Aggregated risk determination method and apparatus, computer-readable medium, and electronic device

Abstract

The present disclosure relates to the technical field of data processing, in particular to a gathering risk determination method, a gathering risk determination device, a computer readable medium and an electronic device. The method comprises: obtaining trajectory data of each monitoring object, and establishing a trajectory database according to the trajectory data; determining contact data corresponding to each monitoring object based on the trajectory database; determining a gathering position in the trajectory database according to a contact position and a contact object, and determining a gathering event level corresponding to the gathering position; determining a target monitoring object in the monitoring objects, and determining a risk position set according to target trajectory data and target contact data of the target monitoring object; and determining a gathering risk level corresponding to each risk position in the risk position set according to the target contact data and the gathering event level. The technical scheme of the embodiment of the present disclosure can determine the monitoring objects that have contact with the target monitoring object, and further determine the risk position and the gathering risk level.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a method for determining cluster risk, a device for determining cluster risk, a computer-readable medium, and an electronic device. Background Technology

[0002] With the rapid development of society and economy, my country's urbanization process is constantly advancing, and the urban population is increasing. Urban public transportation and living facilities often experience high pedestrian density. In special circumstances where infectious diseases can easily spread, this high pedestrian density can easily lead to excessive spread and other hidden dangers.

[0003] Currently, to prevent the aforementioned excessive spread, a large number of staff are often needed to manually obtain epidemiological investigation reports and then manually identify locations and groups of people at risk of clustering. This manual process not only consumes a lot of manpower but also suffers from low efficiency. Low efficiency can easily lead to uncontrolled spread of the virus.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, apparatus, computer-readable medium, and electronic device for determining cluster risk, thereby improving the efficiency of determining risk locations with cluster risk to at least a certain extent, so as to facilitate the management of risk locations with different cluster risk levels.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to a first aspect of this disclosure, a method for determining cluster risk is provided, comprising: acquiring trajectory data of each monitored object and establishing a trajectory database based on the trajectory data; the trajectory data includes trajectory locations; determining contact data corresponding to each monitored object based on the trajectory database; the contact data includes contact locations and contact objects corresponding to the contact locations; determining cluster locations in the trajectory database based on the contact locations and contact objects, and determining the cluster event level corresponding to the cluster locations; identifying target monitored objects among the monitored objects, and determining a set of risk locations based on the target trajectory data and target contact data of the target monitored objects; and determining the cluster risk level corresponding to each risk location in the set of risk locations based on the target contact data and the cluster event level.

[0008] Optionally, based on the aforementioned scheme, the trajectory data also includes attribute data corresponding to the trajectory location; determining the 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 location between each monitored object and other monitored objects; filtering the attribute data corresponding to the common location based on preset contact rules, and determining the contact data based on the filtering results.

[0009] Optionally, based on the aforementioned scheme, the method further includes: calculating the contact time based on the contact data and associating the contact time with the contact data.

[0010] Optionally, based on the aforementioned scheme, before filtering the attribute data corresponding to the common location based on the preset contact rules, the method further includes: classifying and labeling the trajectory locations in the trajectory database based on a preset normalization table; and determining the preset contact rules corresponding to the common location based on the classification labels.

[0011] Optionally, based on the aforementioned scheme, the aggregation location is determined in the trajectory database according to the contact location and the contact object, and the aggregation event level corresponding to the aggregation location is determined, including: filtering the contact location and the contact object according to preset aggregation rules, determining the aggregation location among the contact locations based on the filtering results, and determining the contact object corresponding to the aggregation location; determining the aggregation event level corresponding to the aggregation location based on the event level rules and the contact object corresponding to the aggregation location.

[0012] Optionally, based on the aforementioned scheme, a risk location set is determined according to the target trajectory data and target contact data of the target monitoring object, including: adding the trajectory locations included in the target trajectory data to the risk location set; and adding the contact locations corresponding to the target contact objects included in the target contact data to the risk location set.

[0013] Optionally, based on the aforementioned scheme, the cluster risk level corresponding to each risk location in the risk location set is determined according to the target contact data and the cluster event level, including: determining the basic risk level corresponding to each risk location according to the cluster event level corresponding to each risk location in the risk location set; and updating the basic risk level according to the target contact data to obtain the cluster risk level corresponding to each risk location.

[0014] Optionally, based on the aforementioned scheme, the basic risk level is updated according to the target contact data, including: classifying the target contact objects according to the number of contact locations 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 detection object is greater than that of the second contact object; determining a first risk location based on the first contact object among the risk locations, and increasing the basic risk level corresponding to the first risk location by a first preset level; determining a second risk location based on the second contact object among the risk locations, and decreasing the basic risk level corresponding to the second risk location by a second preset level.

[0015] Optionally, based on the aforementioned scheme, the trajectory data of each monitored object is obtained, including: obtaining registration data from different data sources; the registration data includes the registration location of the monitored object; and integrating the registration data from the perspective of the monitored object to obtain the trajectory data corresponding to each monitored object.

[0016] According to a second aspect of this disclosure, a cluster risk determination device is provided, comprising: a database establishment module for acquiring trajectory data of each monitored object and establishing a trajectory database based on the trajectory data; the trajectory data includes trajectory locations; a contact data determination module for determining contact data corresponding to each monitored object based on the trajectory database; the contact data includes contact locations and contact objects corresponding to the contact locations; a first level determination module for determining cluster locations in the trajectory database based on the contact locations and contact objects, and determining the cluster event level corresponding to the cluster locations; a location determination module for determining target monitored objects among the monitored objects, and determining a set of risk locations based on the target trajectory data and target contact data of the target monitored objects; and a second level determination module for determining the cluster risk level corresponding to each risk location in the set of risk locations based on the target contact data and the cluster event level.

[0017] According to a third aspect of this disclosure, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the foregoing.

[0018] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising:

[0019] Processor; and

[0020] A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to perform any of the methods described above.

[0021] The technical solutions provided in this disclosure may have the following beneficial effects:

[0022] In one embodiment of this disclosure, a method for determining cluster risk involves analyzing the trajectory data of each monitored object in a trajectory database to determine the contact data between each monitored object and other monitored objects. Based on the contact data, the method identifies potential clustering locations in the trajectory database and determines a clustering event level to represent the degree of clustering at each location. When identifying a target monitored object, the method uses the target trajectory data to determine the set of risk locations where the target monitored object may appear, thereby determining the clustering risk level corresponding to each risk location. This disclosure, on the one hand, analyzes the trajectory data of each monitored object to identify monitored objects that have contact with the target monitored object, thus predicting the spread of monitored objects; on the other hand, by determining the risk locations and clustering risk levels, it predicts the geographical range of the spread, facilitating different control measures for risk locations with different clustering risk levels.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0025] Figure 1 This schematically illustrates a flowchart of a cluster risk determination method according to an exemplary embodiment of the present disclosure;

[0026] Figure 2 This schematically illustrates a flowchart of a method for determining contact data in an exemplary embodiment of the present disclosure;

[0027] Figure 3 This schematically illustrates a flowchart of a method for determining aggregation location and aggregation event level in an exemplary embodiment of this disclosure;

[0028] Figure 4 This schematically illustrates a flowchart of a method for determining a set of risky locations in an exemplary embodiment of this disclosure;

[0029] Figure 5 This schematically illustrates a flowchart of a method for determining the clustered risk level corresponding to each risk location in a set of risk locations according to an exemplary embodiment of the present disclosure;

[0030] Figure 6 This schematic diagram illustrates the composition of a cluster risk determination apparatus according to an exemplary embodiment of the present disclosure;

[0031] Figure 7 The schematic diagram illustrates a structural schematic of a computer system suitable for implementing an electronic device according to exemplary embodiments of the present disclosure. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary 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.

[0033] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] Taking infectious diseases as an example, suppose a confirmed patient traveled multiple times before diagnosis. To quickly identify other individuals who may have been infected, it is often necessary to manually obtain epidemiological investigation reports to determine the locations and populations requiring control. Sometimes, it is also necessary to use media and other means to publicize the places where the patient appeared, and then rely on spontaneous reports from pedestrians to identify potentially infected individuals. The above methods not only require a large amount of manpower but also suffer from low processing efficiency, leading to uncontrolled spread of the disease.

[0035] To address one or more of the aforementioned issues, this example implementation provides a method for determining cluster risk. This method can be applied to the analysis and management of various easily spread events.

[0036] refer to Figure 1 As shown, the above-mentioned cluster risk determination method may include the following steps S110 to S150:

[0037] In step S110, trajectory data of each monitored object is acquired, and a trajectory database is established based on the trajectory data.

[0038] The trajectory data of the monitored object includes the location of the monitored object at at least one point in time. For example, the trajectory data of monitored object A may include the location at the train station at 12:00 on January 1st.

[0039] In an exemplary embodiment, when acquiring the trajectory data of the aforementioned monitored objects, registration data from different data sources can be acquired. This registration data includes the registered location of each monitored object at a certain time. By integrating the registration data from different data sources according to the dimension of the monitored object, the location of each monitored object at different times can be obtained, thereby determining the trajectory data of each monitored object.

[0040] The registration data can include travel information from various transportation systems such as entry / exit, trains, domestic flights, ships, city buses, taxis, light rail, and shared bicycles; data from health code scanning, entry / exit registration systems, ticketing systems; or shopping records from hospital visits, pharmacies, and mobile phone Bluetooth pairing records. By organizing the above registration data by time and location, and then integrating it according to the monitoring object dimension, the trajectory data of the monitoring object can be obtained. For example, when the monitoring object is taking a train, based on the travel information, it can be determined that the monitoring object is at the departure station when the train departs and at the arrival station when the train arrives. At this time, the trajectory data of the monitoring object can be obtained: departure time - departure station and arrival time - arrival station.

[0041] In step S120, the contact data corresponding to each monitored object is determined based on the trajectory database.

[0042] The contact data mentioned above can include the contact location and the corresponding contact object. For example, if monitoring object B and monitoring object C go to the same pharmacy to buy medicine at the same time, the contact data for monitoring object B can include the pharmacy (contact location) and monitoring object C (contact object).

[0043] In an exemplary embodiment, the trajectory data may further include attribute data corresponding to the trajectory location, such as the time of arrival at the trajectory location, the time spent at the trajectory location, and the area covered by the trajectory location. In this case, refer to... Figure 2 As shown, determining the contact data corresponding to each monitored object based on the trajectory database can be achieved through the following process:

[0044] Step S210: Calculate the intersection of trajectory data of each monitored object in the trajectory database to determine the common location between each monitored object and other monitored objects.

[0045] Step S220: Filter the attribute data corresponding to the common location based on the preset contact rules, and determine the contact data according to the filtering results.

[0046] Specifically, the trajectory intersection between each monitored object can be calculated in the trajectory database to determine the common location where each monitored object appears with other monitored objects; then, the attribute data corresponding to the common location is filtered based on the preset contact rules, and the contact data is determined according to the filtering results.

[0047] It should be noted that when determining contact data, the common location can be determined as the contact location when the common location and the corresponding attribute data meet the preset contact rules, and then the monitoring objects to which all attribute data that meet the preset contact rules belong can be determined as contact objects.

[0048] For example, suppose a preset contact rule states that if there are other monitored objects scanning a QR code at the same location as the monitored object, and within 3 minutes before or after the monitored object's scanning time, then that location is determined as a contact location, and the other monitored object is the contact object corresponding to that contact location. In this case, if monitored object D scans a QR code at common location 1 at 12:03 on a certain day, and monitored object E scans a QR code at common location 1 at 12:05, both the scanning time and location conform to the preset contact rule. In this case, for monitored object D, common location 1 can be determined as a contact location, and monitored object E is a contact object of monitored object D; similarly, for monitored object E, common location 1 can be determined as a contact location, and monitored object D is a contact object of monitored object E.

[0049] Furthermore, the contact time between the monitored object and the contact object can be determined by the intersection of their dwell times at various contact locations. Once the contact time is determined, it can be correlated with contact data, allowing users to visually observe the contact time between the monitored object and each contact object.

[0050] For example, when there are sweep-in and sweep-out times, the dwell time can be directly calculated by subtracting the sweep-in time from the sweep-out time, and then the intersection can be determined based on the dwell time. Alternatively, when there is only a sweep-in time and no sweep-out time, an estimated time can be determined by subtracting the current sweep-in time from the next sweep-in time at a different location, and then calculating the dwell time. For example, if the dwell time exceeds 12 hours, the contact time can be counted as 0.5 days. Furthermore, when there is only a sweep-in time and no sweep-out time, the contact time for the next sweep-in time with a different name (e.g., a different name more than 100 meters away) can be calculated by subtracting the sweep-in time from the next sweep-in time with a different name, and then dividing by 2.

[0051] It should be noted that, in some embodiments, the contact time between the monitored object and other monitored objects can be calculated based on the attribute data before determining the contact data, and the contact time can be used as part of the attribute data. Then, preset contact rules related to the contact time can be set to filter the attribute data, and the contact data can be determined based on the filtering results.

[0052] In an exemplary embodiment, since trajectory data acquired from different sources or locations have different characteristics, the corresponding contact rules may also differ. Therefore, before filtering attribute data corresponding to common locations based on preset contact rules, all trajectory locations in the trajectory database can be classified and identified according to a preset normalization table. By pre-setting preset contact rules for different categories, different preset contact rules can be selected for filtering based on the classification identifier of each trajectory location. For example, trajectory data can be divided into one or more of the following types: QR code travel, entry and exit, urban transportation, shopping, fever clinics, outpatient clinics, hospitalization, trains, airplanes, etc.

[0053] In step S130, the aggregation location is determined in the trajectory database based on the contact location and the contact object, and the aggregation event level corresponding to the aggregation location is determined.

[0054] In one exemplary embodiment, reference is made to Figure 3 As shown, the above-mentioned determination of the aggregation location in the trajectory database based on the contact location and the contact object, and determination of the aggregation event level corresponding to the aggregation location may include the following steps S310 to S320:

[0055] In step S310, the contact positions and contact objects are filtered according to preset aggregation rules. Based on the filtering results, the aggregation positions are determined among the contact positions, and the contact objects corresponding to the aggregation positions are determined.

[0056] In one exemplary embodiment, when determining the aggregation location, contact locations and contact objects can be filtered according to preset aggregation rules. Then, based on the filtering results, contact locations that satisfy the preset aggregation rules are determined as aggregation locations from among the contact locations where contact occurs. At the same time, contact objects that satisfy the preset aggregation rules are determined as the contact objects corresponding to that aggregation location.

[0057] It should be noted that, similar to the contact rule settings, the aforementioned preset aggregation rules and event level rules can also be configured differently based on the trajectory location. When filtering and determining the aggregation event level, different rules corresponding to different categories are set in advance. Then, different rules can be selected based on the category identifier of each trajectory location to perform the above filtering process and determine the aggregation event level.

[0058] For example, preset clustering rules could include: if more than one person remains at a location within a 100-meter radius, or at the same location, at the same time or within the same time frame (e.g., within 10 minutes), then that location is the current clustering location for all remaining individuals. The individuals remaining at that same time or within the same time frame (e.g., within 10 minutes) are the contact targets corresponding to that current clustering location. By setting preset clustering rules, the clustering location for each monitored object and the contact targets for each clustering location can be determined. It should be noted that a monitored object can have multiple clustering locations, and the contact targets for each clustering location may differ.

[0059] In step S320, the cluster event level corresponding to the cluster location is determined based on the event level rules and the contact objects corresponding to the cluster location.

[0060] In an exemplary embodiment, after determining the aggregation location, the aggregation event level corresponding to the aggregation location can be determined based on event level rules and the contact objects corresponding to the aggregation location. Specifically, the number of monitoring objects aggregated at the aggregation location can be used as the condition for level differentiation; the density of monitoring objects at the aggregation location can also be used as the condition for level differentiation; furthermore, different settings can be made according to different diffusion sources, and this disclosure does not impose any special limitations on this.

[0061] For example, in a shopping scenario, if the number of people at a certain gathering location exceeds 5, or if the number of contacts at a certain gathering location exceeds 10, the gathering event level corresponding to that gathering location can be set as medium. Similarly, for trajectory data such as shared bicycles, if the number of people at a certain gathering location exceeds 100, or if the number of contacts at that gathering location exceeds 1000, the gathering event level corresponding to that gathering location can be set as high.

[0062] It should be noted that, in some exemplary embodiments, a gathering event warning can also be set up for the aforementioned gathering events. For example, when a medium- or high-level gathering event occurs, a prevention and control warning can be issued for the gathering location.

[0063] In step S140, a target monitoring object is identified among the monitoring objects, and a set of risk locations is determined based on the target trajectory data and target contact data of the target monitoring object.

[0064] In one exemplary embodiment, the target monitoring object may include a monitoring object carrying a diffusion source. For example, in the context of infectious diseases, the target monitoring object may be a confirmed patient; or, in the context of message propagation, the target monitoring object may be the primary source of the message.

[0065] In an exemplary embodiment, after identifying the target detection object among the monitoring objects, a set of risk locations can be determined based on the target trajectory data and target contact data of the target monitoring object.

[0066] Specifically, refer to Figure 4 As shown, the steps S410 and S420 may be included:

[0067] In step S410, the trajectory locations included in the target trajectory data are added to the risk location set.

[0068] Specifically, the above process can be understood as using the target trajectory data of the monitored object as the main body, and adding the trajectory locations included in the target trajectory data of the monitored object to the risk location set. For example, in the scenario of infectious diseases, all trajectory locations that confirmed patients have visited are added to the risk location set.

[0069] In step S420, the risk location set is added based on the contact trajectory location of the target contact object contained in the target contact data.

[0070] Specifically, since some of the trajectory locations of the target monitored object may be target contact locations, meaning the target monitored object and the target contact object have come into contact at these locations, the person who came into contact with the target contact object may also become a carrier. Therefore, the contact location corresponding to the target contact object is also added to the risk location set. For example, in the context of infectious diseases, a monitored object G (target contact object) who has been in contact with a confirmed patient F (target monitored object) may have a contact location with another monitored object H (the contact object corresponding to the target contact object) (the contact location between monitored object G and monitored object H). In this case, the contact location is also added to the risk location set as a risk location.

[0071] In step S150, the cluster risk level corresponding to each risk location in the risk location set is determined based on the target contact data and the cluster event level.

[0072] In an exemplary embodiment, after determining the set of risk locations, the aggregation risk level corresponding to each risk location in the set of risk locations can be determined based on target contact data and aggregation event levels.

[0073] Specifically, the basic risk level corresponding to each risk location can be determined first based on the clustering event levels corresponding to each risk location in the risk location combination. For example, the basic risk levels can be 5, 4, 3, and 2 based on the high, medium, low, and no definite clustering event levels of the risk locations; then, the basic risk levels are updated based on the target contact data to obtain the clustering risk level corresponding to each risk location.

[0074] In one exemplary embodiment, reference is made to Figure 5As shown, updating the basic risk level based on target contact data may include the following steps S510 to S530:

[0075] In step S510, the target contact objects are classified according to the number of contact points between the target monitoring object and the target contact object to obtain the first contact object and the second contact object.

[0076] In this context, the number of contact points between the first contact object and the target detection object is greater than that between the second contact object. When the target contact object has only a few contact points with the target detection object, it can be determined that the contact object and the target detection object are only in point contact; however, when there are a large number of contact points between the target contact object and the target detection object, it is very likely that the target contact object and the target detection object are traveling together. The corresponding diffusion may also differ depending on the contact situation. Therefore, the target contact objects can be classified into first contact objects and second contact objects based on the number of contact points. By classifying the first contact objects and second contact objects in this way, the process of predicting primary and secondary close contacts can be realized.

[0077] In step S520, a first risk location is determined based on the first contact object in the risk location, and the basic risk level corresponding to the first risk location is increased by a first preset level.

[0078] Specifically, when there are numerous contact points between the target contact and the target monitoring object, infection is likely to have already occurred. Therefore, it is necessary to identify a first-risk location (e.g., the contact point between the target monitoring object and the first contact) and increase its pre-set risk level based on the base risk level corresponding to the first-risk location. It should be noted that the pre-set risk level can also be determined based on the number of contact points; different pre-set risk levels should be selected for adjustment when the number of contact points between the target contact and the target monitoring object varies.

[0079] In step S530, a second risk location is determined based on the second contact object in the risk location, and the basic risk level corresponding to the second risk location is reduced by a second preset level.

[0080] Specifically, the probability of infection is low when there are a few contact points between the target contact and the target monitoring object. Therefore, for risk locations determined based on the second contact (such as the remaining risk locations after removing the contact points from the trajectory of the second contact), i.e., the second risk location, its risk level can be reduced by a second preset level. Similarly, the second preset level can also be determined based on the number of contact points; different second preset levels can be selected for reduction when the number of contact points between the target contact and the target monitoring object varies.

[0081] The following example, using an infectious disease scenario, illustrates the process of updating the basic risk level.

[0082] (1) Based on target monitoring object I, target contact object J with two or more contact positions with target monitoring object I, and target contact object K with only one contact position with target monitoring object I, a total of 20 risk locations are identified. Among them, 15 are the trajectory locations of target monitoring object I, and the remaining 5 are the trajectory locations of target contact object K excluding the contact positions. Of the 15 trajectory locations, 5 are the contact positions of target contact object J.

[0083] (2) Determine the basic risk level according to the cluster event level corresponding to the 20 risk locations. Assuming that the cluster event level corresponding to the 20 risk locations is high, the basic risk level is set to 5.

[0084] (3) Increase the basic risk level of the first risk position of the five contact positions of the target contact object J by 2, that is, the aggregation risk level of these five first risk positions is 7.

[0085] (4) Reduce the cluster risk level of the five target contact objects K, excluding the contact location, by 3. Then the cluster risk level of these five second risk locations will be 2.

[0086] It should be noted that when the above-mentioned cluster risk level is reduced to 0, it will be removed from the risk location, meaning that there is no risk at that risk location.

[0087] By analyzing the risk levels of each risk location, it is possible to predict the potential location range and impact of the spread originating from the target monitoring object. This facilitates the implementation of different prevention and control strategies or measures for different risk locations based on different cluster risk levels.

[0088] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0089] Furthermore, in an exemplary embodiment of this disclosure, an apparatus for determining cluster risk is also provided. (Refer to...) Figure 6 As shown, the cluster risk determination device 600 includes: a database establishment module 610, a contact data determination module 620, a first level determination module 630, a location determination module 640, and a second level determination module 650.

[0090] The database establishment module 610 can be used to acquire trajectory data of each monitored object and establish a trajectory database based on the trajectory data; the trajectory data includes trajectory location; the contact data determination module 620 can be used to determine the contact data corresponding to each monitored object based on the trajectory database; the contact data includes contact location and the contact object corresponding to the contact location; the first level determination module 630 can be used to determine the aggregation location in the trajectory database based on the contact location and the contact object, and determine the aggregation event level corresponding to the aggregation location; the location determination module 640 can be used to determine the target monitored object among the monitored objects, and determine the risk location set based on the target trajectory data and target contact data of the target monitored object; the second level determination module 650 can be used to determine the aggregation risk level corresponding to each risk location in the risk location set based on the target contact data and the aggregation event level.

[0091] In an exemplary embodiment, the contact data determination module 620 can be used to calculate the intersection of trajectory data of each monitored object in the trajectory database to determine the common position between each monitored object and other monitored objects; filter the attribute data corresponding to the common position based on preset contact rules, and determine the contact data according to the filtering results.

[0092] In one exemplary embodiment, the contact data determination module 620 can be used to calculate the contact time based on the contact data and associate the contact time with the contact data.

[0093] In an exemplary embodiment, the contact data determination module 620 can be used to classify and identify the trajectory positions in the trajectory database based on a preset normalization table; and to determine the preset contact rules corresponding to common positions based on the classification labels.

[0094] In an exemplary embodiment, the first level determination module 630 can be used to filter contact locations and contact objects according to preset aggregation rules, determine aggregation locations among contact locations based on the filtering results, and determine the contact objects corresponding to the aggregation locations; and determine the aggregation event level corresponding to the aggregation location based on event level rules and the contact objects corresponding to the aggregation locations.

[0095] In an exemplary embodiment, the location determination module 640 can be used to add the trajectory locations included in the target trajectory data to the risk location set; and add the contact locations corresponding to the target contact objects included in the target contact data to the risk location set.

[0096] In an exemplary embodiment, the second level determination module 650 can be used to determine the basic risk level corresponding to each risk location based on the clustering event level corresponding to each risk location in the risk location set; and update the basic risk level based on the target contact data to obtain the clustering risk level corresponding to each risk location.

[0097] In an exemplary embodiment, the second level determination module 650 can be used to classify 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 greater than that of the second contact object; a first risk position is determined based on the first contact object in the risk positions, and the basic risk level corresponding to the first risk position is increased by a first preset level; a second risk position is determined based on the second contact object in the risk positions, and the basic risk level corresponding to the second risk position is decreased by a second preset level.

[0098] In an exemplary embodiment, the database establishment module 610 can be used to acquire registration data from different data sources; the registration data includes the registration location of the monitored object; and the registration data is integrated from the perspective of the monitored object to obtain the trajectory data corresponding to each monitored object.

[0099] The specific details of each module in the above-mentioned device have been described in detail in the method section of the implementation. For any undisclosed details, please refer to the implementation content of the method section, and therefore will not be repeated here.

[0100] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0101] also, Figure 7 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown.

[0102] It should be noted that, Figure 7 The computer system 700 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0103] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 702 or programs loaded from storage section 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0104] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. 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 disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0105] In particular, according to embodiments of this disclosure, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the methods and apparatus of this application. In some embodiments, computer system 700 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.

[0106] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0109] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods as described in the following embodiments. For example, the electronic device can implement the methods shown in Figures 1-14. Figure 5 The various steps shown are as follows.

[0110] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0111] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining clustering risk, characterized in that, include: Acquire trajectory data for each monitored object, and establish a trajectory database based on the trajectory data. The trajectory data includes the trajectory location; Based on the trajectory database, contact data corresponding to each monitored object is determined; the contact data includes the contact location and the contact object corresponding to the contact location. Based on the contact location and the contact object, a clustering location is determined in the trajectory database, and the clustering event level corresponding to the clustering location is determined. Among the monitored objects, a target monitoring object is identified, and a set of risk locations is determined based on the target trajectory data and target contact data of the target monitoring object; The cluster risk level corresponding to each risk location in the risk location set is determined based on the target contact data and the cluster event level. The trajectory data also includes attribute data corresponding to the trajectory location, including arrival time, dwell time, and coverage area. The step of determining the contact data corresponding to each of the monitored objects based on the trajectory database includes: calculating the intersection of the trajectory data of each of the monitored objects in the trajectory database to determine the common location between each of the monitored objects and other monitored objects; filtering the attribute data corresponding to the common location based on preset contact rules, and determining the contact data according to the filtering results; The step of filtering the attribute data corresponding to the common location based on preset contact rules and determining the contact data according to the filtering results includes: when the common location and the attribute data corresponding to the common location satisfy the preset contact rules, determining the common location as the contact location; and determining the monitoring objects to which all the attribute data satisfying the preset contact rules belong as contact objects. The contact time is calculated based on the contact data, and the contact time is associated with the contact data; Before filtering the attribute data corresponding to the common location based on the preset contact rules, the method further includes: classifying and identifying the trajectory locations in the trajectory database based on a preset normalization table; and determining the preset contact rules corresponding to the common location based on the classification labels.

2. The method according to claim 1, characterized in that, The step of determining the clustering location based on the contact location and the contact object in the trajectory database, and determining the clustering event level corresponding to the clustering location, includes: The contact positions and contact objects are filtered according to preset aggregation rules. Based on the filtering results, aggregation positions are determined among the contact positions, and the contact objects corresponding to the aggregation positions are determined. The cluster event level corresponding to the cluster location is determined based on the event level rules and the contact objects corresponding to the cluster location.

3. The method according to claim 1, characterized in that, The step of determining the risk location set based on the target trajectory data and target contact data of the target monitoring object includes: Add the trajectory locations included in the target trajectory data to the risk location set; The risk location set is added based on the contact location corresponding to the target contact object contained in the target contact data.

4. The method according to claim 1, characterized in that, The step of determining the cluster risk level corresponding to each risk location in the risk location set based on the target contact data and the cluster event level includes: The basic risk level corresponding to each risk location is determined based on the cluster event level corresponding to each risk location in the set of risk locations. The basic risk level is updated based on the target contact data to obtain the cluster risk level corresponding to each risk location.

5. The method according to claim 4, characterized in that, The step of updating the basic risk level based on the target contact data includes: The target contact objects are classified according to the number of contact points 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 points between the first contact object and the target monitoring object is greater than that between the second contact object; Based on the first contact object, a first risk location is determined in the risk location, and the basic risk level corresponding to the first risk location is increased by a first preset level; Based on the second contact object, a second risk location is determined in the risk location, and the basic risk level corresponding to the second risk location is reduced by a second preset level.

6. The method according to claim 1, characterized in that, The acquisition of trajectory data for each monitored object includes: Acquire registration data from different data sources; the registration data includes the registered location of the monitored objects; The registered data is integrated from the perspective of the monitored objects to obtain the trajectory data corresponding to each monitored object.

7. A clustering risk determination device, characterized in that, include: The database establishment module is used to acquire trajectory data of each monitored object and establish a trajectory database based on the trajectory data; the trajectory data includes trajectory location; The contact data determination module is used to determine the contact data corresponding to each of the monitored objects based on the trajectory database; the contact data includes the contact location and the contact object corresponding to the contact location. The first level determination module is used to determine the aggregation position in the trajectory database based on the contact position and the contact object, and to determine the aggregation event level corresponding to the aggregation position; The location determination module is used to determine the target monitoring object among the monitoring objects, and to determine the risk location set based on the target trajectory data and target contact data of the target monitoring object; The second level determination module is used to determine the cluster risk level corresponding to each risk location in the risk location set based on the target contact data and the cluster event level. The trajectory data also includes attribute data corresponding to the trajectory location, including arrival time, dwell time, and coverage area. The step of determining the contact data corresponding to each of the monitored objects based on the trajectory database includes: calculating the intersection of the trajectory data of each of the monitored objects in the trajectory database to determine the common location between each of the monitored objects and other monitored objects; filtering the attribute data corresponding to the common location based on preset contact rules, and determining the contact data according to the filtering results; The step of filtering the attribute data corresponding to the common location based on preset contact rules and determining the contact data according to the filtering results includes: when the common location and the attribute data corresponding to the common location satisfy the preset contact rules, determining the common location as the contact location; and determining the monitoring objects to which all the attribute data satisfying the preset contact rules belong as contact objects. The contact time is calculated based on the contact data, and the contact time is associated with the contact data; Before filtering the attribute data corresponding to the common location based on the preset contact rules, the method further includes: classifying and identifying the trajectory locations in the trajectory database based on a preset normalization table; and determining the preset contact rules corresponding to the common location based on the classification labels.

8. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 6 by executing the executable instructions.

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