CN111241429A - Method and device for determining space-time relationship, electronic equipment and storage medium - Google Patents
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Abstract
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for determining a spatiotemporal relationship, an electronic device, and a storage medium. According to the method and the device, the time node which is associated when the target person generates the target behavior at the target place can be determined from at least one corresponding time node in the query time period, each time node which is associated with the target person can be determined, the time node and each time node which is separated from the time node by the preset number can be determined, the candidate persons except the target person of the target behavior are generated at the target place, and then the candidate persons are determined to be persons having space-time relation with the target person. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for determining a spatiotemporal relationship, an electronic device, and a storage medium.
Background
The relation network of the knowledge graph is used more and more in the fields of internet social contact, public security, banks, new retail and the like, and more high-value application scenes are also promoted, such as personnel relation strength analysis of the internet social network, fund transaction anti-money laundering analysis in the bank field and the like. However, some relationships are complicated, and space-time factors are introduced for constraint, for example: whether the relation of staying at a hotel and surfing at the same internet bar exists between people and people is defined in the public security field by analyzing the behaviors of the same time, the same place (such as the same hotel room number, the same internet bar and the like) and the like based on the air conditions, wherein the same time and the same place are theoretically understood to be clear, but are not completely the same in most cases in practice, because the check-in time of the hotel and the check-in time of the internet bar can be different due to the fact that the computer logs in the sequence interval and the like, the behavior time periods (the check-in time to the check-out time of the hotel and the internet bar) of the people with the relation are overlapped, but the start time and the end time are not completely the same, and therefore, the definition of the space-time relation based on the behavior data brings great difficulty.
Disclosure of Invention
In view of this, embodiments of the present application at least provide a method, an apparatus, an electronic device, and a storage medium for determining a spatiotemporal relationship between a target person and a person, so as to improve efficiency of determining the person having the spatiotemporal relationship with the target person.
The application mainly comprises the following aspects:
in a first aspect, an embodiment of the present application provides a method for determining a spatiotemporal relationship, where the method for determining a spatiotemporal relationship includes:
determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period;
for each time node associated with the target person, determining each time node between the time node and a preset number of time nodes separated from the time node, and generating candidate persons of the target behavior at the target place except the target person;
and determining the candidate persons as persons having a space-time relationship with the target person.
In a possible implementation manner, before determining, from the at least one corresponding time node in the query time period, a time node associated with the target person when the target person generates the target behavior at the target location, the determining method further includes:
dividing a preset time period into a plurality of time points, and establishing corresponding time nodes according to each time point;
and respectively storing the behavior data generated by each person at a plurality of time points, the places corresponding to the generated behavior data and the identity information of each person in association with the corresponding time nodes.
In a possible embodiment, the associating and storing the behavior data generated by each person at a plurality of time points, the location corresponding to the generated behavior data, and the identity information of each person with the corresponding time node includes:
establishing a corresponding place node according to each place, and establishing a corresponding personnel node according to the identity information of personnel;
and according to behavior data respectively generated by each person at a plurality of time points, performing associated storage on each site node, each person node and each time node in a knowledge graph mode.
In one possible embodiment, the target behavior comprises any one of the following behaviors:
a surfing behavior; lodging behaviors; taking a ride; dining behavior; and (4) shopping behavior.
In a possible implementation manner, if there are a plurality of candidate persons, after the determining the candidate persons as persons having a spatiotemporal relationship with the target person, the determining method further includes:
and sequencing the candidate persons according to the relationship scores between the candidate persons and the target person.
In a possible implementation manner, the ranking the plurality of candidate persons according to the relationship scores between the plurality of candidate persons and the target person respectively includes:
for each candidate person in the candidate persons, acquiring the number of time nodes associated with the candidate person and the target person;
calculating a relationship score between each candidate person and the target person according to the number of time nodes which are respectively associated with the target person by the candidate persons;
and determining the affinity and the sparseness of the space-time relationship between each candidate person and the target person according to the corresponding relationship score of each candidate person, and sequencing the candidate persons.
In a second aspect, an embodiment of the present application further provides an apparatus for determining a spatiotemporal relationship, where the apparatus for determining a spatiotemporal relationship includes:
the first determining module is used for determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period;
a second determining module, configured to determine, for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node, and generate a candidate person, other than the target person, of the target behavior at the target location;
and the third determining module is used for determining the candidate persons as persons having space-time relationship with the target person.
In a possible implementation, the determining means further includes:
the dividing module is used for dividing a preset time period into a plurality of time points and establishing corresponding time nodes according to each time point;
and the storage module is used for storing the behavior data generated by each person at a plurality of time points, the places corresponding to the generated behavior data and the identity information of each person in association with the corresponding time nodes.
In one possible embodiment, the storage module comprises:
the establishing unit is used for establishing corresponding site nodes according to each site and establishing corresponding personnel nodes according to the identity information of personnel;
and the storage unit is used for performing associated storage on each site node, each personnel node and each time node in a knowledge graph mode according to behavior data generated by each personnel at a plurality of time points.
In one possible embodiment, the target behavior comprises any one of the following behaviors:
a surfing behavior; lodging behaviors; taking a ride; dining behavior; and (4) shopping behavior.
In a possible implementation manner, if there are a plurality of candidate persons, the determining device further includes:
and the sequencing module is used for sequencing the candidate persons according to the relationship scores between the candidate persons and the target person.
In one possible implementation, the sorting module includes:
the acquisition unit is used for acquiring the number of time nodes associated with the target person by each candidate person in the candidate persons;
the calculating unit is used for calculating the relationship score between each candidate person and the target person according to the number of time nodes which are respectively associated with the target person by the candidate persons;
and the determining unit is used for determining the affinity and the sparseness of the space-time relationship between each candidate person and the target person according to the corresponding relationship score of each candidate person, and sequencing the candidate persons.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other via the bus when the electronic device is running, and the machine-readable instructions are executed by the processor to perform the steps of the method for determining spatiotemporal relationships according to the first aspect or any one of the possible embodiments of the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for determining a spatiotemporal relationship described in the first aspect or any one of the possible implementation manners of the first aspect.
In the embodiment of the application, the time node associated when the target person generates the target behavior at the target location can be determined from at least one corresponding time node in the query time period, and for each time node associated with the target person, the time node and each time node spaced from the time node by a preset number of time nodes can be determined, and the candidate persons except the target person generating the target behavior at the target location are determined, so that the candidate persons are determined as the persons having a space-time relationship with the target person. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a flow chart illustrating a method for determining spatiotemporal relationships provided by an embodiment of the present application;
FIG. 2 is a functional block diagram of an apparatus for determining spatiotemporal relationships according to an embodiment of the present application;
FIG. 3 is a second functional block diagram of an apparatus for determining spatiotemporal relationships provided by an embodiment of the present application;
FIG. 4 illustrates a functional block diagram of the memory module of FIG. 3;
FIG. 5 illustrates a functional block diagram of the sequencing module of FIG. 3;
fig. 6 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Description of the main element symbols:
in the figure: 200-means for determining a spatiotemporal relationship; 210-a first determination module; 220-a second determination module; 230-a third determination module; 240-a partitioning module; 250-a storage module; 252-a building unit; 254-a storage unit; 260-a sorting module; 262-an acquisition unit; 264-a computing unit; 266-a determination unit; 600-an electronic device; 610-a processor; 620-memory; 630-bus.
Detailed Description
To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be performed in reverse order or concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
To enable one skilled in the art to use the present disclosure in connection with a particular application scenario "spatio-temporal relationship determination," the following embodiments are presented, and it will be apparent to one skilled in the art that the general principles defined herein may be applied to other embodiments and application scenarios without departing from the spirit and scope of the present disclosure.
The following method, apparatus, electronic device or computer-readable storage medium in the embodiments of the present application may be applied to any scenario where a spatio-temporal relationship needs to be determined, and the embodiments of the present application do not limit a specific application scenario, and any scheme using the method, apparatus, electronic device and storage medium for determining a spatio-temporal relationship provided in the embodiments of the present application is within the scope of protection of the present application.
It is noted that, before the present application, the relation is complicated, and a space-time factor is introduced to perform constraint, for example: whether a hotel accommodation relationship and an internet bar internet relationship exist among people is defined in the public security field through analysis based on behaviors of time-space conditions such as whether the people and the people have the same time and the same place, the same time and the same place mentioned herein are clear in theory, but are not completely the same in most practical situations, because check-in time of a hotel and check-in time of an internet bar are different due to the fact that computer login sequence intervals and the like show that behavior time periods of people with the relationship are overlapped, but starting time and stopping time are not completely the same, and therefore, the definition of the time-space relationship based on the behavior data brings great difficulty.
In view of the above problem, in the embodiment of the present application, a time node associated when a target person generates a target behavior at a target location may be determined from at least one corresponding time node in a query time period, and for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node may be determined, and a candidate person other than the target person of the target behavior is generated at the target location, and then the candidate person is determined as a person having a space-time relationship with the target person. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
It should be noted that a Knowledge map (Knowledge Graph) is called Knowledge domain visualization or Knowledge domain mapping map in the book intelligence world, is a series of different graphs for displaying the relationship between the Knowledge development process and the structure, describes Knowledge resources and carriers thereof by using a visualization technology, and excavates, analyzes, constructs, draws and displays Knowledge and the mutual relation between the Knowledge resources and the carriers; neo4j is a high-performance, NOSQL graphical database implemented based on graph theory in mathematics, unlike traditional relational databases that store data in database table fields, which store structured data on the network rather than in tables, specifically, graph databases store data and relationships between data in nodes and edges, which are referred to as "nodes" and "relationships" in graph databases.
For the convenience of understanding of the present application, the technical solutions provided in the present application will be described in detail below with reference to specific embodiments.
Fig. 1 is a flowchart of a method for determining a spatiotemporal relationship according to an embodiment of the present disclosure. The device for executing the method for determining the spatiotemporal relationship may be a cloud platform or a server interacting with the user terminal. The method for determining the spatiotemporal relationship provided in the embodiments of the present application will be described below from the perspective of the execution subject being a server. As shown in fig. 1, the method for determining a spatiotemporal relationship provided in the embodiment of the present application includes the following steps:
s101: and determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period.
In the specific implementation, when querying a person having a space-time relationship with a target person, a query time period needs to be set first, and then at least one time node corresponding to the query time period is determined, where each time point in the query time period corresponds to one time node, and the time nodes are set in advance.
Here, the fact that the target person has a spatiotemporal relationship with one person means that the target person and the person generate the same target behavior at the same place, the same time point or similar time points; the query time period is a time range of the query, for example, a time period from 1/month 1/2019 to 1/month 1/2020; a target location such as internet cafe a.
It should be noted that the target behavior may include any one of the following behaviors: a surfing behavior; lodging behaviors; taking a ride; dining behavior; and (4) shopping behavior.
In an example, the query time period is 10:00-15:00, one hour corresponds to one time node, 6 time nodes corresponding to the 10:00-15:00 time period are respectively a time node 1, a time node 2, a time node 3, a time node 4, a time node 5 and a time node 6, and if the corresponding time when the first person generates the internet surfing behavior in the internet cafe a is 12:00, the time node associated when the first person generates the internet surfing behavior in the internet cafe a is the time node 3.
Further, in order to associate each behavior data generated by each person at each location with a time node, the above-mentioned content needs to be stored in an associated manner, and a corresponding relationship between each time point and the time node needs to be established in advance, that is, before determining the time node associated when the target person generates the target behavior at the target location from at least one corresponding time node in the query time period in step S101, the method further includes the following steps:
step a: the method comprises the steps of dividing a preset time period into a plurality of time points, and establishing corresponding time nodes according to each time point.
In specific implementation, time intervals corresponding to adjacent time nodes may be preset, and the time intervals may be set according to actual needs, preferably 1 minute, so that a past time period may be divided into a plurality of time points, or only a time period that is a distance from the present time period may be divided into a plurality of time points, and then, a corresponding time node is established according to each time point, that is, one time point corresponds to one time node.
In one example, the time interval is 1 minute, the preset time period is 1 year, and 365 × 24 × 60 of 1 year is 525600 minutes, each minute in the 1 year is taken as a time point, and furthermore, 52600 time nodes are correspondingly established for 1 year.
Step b: and respectively storing the behavior data generated by each person at a plurality of time points, the places corresponding to the generated behavior data and the identity information of each person in association with the corresponding time nodes.
In the specific implementation, after time nodes in a preset time period are established, behavior data generated by each person at a plurality of time points in the preset time period, a place corresponding to the generated behavior data and identity information of each person generating the behavior data are acquired, and the information and the corresponding time nodes are stored in an associated manner.
In an example, when the time when the person a generates the internet surfing behavior at the place B is the time point a, the identity information of the person a, the place B, the internet surfing behavior and the time node B corresponding to the time point a are stored in an associated manner.
Further, behavior data generated by each person at a plurality of time points, a place corresponding to the generated behavior data, and identity information of each person may be stored in association with a corresponding time node in a form of a knowledge graph, that is, in step b, the behavior data generated by each person at a plurality of time points, the place corresponding to the generated behavior data, and the identity information of each person may be stored in association with a corresponding time node, including the following steps:
step b 1: and establishing a corresponding place node according to each place, and establishing a corresponding personnel node according to the identity information of the personnel.
In specific implementation, after the place information of various behavior data generated by each person and the identity information of each person are acquired, various types of nodes can be established according to the place information and the identity information, for the application, three types of nodes are mainly established, including time nodes, place nodes and person nodes, wherein the time nodes are established in advance, and the time information generated when each behavior data is generated can be associated with the corresponding time nodes.
Step b 2: and according to behavior data respectively generated by each person at a plurality of time points, performing associated storage on each site node, each person node and each time node in a knowledge graph mode.
In a specific implementation, after the time node, the place node, and the person node are established, the nodes need to be associated, where the behavior data is used as relationship data, and various types of nodes are associated through the behavior data, for example, if the behavior data is an internet behavior, the internet behavior corresponds to the time, the place, and the person generated, and further, various types of nodes can be associated through the internet behavior.
S102: and aiming at each time node associated with the target person, determining each time node between the time node and a preset number of time nodes separated from the time node, and generating candidate persons except the target person of the target behavior at the target place.
In the specific implementation, because the time node is introduced, the people having a spatio-temporal relationship with the target person can be determined through the time node, specifically, after the time node associated when the target person generates the target behavior at the target location within the queried time period is obtained, that is, the target person generates the over-target behavior at the time point corresponding to the associated time node and the target location, considering that one person and the other person generate the same behavior at the same time and the same location, it can be determined that the spatio-temporal relationship exists between the two persons, but in practice, the time points are not necessarily the same, for example, when two persons going together are checked in a hotel, the check-in time points may be different due to the fact that the computer checks-in sequence intervals and the like, which means that the time points generating behavior data are different between the persons having such spatio-temporal relationship, for another example, two persons traveling together do not completely coincide with each other in the stay time period of the hotel, but overlap with each other, and therefore, based on the above consideration, when determining the person having a space-time relationship with the target person, the present application considers that the same behavior data is generated at the same time point and the same behavior data is generated at the similar time point, and specifically, for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node is determined, and a candidate person other than the target person having the target behavior at the target location is generated.
Here, the preset number may be set according to actual needs, for example, when determining whether there is a time-space relationship between people that the same internet cafe has internet behavior, if the time node is a minute node, the preset number may be set to 5 because 5 minutes is appropriate for registering records of two people who come together in one internet cafe, and may also be set to 30 because 30 minutes is also appropriate for two people who surf the internet in the internet cafe.
S103: and determining the candidate persons as persons having a space-time relationship with the target person.
In the specific implementation, the candidate persons generating the target behavior at the target location are all taken as the persons having the space-time relationship with the target person at the time node generating the target behavior of the target person and the time nodes close to the time node.
Further, in the above manner, a plurality of candidate persons having a spatio-temporal relationship with the target person may be determined, but the relationship affinity between each candidate person and the target person is different, for example, some candidate persons may generate the same behavior data at the same place and the same time point as the target person for many times, and some candidate persons may generate the same behavior data only at the same place and the same time point as the target person for less times or once, so that the candidate persons need to be ranked according to the relationship affinity with the target person, that is, after determining the candidate person as a person having a spatio-temporal relationship with the target person in step S103, the method further includes the following steps:
and sequencing the candidate persons according to the relationship scores between the candidate persons and the target person.
In a specific implementation, the relationship scores between the candidate persons and the target persons may be calculated respectively, where the relationship scores may represent the relationship between the candidate persons and the target persons, and further, the candidate persons may be ranked according to the relationship scores between the candidate persons and the target persons.
Further, the method for ranking the plurality of candidate persons according to the relationship scores between the plurality of candidate persons and the target person respectively comprises the following steps:
step A: and acquiring the number of time nodes of each candidate person in the plurality of candidate persons, wherein the candidate person is associated with the target person.
In the specific implementation, for each candidate, the number of time nodes at which the candidate and the target person are associated is obtained, where the time nodes at which the target person is generating the target behavior are the time nodes at which the target person is generating the target behavior, and the time nodes in the time nodes close to the time nodes.
And B: and calculating the relationship score between each candidate person and the target person according to the number of time nodes which are respectively associated with the target person by the candidate persons.
In a specific implementation, the higher the number of time nodes at which a candidate and a target person are associated, the higher the score of the relationship between the candidate and the target person, where the higher the score, the more intimate the relationship between the candidate and the target person is.
And C: and determining the affinity and the sparseness of the space-time relationship between each candidate person and the target person according to the corresponding relationship score of each candidate person, and sequencing the candidate persons.
In a specific implementation, after the relationship scores corresponding to the candidate persons are determined, affinity and sparseness of the time-space relationship between the candidate persons and the target person can be determined according to the relationship scores corresponding to the candidate persons, and then the candidate persons are ranked according to the affinity and sparseness between the candidate persons and the target person, so that the method can be applied to analysis of the target person in various scenes.
Here, taking the gateway in the same internet bar in the public security field as an example, the solution set forth in the present application may adopt a storage structure of a Neo4J graph database and corresponding query statements, and the solution includes the following contents:
step (1): object storage design of Neo4J graph database;
in the actual service scene, in the space condition, the node is generally defined as the internet bar, that is, a necessary condition of the internet relationship with the internet bar is to surf the internet in the same internet bar, and the time condition is more complicated, taking two persons, namely a person A and a person B as an example, if the person A and the person B have the internet relationship with the internet bar, the corresponding internet surfing time and the corresponding internet surfing time mainly have the possible combinations, and the time periods between the registration internet surfing time and the internet surfing registration time are completely or partially overlapped; if Δ t1 is | time of going on the internet of person a — time of going on the internet of person B |, and Δ t2 is | time of going off the internet of person a — time of going off the internet of person B |, then the minimum value of Δ t1, Δ t2 is 0, and the maximum value is a value given in real time at the time of flexible query, for example, may be 5 minutes, which is appropriate when a person in an internet cafe registers an association in succession with 2 records, or may be 30 minutes, which is a necessary condition in a general sense for about 2 people who have access to the internet at the internet cafe. When the same internet bar relationship between the designers and the staff in Neo4J is designed, specific values of Δ t1 and Δ t2 in query cannot be known, and each possible value cannot be well defined and stored in a library, so that the present application introduces time nodes to associate each specific online time point and offline time point to form a storage structure of a database object in the present application, by introducing time nodes, such as minute nodes, all minute nodes in recent years, such as 365 × 24 × 60 ═ 525600 minute nodes in 1 year, can be constructed first, and then each online time point and offline time point of each staff are associated to a corresponding minute node, so that the time difference of the associated query of a designated node is almost constant by combining an important characteristic that no index is adjacent to Neo4J, and the time difference is converted into a discrete minute node, and then the minute node is introduced into a query relation chain between personnel, so that the query performance of the same internet bar relation is hardly influenced by the data scale in the database, and the contradiction between the flexibility and the query performance is thoroughly solved.
Step (2): designing a query statement;
the corresponding query statement is exemplified as follows:
MATCH (p2: personnel { ID card: ": star:": Internet ] - > (t1: Internet behavior) - [: Internet ] - > (m11: min) - [: NEXT 0.. DELTA.t 1] - (: min) - [ r1: min ] - > (wb: Internet bar) < - [: min { personnel ID card: p2. ID card } ] - (m11)
WITH p2，r1，t1，wb
MATCH(p1:Person{gmsfhm:r1.gmsfhm})
RETURN distinct p2
The preset number of time nodes at intervals is set, the time nodes in the preset number are connected in series, and the node relation query with good Neo4J replaces mass data scanning and then is restricted, so that the performance of querying the spatio-temporal relation is greatly improved.
In the embodiment of the application, time nodes associated with target persons when the target persons generate the target behaviors at the target places can be determined from at least one corresponding time node in the query time period, and for each time node associated with the target persons, the time node and each time node among the time nodes with the preset number of intervals can be determined, candidate persons except the target persons of the target behaviors are generated at the target places, and then the candidate persons are determined as persons having space-time relations with the target persons. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
Based on the same application concept, a device for determining a spatiotemporal relationship corresponding to the method for determining a spatiotemporal relationship provided by the above embodiment is also provided in the embodiment of the present application, and since the principle of solving the problem of the device in the embodiment of the present application is similar to the method for determining a spatiotemporal relationship in the above embodiment of the present application, the implementation of the device may refer to the implementation of the method, and repeated details are not repeated.
As shown in fig. 2 to 5, fig. 2 shows one of the functional block diagrams of a spatiotemporal relationship determination apparatus 200 provided in an embodiment of the present application, fig. 3 shows a second functional block diagram of the spatiotemporal relationship determination apparatus 200 provided in an embodiment of the present application, fig. 4 shows a functional block diagram of a storage module 250 in fig. 3, and fig. 5 shows a functional block diagram of a sorting module 260 in fig. 3.
As shown in fig. 2, the apparatus 200 for determining spatiotemporal relationship includes:
the first determining module 210 is configured to determine, from at least one corresponding time node in the query time period, a time node associated when the target person generates the target behavior at the target location;
a second determining module 220, configured to determine, for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node, and generate a candidate person, other than the target person, of the target behavior at the target location;
and a third determining module 230, configured to determine the candidate person as a person having a spatiotemporal relationship with the target person.
In one possible implementation, as shown in fig. 3, the apparatus 200 for determining a spatiotemporal relationship further includes:
a dividing module 240, configured to divide a preset time period into multiple time points, and establish a corresponding time node according to each time point;
the storage module 250 is configured to perform association storage on behavior data generated by each person at a plurality of time points, a location corresponding to the generated behavior data, and identity information of each person, and corresponding time nodes.
In one possible implementation, as shown in fig. 4, the storage module 250 includes:
an establishing unit 252, configured to establish a corresponding location node according to each location, and establish a corresponding personnel node according to the identity information of the personnel;
the storage unit 254 is configured to store each location node, each person node, and each time node in association in a form of a knowledge graph according to behavior data generated by each person at a plurality of time points.
In one possible embodiment, the target behavior comprises any one of the following behaviors:
a surfing behavior; lodging behaviors; taking a ride; dining behavior; and (4) shopping behavior.
In one possible implementation, as shown in fig. 3, if there are a plurality of candidate persons, the apparatus 200 for determining a spatiotemporal relationship further includes:
a ranking module 260, configured to rank the multiple candidate persons according to the relationship scores between the multiple candidate persons and the target person, respectively.
In one possible implementation, as shown in fig. 5, the sorting module 260 includes:
an obtaining unit 262, configured to obtain, for each candidate person in the plurality of candidate persons, the number of time nodes at which the candidate person is associated with the target person;
a calculating unit 264, configured to calculate a relationship score between each candidate person and the target person according to the number of time nodes at which the plurality of candidate persons are respectively associated with the target person;
the determining unit 266 is configured to determine affinity and sparseness of the spatio-temporal relationship between each candidate person and the target person according to the relationship score corresponding to each candidate person, and rank the plurality of candidate persons.
In this embodiment of the application, from at least one corresponding time node in the query time period, a time node associated with the target person when the target person generates the target behavior at the target location may be determined by the first determining module 210, and for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node may be determined by the second determining module 220, a candidate person other than the target person who generates the target behavior at the target location, and further, the candidate person may be determined as a person having a space-time relationship with the target person by the third determining module 230. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
Based on the same application concept, referring to fig. 6, a schematic structural diagram of an electronic device 600 provided in the embodiment of the present application includes: a processor 610, a memory 620 and a bus 630, wherein the memory 620 stores machine-readable instructions executable by the processor 610, when the electronic device 600 runs, the processor 610 and the memory 620 communicate with each other through the bus 630, and the machine-readable instructions are executed by the processor 610 to perform the steps of the method for determining spatiotemporal relationships according to any of the above embodiments.
In particular, the machine readable instructions, when executed by the processor 610, may perform the following:
determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period;
for each time node associated with the target person, determining each time node between the time node and a preset number of time nodes separated from the time node, and generating candidate persons of the target behavior at the target place except the target person;
and determining the candidate persons as persons having a space-time relationship with the target person.
In the embodiment of the application, the time node associated when the target person generates the target behavior at the target location can be determined from at least one corresponding time node in the query time period, and for each time node associated with the target person, the time node and each time node spaced from the time node by a preset number of time nodes can be determined, and the candidate persons except the target person generating the target behavior at the target location are determined, so that the candidate persons are determined as the persons having a space-time relationship with the target person. Based on the mode, the time nodes are introduced, the behavior data of each person generated in each place is associated with the corresponding time nodes, and the efficiency of determining the person having the space-time relation with the target person can be improved.
Based on the same application concept, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining a spatiotemporal relationship provided in the foregoing embodiments are performed.
Specifically, the storage medium may be a general storage medium, such as a mobile disk, a hard disk, or the like, and when a computer program on the storage medium is executed, the method for determining a spatiotemporal relationship may be executed, and by introducing a time node and associating behavior data of each person generated at each location with the corresponding time node, the efficiency of determining a person having a spatiotemporal relationship with a target person may be improved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
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. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for determining spatiotemporal relationships, the method comprising:
determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period;
for each time node associated with the target person, determining each time node between the time node and a preset number of time nodes separated from the time node, and generating candidate persons of the target behavior at the target place except the target person;
and determining the candidate persons as persons having a space-time relationship with the target person.
2. The determination method according to claim 1, wherein before determining, from the at least one corresponding time node in the query time period, a time node associated with the target person when the target person generates the target behavior at the target location, the determination method further comprises:
dividing a preset time period into a plurality of time points, and establishing corresponding time nodes according to each time point;
and respectively storing the behavior data generated by each person at a plurality of time points, the places corresponding to the generated behavior data and the identity information of each person in association with the corresponding time nodes.
3. The method for determining according to claim 2, wherein the associating and storing the behavior data generated by each person at a plurality of time points, the location corresponding to the generated behavior data, and the identity information of each person with the corresponding time node comprises:
establishing a corresponding place node according to each place, and establishing a corresponding personnel node according to the identity information of personnel;
and according to behavior data respectively generated by each person at a plurality of time points, performing associated storage on each site node, each person node and each time node in a knowledge graph mode.
4. The determination method according to claim 1, wherein the target behavior comprises any one of the following behaviors:
a surfing behavior; lodging behaviors; taking a ride; dining behavior; and (4) shopping behavior.
5. The method according to claim 1, wherein if there are a plurality of candidate persons, after said determining the candidate persons as persons having a spatiotemporal relationship with the target person, the method further comprises:
and sequencing the candidate persons according to the relationship scores between the candidate persons and the target person.
6. The method of determining according to claim 5, wherein said ranking the plurality of candidate persons according to the relationship scores between the plurality of candidate persons and the target person respectively comprises:
for each candidate person in the candidate persons, acquiring the number of time nodes associated with the candidate person and the target person;
calculating a relationship score between each candidate person and the target person according to the number of time nodes which are respectively associated with the target person by the candidate persons;
and determining the affinity and the sparseness of the space-time relationship between each candidate person and the target person according to the corresponding relationship score of each candidate person, and sequencing the candidate persons.
7. An apparatus for determining spatiotemporal relationships, the apparatus comprising:
the first determining module is used for determining a time node associated when the target person generates the target behavior at the target place from at least one corresponding time node in the query time period;
a second determining module, configured to determine, for each time node associated with the target person, each time node between the time node and a preset number of time nodes separated from the time node, and generate a candidate person, other than the target person, of the target behavior at the target location;
and the third determining module is used for determining the candidate persons as persons having space-time relationship with the target person.
8. The apparatus according to claim 7, wherein the apparatus further comprises:
the dividing module is used for dividing a preset time period into a plurality of time points and establishing corresponding time nodes according to each time point;
and the storage module is used for storing the behavior data generated by each person at a plurality of time points, the places corresponding to the generated behavior data and the identity information of each person in association with the corresponding time nodes.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when an electronic device is operating, the machine-readable instructions being executable by the processor to perform the steps of the method for determining spatiotemporal relationships of any one of claims 1 to 6.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for determining spatiotemporal relationships according to any one of claims 1 to 6.
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