Network resource maintenance difficulty division method, device and equipment and readable storage medium

Abstract

The invention provides a network resource maintenance difficulty division method, device and equipment and a readable storage medium, and the method comprises the steps: carrying out the data fusion of obtained network resource data and space data, and obtaining resource space fusion data; performing region classification on the resource space fusion data to obtain a maintenance region; performing maintenance difficulty area division on the maintenance area to obtain maintenance difficulty area data; and verifying the maintenance difficulty region data and the spatial data to obtain a network resource maintenance difficulty division result. The network resource maintenance difficulty division result is obtained by fusing the network resources and the spatial data and combining the region classification of the fused data and the region division based on the maintenance difficulty, so that the division accuracy of the network resource maintenance difficulty is improved, and the accurate calculation of the network resource maintenance cost is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of network resource maintenance, and in particular to a method, device, equipment and readable storage medium for dividing the difficulty of network resource maintenance. Background Art

[0002] Existing maps mainly manage regional data based on buildings and natural environments. Regional data includes natural elements and human elements. Among them, various network resources that need to be maintained by communication operators (such as computer rooms, base stations, fiber distribution frames, optical cross boxes, and line laying points, etc.) are distributed in various scene areas of the map. Communication operators will divide and manage the maintenance difficulty of various network resources according to their locations, so as to accurately calculate the maintenance cost. At present, the division of maintenance difficulty of various network resources is mainly determined by subjective judgment of staff based on the location of network resources and other maintenance information. The division of network resource maintenance difficulty is not precise and unified enough, which makes the calculation of maintenance cost inaccurate. Summary of the invention

[0003] The present invention provides a method, device, equipment and readable storage medium for dividing the difficulty of network resource maintenance, so as to solve the defect that the existing division of the difficulty of network resource maintenance is not accurate and unified enough, and realize the accurate calculation of the cost of network resource maintenance.

[0004] The present invention provides a method for dividing the difficulty of network resource maintenance, comprising the following steps.

[0005] Perform data fusion on the acquired network resource data and spatial data to obtain resource space fusion data; Performing regional classification on the resource space fusion data to obtain a maintenance area; Dividing the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; The maintenance difficulty area data and the spatial data are verified to obtain a network resource maintenance difficulty classification result.

[0006] According to a network resource maintenance difficulty classification method provided by the present invention, the data fusion of the acquired network resource data and spatial data to obtain resource space fusion data includes: Acquire network resource data including network resource locations, and spatial data including multiple different coordinate systems; Perform unified coordinate transformation on spatial data in different coordinate systems; The converted spatial data is fused with the network resource location to obtain resource space fusion data.

[0007] According to a network resource maintenance difficulty classification method provided by the present invention, the resource space fusion data is subjected to regional classification to obtain maintenance areas, including: Performing slope analysis on the resource space fusion data to obtain slope classification areas; Dividing the slope classification area into areas; The area division results are sorted and classified into regions to obtain various maintenance areas.

[0008] According to a method for dividing network resource maintenance difficulty provided by the present invention, the maintenance area includes a target area; the maintenance difficulty includes a benchmark maintenance; and the maintenance difficulty area division of the maintenance area to obtain maintenance difficulty area data includes: determining a first buffer area of ​​the target area; determining a second buffer area for road data within the maintenance area; Based on the first buffer area and the second buffer area, reference maintenance area data is determined.

[0009] According to a method for dividing network resource maintenance difficulty provided by the present invention, the maintenance difficulty includes medium maintenance; the maintenance difficulty area division of the maintenance area to obtain maintenance difficulty area data also includes: determining a third buffer area of ​​the target area based on the first buffer area; determining a fourth buffer area of ​​the road data; The third buffer area and the fourth buffer area are merged to obtain medium maintenance area data.

[0010] According to a method for dividing network resource maintenance difficulty provided by the present invention, the maintenance difficulty includes high maintenance; the maintenance difficulty area division of the maintenance area to obtain maintenance difficulty area data also includes: Based on the maintenance area, the reference maintenance area data, and the medium maintenance area data, high maintenance area data is determined.

[0011] The present invention also provides a network resource maintenance difficulty division device, comprising the following modules: The resource space fusion module is used to fuse the acquired network resource data and space data to obtain resource space fusion data; A regional classification module, used for performing regional classification on the resource space fusion data to obtain a maintenance area; A maintenance difficulty area division module, used for dividing the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; The network resource maintenance difficulty classification module is used to verify the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty classification result.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the network resource maintenance difficulty division method as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the network resource maintenance difficulty division method as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for dividing the difficulty of network resource maintenance as described in any one of the above is implemented.

[0015] The network resource maintenance difficulty division method, device, equipment and readable storage medium provided by the present invention fuse the network resource data to be divided and the spatial data to obtain resource space fusion data, then classify the resource space data by region, obtain the maintenance areas of multiple network resources, and then divide the maintenance areas into maintenance difficulty regions to obtain maintenance difficulty regional data, and finally verify the obtained maintenance difficulty regional data to obtain the network resource maintenance difficulty division result. The present application obtains the network resource maintenance difficulty division result by fusing the network resource and spatial data, and then combining the regional classification of the fused data and the regional division based on the maintenance difficulty, thereby improving the division accuracy of the network resource maintenance difficulty and realizing the accurate calculation of the network resource maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is one of the flow charts of the network resource maintenance difficulty division method provided by the present invention.

[0018] Figure 2 This is the second flow chart of the network resource maintenance difficulty division method provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of the network resource maintenance difficulty division device provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Combine the following Figure 1-Figure 4 The invention describes a method, device, equipment and readable storage medium for dividing the difficulty of network resource maintenance.

[0023] Figure 1 This is one of the flow charts of the network resource maintenance difficulty classification method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 100: fusing the acquired network resource data and spatial data to obtain resource spatial fusion data; Specifically, the network resource maintenance difficulty classification method provided in this application mainly includes six parts: data acquisition, data fusion, regional classification, maintenance difficulty level analysis, maintenance difficulty and network resource matching, and data interactive output.

[0024] This embodiment mainly introduces the data acquisition and data fusion parts. The data acquired in the data acquisition part mainly includes network resource data, including network resource classification names (for example, computer rooms, base stations, optical fiber distribution frames, optical cross-connect boxes, and line laying points, etc.) and network resource location information, that is, which network resource is located at what location; the acquired data also includes various types of spatial data, such as satellite image data, regional map data, and electronic fences, etc.

[0025] The main function of the data fusion part is to unify and fuse the network resource data and spatial data from different sources, and obtain map data containing the network resource location coordinates in unified spatial coordinates, that is, the resource space fusion data in this embodiment.

[0026] Step 200: performing regional classification on the resource space fusion data to obtain a maintenance area; Specifically, the map data (in a unified coordinate system) in the resource space fusion data is divided and identified and analyzed. The basis for division and identification can be spatial terrain, terrain slope and elevation, etc., to achieve automatic grading of areas within a certain range. Among them, the slope calculation and analysis method is mainly used. Based on the digital elevation model (DEM) data, the mountain area data with a slope greater than a certain threshold (for example, 10 degrees) is extracted, and the maintenance difficulty data is regenerated. Based on data division and identification, the resource space fusion data is regionally classified to obtain a variety of maintenance areas, such as urban areas, rural areas and mountainous areas.

[0027] Step 300, dividing the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; Specifically, the maintenance areas obtained include multiple types, and different types of maintenance areas are divided into different maintenance difficulty levels to obtain regional data of multiple maintenance difficulties. Based on the determination of the type and scope of the maintenance area, different maintenance difficulty levels are further divided, so that the determination of the maintenance difficulty of network resources in the area is more accurate.

[0028] Step 400: verify the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty classification result.

[0029] The main contents of the above network resource matching part and data interaction output part are as follows: The network resource data is matched and associated with data such as regions, roads, and place names and addresses to accurately locate the location of network resources, such as the location of base stations and other data, and obtain the maintenance difficulty corresponding to the area where they are located. The main technologies used are spatial computing technology, point-surface matching technology, and natural language processing (NLP) address resolution technology.

[0030] Taking the base station as an example, the algorithm flow of network resources and regional distance is as follows: 1. Construct a distance algorithm analysis data model: Construct an analysis data model for the base station and regional distance algorithm, determine the spatial distance between the input regional location and road data and a large amount of base station data in a large area, and determine the base station location within a certain regional range. If the base station location is within the set distance range, it can be determined that the base station belongs to this area and meets the classification criteria for regional maintenance difficulty.

[0031] 2. Build a data model for base stations and regions: It is necessary to build a data model for base stations and regions, and provide an analysis data model based on regional classification and base station location distance.

[0032] 3. Build a spatial distance algorithm: Build a spatial distance algorithm by configuring threshold data, including distance and rules for matching data at the same location, and then perform spatial calculations based on the rules.

[0033] The calculation and matching of the base station surface is as follows: the position of the base station coordinates in the map is judged, and it is superimposed and matched with the calculated regional surface data. For the resource point data falling within the regional surface, the relationship between the region and the resource is bound, and the relevant business data information within the regional surface is extracted, and it is labeled with the type of regional classification.

[0034] The address data and map location matching algorithm based on the NLP model is as follows: Use the NLP word segmentation and parsing model algorithm to parse and split the address data based on address characteristics. The input is the network resource address data that needs to be classified and matched by the region. The address data and location information of the base station are matched and associated with the geographical location and address information after regional classification to obtain the location, address information and matching area information of the base station.

[0035] The data interaction output part is mainly used to output information including regional address information and maintenance difficulty, so as to accurately calculate the regional maintenance cost.

[0036] This embodiment fuses the network resource data to be divided with the spatial data to obtain resource space fusion data, then performs regional classification on the resource space data to obtain maintenance areas of multiple network resources, and then performs maintenance difficulty regional division on the maintenance areas to obtain maintenance difficulty regional data, and finally verifies the obtained maintenance difficulty regional data to obtain the network resource maintenance difficulty division result. This application obtains the network resource maintenance difficulty division result by fusing the network resource and spatial data, and then combines the regional classification of the fused data with the regional division based on the maintenance difficulty, thereby improving the division accuracy of the network resource maintenance difficulty and realizing the accurate calculation of the network resource maintenance cost.

[0037] Figure 2 FIG. 2 is a flow chart of the method for dividing the difficulty of network resource maintenance provided by the present invention. Figure 2 As shown, the method may also include: Step 110: Acquire network resource data including network resource locations and spatial data including multiple different coordinate systems; Step 120, performing unified coordinate transformation on spatial data of different coordinate systems; Step 130: Fusing the converted spatial data with the network resource location to obtain resource space fusion data.

[0038] Specifically, in the data fusion part, due to the diverse sources of spatial data, complex data formats, and lack of correlation between data, it is necessary to verify and fuse the data. Through data fusion technology, various spatial data are processed uniformly, the coordinate system is unified, and correlation relationships are established.

[0039] The method of unifying the coordinate system is as follows: Convert the coordinate systems of map data and network resource data into the standard coordinate system of the exclusive map library.

[0040] Provides coordinate conversion algorithms to achieve the conversion capabilities from WGS84 (World Geodetic System 1984, a coordinate system established for the GPS global positioning system) to GCJ02 (coordinate system for geographic information systems), BD09 (a coordinate system improved based on GCJ02), and CGCS2000 (China Geodetic Coordinate System2000, a national geodetic coordinate system), meeting the needs of coordinate conversion for coordinate systems from different sources and establishing the unified WGS84 coordinate system.

[0041] Take BD09 to WGS84 as an example: The coordinate conversion parameter fitting calculation is performed using the quadratic polynomial model, as shown in Formula 1 and Formula 2. Among them, x and y are the longitude and latitude of the WGS84 coordinate system; X and Y are the longitude and latitude of the BD09 coordinate system; A, B, C, D, E, and F are the latitude coordinate conversion parameters; and G, H, I, J, K, and L are the longitude coordinate conversion parameters.

[0042] ; (1) ; (2) The standard deviation of the calculated coordinate transformation parameters is calculated, and the standard deviation is taken as the final coordinate transformation parameter, as shown in Formula 3.

[0043] ; (3) The calculated coordinate conversion parameters are integrated into the code to realize the interface functions of converting the coordinates to be converted and WGS84 coordinates, and the coordinate conversion capability is realized through the real-time interface.

[0044] This embodiment realizes the fusion of spatial data from different sources by converting different coordinate systems.

[0045] In one embodiment, the network resource maintenance difficulty classification method provided in the embodiment of the present application may further include: Step 210: performing slope analysis on the resource space fusion data to obtain a slope classification area; Step 220, dividing the slope classification area into areas; Step 230: sort out the area division results and classify the regions to obtain multiple maintenance regions.

[0046] Specifically, the main process of slope calculation and analysis method is as follows: 1. Data extraction: Determine the regional boundary data based on the map data of the unified coordinate system mentioned above, and divide the DEM data of a large area into DEM data of multiple small areas based on the mask extraction method.

[0047] 2. Slope analysis algorithm: The slope of the DEM data of each small area is calculated according to the geodesic method to determine the steepness of the terrain at each pixel on the grid surface. The smaller the slope value, the flatter the terrain; the larger the slope value, the steeper the terrain.

[0048] Geodesic calculation: using geodesic coordinates (latitude ,longitude ,high ) Calculates X, Y, Z, Earth-Centered, Earth-Fixed (ECEF) coordinates.

[0049] Geodesic Coordinate Conversion: Reproject the raster to a geographic coordinate system, where each location has geodetic coordinates, and then convert them to the ECEF coordinate system.

[0050] Calculate the slope value: In ECEF coordinates, the slope is calculated by the angle between the ellipsoid normal and the terrain surface normal, in degrees.

[0051] Extract mountain area data: determine the area with a slope of more than 10 degrees as mountain area data, and determine the boundary of the mountain area data.

[0052] Slope analysis results are reclassified: the slope analysis results are classified into two categories: areas with slopes greater than 10 degrees are mountainous areas; the other category is regional data with slopes less than or equal to 10 degrees.

[0053] Data conversion: Convert the reclassified raster map data into vector surface data.

[0054] Vector data screening: Screen regional data in vector surface data and extract mountain area data with a slope greater than 10 degrees.

[0055] Area division and calculation: Merge the extracted mountain area data, split them into multiple parts, split the smaller area data and calculate their area.

[0056] Data classification: Delete areas with smaller areas and determine them as non-mountainous area data.

[0057] This embodiment uses technical features such as slope analysis and area division to perform regional classification on resource space fusion data, obtains multiple maintenance areas, and provides a data basis for determining the difficulty of maintenance.

[0058] In one embodiment, the network resource maintenance difficulty classification method provided in the embodiment of the present application may further include: Step 310: determine a first buffer area of ​​the target area; Step 320, determining a second buffer area for road data within the maintenance area; Step 330: Determine reference maintenance area data based on the first buffer area and the second buffer area.

[0059] Specifically, various maintenance areas (urban, rural, mountainous, etc.) and various road data are identified and analyzed to realize the automatic division of the difficulty level of network resource maintenance in a large range. This embodiment mainly uses the road identification method and the buffer calculation method. The maintenance difficulty of this application can be divided into three types: baseline, medium and high difficulty. The automatic target area of ​​this embodiment is the mountainous area data. Based on the buffer calculation method, the mountainous area boundary is buffered by a certain distance (for example, 100 meters) into the surface.

[0060] Method 1 for dividing the benchmark difficulty area: using the mountain area data to erase the above small area data, obtaining the area data other than the mountain area, merging the area data with the 100-meter buffer area data (i.e., the first buffer area in this embodiment), and obtaining the area of ​​benchmark maintenance difficulty; Method 2 for dividing the benchmark difficulty area: divide the road data into a buffer area of ​​a certain distance (e.g., 1,000 meters), i.e., the second buffer area in this embodiment, determine the part where the buffer area intersects with the mountainous area, and use this part of the area as the benchmark maintenance difficulty area.

[0061] The above two benchmark difficulty area division results are combined to obtain preliminary regional data of benchmark maintenance difficulty. The preliminary regional data of benchmark maintenance difficulty is then verified with spatial data (satellite images) to obtain benchmark maintenance area data.

[0062] This embodiment accurately calculates the benchmark maintenance area data through a road recognition method and a buffer zone calculation method.

[0063] In one embodiment, the network resource maintenance difficulty classification method provided in the embodiment of the present application may further include: Step 340: Determine a third buffer area of ​​the target area based on the first buffer area; Step 350, determining a fourth buffer area of ​​the road data; Step 360: Merge the third buffer area and the fourth buffer area to obtain medium maintenance area data.

[0064] Specifically, method one for dividing the maintenance area with medium difficulty: based on the buffer calculation method, a buffer is drawn for a certain distance (for example, 200 meters) inward from the boundary of the mountainous area, and the boundary range of 100 meters to 200 meters (i.e., the third buffer area in this embodiment) is the area data with medium maintenance difficulty; method two for dividing the maintenance area with medium difficulty: the road data is buffered by 2000 meters, and the area from the 1000-meter buffer of the road to the 2000-meter buffer boundary of the road (i.e., the fourth buffer area in this embodiment) is determined to be a non-mountainous area, and it is designated as an area with medium maintenance difficulty.

[0065] The above two medium-difficulty area division results are combined to obtain preliminary medium-difficulty area data. The preliminary medium-difficulty area data is then verified with the spatial data to obtain medium-maintenance area data.

[0066] This embodiment accurately calculates the medium maintenance area data through a road recognition method and a buffer zone calculation method.

[0067] In one embodiment, the network resource maintenance difficulty classification method provided in the embodiment of the present application may further include: Step 370: Determine high maintenance area data based on the maintenance area, the reference maintenance area data and the medium maintenance area data.

[0068] Specifically, the method for determining the high-difficulty maintenance area is as follows: the small area data is used to eliminate the above-determined benchmark maintenance area data and medium maintenance area data, and the obtained area result is classified as preliminary high-difficulty maintenance area data. Similarly, the preliminary high-difficulty maintenance area data is verified with the spatial data to obtain the high-difficulty maintenance area data.

[0069] This embodiment accurately calculates the high-maintenance area data through a road recognition method and a buffer zone calculation method.

[0070] The network resource maintenance difficulty division device provided by the present invention is described below. The network resource maintenance difficulty division device described below and the network resource maintenance difficulty division method described above can be referenced to each other.

[0071] Please refer to Figure 3 The present invention also provides a network resource maintenance difficulty division device, comprising: The resource space fusion module 301 is used to fuse the acquired network resource data and space data to obtain resource space fusion data; A region classification module 302 is used to perform region classification on the resource space fusion data to obtain a maintenance region; A maintenance difficulty area division module 303 is used to divide the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; The network resource maintenance difficulty classification module 304 is used to verify the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty classification result.

[0072] Optionally, the resource space fusion module includes: A resource space data acquisition unit, used to acquire network resource data including network resource locations, and space data including multiple different coordinate systems; A unified coordinate conversion unit is used to perform unified coordinate conversion on spatial data of different coordinate systems; The resource space data fusion unit is used to fuse the converted spatial data with the network resource location to obtain resource space fusion data.

[0073] Optionally, the area classification module includes: A slope analysis unit, used for performing slope analysis on the resource space fusion data to obtain a slope classification area; An area division unit, used for dividing the slope classification area into areas; The maintenance area determination unit is used to sort out the area division results and classify the areas to obtain various maintenance areas.

[0074] Optionally, the maintenance area includes a target area; the maintenance difficulty includes a baseline maintenance; and the maintenance difficulty area division module includes: A first buffer area determining unit, configured to determine a first buffer area of ​​the target area; A second buffer area determination unit, configured to determine a second buffer area for the road data in the maintenance area; The reference maintenance area data determining unit is configured to determine reference maintenance area data based on the first buffer area and the second buffer area.

[0075] Optionally, the maintenance difficulty includes medium maintenance; and the maintenance difficulty area division module further includes: A third buffer area determining unit, configured to determine a third buffer area of ​​the target area based on the first buffer area; a fourth buffer area determining unit, configured to determine a fourth buffer area of ​​the road data; The medium maintenance area data determining unit is used to merge the third buffer area and the fourth buffer area to obtain medium maintenance area data.

[0076] Optionally, the maintenance difficulty includes high maintenance; and the maintenance difficulty area division module further includes: The high maintenance area data determining unit is configured to determine the high maintenance area data based on the maintenance area, the reference maintenance area data and the medium maintenance area data.

[0077] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the network resource maintenance difficulty division method, which includes: performing data fusion on the acquired network resource data and spatial data to obtain resource space fusion data; performing regional classification on the resource space fusion data to obtain maintenance areas; performing maintenance difficulty regional division on the maintenance area to obtain maintenance difficulty regional data; and verifying the maintenance difficulty regional data and the spatial data to obtain a network resource maintenance difficulty division result.

[0078] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0079] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the network resource maintenance difficulty division method provided by the above methods, and the method includes: performing data fusion on the acquired network resource data and spatial data to obtain resource space fusion data; performing regional classification on the resource space fusion data to obtain maintenance areas; performing maintenance difficulty area division on the maintenance areas to obtain maintenance difficulty area data; verifying the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty division result.

[0080] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the network resource maintenance difficulty division method provided by the above-mentioned methods. The method includes: performing data fusion on the acquired network resource data and spatial data to obtain resource space fusion data; performing regional classification on the resource space fusion data to obtain maintenance areas; performing maintenance difficulty area division on the maintenance areas to obtain maintenance difficulty area data; and verifying the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty division result.

[0081] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0082] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dividing the difficulty of network resource maintenance, characterized in that: include: Perform data fusion on the acquired network resource data and spatial data to obtain resource space fusion data; Performing regional classification on the resource space fusion data to obtain a maintenance area; Dividing the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; The maintenance difficulty area data and the spatial data are verified to obtain a network resource maintenance difficulty classification result.

2. The method for dividing the difficulty of network resource maintenance according to claim 1, characterized in that: The data fusion of the acquired network resource data and spatial data to obtain resource space fusion data includes: Acquire network resource data including network resource locations, and spatial data including multiple different coordinate systems; Perform unified coordinate transformation on spatial data in different coordinate systems; The converted spatial data is fused with the network resource location to obtain resource space fusion data.

3. The method for dividing the difficulty of network resource maintenance according to claim 1, characterized in that: The resource space fusion data is subjected to regional classification to obtain maintenance areas, including: Performing slope analysis on the resource space fusion data to obtain slope classification areas; Dividing the slope classification area into areas; The area division results are sorted and classified into regions to obtain various maintenance areas.

4. The method for dividing the difficulty of network resource maintenance according to claim 1, characterized in that: The maintenance area includes a target area; the maintenance difficulty includes a baseline maintenance; the maintenance difficulty area is divided into maintenance difficulty areas, and the maintenance difficulty area data obtained includes: determining a first buffer area of ​​the target area; determining a second buffer area for road data within the maintenance area; Based on the first buffer area and the second buffer area, reference maintenance area data is determined.

5. The method for dividing the difficulty of network resource maintenance according to claim 4, characterized in that: The maintenance difficulty includes medium maintenance; and the maintenance difficulty area division of the maintenance area to obtain maintenance difficulty area data further includes: determining a third buffer area of ​​the target area based on the first buffer area; determining a fourth buffer area of ​​the road data; The third buffer area and the fourth buffer area are merged to obtain medium maintenance area data.

6. The method for dividing the difficulty of network resource maintenance according to claim 5, characterized in that: The maintenance difficulty includes high maintenance; and the maintenance difficulty area division of the maintenance area to obtain maintenance difficulty area data further includes: Based on the maintenance area, the reference maintenance area data, and the medium maintenance area data, high maintenance area data is determined.

7. A network resource maintenance difficulty classification device, characterized in that: include: The resource space fusion module is used to fuse the acquired network resource data and space data to obtain resource space fusion data; A regional classification module, used for performing regional classification on the resource space fusion data to obtain a maintenance area; A maintenance difficulty area division module is used to divide the maintenance area into maintenance difficulty areas to obtain maintenance difficulty area data; The network resource maintenance difficulty classification module is used to verify the maintenance difficulty area data and the spatial data to obtain a network resource maintenance difficulty classification result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for dividing the difficulty of network resource maintenance according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dividing the difficulty of network resource maintenance as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for dividing the difficulty of network resource maintenance as claimed in any one of claims 1 to 6 is implemented.

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