LBS-based wading information dynamic recommendation method and device

Abstract

The invention provides a method and a device for dynamically recommending wading information based on LBS (Location Based Service). The method comprises the following steps: acquiring at least one wading-related data item; determining a water conservancy object model and a water conservancy topic model; dividing a preset area to obtain a wading information grid model; determining a water conservancy event model; forming a plurality of subject knowledge maps according to wading knowledge data in wading knowledge databases corresponding to the water conservancy object model, the water conservancy subject model, the wading information grid model and the water conservancy event model; according to a target wading information grid to which the position of a target object belongs, obtaining a target theme knowledge graph corresponding to the target wading information grid from the plurality of theme knowledge graphs; and according to the target theme knowledge graph, obtaining at least one type of target push wading data and pushing the target push wading data to a client of a target object. The method has the advantage of improving the effectiveness and timeliness of wading related information pushing.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and also to a method and device for dynamically recommending water-related information based on LBS. Background Art

[0002] Mobile services that provide the public with water-related information, such as water conservancy and water affairs knowledge, primarily include WeChat public accounts, water conservancy-related mini-programs, and water conservancy-related mobile applications. The functions built into the business application layer of these services push water conservancy or water affairs information in a passive and ubiquitous manner, which is not highly relevant to the public serving mobile services and is not what the public cares about or wants to see. To accurately push water conservancy and water affairs information and services that the public cares about, the data resource pool and application support layer must be restructured. The current construction of the data resource layer and application support layer fails to aggregate and associate data based on ontology (audience), subject, and physical mechanisms. It lacks subject-ontology and event-ontology associations, and cannot support refined, master-formulated water conservancy public services. The services constructed are not what the public wants, and water conservancy information cannot be pushed based on the user's location. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for dynamically recommending water-related information based on LBS, so as to improve the effectiveness and timeliness of pushing water-related information.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0005] A first aspect of the present invention provides a method for dynamically recommending water-related information based on LBS, comprising:

[0006] Obtain at least one water-related data item;

[0007] Determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item;

[0008] Divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model;

[0009] Determining a water conservancy event model according to the water conservancy object model, the water conservancy theme model, and the water-related information grid model;

[0010] forming a plurality of subject knowledge graphs according to the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy subject model, the water-related information grid model, and the water conservancy event model;

[0011] According to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs;

[0012] Obtain at least one target push-related data according to the target subject knowledge graph;

[0013] The target push-related data is pushed to the client of the target object.

[0014] Optionally, at least one water-related data item is obtained, including:

[0015] Get the resource directory of preset water conservancy data;

[0016] The object class data of the resource directory library is parsed to obtain at least one water-related data item.

[0017] Optionally, determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item includes:

[0018] Determining a water conservancy object model according to the attributes of the at least one water-related data item;

[0019] Based on the relationship between different data items of the at least one water-related data item, a subcategory of the data item is determined, and based on the major category to which the subcategory belongs, a water conservancy theme model is determined.

[0020] Optionally, the preset area is divided according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, including:

[0021] Obtaining grid unit division parameters corresponding to each water-related theme in the water conservancy theme model;

[0022] Determine the minimum spatial grid unit of each water-related theme according to the grid unit division parameters;

[0023] The preset area is divided according to the minimum spatial grid unit of each water-related theme to obtain a water-related information grid model corresponding to each water-related theme.

[0024] Optionally, determining a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water-related information grid model includes:

[0025] Determining at least one target data item of the water conservancy object model corresponding to each water-related information grid according to the water-related information grid model;

[0026] Determining, based on the water conservancy theme model, a target water-related theme corresponding to at least one target data item;

[0027] Acquire water conservancy events related to the target water-related topic and determine a water conservancy event model.

[0028] Optionally, multiple subject knowledge graphs are formed based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model, and the water conservancy event model, including:

[0029] Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy object model to obtain first entity data;

[0030] performing knowledge extraction on the water-related knowledge data corresponding to the water-related information grid model to obtain second entity data;

[0031] Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy theme model to obtain third entity data;

[0032] performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy event model to obtain fourth entity data;

[0033] performing fusion processing on the first entity data, the second entity data, the third entity data, and the fourth entity data to obtain fused entity data;

[0034] Based on the fused entity data, multiple subject knowledge graphs are formed.

[0035] Optionally, according to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs includes:

[0036] Get the geographic location of the target object;

[0037] According to the geographical location of the target object, determining the target water-related information grid to which the geographical location belongs;

[0038] From the multiple subject knowledge graphs, obtain the target subject knowledge graph corresponding to the target water-related information grid.

[0039] A second aspect of the present invention provides a dynamic recommendation device for water-related information based on LBS, comprising:

[0040] An acquisition module, configured to acquire at least one water-related data item;

[0041] A processing module is used to determine a water conservancy object model and a water conservancy theme model based on the at least one water-related data item; divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model; determine a water conservancy event model based on the water conservancy object model, the water conservancy theme model and the water-related information grid model; form multiple theme knowledge graphs based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model and the water conservancy event model respectively; obtain a target theme knowledge graph corresponding to the target water-related information grid to which the location of the target object belongs from the multiple theme knowledge graphs; obtain at least one target pushed water-related data based on the target theme knowledge graph; and push the target pushed water-related data to the client of the target object.

[0042] According to a third aspect of the present invention, a computing device is provided, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to the first aspect is executed.

[0043] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the method described in the first aspect.

[0044] The above solution of the present invention includes at least the following beneficial effects:

[0045] The above-mentioned solution of the present invention obtains at least one water-related data item to determine a water conservancy object model and a water conservancy theme model, then divides the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, and then determines a water conservancy event model based on the water conservancy object model, the water conservancy theme model and the water conservancy information grid model. Then, based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water conservancy information grid model and the water conservancy event model, multiple theme knowledge graphs are formed. According to the target water-related information grid to which the location of the target object belongs, a target theme knowledge graph corresponding to the target water-related information grid is obtained from the multiple theme knowledge graphs. According to the target theme knowledge graph, at least one target pushed water-related data is obtained, and the target pushed water-related data is pushed to the client of the target object. This not only pushes water-related data to the target object according to the location of the target object, but also has high integrity and relevance, which has the advantage of improving the effectiveness and timeliness of water-related data push. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 11 is a flow chart of a method for dynamically recommending water-related information based on LBS in an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of a spatial grid unit in an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the process of constructing a subject knowledge graph in an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the data layer and model layer of the knowledge graph in an embodiment of the present invention;

[0050] Figure 5 4 is a schematic structural diagram of a dynamic recommendation device for water-related information based on LBS in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0052] like Figure 1 As shown, an embodiment of the present invention proposes a dynamic recommendation method for water-related information based on LBS, comprising the following steps:

[0053] Step 101, obtaining at least one water-related data item;

[0054] Step 102: determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item;

[0055] Step 103: Divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model;

[0056] Step 104: determining a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water-related information grid model;

[0057] Step 105: forming a plurality of subject knowledge graphs based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy subject model, the water-related information grid model, and the water conservancy event model respectively;

[0058] Step 106: According to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the plurality of subject knowledge graphs;

[0059] Step 107: obtaining at least one target push-related data according to the target subject knowledge graph;

[0060] Step 108: Push the target push-related data to the client of the target object.

[0061] The dynamic recommendation method for water-related information based on LBS in an embodiment of the present invention obtains at least one water-related data item, determines a water conservancy object model and a water conservancy theme model, then divides a preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, and then determines a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water conservancy information grid model. Then, based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water conservancy information grid model, and the water conservancy event model, multiple theme knowledge graphs are formed. Based on the target water-related information grid to which the location of the target object belongs, a target theme knowledge graph corresponding to the target water-related information grid is obtained from the multiple theme knowledge graphs. Based on the target theme knowledge graph, at least one target pushed water-related data is obtained, and the target pushed water-related data is pushed to the client of the target object. This method not only pushes water-related data to the target object based on the location of the target object, but also has high integrity and relevance, thereby improving the effectiveness and timeliness of water-related data push.

[0062] Here, LBS is short for Location Based Services, which uses various types of positioning technologies to obtain the current location of a positioning device and provides information resources and basic services to the positioning device via the mobile Internet. In this application, water-related data can be pushed to the target object based on the target object's location.

[0063] In an optional embodiment of the present invention, step 101 includes:

[0064] Step 1011, obtaining a resource directory library of preset water conservancy data;

[0065] Step 1012: parse the object class data of the resource directory library to obtain at least one water-related data item.

[0066] Specifically, the pre-set resource directory for water conservancy data can be formed as shown in Table 1, using public service water-related data items. This resource directory can include a water conservancy project database, a water supply database, an urban flooding database, a rainfall database, a water conservation database, a river database, a disaster avoidance database, a drainage database, a water-related service database, and a spatial database. The water conservancy project database stores information on surrounding reservoirs, sluice gates, pumping stations, weirs, ponds, and irrigation districts, as well as information on water conservancy scenic areas and the management boundaries of water conservancy projects. The water supply database stores data on water tank cleaning, water quality testing, water outage notifications, water supply restoration, secondary water supply, water use suspension notifications, pipe bursts, and water supply failures. The urban flooding database stores data on urban flooding locations, depth, extent, and video footage of urban flooding. The rainfall database stores real-time and forecast rainfall data. The water conservation database stores data on water-saving devices, water-saving guidelines, and water conservation incentives from water administration departments. The river database is used to store data such as water quality testing of surrounding rivers, river health ratings, river water levels during flood prevention periods, and river warning information. Disaster shelter data is primarily used to store key disaster shelters and nearby rescue points during flood seasons and waterlogging. Drainage data is primarily used to store the distribution of surrounding drainage networks, manhole covers, and manhole covers under repair or requiring attention. The social service database is used to store water-related service agencies (payment points, repair points), surrounding water service authorities, and so on. By accessing a resource directory of pre-set water conservancy data, it is convenient to directly extract relevant data items from it, providing a foundation for the subsequent push of water-related data based on the location of the target object.

[0067] The object classes in Tables 1 to 4 include data items related to various objects. Therefore, at least one water-related data item can be directly obtained from the object class data in the resource directory library.

[0068] Table 1 Resource Directory 1

[0069] Table 2 Resource Catalog Library 2

[0070] Table 3 Resource Catalog Library 3

[0071] Table 4 Resource Directory 4

[0072] In an optional embodiment of the present invention, step 102 includes:

[0073] Step 1021: determining a water conservancy object model according to the attributes of the at least one water-related data item;

[0074] Specifically, the attributes of a data item include at least one of basic, spatial, operational, and temporal attributes. The water conservancy object model describes object attributes according to the object class in the resource catalog library, including basic, spatial, operational, and temporal attributes. Basic attributes include name, code, type (such as reservoir, river, pumping station), and administrative affiliation. Spatial attributes include latitude and longitude coordinates, watershed range, and grid unit number. Operational attributes include function (flood control, irrigation, water supply), design standard, and operating status (normal / maintenance). Temporal attributes include construction time, update cycle, and data collection timestamp.

[0075] Step 1022: Determine the subcategory of the data item based on the relationship between different data items of the at least one water-related data item, and determine the water conservancy theme model based on the major category to which the subcategory belongs.

[0076] Specifically, the relationships between different data items can be topological, such as upstream and downstream (e.g., the confluence of a river and a reservoir) or inclusion (e.g., a river basin contains sub-basins). They can also be business-related, such as dependency (e.g., a pump station relies on the power grid for power) or impact range (e.g., a damaged embankment affects surrounding farmland). Based on the relationships between different data items and the resource catalog shown in Table 1, corresponding subcategories and major categories can be determined, and each major category corresponds to a water conservancy theme model. Here, the steps for determining a water conservancy theme model may include:

[0077] Aggregate subcategories. Group subcategories belonging to the same general category into corresponding themes (e.g., "Nature," "Projects and Facilities," and "Main Functional Areas" into "Water Conservancy Projects").

[0078] Define topic relationships. For example, a dependency relationship: the disaster avoidance data topic depends on the water safety data in the river (lake) topic. For example, an impact relationship: gate scheduling in the drainage information topic affects the water safety data in the river (lake) topic (such as upstream water release).

[0079] Create thematic views. For example, a dynamic view like a real-time water level-flow curve (for flood warning information) or a static view like rainfall forecast-drainage information (for water resource scheduling) can be created.

[0080] Thematic models are integrated with grid models. Spatial aggregation: Statistical analysis of thematic data by grid unit (e.g., "average rainfall in grid A"); hierarchical drilling: Drilling down from a thematic overview (e.g., provincial water quality compliance rate) to detailed grid information (e.g., COD concentration within a specific grid).

[0081] In an optional embodiment of the present invention, step 103 includes:

[0082] Step 1031, obtaining grid unit division parameters corresponding to each water-related theme in the water conservancy theme model;

[0083] Specifically, each water-related theme has corresponding grid unit division parameters. Spatial grids are delineated based on different water-related themes, such as water supply, waterlogging, rainfall, water conservation, river channels, and disaster relief. The grid unit division parameters for water-related themes are as follows: For water conservancy project themes, the network unit is the area within a 5-10 km radius of the target location. For water supply themes, the minimum grid unit is the water supply area controlled by the water supply network valves. For waterlogging themes, the default grid unit is the area within a 5 km radius of the target location, but the grid unit can also be based on the route to the destination. For rainfall themes, the grid unit is the smallest administrative division of the target location. For water conservation, the grid unit is the administrative division. For river channels (including all water bodies) and disaster relief sites, grid units are not required; the distance to the target location is the primary factor.

[0084] Step 1032: determining the minimum spatial grid unit for each water-related theme according to the grid unit division parameters;

[0085] Specifically, the minimum spatial grid unit of each water-related theme can be directly extracted from the grid unit division parameters. Taking the water-related theme of water conservancy projects as an example, the corresponding minimum spatial grid unit is 5 to 10 kilometers.

[0086] Step 1033 : Divide the preset area according to the minimum spatial grid unit of each water-related theme to obtain a water-related information grid model corresponding to each water-related theme.

[0087] Specifically, the preset area can be a map of a city or a map of a country. The scope of the preset area can be specifically set according to specific needs. According to the minimum spatial grid unit corresponding to each water-related theme in the grid unit division parameters, the preset area is divided into multiple minimum spatial grid units. It should be noted that the minimum spatial grid units of multiple water-related themes can be displayed on the same map (the color of the minimum spatial grid unit of each water-related theme is different for distinction), or the minimum spatial grid unit of each water-related theme can correspond to a unique map, which can be selected or set according to actual conditions. After the division of the preset area is completed, the water-related information grid model corresponding to the water-related theme is obtained, which can be a map with a spatial grid unit of at least one water-related theme. When dividing the preset area, it can be divided into rectangular or circular grids. In specific implementation, irregular shapes such as polygons can also be used for division.

[0088] Here, when dividing the preset area into spatial grid units, if the preset area is non-rectangular (such as a polygon), it is necessary to first calculate the grid according to the circumscribed rectangle, and then retain the grid units within the polygon by clipping; if the preset area is rectangular, in the plane rectangular coordinate system, the area boundary coordinates are: the minimum x coordinate (left boundary) is , the maximum x coordinate (right boundary) is , the minimum y coordinate (lower boundary) is , the maximum y coordinate (upper boundary) is ; The size of the preset area is: width (length in x direction) W is Calculated; height (length in y direction) H is obtained by Calculated.

[0089] For column i ( i ∈[0, n col -1] )、jth row( j∈[0,n row -1] ) space grid unit, whose coordinate range is:

[0090] x direction ( i ∈[0, n col -1] ): [ X min +i∙dx,min( X min +(i+1)∙dx, X m ax )] ; If the last column exceeds , then truncate it;

[0091] y direction ( j∈[0,n row -1] ): [ Y min +j∙dy,min( Y min +(j+1)∙dy, Y m ax )] ; If the last line exceeds , then truncate it;

[0092] The width dx and height dy of each grid cell are calculated using the following formula to calculate the minimum number of rows and columns required to cover the entire area (rounded up to avoid incomplete coverage):

[0093] Minimum number of columns (number of units in the x-direction) by Calculated; minimum number of rows (Number of units in the y direction) by Calculated, is a rounding function, for example: the area width is 10m and the unit width is 3m, then , take 4 columns.

[0094] like Figure 2 As shown, taking the water supply theme as an example, the preset area 1 is divided according to the water supply area 3 (circle in the figure) controlled by the water supply network valve 2 as the minimum spatial grid unit.

[0095] In an optional embodiment of the present invention, step 104 includes:

[0096] Step 1041: determining at least one target data item of the water conservancy object model corresponding to each water-related information grid according to the water-related information grid model;

[0097] Specifically, such as Figure 3 As shown, by establishing a many-to-many mapping relationship between the water conservancy object model and the water-related information grid model, each water-related information grid in the water-related information grid model corresponds to at least one target data item of the water conservancy object model. For example, a water-related information grid corresponds to data items such as reservoirs, rivers, water plants, and water supply networks in the water conservancy object model.

[0098] Step 1042: determining a target water-related topic corresponding to at least one target data item according to the water conservancy topic model;

[0099] Specifically, the target water-related themes corresponding to different target data items can be determined from the water conservancy theme model. For example, if the target data items include reservoir data, river data, water plant data, and water supply network data, it can be found from the water conservancy theme model that the target water-related themes corresponding to reservoir data and river data are water conservancy project-related themes, and the target water-related themes corresponding to water plant data and water supply network data are water supply-related themes.

[0100] Step 1043: Acquire water conservancy events related to the target water-related theme and determine a water conservancy event model.

[0101] Specifically, water conservancy events occurring under the target water-related theme, such as urban rainstorms, exposure, and water pollution, are acquired in real time. Event attributes, such as event ID, name, occurrence time, duration, and severity (e.g., rainstorm level, pollution level), are determined. Spatial attributes, such as the impact grid range (based on geographic or administrative grids) and the coordinates of key facilities (e.g., drainage outlets, monitoring stations), are also determined. Business attributes, such as associated objects (e.g., river channels, pumping stations), trigger conditions (e.g., rainfall > 50 mm / h), and response status (unprocessed / in progress / resolved), are also determined. Based on pre-set event trigger criteria (e.g., "rainfall ≥ 30 mm for two consecutive hours triggers a rainstorm warning"), an event association network is constructed (e.g., "rainstorm → drainage network overload → road flooding → traffic paralysis → public exposure"). Finally, corresponding response plans are retrieved from historical water conservancy event response plans to form a water conservancy event model, facilitating subsequent event identification, association analysis, spatial location, impact assessment, and response recommendations based on the water conservancy event.

[0102] In an optional embodiment of the present invention, step 105 includes:

[0103] Step 1051: extracting water-related knowledge data corresponding to the water conservancy object model to obtain first entity data;

[0104] Step 1052: extracting the water-related knowledge data corresponding to the water-related information grid model to obtain second entity data;

[0105] Step 1053: extracting knowledge from the water-related knowledge data corresponding to the water conservancy theme model to obtain third entity data;

[0106] Step 1054: extracting the water-related knowledge data corresponding to the water conservancy event model to obtain fourth entity data;

[0107] Step 1055: performing fusion processing on the first entity data, the second entity data, the third entity data, and the fourth entity data to obtain fused entity data;

[0108] Specifically, the water-related knowledge data corresponding to the water conservancy object model, the water-related information grid model, the water conservancy topic model, and the water conservancy event model can be extracted to obtain the first entity data, the second entity data, the third entity data, and the fourth entity data in the following manner:

[0109] Multiple entities are extracted from water-related knowledge data (e.g., water conservancy object entities include: rivers, reservoirs, pumping stations and other specific objects; attribute and indicator entities include: water level, flow, water quality and other attributes and their values; event and phenomenon entities include: rainstorm, waterlogging, dam breach and other events; time and space entities include: event occurrence time and geographical location), forming an entity set ,in, For example, from the text message "Water level of reservoir A exceeds warning level" in water-related knowledge data, entities such as "reservoir A" (reservoir category) and "water level" (indicator category) can be identified. Here, multiple entities can be identified from water-related knowledge data using regular expressions. Relationship extraction can be performed through open information extraction, such as extracting the relationship "reservoir A - flood discharge impact - river B" from "the flood discharge of reservoir A affects river B." Attribute extraction can be performed through keyword matching, template filling, and table parsing, such as extracting the attribute "total storage capacity = 39.3 billion cubic meters" from "the total storage capacity of reservoir A is 39.3 billion cubic meters." Entity data can include entities, relationships, and attributes identified from water-related knowledge data.

[0110] Based on pre-defined ontologies, such as entity types (e.g., reservoir, river, monitoring station), relationship types (e.g., "located in," "belongs to," "monitored"), and attribute types (e.g., numeric, text, date), conflicts between the first, second, third, and fourth entity data describing the same entity (e.g., "reservoir capacity: 39.3 billion cubic meters and 40 billion cubic meters") are resolved. This can be done based on official data sources. Relational expressions are then unified, for example, "water supply to" and "flow direction" are merged into "water supply relationship," resulting in fused entity data.

[0111] Step 1056: Form multiple subject knowledge graphs based on the fused entity data.

[0112] Specifically, triples (subject, attribute, value, or subject, relationship, object) are used to store and fuse entity data to obtain an initial knowledge graph, and then the graph consistency is verified based on logical rules. If the verification passes, the initial knowledge graph is used as the subject knowledge graph, and then the subject knowledge graph is divided according to different water-related topics. We can obtain multiple subject knowledge graphs such as water conservancy object knowledge graph, water conservancy project knowledge graph, water supply knowledge graph, drainage knowledge graph, waterlogging knowledge graph, rainfall knowledge graph, water-saving knowledge graph, event knowledge graph, etc. This not only obtains the knowledge graph corresponding to each water-related topic, but also helps to improve query efficiency. Here, taking the example of selecting a graph database to store fused entity data and obtaining an initial knowledge graph, we first add attributes to the nodes and relationships in the fused entity data. For example, if the node is a "river", its attribute is its specific length, and if the relationship is an "inflow", its attribute is the specific inflow volume. The fused entity data is processed into JSON (an open standard file format and data exchange format) format to reduce real-time parsing overhead. The fused entity data in JSON format is imported into the preset attribute graph model to obtain the initial knowledge graph, which can realize the search for entity-related data through the entity.

[0113] Based on water-related topics, various data items are stored in relevant databases, including water conservancy project databases, water supply databases, waterlogging databases, rainfall databases, water conservation databases, river channel databases, disaster avoidance databases, drainage data, water-related services, and spatial databases. The water conservancy project database stores information on surrounding reservoirs, sluice gates, pumping stations, weirs, ponds, irrigation districts, as well as water conservancy scenic areas and water conservancy project management boundaries. The water supply database stores data on water tank cleaning, water quality testing, water outage notifications, water supply restoration, secondary water supply, water use suspension reports, pipe bursts, and water supply failures. The waterlogging database stores data on waterlogging locations, depth, extent, and video footage. The rainfall database stores real-time and forecast rainfall data. The water conservation database stores data on water-saving devices, water conservation guidelines, and water conservation incentives from water administration departments. The river channel database stores data on surrounding river water quality testing, river health ratings, flood control period water levels, and river warnings. Disaster evacuation data primarily stores information about major disaster shelters and nearby rescue points during flood seasons and waterlogging. Drainage data primarily stores information about the distribution of surrounding drainage networks, manhole covers, and manhole covers under repair or requiring attention. The social service database stores information about water-related service agencies (payment points, repair points), surrounding water service authorities, and more.

[0114] Water-related knowledge data under different water-related themes are described in terms of object attributes according to the data catalog object class, including basic, spatial, business, and temporal attributes. To determine the relationship between objects, a one-to-one mapping relationship must be established with the knowledge graph corresponding to the water-related theme type. Grids are mainly divided according to the major categories in the water conservancy data catalog service specification. The topic model mainly organizes data based on different service items, such as water supply and drainage. The event model needs to form a multi-dimensional, dynamic, and integrated data view based on the mapping relationship between the knowledge engine and the grid according to specific events, such as urban rainstorms, exposure, water pollution, etc.

[0115] Based on the existing structured, unstructured and semi-structured databases, and based on the knowledge engine, knowledge is stored as graph data through knowledge modeling, extraction, fusion and storage. The graph computing engine is used to manage and drive water conservancy knowledge elements, and knowledge graphs corresponding to water-related topics are constructed, including water conservancy object knowledge graph, water conservancy project knowledge graph, water supply knowledge graph, drainage knowledge graph, urban flooding knowledge graph, rainfall knowledge graph, water-saving knowledge graph and event knowledge graph.

[0116] In an optional embodiment of the present invention, step 106 includes:

[0117] Step 1061, obtaining the geographic location of the target object;

[0118] Specifically, the geographic location of the target object may be obtained by receiving the real-time geographic location sent by the target object or the geographic location of the destination.

[0119] Step 1062: determining the target water-related information grid to which the geographic location belongs based on the geographic location of the target object;

[0120] Specifically, according to the geographical location of the target object, the spatial grid unit to which the geographical location belongs, ie the target water information grid, can be found from the water information grid model. It should be noted that the geographical location of the target object is within the range of the target water information grid.

[0121] Step 1063: Obtain a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs.

[0122] Specifically, there is a mapping relationship between the subject knowledge graph and each spatial grid unit in the water-related information grid model, that is, one spatial grid unit can map at least one subject knowledge graph. Therefore, at least one corresponding subject knowledge graph, that is, the target subject knowledge graph, can be determined through the target water-related information grid.

[0123] In an optional embodiment of the present invention, step 107 includes:

[0124] A variety of entities can be extracted from the target subject knowledge graph. Based on the entity and its corresponding attribute relationship, the water-related knowledge data corresponding to the entity and its corresponding attribute relationship can be found from the water-related knowledge data corresponding to the water conservancy object model. The water-related data can be pushed as the target and sent wirelessly to the client of the target object for the target object to view.

[0125] In this embodiment, the target pushed water-related data can be quickly determined through the target subject knowledge graph and the user location, thereby improving the efficiency of water-related data push.

[0126] A specific embodiment of the method for dynamically recommending water-related information based on LBS according to an embodiment of the present invention includes:

[0127] Step 111, obtaining at least one water-related data item;

[0128] According to the resource directory library shown in Table 1, the object class is parsed to obtain at least one water-related data item.

[0129] Step 112: determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item;

[0130] According to the attributes of the data items, the corresponding water conservancy object model is determined; according to the relationship between different data items, the subcategories of the data items are determined, and then the major categories to which the subcategories belong are determined, namely the water conservancy theme model.

[0131] Step 113: Divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model;

[0132] The minimum spatial grid unit is determined according to the preset grid unit division parameters corresponding to the water-related themes, and the water-related information grid models of different water-related themes are obtained by dividing the preset area.

[0133] Step 114: determining a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water-related information grid model;

[0134] According to the water-related information grid model, at least one target data item of the water conservancy object model corresponding to each water-related information grid can be determined; according to the water conservancy theme model, the target water-related theme corresponding to at least one target data item can be determined; water conservancy events occurring under the target water-related theme are obtained in real time, such as urban rainstorms, exposure, water pollution and other water conservancy events to form a water conservancy event model.

[0135] Step 115: forming a plurality of subject knowledge graphs based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy subject model, the water-related information grid model, and the water conservancy event model respectively;

[0136] By extracting entities from multiple models, fusion of entities and segmentation based on relevant topics, multiple thematic knowledge graphs can be obtained.

[0137] like Figure 4 As shown, starting from the data base, knowledge base and external data sources, a series of automatic or semi-automatic technical means can be used to extract water conservancy knowledge elements (i.e. data items) from the data sources and store them in the data layer and model layer of the knowledge graph library, including knowledge modeling, knowledge extraction, knowledge fusion, knowledge processing and knowledge storage.

[0138] Step 116: acquiring a target subject knowledge graph corresponding to the target water-related information grid to which the location of the target object belongs from the plurality of subject knowledge graphs;

[0139] The target water-related information grid to which the target object belongs is determined according to its location, and the corresponding target topic knowledge graph can be determined according to the entities and water-related topics involved in the target water-related information grid.

[0140] Step 117: obtaining at least one target push-related data according to the target subject knowledge graph;

[0141] Based on the entities and relationships in the target subject knowledge graph, water-related knowledge data corresponding to the entity and its corresponding attribute relationships is found in the water-related knowledge data and pushed as the target water-related data. Targeted water-related data pushed may include: water supply area, water supply company, service hotline, whether there is a planned water outage or low-pressure water supply; the nearest public drinking water point, public toilet, water fee payment point, repair point (distance, business hours, phone number), etc.

[0142] Step 118: Push the target push-related data to the client of the target object.

[0143] The target push wading data is sent to the target object's client in a wireless manner for the target object to view.

[0144] The LBS-based dynamic recommendation method for water-related information in the embodiment of the present invention can form an orderly knowledge network by analyzing water-related information on the basis of grid division, and provide users with accurate and effective water conservancy information according to the location of the target object.

[0145] like Figure 5As shown, an embodiment of the present invention provides a dynamic recommendation device 500 for water-related information based on LBS, including:

[0146] An acquisition module 501 is configured to acquire at least one water-related data item;

[0147] Processing module 502 is used to determine a water conservancy object model and a water conservancy theme model based on the at least one water-related data item; divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model; determine a water conservancy event model based on the water conservancy object model, the water conservancy theme model and the water-related information grid model; form multiple theme knowledge graphs based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model and the water conservancy event model respectively; obtain a target theme knowledge graph corresponding to the target water-related information grid to which the location of the target object belongs from the multiple theme knowledge graphs; obtain at least one target pushed water-related data based on the target theme knowledge graph; and push the target pushed water-related data to the client of the target object.

[0148] Optionally, at least one water-related data item is obtained, including:

[0149] Get the resource directory of preset water conservancy data;

[0150] The object class data of the resource directory library is parsed to obtain at least one water-related data item.

[0151] Optionally, determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item includes:

[0152] Determining a water conservancy object model according to the attributes of the at least one water-related data item;

[0153] Based on the relationship between different data items of the at least one water-related data item, a subcategory of the data item is determined, and based on the major category to which the subcategory belongs, a water conservancy theme model is determined.

[0154] Optionally, the preset area is divided according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, including:

[0155] Obtaining grid unit division parameters corresponding to each water-related theme in the water conservancy theme model;

[0156] Determine the minimum spatial grid unit of each water-related theme according to the grid unit division parameters;

[0157] The preset area is divided according to the minimum spatial grid unit of each water-related theme to obtain a water-related information grid model corresponding to each water-related theme.

[0158] Optionally, determining a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water-related information grid model includes:

[0159] Determining at least one target data item of the water conservancy object model corresponding to each water-related information grid according to the water-related information grid model;

[0160] Determining, based on the water conservancy theme model, a target water-related theme corresponding to at least one target data item;

[0161] Acquire water conservancy events related to the target water-related topic and determine a water conservancy event model.

[0162] Optionally, multiple subject knowledge graphs are formed based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model, and the water conservancy event model, including:

[0163] Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy object model to obtain first entity data;

[0164] performing knowledge extraction on the water-related knowledge data corresponding to the water-related information grid model to obtain second entity data;

[0165] Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy theme model to obtain third entity data;

[0166] performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy event model to obtain fourth entity data;

[0167] performing fusion processing on the first entity data, the second entity data, the third entity data, and the fourth entity data to obtain fused entity data;

[0168] Based on the fused entity data, multiple subject knowledge graphs are formed.

[0169] Optionally, according to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs includes:

[0170] Get the geographic location of the target object;

[0171] According to the geographical location of the target object, determining the target water-related information grid to which the geographical location belongs;

[0172] From the multiple subject knowledge graphs, obtain the target subject knowledge graph corresponding to the target water-related information grid.

[0173] The LBS-based dynamic recommendation device for water-related information of an embodiment of the present invention obtains at least one water-related data item to determine a water conservancy object model and a water conservancy theme model, then divides a preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, and then determines a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water conservancy information grid model. Then, based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water conservancy information grid model, and the water conservancy event model, multiple theme knowledge graphs are formed. Based on the target water-related information grid to which the location of the target object belongs, a target theme knowledge graph corresponding to the target water-related information grid is obtained from the multiple theme knowledge graphs. Based on the target theme knowledge graph, at least one target pushed water-related data is obtained, and the target pushed water-related data is pushed to the client of the target object. This device not only pushes water-related data to the target object based on the location of the target object, but also has high integrity and relevance, thereby improving the effectiveness and timeliness of water-related data push.

[0174] It should be noted that the device is a device corresponding to the above method, and all implementations in the above method embodiment are applicable to the embodiment of the device and can achieve the same technical effects, which will not be described in detail in this embodiment.

[0175] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program. When the computer program is executed by the processor, the computer program performs the method described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. These are not further described in this embodiment.

[0176] An embodiment of the present invention further provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. These are not further described in this embodiment.

[0177] It should be noted that, in the apparatus and method of the present invention, it is apparent that each component or step can be decomposed and / or recombined. Such decomposition and / or recombination should be considered equivalent solutions of the present invention. Furthermore, the steps of performing the above series of processes can naturally be performed in chronological order according to the order described, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel, interleaved, or independently of each other.

[0178] It should be noted that, in the above embodiments, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the implementation methods of the above embodiments is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0179] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A dynamic recommendation method for water-related information based on LBS, characterized in that: include: Obtain at least one water-related data item; Determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item; Divide the preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model; Determining a water conservancy event model according to the water conservancy object model, the water conservancy theme model, and the water-related information grid model; forming a plurality of subject knowledge graphs according to the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy subject model, the water-related information grid model, and the water conservancy event model; According to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs; Obtain at least one target push-related data according to the target subject knowledge graph; The target push-related data is pushed to the client of the target object.

2. The LBS-based dynamic recommendation method for water-related information according to claim 1, characterized in that: Obtain at least one water-related data item, including: Get the resource directory of preset water conservancy data; The object class data of the resource directory library is parsed to obtain at least one water-related data item.

3. The LBS-based dynamic recommendation method for water-related information according to claim 1, characterized in that: Determining a water conservancy object model and a water conservancy theme model based on the at least one water-related data item includes: Determining a water conservancy object model according to the attributes of the at least one water-related data item; Based on the relationship between different data items of the at least one water-related data item, a subcategory of the data item is determined, and based on the major category to which the subcategory belongs, a water conservancy theme model is determined.

4. The LBS-based dynamic recommendation method for water-related information according to claim 1, characterized in that: The preset area is divided according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model, including: Obtaining grid unit division parameters corresponding to each water-related theme in the water conservancy theme model; Determine the minimum spatial grid unit of each water-related theme according to the grid unit division parameters; The preset area is divided according to the minimum spatial grid unit of each water-related theme to obtain a water-related information grid model corresponding to each water-related theme.

5. The LBS-based dynamic recommendation method for water-related information according to claim 1, characterized in that: Determining a water conservancy event model according to the water conservancy object model, the water conservancy theme model, and the water-related information grid model includes: Determining at least one target data item of the water conservancy object model corresponding to each water-related information grid according to the water-related information grid model; Determining, based on the water conservancy theme model, a target water-related theme corresponding to at least one target data item; Acquire water conservancy events related to the target water-related topic and determine a water conservancy event model.

6. The dynamic recommendation method for water-related information based on LBS according to claim 1, characterized in that: Based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model, and the water conservancy event model, a plurality of theme knowledge graphs are formed, including: Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy object model to obtain first entity data; performing knowledge extraction on the water-related knowledge data corresponding to the water-related information grid model to obtain second entity data; Performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy theme model to obtain third entity data; performing knowledge extraction on the water-related knowledge data corresponding to the water conservancy event model to obtain fourth entity data; performing fusion processing on the first entity data, the second entity data, the third entity data, and the fourth entity data to obtain fused entity data; Based on the fused entity data, multiple subject knowledge graphs are formed.

7. The LBS-based dynamic recommendation method for water-related information according to claim 1, characterized in that: According to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs includes: Get the geographic location of the target object; According to the geographical location of the target object, determining the target water-related information grid to which the geographical location belongs; From the multiple subject knowledge graphs, obtain the target subject knowledge graph corresponding to the target water-related information grid.

8. A dynamic recommendation device for water-related information based on LBS, characterized in that: include: An acquisition module, configured to acquire at least one water-related data item; a processing module configured to determine a water conservancy object model and a water conservancy theme model based on the at least one water-related data item; divide a preset area according to the minimum spatial grid unit of each water-related theme in the water conservancy theme model to obtain a water-related information grid model; determine a water conservancy event model based on the water conservancy object model, the water conservancy theme model, and the water-related information grid model; and form multiple theme knowledge graphs based on the water-related knowledge data in the water-related knowledge database corresponding to the water conservancy object model, the water conservancy theme model, the water-related information grid model, and the water conservancy event model, respectively; According to the target water-related information grid to which the location of the target object belongs, obtaining a target subject knowledge graph corresponding to the target water-related information grid from the multiple subject knowledge graphs; Obtain at least one target push-related data according to the target subject knowledge graph; The target push-related data is pushed to the client of the target object.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

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