Quality inspection method, device and equipment for grid structure of flood analysis model and medium

Abstract

The invention discloses a quality inspection method and device for a flood analysis model grid structure, equipment and a medium. The quality inspection method comprises the steps of collecting an element data set included in the flood analysis model grid structure; according to the identification number and the associated element number corresponding to each element data, verifying the association relationship between each element data, and obtaining successfully verified target element data; and generating a grid vector layer based on the target element data through a vector graph generation engine, and determining a quality inspection result of the grid structure according to a generation result of the grid vector layer. According to the technical scheme of the embodiment of the invention, the time cost and the labor cost consumed in the quality inspection process of the flood analysis model grid structure can be saved, omission errors are avoided, and the quality inspection efficiency and the accuracy of quality inspection results are improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more particularly to the field of flood simulation technology, specifically to a quality inspection method, apparatus, equipment, and medium for a grid structure of a flood analysis model. Background Technology

[0002] The grid structure in flood analysis models is the fundamental framework for simulating water flow and calculating flood inundation. It's like laying out a "digital grid map" over the study area, allowing the model to accurately calculate key data such as water level, flow velocity, inundation depth, and inundation duration in each small "grid." The grid structure "discretizes" the real terrain, dividing continuous geographical space into countless regular or irregular small units (grids). Each unit has parameters such as elevation and roughness, and numerical calculations simulate the movement of floodwater between grids.

[0003] In existing technologies, the grid structure in flood analysis models is usually inspected manually to verify whether the data topology relationship defined by the grid structure for specific terrain is reasonable. However, this method requires high manpower and time costs, resulting in low quality inspection efficiency and easy omission of errors and difficulty in locating erroneous elements. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for quality inspection of flood analysis model grid structures, which can save time and manpower costs in the quality inspection process of flood analysis model grid structures, avoid omissions and errors, and improve quality inspection efficiency and accuracy of quality inspection results.

[0005] According to one aspect of the present invention, a quality inspection method for the grid structure of a flood analysis model is provided, the method comprising: Collect the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data, and polygon feature data; Based on the identifier number and associated element number corresponding to each element data, the association relationship between each element data is verified, and the target element data that has been successfully verified is obtained. A grid vector map layer is generated based on the target feature data using a vector graphics generation engine, and the quality inspection result of the grid structure is determined based on the generation result of the grid vector map layer.

[0006] Optionally, the point feature data includes the identifier number of each point feature in the grid structure and the spatial coordinates of each point feature; The line feature data includes the identifier number of each line feature in the grid structure, the point feature number associated with each line feature, and the polygon feature number. The surface feature data includes the identifier number of each surface feature in the mesh structure, the mesh cell number associated with each surface feature, and the line feature number associated with each surface feature.

[0007] Optionally, the relationship between the data elements can be verified based on the identifier number and associated element number corresponding to each element data, including: Based on the identifier number and associated element number corresponding to each element data, the relationship between point element data and line element data is verified. Based on the identifier number and associated element number corresponding to each element data, the association relationship between the line element data and the polygon element data is verified. The relationship between surface element data and grid cells is verified based on the corresponding identifier number and associated element number of each element data.

[0008] Optionally, after validating the relationships between the various element data, the following may also be included: Obtain the abnormal element data that failed the verification, and record the abnormal element data and the corresponding abnormal type in a data file with a preset format.

[0009] Optionally, a grid vector layer is generated based on the target feature data, including: Based on the point feature identifier number associated with the target line feature, the spatial coordinates of multiple corresponding points are obtained, and then the Bézier curve interpolation algorithm is used to connect the multiple points to obtain the line vector. Based on the line feature identifier number associated with the target surface feature, the line vector is closed in a counterclockwise order to obtain the surface vector; Based on the surface vector and mesh partitioning rules, target surface features associated with the same mesh unit are combined to obtain the mesh vector layer.

[0010] Optionally, based on the generation result of the mesh vector layer, the quality inspection result of the mesh structure is determined, including: Determine whether the mesh vector layer was successfully generated; If so, and the number of grids and spatial range corresponding to the grid vector layer are consistent with the output of the flood analysis model, then the grid structure is determined to be qualified.

[0011] Optionally, after determining whether the mesh vector layer has been successfully generated, the method further includes: If not, the mesh structure is determined to be unqualified, and abnormal element information in the mesh structure and corresponding correction suggestions are output.

[0012] According to another aspect of the present invention, a quality inspection device for a flood analysis model grid structure is provided, the device comprising: The data acquisition module is used to acquire the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data, and area feature data; The relationship verification module is used to verify the relationship between each element data according to the corresponding identifier number and associated element number, and to obtain the target element data that has been successfully verified. The vector graphics layer generation module is used to generate a grid vector graphics layer based on the target feature data through a vector graphics generation engine, and to determine the quality inspection result of the grid structure based on the generation result of the grid vector graphics layer.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the quality inspection method for the flood analysis model grid structure according to any embodiment of the present invention.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the quality inspection method for the grid structure of the flood analysis model according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the quality inspection method for the grid structure of the flood analysis model according to any embodiment of the present invention.

[0016] The technical solution provided by this invention collects the element dataset included in the grid structure of a flood analysis model, verifies the correlation between the element data according to the corresponding identifier number and associated element number of each element data, obtains the target element data that has been successfully verified, generates a grid vector layer based on the target element data through a vector graphics generation engine, and determines the quality inspection result of the grid structure based on the generation result of the grid vector layer. This provides a fast and effective method for grid structure quality inspection, which can save the time and manpower costs consumed in the quality inspection process of the flood analysis model grid structure, avoid omissions and errors, and improve the efficiency and accuracy of quality inspection results.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1a This is a flowchart of a quality inspection method for a flood analysis model grid structure provided by an embodiment of the present invention; Figure 1b This is a schematic diagram of a flood analysis model grid structure provided by an embodiment of the present invention; Figure 2 This is a flowchart of another quality inspection method for a flood analysis model grid structure provided by an embodiment of the present invention; Figure 3 This is a structural schematic diagram of a quality inspection device for a flood analysis model grid structure provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the quality inspection method for the grid structure of the flood analysis model in this embodiment of the invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Figure 1a This is a flowchart illustrating a quality inspection method for a flood analysis model mesh structure provided in an embodiment of the present invention. This embodiment is applicable to situations where the rationality of a flood analysis model mesh structure needs to be inspected. The method can be executed by a quality inspection device for the flood analysis model mesh structure. This device can be implemented in hardware and / or software and configured in an electronic device, such as... Figure 1a As shown, the method includes: Step 110: Collect the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data and area feature data.

[0023] In this embodiment, specifically, Figure 1b This can be a schematic diagram of a grid structure for a flood analysis model, such as... Figure 1b As shown, the mesh structure may include multiple regular or irregular mesh units. Each mesh unit may consist of multiple nodes and the lines connecting the nodes. Multiple lines enclose the surface corresponding to the closed region.

[0024] In this step, after obtaining the grid structure of the flood analysis model to be inspected, point feature data, line feature data, and area feature data included in the grid structure can be collected.

[0025] Optionally, the point features can be nodes in a grid structure, such as pumping stations or drainage wells. The point feature data stores key data for the point features, such as specific values ​​for flow rate, water level, flow velocity, water depth, and elevation. Furthermore, the point feature data also includes the identifier number of each point feature and its spatial coordinates.

[0026] The line features can be the lines connecting nodes in the grid structure, such as rivers, embankments, railways, sluices, culverts, bridges, and drainage networks. The line feature data stores key data for the line features, such as specific values ​​for flow rate, water level, flow velocity, water depth, and elevation. Furthermore, the line feature data includes the identifier number of each line feature in the grid structure, the point feature numbers associated with each line feature, and the polygon feature numbers.

[0027] The surface feature can be a surface enclosed by multiple lines in a mesh structure, such as ground, water surface, lake, reservoir, etc. The surface feature data stores key data of the surface feature, such as specific values ​​for flow rate, water level, flow velocity, water depth, elevation, and roughness parameters. Furthermore, the surface feature data includes the identifier number of each surface feature in the mesh structure, the mesh cell number associated with each surface feature, and the line feature number associated with each surface feature.

[0028] In a specific embodiment, after collecting the element dataset included in the grid structure of the flood analysis model, the element dataset can be standardized according to a preset data format to uniformly convert the element dataset into GeoJSON format, thereby ensuring the compatibility of multivariate model data.

[0029] Step 120: Verify the relationship between each element data according to the corresponding identifier number and associated element number, and obtain the target element data that has been successfully verified.

[0030] In this step, based on the identifier number and associated element number corresponding to each element data, it is possible to verify whether the associated element data of each element data exists or is duplicated, and obtain the target element data that has been successfully verified.

[0031] Step 130: Generate a grid vector layer based on the target feature data using a vector graphics generation engine, and determine the quality inspection result of the grid structure based on the generation result of the grid vector layer.

[0032] The vector graphics generation engine is a graphics processing tool based on mathematical algorithms that generates vector layers by calculating the positions and relationships of geometric elements such as points, lines, and surfaces.

[0033] In this embodiment, since the mesh structure is a representation of the topological relationship between points, lines, and surfaces, the rationality of the mesh structure can be effectively verified by using a vector graphics generation engine to generate a mesh structure vector layer in reverse based on the self-identification number of the point, line, and surface elements and the associated element number.

[0034] Specifically, if the vector graphics generation engine can successfully generate a grid vector layer based on the target feature data, and the number of grids and spatial range corresponding to the grid vector layer are consistent with the output results of the flood analysis model, then the grid structure design of the flood analysis model can be determined to be reasonable and the quality inspection is qualified; conversely, if the vector graphics generation engine encounters an anomaly during the generation of the grid vector layer, or the number of grids and spatial range corresponding to the generated grid vector layer are inconsistent with the output results of the flood analysis model, then the grid structure quality inspection of the flood analysis model can be determined to be unqualified.

[0035] The technical solution provided by this invention collects the element dataset included in the grid structure of a flood analysis model, verifies the correlation between the element data according to the corresponding identifier number and associated element number of each element data, obtains the target element data that has been successfully verified, generates a grid vector layer based on the target element data through a vector graphics generation engine, and determines the quality inspection result of the grid structure based on the generation result of the grid vector layer. This provides a fast and effective method for grid structure quality inspection, which can save the time and manpower costs consumed in the quality inspection process of the flood analysis model grid structure, avoid omissions and errors, and improve the efficiency and accuracy of quality inspection results.

[0036] Figure 2 A flowchart of another quality inspection method for a flood analysis model grid structure provided in an embodiment of the present invention is shown below. Figure 2 As shown, the method includes: Step 210: Collect the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data and area feature data.

[0037] In this embodiment, a "three-level association verification mechanism (point-line association, line-area association, and area-grid association)" can be used to verify the association relationship between point feature data, line feature data, and area feature data in the grid structure.

[0038] Step 220: Verify the association between point feature data and line feature data based on the corresponding identifier number and associated feature number of each feature data.

[0039] Specifically, in this step, the point feature numbers associated with each line feature data can be traversed, and the existence of the point feature corresponding to each point feature number can be verified. For example, assuming that the line feature numbered L001 is associated with point feature numbers P001 and P002, it can be determined whether point features numbered P001 and P002 exist in the point feature data. If the point feature exists in the point feature data and there are no duplicate features, then the verification of the line feature numbered L001 and the point features numbered P001 and P002 is considered successful.

[0040] Step 230: Verify the association between the line feature data and the polygon feature data based on the identifier number and associated feature number corresponding to each feature data.

[0041] In this step, the line feature numbers associated with each polygon feature data can be traversed, and the existence of the line feature corresponding to each line feature number can be verified. For example, assuming that all line feature numbers associated with the polygon feature number F001 are L003, L004, L005, and L006, it can be determined whether line features numbered L003, L004, L005, and L006 exist in the line feature data. If they exist and there are no duplicate features, then the verification of the polygon feature number F001 and the line features numbered L003, L004, L005, and L006 is successful.

[0042] Step 240: Verify the relationship between surface element data and grid cells based on the corresponding identifier number and associated element number of each element data.

[0043] In this step, it can be determined whether the identifier number of each face feature exists in the set of grid cell numbers output by the model. If it exists, it is determined that the face feature data verification is successful, thereby avoiding the occurrence of isolated face features without corresponding grid cells.

[0044] In this embodiment, after verifying the correlation between the data of each element, the method further includes: obtaining the abnormal element data that failed the verification, and recording the abnormal element data and the corresponding abnormal type in a data file with a preset format.

[0045] The abnormal feature data may include an abnormal feature identifier number and the associated feature number of the abnormal feature, such as "the point feature P102 associated with line feature L005 does not exist" or "the line feature L030 associated with polygon feature F012 is missing".

[0046] Step 250: Obtain the successfully verified target feature data, and generate a grid vector layer based on the target feature data using a vector graphics generation engine.

[0047] In one embodiment of this example, generating a mesh vector layer based on target feature data includes: obtaining the spatial coordinates of multiple points based on the point feature identifier numbers associated with the target line features, and then connecting the multiple points using a Bézier curve interpolation algorithm to obtain a line vector; closing the line vectors in a counterclockwise order based on the line feature identifier numbers associated with the target surface features to obtain a surface vector; and combining the target surface features associated with the same mesh unit based on the surface vectors and mesh partitioning rules (e.g., rectangular mesh, triangular mesh, etc.) to obtain the mesh vector layer.

[0048] In a specific embodiment, after constructing the surface vector in the above manner, the corresponding area and center point coordinates can be calculated based on the surface vector, and this area can be compared with the area and center point coordinates originally recorded in the mesh structure. If the deviation between the two is within 1%, the surface vector generation is determined to be valid.

[0049] Step 260: Determine whether the mesh vector layer has been successfully generated. If yes, proceed to step 270; otherwise, proceed to step 280.

[0050] Step 270: If the number of grids and the spatial range corresponding to the grid vector layer are consistent with the output results of the flood analysis model, then the grid structure is determined to be qualified.

[0051] In this step, if the mesh structure is determined to be qualified, a qualified mesh vector layer and a qualified data report can be output.

[0052] Step 280: If the quality inspection of the mesh structure is determined to be unqualified, output the abnormal element information in the mesh structure and the corresponding correction suggestions.

[0053] In this embodiment, if the mesh vector layer cannot be successfully generated, for example, due to missing associated features or mismatched coordinates, the generation process of the mesh vector layer is interrupted, then the mesh structure quality inspection is determined to be unqualified, and an error diagnosis report of unqualified quality inspection is output.

[0054] Specifically, the error diagnosis report may include the identifier number of the abnormal element, its spatial coordinates, the cause of the abnormality, and correction suggestions (such as "It is recommended to supplement the point element P102 associated with the line element L005").

[0055] The technical solution provided by this invention collects the element dataset included in the grid structure of a flood analysis model. Based on the corresponding identifier and associated element number of each element data, it verifies the relationship between point element data and line element data, the relationship between line element data and area element data, and the relationship between area element data and grid cells. It obtains the successfully verified target element data, generates a grid vector layer based on the target element data using a vector graphics generation engine, and determines whether the grid vector layer was successfully generated. If so, and the number of grids and spatial range corresponding to the grid vector layer are consistent with the output result of the flood analysis model, then the grid structure is deemed to have passed quality inspection. If not, the grid structure is deemed to have failed quality inspection, and abnormal element information and correction suggestions are output. This technical means can save time and manpower costs in the quality inspection process of the flood analysis model grid structure, avoid omissions and errors, and improve quality inspection efficiency and accuracy.

[0056] Figure 3 This is a schematic diagram of a quality inspection device for a flood analysis model grid structure provided in an embodiment of the present invention. The device is configured in an electronic device, such as... Figure 3 As shown, the device includes: a data acquisition module 310, a relationship verification module 320, and a vector layer generation module 330.

[0057] The data acquisition module 310 is used to acquire the element dataset included in the grid structure of the flood analysis model; the element dataset includes point element data, line element data and area element data; The relationship verification module 320 is used to verify the relationship between each element data according to the identifier number and associated element number corresponding to each element data, and to obtain the target element data that has been successfully verified. The vector graphic layer generation module 330 is used to generate a grid vector graphic layer based on the target feature data through a vector graphic generation engine, and to determine the quality inspection result of the grid structure based on the generation result of the grid vector graphic layer.

[0058] The technical solution provided by this invention collects the element dataset included in the grid structure of a flood analysis model, verifies the correlation between the element data according to the corresponding identifier number and associated element number of each element data, obtains the target element data that has been successfully verified, generates a grid vector layer based on the target element data through a vector graphics generation engine, and determines the quality inspection result of the grid structure based on the generation result of the grid vector layer. This provides a fast and effective method for grid structure quality inspection, which can save the time and manpower costs consumed in the quality inspection process of the flood analysis model grid structure, avoid omissions and errors, and improve the efficiency and accuracy of quality inspection results.

[0059] Based on the above embodiments, the point feature data includes the identifier number of each point feature in the grid structure and the spatial coordinates of each point feature; The line feature data includes the identifier number of each line feature in the grid structure, the point feature number associated with each line feature, and the polygon feature number. The surface feature data includes the identifier number of each surface feature in the mesh structure, the mesh cell number associated with each surface feature, and the line feature number associated with each surface feature.

[0060] The relationship verification module 320 includes: The first-level verification unit is used to verify the relationship between point feature data and line feature data based on the corresponding identifier number and associated feature number of each feature data. The secondary verification unit is used to verify the relationship between the line feature data and the polygon feature data based on the identifier number and associated feature number corresponding to each feature data. The three-level verification unit is used to verify the relationship between surface element data and grid cells based on the corresponding identifier number and associated element number of each element data. An abnormal data recording unit is used to acquire abnormal element data that failed the verification and record the abnormal element data and the corresponding abnormal type in a data file with a preset format.

[0061] Vector layer generation module 330 includes: The layer generation unit is used to obtain the spatial coordinates of multiple points based on the point feature identifiers associated with the target line features, and then connect the multiple points using a Bézier curve interpolation algorithm to obtain a line vector; based on the line feature identifiers associated with the target surface features, the line vectors are closed in a counterclockwise order to obtain a surface vector; based on the surface vectors and the grid partitioning rules, the target surface features associated with the same grid unit are combined to obtain the grid vector layer; The layer judgment unit is used to determine whether the grid vector layer has been successfully generated. If so, and the number of grids and spatial range corresponding to the grid vector layer are consistent with the output results of the flood analysis model, then the grid structure is determined to be qualified. If not, the grid structure is determined to be unqualified, and abnormal element information in the grid structure and correction suggestions corresponding to the abnormal element information are output.

[0062] The above-described apparatus can execute the methods provided in all the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the above methods. Technical details not described in detail in the embodiments of the present invention can be found in the methods provided in all the foregoing embodiments of the present invention.

[0063] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0064] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0065] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0066] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processing (DSP) processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the quality control method for a flood analysis model grid structure.

[0067] In some embodiments, the quality control method for the flood analysis model mesh structure can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the quality control method for the flood analysis model mesh structure described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the quality control method for the flood analysis model mesh structure by any other suitable means (e.g., by means of firmware).

[0068] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0071] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD)) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0072] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0073] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and Virtual Private Servers (VPS) in terms of management difficulty and weak business scalability.

[0074] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0075] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A quality inspection method for the grid structure of a flood analysis model, characterized in that, The method includes: Collect the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data, and polygon feature data; Based on the identifier number and associated element number corresponding to each element data, the association relationship between each element data is verified, and the target element data that has been successfully verified is obtained. A grid vector map layer is generated based on the target feature data using a vector graphics generation engine, and the quality inspection result of the grid structure is determined based on the generation result of the grid vector map layer.

2. The method according to claim 1, characterized in that, The point feature data includes the identifier number of each point feature in the grid structure and the spatial coordinates of each point feature; The line feature data includes the identifier number of each line feature in the grid structure, the point feature number associated with each line feature, and the polygon feature number. The surface feature data includes the identifier number of each surface feature in the mesh structure, the mesh cell number associated with each surface feature, and the line feature number associated with each surface feature.

3. The method according to claim 1, characterized in that, Based on the identifier number and associated element number corresponding to each element data, the relationship between the element data is verified, including: Based on the identifier number and associated element number corresponding to each element data, the relationship between point element data and line element data is verified. Based on the identifier number and associated element number corresponding to each element data, the association relationship between the line element data and the polygon element data is verified. The relationship between surface element data and grid cells is verified based on the corresponding identifier number and associated element number of each element data.

4. The method according to claim 1, characterized in that, After verifying the relationships between the various element data, the process also includes: Obtain the abnormal element data that failed the verification, and record the abnormal element data and the corresponding abnormal type in a data file with a preset format.

5. The method according to claim 2, characterized in that, Generate a grid vector layer based on the target feature data, including: Based on the point feature identifier number associated with the target line feature, the spatial coordinates of multiple corresponding points are obtained, and then the Bézier curve interpolation algorithm is used to connect the multiple points to obtain the line vector. Based on the line feature identifier number associated with the target surface feature, the line vector is closed in a counterclockwise order to obtain the surface vector; Based on the surface vector and mesh partitioning rules, target surface features associated with the same mesh unit are combined to obtain the mesh vector layer.

6. The method according to claim 1, characterized in that, Based on the generation result of the mesh vector layer, the quality inspection result of the mesh structure is determined, including: Determine whether the mesh vector layer was successfully generated; If so, and the number of grids and spatial range corresponding to the grid vector layer are consistent with the output of the flood analysis model, then the grid structure is determined to be qualified.

7. The method according to claim 6, characterized in that, After determining whether the mesh vector layer has been successfully generated, the process also includes: If not, the mesh structure is determined to be unqualified, and abnormal element information in the mesh structure and corresponding correction suggestions are output.

8. A quality inspection device for a grid structure of a flood analysis model, characterized in that, The device includes: The data acquisition module is used to acquire the feature dataset included in the grid structure of the flood analysis model; the feature dataset includes point feature data, line feature data, and area feature data; The relationship verification module is used to verify the relationship between each element data according to the corresponding identifier number and associated element number, and to obtain the target element data that has been successfully verified. The vector graphics layer generation module is used to generate a grid vector graphics layer based on the target feature data through a vector graphics generation engine, and to determine the quality inspection result of the grid structure based on the generation result of the grid vector graphics layer.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quality inspection method for the flood analysis model grid structure according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the quality inspection method for the grid structure of the flood analysis model according to any one of claims 1-7.

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