Data validity identification method and apparatus

Abstract

The application discloses a data validity identification method and device. The method comprises the following steps: performing regional rasterization processing on a to-be-identified region, and determining the raster attributes of each regional raster; performing regional meshing processing on the to-be-identified region after the regional rasterization, calculating the first space code corresponding to each regional mesh, and storing the first space code corresponding to the regional mesh and the regional mesh data in a hash table in association; calculating the second space code corresponding to the longitude and latitude coordinates in the to-be-identified road test data, querying the hash table to determine the regional mesh data corresponding to the first space code matched with the second space code; determining the regional raster in which the road test point corresponding to the to-be-identified road test data is located according to the longitude and latitude coordinates and the regional mesh data, and determining the validity of the to-be-identified road test data according to the raster attributes of the regional raster, thereby improving the accuracy and identification efficiency of road test data identification, performing one-time initialization processing on the to-be-identified region, avoiding repeated calculation, and saving resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a data validity identification method and device. BACKGROUND

[0002] Road testing is a most commonly used test method for road wireless signals in the communication industry. By analyzing road testing data, it can be determined whether the actual coverage of a base station cell meets the design expectation. If not, the base station cell can be adjusted according to the road testing data. Abnormal road testing data increases the complexity and time of program processing, affects the accuracy of result analysis, and is not conducive to network quality evaluation and wireless network optimization.

[0003] At present, the collected road testing data has the characteristics of large data volume, many sampling points with empty latitude and longitude, and many abnormal data. It is a difficult problem to quickly screen out effective road testing data from massive road testing data. When cleaning massive road testing data, the existing technology mainly uses the following two methods: (1) opening each test LOG file and checking the LOG test file content abnormal data through test software to record the abnormal conditions and related data of each test file; (2) using a screening test software to analyze road testing data. However, the above two methods have the following problems: low efficiency, incomplete deletion of abnormal data, large workload for checking data one by one in the later stage, low accuracy, and it is difficult to find latitude and longitude abnormal data, which ultimately leads to a large amount of abnormal data, affecting analysis effect and optimization measures. Therefore, there is an urgent need for a data validity identification scheme that can effectively improve data identification efficiency and accuracy. SUMMARY

[0004] In view of the above problems, the embodiments of the present application are proposed to provide a data validity identification method and device that can overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of an embodiment of the present application, a data validity identification method is provided, comprising:

[0006] performing regional gridding processing on the to-be-identified region to determine the grid attributes of each regional grid, wherein the grid attributes include: an in-road attribute or an out-of-road attribute;

[0007] performing regional meshing processing on the regional gridded to-be-identified region, calculating the first space code corresponding to each regional mesh, and storing the first space code corresponding to the regional mesh and the regional mesh data in the hash table in association;

[0008] calculating the second space code corresponding to the latitude and longitude coordinates in the to-be-identified road testing data, querying the hash table to determine the regional mesh data corresponding to the first space code matched with the second space code;

[0009] The region grid where the road test point corresponding to the road test data to be identified is located is determined based on the latitude and longitude coordinates and regional grid data. The validity of the road test data to be identified is determined based on the grid attributes of the region grid.

[0010] According to another aspect of the present invention, a data validity identification device is provided, comprising:

[0011] The rasterization module is suitable for performing rasterization processing on the area to be identified, and determining the raster attributes of each raster area. The raster attributes include: in-road attributes or out-of-road attributes.

[0012] The gridding module is suitable for performing gridding processing on the region to be identified after the region has been rasterized, and calculating the first spatial code corresponding to each region grid.

[0013] The associated storage module is suitable for associating and storing the first spatial code corresponding to the area grid and the area grid data in a hash table;

[0014] The query module is suitable for calculating the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and querying the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

[0015] The identification module is suitable for determining the area grid where the road test point corresponding to the road test data to be identified is located based on latitude and longitude coordinates and area grid data, and determining the validity of the road test data to be identified based on the grid attributes of the area grid.

[0016] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0017] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above data validity identification method.

[0018] According to another aspect of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, the executable instruction causing a processor to perform an operation corresponding to the data validity identification method described above.

[0019] According to the solution provided in the above embodiments of the present invention, the region to be identified is rasterized to determine the raster attributes of each region raster; the rasterized region to be identified is then meshed to calculate the first spatial code corresponding to each region mesh, and the first spatial code corresponding to the region mesh and the region mesh data are associated and stored in a hash table; the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified is calculated, and the hash table is queried to determine the region mesh data corresponding to the first spatial code that matches the second spatial code; the region raster where the road test point corresponding to the road test data to be identified is located is determined according to the latitude and longitude coordinates and the region mesh data, and the validity of the road test data to be identified is determined according to the raster attributes of the region raster. This improves the accuracy of road test data identification and increases the identification efficiency. In addition, the present invention only requires one initialization process for the region to be identified, avoiding repeated calculations and saving resources.

[0020] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 A flowchart of the data validity identification method provided in an embodiment of the present invention is shown;

[0023] Figure 2 A flowchart of a data validity identification method provided by another embodiment of the present invention is shown;

[0024] Figure 3 A schematic diagram of the data validity identification device provided in an embodiment of the present invention is shown;

[0025] Figure 4 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0026] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0027] Figure 1 A flowchart of the data validity identification method provided by an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps:

[0028] Step S101: Perform region rasterization processing on the region to be identified and determine the raster attributes of each region raster.

[0029] The area to be identified refers to the region where data validity verification will be performed. This area can be set according to actual identification needs. For example, the area to be identified can be the entire city of Beijing, or the area within the Third Ring Road of Beijing, etc. There is no specific limitation here. Once the area to be identified is determined, the corresponding road test data to be identified can be determined. The road test data to be identified is pre-collected data and is the processing object of data validity verification in this embodiment.

[0030] Specifically, in order to perform data validity identification, this embodiment needs to perform region initialization processing on the region to be identified. The region initialization processing includes region rasterization processing and region grid processing. It should be emphasized that only one region initialization processing is needed for the same region to be identified, without repeated processing, thereby avoiding a large amount of redundant calculation and saving computing resources.

[0031] After identifying the region to be identified, it needs to be rasterized, specifically divided into multiple raster regions. The size of each raster region can be determined based on actual needs. After dividing the region into raster regions, the raster attributes of each region are determined. These raster attributes serve as the basis for the validity of the identification data. Depending on whether the region falls within the road, the raster attributes include either "inside the road" or "outside the road." The "inside the road" attribute indicates that the region falls within the road, while the "outside the road" attribute indicates that the region falls outside the road. By rasterizing the region to be identified and determining the raster attributes of each region, the accuracy of valid identification can be further improved.

[0032] Step S102: Perform region gridding processing on the region to be identified after region rasterization, calculate the first spatial code corresponding to each region grid, and associate and store the first spatial code corresponding to the region grid and the region grid data in a hash table.

[0033] To effectively improve recognition efficiency and avoid performing a large number of data queries and matches, this step requires performing region gridding processing on the rasterized region to be recognized. Specifically, the rasterized region to be recognized is divided into multiple region grids, where the size of the region grid is larger than the size of the region raster, and one region grid contains multiple region rasters.

[0034] For each region grid, calculate the first spatial code corresponding to that region grid. For example, the first spatial code corresponding to the latitude and longitude coordinates of the points in the region grid can be calculated using a preset method. The calculated first spatial code is used as the first spatial code of the region grid (for example, calculate the Geohash code). The first spatial code is an encoded string used to identify a region grid. Different region grids have different first spatial codes, so that region grids can be distinguished according to the first spatial code.

[0035] After obtaining the first spatial code corresponding to the regional grid, the first spatial code and the regional grid data are associated and stored in a hash table. For example, the first spatial code of the regional grid is used as the KEY, and the regional grid data is used as the VALUE, and the first spatial code and the regional grid data are associated and stored in the hash table in a key-value (KV) format. The regional grid data is data related to the regional grid.

[0036] Step S103: Calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

[0037] The road test data to be identified is the data collected during road testing. This data contains the latitude and longitude coordinates of the road test points. In order to effectively narrow down the identification range of data validity identification, this step needs to calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified. Then, based on the second spatial code, a hash table is queried to determine the area grid data corresponding to the first spatial code that matches the second spatial code in the hash table. The area grid data includes area grid information and grid attributes. By querying the hash table, the area grid to which the road test point belongs can be determined, thereby narrowing the identification range and improving identification efficiency.

[0038] Step S104: Determine the area grid where the road test point corresponding to the road test data to be identified is located based on the latitude and longitude coordinates and area grid data, and determine the validity of the road test data to be identified based on the grid attributes of the area grid.

[0039] Although step S103 determines the area grid to which the road test point belongs, since the area grid contains multiple area grids, further processing is needed to identify the validity of the road test data to be identified. Specifically, after obtaining the area grid data, the area grid where the road test point corresponding to the road test data to be identified is located can be determined based on the latitude and longitude coordinates and the area grid data. Since the grid attributes of the area grid have been determined, after determining the area grid where the road test point is located, the validity of the road test data to be identified is determined based on the grid attributes of that area grid. For example, if the grid attribute of the area grid is "inside the road," the road test data to be identified can be determined to be valid; if the grid attribute of the area grid is "outside the road," the road test data to be identified can be determined to be invalid.

[0040] According to the method provided in the above embodiments of the present invention, the region to be identified is rasterized to determine the raster attributes of each region raster; the rasterized region to be identified is then meshed to calculate the first spatial code corresponding to each region mesh, and the first spatial code corresponding to the region mesh and the region mesh data are associated and stored in a hash table; the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified is calculated, and the hash table is queried to determine the region mesh data corresponding to the first spatial code that matches the second spatial code; the region raster where the road test point corresponding to the road test data to be identified is located is determined according to the latitude and longitude coordinates and the region mesh data, and the validity of the road test data to be identified is determined according to the raster attributes of the region raster. This improves the accuracy of road test data identification and increases the identification efficiency. In addition, the present invention only requires one initialization process for the region to be identified, avoiding repeated calculations and saving resources.

[0041] Figure 2 A flowchart of a data validity identification method provided by another embodiment of the present invention is shown. Figure 2 As shown, the method includes the following steps:

[0042] Step S201: Perform region rasterization processing on the region to be identified according to the first preset spacing to obtain L*W region rasteres.

[0043] The area to be identified refers to the region where data validity verification will be performed. This area can be set according to actual identification needs. For example, the area to be identified can be the entire city of Beijing, or the area within the Third Ring Road of Beijing, etc. There is no specific limitation here. Once the area to be identified is determined, the corresponding road test data to be identified can be determined. The road test data to be identified is pre-collected data and is the processing object of data validity verification in this embodiment.

[0044] The first preset spacing specifically defines the size of the area grid. For example, if the size of the area grid obtained after division is limited to 1m*1m, then the first preset spacing is specifically 1m*1m.

[0045] In this step, the area to be identified is divided into multiple region grids according to a first preset spacing. For example, the upper left corner of the area to be identified can be used as the starting point to divide the area into L*W region grids, where L is the number of region grids along the vertical axis and W is the number of region grids along the horizontal axis. The number of region grids is related to the size of the area to be identified and the size of the region grids. To facilitate region rasterization, a Cartesian coordinate system can be established, with the upper left corner of the area to be identified as the origin.

[0046] Step S202: Create a 2D array of size L*W, where each array element represents a region grid.

[0047] To facilitate the determination of the raster attributes of the region raster, this step requires the creation of an L*W 2D array. The number of rows in the 2D array is the same as the number of region raster cells L in the vertical direction, and the number of columns in the 2D array is the same as the number of region raster cells W in the horizontal direction. Each array element represents a region raster.

[0048] Step S203: Fill the array elements of the 2D array according to the road vector data to determine the raster attributes of each area raster, wherein the raster attributes include: road interior attributes or road exterior attributes.

[0049] The road vector data is obtained in advance from the road department. The array elements of the 2D array are filled based on the road vector data to determine the raster attributes of each area grid. For example, the road vector data can be used to draw the outline on the 2D array (area grid). After drawing the outline, it is filled. The raster attributes include: road interior attributes or road exterior attributes. The road interior attributes are set to 1, and the road exterior attributes are set to 0. By filling the array elements, a 2D array representing the raster attributes is obtained.

[0050] Step S204: Perform region gridding processing on the rasterized region to be identified according to the second preset spacing.

[0051] To effectively improve recognition efficiency and avoid performing a large amount of data query matching, this step requires performing regional gridding on the rasterized region to be recognized. The second preset spacing specifically limits the size of the regional grid. For example, if the size of the regional grid obtained after division is limited to 38m*19m, then the second preset spacing is specifically 38m*19m, and the rasterized region to be recognized is divided into a 38m*19m grid.

[0052] Step S205: Determine the starting point of the area to be identified, find discrete points within the area to be identified based on the latitude and longitude coordinates of the starting point and the preset step size, and calculate the first spatial code corresponding to each discrete point.

[0053] To facilitate subsequent validity identification and determine the identification range, it is necessary to calculate the first spatial code of the region grid. Specifically, the first spatial code of the region grid can be calculated as follows: By determining discrete points within the region to be identified, the first spatial code of the entire region grid within the region to be identified is calculated based on these discrete points. The starting point of the region to be identified is determined. To facilitate finding discrete points and avoid omissions that could prevent coverage of all region grids, any point located at one of the four corners of the region to be identified can be used as the starting point. Then, based on the latitude and longitude coordinates of the starting point and a preset step size, discrete points within the region to be identified are found, and their coordinates are recorded. The coordinates of each discrete point are its corresponding latitude and longitude coordinates. The first spatial code corresponding to each discrete point is calculated. For example, the Geohash code corresponding to the discrete point can be calculated using functions provided by PostGIS. The specific process for calculating the Geohash code can be found in existing technologies and will not be elaborated here. The preset step size is less than half of the second preset spacing. By setting the preset step size to less than half of the second preset spacing, it is ensured that discrete points cover all region grids, avoiding omissions.

[0054] For example, the size of the region grid is 38m*19m. The step size to the right is set to 17m (less than half the length of the long side of the region grid) and the step size to the down is 9m (less than half the length of the short side of the region grid). The top left point of the region to be identified is determined as the starting point. Discrete points are found and recorded sequentially to the right with a step size of 17m (less than half the length of the long side of the grid) until the right edge of the region. The point at the step size of 17m is determined as a discrete point.

[0055] Then, move down 9m (less than half the short side of the grid) from the starting point of the previous round to obtain a new starting point. Find discrete points to the right in sequence with a step size of 17m (less than half the long side of the grid) and record the coordinates of the discrete points until the right edge of the region is reached. Repeat this step until all discrete points in the entire region to be identified are found.

[0056] Step S206: Determine the first spatial code corresponding to each region grid based on the first spatial code corresponding to each discrete point.

[0057] For each discrete point found, the first spatial code corresponding to each discrete point is calculated. It should be noted that discrete points belonging to the same region grid have the same first spatial code. The region grid to which each discrete point belongs is known. By deduplicating the first spatial codes corresponding to each discrete point, the first spatial code corresponding to each region grid is finally determined.

[0058] Step S207: Determine whether the area grid contains at least one area grid with a grid attribute of "inside road". If not, proceed to step S208; if yes, proceed to step S209.

[0059] To reduce subsequent query time, reduce the number of queries, and improve recognition efficiency, this embodiment needs to determine whether the regional grid contains at least one regional grid with a road-in-the-way attribute before associating and storing the first spatial code corresponding to the regional grid and the regional grid data in the hash table. The road-in-the-way attribute is the basis for data validity. If the regional grid contains regional grids whose raster attributes are all road-out-of-the-way attributes, it can be determined that the data is invalid, and the regional grid can be filtered out without matching, thereby reducing the number of matching times.

[0060] Step S208: Filter out the grid in this area and proceed to step S210.

[0061] If it is determined that all the raster attributes of the area grid contained in the area grid are road-external attributes, the area grid can be filtered out. There is no need to associate and store the first spatial code and area grid data of the area grid in the hash table, thereby reducing the number of subsequent matching operations.

[0062] Step S209: Preserve the grid in this area and proceed to step S210.

[0063] If a region grid is determined to contain at least one region grid with a road attribute, the region grid can be retained, and the first spatial code corresponding to the region grid and the region grid data can be associated and stored in a hash table to facilitate subsequent data validity identification.

[0064] It should be noted that steps S206-S209 are optional steps.

[0065] Step S210: The first spatial code corresponding to the filtered region grid and the region grid data are associated and stored in a hash table.

[0066] After determining the filtered regional grid, the first spatial code corresponding to the filtered regional grid and the regional grid data are associated and stored in a hash table. For example, the first spatial code corresponding to the filtered regional grid is used as the KEY, and the regional grid data is used as the VALUE value, and the first spatial code corresponding to the regional grid and the regional grid data are associated and stored in the hash table in a key-value (KV) format. The regional grid data is data related to the regional grid. The regional grid data includes: the regional grid data file name, the number of grid cells along the horizontal axis, the number of grid cells along the vertical axis, the longitude coordinate of the lower left corner of the regional grid, the latitude coordinate of the lower left corner of the regional grid, the total longitude width of the regional grid, the total latitude height of the regional grid, the side length of the regional grid, and the grid attributes of the regional grid cells within the regional grid.

[0067] Combining the above steps, the raster properties of the region grid within the region grid are specifically: a two-dimensional array corresponding to the region grid within the filtered region grid, wherein at least one array element in the two-dimensional array has an element value of 1.

[0068] Step S211: Calculate the circumscribed rectangle of the road.

[0069] For roads, calculate the bounding rectangle of the road. For example, the bounding rectangle of the road can be calculated based on the coordinates of the starting point and the ending point of the road. The bounding rectangle of the road covers the entire road and includes some non-road areas. Set the four coordinate points of the bounding rectangle as: bottom left corner (minx, miny), top left corner (minx, maxy), top right corner (maxx, maxy), and bottom right corner (maxx, miny). The purpose of calculating the bounding rectangle of the road is to reduce the number of subsequent matching operations.

[0070] Step S212: Based on the latitude and longitude coordinates in the road test data to be identified, determine whether the road test point corresponding to the road test data to be identified is located within the outer rectangle of the road; if yes, proceed to step S213; if no, the method ends.

[0071] Since the circumscribed rectangle of the road covers the entire road, road test points located within the circumscribed rectangle may still be valid locations, and only the road test data corresponding to these points can be considered valid data. To improve recognition efficiency, the following judgment can be made first: Based on the latitude and longitude coordinates in the road test data to be recognized, determine whether the road test point corresponding to the data is located within the circumscribed rectangle. If the road test point is located within the circumscribed rectangle, it indicates that the road test point may be a valid location point. To further verify the validity of the road test data, further processing is required, and the process can proceed to step S213. If the road test point is located outside the circumscribed rectangle, it indicates that the road test point is an invalid location point, and the method ends.

[0072] Assuming the latitude and longitude coordinates in the road test data to be identified are (lng, lat), the following method can be used to determine whether the road test point corresponding to the road test data is located within the road's outer rectangle: determine whether lng is greater than or equal to minx and lng is less than or equal to maxx and lat is greater than or equal to miny and lat is less than or equal to maxy. If yes, the road test point is determined to be located within the road's outer rectangle; otherwise, the road test point is determined to be located outside the road's outer rectangle.

[0073] It should be noted that steps S211-S212 are optional steps.

[0074] Step S213: Calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

[0075] The road test data to be identified is the data collected during road testing. This data contains the latitude and longitude coordinates of the road test points. In order to effectively narrow down the identification range of data validity identification, this step needs to calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified. Then, based on the second spatial code, a hash table is queried to determine the area grid data corresponding to the first spatial code that matches the second spatial code in the hash table. The area grid data includes area grid information and grid attributes. By querying the hash table, the area grid to which the road test point belongs can be determined, thereby narrowing the identification range and improving identification efficiency.

[0076] Step S214: Calculate the grid coordinates corresponding to the road test points based on the latitude and longitude coordinates and regional grid data in the road test data to be identified.

[0077] Specifically, the regional grid data includes data related to the regional grid, such as the longitude of the lower left corner of the grid, the latitude of the lower left corner of the grid, the total longitude width of the grid, the total latitude height of the grid, and the grid side length. Therefore, the grid coordinates (x, y) corresponding to the road test point can be calculated using the following formula (1):

[0078] x = (longitude coordinates in the road test data to be identified - longitude coordinates of the lower left corner of the area grid) / total longitude width of the area grid * side length of the area grid

[0079] y = (Latitude coordinates of the lower left corner of the area grid - Latitude coordinates in the road test data to be identified) / Total longitude and height of the area grid * Area grid side length Formula (1)

[0080] Assuming the latitude and longitude coordinates in the road test data to be identified are (lng, lat), the longitude of the bottom left corner of the grid is lng0, the latitude of the bottom left corner of the grid is lat0, the total width of the grid by longitude is W, the total height of the grid by latitude is H, and the grid side length is G, then x = (lng - lng0) / W * G

[0081] y = (lat0 - lat) / H * G

[0082] Step S215: Determine the grid area where the road test point is located based on the calculated grid coordinates, and determine the validity of the road test data to be identified based on the grid attributes of the grid area.

[0083] Although step S214 determines the grid coordinates corresponding to the road test point, further processing is needed to identify the validity of the road test data to be identified because the area grid contains multiple area grids. Specifically, the grid coordinates corresponding to the area grids are known. After obtaining the grid coordinates, the area grid where the road test point is located can be determined based on the calculated grid coordinates. Since the grid attributes of the area grids have been determined, the validity of the road test data to be identified can be determined based on the grid attributes of the area grids after the area grids where the road test point is located. For example, if the grid attribute of the area grid is an in-road attribute, the road test data to be identified can be determined to be valid; if the grid attribute of the area grid is an out-of-road attribute, the road test data to be identified can be determined to be invalid.

[0084] It should be noted that this example uses one piece of road test data to illustrate the data validity identification process. When there are multiple pieces of road test data to be identified, after the validity identification of one piece of road test data is completed, the validity identification of the next piece of road test data can be carried out. Steps S212-S215 can be repeated.

[0085] The data validity identification method provided in the above embodiments is applicable to all mobile communication network standards including road test data containing latitude and longitude, including GSM900, DCS1800, CDMA1X, CDMA2000, WCDMA, TD-SCDMA, TD-LTE, FDDLTE, 5G NSA, 5G SA, etc. It has a wide range of applications, and the system designed based on the above method has the characteristics of simplicity, clarity, strong feasibility, and is easy to form large-scale production and application.

[0086] According to the method provided in the above embodiments of the present invention, the area to be identified is rasterized, and the raster attributes of the area raster are set to identify whether the area raster is within the road; the area to be identified is gridded, and the grid data is stored in a hash table. The identification range of road test data is narrowed based on the area grid affiliation, thereby improving computational efficiency; only one area rasterization and area gridding are required, avoiding a large number of repeated calculations; the latitude and longitude coordinates of the current road test point are sequentially determined to be within the road's circumscribed rectangle and hash table, and the raster to which the road test data to be identified belongs is calculated, reducing the number of matching times and greatly improving the efficiency and accuracy of effective data identification.

[0087] Figure 3 A schematic diagram of the data validity identification device provided in an embodiment of the present invention is shown. Figure 3 As shown, the device includes: a rasterization processing module 301, a grid processing module 302, an associated storage module 303, a query module 304, and an identification module 305.

[0088] The rasterization processing module 301 is adapted to perform regional rasterization processing on the region to be identified and determine the raster attributes of each region raster, wherein the raster attributes include: in-road attributes or out-of-road attributes;

[0089] The gridding module 302 is adapted to perform gridding processing on the region to be identified after the region is rasterized, and to calculate the first spatial code corresponding to each region grid.

[0090] The associated storage module 303 is adapted to associate and store the first spatial code corresponding to the area grid and the area grid data in a hash table;

[0091] The query module 304 is adapted to calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and to query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

[0092] The identification module 305 is adapted to determine the area grid where the road test point corresponding to the road test data to be identified is located based on latitude and longitude coordinates and area grid data, and to determine the validity of the road test data to be identified based on the grid attributes of the area grid.

[0093] Optionally, the rasterization module is further adapted to: perform region rasterization processing on the region to be identified according to the first preset spacing to obtain L*W region rasteres;

[0094] Create an L*W 2D array, where each array element represents a region grid;

[0095] The array elements of the 2D array are filled based on the road vector data to determine the raster attributes of each region's raster. The raster attributes include: attributes within the road or attributes outside the road.

[0096] Optionally, the gridding module is further adapted to: perform region gridding processing on the rasterized region to be identified according to the second preset spacing;

[0097] Determine the starting point of the area to be identified, find discrete points within the area to be identified based on the latitude and longitude coordinates of the starting point and the preset step size, and calculate the first spatial code corresponding to each discrete point. The preset step size is less than half of the second preset interval.

[0098] The first spatial code corresponding to each region grid is determined based on the first spatial code corresponding to each discrete point.

[0099] Optionally, the device further includes: a first determination module, adapted to determine whether the area grid contains at least one area grid with a grid attribute of "inside road"; if not, the area grid is filtered out.

[0100] The associated storage module is further adapted to: associate and store the first spatial code corresponding to the filtered area grid and the area grid data in a hash table.

[0101] Optionally, the device further includes: a calculation module adapted to calculate the road's circumscribed rectangle;

[0102] The second judgment module is adapted to determine whether the road test point corresponding to the road test data to be identified is located within the outer rectangle of the road based on the latitude and longitude coordinates in the road test data to be identified.

[0103] The query module is further adapted to: if the road test point corresponding to the road test data to be identified is located within the outer rectangle of the road, then calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

[0104] Optionally, the identification module is further adapted to: calculate the grid coordinates corresponding to the road test points based on the latitude and longitude coordinates and regional grid data in the road test data to be identified;

[0105] The grid area where the test point is located is determined based on the calculated grid coordinates.

[0106] Optionally, the regional grid data includes: the regional grid data file name, the number of regional grid grids along the horizontal axis, the number of regional grid grids along the vertical axis, the longitude coordinates of the lower left corner of the regional grid, the latitude coordinates of the lower left corner of the regional grid, the total longitude width of the regional grid, the total latitude height of the regional grid, the side length of the regional grid, and the grid attributes of the regional grids within the regional grid.

[0107] According to the apparatus provided in the above embodiments of the present invention, the region to be identified is rasterized to determine the raster attributes of each region raster; the rasterized region to be identified is then meshed to calculate the first spatial code corresponding to each region mesh, and the first spatial code corresponding to the region mesh and the region mesh data are associated and stored in a hash table; the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified is calculated, and the hash table is queried to determine the region mesh data corresponding to the first spatial code that matches the second spatial code; the region raster where the road test point corresponding to the road test data to be identified is located is determined according to the latitude and longitude coordinates and the region mesh data, and the validity of the road test data to be identified is determined according to the raster attributes of the region raster. This improves the accuracy of road test data identification and increases the identification efficiency. In addition, the present invention only requires one initialization process for the region to be identified, avoiding repeated calculations and saving resources.

[0108] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the data validity identification method in any of the above method embodiments.

[0109] Figure 4 The diagram shows a structural schematic of a computing device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0110] like Figure 4 As shown, the computing device may include a processor, a communications interface, memory, and a communications bus.

[0111] The processor, communication interface, and memory communicate with each other via a communication bus. The communication interface is used to communicate with other network elements, such as clients or other servers. The processor executes programs, specifically the steps described in the embodiment of the data validity identification method for computing devices.

[0112] Specifically, the program may include program code, which includes computer operation instructions.

[0113] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0114] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0115] Specifically, the program can be used to cause the processor to execute the data validity identification method in any of the above method embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units in the above data validity identification embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0116] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of the present invention.

[0117] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0118] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0119] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0120] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0121] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0122] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A data validity identification method, comprising: The region to be identified is rasterized to determine the raster attributes of each raster. Specifically, the region to be identified is rasterized according to a first preset interval to obtain L×W raster grids. An L×W 2D array is established, where each array element represents a raster grid. The array elements of the 2D array are filled according to road vector data to determine the raster attributes of each raster grid. The raster attributes include: in-road attributes or out-of-road attributes. The rasterized region to be identified is subjected to region gridding processing. The first spatial code corresponding to each region grid is calculated, and the first spatial code corresponding to the region grid and the region grid data are associated and stored in a hash table. Specifically, the rasterized region to be identified is subjected to region gridding processing according to a second preset interval. The starting point of the region to be identified is determined. Discrete points within the region to be identified are found based on the latitude and longitude coordinates of the starting point and a preset step size. The first spatial code corresponding to each discrete point is calculated. The preset step size is less than half of the second preset interval. The first spatial code corresponding to each region grid is determined based on the first spatial code corresponding to each discrete point. Calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code; The region grid where the road test point corresponding to the road test data to be identified is located is determined based on the latitude and longitude coordinates and the region grid data. The validity of the road test data to be identified is determined based on the grid attributes of the region grid. Specifically, the grid coordinates corresponding to the road test point are calculated based on the latitude and longitude coordinates and the region grid data in the road test data to be identified. The region grid where the road test point is located is determined based on the calculated grid coordinates.

2. The method according to claim 1, wherein, Before associating and storing the first spatial code corresponding to the region grid and the region grid data in the hash table, the method further includes: Determine whether the area grid contains at least one area grid with a grid attribute of "inside road". If not, filter out the area grid. The step of associating and storing the first spatial code corresponding to the region grid and the region grid data in the hash table further includes: The first spatial code and the region grid data corresponding to the filtered region grid are associated and stored in a hash table.

3. The method according to claim 1 or 2, wherein, Before calculating the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and querying the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code, the method further includes: Calculate the circumscribed rectangle of the road; Based on the latitude and longitude coordinates in the road test data to be identified, determine whether the road test point corresponding to the road test data to be identified is located within the outer rectangle of the road; If so, calculate the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and query the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code.

4. The method according to claim 1 or 2, wherein, The regional grid data includes: regional grid data file name, number of grid cells along the horizontal axis of the regional grid, number of grid cells along the vertical axis of the regional grid, longitude coordinates of the lower left corner of the regional grid, latitude coordinates of the lower left corner of the regional grid, total longitude width of the regional grid, total latitude height of the regional grid, side length of the regional grid, and grid attributes of the regional grid cells within the regional grid.

5. A data validity identification device, comprising: The rasterization module is suitable for performing rasterization processing on the area to be identified and determining the raster attributes of each area raster. Specifically, it performs rasterization processing on the area to be identified according to a first preset interval to obtain L×W area rasteres; establishes an L×W 2D array, where each array element represents an area raster; and fills the array elements of the 2D array according to road vector data to determine the raster attributes of each area raster, including: in-road attributes or out-of-road attributes. The gridding module is suitable for performing gridding processing on the rasterized region to be identified, calculating the first spatial code corresponding to each region grid, wherein the rasterized region to be identified is subjected to gridding processing according to a second preset interval; the starting point of the region to be identified is determined, and discrete points within the region to be identified are found according to the latitude and longitude coordinates of the starting point and a preset step size, and the first spatial code corresponding to each discrete point is calculated, wherein the preset step size is less than half of the second preset interval; the first spatial code corresponding to each region grid is determined according to the first spatial code corresponding to each discrete point. The associated storage module is suitable for associating and storing the first spatial code corresponding to the area grid and the area grid data in a hash table; The query module is suitable for calculating the second spatial code corresponding to the latitude and longitude coordinates in the road test data to be identified, and querying the hash table to determine the area grid data corresponding to the first spatial code that matches the second spatial code. The identification module is adapted to determine the area grid where the road test point corresponding to the road test data to be identified is located based on the latitude and longitude coordinates and the area grid data, and to determine the validity of the road test data to be identified based on the grid attributes of the area grid. Specifically, the grid coordinates corresponding to the road test point are calculated based on the latitude and longitude coordinates and the area grid data in the road test data to be identified; and the area grid where the road test point is located is determined based on the calculated grid coordinates.

6. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the data validity identification method as described in any one of claims 1-4.

7. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the data validity identification method as described in any one of claims 1-4.

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