Method for generating a location for sensing front-end equipment and related equipment

Abstract

The present application relates to a method for generating a location for the deployment of a perception front-end device and its related equipment. The method includes: obtaining geographic data, the geographic data includes point of interest data and interest surface data, the point of interest data includes first point of interest data and second point of interest data, wherein the position corresponding to the first point of interest data is located within the area corresponding to the interest surface data, the position corresponding to the second point of interest data is located outside the area corresponding to the interest surface data, and the text similarity between the second point of interest data and the interest surface data is not less than a preset threshold; generating a first location surface according to the interest surface data; mapping the first point of interest corresponding to the first point of interest data and the second point of interest corresponding to the second point of interest data to the first location surface. Through the present application, the problem of inaccurate locations created by related technologies is solved, and the accuracy of the created locations is improved.

Description

Technical Field

[0001] The present application relates to the field of geographic data processing technology, and in particular to a method for generating a location for sensing front-end equipment deployment and related equipment. Background Art

[0002] The place where the sensing front-end equipment is deployed refers to the physical space area associated with social functions, purposes, ownership or use rights, such as schools, shopping malls, ports, etc. In related technologies, existing geographic information, such as point of interest (POI) data and area of ​​interest (AOI) data, is used to map a point of interest to an area of ​​interest and create a place (the place here is equivalent to the area of ​​interest) by judging whether the point of interest is located within the area of ​​interest. However, for those points of interest that are not located within any area of ​​interest, they cannot be mapped to any place, so there is a large error in the boundary range of the place, which cannot meet the actual needs. Summary of the invention

[0003] The method for generating a location for the perception front-end device deployment and its related equipment provided in the embodiments of the present application at least solve the problem of inaccurate locations created by related technologies.

[0004] A method for generating a location for the deployment of a perception front-end device comprises: acquiring geographic data, the geographic data comprising point of interest data and interest surface data, the point of interest data comprising first point of interest data and second point of interest data, wherein a position corresponding to the first point of interest data is located within an area corresponding to the interest surface data, a position corresponding to the second point of interest data is located outside the area corresponding to the interest surface data, and a text similarity between the second point of interest data and the interest surface data is not less than a preset threshold; generating a first location surface according to the interest surface data; and mapping a first point of interest corresponding to the first point of interest data and a second point of interest corresponding to the second point of interest data to the first location surface.

[0005] In some of the embodiments, the point of interest data also includes third point of interest data, the position corresponding to the third point of interest data is outside the area corresponding to the interest surface data, and the text similarity between the third point of interest data and the interest surface data is less than a preset threshold; the method also includes: clustering the third point of interest corresponding to the third point of interest data according to the text similarity, and generating a second place surface based on the point of interest data in the category obtained by clustering.

[0006] In some of the embodiments, after acquiring the geographic data, the method also includes: deleting at least one of the following interest surface data: interest surface data corresponding to a set type, interest surface data containing set words; merging at least one of the following interest surface data: multiple interest surface data with the same name and an inclusion relationship between regions, multiple interest surface data with similar names and an inclusion relationship between regions; deleting the first set word in the name field of the interest point data as an alias field of the interest point data; deleting the interest point data whose name field only contains the second set word.

[0007] In some of the embodiments, mapping the first interest point corresponding to the first interest point data to the first place surface according to the position relationship includes: judging whether the first interest point is located in the area corresponding to the interest surface data according to the position information of the first interest point data and the area information of the interest surface data; and mapping the first interest point to the first place surface when the first interest point is located in the area corresponding to the interest surface data.

[0008] In some of the embodiments, mapping the second point of interest corresponding to the second point of interest data to the first place surface includes: mapping the second point of interest to the first place surface based on the distance between the second point of interest and the interest surface, and at least one of the following information: whether the alias field of the second point of interest data is the same as the name field of the interest surface data; whether the address field of the second point of interest data contains the name field of the interest surface data; whether the text similarity between the address field of the second point of interest data and the name field of the interest surface data is greater than a threshold; whether the text similarity between the address field of the second point of interest data and the target field is greater than a threshold, and the target field is the field after the third set word is deleted from the name field of the interest surface data.

[0009] In some of the embodiments, before clustering the third interest points corresponding to the third interest point data according to text similarity, the method also includes: deleting the interest point data whose name field of the third interest point data starts with the fourth set word and ends with the fifth set word; deleting the interest point data whose name field of the third interest point data contains the sixth set word and whose alias field contains the seventh set word.

[0010] In some of the embodiments, clustering the third points of interest corresponding to the third points of interest data according to text similarity includes: clustering the third points of interest according to the text similarity of the alias field of the third points of interest data to obtain multiple categories; clustering the unclustered points of interest in the third points of interest into multiple categories according to the distance between the unclustered points of interest in the third points of interest and the points of interest in multiple categories, and the text similarity between the name field of the unclustered points of interest in the third points of interest and the name field of the points of interest in multiple categories; clustering the unclustered points of interest in the third points of interest into multiple categories according to the distance between the unclustered points of interest in the third points of interest and the points of interest in multiple categories, and the text similarity between the alias field of the unclustered points of interest in the third points of interest and the name field of the points of interest in multiple categories; clustering the unclustered points of interest in the third points of interest into multiple categories according to the distance between the unclustered points of interest in the third points of interest and the points of interest in multiple categories, and the text similarity between the address field of the unclustered points of interest in the third points of interest and the name field of the points of interest in multiple categories.

[0011] In some of the embodiments, generating a second place surface based on the interest point data in the clustered category includes: generating a concave shell of the interest points in the clustered category, expanding the concave shell outward by a set width to obtain the second place surface, the concave shell being the minimum polygon surrounding the interest points in the clustered category.

[0012] In some of the embodiments, the method further includes: identifying a point of interest whose name field and address segment of the point of interest data in the place plane contain an eighth set word as a place part of the place plane.

[0013] In some of the embodiments, the method further includes: obtaining location information of a setting point of the perception front-end device; and binding the perception front-end device to the venue surface according to a positional relationship between the setting point of the perception front-end device and the venue surface.

[0014] An electronic device includes: a processor and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the above method.

[0015] A non-transitory machine-readable medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above method.

[0016] The method for generating a location for the layout of a perception front-end device and its related equipment provided in an embodiment of the present application adopt the method of acquiring geographic data, wherein the geographic data includes point of interest data and interest surface data, wherein the point of interest data includes first point of interest data and second point of interest data, wherein the position corresponding to the first point of interest data is located within the area corresponding to the interest surface data, the position corresponding to the second point of interest data is located outside the area corresponding to the interest surface data, and the text similarity between the second point of interest data and the interest surface data is not less than a preset threshold; a first location surface is generated according to the interest surface data; and a first point of interest corresponding to the first point of interest data and a second point of interest corresponding to the second point of interest data are mapped to the first location surface, thereby solving the problem of inaccurate locations created by related technologies and improving the accuracy of the created locations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other embodiments can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a flowchart of a method for generating a deployment location of a perception front-end device according to an embodiment of the present application.

[0019] Figure 2 It is a preferred flow chart of the method for generating the layout location of the perception front-end device of the embodiment of the present application.

[0020] Figure 3 This is a flowchart of the present application for obtaining general geographic data from Carlead geographic data.

[0021] Figure 4 It is a schematic diagram of generating a second location scene according to an embodiment of the present application.

[0022] Figure 5 It is a flow chart of the interaction of venue location analysis in an embodiment of the present application.

[0023] Figure 6 It is an interactive flow chart of the site automation associated points of an embodiment of the present application.

[0024] Figure 7 It is a preferred flow chart of the method for generating the layout location of the perception front-end device of the embodiment of the present application.

[0025] Figure 8 It is a schematic diagram of the structure of the electronic device of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0027] Explanation of terms

[0028] Geographic data: information describing objects, events, or other features on or near the surface of the Earth, primarily including point of interest data and area of ​​interest data.

[0029] Perception front-end equipment: a collection of equipment with image and video shooting functions; the collection of equipment can also include various sensors, such as infrared sensors, light sensors, sound sensors, pressure sensors, gas sensors, temperature and humidity sensors, GPS receivers, RFID readers, etc.

[0030] Location: Locations in the venue where front-end sensing equipment needs to be deployed, such as entrances and exits of residential areas, commanding points of shopping malls, etc.

[0031] In order to improve the accuracy of the created place, an embodiment of the present application provides a method for generating a place for sensing the layout of a front-end device. Figure 1 is a flow chart of a method for generating a location for a sensing front-end device according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0032] Step S101, obtaining geographic data, which includes point of interest data and surface of interest data.

[0033] The point of interest data includes first point of interest data, second point of interest data, and third point of interest data. In this embodiment, the first point of interest data, the second point of interest data, and the third point of interest data refer to three data sets, each of which may include one or more point of interest data. In addition, the three data sets have no overlapping data. For example, any point of interest data in the first point of interest data no longer belongs to the second point of interest data or the third point of interest data.

[0034] Among them, the position corresponding to the first point of interest data is located in the area corresponding to the surface of interest data, the positions corresponding to the second point of interest data and the third point of interest data are both located outside the area corresponding to the surface of interest data, and the text similarity between the second point of interest data and the surface of interest data is greater than the text similarity between the third point of interest data and the surface of interest data; in this embodiment, a preset threshold is set to distinguish the point of interest data located outside the area corresponding to the surface of interest data, for example, among these point of interest data, the point of interest data with a text similarity not less than the preset threshold is classified as the second point of interest data, and the point of interest data with a text similarity greater than the preset threshold is classified as the third point of interest data. The preset threshold can be, for example, 70%, 80%, 90% or other values.

[0035] The method of defining the point of interest data is to first determine whether the position corresponding to a point of interest data (determined by the latitude and longitude surface fields in the data) is located in the area corresponding to any interest surface data (also determined by the latitude and longitude surface fields in the data). If so, this point of interest data is defined as the first point of interest data; otherwise, the second point of interest data and the third point of interest data are defined based on the text similarity between the point of interest data and the interest surface data.

[0036] It should also be noted that, although the first point of interest data, the second point of interest data and the third point of interest data are defined in the above steps, it does not mean that the point of interest data are divided immediately when the geographic data is obtained or thereafter. Instead, these points of interest can be divided in subsequent processing steps.

[0037] In step S101, geographic data may be obtained from a geographic data service provider. The geographic data service provider may be, for example, a map data service provider or a navigation data service provider, such as AutoNavi, Carlife Navigation, etc. Taking Carlife Navigation as an example, the original geographic data format of Carlife Navigation is a private format .shp. Figure 2 This is a flowchart of the present application for obtaining general geographic data from Carlidata geographic data, such as Figure 2 As shown, first use the authorization code provided by the Carlife manufacturer to decompress the .zip format geographic data file compressed package exported from Carlife to obtain the original geographic data in .shp format. For easy processing, the original geographic data can be parsed using conversion tools, such as the open source GeoTools, to obtain a .sql format database file. After importing the database file into the database system, use the Java parsing script to convert the data in the imported original data table into POI data and AOI data according to the model of parsing place attributes. The system will record the import record during import, and the import record can be viewed later.

[0038] The POI data mainly includes fields such as point of interest ID, name, name pinyin, alias, administrative division code, type code, type name, address, longitude and latitude, as shown in Table 1.

[0039]

[0040] Table 1 Table structure of POI data

[0041] The AOI data mainly includes fields such as interest surface ID, name, name pinyin, administrative division code, type code, type name, address, longitude and latitude, and area, as shown in Table 2.

[0042]

[0043] Table 2 AOI data table structure

[0044] After obtaining geographic data and processing the geographic data into a standardized format capable of text processing (the table structure shown in Table 1 and Table 2), places are generated based on the geographic data according to the method provided in the embodiment of the present application.

[0045] The method for generating a place in the present application is implemented fully automatically or semi-automatically by a computer, and all the programs and configurations required for the operation can be set in advance or manually input or specified during the semi-automatic implementation process. The generation process uses a visual interface to display the intermediate results and final results involved in the process.

[0046] In a specific place generation scenario, there are geographic data that are of interest in the scenario and geographic data that are not of interest in the scenario. For example, in the scenario of generating a place of a school, a shopping mall or a port, the AOI data such as railways, national roads, provincial roads, county roads, rural roads, highways, expressways, urban roads (main roads, secondary roads, branches, pedestrian streets), overpasses, underground passages, cableways / ferries and other traffic facilities, administrative division boundaries, woodlands, etc. near these schools, shopping malls or ports are geographic data that are not of interest. Therefore, in some embodiments, after obtaining the geographic data, the interest surface data corresponding to the set type will be deleted, and the place surface will not be generated based on these interest surface data. The set type includes but is not limited to the above-mentioned railways, national roads, provincial roads, county roads, rural roads, highways, expressways, urban roads (main roads, secondary roads, branches, pedestrian streets), overpasses, underground passages, cableways / ferries and other traffic facilities, administrative division boundaries, woodlands, etc. It can be understood that in different scenarios, the AOI data of the set type that needs to be deleted may be different.

[0047] In the AOI data, there are also types of AOI data (area greater than 0), but the information described by the name is XX office building or XX commercial building. This type of AOI data should be regarded as a facility in a certain place, but not as a place alone. Therefore, after obtaining the geographic data, the interest surface data containing the set words are also deleted, so that the place surface is not generated based on these interest surface data. The set words include but are not limited to: words composed of "number + building / building / number / office building / commercial building".

[0048] In some embodiments, the interest surface data or the first place surface data may also be merged. It is understandable that, whether the interest surface data is merged before the place surface is generated, or the first place surface is merged in the same manner after the first place surface is generated, similar place generation results can be obtained in the end, so the merging operation is not limited to before or after the first place surface is generated.

[0049] In some embodiments, repeated AOI data may also be merged. The AOI data that needs to be merged includes but is not limited to the following two situations: multiple interest surface data with the same name and an inclusion relationship in the region, and multiple interest surface data with similar names and an inclusion relationship in the region.

[0050] Specifically, multiple interest surface data with the same name and an inclusion relationship in the area refer to that the names of the AOI data are the same and the surface data are overlapping. For example, there are two AOI data for "XX Building" in a city, and the two AOI data have the same administrative division code. The type of the AOI data with a larger surface data is a commercial building, and the type of the AOI data with a smaller surface data is an ordinary house, and based on the latitude and longitude data, it can be determined that the surface data of the two AOI data are in an inclusion relationship; then, in this embodiment, the two AOI data are merged into one AOI data named "XX Building", and its type and surface data are based on the AOI data with a larger area.

[0051] Specifically, multiple interest surface data with similar names and inclusion relationships in the regions refer to AOI data with different names, but similar text similarity, and inclusion relationships between the surface data. For example, there are 6 AOI data for "XX Science and Technology Park" in a certain city, namely XX Science and Technology Park, XX Science and Technology Park A, XX Science and Technology Park B, XX Science and Technology Park C, XX Science and Technology Park D, and XX Science and Technology Park E. Among them, "XX Science and Technology Park" has the largest area, and its surface data contains the other five AOI data. In this embodiment, these 6 AOI data are merged into one AOI data named "XX Science and Technology Park", and its type and surface data are based on the AOI data with a larger area.

[0052] The logic of the above AOI data merging may be, for example: first, the name of the AOI data is cleaned, and after removing the sub-site information such as Building A and Building B, the alias field information in the AOI data is used, for example, the alias field information of the AOI data named "Building B of XX Science and Technology Park" is "XX Science and Technology Park". After the names of the six AOI data are cleaned and the alias field information is set, the alias field of the AOI data, the administrative division code, the surface data and other multiple data are cross-checked to determine whether there is a surface data inclusion relationship between the six AOI data. If there is an inclusion relationship, the AOI data with smaller surface data is merged into the AOI data with larger surface data.

[0053] Step S102: Generate a first place surface according to the interest surface data.

[0054] After deleting and merging the interest surface data, the remaining interest surface data can generate a first place surface, and each data of the first place surface inherits the data of the interest surface data.

[0055] In some embodiments, the point of interest data can also be cleaned and deleted. After acquiring the geographic data, the first set word in the name field of the point of interest data is deleted as the alias field of the point of interest data; and the point of interest data whose name field only contains the second set word is deleted.

[0056] Cleaning refers to deleting the first set word in the name field of the point of interest data as the alias field of the point of interest data. Specifically, some POI data contain location information such as XX Gate, XX Hall, XX Building, XX Building, XX Block, XX Parking Lot, South 1, North 1, South 2, and North 2. Although this location information is important, it is easy to introduce text similarity interference items in the process of generating places. Therefore, in this embodiment, after deleting the first set word representing the location information in the name field of the POI data, it is used as the information of the alias field of the POI data. In this way, not only can the information of the POI alias field be used to generate places in the future, but the information representing the location in the POI data will not be lost.

[0057] Specifically, deleting the point of interest data whose name field only contains the second set word means deleting the POI data whose name field information is the full name of the underground garage entrance, parking lot, basketball court, football field, etc. that cannot accurately describe the location information of the place.

[0058] Step S103: Mapping the first point of interest corresponding to the first point of interest data and the second point of interest corresponding to the second point of interest data to the first location surface.

[0059] In this embodiment, if the point of interest is located in the area corresponding to a certain place surface, it is considered that the point of interest has a strong correlation with the place surface, that is, the first point of interest is a point of interest with a strong correlation with the first place surface; in contrast, the second point of interest and the third point of interest are points of interest with a weak correlation with the first place surface. For the points of interest and place surfaces with a strong correlation, according to the location information of the first point of interest data and the area information of the interest surface data, the st_Within method provided by the database is used to determine whether the longitude and latitude data of the POI data are included in the area corresponding to a certain AOI data (that is, the first place surface data). When the first point of interest is located in the area corresponding to the interest surface data, a mapping relationship between the point of interest and the corresponding first place surface is established to map the first point of interest to the first production surface. The mapping fields of AOI data and POI data are shown in Table 3.

[0060]

[0061] Table 3 Mapping table fields of AOI data and POI data

[0062] For the second and third interest points with weak correlation, different mapping strategies are adopted according to their text similarity with the data of the first place surface. For the second interest point with higher text similarity, the following mapping strategy is adopted: the second interest point is mapped to the first place surface according to the distance between the second interest point and the interest surface, and at least one of the following information: whether the alias field of the second interest point data is the same as the name field of the interest surface data; whether the address field of the second interest point data contains the name field of the interest surface data; whether the text similarity between the address field of the second interest point data and the name field of the interest surface data is greater than a threshold; whether the text similarity between the address field of the second interest point data and the target field is greater than a threshold, and the target field is the field after deleting the third set word from the name field of the interest surface data.

[0063] For example, for a POI that is not bound to a corresponding AOI, a mapping relationship between the POI and the AOI is established by determining whether the alias of the POI is the same as the name of the AOI and whether the longitude and latitude distance is within the allowable range (for example, 250 meters, which can be adjusted according to the actual data of the project).

[0064] For another example, for a POI that has not yet been bound to a corresponding AOI, a mapping relationship between the POI and the AOI is established by determining whether the address of the POI contains the AOI name and whether the longitude and latitude distance is within the allowable range (for example, 250 meters, which can be adjusted according to the actual data of the project).

[0065] For another example, for a POI that has not yet been bound to a corresponding AOI, a mapping association relationship between the POI and the AOI is established by determining whether the POI address contains the POI address corresponding to the AOI, and the longitude and latitude distance between the POI and the AOI is within an allowable range (for example, 30 meters, which can be adjusted according to the actual project data), and the text similarity between the POI address and the AOI name is within an allowable range (for example, the similarity threshold is set to 0.13, which can be adjusted according to the actual project data).

[0066] For another example, for a POI that has not yet been bound to a corresponding AOI, a mapping relationship between the POI and the AOI is established by determining whether the address of the POI contains the AOI name after removing the real estate company name (equivalent to the third set word mentioned above), and whether the longitude and latitude distance is within the allowable range (for example, the distance threshold is set to 100 meters, which can be adjusted according to the actual data of the project).

[0067] Figure 3 is a preferred flow chart of a method for generating a location for a sensing front-end device according to an embodiment of the present application, such as Figure 3 As shown, Figure 1 The difference between the process shown is that the process further includes step S305. Figure 3 The process shown includes the following steps:

[0068] Step S301, obtaining geographic data.

[0069] Step S302: Generate a first place surface according to the interest surface data.

[0070] Step S303: Map the first point of interest corresponding to the first point of interest data to the first location plane.

[0071] Step S304: Map the second point of interest corresponding to the second point of interest data to the first location surface.

[0072] Step S305: clustering the third points of interest corresponding to the third point of interest data according to the text similarity, and generating a second place surface according to the point of interest data in the category obtained by clustering.

[0073] For the third interest point whose text similarity is lower than that of the second interest point, a clustering strategy is adopted to generate a new place surface, namely, the second place surface.

[0074] In order to eliminate the text similarity interference items existing in the second point of interest when generating the second place scene, the third point of interest data is also filtered before clustering: the point of interest data starting with the fourth set word and ending with the fifth set word in the name field of the third point of interest data is deleted; the point of interest data whose name field contains the sixth set word and whose alias field contains the seventh set word is deleted.

[0075] Specifically, deleting the point of interest data that starts with the fourth set word and ends with the fifth set word in the name field of the third point of interest data means: for POIs that have not yet been bound to the corresponding AOI, filtering out POI data that starts with numbers, letters, directions, etc. (equivalent to the fourth set word) and ends with building numbers, buildings, floors, units, plots, etc. (equivalent to the fifth set word), and that do not clearly describe the specific places.

[0076] Specifically, deleting the point of interest data whose name field contains the sixth setting word and whose alias field contains the seventh setting word means: for POIs that have not yet been bound to the corresponding AOI, filtering out POIs whose names contain chain stores (equivalent to the above-mentioned sixth setting word) and POI data whose aliases contain supermarkets, hotels, barbecue restaurants, guesthouses, famous cigarettes and liquors, barbershops, etc. (equivalent to the above-mentioned seventh setting word) that cannot accurately describe the place information.

[0077] Through the above filtering operation, it is convenient to merge POI data and generate AOI through POI name, alias and address, so as to complete the creation of the second place surface.

[0078] In step S305, clustering is performed on the filtered POI data using the following strategy:

[0079] a. Clustering the third interest points according to the text similarity of the alias field of the third interest point data to obtain multiple categories.

[0080] Specifically, for POI names, POI data with the same alias are clustered into the same category.

[0081] b. clustering the unclustered points of interest in the third points of interest into multiple categories based on the distances between the unclustered points of interest in the third points of interest and the points of interest in multiple categories, and the text similarities between the name fields of the unclustered points of interest in the third points of interest and the name fields of the points of interest in multiple categories.

[0082] Specifically, for the remaining POIs, determine whether the POI name is the same as the POI name in a above, and the longitude and latitude distance is within the allowable range (for example, the distance threshold is set to 400 meters, which can be adjusted according to the actual data of the project), and cluster them into the same category.

[0083] c. Clustering the unclustered points of interest in the third points of interest into multiple categories based on the distances between the unclustered points of interest in the third points of interest and the points of interest in multiple categories, and the text similarities between the alias fields of the unclustered points of interest in the third points of interest and the name fields of the points of interest in multiple categories.

[0084] Specifically, for the remaining POIs, determine whether the POI alias is the same as the POI name in b above, and the longitude and latitude distance is within the allowable range (for example, the distance threshold is set to 400 meters, which can be adjusted according to the actual data of the project), and cluster them into the same category.

[0085] d. clustering the unclustered points of interest in the third points of interest into multiple categories based on distances between the unclustered points of interest in the third points of interest and the points of interest in multiple categories and text similarities between address fields of the unclustered points of interest in the third points of interest and name fields of the points of interest in multiple categories.

[0086] Specifically, for the remaining POIs, determine whether the POI address is the same as the POI name in c above, and the longitude and latitude distance is within the allowed range (for example, the distance threshold is set to 400 meters, which can be adjusted according to the actual data of the project), and cluster them into the same category.

[0087] After clustering the third points of interest into different categories through the above strategy, each category contains multiple points of interest. In this embodiment, the multiple longitudes and latitudes corresponding to the POI data that can be merged are processed through the ST_ConcaveHull method provided by the database to generate a concave hull. Then, the boundary of the concave hull is expanded outward by a certain distance through the ST_Buffer method provided by the database to generate a second place surface, and the center point of the second place surface is obtained. Figure 4 is a schematic diagram of generating a second location screen according to an embodiment of the present application, such as Figure 4 As shown, in Figure 4 The black origin represents the point of interest, the black solid line represents the initially generated concave hull, and the gray polygon represents the generated second place surface. For each category, the concave hull of the point of interest in the category obtained by clustering is generated, and the concave hull is expanded outward by a set width (for example, 30 meters) to obtain the second place surface.

[0088] Among them, the concave hull refers to a geometric shape used to approximately contain a set of points while maintaining the original shape characteristics of these points as much as possible. Compared with the traditional convex hull, the concave hull can better reflect the actual distribution of the point set because it allows concave parts to be formed inside the shell. The convex hull is a classic geometric concept, which refers to the smallest convex polygon or polyhedron containing a set of points, and no point in this polygon or polyhedron is outside its boundary. In other words, the convex hull does not allow any concave parts, and all points are located within a continuous convex boundary. The concave hull is different. It tries to fit the real shape of the point set more closely, allowing some parts to be concave inward. As a result, the generated shape is usually closer to the actual distribution of the point set than the convex hull, especially when the point set has a complex structure.

[0089] The first place surface and the second place surface generated through the above steps S301 to S305 are compared with the related art that can only rely on POI data with strong correlation to generate place surfaces, resulting in missing and inaccurate place surfaces. The place generation method provided in this embodiment makes full use of POI data with weak correlation and adopts text similarity and clustering to maximize the retention of POI data information and ensure the accuracy of the generated places.

[0090] Some parts of a venue are key management areas that require the deployment of perception front-end devices, such as entrances and exits, doors and other parts of the venue. The conventional practice is to manually mark these parts of the venue in the venue surface data after obtaining the venue surface data, so as to ensure that these parts of the venue are covered when the perception front-end devices are deployed later. In order to further reduce the workload of manually managing venue surface data in the field of perception front-end device deployment, in this embodiment, full use is made of the information in the name field and address field of the POI data, and points of interest whose name field or address field of the point of interest data in the venue surface contains the eighth set word (such as "entrance and exit", "door", etc.) are identified, and these points of interest are used as venue parts of the venue surface. Figure 5 is a flow chart of the site location analysis interaction of an embodiment of the present application, such as Figure 5 As shown in the figure, the user platform selects POI data and submits an automated governance request to the platform. The platform returns the ID of the automated governance task for the platform user to query the task status later. When the automated governance task is run on the platform, the platform automatically performs the following tasks: the platform queries the database for all the venue data in the task; the platform queries the venue location data from the database by text regular matching; the platform filters the venue location by keywords to obtain the venues and venue locations that need to be focused on; and the platform updates the venue governance status information in the database.

[0091] In some of the embodiments, the method further includes: obtaining the location information of the setting point of the perception front-end device; binding the perception front-end device to the venue surface according to the positional relationship between the setting point of the perception front-end device and the venue surface. After the venue is generated, for the existing venue data, the venue and the point can be automatically bound based on the analysis of the device name and venue name, and the inclusion analysis of the device longitude and latitude and venue surface data to achieve automated management. For example, if the point is within a distance of 30 meters around the venue surface data, it can be considered that the point belongs to the venue. Finally, the association information between the venue and the point is written into the database to complete the closed-loop operation of venue management. Figure 6 is an interactive flow chart of the site automation associated points of the embodiment of the present application, such as Figure 6As shown in the figure, the platform user selects POI data and submits an automated governance request. The platform returns the ID of the automated governance task for the platform user to query the task status later. When running an automated governance task on the platform, the platform automatically performs the following tasks: the platform queries the database for information about surrounding points that can be associated based on the location surface data; the platform performs text analysis based on the location name and the point name to query the database for associated points; after associating the point with the location, the platform stores the location-associated points in the database; the platform batch queries the associated points from the database to associate the location; and updates the database when the location governance status is updated.

[0092] Figure 7 It is a preferred flow chart of the method for generating the layout location of the perception front-end device of the embodiment of the present application, and the process mainly includes: with the help of a third-party data source, importing POI data and AOI data into the system; based on the imported POI data and AOI data, the system automatically completes location generation, location analysis, and location-related points.

[0093] Through the above-mentioned embodiments or preferred implementation modes of the present application, the POI and AOI data imported from the third-party data source are cleaned, filtered and merged to complete the place creation process, analyze the location of the place, and use the text similarity between the place and the point to realize the place-associated point. The above-mentioned embodiments perform rapid automated place management of places of concern to users, such as government agencies, schools and their surrounding areas, through the management of equipment in key places, thereby enhancing the management of people and vehicles entering the area. After the place generation achieved by the above-mentioned method is completed by computer automation or semi-automation, it can be routinely reused in multiple subsequent projects.

[0094] An embodiment of the present application further provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present application.

[0095] An embodiment of the present application further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present application.

[0096] The embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program executable by the at least one processor, and the computer program is used to enable the electronic device to perform the method of the embodiment of the present application when executed by the at least one processor.

[0097] refer to Figure 8, the structural block diagram of the electronic device that can be used as the server or client of the embodiment of the present application will now be described, which is an example of the hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0098] like Figure 8 As shown, the electronic device includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0099] Multiple components in the electronic device are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 can be any type of device that can input information to the electronic device, and the input unit 806 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device. The output unit 807 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 can include but is not limited to a disk, an optical disk. The communication unit 809 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0100] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present application may be implemented as a computer program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via a ROM 802 and / or a communication unit 809. In some embodiments, the computing unit 801 may be configured to perform the above method in any other appropriate manner (e.g., by means of firmware).

[0101] The computer program for implementing the method of the embodiment of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the embodiments of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] It should be noted that the term "including" and its variations used in the embodiments of the present application are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present application are illustrative and not restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0104] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0105] The various steps described in the method implementation methods provided by the embodiments of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present application is not limited in this respect.

[0106] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments refer to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0107] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. A method for generating a location for sensing front-end equipment, characterized in that include: Acquire geographic data, the geographic data including point of interest data and surface of interest data, the point of interest data including first point of interest data, second point of interest data and third point of interest data, wherein a position corresponding to the first point of interest data is located within an area corresponding to the surface of interest data, a position corresponding to the second point of interest data is located outside an area corresponding to the surface of interest data, and a text similarity between the second point of interest data and the surface of interest data is not less than a preset threshold, a position corresponding to the third point of interest data is located outside an area corresponding to the surface of interest data, and a text similarity between the third point of interest data and the surface of interest data is less than the preset threshold; generating a first location surface according to the interest surface data; Mapping a first point of interest corresponding to the first point of interest data and a second point of interest corresponding to the second point of interest data to the first location surface; Deleting a first setting word of the name field of the third interest point data as an alias field of the interest point data, wherein the first setting word includes location information; clustering the third interest points corresponding to the third interest point data according to text similarity, and generating a second location surface according to the interest point data in the category obtained by clustering; Wherein, clustering the third points of interest corresponding to the third points of interest data according to text similarity comprises: clustering the third points of interest according to text similarity of alias fields of the third points of interest data to obtain a plurality of categories; clustering the unclustered points of interest in the third points of interest into the plurality of categories according to distances between the unclustered points of interest in the third points of interest and the points of interest in the plurality of categories, and text similarities between name fields of the unclustered points of interest in the third points of interest and the name fields of the points of interest in the plurality of categories; clustering the unclustered points of interest in the third points of interest into the plurality of categories according to distances between the unclustered points of interest in the third points of interest and the points of interest in the plurality of categories, and text similarities between alias fields of the unclustered points of interest in the third points of interest and the name fields of the points of interest in the plurality of categories; clustering the unclustered points of interest in the third points of interest into the plurality of categories according to distances between the unclustered points of interest in the third points of interest and the points of interest in the plurality of categories, and text similarities between address fields of the unclustered points of interest in the third points of interest and the name fields of the points of interest in the plurality of categories; Among them, generating the second place surface according to the interest point data in the category obtained by clustering includes: merging the longitude and latitude of the interest points in the category obtained by clustering through the ST_ConcaveHull method provided by the database to generate a concave shell, and expanding the concave shell outward to a set width through the ST_Buffer method provided by the database to obtain the second place surface, and the concave shell is the minimum polygon surrounding the interest points in the category obtained by clustering.

2. The method according to claim 1, characterized in that After acquiring the geographic data, the method further includes: Deleting at least one of the following interest surface data: interest surface data corresponding to a set type, and interest surface data containing set words; Merging at least one of the following interest surface data: a plurality of interest surface data having the same name and having an inclusion relationship in their regions, and a plurality of interest surface data having similar names and having an inclusion relationship in their regions; Deleting the first setting word in the name field of the interest point data as the alias field of the interest point data; The point of interest data whose name field only includes the second set word is deleted.

3. The method according to claim 1, characterized in that Mapping the first point of interest corresponding to the first point of interest data to the first location surface according to the position relationship includes: determining, according to the location information of the first point of interest data and the area information of the surface of interest data, whether the first point of interest is located in the area corresponding to the surface of interest data; When the first interest point is located in the area corresponding to the interest surface data, the first interest point is mapped to the first location surface.

4. The method according to claim 1, characterized in that: Mapping the second point of interest corresponding to the second point of interest data to the first location surface includes: The second interest point is mapped to the first place surface according to the distance between the second interest point and the interest surface and at least one of the following information: whether the alias field of the second interest point data is the same as the name field of the interest surface data; whether the address field of the second interest point data includes the name field of the interest surface data; whether the text similarity between the address field of the second interest point data and the name field of the interest surface data is greater than a threshold; Whether the text similarity between the address field of the second interest point data and the target field is greater than a threshold, the target field is the field after deleting the third set word from the name field of the interest surface data.

5. The method according to claim 1, characterized in that Before clustering the third points of interest corresponding to the third points of interest data according to text similarity, the method further includes: Deleting the point of interest data starting with the fourth set word and ending with the fifth set word in the name field of the third point of interest data; The point of interest data of the third point of interest data whose name field includes the sixth setting word and whose alias field includes the seventh setting word is deleted.

6. The method according to claim 1, characterized in that The method further comprises: The name field and the address segment of the point of interest data in the place plane are identified as points of interest containing the eighth setting word as the place part of the place plane.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining location information of the setting points of the sensing front-end device; The perception front-end device is bound to the venue surface according to the positional relationship between the setting point of the perception front-end device and the venue surface.

8. An electronic device comprising: A processor and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

9. A non-transitory machine-readable medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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