Marine grid inspection method and device based on GIS information, equipment and medium

Abstract

The application discloses a GIS information-based marine gridding inspection method and device, equipment and medium, comprising: dividing an inspection area into a plurality of initial inspection grids based on obtained target plot data; obtaining image data of the inspection area, and optimizing each initial inspection grid based on the image data to obtain a plurality of target inspection grids; monitoring the target inspection grids, if there are abnormal grids in the target inspection grids, determining the abnormal types corresponding to each abnormal grid, and automatically generating a plurality of candidate inspection paths based on the abnormal types and the abnormal grids, determining a target inspection path based on real-time obtained inspection data, and inspecting each abnormal grid according to the target inspection path. The application can improve the inspection efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent inspection, in particular to a marine gridding inspection method and device based on GIS information, equipment and medium. BACKGROUND

[0002] With the rapid development of marine economy, the development and utilization of sea islands are also increasingly frequent. Through sea island supervision, the development and utilization of sea islands can be standardized to prevent overdevelopment and misuse of resources, thereby ensuring the sustainable development of marine economy. At the same time, marine disasters can be discovered and warned in a timely manner, and timely response and prevention measures can be provided to relevant departments and the public, thereby reducing the loss caused by disasters. Therefore, how to efficiently and intelligently detect the sea area becomes increasingly important.

[0003] Currently, satellite remote sensing, unmanned aerial vehicle inspection, manual inspection, and marine monitoring buoys are mainly used to inspect the sea. However, the frequency and quality of data obtained by these technologies are not effective, and the cost is high. At the same time, there are problems of small inspection range and low inspection efficiency. Moreover, when the marine environment changes, traditional inspection technologies are difficult to capture these changes in a timely manner, and thus it is difficult to adaptively adjust the inspection plan, i.e., dynamic supervision cannot be performed. Therefore, an intelligent marine inspection method is urgently needed to improve the inspection efficiency. SUMMARY

[0004] The present application provides a marine gridding inspection method and device based on GIS information to improve the inspection efficiency.

[0005] In a first aspect, the present application provides a marine gridding inspection method based on GIS information, comprising:

[0006] dividing the inspection area into a plurality of initial inspection grids based on the obtained target plot data;

[0007] obtaining image data of the inspection area, and optimizing each initial inspection grid based on the image data to obtain a plurality of target inspection grids;

[0008] monitoring the target inspection grids, determining the abnormal types corresponding to each abnormal grid if there is an abnormal grid in the target inspection grid, automatically generating a plurality of candidate inspection paths based on the abnormal types and the abnormal grids, determining a target inspection path based on real-time obtained inspection data, and inspecting each abnormal grid according to the target inspection path.

[0009] The embodiments of the present application can divide the inspection area into more manageable initial inspection grids by dividing the inspection area into a plurality of initial inspection grids. By obtaining image data of the inspection area, the area information of the inspection area can be understood in more detail. By optimizing the initial inspection grids, target inspection grids that more accurately reflect the actual situation of the inspection area can be obtained, facilitating subsequent rapid detection of abnormal information in each target inspection grid. By monitoring the target inspection grids, it can be accurately determined whether there is an abnormal grid in the target inspection grid, and then a corresponding candidate inspection path is quickly generated. Based on the real-time obtained inspection data, the target inspection path can be determined. The inspection path can be continuously adjusted based on the real-time obtained inspection data to obtain the target inspection path and perform inspection according to the target inspection path, so that the abnormal grid can be quickly and accurately inspected. Compared with the prior art, the present application can improve the inspection efficiency.

[0010] Further, the target plot data is obtained based on the obtained target plot data, and the inspection area is divided into a plurality of initial inspection grids, specifically:

[0011] The shoreline data and the first plot data are input into a geographic information system to determine corresponding target plot data.

[0012] The second plot data that does not meet the condition in the target plot data is removed according to a preset removal rule to obtain third plot data, and the inspection area is determined based on the third plot data, and the inspection area is sequentially divided into grids according to a preset division rule to obtain initial inspection grids; wherein the second plot data is an area that personnel cannot reach or cannot effectively inspect, and the preset division rule includes a division direction and a grid size.

[0013] In this way, the inspection area can be divided into more manageable initial inspection grids by dividing the inspection area into a plurality of initial inspection grids.

[0014] Further, the initial inspection grids are optimized based on the image data to obtain a plurality of target inspection grids, specifically:

[0015] The image data and the initial inspection grids are compared to obtain a comparison result, and the accessible area and the obstacle area are determined based on the comparison result.

[0016] It is determined whether the accessible area or the obstacle area exists in each of the target inspection grids. If the accessible area does not exist in each of the target inspection grids or the obstacle area exists in each of the target inspection grids, each of the target inspection grids is updated based on the accessible area or the obstacle area.

[0017] In this way, the image data of the inspection area is acquired, so that the area information of the inspection area can be understood in detail, and the target inspection grid that can accurately reflect the actual situation of the inspection area can be obtained by optimizing the initial inspection grid, thereby facilitating subsequent rapid discovery of abnormal information of each target inspection grid.

[0018] Further, the target inspection grid is monitored, and if the target inspection grid has an abnormal grid, the abnormal type corresponding to each abnormal grid is determined, specifically:

[0019] The sensor data and historical inspection records in each target inspection grid are acquired, and the sensor data and the historical inspection records are input into a pre-trained abnormal detection model to determine the abnormal grid and the abnormal type corresponding to each abnormal grid.

[0020] In this way, the target inspection grid is monitored, so that whether the target inspection grid has an abnormal grid and the abnormal type can be accurately determined, thereby facilitating subsequent generation of a candidate inspection path.

[0021] Further, the abnormal type and the abnormal grid are used to automatically generate a plurality of candidate inspection paths, specifically:

[0022] The inspection importance level corresponding to each abnormal grid is determined based on each abnormal type.

[0023] A plurality of candidate inspection paths reaching each abnormal grid are automatically generated in the order of the inspection importance level.

[0024] In this way, the inspection importance level corresponding to each abnormal grid is determined, so that the corresponding candidate inspection path can be quickly generated according to the inspection importance level.

[0025] Further, the target inspection path is determined based on real-time acquired inspection data, specifically:

[0026] The inspection data of the abnormal grid is acquired in real time, and the inspection difficulty level of each abnormal grid is determined based on the inspection data.

[0027] Each abnormal grid is sorted according to the inspection difficulty level to obtain a sorting result, and the target inspection path is determined from the candidate inspection path based on the sorting result.

[0028] In this way, the target inspection path is determined based on the real-time acquired inspection data, so that the inspection path can be continuously adjusted based on the real-time acquired inspection data to obtain the target inspection path, thereby facilitating subsequent rapid and accurate inspection of the abnormal grid.

[0029] Further, the marine gridding inspection method based on GIS information further comprises: updating each target inspection grid based on the sensor data and the inspection data.

[0030] In this way, by updating each target inspection grid, the target inspection grid can be continuously corrected and adjusted, so that a target inspection grid that more accurately reflects the actual situation of the inspection area can be obtained.

[0031] In a second aspect, the application provides a marine gridding inspection device based on GIS information, comprising: an acquisition module, an optimization module, and an inspection module.

[0032] The acquisition module is configured to divide the inspection area into a plurality of initial inspection grids based on the acquired target plot data.

[0033] The optimization module is configured to acquire image data of the inspection area, and optimize each initial inspection grid based on the image data to obtain a plurality of target inspection grids.

[0034] The inspection module is configured to monitor the target inspection grid, determine the abnormal type corresponding to each abnormal grid if there is an abnormal grid in the target inspection grid, automatically generate a plurality of candidate inspection paths based on the abnormal type and the abnormal grid, determine a target inspection path based on real-time acquired inspection data, and inspect each abnormal grid according to the target inspection path.

[0035] The embodiments of the application can divide the inspection area into a plurality of initial inspection grids, so that the inspection area can be divided into initial inspection grids that are easier to manage. By acquiring image data of the inspection area, the area information of the inspection area can be more detailed. By optimizing the initial inspection grid, a target inspection grid that more accurately reflects the actual situation of the inspection area can be obtained, which facilitates subsequent rapid discovery of abnormal information of each target inspection grid. By monitoring the target inspection grid, it can be determined whether there is an abnormal grid in the target inspection grid, and then a corresponding candidate inspection path can be quickly generated. Based on real-time acquired inspection data, a target inspection path can be determined, which can continuously adjust the inspection path based on real-time acquired inspection data to obtain a target inspection path and perform inspection according to the target inspection path, so that the abnormal grid can be quickly and accurately inspected. Compared with the prior art, the application can improve the inspection efficiency.

[0036] In a third aspect, the application further provides a terminal device, comprising: one or more processors; a memory coupled to the processors, configured to store one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement the marine gridding inspection method based on GIS information as described in the application.

[0037] In a fourth aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which when executed by a processor implements the method for marine grid patrol based on GIS information as described in the present application. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flow diagram of an embodiment of the method for marine grid patrol based on GIS information provided by the present application;

[0039] Figure 2 is a structural diagram of an embodiment of the device for marine grid patrol based on GIS information provided by the present application;

[0040] Figure 3 is a structural diagram of an embodiment of the terminal device provided by the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0042] It should be understood that the step numbers used herein are only for the convenience of description, and are not limited to the execution sequence of the steps.

[0043] It should be understood that the terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0044] The terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0045] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations thereof, and includes these combinations.

[0046] With the rapid development of marine economy, the development of sea areas and islands is frequent. The supervision of sea areas and islands can regulate the development and utilization of sea areas and islands, and timely discover and warn marine disasters, thereby reducing the loss caused by disasters. At present, satellite remote sensing, unmanned aerial vehicles, artificial patrol, buoys and other technologies are used for marine inspection, but these technologies have the disadvantages of low data acquisition frequency, limited quality, high cost, low efficiency, and difficulty in timely adapting to changes in the marine environment, and cannot realize dynamic supervision of sea areas and islands. Therefore, it is urgent to improve the efficiency of intelligent inspection methods.

[0047] Next, the terms involved in the present application are analyzed:

[0048] GIS is the abbreviation of Geographic Information System, and its Chinese name is geographic information system. It is a technical system that collects, stores, manages, calculates, analyzes, displays and describes the spatial geographic distribution data of the whole or part of the earth's surface (including the atmosphere) under the support of computer software and hardware system. It combines geography, cartography, remote sensing and computer science, and has been widely applied in different fields. It is a computer system for input, storage, query, analysis and display of geographic data.

[0049] Based on this, the present application embodiment provides a marine grid-based inspection method and device based on GIS information, which can improve the inspection efficiency.

[0050] The marine grid-based inspection method and device based on GIS information provided by the embodiments of the present application are specifically explained by the following embodiments. First, the marine grid-based inspection method based on GIS information in the embodiments of the present application is described.

[0051] The marine grid-based inspection method based on GIS information provided by the embodiments of the present application relates to the field of intelligent inspection. The marine grid-based inspection method based on GIS information provided by the embodiments of the present application can be applied in a terminal, can be applied in a server end, and can also be software running in a terminal or a server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and basic cloud computing services such as big data and artificial intelligence platforms; the software can be an application that implements a marine grid-based inspection method based on GIS information, but is not limited to the above forms.

[0052] The application can also be implemented in a variety of general purpose or special purpose computer systems environments or configurations. Examples of computer environments include personal computer systems, server computer systems, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PC's, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0053] Embodiment one

[0054] Please refer to Figure 1 , Figure 1 is a flowchart of an embodiment of the GIS information-based marine gridding inspection method provided by the application, comprising steps S101 to S103;

[0055] Step S101: dividing an inspection area into a plurality of initial inspection grids based on acquired target polygon data;

[0056] In some embodiments, dividing the inspection area into a plurality of initial inspection grids based on the acquired target polygon data comprises: inputting coastline data and first polygon data into a geographic information system to determine corresponding target polygon data; removing second polygon data that does not meet the conditions in the target polygon data according to a preset removal rule to obtain third polygon data, and determining an inspection area based on the third polygon data, and sequentially performing grid division on the inspection area according to a preset division rule to obtain initial inspection grids; wherein the second polygon data is an area that cannot be reached by personnel or cannot be effectively inspected, and the preset division rule includes a division direction and a grid size. Specifically, first, coastline data and national land-use survey polygon data, i.e., first polygon data, are acquired from a website or materials, then the coastline data and the first polygon data are input into a geographic information system, and the target polygon data located on the sea side of the coastline is selected according to the coastline data using the spatial selection function of the GIS; second, the second polygon data that does not meet the conditions is removed from the target polygon data according to the preset removal rule, and the third polygon data that meets the conditions is left, and the spatial range of the third polygon data is aggregated and merged to form an inspection area; finally, the inspection area is divided into grids according to the preset division rule (including the division direction and the grid size, etc.) to obtain the initial inspection grids.

[0057] It should be noted that the second polygon data refers to those areas that personnel cannot reach or cannot effectively patrol due to terrain, environment or policy restrictions, such as shrub bogs, river surfaces, mangrove lands, etc.

[0058] It should be noted that the preset elimination rule is based on field investigation and historical patrol data to ensure the accessibility and practicability of the patrol area, and the preset elimination rule includes division direction and grid size, wherein the division direction is determined based on geographical features (such as the direction of coastline) or administrative boundaries, and the grid size is determined according to patrol requirements (such as personnel allocation, equipment coverage range, etc.).

[0059] In some embodiments, the spatial range of the third polygon data is summarized and merged to form a patrol area, specifically: taking the coastline data as the starting point, a buffer zone of a preset distance is generated towards the land side; at the same time, taking the sea-land boundary after eliminating the unreachable area as the starting point, a buffer zone of a preset distance is also generated towards the sea side, and then the above two buffer zones and the area between the coastline and the sea-land boundary are merged to constitute the patrol area.

[0060] It should be noted that the patrol area includes marine patrol area and land patrol area, wherein the marine patrol area is an area formed by extending a certain distance towards the sea and land with the coastline data as the middle line, and then the marine patrol area is removed from the patrol area to obtain the land patrol area.

[0061] It should be noted that if the marine patrol area has polygon data that is small and discontinuous, or polygon data that is distributed in a narrow strip along the coastline, etc., these polygon data are merged with the land patrol area, and no grid is separately set, and if the marine patrol area is large and continuous, a grid needs to be separately set.

[0062] It should be noted that the division direction is determined based on geographical features (such as the direction of coastline) or administrative boundaries, and generally the grid is divided along the coastline data based on administrative boundaries, and the grid size is determined according to patrol requirements (such as personnel allocation, equipment coverage range, etc.). For the land patrol grid, it is necessary to ensure that the length of the coastline corresponding to each grid is about 4-6 kilometers, and for the marine grid, according to the size and continuity of the marine patrol area, it is necessary to ensure that the area of each grid is about 40-60 hectares, and for the marine patrol area polygon data that is small and discontinuous or distributed in a narrow strip, it can be considered to be merged with the land grid.

[0063] It should be noted that when dividing the initial patrol grid, the singular points and burrs in it also need to be eliminated.

[0064] In this way, by dividing the patrol area into a plurality of initial patrol grids, the patrol area can be divided into initial patrol grids that are easier to manage.

[0065] Step S102: Obtain image data of the inspection area, and optimize each initial inspection grid based on the image data to obtain a plurality of target inspection grids;

[0066] In some embodiments, the image data of the inspection area is obtained, specifically by satellite remote sensing image or unmanned aerial vehicle aerial image technology, to obtain the image data of the inspection area, so as to understand the information of the terrain, topography, building distribution and the like in more detail, and to ensure that the grid accurately reflects the actual situation.

[0067] In some embodiments, the initial inspection grid is optimized based on the image data to obtain a plurality of target inspection grids, including: comparing the image data and the initial inspection grid to obtain a comparison result, and determining the accessible area and the obstacle area based on the comparison result; determining whether the accessible area or the obstacle area exists in each target inspection grid, if the accessible area does not exist in each target inspection grid or the obstacle area exists in each target inspection grid, updating each target inspection grid based on the accessible area or the obstacle area. Specifically, first, the real-time obtained image data is compared with the initial inspection grid in space to obtain a comparison result, and the actual accessible area and obstacle area are determined based on the comparison result; second, the determined accessible area and obstacle area are compared with the target inspection grid to determine whether these areas are correctly included in the grid or excluded from the grid, if the accessible area is not correctly included in the target inspection grid or the obstacle area is incorrectly included, the initial inspection grid needs to be adjusted and optimized based on the actual distribution of the accessible area and the obstacle area to obtain the target inspection grid, so as to ensure that the target inspection grid accurately reflects the actual situation.

[0068] It should be noted that the actual accessible area is an area without obstacles and easy to pass through, and the obstacle area is an area with obstacles such as vegetation coverage, water body and building.

[0069] It should be noted that the comparison method includes but is not limited to pixel-level analysis, texture feature extraction and semantic segmentation technology, to identify the obstacle area (such as vegetation coverage area, water body and the like) and the accessible area in the image.

[0070] In this way, by obtaining the image data of the inspection area, the area information of the inspection area can be understood in more detail, by optimizing the initial inspection grid, the target inspection grid which accurately reflects the actual situation of the inspection area can be obtained, and the subsequent rapid discovery of abnormal information of each target inspection grid is facilitated.

[0071] Step S103: monitoring the target inspection grid, if there are abnormal grids in the target inspection grid, determining the abnormal type corresponding to each abnormal grid, and automatically generating a plurality of candidate inspection paths based on the abnormal type and the abnormal grid, determining a target inspection path based on real-time acquired inspection data, and inspecting each abnormal grid according to the target inspection path.

[0072] In some embodiments, monitoring the target inspection grid, if there are abnormal grids in the target inspection grid, determining the abnormal type corresponding to each abnormal grid, comprises: acquiring sensor data and historical inspection records in each target inspection grid, and inputting the sensor data and the historical inspection records into a pre-trained abnormal detection model to determine the abnormal grid and the abnormal type corresponding to each abnormal grid. Specifically, first, real-time inspection data is collected from each target inspection grid through sensors, monitoring devices, handheld terminals of inspection personnel, and the like, and past historical inspection records are acquired, and the sensor data and the historical inspection records are input into a pre-trained abnormal detection model. The abnormal detection model can identify abnormal networks and determine the abnormal type corresponding to each abnormal network.

[0073] It should be noted that the abnormal detection model uses a convolutional neural network (CNN) in deep learning, which is trained by historical inspection data and sensor data to automatically identify abnormal types such as pollution and equipment failure.

[0074] In this way, by monitoring the target inspection grid, it can be accurately determined whether there are abnormal grids and abnormal types in the target inspection grid, facilitating the generation of subsequent candidate inspection paths.

[0075] In some embodiments, automatically generating a plurality of candidate inspection paths based on the abnormal type and the abnormal grid comprises: determining a patrol importance level corresponding to each abnormal grid based on each abnormal type; and automatically generating a plurality of candidate inspection paths reaching each abnormal grid in order of the patrol importance level. Specifically, first, after determining the abnormal type corresponding to each abnormal grid, the abnormal type needs to be analyzed to understand the influence of each abnormal type on the abnormal grid, and each abnormal grid is divided into a patrol importance level according to the influence degree; second, according to the patrol importance level, all abnormal grids are sorted to obtain a sorting result, so as to ensure that the abnormal grids with high priority are accessed first in the inspection path, and a path planning algorithm (such as Dijkstra algorithm, A* algorithm, etc.) or a heuristic method is used to generate a plurality of candidate inspection paths capable of covering all abnormal grids according to the sorted grid order.

[0076] It should be noted that the inspection importance level can be a numerical value (such as 1-5 levels, where level 1 represents the highest importance) or a descriptive label (such as urgent, high, medium, and low).

[0077] It should be noted that the path planning also needs to consider the spatial relationship, connectivity, and obstacles between the abnormal grids.

[0078] In this way, by determining the inspection importance level corresponding to each abnormal grid, the corresponding candidate inspection path can be quickly generated according to the inspection importance level.

[0079] It should be noted that the inspection importance level can be used to distinguish the levels of each abnormal grid according to different colors.

[0080] In some embodiments, the target inspection path is determined based on the real-time acquired inspection data, including: acquiring the inspection data of the abnormal grid in real time, and determining the inspection difficulty level of each abnormal grid based on the inspection data; sorting each abnormal grid according to the inspection difficulty level to obtain a sorting result, and determining the target inspection path from the candidate inspection path based on the sorting result. Specifically, after determining the candidate inspection path, the inspection data of the abnormal grid needs to be collected in real time, and an inspection difficulty level is determined for each abnormal grid according to these inspection data. The candidate inspection path is sorted according to the inspection difficulty level to obtain a sorting result, and the optimal or specific standard path in the sorting result is selected as the target inspection path.

[0081] It should be noted that the inspection difficulty level can be affected by weather, traffic, personnel availability, inspection speed, and other factors. When these factors change, real-time judgment needs to be made to determine whether the target inspection path needs to be adjusted.

[0082] It should be noted that the inspection difficulty level is determined by acquiring the inspection data of the abnormal grid in real time during the inspection process, that is, judging the road conditions in the current inspection grid, whether there are areas that are difficult to reach due to non-human factors, and the like to further determine the inspection difficulty level.

[0083] In this way, the target inspection path is determined based on the real-time acquired inspection data, which can continuously adjust the inspection path based on the real-time acquired inspection data to obtain the target inspection path, facilitating subsequent rapid and accurate inspection of the abnormal grid.

[0084] In some embodiments, each of the abnormal grids is inspected according to the target inspection path, specifically, the target inspection path is packaged into a task and sent to an inspection personnel, an inspection system or an inspection machine, so as to execute an inspection task according to the target inspection path, continuously record and upload inspection data in the inspection process, analyze unreachable areas and difficult-to-inspect areas in the abnormal grid according to the target inspection path, and adaptively adjust the target inspection path based on these factors. The task includes detailed abnormal description, location coordinates, and specific checks and measures to be taken.

[0085] In some embodiments, each of the target inspection grids is updated based on the sensor data and the inspection data. Specifically, the target inspection grids are checked one by one according to the latest remote sensing images obtained in real time, the existing shoreline data, the sensor data and the inspection data. When the target inspection grid does not match the actual situation, the inspection range and the target inspection grid need to be adjusted, and the specific adjustment includes deleting unreachable areas and supplementing in the inspection area for uncovered areas.

[0086] It should be noted that the target inspection grid can be updated within a preset period of time, and the shoreline that has changed significantly within a certain period of time is screened regularly, and the inspection range of the grid is corrected and adjusted according to the shoreline change. Generally, the update iteration frequency of this item is higher in the early stage of grid issuance (updated once a quarter or half a quarter), and the update frequency gradually decreases as the grid inspection range tends to be stable.

[0087] In this way, by updating each of the target inspection grids, the target inspection grids can be continuously corrected and adjusted, and a target inspection grid that more accurately reflects the actual situation of the inspection area can be obtained.

[0088] The embodiments of the present application can divide the inspection area into a plurality of initial inspection grids, so as to divide the inspection area into initial inspection grids that are easier to manage. By obtaining image data of the inspection area, the area information of the inspection area can be understood in more detail. By optimizing the initial inspection grids, a target inspection grid that more accurately reflects the actual situation of the inspection area can be obtained, which facilitates subsequent rapid discovery of abnormal information of each target inspection grid. By monitoring the target inspection grids, it can be determined whether there is an abnormal grid in the target inspection grid, and a corresponding candidate inspection path can be quickly generated. Based on the real-time obtained inspection data, the target inspection path can be determined. The inspection path can be adjusted in real time based on the real-time obtained inspection data to obtain the target inspection path and perform inspection according to the target inspection path, so as to quickly and accurately inspect the abnormal grid. Compared with the prior art, the present application can improve the inspection efficiency.

[0089] Embodiment two

[0090] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of an embodiment of the marine grid-based inspection device based on GIS information provided by the present application, comprising an acquisition module 100, an optimization module 200 and an inspection module 300;

[0091] The acquisition module 100 is configured to divide an inspection area into a plurality of initial inspection grids based on acquired target polygon data;

[0092] The optimization module 200 is configured to acquire image data of the inspection area, and optimize each of the initial inspection grids based on the image data to obtain a plurality of target inspection grids;

[0093] The inspection module 300 is configured to monitor the target inspection grids, determine the abnormal types corresponding to each of the abnormal grids if there are abnormal grids in the target inspection grids, automatically generate a plurality of candidate inspection paths based on the abnormal types and the abnormal grids, determine a target inspection path based on real-time acquired inspection data, and inspect each of the abnormal grids according to the target inspection path.

[0094] The information interaction, execution process and other contents between the modules in the marine grid-based inspection device based on GIS information described above are based on the same concept as the embodiment of the marine grid-based inspection method based on GIS information of the first aspect of the present application, and achieve basically the same technical effects. For specific content, please refer to the description in the first embodiment of the method of the present application, which will not be repeated here.

[0095] The device embodiments described above are only schematic, and the modules described as separate components can or can not be physically separated, i.e. they can be located in one place or distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the method of the present embodiment.

[0096] Please refer to Figure 3 , Figure 3 a hardware structure of a terminal device of an embodiment is shown, the terminal device comprising:

[0097] The processor 301 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application;

[0098] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 302 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 302 and are called and executed by the processor 301 to implement the large model-based dialogue risk assessment method of the embodiments of the present application;

[0099] The input / output interface 303 is used to realize information input and output.

[0100] The communication interface 304 is used to realize the communication interaction between the device and other devices, which can realize communication through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0101] The bus 305 transmits information between various components (such as the processor 301, the memory 302, the input / output interface 303, and the communication interface 304) of the device.

[0102] The processor 301, the memory 302, the input / output interface 303, and the communication interface 304 are connected to each other through the bus 305 for internal communication connection.

[0103] The present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the GIS information-based marine gridding inspection method according to the first embodiment.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0105] The above-described specific embodiments further illustrate the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and do not limit the protection scope of the present application.

[0106] It is specifically intended that any modifications, equivalent replacements, improvements, etc., be included within the scope of the application.

Claims

1. A marine grid-based inspection method based on GIS information, characterized in that, include: Based on the acquired target patch data, the inspection area is divided into several initial inspection grids. Specifically, the shoreline data and first patch data are input into a geographic information system to determine the corresponding target patch data; second patch data that does not meet the conditions in the target patch data are removed according to a preset removal rule to obtain third patch data, and the inspection area is determined based on the third patch data. The inspection area is then divided into grids according to a preset division rule, and singularities and rough edges are removed to obtain the initial inspection grid. The second patch data refers to areas that are inaccessible to personnel or where effective inspection is not possible. The preset division rule includes the division direction and grid size, and the division direction is determined based on geographical features or administrative boundaries. Image data of the inspection area is acquired, and each initial inspection grid is optimized based on the image data to obtain several target inspection grids. Specifically, the image data and the initial inspection grids are compared to obtain a comparison result, and the reachable area and obstacle area are determined based on the comparison result. It is then determined whether the reachable area or the obstacle area exists in each of the target inspection grids. If the reachable area does not exist in each of the target inspection grids or the obstacle area exists in each of the target inspection grids, the target inspection grids are updated based on the reachable area or the obstacle area. The target inspection grid is monitored. If there are abnormal grids in the target inspection grid, the abnormality type corresponding to each abnormal grid is determined. Based on the abnormality type and the abnormal grid, several candidate inspection paths are automatically generated. The target inspection path is determined based on the real-time acquired inspection data. The abnormal grids are inspected according to the target inspection path. It also includes updating each target inspection grid based on sensor data and the inspection data. Specifically, it involves acquiring inspection data of abnormal grids in real time, and checking each target inspection grid one by one based on the latest remote sensing images, existing shoreline data, sensor data, and inspection data. When the target inspection grid does not match the actual situation, the inspection range and the target inspection grid are adjusted accordingly.

2. The marine grid-based inspection method based on GIS information according to claim 1, characterized in that, The process involves monitoring the target inspection grid. If abnormal grids are found within the target inspection grid, the abnormality type corresponding to each abnormal grid is determined, specifically as follows: Acquire sensor data and historical inspection records from each of the target inspection grids, and input the sensor data and historical inspection records into a pre-trained anomaly detection model to determine the abnormal grids and the anomaly types corresponding to each abnormal grid.

3. The marine grid-based inspection method based on GIS information according to claim 2, characterized in that, The automatic generation of several candidate inspection paths based on the anomaly type and the anomaly grid is specifically as follows: The inspection importance level corresponding to each of the aforementioned anomaly types is determined based on the anomaly grid. Several candidate inspection paths are automatically generated to reach each of the abnormal grids according to the order of the inspection importance level.

4. The marine grid-based inspection method based on GIS information according to claim 3, characterized in that, The determination of the target inspection path based on real-time acquired inspection data specifically involves: Real-time acquisition of inspection data for abnormal grids, and determination of the inspection difficulty level of each abnormal grid based on the inspection data; The abnormal grids are sorted according to the inspection difficulty level to obtain a sorting result, and the target inspection path is determined from the candidate inspection paths based on the sorting result.

5. A marine grid-based inspection device based on GIS information, characterized in that, include: Acquisition module, optimization module, and inspection module; The acquisition module is used to divide the inspection area into several initial inspection grids based on the acquired target patch data. Specifically, it inputs shoreline data and first patch data into a geographic information system to determine the corresponding target patch data; it removes second patch data that does not meet the conditions from the target patch data according to a preset removal rule to obtain third patch data, and determines the inspection area based on the third patch data. It then divides the inspection area into grids according to a preset division rule, removing singularities and rough edges to obtain the initial inspection grid. The second patch data represents areas that are inaccessible to personnel or where effective inspection is impossible. The preset division rule includes a division direction and grid size, where the division direction is determined based on geographical features or administrative boundaries. The optimization module is used to acquire image data of the inspection area and optimize each of the initial inspection grids based on the image data to obtain several target inspection grids. Specifically, it compares the image data with the initial inspection grids to obtain a comparison result and determines the reachable area and obstacle area based on the comparison result; it determines whether the reachable area or the obstacle area exists in each of the target inspection grids. If the reachable area does not exist in each of the target inspection grids or the obstacle area exists in each of the target inspection grids, it updates each of the target inspection grids based on the reachable area or the obstacle area. The inspection module is used to monitor the target inspection grid. If there is an abnormal grid in the target inspection grid, the module determines the abnormal type corresponding to each abnormal grid, and automatically generates several candidate inspection paths based on the abnormal type and the abnormal grid. The module determines the target inspection path based on the real-time acquired inspection data, and inspects each abnormal grid according to the target inspection path. It also includes updating each target inspection grid based on sensor data and the inspection data. Specifically, it involves acquiring inspection data of abnormal grids in real time, and checking each target inspection grid one by one based on the latest remote sensing images, existing shoreline data, sensor data, and inspection data. When the target inspection grid does not match the actual situation, the inspection range and the target inspection grid are adjusted accordingly.

6. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the marine gridded inspection method based on GIS information as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the marine gridded inspection method based on GIS information as described in any one of claims 1-4.

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