CN115188488A - Aggregation risk determination method and device, computer readable medium and electronic device - Google Patents
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Abstract
The present disclosure relates to the field of data processing technologies, and in particular, to an aggregation risk determination method, an aggregation risk determination apparatus, a computer-readable medium, and an electronic device, where the method includes: acquiring track data of each monitored object, and establishing a track database according to each track data; determining contact data corresponding to each monitored object based on a track database; determining an aggregation position in the track database according to the contact position and the contact object, and determining an aggregation event grade corresponding to the aggregation position; determining a target monitoring object in the monitoring objects, and determining a risk position set according to target track data and target contact data of the target monitoring object; and determining an aggregation risk level corresponding to each risk position in the risk position set according to the target contact data and the aggregation event level. According to the technical scheme, the monitoring object which is in contact with the target monitoring object can be determined, and then the risk position and the aggregation risk level are determined.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an aggregation risk determination method, an aggregation risk determination apparatus, a computer-readable medium, and an electronic device.
Background
With the rapid development of social economy, the urbanization process of China is continuously promoted, the population quantity of cities is more and more, and the situation of higher pedestrian density often occurs in public transportation, living facilities and other areas of the cities. Under the special condition that the spread is easy to occur, such as infectious diseases, the condition of high pedestrian density easily causes the hidden trouble of excessive spread, and the like.
At present, in order to avoid the situation of excessive spreading, a large number of workers are often required to manually obtain the flow regulation report, and then positions and groups with aggregation risks are manually defined. The manual processing process not only needs to consume a large amount of manpower, but also has the problem of low processing efficiency. In the case of low processing efficiency, the problem of uncontrolled flooding is also likely to occur.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to an aggregation risk determining method, an aggregation risk determining apparatus, a computer readable medium, and an electronic device, so as to improve efficiency of determining a risk location with an aggregation risk at least to a certain extent, so as to manage and control risk locations with different aggregation risk levels.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided an aggregation risk determination method, comprising: acquiring track data of each monitored object, and establishing a track database according to each track data; the trajectory data includes a trajectory position; determining contact data corresponding to each monitored object based on a track database; the contact data comprises a contact position and a contact object corresponding to the contact position; determining an aggregation position in the track database according to the contact position and the contact object, and determining an aggregation event grade corresponding to the aggregation position; determining a target monitoring object in the monitoring objects, and determining a risk position set according to target track data and target contact data of the target monitoring object; and determining an aggregation risk level corresponding to each risk position in the risk position set according to the target contact data and the aggregation event level.
Optionally, based on the foregoing scheme, the trajectory data further includes attribute data corresponding to the trajectory position; determining contact data corresponding to each monitored object based on a track database, wherein the contact data comprises the following steps: calculating the intersection of the track data of each monitored object in a track database to determine the common position of each monitored object and other monitored objects; and screening the attribute data corresponding to the common position based on a preset contact rule, and determining the contact data according to a screening result.
Optionally, based on the foregoing scheme, the method further includes: a contact time is calculated based on the contact data and the contact time is associated with the contact data.
Optionally, based on the foregoing scheme, before the attribute data corresponding to the common location is filtered based on the preset contact rule, the method further includes: classifying and identifying the track positions in the track database based on a preset normalization table; and determining a preset contact rule corresponding to the common position based on the classification identification.
Optionally, based on the foregoing scheme, determining an aggregation position in the trajectory database according to the contact position and the contact object, and determining an aggregation event level corresponding to the aggregation position, includes: screening the contact positions and the contact objects according to a preset aggregation rule, determining aggregation positions in the contact positions based on a screening result, and determining the contact objects corresponding to the aggregation positions; and determining an aggregation event grade corresponding to the aggregation position based on the event grade rule and the contact object corresponding to the aggregation position.
Optionally, based on the foregoing scheme, determining a risk location set according to target trajectory data and target contact data of the target monitoring object includes: adding the track positions included in the target track data into a risk position set; and adding a risk position set based on the contact position corresponding to the target contact object contained in the target contact data.
Optionally, based on the foregoing scheme, determining an aggregated risk level corresponding to each risk position in the risk position set according to the target contact data and the aggregated event level includes: determining a basic risk level corresponding to each risk position according to the aggregation event level corresponding to each risk position in the risk position set; and updating the basic risk level according to the target contact data so as to obtain the aggregation risk level corresponding to each risk position.
Optionally, based on the foregoing scheme, updating the basic risk level according to the target contact data includes: classifying the target contact object according to the number of contact positions between the target monitoring object and the target contact object to obtain a first contact object and a second contact object; wherein the number of contact positions between the first contact object and the target detection object is larger than that of the second contact object; determining a first risk position in the risk positions based on the first contact object, and increasing a first preset level for a basic risk level corresponding to the first risk position; and determining a second risk position in the risk positions based on the second contact object, and reducing the basic risk level corresponding to the second risk position by a second preset level.
Optionally, based on the foregoing scheme, acquiring trajectory data of each monitored object includes: acquiring registration data of different data sources; the registration data includes a registration location of the monitoring object; and integrating the registered data from the dimension of the monitoring object to obtain the track data corresponding to each monitoring object.
According to a second aspect of the present disclosure, there is provided an aggregation risk determination apparatus comprising: the database establishing module is used for acquiring the track data of each monitored object and establishing a track database according to each track data; the trajectory data includes a trajectory position; the contact data determining module is used for determining contact data corresponding to each monitored object based on the track database; the contact data comprises a contact position and a contact object corresponding to the contact position; the first grade determining module is used for determining an aggregation position in the track database according to the contact position and the contact object and determining an aggregation event grade corresponding to the aggregation position; the position determining module is used for determining a target monitoring object in the monitoring objects and determining a risk position set according to target track data and target contact data of the target monitoring object; and the second grade determining module is used for determining the aggregation risk grade corresponding to each risk position in the risk position set according to the target contact data and the aggregation event grade.
According to a third aspect of the disclosure, there is provided a computer-readable medium, on which a computer program is stored, which program, when executed by a processor, performs the method of any of the above.
According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic apparatus including:
a processor; and
storage means for storing one or more programs which, when executed by one or more processors, cause the one or more processors to carry out a method as claimed in any preceding claim.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the aggregation risk determining method provided by an embodiment of the present disclosure, by analyzing trajectory data of each monitoring object in a trajectory database, contact data between each monitoring object and other monitoring objects may be determined, then an aggregation position where an aggregation event may exist in the trajectory database is determined according to the contact data, and an aggregation event level used for indicating an aggregation degree of each aggregation position is determined; when a target monitoring object is determined, a risk position set appearing in the target monitoring object can be determined through target track data of the target monitoring object, and then an aggregation risk level corresponding to each risk position is determined. According to the method, on one hand, the monitoring objects which are in contact with the target monitoring object can be determined through analysis of the track data of all the monitoring objects, and then the extended monitoring object range is predicted; on the other hand, by determining the risk positions and the aggregation risk levels, the extended geographical position range can be predicted, so that different management and control can be performed on the risk positions with different aggregation risk levels.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It should be apparent that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived by those of ordinary skill in the art without inventive effort. In the drawings:
FIG. 1 schematically illustrates a flow chart of a method of aggregate risk determination in an exemplary embodiment of the disclosure;
FIG. 2 schematically illustrates a flow chart of a method of determining contact data in an exemplary embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining aggregate location and aggregate event rating in an exemplary embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of a method of determining a set of risk locations in an exemplary embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart of a method of determining an aggregate risk level for each risk location in a set of risk locations in an exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a composition diagram of an aggregation risk determination apparatus in an exemplary embodiment of the present disclosure;
fig. 7 schematically illustrates a structural schematic diagram of a computer system of an electronic device suitable for implementing exemplary embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
For example, if a certain diagnosed patient has been going out many times before the confirmed patient is diagnosed, in order to find other people possibly infected by the diagnosed patient as soon as possible, it is often necessary to manually obtain a circulation report to determine the location and the population to be controlled, and sometimes it is necessary to publicize the occurrence of the patient by means of media, etc., and then determine the people possibly infected by the spontaneous report of the pedestrian. The mode not only needs to consume a large amount of manpower, but also has the problem of low processing efficiency, and then has the problem of uncontrolled diffuse situation.
Based on one or more of the problems described above, the present example embodiment provides an aggregate risk determination method. The method can be applied to the process of analyzing and controlling various easily-delayed events.
Referring to fig. 1, the above aggregation risk determination method may include the following steps S110 to S150:
in step S110, trajectory data of each monitored object is acquired, and a trajectory database is established according to each trajectory data.
The track data of the monitoring object comprises the position of the monitoring object at least one time point. For example, the trajectory data of monitoring object a may include at a train station at 12: 1/1.
In an exemplary embodiment, when acquiring the trajectory data of the detection object, registration data of different data sources may be acquired, and the registration data includes a registration position of each monitoring object at a certain time. And integrating the registered data of different data sources according to the dimension of the monitoring object to obtain the position of each monitoring object at different time, thereby determining the track data of each monitoring object.
The registered data can include the taking information of each travel system such as entry and exit, trains, domestic flights, ships, buses in cities, taxis, city railways and shared bicycles, or health code scanning, entry and exit registration systems and ticketing system data, or the shopping registration of shopping scenes such as hospital visit records and drug stores, and the mobile phone Bluetooth pairing records. The track data of the monitoring object can be obtained by sorting the registration data according to time and position and integrating the registration data according to the dimension of the monitoring object. For example, when the monitoring object takes a train, the monitoring object can be determined to be located at a train departure station when the train departs and be located at a train arrival station when the train arrives. At this time, trajectory data of the monitored object, departure time-departure station and arrival time-arrival station, can be obtained.
In step S120, contact data corresponding to each monitoring target is determined based on the trajectory database.
The contact data may include a contact position and a contact object corresponding to the contact position. For example, monitoring object B goes to the same pharmacy to buy a medicine at the same time as monitoring object C, and for monitoring object B, the contact data may include pharmacy (contact location) -monitoring object C (contact object).
In an exemplary embodiment, the track data may further include attribute data corresponding to the track position, such as time to reach the track position, time to stay at the track position, coverage of the track position, and the like. At this time, referring to fig. 2, determining the contact data corresponding to each monitored object based on the trajectory database may be implemented by:
step S210, calculating an intersection of the trajectory data of each monitored object in the trajectory database to determine a common position existing between each monitored object and other monitored objects.
Step S220, screening the attribute data corresponding to the common position based on a preset contact rule, and determining the contact data according to the screening result.
Specifically, a trajectory intersection existing between each monitored object may be calculated in the trajectory database to determine a common position where each monitored object and other monitored objects commonly appear; and then screening the attribute data corresponding to the common position based on a preset contact rule, and determining the contact data according to the screening result.
It should be noted that, when determining the contact data, when the common location and the corresponding attribute data satisfy the preset contact rule, the common location may be determined as the contact location, and then all monitoring objects to which the attribute data satisfying the preset contact rule belong may be determined as the contact objects.
For example, if a preset contact rule is that there are other monitoring objects which scan codes at the same position as the monitoring object and scan codes for 3 minutes before and after the time of the monitoring object, the position is determined as a contact position, and the other monitoring objects are contact objects corresponding to the contact position. At this time, if the monitoring object D is 12:03 is divided into 1 common position and code scanning is carried out, and the monitoring object E is arranged at 12: 05 scanning the code at the common position 1, wherein the code scanning time and the code scanning position both accord with the preset contact rule. At this time, it may be determined that the common position 1 is a contact position for the monitoring object D, and the monitoring object E is a contact object of the monitoring object D; for the monitoring object E, the common location 1 can be determined as a contact location, and the monitoring object D as a contact object of the monitoring object E.
In addition, the contact time of the monitoring object and the contact object can be determined according to the intersection of the residence time of the monitoring object and the contact object at each contact position. After the contact time is determined, the contact time can be associated with the contact data so that the user can visually see the contact time between the monitoring object and each contact object.
For example, when there is a sweep-in time and a sweep-out time, the sweep-in time can be directly used as the stay time, and then the intersection can be determined according to the stay time; for another example, in the case that only the scan-in time does not have the scan-out time, the scan-in time of the current time can be subtracted from the scan-in time of the next different position, and then an estimated time is determined according to the length of the stay time. For example, the contact time may be counted as 0.5 day when the residence time exceeds 12 hours; for another example, when only the scan-in time is not the scan-out time, the scan-in time (for example, a different name other than 100 meters) of the next different name is calculated, and the scan-in time is subtracted from the scan-in time of the next different name, and then the result is divided by 2 to obtain the current contact time.
It should be noted that, in some embodiments, before determining the contact data, the contact time between the monitoring object and another monitoring object may be calculated according to the attribute data, and the contact time is used as a part of the attribute data, and then a preset contact rule related to the contact time is set to screen the attribute data, and then the contact data is determined according to the screening result.
In an exemplary embodiment, the corresponding set contact rules may also differ due to differences in characteristics of trajectory data obtained from different sources or different locations. Therefore, before the attribute data corresponding to the common position is screened based on the preset contact rule, all the track positions in the track database can be classified and identified according to the preset normalization table. And preset contact rules corresponding to different classifications are set in advance, and when the contact rules are screened, different preset contact rules can be selected according to the classification identification of each track position. For example, the trajectory data may be divided into one or more of the following types: sweep yard trip, entry and exit, urban traffic, shopping, heating outpatient service, hospitalization, train, plane, etc.
In step S130, an aggregation position is determined in the trajectory database according to the contact position and the contact object, and an aggregation event level corresponding to the aggregation position is determined.
In an exemplary embodiment, as shown in fig. 3, the determining the aggregation position in the trajectory database according to the contact position and the contact object, and determining the aggregation event level corresponding to the aggregation position may include the following steps S310 to S320:
in step S310, the contact positions and the contact objects are filtered according to a preset aggregation rule, an aggregation position is determined in the contact positions based on the filtering result, and a contact object corresponding to the aggregation position is determined.
In an exemplary embodiment, when determining the aggregation positions, the contact positions and the contact objects may be screened according to a preset aggregation rule, and then a contact position satisfying the preset aggregation rule among the contact positions where the contact occurs may be determined as the aggregation position according to a screening result. Meanwhile, a contact object satisfying a preset aggregation rule is determined as a contact object corresponding to the aggregation position.
It should be noted that, similar to the setting of the contact rule, the preset aggregation rule and the event level rule may also be set differently according to the different track positions. When screening and determining the grade of the aggregation event, different rules corresponding to different classifications are set in advance, and then different rules can be selected according to the classification mark of each track position to carry out the screening process and the process of determining the grade of the aggregation event.
For example, the preset aggregation rule may include: the positions are different but within the range of hundred meters or the same, the number of people staying in the same time point or the same time range (such as within 10 minutes) exceeds 1 person, the current position is the gathering position of all the staying people, and the staying people at the same time point or the same time range (such as within 10 minutes) are the contact objects corresponding to the current gathering position. By setting a preset aggregation rule, the aggregation position of each monitoring object and the contact object of each aggregation position can be obtained. It should be noted that one monitoring object may have a plurality of aggregation positions, and the contact object of each aggregation position may be different.
In step S320, an aggregate event rank corresponding to the aggregate location is determined based on the event rank rule and the contact object corresponding to the aggregate location.
In an exemplary embodiment, after determining the aggregation location, an aggregation event level corresponding to the aggregation location may be determined based on the event level rule and the contact object corresponding to the aggregation location. Specifically, the condition that can be discriminated in the order of the number of monitoring objects gathered at the gathering position; the condition of grade discrimination can also be the density of the monitored objects at the gathering position; in addition, different settings can be made according to different diffuse sources, and the disclosure is not limited to this.
For example, for a shopping scene, the number of people in a certain gathering location exceeds 5, or the number of contact persons in a certain gathering location exceeds 10, the gathering event level corresponding to the gathering location is set to be a middle level; for example, for track data such as a shared bicycle, if the number of people at a certain aggregation location exceeds 100 or the number of contact objects at the aggregation location exceeds 1000, the aggregation event level corresponding to the aggregation location is high.
It should be noted that, in some exemplary embodiments, an aggregated event early warning may also be set for the aggregated event. For example, when a medium-level or high-level aggregation event occurs, a prevention and control early warning can be carried out on the aggregation position.
In step S140, a target monitoring object is determined among the monitoring objects, and a risk location set is determined according to target trajectory data and target contact data of the target monitoring object.
In an exemplary embodiment, the target monitoring object may comprise a monitoring object carrying a diffuse source. For example, in the context of an infectious disease, the target monitoring subject may be a diagnosed patient; as another example, in the context of message propagation, the target monitoring object may be the first source of the message.
In an exemplary embodiment, after the target detection object is determined in the monitored objects, a set of risk locations may be determined from the target trajectory data and the target contact data of the target monitoring object.
Specifically, as shown in fig. 4, the method may include the following steps S410 and S420:
in step S410, the trajectory positions included in the target trajectory data are added to the risk position set.
Specifically, the above process may be understood as adding the trajectory position included in the target trajectory data corresponding to the target monitoring object into the risk position set, with the target trajectory data of the target monitoring object as a main body. For example, in the case of infectious diseases, the track positions that the confirmed patient has arrived are all added to the risk position set.
In step S420, a risk location set is added based on the contact trajectory location corresponding to the target contact object included in the target contact data.
Specifically, some trajectory positions may be target contact positions, that is, the target monitoring object makes contact with the target contact object at the target contact positions. At this time, the target contact person may also become a carrier, and therefore the contact position corresponding to the monitoring object, that is, the target contact object, is also added to the risk position set. For example, in a scene of an infectious disease, a contact position (a contact position between the monitoring object G and the monitoring object H) may exist between the monitoring object G (a target contact object) having a contact relationship with a diagnosed patient F (a target monitoring object) and another monitoring object H (a contact object corresponding to the target contact object), and the contact position is also added to the risk position set as a risk position.
In step S150, an aggregated risk level corresponding to each risk location in the risk location set is determined according to the target contact data and the aggregated event level.
In an exemplary embodiment, after the risk location set is determined, an aggregate risk level corresponding to each risk location in the risk location set may be determined according to the target contact data and the aggregate event level.
Specifically, the basic risk level corresponding to the risk position may be determined according to the aggregation event level corresponding to each risk position in the risk position combination. For example, the high, medium, low, and no-determined base risk levels of the corresponding aggregated event levels according to risk location may be 5, 4, 3, and 2; and then updating the basic risk level based on the target contact data to obtain the aggregation risk level corresponding to each risk position.
In an exemplary embodiment, as shown with reference to fig. 5, updating the base risk level based on the target contact data may include the following steps S510 to S530:
in step S510, the target contact objects are classified according to the number of contact positions between the target monitoring object and the target contact object, and a first contact object and a second contact object are obtained.
Wherein the number of contact positions between the first contact object and the target detection object is larger than that of the second contact object. When the target contact object has only a small number of contact positions with the target monitoring object, it may be determined that the target contact object is only in point contact with the target detection object; when a large number of contact positions exist between the target contact object and the target monitoring object, it is likely that the target contact object and the target monitoring object are the same person. When the contact conditions are different, the corresponding spread may also be different. The target contact object can therefore be classified into a first contact object and a second contact object on the basis of the number of contact positions. By the above-described manner of dividing the first contact object and the second contact object, the process of predicting the primary close contact and the secondary close contact can be realized.
In step S520, a first risk position is determined in the risk positions based on the first contact object, and a first preset level is increased for a basic risk level corresponding to the first risk position.
In particular, when there are a large number of contact locations between the target contact object and the target monitoring object, infection is likely to have occurred, and therefore it is desirable to determine a first risk location (e.g., a contact location between the target monitoring object and the first contact object) in the risk locations and increase the first preset level to the first risk location based on a base risk level corresponding to the first risk location. It should be noted that the first preset level may also be determined according to the number of the contact positions, and when the number of the contact positions existing between the target contact object and the target monitoring object is different, different first preset levels are selected to be increased.
In step S530, a second risk position is determined in the risk positions based on the second contact object, and the basic risk level corresponding to the second risk position is decreased by a second preset level.
In particular, when there are a small number of contact locations between the target contact object and the target monitoring object, the probability of infection occurring is small. The risk level of the second risk position determined on the basis of the second contact object (e.g. the risk position remaining from the contact position among the trajectory positions of the second contact object in the risk position), i.e. the second risk position, may be reduced by a second predetermined level. Similarly, the second preset level may also be determined according to the number of the contact positions, and when the number of the contact positions existing between the target contact object and the target monitoring object is different, different second preset levels are selected for adjustment and reduction.
The above process of updating the basic risk level is exemplified below by taking an infectious disease scenario as an example.
(1) And determining 20 risk positions in total according to the target monitoring object I, the target contact object J with more than 2 contact positions with the target monitoring object I and the target contact object K with only 1 contact position with the target monitoring object I. Of these, 15 are the track positions of the target monitoring object I, and the remaining 5 are the track positions of the target contact object K excluding the contact positions. Wherein 5 of the 15 track positions are the contact positions of the target contact object J,
(2) And respectively determining basic risk levels according to the aggregation event levels corresponding to the 20 risk positions, and setting the basic risk levels to be 5 if the aggregation event levels corresponding to the 20 risk positions are all high levels.
(3) The base risk levels corresponding to 5 first risk positions as the contact positions of the target contact object J are all increased by 2, that is, the aggregation risk levels corresponding to the 5 first risk positions are all 7.
(4) And (3) reducing the aggregation risk levels of the 5 track positions except the contact position of the target contact object K by 3, wherein the aggregation risk levels corresponding to the 5 second risk positions are 2.
It should be noted that, when the aggregate risk level is reduced to 0, the aggregate risk level is deleted from the risk position, that is, the risk position has no risk.
Through risk level analysis of each risk position, the position range possibly related to the spreading time taking the target monitoring object as a spreading source and the influence on the position can be predicted, and further different prevention and control strategies or measures can be conveniently executed on different risk positions according to different aggregation risk levels.
It is noted that the above-mentioned figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes illustrated in the above figures are not intended to indicate or limit the temporal order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Furthermore, in an exemplary embodiment of the present disclosure, an aggregation risk determination apparatus is also provided. Referring to fig. 6, the aggregate risk determination apparatus 600 includes: a database creation module 610, a contact data determination module 620, a first tier determination module 630, a location determination module 640, and a second tier determination module 650.
The database establishing module 610 may be configured to obtain trajectory data of each monitored object, and establish a trajectory database according to each trajectory data; the trajectory data includes a trajectory position; the contact data determining module 620 may be configured to determine contact data corresponding to each monitored object based on the trajectory database; the contact data comprises a contact position and a contact object corresponding to the contact position; the first rank determination module 630 may be configured to determine an aggregation position in the trajectory database according to the contact position and the contact object, and determine a rank of an aggregation event corresponding to the aggregation position; the position determination module 640 may be configured to determine a target monitoring object among the monitoring objects, and determine a risk position set according to target trajectory data and target contact data of the target monitoring object; the second rank determination module 650 may be configured to determine an aggregated risk rank corresponding to each risk location in the set of risk locations according to the target contact data and the aggregated event rank.
In an exemplary embodiment, the contact data determination module 620 may be configured to calculate an intersection of trajectory data of each monitored object in the trajectory database to determine a common location existing between each monitored object and other monitored objects; and screening the attribute data corresponding to the common position based on a preset contact rule, and determining the contact data according to a screening result.
In an exemplary embodiment, the contact data determination module 620 may be configured to calculate a contact time based on the contact data and associate the contact time with the contact data.
In an exemplary embodiment, the contact data determining module 620 may be configured to perform classification and identification on the trajectory position in the trajectory database based on a preset normalization table; and determining a preset contact rule corresponding to the common position based on the classification identification.
In an exemplary embodiment, the first rank determining module 630 may be configured to filter the contact locations and the contact objects according to a preset aggregation rule, determine an aggregation location in the contact locations based on a filtering result, and determine a contact object corresponding to the aggregation location; and determining an aggregation event grade corresponding to the aggregation position based on the event grade rule and the contact object corresponding to the aggregation position.
In an exemplary embodiment, the location determination module 640 may be configured to add the trajectory locations included in the target trajectory data to the set of risk locations; and adding a risk position set based on the contact position corresponding to the target contact object contained in the target contact data.
In an exemplary embodiment, the second level determining module 650 may be configured to determine a basic risk level corresponding to each risk location according to the aggregation event level corresponding to each risk location in the risk location set; and updating the basic risk level according to the target contact data so as to obtain the aggregation risk level corresponding to each risk position.
In an exemplary embodiment, the second rank determination module 650 may be configured to classify the target contact object according to the number of contact positions between the target monitoring object and the target contact object, resulting in a first contact object and a second contact object; wherein the number of contact positions between the first contact object and the target detection object is larger than that of the second contact object; determining a first risk position in the risk positions based on the first contact object, and increasing a first preset level for a basic risk level corresponding to the first risk position; and determining a second risk position in the risk positions based on the second contact object, and reducing the basic risk level corresponding to the second risk position by a second preset level.
In an exemplary embodiment, the database building module 610 may be configured to obtain enrollment data for different data sources; the registration data includes a registration location of the monitoring object; and integrating the registered data from the dimension of the monitoring object to obtain the track data corresponding to each monitoring object.
The specific details of each module in the above apparatus have been described in detail in the method section, and details that are not disclosed may refer to the method section, and thus are not described again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Further, fig. 7 shows a schematic structural diagram of a computer system suitable for an electronic device used to implement the embodiments of the present disclosure.
It should be noted that the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by a Central Processing Unit (CPU) 701, performs various functions defined in the methods and apparatus of the present application. In some embodiments, the computer system 700 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method in the embodiments described below. For example, the electronic device may implement the steps shown in fig. 1 to 5, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (14)
1. An aggregate risk determination method, comprising:
acquiring track data of each monitored object, and establishing a track database according to each track data; the trajectory data comprises a trajectory position;
determining contact data corresponding to each monitored object based on the track database; the contact data comprises a contact position and a contact object corresponding to the contact position;
determining an aggregation position in the contact positions of the track database according to the contact position and the contact object, and determining an aggregation event level corresponding to the aggregation position;
determining a target monitoring object in the monitoring objects, and determining a risk position set according to target track data and target contact data of the target monitoring object;
and determining an aggregation risk level corresponding to each risk position in the risk position set according to the target contact data and the aggregation event level.
2. The method of claim 1, wherein the trajectory data further comprises attribute data corresponding to the trajectory position;
the determining contact data corresponding to each monitored object based on the trajectory database includes:
calculating the intersection of the trajectory data of each monitored object in the trajectory database to determine the common position existing between each monitored object and other monitored objects;
and screening the attribute data corresponding to the common position based on a preset contact rule, and determining the contact data according to a screening result.
3. The method of claim 2, further comprising:
calculating a contact time based on the contact data and associating the contact time with the contact data.
4. The method according to claim 2, wherein before the filtering the attribute data corresponding to the common location based on the preset contact rule, the method further comprises:
classifying and identifying the track positions in the track database based on a preset normalization table;
and determining a preset contact rule corresponding to the common position based on the classification identification.
5. The method of claim 1, wherein determining an aggregate location from the contact locations and the contact object in the contact locations of the trajectory database and determining an aggregate event rank corresponding to the aggregate location comprises:
screening the contact positions and the contact objects according to a preset aggregation rule, determining aggregation positions in the contact positions based on a screening result, and determining the contact objects corresponding to the aggregation positions;
and determining an aggregation event grade corresponding to the aggregation position based on an event grade rule and the contact object corresponding to the aggregation position.
6. The method of claim 1, wherein determining a set of risk locations from target trajectory data and target contact data of the target monitoring object comprises:
adding the track positions included in the target track data into a risk position set;
and adding the risk position set based on the contact position corresponding to the target contact object contained in the target contact data.
7. The method of claim 1, wherein determining an aggregated risk level corresponding to each risk location in the set of risk locations from the target contact data and the aggregated event level comprises:
determining a basic risk level corresponding to each risk position according to the aggregation event level corresponding to each risk position in the risk position set;
and updating the basic risk level according to the target contact data so as to obtain an aggregated risk level corresponding to each risk position.
8. The method of claim 7, wherein said updating the base risk level based on the target contact data comprises:
classifying the target contact object according to the number of contact positions between the target monitoring object and the target contact object to obtain a first contact object and a second contact object; wherein the number of contact locations between the first contact object and the target monitoring object is greater than the second contact object;
determining a first risk position in the risk positions based on the first contact object, and increasing a first preset level for a basic risk level corresponding to the first risk position;
and determining a second risk position in the risk positions based on the second contact object, and reducing the basic risk level corresponding to the second risk position by a second preset level.
9. The method of claim 1, wherein the obtaining trajectory data for each monitored object comprises:
acquiring registration data of different data sources; the registration data includes a registration location of the monitoring object;
and integrating the registration data from the dimension of the monitored object to obtain the track data corresponding to each monitored object.
10. The method of claim 2, wherein the attribute data includes arrival time, dwell time, and coverage.
11. The method according to claim 2, wherein the screening the attribute data corresponding to the common location based on the preset contact rule, and determining the contact data according to the screening result comprises:
when the common position and the attribute data corresponding to the common position meet the preset contact rule, determining the common position as a contact position;
and determining all monitoring objects which meet the preset contact rule and to which the attribute data belong as contact objects.
12. An aggregate risk determination device, comprising:
the database establishing module is used for acquiring the track data of each monitored object and establishing a track database according to each track data; the trajectory data comprises a trajectory position;
the contact data determining module is used for determining contact data corresponding to each monitored object based on the track database; the contact data comprises a contact position and a contact object corresponding to the contact position;
the first grade determining module is used for determining an aggregation position in the contact positions of the track database according to the contact position and the contact object, and determining an aggregation event grade corresponding to the aggregation position;
the position determining module is used for determining a target monitoring object in the monitoring objects and determining a risk position set according to target track data and target contact data of the target monitoring object;
and the second grade determining module is used for determining the aggregation risk grade corresponding to each risk position in the risk position set according to the target contact data and the aggregation event grade.
13. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 11.
14. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any of claims 1 to 11 via execution of the executable instructions.
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