CN118708789B - Generating method for sensing front-end equipment layout place and related equipment thereof - Google Patents
Generating method for sensing front-end equipment layout place and related equipment thereof Download PDFInfo
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Abstract
The application relates to a generating method for a layout place of a sensing front-end device and related devices thereof. The method comprises the steps of obtaining geographic data, wherein the geographic data comprises interest point data and interest surface data, the interest point data comprises first interest point data and second interest point data, the position corresponding to the first interest point data is located in an area corresponding to the interest surface data, the position corresponding to the second interest point data is located outside the area corresponding to the interest surface data, the text similarity between the second interest point data and the interest surface data is not smaller than a preset threshold value, generating a first place surface according to the interest surface data, and mapping the first interest point corresponding to the first interest point data and the second interest point corresponding to the second interest point data to the first place surface. The application solves the problem of inaccurate place created by the related technology and improves the accuracy of the created place.
Description
Technical Field
The application relates to the technical field of geographic data processing, in particular to a generating method for a layout place of a sensing front-end device and related devices thereof.
Background
The place where the front-end equipment is perceived refers to a physical space area associated with a social function, use, ownership or usage right, such as a school, mall, port, etc. In the related art, existing geographic information, such as point of Interest (Point of Interest, abbreviated as POI) data and Area of Interest (AOI) data, is used to map a certain point of Interest to an Interest surface by determining whether the point of Interest is located in a positional relationship in the Interest surface, so as to create a place (the place corresponds to the Interest surface). However, for those points of interest that are not located in any of the interest planes, they cannot be mapped to any of the places, and therefore, there is a large error in the boundary range of the places, which cannot meet the actual needs.
Disclosure of Invention
The generation method of the layout places of the sensing front-end equipment and the related equipment at least solve the problem that places created by the related technology are inaccurate.
A generating method for a layout place of a perception front-end device comprises the steps of obtaining geographic data, wherein the geographic data comprise interest point data and interest surface data, the interest point data comprise first interest point data and second interest point data, the position corresponding to the first interest point data is located in an area corresponding to the interest surface data, the position corresponding to the second interest point data is located outside the area corresponding to the interest surface data, the text similarity between the second interest point data and the interest surface data is not smaller than a preset threshold value, generating a first place surface according to the interest surface data, and mapping a first interest point corresponding to the first interest point data and a second interest point corresponding to the second interest point data to the first place surface.
In some embodiments, the interest point data further comprises third interest point data, the position corresponding to the third interest point data is located outside the region corresponding to the interest surface data, the text similarity between the third interest point data and the interest surface data is smaller than a preset threshold value, and the method further comprises clustering the third interest point corresponding to the third interest point data according to the text similarity and generating a second place surface according to the interest point data in the class obtained by clustering.
In some embodiments, after the geographic data is acquired, the method further comprises deleting at least one of the interest surface data corresponding to the interest surface data of the set type and containing the set words, merging at least one of the interest surface data including the plurality of interest surface data having the same name and having the containing relation in the region, the plurality of interest surface data having the similar name and having the containing relation in the region, deleting the first set word of the name field of the interest point data as the alias field of the interest point data, and deleting the name field of the interest point data to contain only the interest point data of the second set word.
In some embodiments, mapping the first interest point corresponding to the first interest point data to the first place plane according to the position relation includes judging whether the first interest point is located in a region corresponding to the interest plane data according to the position information of the first interest point data and the region information of the interest plane data, and mapping the first interest point to the first place plane when the first interest point is located in the region corresponding to the interest plane data.
In some embodiments, mapping the second interest point corresponding to the second interest point data to the first place plane comprises mapping the second interest point to the first place plane according to the distance between the second interest point and the interest plane and at least one of the information that whether an alias field of the second interest point data is identical to a name field of the interest plane data, whether an address field of the second interest point data contains the name field of the interest plane data, whether the text similarity between the address field of the second interest point data and the name field of the interest plane data is greater than a threshold value, whether the text similarity between the address field of the second interest point data and a target field is greater than the threshold value, and deleting the target field from the name field of the interest plane data after the third set word.
In some embodiments, before clustering the third interest point corresponding to the third interest point data according to the text similarity, the method further includes deleting the interest point data beginning with the fourth set word and ending with the fifth set word in the name field of the third interest point data, deleting the interest point data of which the name field contains the sixth set word and the alias field contains the seventh set word.
In some embodiments, clustering the third interest point corresponding to the third interest point data according to the text similarity includes clustering the third interest point according to the text similarity of the alias field of the third interest point data to obtain a plurality of categories, clustering the non-clustered interest point in the third interest point to the plurality of categories according to the distance between the non-clustered interest point in the third interest point and the interest points in the plurality of categories and the text similarity of the name field of the non-clustered interest point in the third interest point to the name field of the interest points in the plurality of categories, and clustering the non-clustered interest point in the third interest point to the plurality of categories according to the distance between the non-clustered interest point in the third interest point and the interest points in the plurality of categories and the text similarity of the non-clustered interest point in the third interest point to the name field of the non-clustered interest points in the plurality of categories.
In some embodiments, generating the second place face according to the interest point data in the clustered class comprises generating a concave surface shell of the interest points in the clustered class, expanding the concave surface shell outwards by a set width to obtain the second place face, wherein the concave surface shell is a minimum polygon surrounding the interest points in the clustered class.
In some of these embodiments, the method further includes identifying a point of interest for which the name field and address field of the point of interest data in the venue surface contains an eighth set word as a venue location for the venue surface.
In some embodiments, the method further comprises the steps of obtaining position information of the set point position of the sensing front-end equipment, and binding the sensing front-end equipment with the place face according to the position relation between the set point position of the sensing front-end equipment and the place face.
An electronic device comprising a processor and a memory storing a program comprising instructions which, when executed by the processor, cause the processor to perform the method described above.
A non-transitory machine readable medium having stored thereon computer instructions for causing a computer to perform the method described above.
The method for generating the layout places of the sensing front-end equipment and the related equipment thereof provided by the embodiment of the application adopt the mode of acquiring the geographic data, wherein the geographic data comprises the interest point data and the interest plane data, the interest point data comprises the first interest point data and the second interest point data, the position corresponding to the first interest point data is positioned in the area corresponding to the interest plane data, the position corresponding to the second interest point data is positioned outside the area corresponding to the interest plane data, the text similarity of the second interest point data and the interest plane data is not less than the preset threshold value, the first place plane is generated according to the interest plane data, and the first interest point corresponding to the first interest point data and the second interest point corresponding to the second interest point data are mapped to the first place plane, so that the problem of inaccuracy of places created by related technologies is solved, and the accuracy of created places is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the application, from which other embodiments can be obtained for a person skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for generating a layout place of a front-end aware device according to an embodiment of the present application.
Fig. 2 is a preferred flow chart of a method of generating a layout place of a perceived front-end device according to an embodiment of the present application.
FIG. 3 is a flow chart of the present application for obtaining generic geographic data from Kai-Lide geographic data.
FIG. 4 is a schematic diagram of generating a second venue surface according to an embodiment of the present application.
Fig. 5 is a place-and-position analysis interaction flow chart according to an embodiment of the present application.
FIG. 6 is an interactive flow diagram of a venue automation association point in accordance with an embodiment of the present application.
Fig. 7 is a preferred flowchart of a method of generating a layout place of a perceived front-end device according to an embodiment of the present application.
Fig. 8 is a schematic structural view of the electronic device of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the application is susceptible of embodiment in the drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.
Interpretation of the terms
Geographic data-information describing objects, events, or other features at or near the earth's surface, primarily including point of interest data and surface of interest data.
The sensing front-end equipment comprises an equipment assembly with image and video shooting functions, and the equipment assembly can also comprise various sensors, such as an infrared sensor, a light sensor, a sound sensor, a pressure sensor, a gas sensor, a temperature and humidity sensor, a GPS receiver, an RFID reader and the like.
The place is a place where front-end equipment is required to be perceived, such as a community entrance and a community high point.
In order to improve the accuracy of the created place, the embodiment of the application provides a generating method for a place where front-end equipment is arranged. Fig. 1 is a flowchart of a generating method of a layout place of a sensing front-end device according to an embodiment of the present application, as shown in fig. 1, the flowchart includes the following steps:
step S101, obtaining geographic data, where the geographic data includes point of interest data and interest surface data.
The interest point data comprises first interest point data, second interest point data and third interest point 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, and each data set may include one or more point of interest data. And, the three data sets do not overlap with each other, for example, any one of the first interest point data does not belong to the second interest point data or the third interest point data any more.
The method comprises the steps of setting a preset threshold value to distinguish the interest point data located outside the area corresponding to the interest surface data, wherein the position corresponding to the first interest point data is located in the area corresponding to the interest surface data, the positions corresponding to the second interest point data and the third interest point data are located outside the area corresponding to the interest surface data, the text similarity of the second interest point data and the interest surface data is larger than the text similarity of the third interest point data, and the interest point data with the text similarity larger than the preset threshold value are classified as the third interest point data. The preset threshold may be, for example, 70%,80%,90%, or other value.
The method for defining the interest point data comprises the steps of firstly judging whether a position corresponding to one interest point data (determined by longitude and latitude surface fields in the data) is located in an area corresponding to any one interest surface data (also determined by longitude and latitude surface fields in the data), if so, defining the interest point data as first interest point data, and otherwise, defining second interest point data and third interest point data according to the text similarity of the interest point data and the interest surface data.
In addition, although the first, second, and third point of interest data are defined in the above steps, it is not meant to divide the point of interest data simultaneously with or immediately after the acquisition of the geographic data, but the points of interest may be divided in subsequent processing steps.
In step S101, geographic data may be acquired 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 a Goldmap, a Karland navigation, or the like. Taking the Kailide navigation as an example, the original geographic data format of the Kailide navigation is a private format shp. FIG. 2 is a flowchart of the present application for obtaining general geographic data from Kadrid geographic data, as shown in FIG. 2, firstly, using an authorization code provided by Kadrid manufacturer to decompress a compressed package of a geographic data file in a zip format derived from Kadrid to obtain original geographic data in a shp format. In order to facilitate processing, the original geographic data can be analyzed by using a conversion tool, such as GeoTools with an open source, so as to obtain a database file in the sql format, after the database file is imported into a database system, the Java analysis script is utilized to analyze the data in the imported original data list, and the data is converted into POI data and AOI data according to a model for analyzing the location attribute. The system will record the import record at the time of import and can view the import record later.
The POI data mainly includes fields of interest point ID, name pinyin, alias, administrative division code, type name, address, longitude and latitude, etc., as shown in table 1.
Table 1 POI Table Structure of data
The AOI data mainly includes fields such as an interest plane ID, a name pinyin, an administrative division code, a type name, an address, a latitude and longitude, and an area, as shown in table 2.
Table 2 AOI Table Structure of data
After obtaining the geographic data and processing the geographic data into a standardized format (table structure shown in table 1 and table 2) capable of performing text processing, generating a place according to the geographic data by using the method provided by the embodiment of the application.
The generating method of the place is realized by full automation or semi-automation of a computer, and all programs and configurations required by operation can be set in advance or manually input or designated in the semi-automation realization process. The generation process adopts intermediate results and final results involved in the visual interface display process.
In a particular venue-generated scenario, there is geographic data of interest in the scenario and geographic data of no interest in the scenario. For example, in the context of creating a place of a school, mall or port, AOI data such as railways, national roads, provinces, counties, rural roads, highways, express ways, urban roads (thoroughfares, secondary thoroughfares, branches, pedestrian streets), overpasses, underground passages, cableways/ferries, and other traffic facilities, administrative division boundaries, woodlands, and the like, are not geographical data of interest, and thus, in some embodiments, the place of interest corresponding to a set type will be deleted after the geographical data is acquired, without creating a place of interest from the interest data. The setting types include but are not limited to railways, national roads, provinces, counties, rural roads, highways, expressways, urban roads (arterial roads, secondary arterial roads, branches, pedestrian streets), overpasses, underground passages, cableways/ferries and other traffic facilities, administrative division boundaries, forest lands and the like. It will be appreciated that the type of setting AOI data that needs to be deleted may be different in different scenarios.
Among the AOI data, there is also one in which although the type of AOI data (area is more than 0), the information described by the name thereof is a XX office building or a XX business building, and such AOI data should be regarded as one facility in a certain place but not enough to be a place alone, and therefore, after the geographical data is acquired, the face of interest data containing the set word is deleted so that the place face is not generated from these face of interest data. The set words include, but are not limited to, words consisting of "number + building/number/office building/business building".
The interest surface data or the first venue surface data may also be merged in some embodiments. It will be appreciated that similar locale generation results can ultimately be obtained whether the merging of the interest surface data occurs before the locale surface is generated or after the first locale surface is generated, and thus the merging operation is not limited to before or after the first locale surface is generated.
Repeated AOI data may also be combined in some embodiments. AOI data that needs to be merged includes, but is not limited to, two cases, multiple face of interest data with the same name and region having an inclusion relationship, multiple face of interest data with the approximate name and region having an inclusion relationship.
Specifically, the plurality of interest surface data with the same name and the area having the inclusion relationship means that the AOI data are the same in name, the surface data overlap, for example, the "XX building" in a certain city has 2 pieces of AOI data, the two pieces of AOI data have the same administrative division codes, the type of AOI data with larger surface data is a commercial building, the type of AOI data with smaller surface data is a common building, and the surface data of the two pieces of AOI data can be determined to be the inclusion relationship according to the longitude and latitude data.
Specifically, the plurality of interest surface data having approximate names and containing relations in areas means that the names of the AOI data are not identical, but the text similarity is similar, and containing relations exist between the surface data. For example, a "XX technical garden" in a certain city has 6 pieces of AOI data, which are respectively a XX technical garden, a XX technical garden A, XX technical garden B, XX technical garden C, XX technical garden D, XX technical garden E seat. The area of "XX technical park" is the largest, and its face data contains five other pieces of AOI data, and in this embodiment, these 6 pieces of AOI data are combined into one piece of AOI data named "XX technical park", and its type and face data are based on the large-area AOI data.
The logic of the above-mentioned AOI data merging may be, for example, that the name of the AOI data is first cleaned, and after the sub-location information such as the A seat and the B seat is removed, the information of the alias field in the AOI data, for example, the information of the alias field of the AOI data named as "XX science and technology park B seat" is "XX science and technology park". And after the name cleaning and alias field setting information is carried out on all 6 pieces of AOI data, judging whether the 6 pieces of AOI data have the inclusion relation of the surface data or not through cross checking of multiple pieces of data such as the alias field, administrative division codes, the surface data and the like of the AOI data, and if the inclusion relation exists, merging the AOI data with smaller surface data into the AOI data with larger surface data.
Step S102, a first place plane is generated according to the interest plane data.
After deleting and merging the interest surface data, the rest of the interest surface data can generate a first place surface, and all data of the first place surface inherit the data of the interest surface data.
The point of interest data may also be cleaned and deleted in some embodiments. After the geographic data is acquired, deleting a first set word of a name field of the point of interest data as an alias field of the point of interest data, wherein the name field of the point of interest data only contains the point of interest data of a second set word.
The cleaning refers to deleting a first set word of a name field of the point of interest data as an alias field of the point of interest data. Specifically, the part of POI data contains part information such as XX door, XX museum, XX building, XX block, XX seat, XX parking lot, south 1, north 1, south 2, north 2 and the like, which are naturally important, but text similarity interference items are easily introduced in the process of generating places, so that after deleting a first set word representing part information in a name field of the POI data in the embodiment, the first set word is used as information of an alias field of the POI data, so that the information of the POI alias field can be used for generating places in the follow-up process, and the information of the representing places in the POI data is not lost.
Specifically, deleting the point of interest data whose name field contains only the second set word means deleting POI data whose name field information for the POI data is totally called as position information of underground garage entrance, parking lot, basketball court, football court, etc. which cannot describe the place accurately.
Step S103, mapping the first interest point corresponding to the first interest point data and the second interest point corresponding to the second interest point data to the first place plane.
In this embodiment, the interest point is located in a region corresponding to a certain location surface, and the interest point and the location surface are considered to have a strong association relationship, that is, the first interest point is an interest point having a strong association relationship with the first location surface, and the second interest point and the third interest point are relatively interest points having a weak association relationship with the first location surface. For the interest points and the place planes with strong association relations, judging whether longitude and latitude data of POI data are contained in a region corresponding to certain AOI data (namely first place plane data) or not according to the position information of first interest point data and the region information of the interest plane data by a st_Within method provided by a database, and under the condition that the first interest points are located in the region corresponding to the interest plane data, establishing a mapping relation between the interest points and the corresponding first place planes so as to map the first interest points into the first place planes. The mapping fields of AOI data and POI data are shown in table 3.
Table 3 mapping table field for AOI data and POI data
And respectively adopting different mapping strategies for the second interest point and the third interest point of the weak association relation according to the text similarity between the second interest point and the first place face data. And mapping the second interest point with higher text similarity to the first place plane according to the distance between the second interest point and the interest plane and at least one of the information that whether the alias field of the second interest point data is identical to the name field of the interest plane data, whether the address field of the second interest point data contains the name field of the interest plane data, whether the text similarity between the address field of the second interest point data and the name field of the interest plane data is greater than a threshold value, whether the text similarity between the address field of the second interest point data and the target field is greater than the threshold value, and deleting the field after the third set word for the name field of the interest plane data.
For example, for the POI not bound to the corresponding AOI, the mapping association relationship between the POI and the AOI is established by judging whether the name of the POI is the same as the name of the AOI and the latitude and longitude distance is within the allowable range (for example, 250 meters, which can be adjusted according to the actual data condition of the project).
For another example, for the POIs which are not bound to the corresponding AOI, the mapping association relationship between the POIs and the AOI is established by judging whether the address of the POI contains the AOI name and whether the latitude and longitude distance is within the allowable range (for example, 250 meters, which can be adjusted according to the actual data condition of the project).
For another example, for the POI which is not bound to the corresponding AOI yet, the mapping association relationship between the POI and the AOI is established by judging whether the POI address contains the POI address corresponding to the AOI, and the POI is within the allowed range (for example, 30 meters, and can be adjusted according to the actual data condition of the project) from the longitude and latitude distance of the AOI, and the text similarity of the POI address and the AOI name is within the allowed range (for example, the similarity threshold is set to 0.13, and can be adjusted according to the actual data condition of the project).
For another example, for the POI that is not yet bound to the corresponding AOI, the mapping association relationship between the POI and the AOI is established by determining whether the address of the POI includes the AOI name after removing the name of the property company (corresponding to the third setting word described above), and the latitude and longitude distance is within the allowable range (for example, the distance threshold is set to 100 meters, and can be adjusted according to the actual data condition of the project).
Fig. 3 is a preferred flowchart of a method for generating a layout place of a front-end device according to an embodiment of the present application, and as shown in fig. 3, the flowchart is different from the flowchart shown in fig. 1 in that the flowchart further includes step S305. The flow shown in fig. 3 includes the following steps:
Step S301, obtaining geographic data.
Step S302, a first place plane is generated according to the interest plane data.
Step S303, mapping the first interest point corresponding to the first interest point data to the first place plane.
Step S304, mapping a second interest point corresponding to the second interest point data to the first place plane.
Step S305, clustering third interest points corresponding to the third interest point data according to the text similarity, and generating a second place face according to the interest point data in the class obtained by clustering.
And for a third interest point with lower text similarity relative to the second interest point, adopting a clustering strategy to generate a new place plane, namely a second place plane.
In order to eliminate text similarity interference items existing in the second interest points when the second place surface is generated, filtering processing is further performed on the third interest point data before clustering is performed on the third interest points, wherein the third interest point data is deleted, the interest point data starts with a fourth set word and ends with a fifth set word in a name field of the third interest point data, the name field of the third interest point data contains a sixth set word, and the alias field contains interest point data of a seventh set word.
Specifically, deleting the point of interest data beginning with the fourth set word and ending with the fifth set word in the name field of the third point of interest data means that, for POIs that have not yet been bound to the corresponding AOI, POI data beginning with numbers, letters, directions, etc. (equivalent to the fourth set word) and ending with buildings, blocks, buildings, floors, units, plots, etc. (equivalent to the fifth set word) are filtered out, and the specific location is not described.
Specifically, deleting the point of interest data whose name field contains a sixth set word and whose alias field contains a seventh set word means that, for POIs that are not yet bound to the corresponding AOI, POI data whose names are in interlocking stores (equivalent to the sixth set word) and POI alias are in supermarkets, hotels, barbecue stores, hotels, famous cigarettes, haircuts, and the like (equivalent to the seventh set word) for which location information cannot be accurately described are filtered out.
Through the filtering operation, the subsequent merging of POI data through POI names, aliases and addresses and the generation of AOI are facilitated, and the creation of the second place face is completed.
In step S305, the POI data subjected to the filtering processing is subjected to clustering processing by the following policies:
a. and clustering the third interest points according to the text similarity of the alias field of the third interest point data to obtain a plurality of categories.
Specifically, for POI names, POI data with the same alias are clustered into the same category.
B. And clustering the non-clustered interest points in the third interest points into a plurality of categories according to the distances between the non-clustered interest points in the third interest points and the interest points in the plurality of categories and the text similarity between the name fields of the non-clustered interest points in the third interest points and the name fields of the interest points in the plurality of categories.
Specifically, for the remaining POIs, it is determined whether the POI name is the same as the POI name in the above a, and the latitude and longitude 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 condition of the project), and the POI names are clustered into the same category.
C. and clustering the non-clustered interest points in the third interest points into a plurality of categories according to the distances between the non-clustered interest points in the third interest points and the interest points in the plurality of categories and the text similarity between the alias fields of the non-clustered interest points in the third interest points and the name fields of the interest points in the plurality of categories.
Specifically, for the rest POIs, whether the POI alias is the same as the POI name in the above b is judged, and the longitude and latitude distance is within the allowable range (for example, the distance threshold is set to 400 meters and can be adjusted according to the actual data condition of the project), and the POIs are clustered into the same category.
D. and clustering the non-clustered interest points in the third interest points into a plurality of categories according to the distances between the non-clustered interest points in the third interest points and the interest points in the plurality of categories and the text similarity between the address field of the non-clustered interest points in the third interest points and the name field of the interest points in the plurality of categories.
Specifically, for the remaining POIs, it is determined whether the POI address is the same as the POI name in the above c, and the latitude and longitude 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 condition of the project), and the POI addresses are clustered into the same category.
After the third points of interest are clustered into different categories by the above strategy, a plurality of points of interest are included in each category. In this embodiment, the st_ ConcaveHull method provided by the database processes a plurality of longitudes and latitudes corresponding to the POI data that can be combined, so as to generate the concave shell. And then expanding the boundary of the concave shell outwards by a certain distance through an ST_buffer method provided by the database to generate a second place face, and obtaining the center point of the second place face. Fig. 4 is a schematic diagram of generating a second venue surface according to an embodiment of the present application, as shown in fig. 4, where black origin represents a point of interest, black solid represents an initially generated concave hull, and gray polygon represents the generated second venue surface. For each category, generating concave shells of interest points in the clustered categories, and expanding the concave shells outwards by a set width (for example, 30 meters) to obtain a second place surface.
The Concave shell (Concave Hull) refers to a geometric shape for approximately containing a set of point sets while maintaining the original shape characteristics of the point sets as much as possible. The concave shell may better reflect the actual distribution of the point set than a conventional convex hull (Convex Hull) because it allows for the formation of concave portions within the shell. Convex hull is a classical geometrical concept that refers to a smallest convex polygon or polyhedron containing a set of points, within which no point lies outside its boundaries. In other words, the convex hull does not allow for a concave portion, all points being located within one continuous convex boundary. The concave housing is different in that it attempts to more closely conform to the true shape of the point set, allowing some portions to be recessed inward. As a result of this, the resulting shape is generally closer to the actual distribution of the point set than the convex hull, especially if the point set has a complex structure.
Through the steps S301 to S305, compared with the situation that the situation surface is missing and inaccurate due to the fact that the situation surface is generated only by means of the POI data with the strong association relationship in the related technology, the situation generation method provided by the embodiment fully utilizes the POI data with the weak association relationship and adopts the text similarity and clustering mode to maximally reserve the POI data information and ensure the accuracy of the generated places.
Some places in the place are important treatment areas where sensing front-end equipment needs to be arranged, such as places including entrances and exits, doors and the like in the place. It is conventional practice to manually annotate the venue locations in venue data after venue data is obtained in order to ensure that the venue locations are covered when the deployment of the sensing front-end equipment is subsequently performed. In order to further reduce the workload of manually managing the site surface data in the field of layout of the sensing front-end equipment, in this embodiment, the information of the name field and the address field of the POI data is fully utilized, the interest points of which the name field or the address field of the interest point data in the site surface contains eighth set words (for example, "entrance and exit", "door", etc.) are identified, and the interest points are used as site parts of the site surface. Fig. 5 is a place-location analysis interaction flow chart of an embodiment of the application, as shown in fig. 5, in which a user platform selects POI data, submits a request for automated administration to the platform, and the platform returns an ID of an automated administration task for a platform user to subsequently query the task status. When the automatic management task is operated on the platform, the platform automatically executes the following tasks that the platform inquires all place data in the task from the database, the platform inquires place position data from the database in a text regular matching mode, the platform filters and screens the place positions through keywords to obtain places and place positions needing to be controlled in an important mode, and the platform updates place management state information in the database.
In some embodiments, the method further comprises the steps of obtaining position information of the set point position of the sensing front-end equipment, and binding the sensing front-end equipment with the place face according to the position relation between the set point position of the sensing front-end equipment and the place face. After the generation of the place is completed, the place and the point location can be automatically bound according to the analysis of the equipment name and the place name and the analysis of the equipment longitude and latitude and the place surface data inclusion, so that automatic treatment is realized, for example, the point location is located in the distance of 30 meters of the peripheral range of the place surface data, the point location can be considered to belong to the place, and finally, the association information of the place and the point location is written into a database to complete the closed-loop operation of the place treatment. FIG. 6 is an interactive flow diagram of a venue automation association point location according to an embodiment of the present application, as shown in FIG. 6, where a platform user selects POI data, submits an automated abatement request, and the platform returns an ID of an automated abatement task for the platform user to subsequently query the task status. When the automatic treatment task is operated on the platform, the platform automatically executes the following tasks that the platform inquires the information of the peripheral associable points from the database according to the place face data, the platform inquires the associable points from the database according to the place names and the point names by text analysis, the platform stores the place associable points into the database after the association of the points and the places is carried out, the platform inquires the associable points in batches from the database to carry out the place association, and the database is updated when the treatment state of the place is updated.
Fig. 7 is a preferred flow chart of a method for generating a layout place of a perceived front-end device according to an embodiment of the present application, the flow mainly including importing POI data and AOI data to the present system by means of a data source of a third party, and automatically completing place generation, place location analysis, and place association points of the system based on the imported POI data and AOI data.
Through the embodiment or the preferred implementation mode of the application, the POI and AOI data imported by the third party data source are cleaned, filtered and combined to complete the site creation process, analyze site parts, and realize site association points by using the text similarity of sites and points. The above embodiments provide for rapid automated management of sites of interest to users, such as institutions, schools and their surroundings, through the management of critical site equipment, thereby enhancing the management of personnel and vehicles entering the area. After the place generation realized by the method is finished by computer automation or semi-automation, the place can be normalized and reused in a plurality of subsequent projects.
Embodiments of the present application also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform the method of the embodiments of the present application.
Embodiments of the application also provide a computer program product comprising a computer program, wherein the computer program, when being executed by a processor of a computer, is for causing the computer to carry out the method of embodiments of the application.
Embodiments of the present application also provide an electronic device comprising at least one processor and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, which when executed by the at least one processor is adapted to cause an electronic device to perform a method of an embodiment of the application.
With reference to fig. 8, a block diagram of an electronic device that may be a server or a client of an embodiment of the present application will now be described, which is an example of a hardware device that may be applied to aspects of the present application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 8, the electronic device includes a computing unit 801 that 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 into 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 by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
A number of 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 may be any type of device capable of inputting information to an electronic device, and the input unit 806 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 807 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. The storage unit 808 may include, but is not limited to, magnetic disks, optical disks. The communication unit 809 allows the electronic device to exchange information/data with other devices over computer networks, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and/or wireless communication transceivers, such as bluetooth devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a CPU, a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing units, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), as well as any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, method embodiments of the present application may be implemented as a computer program tangibly embodied on a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device via the ROM 802 and/or the communication unit 809. In some embodiments, the computing unit 801 may be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).
A computer program for implementing the methods of embodiments of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of embodiments of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the term "comprising" and its variants as used in the embodiments of the present application are open-ended, i.e. "including but not limited to". The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment," another embodiment "means" at least one additional embodiment, "and" some embodiments "means" at least some embodiments. The references to "one" or "a plurality" of modifications in the embodiments of the application are intended to be illustrative rather than limiting, and it will be understood by those skilled in the art that "one or more" is intended to be interpreted as "one or more" unless the context clearly indicates otherwise.
User information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) according to embodiments of the present application are information and data authorized by a user or sufficiently authorized by parties, and the collection, use and processing of relevant data requires compliance with relevant laws and regulations and standards of relevant countries and regions, and is provided with corresponding operation portals for the user to select authorization or denial.
The steps recited in the method embodiments provided by the embodiments of the present application may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the application is not limited in this respect.
The term "embodiment" in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive. The various embodiments in this specification are described in a related manner, with identical and similar parts being referred to each other. In particular, for apparatus, devices, system embodiments, the description is relatively simple as it is substantially similar to method embodiments, see for relevant part of the description of method embodiments.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of protection. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
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