CN113032504A - Method and device for gathering public service space-time data of village and town community - Google Patents
Method and device for gathering public service space-time data of village and town community Download PDFInfo
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Abstract
The invention provides a method and a device for converging public service space-time data of a village and town community, wherein the method comprises the following steps: carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data; respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community; determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features; and sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode. The method establishes the association construction among the data, can perform expanded query according to retrieval requirements, helps to search potential data, has scientific basis for calculation and analysis compared with an experience-dependent manual method for searching a data set, and improves the utilization rate of mass village and town community public service data.
Description
Technical Field
The invention relates to the technical field of data analysis and mining, in particular to a method and a device for converging public service space-time data of a town community.
Background
With the construction of digital villages and the continuous development of data acquisition means such as various sensors, earth observation and the like, the rapid and efficient acquisition of village and town community public service data with rich content elements and various types becomes possible. The village and town community public service data collection has various uncertain, semi-open and open contents, the data sources are various and comprise the existing system data, statistical form data, village and town planning data, text graphs, paper files and the like, the data formats comprise structured data, semi-structured data, unstructured data and the like, and the contents cover various aspects of village and town planning construction, infrastructure, natural environment, social economy, production technology, commercial and trade logistics, health medical care, education social security, service management, old-age care and disaster prevention and the like. With the increasing of various intelligent services and applications, the types and scales of data are further increased, such as agricultural production and management data, village and town resident social activity data, application data such as mobile phone APP applet and the like, video monitoring data, environment and weather data and the like.
The village and town community public service data has wide sources and various types, and has obvious difference in data structure and semantics. The public service data of the villages and small towns are typical space-time data, and have the characteristics of multi-source isomerism, large size and strong timeliness.
Existing spatiotemporal data management typically uses a spatial database engine with spatiotemporal indices to provide an interface for spatial computation and querying to a relational database. Although the spatial data management engine can uniformly store time and space data (such as point data, road data and the like) and business data (such as special data of land utilization, disaster prevention and risk avoidance, life service and the like) and support a certain degree of spatial information correlation query, the standards of the management system are different due to lack of uniform semantic description, and the data lack of connection. Such as raster data, vector data, text, multimedia, etc., and the increase of the amount of data makes it difficult for the traditional database design based on SQL storage and query retrieval to meet the business application requirements of multi-source heterogeneous data. The other method is to utilize methods such as object-oriented methods to map and convert data expressing and describing different data to standardized names, provide data exchange and interoperation methods, and provide basic data association query and analysis by recording interoperation and relation expression among data. However, in the data conversion process, because the data mapping relation expression is simple, the semantic description is limited, and the hierarchical description is lacked, the retrieval and the query of the data still depend on manual experience.
The conventional simple entry type index management method is generally adopted in the conventional village and town community data management, data information stored in entries is mainly dominant, and the incidence relation between heterogeneous data and deep features of the data are rarely considered and reflected. The data retrieval mainly depends on manual experience, metadata keywords and other modes, and the requirement for rapidly and accurately acquiring massive public service space-time data of village and town communities cannot be met. Therefore, research and realization of a method for associating and converging space-time data of village and town communities are urgently needed, effective query and retrieval of the space-time data of the village and town communities under a large volume are guaranteed, a most appropriate data set is timely provided for village and town community managers, village and town residents and the like, and data support is better provided for fine management, intelligent service and the like of the village and town.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for converging space-time data of public service of a town community.
The invention provides a method for converging space-time data of public service of villages and towns communities, which comprises the following steps: carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data; respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community; determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features; and sequentially narrowing the data search range step by step according to the type characteristic matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
According to the method for aggregating the public service space-time data of the village and town communities, the classification of the types of the public service data of the village and town communities according to the type elements comprises the following steps: dividing public service space-time data of village and town communities according to the overall type, wherein the divided type comprises basic geographic data, remote sensing image data, social and economic data, community administration data and social service data; dividing the public service space-time data of the village and town communities according to the functional requirements, wherein the division types comprise planning construction, resource management, disaster prevention and reduction, community management and public service; dividing public service space-time data of the village and town communities respectively according to the administrative district scale, the space scale, the acquisition time and the data format; the type of each data is determined by the form of the combination tag.
According to the method for converging the public service space-time data of the village and town communities, disclosed by the embodiment of the invention, the space semantic extraction is carried out on the public service space-time data of the village and town communities, and the method comprises the following steps: and extracting attribute information related to the position, the spatial range and the place name from metadata of the public service data of the village and town community, and determining the spatial position type, the spatial resolution and the auxiliary spatial scale characteristic.
According to the method for aggregating the public service space-time data of the village and town communities, the time correlation degree between the data is determined, and the method comprises the following steps: determining the association degree according to the relation between the starting time and the ending time of the two data objects; the degrees of association include preceding, succeeding, meeting, overlapping, beginning, including, contained, ending, and the like.
According to the method for gathering the public service space-time data of the village and town communities, the method for determining the space association degree among the data comprises the following steps: determining a spatial association degree according to a Region connection algorithm (RCC) spatial representation model; the spatial association includes overlapping, covered, contained, equal, containing, covering, meeting, and separating.
According to the method for aggregating the public service space-time data of the village and town communities, the method for determining the space-time association degree between the data comprises the following steps:
wherein, wtAnd wgRespectively, the weighted parameters of the time proximity and the space overlapping degree, and the value model is [0, 1%]；
Is the temporal proximity between two objects,
is the spatial overlap of the two objects; a isy、ad、ah、amIs a time correlation attenuation factor, and the range is 0-1; | Yj-Yi|、|Dj-Di|、|Hj-HiI and I Mj-MiL represents the distance in months, days, hours and minutes, respectively, at four time scales; beta is the attenuation factor of the spatial correlation of the object, ranging from 0, 1]；intersect(areai，areaj) And union (area)i，areaj) The intersection space range and the union space range of the two objects are respectively; gtijIs the spatiotemporal correlation between data i and j.
According to the method for gathering the public service space-time data of the village and town communities, the semantic similarity between the data is determined, and the method comprises the following steps: determining all key word attribute values of the two data object class attributes; and determining semantic similarity between the two data according to the same quantity and different quantities of all the keyword attribute values.
The invention also provides a device for converging the public service space-time data of the village and town community, which comprises: the data ontology construction module is used for carrying out type division on the public service data of the village and town communities according to the type elements and determining the characteristic elements of the data; the semantic feature extraction module is used for respectively performing type semantic extraction, time semantic extraction and space semantic extraction on feature elements of the public service space-time data of the village and town communities; the relevance measurement module is used for determining time relevance, space-time relevance and semantic similarity among data according to the extracted semantic features; and the spatio-temporal data retrieval module is used for reducing the data search range step by step and screening the data set in a quantitative sorting mode according to the type feature matching, the time correlation degree, the space correlation degree, the spatio-temporal correlation degree and the semantic similarity in sequence.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method for gathering the public service space-time data of the village and town communities.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for aggregating public service spatio-temporal data of village and town communities as described in any one of the above.
According to the method and the device for converging the public service space-time data of the village and town communities, the data attribute items meeting the retrieval requirements are respectively defined from the type semantics and the space-time semantics, the association construction between the data is established, the extended query can be carried out according to the retrieval requirements, the potential data is searched for, compared with the method of manually searching for a proper data set by depending on experience, the method and the device have scientific basis of calculation and analysis, the utilization rate of the public service data of the village and town communities is improved, and accurate and reliable data support is provided for the refined management and intelligent service of the village and town communities.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the following briefly introduces the drawings needed to be used in the description of the embodiments or the prior art, and obviously, the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;
FIG. 1 is a schematic flow chart of a method for aggregating public service spatiotemporal data of village and town communities, provided by the present invention;
FIG. 2 is a second schematic flowchart of a method for collecting public service spatio-temporal data of villages and small towns provided by the present invention;
FIG. 3 is a schematic structural diagram of a village and town community public service spatio-temporal data aggregation device provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention; all other embodiments, which can be obtained by a person skilled in the art without any inventive work based on the embodiments of the present invention, are within the scope of the present invention;
aiming at the accurate and rapid retrieval requirement of the public service space-time data of the village and town communities, a unified data description model is established according to the characteristics of massive heterogeneous public service space-time data of the village and town communities, the type characteristics and the space-time characteristics of the public service space-time data of the village and town communities are subjected to semantic description, multi-level semantic mapping is established on an ontology description model, the association relationship of the data is established from a time layer, a space layer and a semantic layer, the semantic association among the public service data of the village and town communities is realized, the public service information of the village and town communities is conveniently retrieved by combining space-time information and semantic information, and timely and accurate data support is provided for improving the public service.
The method and the device for gathering the public service space-time data of the village and town community are described in the following with reference to fig. 1-4; fig. 1 is a schematic flow diagram of a method for aggregating public service spatio-temporal data of a village and town community, as shown in fig. 1, the method for aggregating public service spatio-temporal data of a village and town community, provided by the present invention, includes:
101. and performing type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data.
The public service data of villages and small towns mainly comprises five categories of basic geographic data, remote sensing image data, social and economic data, community administration data, social service data and the like. The base geographic data includes vector data and raster data. The vector data includes attribute information and spatial distribution state of objects such as administrative divisions, roads, rivers, public facilities, and the like, and vector graphics such as village and town planning drawings, current land use situations, and right to confirm land. The grid data comprises a landscape feature survey map, a village and town important public facility survey map and the like, and also comprises a video monitoring image shot by a camera and the like. The remote sensing image data comprises image data such as satellite remote sensing, aerial remote sensing, oblique photogrammetry and unmanned aerial vehicles, and mainly provides earth surface optical image information for large-scale region identification and interpretation and base map background display. The social and economic data mainly refers to statistical data of village and town communities, including statistical report data of a series of social, economic and environmental aspects such as cultivated land area, industrial development, leading enterprises, novel business bodies and the like, and each statistical data is mainly based on a statistical report of villages and towns, a work report of government and the like. The community management data comprises population distribution, community construction, civil affair social security, comprehensive management and other types of data; the social service data comprises production technology, education culture, health medical treatment, commodity and trade logistics, endowment service, disaster prevention and risk avoidance and other types of data.
The village and town community public service data has the characteristic of heterogeneity in structure and semantics, so that a uniform data expression form needs to be established for quick access and effective management of multi-source heterogeneous data, and the data can be conveniently and quickly found and retrieved; the method comprises the steps of firstly, providing uniform metadata description based on an ontology for public service data of villages and towns communities, and describing necessary information of a data set which is subjected to data matching and filtering by using a uniform expression in a retrieval process; the village and town community public service data ontology is expressed by the following three elements in a form:
Od＝<C，F|R>
the village and town community public service data consists of three elements of a type C, a characteristic F and a relation R.
The characteristic elements are as follows: the explicit characteristics of the public service data of the village and town communities are described in terms of time, space and attributes. The dominant features include data name, data source, data type, data format, keywords, spatial coverage, acquisition date, responsible unit, responsible person, etc., for example, the remote sensing image data further includes sensor type, waveband information, temporal resolution, spatial resolution, etc., the video data further includes video object, event, etc., and these features are derived from metadata.
Relationship elements: the method mainly establishes the relation among the public service data of the village and town communities from the aspects of time, space and semantics, and is used for describing the association among the heterogeneous community public service data of different types. The heterogeneous degree of the data is judged by counting the label similarities and differences of the data on the types and the characteristics, and the data with lower heterogeneous degree is automatically clustered, so that the retrieval and the sequencing of the data with the same type are facilitated. And acquiring the association degree between the data sets with higher isomerism degrees by utilizing the time association, the space association, the spatio-temporal feature association and the semantic similarity. See step 103 for details.
102. And respectively carrying out type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town communities.
The type semantic features are used for representing hard constraints of query requirements on public service data of the villages and small towns, the conditions for satisfying the query requirements and the type features are judged through Boolean operation and logic operation, namely 'satisfied' and 'not satisfied', and whether the current access data resources meet the query requirements or not is judged among the multiple type features through logic 'AND' operation. The semantic constraint can provide rapid resource screening for the fuzzy query of the data, and avoid blind search of the data resources under the condition of unclear retrieval requirements.
Temporal semantic extraction includes temporal granularity and time span. The time granularity describes the discretization degree of time and the accuracy degree of a recording time system, and also reflects the updating frequency and timeliness of data. According to the traditional measurement division of a time system, the time granularity of the public service data of the village and town communities is divided into a plurality of scales of year, month, day, hour, minute, second and the like. Time span description village and town public service data is reflected on a time line and continuously spans a length, such as land utilization in the last 10 years, NDVI data products.
The spatial semantic extraction comprises the steps of extracting attribute information related to positions, spatial ranges and place names from metadata of the public service data of the village and town communities, and performing classification conversion.
103. And determining the time correlation degree, the space-time correlation degree and the semantic similarity among the data according to the extracted semantic features.
Aiming at the characteristics of the public service space-time data of the village and town communities, relevance measurement among the data is carried out from four aspects of time, space-time characteristics and semantics, and the relevance among the data is utilized to calculate the characteristic difference between a potential data set and a target data set so as to obtain an ideal data set. Representing village and town community public service space-time data as set Y, YiAnd YjRepresenting two spatio-temporal data elementary unit objects to be associated within a set, where i<j and are positive integers; the basic unit object of the public service space-time data of the village and town community refers to the data itself, is the minimum unit of the data, and for example, one or more pictures, remote sensing images and the like are used as the basic unit object, and the vector data takes the space object points, lines and planes contained in the vector data as the basic unit object.
The public service space-time data association state matrix M of the village and town community represents the association state information among all data, as shown in the following formula:
wherein m isijRepresents YiAnd YjCorrelation state between mijIs a four-dimensional associative state vector, tij、gij、gtij、sijRespectively represent objects YiAnd YjThe time correlation degree, the space-time correlation degree and the semantic correlation degree between the two.
104. And sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
Fig. 2 is a second flow chart of the public service spatio-temporal data aggregation method for village and town communities, which is provided by the invention, and on the basis of the steps, the data retrieval of multi-level semantic matching is performed, namely the data retrieval of type feature matching is performed firstly, the spatio-temporal associated data filtering is performed secondly, then the similarity judgment of semantic levels is performed, the data search norm is reduced step by step, the alternative data set is optimized, and finally the data set is screened in a quantitative sorting mode.
And (3) matching type features, wherein the type matching is carried out in a field-by-field query matching mode, and according to the division rule of the type elements in the data body, the type elements are sequentially queried from the type parent elements to the type child elements. The initial query is a metadata list facing global data, all data sets meeting conditions are searched out, and a search result is returned to form a new metadata set. And repeating the above operations, continuing to inquire based on the next type predicate, filtering metadata in the existing metadata set, deleting the metadata records which do not contain the current field or the field value and do not match with the requirement, and reducing the set quantity. And circulating the query operation until all the type predicates of all the retrieval requirements are used as query conditions and completely retrieved, wherein the obtained metadata set is a data retrieval result matched with the type.
The retrieval process of the space-time coverage can be carried out by using only the space-time relevance degree because the space-time relevance degree already contains the information of time and space. The data retrieval of the space-time coverage is that firstly, the overlapping proximity degree between the time and space range related to the retrieval task and the coverage range and the duration span of the initial search set is calculated, and then whether the data set meets the requirement or not is judged according to the space-time characteristics extracted facing to the current retrieval task. And (2) performing space-time distance calculation on the current retrieval task and a data set meeting the type condition by introducing a space-time association degree (represented as space-time characteristic association degree in fig. 2) calculation formula, and automatically selecting a potential alternative data set by a threshold setting method.
The data retrieval step of the spatiotemporal correlation is that firstly, spatiotemporal semantic information of a retrieval task is extracted, and a new data table is established for storing metadata records meeting the spatiotemporal correlation threshold. And sequentially traversing the records in the table, extracting the time information and the space range of the records, calculating by using a time-space association measurement formula, comparing the result with an association threshold, storing the metadata records which are more than or equal to the threshold into a data table, and acquiring a data retrieval result matched with the time-space association after the traversal is finished.
And (3) data retrieval of semantic similarity, wherein the data retrieval of the semantic similarity is to convert a query requirement into semantic description, query spatiotemporal data with the semantic similarity meeting conditions with the semantic description content, and the semantic description content is usually represented by a plurality of keywords. The semantic index adopts an inverted index, and the semantic inverted structure comprises a word dictionary and an inverted file. The word dictionary takes keywords of normalized semantic expression of public service spatio-temporal data of village and town communities as words of the dictionary, each keyword points to an inverted arrangement table, each inverted index item in the inverted arrangement table comprises an object of the spatio-temporal data and word frequency of the keyword in attribute description of a corresponding subclass of the object, so that the object can be quickly positioned and inquired, semantic similarity can be calculated, meanwhile, each inverted index item is provided with a pointer pointing to the next inverted index item, the last inverted index item is provided with a pointer pointing to the semantic similarity, the semantic similarity between the objects is stored by using a B + tree structure, and is calculated by adopting a semantic similarity calculation formula.
Through the steps, the optimal data set meeting the retrieval requirements is screened from the global database step by step, the data range is narrowed layer by layer, and finally the satisfaction degree of the data to the query task is expressed and sequenced in a percentage mode by utilizing a space-time and semantic quantization method, so that the optimal data is found quickly and accurately.
According to the method for converging the public service space-time data of the village and town communities, the data attribute items meeting the retrieval requirements are respectively defined from the type semantics and the space-time semantics, the association construction between the data is established, the extended query can be carried out according to the retrieval requirements, the potential data is helped to be searched, compared with the method of manually searching a proper data set by depending on experience, the method has the scientific basis of calculation analysis, the utilization rate of mass public service data of the village and town communities is improved, and accurate and reliable data support is provided for the refined management and intelligent service of the village and town communities.
In one embodiment, the classifying the types of the village and town community public service data according to the type elements includes: dividing public service space-time data of village and town communities according to the overall type, wherein the divided type comprises basic geographic data, remote sensing image data, social and economic data, community administration data and social service data; dividing the public service space-time data of the village and town communities according to the functional requirements, wherein the division types comprise planning construction, resource management, disaster prevention and reduction, community management and public service; dividing public service space-time data of the village and town communities respectively according to the administrative district scale, the space scale, the acquisition time and the data format; the type of each data is determined by the form of the combination tag.
Type elements: data is primarily typed from a qualitative perspective, with each data set having multiple type labels. Dividing data into a village scale, a ballast scale, a county scale, a city scale and a provincial scale from an administrative division scale; dividing the data into 1:500, 1:1000, 1:2000, 1:5000 and the like from a spatial scale; dividing data into real-time data, recent data and historical data from the angle of acquisition time; data is divided into vectors, grids, text, tables, video, etc. from a data format perspective. The type element provides rapid screening of coarse target data clusters for data retrieval in the form of a composite tag.
In one embodiment, the spatial semantic extraction is performed on the village and town community public service spatio-temporal data, and comprises the following steps: and extracting attribute information related to the position, the spatial range and the place name from metadata of the public service data of the village and town community, and determining the spatial position type, the spatial resolution and the auxiliary spatial scale characteristic.
The spatial position type describes spatial coverage of public service data of villages and towns, and the spatial position type comprises three forms of a point form, a plane form and a curved surface form. The dot coverage is expressed by a two-dimensional coordinate, such as data of village health rooms, agricultural stores, large-scale farm distribution and the like. Planar coverage, i.e., a two-dimensional plane, such as a land use status/planning map, an infrastructure survey map, etc., is expressed by a set of two-dimensional spatial point strings. Curved surfaces cover, for example, terrain, a continuous three-dimensional spatial range representation of surface information, such as a digital elevation model, and the like.
The spatial resolution describes the size of a minimum unit which can be distinguished in detail in the range of plane and curved surface data, and the unit is divided into three dimensions of kilometers, meters and decimeters. The spatial resolution is used for representing indexes of the remote sensing image for distinguishing ground target details, and the size of the indexes determines the richness and the identifiability of information in a unit space.
And the auxiliary space scale characteristic is used for performing characteristic expansion on the public service data of the village and town communities from the perspective of administrative divisions, and increasing the condition basis of data space retrieval. For example, an administrative division reference is established for a village and a town of a certain region, five-level nodes of a city, a county, a village and a group are provided, the nodes are mapped into corresponding administrative divisions according to the scale of data, semantic features such as the administrative divisions except a simple space are added to the data, the data can be quickly analyzed and positioned to an accurate spatial range, and auxiliary conditions are provided for high-proximity data screening. For example, when data is retrieved for a town, data for all villages and all groups in the town may also be retrieved.
In one embodiment, determining a degree of temporal correlation between data comprises: determining the association degree according to the relation between the starting time and the ending time of the two data objects; the degrees of association include preceding, succeeding, meeting, overlapping, beginning, including, contained, ending, and the like.
Objects contained in the public service space-time data of the village and town community have different one-dimensional time interval relations, and according to a JPED time model, 13 time relations are defined according to the initial time relation of the two objects: before (P), after (P), meet (M), overlap (O), start (S), contain (C), end (F), equal (e). Establishing time association t among the spatial data of the public service of the village and town communities by calculating and judging the time range relation among different object time points, starting time and ending timeij，tijIs one of 13 time-correlated states.
In one embodiment, determining a spatial correlation between data comprises: determining the spatial correlation degree according to the RCC spatial representation model; the spatial association includes overlapping, covered, contained, equal, containing, covering, meeting, and separating.
The objects contained in the village and town community public service space-time data have different spatial position relations, namely, topology invariants under topology transformation (such as translation, rotation and scaling). According to the RCC spatial representation model, the spatial relationships can be divided into 8 types, i.e. overlap (PO), covered (TPP), contained (NTPP), Equal (EQ), contained (NTPP)-1) Overlay (TPP)-1) Encounter (EC), phase separation (DC). The spatial association g among the time-space data of the public service of villages and towns is established by judging the inclusion and intersection relations of the positions and coverage areas of different objectsij，gijIs one of 8 spatial correlation states.
The method for determining the time correlation degree and the space correlation degree can be used for carrying out data search together with the time-space correlation degree, so that the search accuracy is improved, and the accurate and quick retrieval requirement is met.
In one embodiment, determining a spatiotemporal degree of correlation between data comprises:
wherein, wtAnd wgRespectively, the weighted parameters of the time proximity and the space overlapping degree, and the value model is [0, 1%]；
Is the temporal proximity between two objects,
is the spatial overlap of the two objects; a isy、ad、ah、amIs a time correlation attenuation factor, and the range is 0-1; | Yj-Yi|、|Dj-Di|、|Hj-HiAnd | Mj-MiL represents the distance in months, days, hours and minutes, respectively, at four time scales; beta is the attenuation factor of the spatial correlation of the object, ranging from 0, 1]；intersect(areai，areaj) And union (area)i，areaj) The intersection space range and the union space range of the two objects are respectively; gtijIs the spatiotemporal correlation between data i and j.
The spatio-temporal relevance is an index for quantifying the 'distance' between data and data in time and space, and the spatio-temporal relation between data is expressed by using the time proximity and the space overlapping degree. Quantification of the spatiotemporal distance between two objects gt by a spatiotemporal correlation metric calculation formulaij. Interpect (area) when two objects are aparti，areaj) Is equal to 0; interselect (area)i，areaj) And union (area)i，areaj) When values of (1) are close, SgijIs close to 1, when the two objects have a high degree of spatial overlap.
The time-space correlation degree calculation of the invention can further improve the expression of the time-space correlation and improve the accuracy of the search result.
In one embodiment, determining semantic similarity between data comprises: determining all key word attribute values of the two data object class attributes; and determining semantic similarity between the two data according to the same quantity and different quantities of all the keyword attribute values.
The public service space-time data of the village and town community has rich semantic information, and the semantic information amount of different types of data is different. Point data such as government agencies, bus stops and the like have the characteristic of single semantic unobvious theme, and the semantic information of the data is expressed as address description or object names, so that the semantic information amount is small. The vector data and the remote sensing data have the characteristic of definite and various semantics of the theme, for example, a road layer contains a plurality of line objects, and each line contains basic information such as road names. The spatiotemporal data such as pictures and videos have the characteristics of various themes and complex semantics, and one picture may contain description of a certain event, including objects, behaviors, environments and the like, and the semantic information content of the picture is rich and complex. The village and town community public service spatio-temporal data basic unit objects have not only correlation in time and space, but also complex semantic correlation, different types of spatio-temporal data possibly express similar contents in semantics, and semantic similarity can reflect the correlation degree of different objects in semantics.
The semantic description of the public service space-time data content of the village and town community is subjected to normalized description based on the keywords, so that the semantic relevance of the data is measured by calculating the similarity of the attribute information semantic keywords and mainly measured based on the number and importance of the keywords. Definition of YiAnd YjBasic unit object, P (Y), representing public service spatio-temporal data of two village and town communities to be associatedi) And P (Y)i) Is represented by YiAnd YjAll object class attributes. PYi，kRepresents YiAnd the attribute value is formed by k keywords, k is 1, 2, 3 and …, and n is an integer. For example, for medical facility distribution data of a certain town, the village and health room distribution point element data comprises a plurality of keywords such as the belonged town, the belonged village, address information, service capability, medical facility condition and the like. According to the semantic similarity calculation model based on the ontology, the similarity calculation of each semantic is as follows:
wherein, the value range of the lambda is
Represent the same keyword;
representing different keywords, sijIs YiAnd semantic similarity between Y.
The device for converging the public service space-time data of the village and town communities provided by the invention is described below, and the device for converging the public service space-time data of the village and town communities described below and the method for converging the public service space-time data of the village and town communities described above can be referred to in a corresponding way;
fig. 3 is a schematic structural diagram of a village and town community public service spatio-temporal data aggregation device according to an embodiment of the present invention, and as shown in fig. 3, the village and town community public service spatio-temporal data aggregation device includes: the system comprises a data ontology construction module 301, a semantic feature extraction module 302, a relevance measurement module 303 and a spatiotemporal data retrieval module 304; the data ontology construction module 301 is configured to perform type division on the public service data of the village and town communities according to type elements, and determine characteristic elements of the data; the semantic feature extraction module 302 is used for respectively performing type semantic extraction, time semantic extraction and space semantic extraction on feature elements of the public service space-time data of the village and town communities; the relevance measurement module 303 is configured to determine a time relevance degree, a space-time relevance degree and a semantic similarity degree between data according to the extracted semantic features; the spatio-temporal data retrieval module 304 is configured to narrow a data search range step by step according to the type feature matching, the time association degree, the space association degree, the spatio-temporal association degree, and the semantic similarity in sequence, and screen a data set in a quantitative sorting manner.
The device embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the content, reference is made to the above method embodiments, which are not described herein again;
the village and town community public service space-time data gathering device provided by the embodiment of the invention defines the data attribute items meeting the retrieval requirements from the type semantics and the space-time semantics respectively, establishes the association construction between the data, can perform extended query according to the retrieval requirements, helps to search potential data, has more scientific basis for calculation and analysis compared with the method of manually searching a proper data set by depending on experience, improves the utilization rate of massive village and town community public service data, and provides accurate and reliable data support for refined management and intelligent service of village and town communities.
Fig. 4 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)401, a communication Interface (Communications Interface)402, a memory (memory)403 and a communication bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the communication bus 404; the processor 401 may invoke logic instructions in the memory 403 to perform a method of village to town community public service spatio-temporal data aggregation, the method comprising: carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data; respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community; determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features; and sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products; based on such understanding, the technical solution of the present invention or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention; and the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the method for aggregating public service spatiotemporal data of village and town communities provided by the above methods, the method including: carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data; respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community; determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features; and sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the method for aggregating public service spatiotemporal data of village and town communities provided in the foregoing embodiments, the method including: carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data; respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community; determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features; and sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units; part or all of the modules can be selected according to actual needs to realize the purpose of the scheme of the embodiment; one of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware; with this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for gathering public service space-time data of village and town communities is characterized by comprising the following steps:
carrying out type division on the public service data of the village and town communities according to the type elements, and determining the characteristic elements of the data;
respectively performing type semantic extraction, time semantic extraction and space semantic extraction on the characteristic elements of the public service space-time data of the village and town community;
determining time correlation degree, space-time correlation degree and semantic similarity among data according to the extracted semantic features;
and sequentially narrowing the data search range step by step according to the type feature matching, the time correlation degree, the space-time correlation degree and the semantic similarity, and screening the data set in a quantitative sorting mode.
2. The method for aggregating public service spatiotemporal data of village-town communities according to claim 1, wherein the classifying the village-town community public service data according to type elements comprises:
dividing public service space-time data of village and town communities according to the overall type, wherein the divided type comprises basic geographic data, remote sensing image data, social and economic data, community administration data and social service data;
dividing the public service space-time data of the village and town communities according to the functional requirements, wherein the division types comprise planning construction, resource management, disaster prevention and reduction, community management and public service;
dividing public service space-time data of the village and town communities respectively according to the administrative district scale, the space scale, the acquisition time and the data format;
the type of each data is determined by the form of the combination tag.
3. The method for gathering public service spatio-temporal data of village and town communities according to claim 1, wherein the spatial semantic extraction of the public service spatio-temporal data of the village and town communities comprises:
and extracting attribute information related to the position, the spatial range and the place name from metadata of the public service data of the village and town community, and determining the spatial position type, the spatial resolution and the auxiliary spatial scale characteristic.
4. The method for aggregating public service spatio-temporal data of village and town communities according to claim 1, wherein determining temporal relevance between data comprises:
determining the association degree according to the relation between the starting time and the ending time of the two data objects;
the degrees of association include preceding, succeeding, meeting, overlapping, beginning, including, contained, ending, and the like.
5. The method for aggregating public service spatio-temporal data of village and town communities according to claim 1, wherein determining spatial correlation between data comprises:
calculating an RCC space representation model according to the region connection, and determining the space correlation degree;
the spatial association includes overlapping, covered, contained, equal, containing, covering, meeting, and separating.
6. The method for aggregating public service spatio-temporal data of village and town communities according to claim 1, wherein determining spatio-temporal relevance between data comprises:
whereinijIs the degree of spatiotemporal correlation between data i and j, wtAnd wgRespectively, the weighted parameters of the time proximity and the space overlapping degree, and the value model is [0, 1%]；
Is the temporal proximity between two objects,
is the spatial overlap of the two objects; a isy、ad、ah、amIs a time correlation attenuation factor, and the range is 0-1; | Yj-Yi|、|Dj-Di|、|Hj-HiI and I Mj-MiL represents the distance in months, days, hours and minutes, respectively, at four time scales; beta is the attenuation factor of the spatial correlation of the object, ranging from 0, 1]；intersect(areai，areaj) And union (area)i，areaj) Respectively, the intersection spatial range and the union spatial range of the two objects.
7. The method for aggregating public service spatiotemporal data of village-town communities according to claim 1, wherein determining semantic similarity between data comprises:
determining all key word attribute values of the two data object class attributes;
and determining semantic similarity between the two data according to the same quantity and different quantities of all the keyword attribute values.
8. A device for converging space-time data of public services of village and town communities is characterized by comprising:
the data ontology construction module is used for carrying out type division on the public service data of the village and town communities according to the type elements and determining the characteristic elements of the data;
the semantic feature extraction module is used for respectively performing type semantic extraction, time semantic extraction and space semantic extraction on feature elements of the public service space-time data of the village and town communities;
the relevance measurement module is used for determining time relevance, space-time relevance and semantic similarity among data according to the extracted semantic features;
and the spatio-temporal data retrieval module is used for reducing the data search range step by step and screening the data set in a quantitative sorting mode according to the type feature matching, the time correlation degree, the space correlation degree, the spatio-temporal correlation degree and the semantic similarity in sequence.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for aggregating public service spatiotemporal data of village and town communities as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method for village and town community public service spatio-temporal data aggregation according to any one of claims 1 to 7.
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