A method, device and equipment for constructing a semantically rich event spatiotemporal data model

Abstract

The present application relates to a method, device and equipment for constructing a semantically rich event spatiotemporal data model. The method includes: hierarchically dividing events according to the semantically rich event features of events and the complexity of event composition, constructing an event hierarchy composed of events and entities, and obtaining the semantically rich event features of each element in the event hierarchy and the semantic logical relationship between each element; from an object-oriented perspective, the semantically rich event features of each element are abstracted into different classes, and the directed graph theory is used to take different classes as nodes and the semantic logical relationship between different classes as edges to construct a semantically rich event spatiotemporal data model for event modeling expression. This method can be used to aggregate the hierarchical structure of event development based on the semantically rich event features and association relationships of events in different fields, and to construct a semantically rich event spatiotemporal data model for event modeling expression, thereby reshaping the spatiotemporal evolution of events.

Description

Technical Field

[0001] The present application relates to the technical field of spatiotemporal data models, and in particular to a method, device and equipment for constructing a semantically rich event spatiotemporal data model. Background Art

[0002] Time and space are one of the essential characteristics of any event. A complex event is a comprehensive process of multiple events, with multi-scale time and space characteristics and multi-level semantic characteristics. Its development is affected by many factors, and it has multi-dimensional and changeable characteristics, full of uncertainty. In order to realize the dynamic cognition of the historical change process of complex events and the knowledge mining of the internal logic of time and space evolution, as well as the simulation evaluation and reasoning prediction of actions that have not yet occurred but are planned to be carried out and have uncertainty, referring to the current research on events in the field of geographic information science, completing the modeling of events with a certain time and space data model, and then analyzing the mutual influence and action mechanism between events is one of the key ways to understand events. Therefore, how to mine the essential characteristics and related influences of complex events based on the time and space characteristics of events, and using appropriate time and space data models to complete the data modeling of complex events is particularly critical.

[0003] Since the birth of spatiotemporal data models in the 1960s, many methods and theories for modeling spatiotemporal expression domains have emerged, such as the spatiotemporal cube model, the sequence snapshot model, the ground state correction model, the temporal composite model, the object-oriented spatiotemporal data model, the event spatiotemporal data model, the feature-based spatiotemporal data model, the graph theory spatiotemporal data model, the process spatiotemporal data model, the dynamic object storage model, the agent-based model, the construction of a full-space information system, multi-granularity spatiotemporal object modeling, geographic system simulation research, geoscience knowledge graph, etc., as well as improved models derived from the above models, etc. The above models describe the dynamic properties of the spatiotemporal change process of entities from different dimensions, such as timestamps, changes, states, events, processes, actions, movements, dynamic objects, multi-granularity spatiotemporal objects, etc., and then use time slices to describe the spatiotemporal change process.

[0004] However, in terms of the event content description dimension, the existing spatiotemporal data models have different focuses on events, and most studies are difficult to expand to other fields, resulting in a one-sided understanding of events. In terms of the spatiotemporal granularity of events, the traditional event spatiotemporal data models have not realized the description of events across spatiotemporal dimensions, making it difficult to capture the time-varying trajectories and spatiotemporal evolution relationships between multiple events and multiple geographic objects that occur simultaneously in an event, and have not established a unified description framework for the spatiotemporal process of continuous and discrete changes in entities. In terms of model application fields, most existing models are based on research driven by specific applications and designed for specific scientific problems. The description of events is mostly based on the change process of a single object or the same type of elements. Few models can rely on the dynamic changes of events in different fields to achieve the spatiotemporal evolution of complex events with the interaction of multiple elements in a complex environment. In terms of event spatiotemporal and causal relationship analysis, previous studies have mostly analyzed the causal relationship between events in fixed process events, integrating the dynamic changes of event semantics at different levels with the inherent spatiotemporal logical relationship between the multi-dimensional characteristics of events, and analyzing the causal relationship driven by event changes is still rare. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and equipment for constructing a semantically rich event spatiotemporal data model that can express the multi-dimensional characteristics of events according to the richness of event semantics in different fields, aggregate the hierarchical structure of event development with the multi-dimensional characteristics and correlation relationships of events, and realize the tracing of the spatiotemporal evolution of events.

[0006] A method for constructing a semantically rich event spatiotemporal data model, the method comprising:

[0007] Acquire semantically rich event features of the event; wherein the semantically rich event features include semantic features, time features, spatial features, attribute features, and existence features;

[0008] According to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained;

[0009] From an object-oriented perspective, the semantic-rich event features of each element in the event hierarchy are abstracted into different classes. The directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0010] In one embodiment, according to the rich semantic event features of the events and the complexity of the event composition, the events are hierarchically divided into rich semantic event features, and an event hierarchy consisting of events and entities is constructed, including:

[0011] According to the rich semantic event features of events and the complexity of event composition, events are divided into rich semantic event feature hierarchies, and an event hierarchy consisting of compound events, simple events, atomic events and entities is constructed; among them, compound events are aggregated from simple events, simple events are aggregated from atomic events, atomic events are composed of entities, entities are divided into dynamic entities and static entities according to whether they interact with the outside world, and entities are divided into point entities, line entities, surface entities and body entities according to their attributes.

[0012] In one embodiment, obtaining the semantically rich event features of each element in the event hierarchy and the semantic logical relationship between the elements includes:

[0013] The semantically rich event features of compound events, simple events, atomic events, dynamic entities and static entities in the event hierarchy are obtained, and the semantic logical relationships between events, between events and entities, and between entities are obtained; among them, the semantic logical relationship between events includes hierarchical relationship, spatiotemporal relationship and causal relationship, the semantic logical relationship between events and entities is a hierarchical relationship, and the semantic logical relationship between entities includes attribute relationship and spatiotemporal relationship.

[0014] In one embodiment, from an object-oriented perspective, the semantically rich event features of each element in the event hierarchy are abstracted into different classes, including:

[0015] From an object-oriented perspective, the semantically rich event features of compound events, simple events, atomic events, dynamic entities, and static entities in the event hierarchy are abstracted into compound event classes, simple event classes, atomic event classes, dynamic entity classes, and static entity classes, respectively.

[0016] Among them, the compound event class includes the semantic features, time features, spatial features, entity sets and simple event sequence sets of compound events; the simple event class includes the semantic features, time features, spatial features, entity sets, atomic event sequence sets and the relationship between simple events and their predecessors and subsequent events; the atomic event class includes the semantic features, time features, spatial features, entity sets, entity existence feature sequence sets and the relationship between atomic events and their predecessors and subsequent events; the dynamic entity class includes the semantic features, time features, spatial features, attribute feature sets, existence feature sets of dynamic entities and the relationship between them and other dynamic entities; the static entity class includes the semantic features, time features, spatial features, attribute feature sets, existence feature sets and the relationship between them and other static entities of static entities.

[0017] In one embodiment, directed graph theory is used to take different classes as nodes and semantic logical relationships between different classes as edges to construct a semantically rich event spatiotemporal data model for event modeling expression, including:

[0018] Using directed graph theory, composite event classes, simple event classes and atomic event classes are used as event class nodes, dynamic entity classes and static entity classes are used as entity class nodes, and the semantic logical relationships between different classes are used as edges to construct a semantically rich event spatiotemporal data model for event modeling and expression.

[0019] Among them, in the semantically rich event spatiotemporal data model, the compound event class node is the parent node, the simple event class node is the child node of the compound event class node, the atomic event class node is the child node of the simple event class node, and the entity class node is the child node of the atomic event class node;

[0020] Among them, the edges in the rich semantic event spatiotemporal data model include event composition relationship edges, spatiotemporal influence edges between events and events, entities and entities, and association relationship edges between entities in events; among them, event composition relationship edges represent the hierarchical relationships between event class nodes of different levels and between atomic event class nodes and entity class nodes; the spatiotemporal influence edges between events and events, entities and entities represent the spatiotemporal relationships between event class nodes of the same level and between entity class nodes contained in atomic event class nodes; and the association relationship edges between entities in events represent the attribute relationships between entity class nodes in atomic event class nodes.

[0021] In one of the embodiments, the above method also includes: from an object-oriented perspective, abstracting the time characteristics of events and entities in the event hierarchy into time classes, abstracting the semantic logical relationships between events and events and between entities in the event hierarchy into event-event relationship classes and entity-entity relationship classes, respectively, and using a unified modeling language to construct compound event classes, simple event classes, atomic event classes, dynamic entity classes, static entity classes, time classes, event-event relationship classes, entity-entity relationship classes, and connections between different classes into a hierarchical semantic logical relationship tree to achieve the logical structure design of a rich semantic event spatiotemporal data model.

[0022] In one embodiment, the time class includes time ID, effective time start, effective time end, transaction time start and transaction time end; the event and event relationship class includes event and event relationship ID, event ID, relationship type, relationship name and relationship details; the entity and entity relationship class includes entity and entity relationship ID, entity ID, relationship type, relationship name and relationship details.

[0023] A device for constructing a semantically rich event spatiotemporal data model, the device comprising:

[0024] A feature expression module is used to obtain semantically rich event features of an event; wherein the semantically rich event features include semantic features, temporal features, spatial features, attribute features, and existence features;

[0025] A hierarchical division module is used to perform rich semantic event feature hierarchical division of events according to the rich semantic event features of the events and the complexity of the event composition, to construct an event hierarchy composed of events and entities, and to obtain the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements;

[0026] The model building module is used to abstract the semantic-rich event features of each element in the event hierarchy into different classes from an object-oriented perspective, and adopt directed graph theory to use different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0027] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Acquire semantically rich event features of the event; wherein the semantically rich event features include semantic features, time features, spatial features, attribute features, and existence features;

[0029] According to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained;

[0030] From an object-oriented perspective, the semantic-rich event features of each element in the event hierarchy are abstracted into different classes. The directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0032] Acquire semantically rich event features of the event; wherein the semantically rich event features include semantic features, time features, spatial features, attribute features, and existence features;

[0033] According to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained;

[0034] From an object-oriented perspective, the semantic-rich event features of each element in the event hierarchy are abstracted into different classes. The directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0035] Compared with the existing spatiotemporal data model, the above-mentioned method, device and equipment for constructing a semantically rich event spatiotemporal data model have the following technical effects:

[0036] 1. This application, by acquiring semantically rich event features of events, can, on the one hand, reflect the multi-dimensional features of events in different fields and the multi-level semantics expressed at different stages of development, embody the multi-dimensional semantic information and multi-scale spatiotemporal information of events, extend the semantic richness and dynamic changes of event descriptions, and realize multi-dimensional description of events and entities across spatiotemporal granularity; on the other hand, semantically rich event features can be used to deduce the explicit and implicit complex spatiotemporal logical connections and causal relationships between events at different levels.

[0037] 2. This application divides events into rich semantic event feature hierarchies according to the rich semantic event features of events and the complexity of event composition, and constructs an event hierarchy composed of events and entities, which can reflect the rich semantic event features and semantic logical relationships of events and entities, aggregate the development context of events, and realize the reconstruction of the temporal and spatial evolution of events.

[0038] 3. This application adopts a combination of object-oriented and directed graph theory to construct a rich semantic event spatiotemporal data model for event modeling and expression. The rich semantic event spatiotemporal data model describes the rich semantic event characteristics of each element in the event hierarchy through nodes, and describes the semantic logical relationship between elements through edges, so as to facilitate the reconstruction of the evolution sequence of complex events, realize the correlation relationship between events with the interaction of multiple factors in a complex environment, and the entity changes with the semantic transformation of events and the spatiotemporal evolution of the relationship between entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a method for constructing a semantically rich event spatiotemporal data model in an embodiment;

[0040] Figure 2 A schematic diagram of an event hierarchy structure in one embodiment;

[0041] Figure 3 Schematic diagram of the organizational structure of a semantically rich event spatiotemporal data model in one embodiment;

[0042] Figure 4 A schematic diagram of a semantic logic relationship tree in an embodiment;

[0043] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] In one embodiment, Figure 1 As shown, a method for constructing a semantically rich event spatiotemporal data model is provided, comprising the following steps:

[0046] Step S1, obtaining semantically rich event features of the event; wherein the semantically rich event features include semantic features, temporal features, spatial features, attribute features and existence features. Among them, "rich semantics" reflects the multi-dimensional features of events in different fields and their multi-level semantics expressed at different stages of development, reflecting the multi-dimensional semantic information and multi-scale spatiotemporal information of events, and on the other hand, represents the apparent and implicit complex spatiotemporal logical connections and causal relationships between events at different levels.

[0047] Among them, the semantic feature (What) is the event theme with a hierarchical structure. The semantic features of an event can describe the historical evolution and origin distribution of things. Depending on the richness of the semantics expressed at different stages, the event has different hierarchical structures.

[0048] The time feature (When) uses dual time series (i.e., effective time and transaction time) to express time. The effective time describes the actual time when an event occurs. Based on the semantic characteristics of the event, it realizes the description of the event with variable time resolution. The transaction time refers to the time when the event occurs in the database, reflecting the life cycle of the event.

[0049] Spatial features (Where) reflect the spatial distribution and change patterns of entities in the evolution of an event. According to the semantics expressed by the event, it is the distribution range of entities contained in the event, the characteristics of position changes, or a close-up of the location of entities that have special significance to the event.

[0050] The attribute feature (Who) indicates the entity contained in the event, which is a single entity or a collection of entities.

[0051] Existence characteristics (How) describe how the entity exists in the event.

[0052] Step S2, according to the rich semantic event features of the events and the complexity of the event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained.

[0053] Specifically, according to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, and an event hierarchy consisting of compound events, simple events, atomic events, and entities is constructed, such as Figure 2 As shown, events and entities in the event hierarchy are organized in time series; among them, compound events are aggregated from simple events, simple events are aggregated from atomic events, atomic events are composed of entities, entities are divided into dynamic entities and static entities according to whether they interact with the outside world, and entities are divided into point entities, line entities, surface entities and body entities according to their attributes.

[0054] Based on Figure 2 The event hierarchy shown obtains the semantically rich event features of compound events, simple events, atomic events, dynamic entities, and static entities, and obtains the semantic logical relationships between events, between events and entities, and between entities.

[0055] Among them, the semantic logical relationship between events includes hierarchical relationship, spatiotemporal relationship and causal relationship. The hierarchical relationship and spatiotemporal relationship are generated when describing events, and their order of strength is hierarchical relationship > spatiotemporal relationship.

[0056] The hierarchical relationship is based on the semantic richness of the event expression. It is the composition structure of compound event - simple event - atomic event, reflecting the structure of events from complex to simple.

[0057] The spatiotemporal relationship represents the spatiotemporal association between events of the same level during the evolution of events. The spatiotemporal relationship can be refined into temporal relationship and spatial relationship. The temporal relationship is described based on the effective time of the event, such as before, after, during, equal, connected, intersecting, containing, same beginning, same end, etc., reflecting the temporal relationship of the advancement of events. Spatial relationships include metric spatial relationships, such as distance and area; directional spatial relationships, such as east, south, west, north, left, right, up, down, front and back, etc.; topological spatial relationships represent the spatial scope of events, such as connected, separated, overlapped, contained, etc. At some stages, the spatial relationship between events has a very weak impact on the advancement of events, and only the temporal relationship between events is considered. On the contrary, only the spatial relationship between events is considered. At other stages of events, the temporal and spatial relationships between events have an important impact on the advancement of events, and the two relationships are considered at the same time.

[0058] The causal relationship represents the relationship between an event and the event that triggers it. The causal relationship between events is derived from event characteristics, entity characteristics, hierarchical relationships of events, and spatiotemporal relationships.

[0059] The semantic logical relationship between events and entities is a hierarchical relationship. The semantic logical relationship between entities includes attribute relationship and spatiotemporal relationship. Attribute relationship represents the composition relationship between entities, such as splitting, aggregation, reorganization, merger, etc., or position relationship, such as hostility, friendship, etc. According to the content of event expression, attribute relationship is an extensible relationship representation. The spatiotemporal relationship between entities is one of the temporal relationship and spatial relationship, or both exist at the same time. According to the degree of influence on subsequent events, the spatiotemporal relationship between entities has different emphases in the description process. Spatial relationship includes metric spatial relationship, directional spatial relationship, and topological spatial relationship. The spatial relationship between entities will continue to evolve with the occurrence of events. Temporal relationship refers to the time relationship between the effective times of different entities. The causal relationship between entities describes the causal relationship between entities in events, which is derived from event features, entity features, event relationships, and entity relationships.

[0060] In summary, the components of the above event hierarchy are events and entities, and the relationships between the elements are hierarchy, time and space, and attributes. Among them, the time and space relationships between events and entities, and the attribute relationships between entities are the driving factors for the semantic transformation of events and the construction of relationships between events, and are also important bases for analyzing the causes and consequences of the evolution of events. Since events are the driving factors of the spatiotemporal dynamic process, the occurrence of events will lead to changes in entity attributes and changes in the spatiotemporal relationships between entities. The above evolutions are constantly changing and deriving new entity attributes and new entity-entity relationships, triggering new events, and realizing the reasoning and argumentation of the preceding causality between different semantic events and the trend analysis of the subsequent development of events. For example, in an event, the attribute of entity A is a value, and the spatiotemporal relationship between entities A and B is separated. After the event occurs, the attribute of entity A evolves to another value, and the spatiotemporal relationship with entity B becomes adjacent. The new attributes of entity A and the new entity relationship between entity A and B trigger new events, which promote the transformation and evolution of event semantics, thereby further generating new events and spatiotemporal associations between entities. This cycle is repeated, constantly promoting the evolution of events.

[0061] Step S3, from an object-oriented perspective, abstract the semantically rich event features of each element in the event hierarchy into different classes, and use directed graph theory to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantically rich event spatiotemporal data model for event modeling and expression.

[0062] Among them, object-oriented means organizing related data and methods as a whole, modeling the system from a higher level, and being closer to the natural operating mode of things.

[0063] Specifically, from an object-oriented perspective, the semantically rich event features of compound events, simple events, atomic events, dynamic entities and static entities in the event hierarchy are abstracted into compound event classes, simple event classes, atomic event classes, dynamic entity classes and static entity classes respectively.

[0064] Compound event class:

[0065] CompositeEvent

[0066] ={Semantic,Time,SpatialScope,[Entityset],[SimpleEventSeries]}

[0067] The symbols in the formula represent the semantic features of the compound event (event ID (number), semantics), time features (effective time, transaction time), spatial features (spatial scope of the event, spatial regularity), entity set (event contains all entities or collection units) and simple event sequence set.

[0068] Simple event class:

[0069] SimpleEvent={Semantic,Time,SpatialAttr,[Entityset],[AtomEventSeries],[EventRelation]}

[0070] The symbols in the formula represent the semantic features of simple events (event ID, semantics), time features (effective time, transaction time), spatial features (spatial range of event occurrence, start position, end position), entity set (event contains all entities or their collection units), atomic event sequence set, and the relationship between simple events and their predecessors and successors.

[0071] Atomic event class:

[0072] AtomEvent={Semantic,Time,SpatialAttr,[Entityset],[EntityBehaviorSeries],[EventRelation]}

[0073] The symbols in the formula represent the semantic features of the atomic event (event ID, semantics), time features (effective time, transaction time), spatial features (spatial location or range of the event, start position, end position), entity set (entities included in the event), entity existence feature sequence set, and the relationship between the atomic event and its predecessor and subsequent events.

[0074] Dynamic entity class:

[0075] DynamicEntity={Semantic,Time,[SpatialAttr],[Attributes],[EntityBehavior],[EntityRelationAttr]}

[0076] The symbols in the formula represent the semantic features (semantics, entity ID, entity category), time features (effective time, transaction time), spatial features (starting position, ending position), attribute feature set (data type, actual category of entity, level, scale, combat value, etc.), existence feature set (entity behavior formula) of the dynamic entity, and the relationship with other dynamic entities.

[0077] Static entity class:

[0078] StaticEntity={Semantic,Time,[SpatialAttr],[Attributes],[BehaviorAttr],[EntityRelationAttr]}

[0079] The symbols in the formula represent the semantic features (semantics, entity ID, entity category), time features (effective time, transaction time), spatial features (distribution location), attribute feature set (data type, actual category of entity, scale, etc.), existence feature set and the relationship between static entities and other static entities.

[0080] Specifically, the directed graph theory is used to construct a semantically rich event spatiotemporal data model by taking compound event classes, simple event classes, and atomic event classes as event class nodes, dynamic entity classes and static entity classes as entity class nodes, and the semantic logical relationships between different classes as edges. The organizational structure of the semantically rich event spatiotemporal data model is as follows: Figure 3 shown.

[0081] Among them, in the semantically rich event spatiotemporal data model, the compound event class node is the parent node, expressing the semantics, time, space, attributes, and simple event sequence of the compound event; the simple event class node is the child node of the compound event class node, describing the semantics, time, space, attributes, the association relationship between the simple event and the preceding and subsequent events, and the atomic event sequence included in the simple event; the atomic event class node is the child node of the simple event class node, expressing the semantics, time, space, attributes, entity existence feature sequence of the atomic event, and the association relationship between the atomic event and its preceding and subsequent events; the entity class node is the child node of the atomic event class node, expressing the semantics, time, space, attributes, entity existence features, and relationship between entities in a certain event;

[0082] Among them, the edges in the semantically rich event spatiotemporal data model include event composition relationship edges, spatiotemporal interaction edges between events and events, entities and entities, and association relationship edges between entities in events.

[0083] Among them, the event composition relationship edge represents the hierarchical relationship between event nodes of different levels and between atomic event nodes and entity nodes, expressing how many lower-level sub-events are contained in the upper-level event and which entities are contained in an atomic event; the spatiotemporal influence edges between events and events, and between entities represent the spatiotemporal relationship between event nodes of the same level and between entity nodes contained in atomic event nodes, such as the temporal predecessor and successor of events, the spatial orientation relationship and distance relationship, as well as the topological relationship and distance relationship between entities; the association relationship edges between entities in events represent the attribute relationship between entity nodes in atomic event nodes, such as reorganization, merger, hostility, and friendship.

[0084] In one of the embodiments, the method further includes: from an object-oriented perspective, abstracting the time characteristics of events and entities in the event hierarchy into time classes, abstracting the semantic logical relationships between events and events and between entities in the event hierarchy into event-event relationship classes and entity-entity relationship classes, respectively, and using a unified modeling language (UML) to construct a hierarchical semantic logical relationship tree of composite event classes, simple event classes, atomic event classes, dynamic entity classes, static entity classes, time classes, event-event relationship classes, entity-entity relationship classes, and connections between different classes, such as Figure 4 As shown in the figure, the logical structure design of the semantically rich event spatiotemporal data model is realized. Figure 4 In the model, the event class derives compound event class, simple event class, and atomic event class, and the entity class derives dynamic entity class and static entity class. Both event class and entity class have five characteristics: semantic, time, space, attribute, and existence. The time class describes the time characteristics of events or entities, the entity-entity relationship class describes the relationship between different entities, and the event-event relationship class expresses the relationship between events. The object-oriented method is used to design the logical structure of the semantic event spatiotemporal data model, so that the semantic description between event class and entity class can conform to the cognitive law of real events.

[0085] Among them, time category:

[0086] TimeClass

[0087] ={TimeID,ValidStartTime,ValidEndTime,TraStartTime,TraEndTime}

[0088] The symbols in the formula represent time ID, effective time start, effective time end, transaction time start, and transaction time end. Time is represented by a time point or a time interval. The granularity of effective time is year, month, day, hour, minute, and second. The time granularity is customized based on the semantic characteristics of the event and the event advancement logic. The granularity of transaction time is seconds and minutes.

[0089] Event and event relationship classes:

[0090] EventRelationType={EventRelationID,EventID,EventRelationType,EventRelationTypeName,EventRelationDescription}

[0091] The symbols in the formula represent event and event relationship ID, event ID, relationship type, relationship name and relationship details respectively.

[0092] Entity and entity relationship classes:

[0093] EntityRelationType={EntityRelationID,EntityID,EntityRelationType,EntityRelationTypeName,EntityRelationDescription}

[0094] The symbols here represent entity-entity relationship ID, entity ID, relationship type, relationship name, and relationship details, respectively.

[0095] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0096] In one embodiment, a device for constructing a semantically rich event spatiotemporal data model is provided, comprising:

[0097] A feature expression module is used to obtain semantically rich event features of an event; wherein the semantically rich event features include semantic features, temporal features, spatial features, attribute features, and existence features;

[0098] A hierarchical division module is used to perform rich semantic event feature hierarchical division of events according to the rich semantic event features of the events and the complexity of the event composition, to construct an event hierarchy composed of events and entities, and to obtain the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements;

[0099] The model building module is used to abstract the semantic-rich event features of each element in the event hierarchy into different classes from an object-oriented perspective, and adopt directed graph theory to use different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0100] For the specific limitations on the construction device of the rich semantic event spatiotemporal data model, please refer to the limitations on the construction method of the rich semantic event spatiotemporal data model in the above text, which will not be repeated here. Each module in the above-mentioned construction device of the rich semantic event spatiotemporal data model can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0101] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a semantically rich event spatiotemporal data model is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0102] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0104] Acquire semantically rich event features of the event; wherein the semantically rich event features include semantic features, time features, spatial features, attribute features, and existence features;

[0105] According to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained;

[0106] From an object-oriented perspective, the semantic-rich event features of each element in the event hierarchy are abstracted into different classes. The directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0107] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0108] Acquire semantically rich event features of the event; wherein the semantically rich event features include semantic features, time features, spatial features, attribute features, and existence features;

[0109] According to the rich semantic event features of events and the complexity of event composition, the events are divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained;

[0110] From an object-oriented perspective, the semantic-rich event features of each element in the event hierarchy are abstracted into different classes. The directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantic-rich event spatiotemporal data model for event modeling and expression.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a semantically rich event spatiotemporal data model, characterized in that: The method comprises: Acquire semantically rich event features of events; wherein, the semantically rich event features include semantic features, time features, spatial features, attribute features and existence features; wherein, "rich semantics" on the one hand reflects the multi-dimensional features of events in different fields and the multi-level semantics expressed at different development stages, embodies the multi-dimensional semantic information and multi-scale spatiotemporal information of events, and on the other hand represents the apparent and implicit complex spatiotemporal logical connections and causal associations between events at different levels; wherein, semantic features are event themes with a hierarchical structure, which are used to describe the historical evolution and origin distribution of things; temporal features use a dual time series of effective time and transaction time to express time, and effective time is used to describe the actual time of an event, and according to the semantic features of the event, the variable time resolution of the event is described; transaction time refers to the time of an event in the database, reflecting the life cycle of the event; spatial features reflect the spatial distribution and change law of entities in the evolution of events, which are the distribution range and position change characteristics of entities contained in the event, or the close-up of the entity position that has special significance to the event; attribute features represent the entities contained in the event, which are single entities or entity collection units; existence features are used to describe the existence mode of entities in the event; According to the rich semantic event features of the event and the complexity of the event composition, the event is divided into rich semantic event feature hierarchies, an event hierarchy consisting of events and entities is constructed, and the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements are obtained; From an object-oriented perspective, the semantically rich event features of each element in the event hierarchy are abstracted into different classes, and directed graph theory is used to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantically rich event spatiotemporal data model for event modeling and expression. From an object-oriented perspective, the semantically rich event features of each element in the event hierarchy are abstracted into different classes, including: From an object-oriented perspective, the semantically rich event features of compound events, simple events, atomic events, dynamic entities and static entities in the event hierarchy are abstracted into compound event classes, simple event classes, atomic event classes, dynamic entity classes and static entity classes respectively; wherein the compound event class includes the semantic features, time features, space features, entity sets and simple event sequence sets of compound events; the simple event class includes the semantic features, time features, space features, entity sets, atomic event sequence sets and the relationship between simple events and their predecessors and subsequent events; the atomic event class includes the semantic features, time features, space features, entity sets, entity existence feature sequence sets and the relationship between atomic events and their predecessors and subsequent events; the dynamic entity class includes the semantic features, time features, space features, attribute feature sets, existence feature sets of dynamic entities and the relationship between them and other dynamic entities; the static entity class includes the semantic features, time features, space features, attribute feature sets, existence feature sets and the relationship between them and other static entities of static entities; The method further comprises: from an object-oriented perspective, abstracting the time features of events and entities in the event hierarchy into time classes, abstracting the semantic logical relationships between events and events and between entities in the event hierarchy into event-event relationship classes and entity-entity relationship classes respectively, and using a unified modeling language (UML) to construct the composite event class, simple event class, atomic event class, dynamic entity class, static entity class, time class, event-event relationship class, entity-entity relationship class and the connections between different classes into a semantic logical relationship tree with a hierarchical structure, so as to realize the logical structure design of a semantically rich event spatiotemporal data model; wherein both the event class and the entity class have five types of features, namely, semantic features, time features, spatial features, attribute features and existence features, the time class describes the time features of events or entities, the entity-entity relationship class describes the relationship between different entities, and the event-event relationship class expresses the relationship between events.

2. The method according to claim 1, characterized in that According to the semantically rich event features of the event and the complexity of the event composition, the event is hierarchically divided into semantically rich event features, and an event hierarchy consisting of events and entities is constructed, including: According to the rich semantic event features of the events and the complexity of the event composition, the events are divided into rich semantic event feature hierarchies to construct an event hierarchy consisting of compound events, simple events, atomic events and entities; wherein the compound events are aggregated from simple events, the simple events are aggregated from atomic events, the atomic events are composed of entities, the entities are divided into dynamic entities and static entities according to whether they interact with the outside world, and the entities are divided into point entities, line entities, surface entities and body entities according to their attributes.

3. The method according to claim 2, characterized in that Obtaining the semantically rich event features of each element in the event hierarchy and the semantic logical relationship between the elements, including: The semantically rich event features of compound events, simple events, atomic events, dynamic entities and static entities in the event hierarchy are obtained, and the semantic logical relationships between events, between events and entities, and between entities are obtained; wherein the semantic logical relationships between events include hierarchical relationships, spatiotemporal relationships and causal relationships, the semantic logical relationships between events and entities are hierarchical relationships, and the semantic logical relationships between entities include attribute relationships and spatiotemporal relationships.

4. The method according to claim 1, characterized in that: Using directed graph theory, different classes are used as nodes, and the semantic logical relationships between different classes are used as edges to construct a semantically rich event spatiotemporal data model for event modeling and expression, including: Adopting directed graph theory, the composite event class, simple event class and atomic event class are taken as event class nodes, the dynamic entity class and static entity class are taken as entity class nodes, and the semantic logical relationship between different classes is taken as the edge, so as to construct a semantically rich event spatiotemporal data model for event modeling and expression; Wherein, in the semantically rich event spatiotemporal data model, the compound event class node is the parent node, the simple event class node is the child node of the compound event class node, the atomic event class node is the child node of the simple event class node, and the entity class node is the child node of the atomic event class node; Among them, the edges in the semantically rich event spatiotemporal data model include event composition relationship edges, spatiotemporal mutual influence edges between events and events, entities and entities, and association relationship edges between entities in events; wherein, the event composition relationship edges represent the hierarchical relationships between event class nodes of different levels and between atomic event class nodes and entity class nodes, the spatiotemporal mutual influence edges between events and events, entities and entities represent the spatiotemporal relationships between event class nodes of the same level and between entity class nodes contained in atomic event class nodes, and the association relationship edges between entities in events represent the attribute relationships between entity class nodes in atomic event class nodes.

5. The method according to claim 1, characterized in that The time class includes time ID, effective time start, effective time end, transaction time start and transaction time end; the event and event relationship class includes event and event relationship ID, event ID, relationship type, relationship name and relationship details; The entity-entity relationship class includes entity-entity relationship ID, entity ID, relationship type, relationship name and relationship details.

6. A device for constructing a semantically rich event spatiotemporal data model, characterized in that: The device comprises: A feature expression module is used to obtain semantically rich event features of events; wherein, the semantically rich event features include semantic features, time features, spatial features, attribute features and existence features; wherein, "rich semantics" reflects the multi-dimensional features of events in different fields and the multi-level semantics expressed at different development stages, reflecting the multi-dimensional semantic information and multi-scale spatiotemporal information of events, and on the other hand, represents the apparent and implicit complex spatiotemporal logical connections and causal associations between events at different levels; wherein, semantic features are event themes with a hierarchical structure, which are used to describe the historical evolution and origin distribution of things; temporal features use a dual time series of effective time and transaction time to express time, and effective time is used to describe the actual time of an event, and according to the semantic features of the event, the variable time resolution of the event is described; transaction time refers to the time of an event in the database, reflecting the life cycle of the event; spatial features reflect the spatial distribution and change law of entities in the evolution of events, which are the distribution range and position change characteristics of entities contained in the event, or the close-up of the entity position that has special significance to the event; attribute features represent the entities contained in the event, which are single entities or entity collection units; existence features are used to describe the existence of entities in the event; A hierarchical division module is used to perform rich semantic event feature hierarchical division of events according to the rich semantic event features of the events and the complexity of the event composition, construct an event hierarchy composed of events and entities, and obtain the rich semantic event features of each element in the event hierarchy and the semantic logical relationship between the elements; A model building module is used to abstract the semantically rich event features of each element in the event hierarchy into different classes from an object-oriented perspective, and adopt directed graph theory to take different classes as nodes and the semantic logical relationships between different classes as edges to construct a semantically rich event spatiotemporal data model for event modeling and expression; From an object-oriented perspective, the semantically rich event features of each element in the event hierarchy are abstracted into different classes, including: From an object-oriented perspective, the semantically rich event features of compound events, simple events, atomic events, dynamic entities and static entities in the event hierarchy are abstracted into compound event classes, simple event classes, atomic event classes, dynamic entity classes and static entity classes respectively; wherein the compound event class includes the semantic features, time features, space features, entity sets and simple event sequence sets of compound events; the simple event class includes the semantic features, time features, space features, entity sets, atomic event sequence sets and the relationship between simple events and their predecessors and subsequent events; the atomic event class includes the semantic features, time features, space features, entity sets, entity existence feature sequence sets and the relationship between atomic events and their predecessors and subsequent events; the dynamic entity class includes the semantic features, time features, space features, attribute feature sets, existence feature sets of dynamic entities and the relationship between them and other dynamic entities; the static entity class includes the semantic features, time features, space features, attribute feature sets, existence feature sets and the relationship between them and other static entities of static entities; It also includes: from an object-oriented perspective, abstracting the time characteristics of events and entities in the event hierarchy into time classes, abstracting the semantic logical relationships between events and events and between entities in the event hierarchy into event-event relationship classes and entity-entity relationship classes, respectively, and using a unified modeling language (UML) to construct the composite event class, simple event class, atomic event class, dynamic entity class, static entity class, time class, event-event relationship class, entity-entity relationship class and the connections between different classes into a semantic logical relationship tree with a hierarchical structure to achieve the logical structure design of a semantically rich event spatiotemporal data model; wherein both the event class and the entity class have five types of features, namely, semantic features, time features, spatial features, attribute features and existence features; the time class describes the time characteristics of events or entities, the entity-entity relationship class describes the relationship between different entities, and the event-event relationship class expresses the relationship between events.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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