yield "50-100 mm / d."

[0055] The parameter verification unit 230 is used to interface with the configuration database through the stream processing engine, to verify the reference parameter set based on the configuration database, and to determine the verified reference parameter set as a structured parameter set.

[0056] The configuration database is a knowledge base storing all business rules, constraints, and metadata of the system. It may include a model capability matrix, reasonable parameter ranges, a data source mapping table, and geographical boundaries. The model capability matrix specifies the simulation types, spatial scales, and minimum required data for each model. Reasonable parameter ranges include, for example, the reasonable range of Manning coefficients (e.g., 0.02-0.8) and the maximum rainfall limit. The data source mapping table includes paths to specific data files or service interfaces. Geographical boundaries may include the geographical coordinates of a predefined area. The configuration database may be, for example, a MySQL database, and there are no specific limitations. The stream processing engine refers to a computing framework capable of processing continuous data streams in real-time or near real-time. It can simultaneously process the validation of multiple parameter sets and interact efficiently with the configuration database to ensure system response speed. The exemplary embodiments of this disclosure do not limit the specific type of this computing framework.

[0057] The parameter validation unit 230 connects to the configuration database via a stream processing engine to validate the reference parameter set, which may include parameter completion and business logic validation. For example, the parameter validation unit 230 connects to the MySQL configuration database via the stream processing engine to automatically complete missing parameters (such as the default Manning coefficient of 0.035) and finally generate a structured parameter set (such as JSON). The structured parameter set includes core fields such as model type, rainfall data (such as start and end times, intervals, and numerical sequences) and terrain configuration (such as DEM file path and cell size), which can be used by downstream modules.

[0058] The exemplary embodiments disclosed herein, by introducing a large language model as the core of intent recognition, enable the system to understand the semantics and context of the natural language input by the user, thereby lowering the operational threshold of the system. Through an independent parameter acquisition unit, named entity recognition and numerical range parsing are performed on the output of the large language model, ensuring that the generated structured parameter set is rich in detail and has a regular format, providing a foundation for the accurate operation of the subsequent model. The parameter validation unit connects to the configuration database in real time through a stream processing engine, enhancing the reliability of the entire system output and avoiding model calculation failures or result distortions due to input errors. Using a stream processing engine to handle parameter validation tasks means that validation requests are processed as a continuous data stream. The engine can process a large number of requests in parallel and asynchronously, ensuring that the system can maintain a fast response with low latency and high throughput even when multiple users submit complex requests simultaneously, improving the overall system performance and user experience.

[0059] The data interaction module 120 is configured to complete the standardized interaction of the model input file through algorithms, databases, and local configuration files to obtain the model input file.

[0060] In one exemplary embodiment, such as Figure 3 As shown, the data interaction module 120 includes a data processing stream construction unit 310, a data processing unit 320, a template calling unit 330, and a file generation unit 340, specifically:

[0061] The data processing flow construction unit 310 is used to construct a data processing flow based on a predetermined algorithm. A data processing flow can be understood as a customizable, sequentially executed sequence of data processing steps, which can decompose complex data processing tasks into multiple independent, reusable processing stages. The predetermined algorithm is the algorithm used to construct the data processing flow, such as ETL (Extract, Transform, Load) algorithms, MapReduce algorithms, etc. Based on these algorithms, a data cleaning pipeline can be constructed for subsequent modules / units to process data based on the data processing flow.

[0062] The data processing unit 320 is used to perform data standardization processing on the structured parameter set through the data processing stream to obtain the standardized structured parameter set.

[0063] Data standardization is the process of converting data into a unified format, unit, coordinate system, and value range to convert structural parameters into a model-compatible format. The specific operation is related to the currently selected model.

[0064] Template calling unit 330 is used to call a preset file template and render the standardized structured parameter set to the preset file template to obtain the target file template.

[0065] The preset file template is a template file that contains the input file structure, keywords and placeholders of the target hydrological and hydrodynamic model. The template may contain placeholders for fixed formats and variable parameters required by the model.

[0066] In an exemplary embodiment, the data processing unit 320 is further configured to call the verification file and parameter mapping rules, and perform legality verification on the structured parameter set based on the verification file. Then, the template calling unit 330 is further configured to call the preset file template according to the model type, and fill the standardized structured parameter set into the preset file template based on the parameter mapping rules to obtain the target file template.

[0067] The validation file is a configuration file that defines the rules for parameter validity. It can be a structured file in JSON, YAML, or XML format and can check the validity, rationality, and completeness of the input data. The parameter mapping rule definition is a set of rules that maps fields in the standardized structured parameter set to placeholders or positions in a specific model template. The data processing unit 320 can perform validity validation on the structured parameter set by calling the validation file.

[0068] Specifically, at the database layer, PostgreSQL + PostGIS (an open-source object-relational database management system) can be used to store spatial data (or a MySQL database). ORM (Object-Relational Mapping Framework) is used to achieve bidirectional synchronization between the structured parameter set and the database tables. During validation, the core table structure includes model parameter ID, model type, parameter name, data type, default value, and validation rules. JSON Schema can be used to validate the format of the structured parameter set. Then, the data processing unit obtains the standardized structured parameter set, and the template calling unit 330 calls a preset file template based on the required model type (e.g., model ID). The standardized structured parameter set is then filled into the preset file template based on parameter mapping rules to obtain the target file template, such as the .inp file for SWMM (Storm Water Management Model). The file generation unit 340 is used to write the target file template to the model input file.

[0069] This can be understood as the data interaction module 120 loading templates, rendering parameters, and writing files through each unit to generate standardized model input files.

[0070] In the exemplary embodiments disclosed herein, users can interact with the system using natural language, eliminating the need to manually write scripts or set parameters item by item through a graphical interface, thus avoiding time-consuming and error-prone operations. Simultaneously, during data interaction, the system reduces the possibility of user errors through standardized processing, improving the user experience, and makes the system more flexible and scalable through parameter mapping.

[0071] The model invocation module 130 is used to invoke the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task. It then uses an asynchronous processing method to invoke the corresponding target hydrological and hydrodynamic model based on the target model input file for task processing, obtaining a reference result file. The hydrological and hydrodynamic model can be invoked through a standardized interface.

[0072] In one exemplary embodiment, the model invocation module 130 includes:

[0073] The interface unit is used to respond to model invocation operations and obtain model invocation tasks. The model invocation operation carries at least the model type, the identifier of the input information, and the model execution parameters.

[0074] The model invocation unit is used to process model invocation tasks asynchronously to obtain reference result files.

[0075] Specifically, a RESTful interface (a type of interface) can be developed based on the Spring Boot framework (Spring scaffolding framework), defining the model call endpoint as POST / api / model / execute. The request body can include the model type, input file ID (an identifier of the input information), and model execution parameters (such as start and end times, time step), etc. The interface can use OAuth 2.0 (Open Authorization Protocol 2.0) authentication. Of course, the Flask framework can also be used to develop the RESTful interface. The exemplary embodiments disclosed herein include, but are not limited to, the above-described methods for developing standardized interfaces. Furthermore, the interface unit can respond to the model call operation and determine the model call task. This can be understood as follows: when a user submits a model call task, the interface unit can first determine the model call task, and can first send the model call task back to the front end. Then, the backend executes the corresponding model call task based on the model call unit.

[0076] In one exemplary embodiment, such as Figure 4 As shown, the model invocation unit includes a memory unit 410, a message feedback unit 420, and a task processing unit 430, specifically:

[0077] Memory unit 410 is used to record the task status of model invocation tasks. The task status describes the stage a model invocation task is in during its lifecycle, such as queued, running, successfully executed, or failed. Asynchronous processing can be implemented using message queues, for example, using Redis as the task status storage.

[0078] The message feedback unit 420 is used to send the task identifier of the model invocation task back to the front end. The task identifier is used to query the task status in the memory unit. The task identifier is a globally unique ID used to uniquely identify a specific model invocation task in the system, and can be automatically generated by the system (such as the interface unit) when the task is created. The front end can query the system for the latest status of the model invocation task by carrying this ID through polling or other methods.

[0079] The task processing unit 430 is used to run the target hydrological and hydrodynamic model in the container to perform task processing based on the target model input file and obtain a reference result file; wherein, different hydrological and hydrodynamic models are run in different containers.

[0080] Containers are lightweight, portable software packaging and runtime technologies that package hydrological and hydrodynamic models and all their dependencies (libraries, environment variables, configuration files) into a self-contained, isolated sandbox environment. For example, model calls can be containerized using a Java Docker client, with configuration including image name, command parameters, data volume mounts, and the model's background running mode. Alternatively, Python's Docker SDK can be used to replace the Java Docker client for containerized model calls.

[0081] By invoking the various units within the model invocation unit, containerized model scheduling and asynchronous task management are implemented. A Java Docker client and message queues are used to achieve asynchronous scheduling of containerized models, enabling model deployment and execution to better match the architecture of the BS system, improving system response speed and processing capacity. Simultaneously, containers provide an independent runtime environment for each model, completely resolving environment dependency conflicts. The system possesses effective security mechanisms when integrating with the BS system, while also facilitating the deployment of different types of models.

[0082] The result reading module 140 is used to read the reference result file and sequentially perform time serialization, cleaning, and format conversion processing to obtain the target result file. That is, the result reading module 140 can automatically read and process the reference result file.

[0083] In one exemplary embodiment, such as Figure 5As shown, the result reading module 140 includes a parsing unit 510, a time series standardization unit 520, a data standardization unit 530, and a format conversion unit 540, specifically:

[0084] The parsing unit 510 is used to read and parse the reference result file to obtain the first parsing file, which includes at least time dimension information, spatial coordinate information and hydrological variable information.

[0085] The reference result file is the raw result file directly output by the hydrological and hydrodynamic model. Its format is highly dependent on the model itself and is a proprietary, non-standard binary or text format. Parsing is the process of interpreting and extracting the raw data from the reference result file. For example, the Java NetCDF library can be used to parse NetCDF (Network Common Data Form) format result files, and parallel data processing can be achieved through Apache Spark. Alternatively, the Python xarray library can be used to parse NetCDF format result files, or the Dask library can be used to replace Apache Spark for parallel data processing. Different specific processing methods can be adopted in different scenarios.

[0086] The first parsing file is obtained by parsing the reference result file, and its dimensions usually correspond to time, space and variables.

[0087] The time series standardization unit 520 is used to standardize the time step of the parsed file to obtain a second parsed file. Time step standardization refers to the process of converting the potentially non-uniform time series data output by the model into a standard time series with constant time intervals through methods such as interpolation or resampling, thereby obtaining the second parsed file. Exemplary embodiments of this disclosure may employ a uniform sampling frequency such as 15 minutes / sample, without specific limitations.

[0088] Data standard unit 530 is used to process outliers and missing values ​​in the second parsing file to obtain the third parsing file.

[0089] Outliers are data points that significantly deviate from the normal range due to model instability, abrupt changes in boundary conditions, or numerical errors. Missing values ​​are data gaps caused by the model failing to calculate or failing to calculate in certain regions or time points. The processing flow of the data standard unit 530 may include dataset reading, timestamp formatting, resampling, outlier filtering, and outputting cleaning results. Specifically, outliers can be identified using the 3σ criterion combined with the IQR (Interquartile Range Method), and missing values ​​can be filled using a combination of linear interpolation and the K-nearest neighbor algorithm.

[0090] The format conversion unit 540 is used to convert the third parsed file into the target file format to obtain the target result file.

[0091] The target file format includes file type and / or file size. For example, a third-party parsed file can be converted into a .shp file and an .img image file according to its file size to obtain the target result file.

[0092] In one exemplary embodiment, such as Figure 6 As shown, the format conversion unit 540 may include a vector conversion unit 610, a raster conversion unit 620, and a pyramid construction unit 630, specifically:

[0093] The vector transformation unit 610 is used to filter third-party parsing files into data subsets corresponding to different time periods based on the time dimension. This can be understood as the third-party parsing file containing data subsets at multiple time points, thus allowing filtering based on the time dimension to select data subsets corresponding to different time periods. For example, the GDAL (Geospatial Data Abstraction Library) toolchain can be used to convert NetCDF data into ESRI Shapefile (a vector data storage format), allowing for data subset filtering by the time dimension. Furthermore, a file size threshold can be set, and files exceeding the threshold can be processed in blocks using the GDAL library.

[0094] Raster conversion unit 620 is used to rasterize a subset of data to obtain an image file in the target format, and outputs the spatial reference, resolution, and compression method of the raster. The raster conversion can generate an image format (e.g., TIFF), resolution (e.g., 10m), and compression method (e.g., LZW). Of course, the parameters of the output raster can also be determined according to the actual needs of the scenario. It can be understood that the output of raster conversion unit 620 is an image file in the target format representing a specific time, along with its spatial reference, resolution, and other metadata information.

[0095] The pyramid construction unit 630 is used to construct image pyramids based on raster spatial reference, resolution, and compression methods from image files to obtain target result files. The image pyramids support different levels of scaling. Constructing the image pyramid involves building an independent pyramid for each single-time-point image. The pyramid construction unit 630 can accurately calculate how each pyramid layer should be sampled or aggregated using the image's spatial reference and resolution. Then, following the pyramid model, it generates lower-resolution images layer by layer and organizes all layers of images according to a standard, outputting N target result files. Each file is an image pyramid supporting multi-level scaling, corresponding to a specific time point. The pyramid levels can be set to scaling levels of 2, 4, 8, and 16 times.

[0096] In an exemplary embodiment of this disclosure, the raster conversion unit ensures that all output image files have a uniform spatial reference, resolution, and compression method, avoiding subsequent data misalignment or display errors due to inconsistent parameters. The vector conversion unit filters data by the time dimension, enabling the massive amount of time-series data to be divided and conquered, with each data subset corresponding to a clear spatiotemporal snapshot, facilitating time-based retrieval, updating, and management. The pyramid building unit generates multiple levels of images, ensuring fast loading and display of clear images at any zoom level, resulting in a smooth user experience. When a user requests an image, the server simply returns the corresponding level of image, ensuring a fast response time.

[0097] The visualization module 150 is used to load the target result file into the browser / server BS system so that the target result file can be visualized in the visualization interface of the BS system. In other words, the visualization module 150 can load the target result file into the browser / server BS system.

[0098] In one exemplary embodiment, the visualization module includes:

[0099] The loading unit is used to load the target result file into the corresponding rendering model of the BS system, so that the target result file can be rendered by the rendering model to obtain the rendering result. An example of a rendering model is the Cesium Ion platform (a cloud service platform designed for 3D geospatial data).

[0100] The display unit is used to visualize the rendering results in the visual interface of the BS system.

[0101] The system utilizes the Cesium Ion platform to process shapefiles and tif files, generating a 3D Tiles dataset. The front-end uses Vue 3 and Cesium JS to develop the visualization interface, initializing a Cesium Viewer instance and loading the 3D Tiles dataset for visualization. For example, time-series animations are dynamically bound to Sampled, Position, and Property attributes; camera flight paths and timeline event listeners are set; and the display style is updated in real-time based on water level data. No special restrictions are placed on the specific visualization process.

[0102] The integrated system based on a hydrological and hydrodynamic model in the exemplary embodiments of this disclosure, on the one hand, reduces operational complexity. Through the intent recognition module, users can describe their modeling needs using natural language, and the system automatically parses and generates structured parameters, greatly reducing the requirements for users' professional backgrounds. Even non-model experts can easily initiate complex simulation calculations, improving the system's usability. Furthermore, users can interact end-to-end with the system rendering using natural language, eliminating the need for manually writing scripts or setting parameters item by item through a graphical interface, avoiding time-consuming and error-prone operations, and significantly improving the overall work efficiency from requirements to results. On the other hand, the data interaction module ensures the uniformity of input data format and standards. The final generated model input file follows preset specifications, effectively solving the problems of model calculation errors or incomparable results caused by inconsistent data formats and chaotic parameter settings. Moreover, the result reading module performs a series of standardization processes on the original model output (time serialization, cleaning, and format conversion), ensuring the quality and consistency of the final target result file, providing a reliable and well-organized data foundation for subsequent analysis and visualization. The model invocation module uses an asynchronous approach to call models for calculations. This means that after a user submits a task, they do not need to wait for the calculation to complete and can continue with other operations. This is crucial for time-consuming hydrological and hydrodynamic simulations, avoiding front-end interface blocking, improving user experience, and facilitating reasonable scheduling and resource allocation of computational tasks on the server side. Furthermore, the visualization module directly loads the processed standard results into the BS system for display, allowing users to intuitively view simulation results through a browser without the need for other specialized desktop software. Simultaneously, the modular system ensures better compatibility between this system and the BS system, providing a standardized and intelligent implementation path for the engineering application of hydrological and hydrodynamic models.

[0103] It should be understood that although several modules or units of the integrated system based on the hydrological and hydrodynamic model are mentioned, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0104] According to one aspect of an exemplary embodiment of this disclosure, a system integration method based on a hydrological and hydrodynamic model is also provided, applied to an integrated system based on a hydrological and hydrodynamic model, such as... Figure 7 As shown, the method includes:

[0105] Step S710: Obtain the requirement description information for the hydrological and hydrodynamic model through the intent recognition module, and perform intent recognition on the requirement description information to generate a structured parameter set based on the intent recognition result. The requirement description information is natural language description information, which is used to indicate the input information required for the calculation of the hydrological and hydrodynamic model. The input information includes at least the model type, rainfall data and topographic data.

[0106] Step S720: Standardize the structured parameter set through the data interaction model, and generate the model input file from the standardized structured parameter set using a preset file template.

[0107] Step S730: Utilize the model invocation module to invoke the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task, and use asynchronous processing to invoke the corresponding target hydrological and hydrodynamic model based on the target model input file to perform task processing and obtain a reference result file.

[0108] Step S740: Based on the result reading module, read the reference result file and perform time serialization, cleaning and format conversion processing in sequence to obtain the target result file.

[0109] Step S750: Load the target result file into the browser / server BS system through the visualization module, so as to visualize the target result file in the visualization interface of the BS system.

[0110] It should be noted that the details of steps S710 to S750 have been described in the above exemplary embodiments and will not be repeated here.

[0111] Exemplary embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the aforementioned system integration method based on a hydrological and hydrodynamic model.

[0112] In one implementation, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0113] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0114] Computer program code can be written in one or more programming languages. The program code can execute entirely on the user's computing device, or partially on the user's computing device, or as a standalone software package, or partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device through any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or it can be connected to an external computing device (e.g., through an internet connection provided by a mobile network operator).

[0115] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic fields, and infrared radiation. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to execute) the method steps of various exemplary embodiments of this disclosure, such as the steps of the system integration method based on the hydrological and hydrodynamic model described above.

[0116] Furthermore, in exemplary embodiments of this disclosure, an electronic device capable of implementing the above-described methods is also provided. Those skilled in the art will understand that various aspects of this disclosure can be implemented as systems, methods, or program products. Therefore, various aspects of this disclosure can be specifically implemented as: entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as "circuit," "module," or "system."

[0117] The following reference Figure 8To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0118] like Figure 8 As shown, the electronic device 800 is presented in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including storage unit 820 and processing unit 810), and a display unit 840.

[0119] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure.

[0120] Storage unit 820 may include readable media in the form of volatile storage units, such as random access memory (RAM) 821 and / or cache memory 822, and may further include read-only memory (ROM) 823.

[0121] The storage unit 820 may also include a program / utility 824 having a set (at least one) of program modules 825, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0122] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0123] Electronic device 800 can also communicate with one or more external devices 900 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0124] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0125] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. An integrated system based on a hydrological and hydrodynamic model, characterized in that, include: The intent recognition module is used to obtain the demand description information for the hydrological and hydrodynamic model, and to perform intent recognition on the demand description information to generate a structured parameter set based on the intent recognition result. The demand description information is natural language description information used to indicate the input information required for the calculation of the hydrological and hydrodynamic model. The input information includes at least the model type, rainfall data and topographic data. The data interaction module is used to perform data standardization processing on the structured parameter set and generate a model input file from the standardized structured parameter set using a preset file template. The model invocation module is used to invoke the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task, and to perform task processing by invoking the corresponding target hydrological and hydrodynamic model based on the target model input file in an asynchronous manner to obtain a reference result file. The result reading module is used to read the reference result file and sequentially perform time serialization, cleaning, and format conversion processing to obtain the target result file. A visualization module is used to load the target result file into a browser / server BS system, so as to visualize the target result file in the visualization interface of the BS system; The intent recognition module includes: An intent recognition unit is used to receive the requirement description information and perform intent recognition on the requirement description information based on a large language model to obtain the intent recognition result; The parameter acquisition unit is used to perform named entity recognition on the intent recognition result to obtain the entity recognition result, and to perform numerical range parsing on the entity recognition result to obtain a reference parameter set; The parameter verification unit is used to interface with the configuration database through the stream processing engine to verify the reference parameter set based on the configuration database, and to determine the verified reference parameter set as the structured parameter set; the configuration database includes a model capability matrix, a reasonable range of parameters, a data source mapping table, and a geographical range boundary. The model invocation module includes: An interface unit is used to obtain the model invocation task in response to a model invocation operation; The model invocation unit is used to process the model invocation task in an asynchronous manner to obtain the reference result file; The model invocation unit includes: Memory unit, used to record the task status of the task called by the model; The message feedback unit is used to feed back the task identifier of the model calling task to the front end. The task identifier is used to query the task status in the memory unit. The task processing unit is used to run the target hydrological and hydrodynamic model in a container to perform task processing based on the target model input file and obtain the reference result file; wherein different hydrological and hydrodynamic models are run in different containers.

2. The system according to claim 1, characterized in that, The data interaction module includes: A data processing stream construction unit, used to construct a data processing stream based on a predetermined algorithm; The data processing unit is used to perform data standardization processing on the structured parameter set through the data processing stream to obtain a standardized structured parameter set; The template invocation unit is used to invoke the preset file template and render the standardized structured parameter set to the preset file template to obtain the target file template; The file generation unit is used to write the target file template into the model input file.

3. The system according to claim 2, characterized in that, The data processing unit is also used to call the verification file and parameter mapping rules, and to perform legality verification on the structured parameter set based on the verification file; The template calling unit is also used to call the preset file template according to the model type, and fill the standardized structured parameter set into the preset file template based on the parameter mapping rules to obtain the target file template.

4. The system according to claim 1, characterized in that, The model invocation operation carries at least the model type, the identifier of the input information, and the model execution parameters.

5. The system according to claim 1, characterized in that, The result reading module includes: The parsing unit is used to read the reference result file and parse it to obtain a first parsing file, wherein the first parsing file includes at least time dimension information, spatial coordinate information and hydrological variable information. A time series normalization unit is used to unify the time step size of the parsed file to obtain a second parsed file; A data standard unit is used to process outliers and missing values ​​in the second parsed file to obtain a third parsed file; The format conversion unit is used to convert the third parsed file into the target file format to obtain the target result file.

6. The system according to claim 5, characterized in that, The format conversion unit includes: The vector transformation unit is used to filter the third parsed file into data subsets corresponding to different times based on the time dimension; The raster conversion unit is used to perform raster conversion on the data subset to obtain an image file in the target format, and output the spatial reference, resolution and compression method of the raster. The pyramid building unit is used to construct an image pyramid based on the image file according to the spatial reference, resolution and compression method of the raster, so as to obtain the target result file. The image pyramid supports different levels of scaling display.

7. The system according to claim 1, characterized in that, The visualization module includes: The loading unit is used to load the target result file into the rendering model corresponding to the BS system, so as to render the target result file through the rendering model to obtain the rendering result; The display unit is used to visualize the rendering results in the visualization interface of the BS system.

8. A system integration method based on a hydrological and hydrodynamic model, characterized in that, The method, applied to an integrated system based on a hydrological and hydrodynamic model, includes: The intention recognition module obtains the requirement description information for the hydrological and hydrodynamic model, and performs intention recognition on the requirement description information to generate a structured parameter set based on the intention recognition result. The requirement description information is natural language description information, used to indicate the input information required for the calculation of the hydrological and hydrodynamic model. The input information includes at least the model type, rainfall data and topographic data. The structured parameter set is standardized using a data interaction model, and a model input file is generated from the standardized structured parameter set using a preset file template. The model invocation module is used to invoke the target model input file and the corresponding target hydrological and hydrodynamic model based on the model invocation task. The target model input file is used to invoke the corresponding target hydrological and hydrodynamic model for task processing in an asynchronous manner to obtain a reference result file. The result reading module reads the reference result file and performs time serialization, cleaning, and format conversion processing in sequence to obtain the target result file. The target result file is loaded into the browser / server BS system through the visualization module, so that the target result file can be visualized in the visualization interface of the BS system; The intent recognition module includes an intent recognition unit, a parameter acquisition unit, and a parameter verification unit. The intent recognition unit receives the requirement description information and performs intent recognition on the requirement description information based on a large language model to obtain the intent recognition result. The parameter acquisition unit performs named entity recognition on the intent recognition result to obtain the entity recognition result, and performs numerical range analysis on the entity recognition result to obtain a reference parameter set. The parameter verification unit connects to the configuration database through the stream processing engine to verify the reference parameter set based on the configuration database, and determines the verified reference parameter set as the structured parameter set; the configuration database includes a model capability matrix, a reasonable range of parameters, a data source mapping table, and a geographical boundary. The model invocation module includes an interface unit and a model invocation unit: The interface unit responds to the model invocation operation and obtains the model invocation task; The model invocation unit processes the model invocation task asynchronously to obtain the reference result file; The model invocation unit includes a memory unit, a message feedback unit, and a task processing unit: The memory unit records the task status of the model's invocation task; The task identifier of the model invocation task is fed back to the front end through the message feedback unit. The task identifier is used to query the task status in the memory unit. The target hydrological and hydrodynamic model is run in the container through the task processing unit to perform task processing based on the target model input file and obtain the reference result file; wherein, different hydrological and hydrodynamic models are run in different containers.

9. A program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of claim 8.

10. A device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of claim 8 by executing the executable instructions.

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