Hydrological test recommendation method and device based on knowledge graph, and medium

Abstract

The invention discloses a hydrological test recommendation method and device based on a knowledge graph, and a medium. The method comprises the steps: obtaining hydrological test data of each hydrological station in a target region, sorting the hydrological test data, carrying out the modeling of a hydrological test mode, and obtaining a plurality of initial graph models; synchronizing hydrological survey data in the MySQL database into the initial atlas model to obtain a plurality of survey station atlas models; and based on the query parameters, determining a target hydrological survey scheme in the plurality of survey station map models. According to the method, a hydrological test knowledge base is constructed, and a hydrological test map model is defined, so that the method for automatically updating the hydrological test map base based on kettle tool visual interface operation is realized. According to the method, the hydrological test mode is displayed in a visualized graph, corresponding test modes can be recommended according to different conditions, and a recommendation report is generated.

Description

Technical Field

[0001] The present application relates to the field of hydrological survey, and specifically to a hydrological survey recommendation method, device and medium based on knowledge graph. Background Art

[0002] Hydrological information is an important basis for flood control dispatch and command decision-making, and is an important support for winning the tough battle of flood defense. Hydrological work obtains data such as water level, precipitation, evaporation, and flow through hydrological tests, and timely understands the dynamic changes of water bodies, providing important decision-making basis for flood control, drought relief, water resources management and other work. In order to ensure that hydrological tests can measure data under any circumstances, hydrological tests are divided into daily tests, flood tests and excessive flood tests, and the test methods of each category of hydrological elements are specified.

[0003] At present, due to the different flood measurement capabilities and geographical locations of each hydrological station, each hydrological station has its own daily test plan, flood test plan and excessive flood test plan, and the contents of each plan are different, resulting in complex test rules. In addition, these plans are generally stored and managed in the form of documents, and the query test method is inefficient, which cannot provide convenient operation guidance for test staff. Especially in emergency situations, it is difficult to provide users with comprehensive and visual decision support. Summary of the invention

[0004] In order to solve the above problems, this application proposes a hydrological test recommendation method, device and medium based on knowledge graph, wherein the method includes:

[0005] Obtain the hydrological test data of each hydrological station in the target area, wherein the test data includes basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; organize the hydrological test data, and store the organized hydrological test data in a structured manner in a MySQL database; use ontology modeling technology to model the hydrological test method to obtain multiple initial graph models that combine the test station and the test method; synchronize the hydrological test data in the MySQL database to the initial graph model to obtain multiple station graph models; obtain the query parameters input by the user, and based on the query parameters, determine the target hydrological test plan corresponding to the target hydrological station in the multiple station graph models.

[0006] In one example, after the hydrological test method is modeled using ontology modeling technology to obtain multiple initial graph models combining test stations and test methods, the method also includes: obtaining the hydrological station and test method corresponding to the initial graph model, determining the name of the initial graph model; and saving the initial graph model to the Neo4j database.

[0007] In one example, synchronizing the hydrological test data in the MySQL database to the initial graph model specifically includes: determining the hydrological test data to be synchronized; creating a job in the kettle data acquisition tool and connecting to the MySQL database; at the output end, performing label and model mapping configuration in the Neo4j database; and executing a script to synchronize the hydrological test data to be synchronized to the initial graph model.

[0008] In one example, after synchronizing the hydrological test data to be synchronized into the initial graph model, the method further includes: displaying the hydrological test data synchronization process in a visual interface by calling the kettleAPI; receiving the job execution frequency set by the user through the visual interface; and scheduling the kettle to implement the scheduled job according to the job execution frequency.

[0009] In one example, after synchronizing the hydrological test data to be synchronized to the initial map model, the method also includes: developing a map visualization interface of the multiple station map models based on the Neovis.js tool; displaying the name of each station map model in the map visualization interface; receiving a user's click operation in the map visualization interface, and displaying the corresponding station map model to the user according to the click operation.

[0010] In one example, the method of obtaining query parameters input by a user and determining a target hydrological test plan corresponding to a target hydrological station in the multiple station map models based on the query parameters specifically includes: providing a retrieval interface in the map visualization interface; receiving retrieval information input by a user in the retrieval interface; determining a target hydrological station and a station map model corresponding to the target hydrological station according to the retrieval information; presenting the station map model corresponding to the target hydrological station to the user in the map visualization interface, and generating a test method recommendation report according to the retrieval information.

[0011] In one example, the basic data of the hydrological station include the station code, station name, river basin, river, water system, downstream station, municipal administrative division, county administrative division, base surface, longitude and latitude, warning water level, warning flow, guaranteed water level, guaranteed flow, catchment area, main stream length, flood limit water level, and flood classification.

[0012] In one example, the daily test plan data include: test methods of various monitoring elements, the monitoring elements include water level, flow, single sand, sediment transport rate, and water quality; the test method includes at least one of the test location, test equipment, and test requirements; the flood measurement plan data include: water level test methods, flow test methods, sediment test methods, flood reporting plans, and support personnel information under different flood conditions; water level test methods include test locations and test requirements; flow and sediment test methods include test plans and backup plans, and the test plans and backup plans include test equipment and test requirements; the excessive flood test plan data include: water level test methods, flow test plans, sediment test plans, flood reporting plans, support personnel information, and personnel and equipment transfer plans.

[0013] The present application also provides a hydrological test recommendation device based on a knowledge graph, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute: obtaining hydrological test data of each hydrological station in a target area, the test data including basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; organizing the hydrological test data, and storing the organized hydrological test data in a structured manner in a MySQL database; modeling the hydrological test method using ontology modeling technology to obtain multiple initial graph models combining the station and the test method; synchronizing the hydrological test data in the MySQL database to the initial graph model to obtain multiple station graph models; obtaining query parameters input by the user, and based on the query parameters, determining the target hydrological test plan corresponding to the target hydrological station in the multiple station graph models.

[0014] The present application also provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: obtain hydrological test data of each hydrological station in a target area, wherein the test data includes basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; organize the hydrological test data, and store the organized hydrological test data in a structured manner in a MySQL database; use ontology modeling technology to model the hydrological test method to obtain multiple initial graph models combining the test station and the test method; synchronize the hydrological test data in the MySQL database to the initial graph model to obtain multiple station graph models; obtain query parameters input by a user, and based on the query parameters, determine the target hydrological test plan corresponding to the target hydrological station in the multiple station graph models.

[0015] The method proposed in this application can bring the following beneficial effects: a hydrological test knowledge base is constructed, and by defining a hydrological test map model, an automatic update method of the hydrological test map library based on the visual interface operation of the kettle tool is realized. The visual map displays the hydrological test method, and can recommend the corresponding test method according to different situations, and generate a recommendation report. By constructing a comprehensive hydrological test knowledge base, a hydrological station test map model, as well as map display and test method recommendation, it can provide users with test operation guidance, help improve the accuracy of the test, quickly respond to hydrological test work during the flood period, provide hydrological information in a timely manner, and strongly support flood prevention and disaster reduction work. At the same time, for hydrological station managers, it is helpful to review the hydrological test work and optimize and improve the subsequent test plans; for managers across the province, it is helpful to fully grasp the test plans of each hydrological station and make comprehensive overall decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A schematic diagram of a flow chart of a hydrological test recommendation method based on a knowledge graph in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of a station map model in an embodiment of the present application;

[0019] Figure 3 This is a structural schematic diagram of a hydrological test recommendation device based on a knowledge graph in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0022] Figure 1A flow chart of a hydrological test recommendation method based on a knowledge graph provided for one or more embodiments of this specification. This method can use knowledge graph technology to sort out the test methods of hydrological stations under different circumstances, build a comprehensive hydrological test knowledge base, and construct a hydrological station test graph model. It can intuitively display the test methods under any circumstances in the form of a graph, and can recommend corresponding test methods according to the set parameters, provide test operation guidance to users, help improve the accuracy of the test, quickly respond to hydrological test work during flood seasons, provide hydrological information in a timely manner, and strongly support flood prevention and disaster reduction work. Certain input parameters or intermediate results in the process allow manual intervention and adjustment to help improve accuracy.

[0023] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0024] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and this application does not make any specific limitations on this.

[0025] like Figure 1 As shown, the embodiment of the present application provides a hydrological test recommendation method based on a knowledge graph, comprising:

[0026] S101: Acquire hydrological test data of each hydrological station in the target area, wherein the test data includes basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data.

[0027] Since each hydrological station has different flood measurement capabilities, geographical locations, service objects and other factors, each hydrological station has its own unique daily test plan, flood measurement plan and excessive flood test plan. Therefore, it is necessary to build a hydrological test knowledge base, facing the hydrological stations across the province, and sort out the data of each hydrological station's daily test plan, flood measurement plan and excessive flood test plan. First, it is necessary to obtain the hydrological test data of each hydrological station in the target area. The test data here includes the basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data.

[0028] Among them, the basic data of the hydrological station include: station code, station name, river basin, river, water system, downstream station, municipal administrative division, county administrative division, base surface, longitude and latitude, warning water level, warning flow, guaranteed water level, guaranteed flow, catchment area, main stream length, flood limit water level, and flood classification.

[0029] Daily test plan data include: water level test method, flow test method, single sand test method, sediment transport rate test method, water quality test method and other monitoring element test methods. Each type of test method includes test location, test equipment, test requirements, etc.

[0030] Flood measurement plan data include: water level test methods, flow test methods, sediment test methods, flood reporting plans and support personnel information under general flood, major flood, large flood and extremely large flood conditions. Water level test methods include test locations and test requirements; flow and sediment test methods include test plans and backup plans, and the plans include test equipment and test requirements, etc.

[0031] The basis for classifying floods and water levels into general floods, relatively large floods, major floods, and extremely large floods is the height of the water level and the degree of threat it poses to the safety of dikes. General floods are floods with a peak flow or volume that recurs once every 5 to 10 years. Relatively large floods are floods with a peak flow or volume that recurs once every 10 to 20 years. Major floods are floods with a peak flow or volume that recurs once every 20 to 50 years. Extremely large floods are floods with a peak flow or volume that recurs once more than once every 50 years.

[0032] The data of the flood test plan for excessive floods include: water level test method, flow test plan, sediment test plan, flood reporting plan, support personnel information and personnel and equipment transfer plan. The water level test method includes the test location and test requirements.

[0033] S102: Arrange the hydrological test data, and store the arranged hydrological test data in a MySQL database in a structured manner.

[0034] When sorting, the content of flood test files of different hydrological stations can be identified through natural language processing technology. For flood test files that cannot be successfully identified, the hydrological test data in the flood test files can be extracted through manual annotation. The sorted data is stored in a structured manner in the MySQL database.

[0035] S103: Modeling the hydrological test method using ontology modeling technology to obtain multiple initial map models combining the test stations and the test method.

[0036] In one embodiment, when modeling, ontology modeling technology is used to model the hydrological test method with objects, attributes, and relationships as elements, and establish the following Figure 2 The graph models shown are "Measuring station-basic information", "Measuring station-daily test", "Measuring station-flood test", "Measuring station-exceeding flood" and so on. When modeling, it is also necessary to obtain the hydrological station and test method corresponding to the initial graph model, determine the name of the initial graph model, and then save the initial graph model to the Neo4j database.

[0037] S104: Synchronize the hydrological test data in the MySQL database to the initial map model to obtain multiple station map models.

[0038] In one embodiment, the graph data is stored in a Neo4j database. For the constructed graph model, the kettle data acquisition tool is used to synchronize and update the data in the structured MySQL database to the graph database. Create a job in kettle to synchronize data. First, connect to the MySQL database and configure the extracted data. Then, at the output end, configure the label and model mapping in the Neo4j database, and then execute the script to generate the graph data.

[0039] Furthermore, by calling the kettle API, all graph conversion jobs can be displayed in a visual interface, and the job execution frequency can be set in the interface to achieve scheduled scheduling of kettle jobs and scheduled automatic updating of graph data.

[0040] S105: Acquire query parameters input by the user, and determine a target hydrological test plan corresponding to a target hydrological station in the plurality of station map models based on the query parameters.

[0041] In one embodiment, when a user needs to search, he can enter the query parameters, and the server will determine the target hydrological station and the corresponding target hydrological test plan in multiple station map models based on the query parameters. Specifically, a visualization interface for the hydrological test map construction results can be developed based on the Neovis.js tool, and a search interface can be provided. By entering the hydrological station code or hydrological station name, the hydrological station daily test, flood test and excessive flood test plan can be displayed in the form of a map. When a test method needs to be recommended, the user can enter the station code, water level, and flow parameters, so as to make a judgment based on the knowledge map and determine the recommended test method in different situations.

[0042] like Figure 3 As shown, the embodiment of the present application also provides a hydrological test recommendation device based on a knowledge graph, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0043] Obtain the hydrological test data of each hydrological station in the target area, wherein the test data includes basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; organize the hydrological test data, and store the organized hydrological test data in a structured manner in a MySQL database; use ontology modeling technology to model the hydrological test method to obtain multiple initial graph models that combine the test station and the test method; synchronize the hydrological test data in the MySQL database to the initial graph model to obtain multiple station graph models; obtain the query parameters input by the user, and based on the query parameters, determine the target hydrological test plan corresponding to the target hydrological station in the multiple station graph models.

[0044] The embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows:

[0045] Obtain the hydrological test data of each hydrological station in the target area, wherein the test data includes basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; organize the hydrological test data, and store the organized hydrological test data in a structured manner in a MySQL database; use ontology modeling technology to model the hydrological test method to obtain multiple initial graph models that combine the test station and the test method; synchronize the hydrological test data in the MySQL database to the initial graph model to obtain multiple station graph models; obtain the query parameters input by the user, and based on the query parameters, determine the target hydrological test plan corresponding to the target hydrological station in the multiple station graph models.

[0046] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0047] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0048] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0049] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0053] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0054] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0055] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0056] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A hydrological test recommendation method based on knowledge graph, characterized in that: include: Obtaining hydrological test data of each hydrological station in the target area, the test data including basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; Arrange the hydrological test data, and store the arranged hydrological test data in a MySQL database in a structured manner; The hydrological test method is modeled by ontology modeling technology to obtain multiple initial map models combining the test station and the test method; Synchronize the hydrological test data in the MySQL database into the initial map model to obtain multiple station map models; The query parameters input by the user are obtained, and based on the query parameters, a target hydrological test plan corresponding to the target hydrological station is determined in the plurality of station map models.

2. The method according to claim 1, characterized in that After the hydrological test method is modeled by ontology modeling technology to obtain a plurality of initial atlas models combining the test stations with the test methods, the method further includes: Obtaining the hydrological station and test method corresponding to the initial atlas model, and determining the name of the initial atlas model; The initial graph model is saved in the Neo4j database.

3. The method according to claim 1, characterized in that The step of synchronizing the hydrological test data in the MySQL database to the initial graph model specifically includes: Determine the hydrological test data to be synchronized; Create a job in the kettle data collection tool and connect to the MySQL database; On the output side, configure the label and model mapping in the Neo4j database; Execute the script to synchronize the hydrological test data to be synchronized into the initial map model.

4. The method according to claim 3, characterized in that After synchronizing the hydrological test data to be synchronized into the initial graph model, the method further includes: By calling kettleAPI, the hydrological test data synchronization process is displayed in the visual interface; Receiving a job execution frequency set by a user through the visual interface; According to the job execution frequency, the kettle is scheduled to implement the scheduled job.

5. The method according to claim 4, characterized in that After synchronizing the hydrological test data to be synchronized into the initial graph model, the method further includes: Developing a map visualization interface of the multiple station map models based on the Neovis.js tool; Displaying the name of each station map model in the map visualization interface; Receive a click operation from the user in the atlas visualization interface, and display the corresponding station atlas model to the user according to the click operation.

6. The method according to claim 5, characterized in that The obtaining of the query parameters input by the user and determining, based on the query parameters, the target hydrological test scheme corresponding to the target hydrological station in the plurality of station map models specifically includes: Providing a search interface in the graph visualization interface; Receiving search information input by the user in the search interface; Determine a target hydrological station and a station atlas model corresponding to the target hydrological station according to the search information; The station map model corresponding to the target hydrological station is presented to the user in the map visualization interface, and a test method recommendation report is generated based on the search information.

7. The method according to claim 1, characterized in that The basic data of the hydrological station include the station code, station name, river basin, river, water system, downstream station, municipal administrative division, county administrative division, base surface, longitude and latitude, warning water level, warning flow, guaranteed water level, guaranteed flow, catchment area, main stream length, flood limit water level, and flood classification.

8. The method according to claim 1, characterized in that The daily test program data includes: test methods of various monitoring elements, the monitoring elements include water level, flow, single sediment, sediment transport rate, water quality; the test method includes at least one of the test location, test equipment, and test requirements; The flood measurement scheme data includes: water level test methods, flow test methods, sediment test methods, flood reporting schemes and support personnel information under different flood conditions; water level test methods include test locations and test requirements; flow and sediment test methods include test schemes and backup schemes, and the test schemes and backup schemes include test equipment and test requirements; The excessive flood test plan data include: water level test method, flow test plan, sediment test plan, flood reporting plan, support personnel information and personnel and equipment transfer plan.

9. A hydrological test recommendation device based on knowledge graph, characterized in that: include: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute: Obtaining hydrological test data of each hydrological station in the target area, the test data including basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; Arrange the hydrological test data, and store the arranged hydrological test data in a MySQL database in a structured manner; The hydrological test method is modeled by ontology modeling technology to obtain multiple initial map models combining the test station and the test method; Synchronize the hydrological test data in the MySQL database into the initial map model to obtain multiple station map models; The query parameters input by the user are obtained, and based on the query parameters, a target hydrological test plan corresponding to the target hydrological station is determined in the plurality of station map models.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Obtaining hydrological test data of each hydrological station in the target area, the test data including basic data of the hydrological station, daily test plan data, flood measurement plan data, and excessive flood test plan data; Arrange the hydrological test data, and store the arranged hydrological test data in a MySQL database in a structured manner; The hydrological test method is modeled by ontology modeling technology to obtain multiple initial map models combining the test station and the test method; Synchronize the hydrological test data in the MySQL database into the initial map model to obtain multiple station map models; The query parameters input by the user are obtained, and based on the query parameters, a target hydrological test plan corresponding to the target hydrological station is determined in the plurality of station map models.

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