WO2022142753A1 - 聚集风险确定方法及装置、计算机可读介质及电子设备 - Google Patents

聚集风险确定方法及装置、计算机可读介质及电子设备 Download PDF

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WO2022142753A1
WO2022142753A1 PCT/CN2021/129851 CN2021129851W WO2022142753A1 WO 2022142753 A1 WO2022142753 A1 WO 2022142753A1 CN 2021129851 W CN2021129851 W CN 2021129851W WO 2022142753 A1 WO2022142753 A1 WO 2022142753A1
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contact
risk
data
target
aggregation
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PCT/CN2021/129851
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English (en)
French (fr)
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朱马丽
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医渡云（北京）技术有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2474—Sequence data queries, e.g. querying versioned data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present disclosure relates to the technical field of data processing, and in particular, to a method for determining an aggregation risk, a device for determining an aggregation risk, a computer-readable medium, and an electronic device.
  • a method for determining an aggregation risk including: acquiring trajectory data of each monitoring object, and establishing a trajectory database according to each trajectory data, where the trajectory data includes trajectory positions; and determining the corresponding monitoring objects based on the trajectory database.
  • the contact data includes the contact position and the contact object corresponding to the contact position; according to the contact position and the contact object, the aggregation position is determined in the trajectory database, and the aggregation event level corresponding to the aggregation position is determined; the target monitoring object is determined in the monitoring object , and determine the risk location set according to the target trajectory data and target contact data of the target monitoring object; according to the target contact data and the aggregation event level, determine the aggregation risk level corresponding to each risk location in the risk location set.
  • an aggregation risk determination device comprising: a database establishment module for acquiring trajectory data of each monitoring object, and establishing a trajectory database according to each trajectory data, the trajectory data including trajectory positions; contact data The determination module is used to determine the contact data corresponding to each monitoring object based on the trajectory database, and the contact data includes the contact position and the contact object corresponding to the contact position; the first-level determination module is used to determine in the trajectory database according to the contact position and the contact object Aggregation location, and determine the aggregation event level corresponding to the aggregation location; location determination module, used to determine the target monitoring object among the monitoring objects, and determine the risk location set according to the target trajectory data and target contact data of the target monitoring object; the second level determines The module is used to determine the aggregation risk level corresponding to each risk location in the risk location set according to the target contact data and the aggregation event level.
  • a computer-readable medium having a computer program stored thereon, the program implementing the method as described above when executed by a processor.
  • an electronic device comprising: a processor; and a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors , causing one or more processors to implement a method as in any of the above.
  • FIG. 1 schematically shows a flowchart of a method for determining aggregation risk in an exemplary embodiment of the present disclosure
  • FIG. 2 schematically shows a flowchart of a method for determining contact data in an exemplary embodiment of the present disclosure
  • FIG. 3 schematically shows a flowchart of a method for determining an aggregation location and an aggregation event level in an exemplary embodiment of the present disclosure
  • FIG. 4 schematically shows a flowchart of a method for determining a set of risk locations in an exemplary embodiment of the present disclosure
  • FIG. 5 schematically shows a flowchart of a method for determining an aggregated risk level corresponding to each risk location in a risk location set in an exemplary embodiment of the present disclosure
  • FIG. 6 schematically shows a schematic composition diagram of an apparatus for determining an aggregation risk in an exemplary embodiment of the present disclosure
  • FIG. 7 schematically shows a schematic structural diagram of a computer system suitable for implementing an electronic device according to an exemplary embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various 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.
  • this example embodiment provides a method for determining aggregation risk. This method can be applied to the analysis and management of various protracted events.
  • the above-mentioned aggregation risk determination method may include the following steps S110 to S150:
  • step S110 the trajectory data of each monitoring object is acquired, and a trajectory database is established according to each trajectory data.
  • the track data of the monitoring object includes the position of the monitoring object at at least one time point.
  • the trajectory data of the monitored object A may include being at the railway station at 12:00 on January 1st.
  • registration data from different data sources may be acquired, and the registration data includes the registration position of each monitoring object at a certain time.
  • the position of each monitoring object at different times can be obtained, and then the trajectory data of each monitoring object can be determined.
  • the registration data may include the ride information of various travel systems such as entry and exit, trains, domestic flights, ships, intra-city buses, taxis, urban railways, shared bicycles, etc., or health code scanning code, entry and exit registration system, ticketing system data, or Hospital visit records, shopping registration in pharmacies and other shopping scenarios, mobile phone Bluetooth pairing records, etc.
  • the trajectory data of the monitoring object can be obtained. For example, when the monitoring object takes a train, it can be determined according to the riding information that the monitoring object is located at the train departure station when the train departs, and is located at the train arrival station when the train arrives at the station. At this time, the trajectory data of the monitoring object can be obtained, the departure time-departure site and the arrival time-arrival site.
  • step S120 contact data corresponding to each monitoring object is determined based on the trajectory database.
  • the above-mentioned contact data may include a contact position and a contact object corresponding to the contact position.
  • monitoring object B and monitoring object C go to the same pharmacy to buy medicine at the same time, and for monitoring object B, the contact data may include pharmacy (contact location) - monitoring object C (contact object).
  • the above-mentioned track data may further include attribute data corresponding to the track position, such as the time to arrive at the track position, the time to stay at the track position, the range covered by the track position, and the like.
  • attribute data corresponding to the track position such as the time to arrive at the track position, the time to stay at the track position, the range covered by the track position, and the like.
  • step S210 the intersection of the trajectory data of each monitoring object is calculated in the trajectory database to determine the common position existing between each monitoring object and other monitoring objects.
  • Step S220 Screen attribute data corresponding to the common location based on a preset contact rule, and determine contact data according to the screening result.
  • intersection of trajectories existing between each monitoring object can be calculated in the trajectory database to determine the common position that occurs between each monitoring object and other monitoring objects; and then based on the preset contact rules, the attributes corresponding to the common position are determined. Data is screened and exposure data is determined based on the screening results.
  • the common location when the common location and the corresponding attribute data satisfy the preset contact rules, the common location can be determined as the contact location, and then the monitoring objects to which all the attribute data that satisfy the preset contact rules belong. Determined as a contact object.
  • a preset contact rule is that there are other monitoring objects that scan the code at the same location as the monitoring object, and 3 minutes before and after the scanning time of the monitoring object, the location is determined to be the contact location, and the other monitoring objects are The contact object corresponding to the contact position.
  • the monitoring object D scans the code at the common location 1 at 12:03 on a certain day
  • the monitoring object E scans the code at the common location 1 at 12:05
  • the scanning time and scanning position conform to the above preset contact rules.
  • the monitoring object D it can be determined that the common position 1 is the contact position, and the monitoring object E is the contact object of the monitoring object D; for the monitoring object E, it can be determined that the common position 1 is the contact position, and the monitoring object D is the contact object of the monitoring object E. .
  • the contact time of the monitoring object and the contact object can also be determined according to the intersection of the stay 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 intuitively see the contact time between the monitoring object and each contact object.
  • the sweep-in time can be directly determined by the sweep-in time as the dwell time, and then the intersection is determined according to the dwell time; for another example, when there is only sweep-in time and no sweep-out time, An estimated time can be determined by the length of the dwell time after subtracting the current sweep-in time from the next sweep-in time at different positions.
  • the contact time can be counted as 0.5 days; for another example, in the case where there is only scan-in time but no scan-out time, the next scan-in time with a different name (such as 100 meters away) different names), the current contact time is obtained by calculating the scan-in time of the next different name minus the scan-in time and dividing by 2.
  • the contact time between the monitoring object and other monitoring objects may be calculated according to the attribute data before the contact data is determined, and the contact time may be taken as a part of the attribute data, and then the contact time may be set with the contact time.
  • Relevant preset contact rules filter the attribute data, and then determine the contact data according to the filtering result.
  • trajectory data due to different characteristics of trajectory data obtained from different sources or different locations, there may also be differences in the corresponding set contact rules. Therefore, before screening the attribute data corresponding to the common position based on the preset contact rule, all the trajectory positions in the trajectory database can be classified and identified according to the preset normalization table. Preset contact rules corresponding to different classifications are set in advance. When screening, different preset contact rules can be selected for screening according to the classification identification of each track position.
  • the trajectory data can be divided into one or more of the following types: scan code travel, entry and exit, intra-city traffic, shopping, fever clinic, outpatient clinic, hospitalization, train, plane, etc.
  • step S130 according to the contact position and the contact object, the aggregation position is determined in the trajectory database, and the aggregation event level corresponding to the aggregation position is determined.
  • the above-mentioned determining the gathering position in the trajectory database according to the contact position and the contact object, and determining the gathering event level corresponding to the gathering position may include the following steps S310 to S320:
  • step S310 the contact position and the contact object are screened according to the preset aggregation rule, the aggregation position is determined in the contact position based on the screening result, and the contact object corresponding to the aggregation position is determined.
  • the contact position and the contact object may be screened according to the preset aggregation rule, and then according to the screening result, the contact position that satisfies the preset aggregation rule is determined as the gathering location. At the same time, a contact object that satisfies the preset aggregation rule is determined as the contact object corresponding to the aggregation position.
  • the above-mentioned preset aggregation rules and event level rules can also be set differently according to different track positions.
  • different rules corresponding to different classifications are set in advance, and then different rules can be selected according to the classification identification of each track position to perform the above screening process and the process of determining the aggregation event level.
  • the preset aggregation rules may include: different locations but within a 100-meter range or the same location, more than 1 person staying at the same time point or within the same time range (for example, within 10 minutes), then this is all current
  • the gathering location of the staying person, the staying person at the same time point or within the same time range (eg, within 10 minutes) is the contact object corresponding to the current gathering location.
  • the aggregation position of each monitoring object and the contact objects at each aggregation position can be obtained. It should be noted that a monitoring object may have multiple gathering locations, and the contact objects at each gathering location may be different.
  • step S320 the aggregation event level corresponding to the aggregation position is determined based on the event grade rule and the contact object corresponding to the aggregation position.
  • the gathering event level corresponding to the gathering location may be determined based on the event level rule and the contact object corresponding to the gathering location.
  • the number of monitoring objects gathered at the gathering location can be used as the condition for level distinction; the density of monitoring objects at the gathering location can also be used as the condition for level distinction; in addition, different settings can be made according to different diffusion sources. There is no special restriction on this.
  • the gathering event level corresponding to this gathering location can be set as intermediate; , for the trajectory data of shared bicycles, if the number of people at a certain gathering location exceeds 100, or the contact objects corresponding to the gathering location exceed 1000, the gathering event level corresponding to this gathering location is high.
  • an aggregation event early warning may also be set.
  • prevention and control warnings for the gathering location can be carried out.
  • step S140 a target monitoring object is determined among the monitoring objects, and a set of risk locations is determined according to target trajectory data and target contact data of the target monitoring object.
  • the target monitoring object may include a monitoring object carrying a diffuse source.
  • the target monitoring object may be a confirmed patient; for another example, in the scenario of news dissemination, the target monitoring object may be the first source of the news.
  • the risk location set may be determined according to target trajectory data and target contact data of the target monitoring object.
  • steps S410 and S420 may be included:
  • step S410 the trajectory position in the target trajectory data is added to the risk position set.
  • the above process can be understood as taking the target trajectory data of the target monitoring object as the main body, and adding the trajectory position included in the target trajectory data corresponding to the target monitoring object to the risk position set.
  • the trajectory positions that the confirmed patients have reached are added to the set of risk positions.
  • step S420 the contact trajectory position corresponding to the target contact object in the target contact data is added to the risk position set.
  • some track positions may be target contact positions, that is, the target monitoring object contacts the target contact object at the target contact position.
  • the target contact person may also become a carrier, so the contact position corresponding to the monitoring object of the target contact object is also added to the risk position set.
  • the monitoring object G target contact object
  • the monitoring object H contact objects corresponding to the target contact object. If there is a contact position (contact position between the monitoring object G and the monitoring object H), the contact position is also added to the risk position set as a risk position.
  • step S150 according to the target contact data and the aggregated event level, the aggregated risk level corresponding to each risk location in the risk location set is determined.
  • the aggregated risk level corresponding to each risk location in the risk location set may be determined according to the target contact data and the aggregated event level.
  • the basic risk level corresponding to the risk location may be determined first according to the aggregate event level corresponding to each risk location in the risk location combination.
  • the high, medium, low, and undetermined basic risk levels can be 5, 4, 3, and 2 according to the corresponding aggregated event levels of the risk locations; then, the basic risk levels can be updated based on the target contact data, and the corresponding risk locations can be obtained. aggregation risk level.
  • updating the basic risk level based on the target contact data may include the following steps S510 to S530:
  • 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 to obtain a first contact object and a second contact object.
  • the number of contact positions between the first contact object and the target detection object is greater than that of the second contact object.
  • the target contact objects can be classified based on the number of contact positions to obtain the first contact object and the second contact object.
  • step S520 based on the first contact object, a first risk location is determined in the risk location, and a first preset level is increased to the basic risk level corresponding to the first risk location.
  • the first preset level is increased. It should be noted that the first preset level can also be determined according to the number of contact positions. When the number of contact positions existing between the target contact object and the target monitoring object is different, a different first preset level is selected to increase. .
  • step S530 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.
  • the probability of infection is small. Therefore, for the risk position determined based on the second contact object (for example, in the trajectory position of the second contact object in the risk position, the risk position remaining in the contact position is removed), that is, the second risk position, its risk level can be adjusted by the second Default level.
  • the second preset level can also be determined according to the number of contact positions. When the number of contact positions existing between the target contact object and the target monitoring object is different, a different second preset level is selected for adjustment.
  • the following takes an infectious disease scenario as an example to illustrate the above-mentioned process of updating the basic risk level.
  • the target contact object J that has more than 2 contact positions with the target monitoring object I, and the target contact object K that has only one contact position with the target monitoring object I, determine a total of 20 risk positions.
  • 15 are the trajectory positions of the target monitoring object I, and the remaining 5 are the trajectory positions of the target contact object K excluding the contact position.
  • 5 of the 15 trajectory positions are the contact positions of the target contact object J,
  • the aggregation risk determination apparatus 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 .
  • the database establishment module 610 can be used to obtain the trajectory data of each monitoring object, and establish a trajectory database according to each trajectory data, and the trajectory data includes the trajectory position;
  • the contact data determination module 620 can be used to determine the contact corresponding to each monitoring object based on the trajectory database Data, the contact data includes the contact position and the contact object corresponding to the contact position;
  • the first level determination module 630 can be used to determine the aggregation position in the trajectory database according to the contact position and the contact object, and determine the aggregation event level corresponding to the aggregation position;
  • location The determination module 640 can be used to determine the target monitoring object among the monitoring objects, and determine the set of risk locations according to the target trajectory data and target contact data of the target monitoring object;
  • the second level determination module 650 can be used according to the target contact data and the aggregated event level. , and determine the aggregated risk level corresponding to each risk location in the risk location set.
  • the contact data determination module 620 is used to calculate the intersection of the trajectory data of each monitoring object in the trajectory database, so as to determine the common position existing between each monitoring object and other monitoring objects; based on preset contact rules The attribute data corresponding to the common location is filtered, and the contact data is determined according to the filtering result.
  • the contact data determination module 620 is used to determine the contact time based on the contact data, and associate the contact time with the contact data.
  • the contact data determination module 620 is configured to classify and identify the track positions in the track database based on a preset normalization table; and determine a preset contact rule corresponding to the common location based on the classification identification.
  • the first level determination module 630 is configured to screen the contact positions and the contact objects according to a preset aggregation rule, determine the aggregation position in the contact positions based on the screening result, and determine the contact object corresponding to the aggregation position;
  • the aggregation event level corresponding to the aggregation location is determined based on the event level rule and the contact object corresponding to the aggregation location.
  • the position determination module 640 is configured to add the trajectory position in the target trajectory data to the risk position set; and add the contact position corresponding to the target contact object in the target contact data to the risk position set.
  • the second level determination module 650 is configured to determine the basic risk level corresponding to each risk location according to the aggregate event level corresponding to each risk location in the risk location set; update the basic risk level according to the target contact data , to obtain the aggregated risk level corresponding to each risk location.
  • the second level determination module 650 is configured to classify the target contact objects according to the number of contact positions between the target monitoring object and the target contact object to obtain the first contact object and the 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; based on the first contact object, determine the first risk position in the risk position, and increase the basic risk level corresponding to the first risk position The first preset level; based on the second contact object, determine the second risk location in the risk location, and reduce the basic risk level corresponding to the second risk location by the second preset level.
  • the database establishment module 610 is used to obtain registration data from different data sources, and the registration data includes the registration position of the monitoring object; the registration data is integrated from the monitoring object dimension to obtain the trajectory data corresponding to each monitoring object. .
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • FIG. 7 shows a schematic structural diagram of a computer system suitable for implementing the electronic device of the embodiment of the present disclosure.
  • a computer system 700 includes a central processing unit (CPU) 701 which can be loaded into a random access memory (RAM) 703 according to a program stored in a read only memory (ROM) 702 or a program from a storage section 708 Instead, various appropriate actions and processes are performed.
  • RAM random access memory
  • ROM read only memory
  • various programs and data required for system operation are also stored.
  • the CPU 701, the ROM 702, and the RAM 703 are connected to each other through 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 section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, 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, a modem, and 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, etc., is mounted on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.
  • embodiments of the present 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 method illustrated in the flowchart.
  • the computer program may be downloaded and installed from the network via the communication portion 709 and/or installed from the removable medium 711 .
  • the computer program is executed by the central processing unit (CPU) 701, various functions defined in the method and apparatus of the present application are performed.
  • the computer system 700 may further include an AI (Artificial Intelligence, artificial intelligence) processor for processing computing operations related to machine learning.
  • AI Artificial Intelligence, artificial intelligence
  • 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 above two.
  • the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • 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 therein. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions.
  • the functions noted in the blocks 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.
  • the units involved in the embodiments of the present disclosure may be implemented in software or hardware, and the described units may also be provided in a processor. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the present application also provides a computer-readable medium.
  • the computer-readable medium may be included in the electronic device described in the above embodiments; it may also exist alone without being assembled into the electronic device. middle.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by an electronic device, the electronic device enables the electronic device to implement the methods in the following embodiments. For example, the electronic device of the above can implement the various steps shown in FIG. 1 to FIG.

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Abstract

涉及数据处理技术领域，具体涉及一种聚集风险确定方法、聚集风险确定装置、计算机可读介质及电子设备，方法包括：获取各监测对象的轨迹数据，并根据各轨迹数据建立轨迹数据库(S110)；基于轨迹数据库确定各监测对象对应的接触数据(S120)；根据接触位置和接触对象，在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级(S130)；在监测对象中确定目标监测对象，并根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合(S140)；根据目标接触数据和聚集事件等级，确定风险位置集合中的各风险位置对应的聚集风险等级(S150)。可以确定与目标监测对象之间发生接触的监测对象，进而确定风险位置和聚集风险等级。

Description

聚集风险确定方法及装置、计算机可读介质及电子设备
交叉引用
本公开要求于2020年12月31日提交的申请号为202011622829.7名称为“聚集风险确定方法及装置、计算机可读介质及电子设备”的中国专利申请的优先权，该中国专利申请的全部内容通过引用全部并入本文。
技术领域
本公开涉及数据处理技术领域，具体而言，涉及一种聚集风险确定方法、聚集风险确定装置、计算机可读介质及电子设备。
背景技术
随着社会经济的高速发展，我国的城市化进程不断推进，城市的人口数量越来越多，城市的公共交通、生活设施等区域经常会出现行人密度比较大的情况。在出现传染疾病等容易漫延的特殊情况下，这种行人密度较大的情况很容易造成过度漫延等隐患。
目前，为了避免上述过度蔓延的情况，往往需要大量工作人员人工获取流调报告，进而人工圈定存在聚集风险的位置和人群。这种人工处理的过程不仅需要耗费大量的人力，同时也存在处理效率低的问题。在处理效率较低的情况下，也很容易出现漫延情况不受控的问题。
公开内容
根据本公开的第一方面，提供了一种聚集风险确定方法，包括：获取各监测对象的轨迹数据，并根据各轨迹数据建立轨迹数据库，轨迹数据包括轨迹位置；基于轨迹数据库确定各监测对象对应的接触数据，接触数据包括接触位置和接触位置对应的接触对象；根据接触位置和接触对象，在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级；在监测对象中确定目标监测对象，并根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合；根据目标接触数据和聚集事件等级，确定风险位置集合中的各风险位置对应的聚集风险等级。
根据本公开的第二方面，提供了一种聚集风险确定装置，包括：数据库建立模块，用于获取各监测对象的轨迹数据，并根据各轨迹数据建立轨迹数据库，轨迹数据包括轨迹位置；接触数据确定模块，用于基于轨迹数据库确定各监测对象对应的接触数据，接触数据包括接触位置和接触位置对应的接触对象；第一等级确定模块，用于根据接触位置和接触 对象，在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级；位置确定模块，用于在监测对象中确定目标监测对象，并根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合；第二等级确定模块，用于根据目标接触数据和聚集事件等级，确定风险位置集合中的各风险位置对应的聚集风险等级。
根据本公开的第三方面，提供了一种计算机可读介质，其上存储有计算机程序，程序被处理器执行时实现如上述任一项的方法。
根据本公开实施例的第四方面，提供了一种电子设备，包括：处理器；以及存储装置，用于存储一个或多个程序，当一个或多个程序被一个或多个处理器执行时，使得一个或多个处理器实现如上述任一项的方法。
附图说明
图1示意性示出本公开示例性实施例中一种聚集风险确定方法的流程图；
图2示意性示出本公开示例性实施例中一种确定接触数据方法的流程图；
图3示意性示出本公开示例性实施例中一种确定聚集位置和聚集事件等级方法的流程图；
图4示意性示出本公开示例性实施例中一种确定风险位置集合方法的流程图；
图5示意性示出本公开示例性实施例中一种确定风险位置集合中的各风险位置对应的聚集风险等级方法的流程图；
图6示意性示出本公开示例性实施例中一种聚集风险确定装置的组成示意图；
图7示意性示出了适于用来实现本公开示例性实施例的电子设备的计算机系统的结构示意图。
具体实施方式
现在将参照附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施方式中。
此外，附图仅为本公开的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现这些功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。
在新冠疫情等易漫延事件的背景下，行人密集的情况很容易出现过度传播等情况。以传染疾病为例，假设某一确诊患者在确诊前曾多次出行，为了尽快找到可能被确诊患者传 染的其它人员，往往需要人工获取流调报告确定需要管控的位置和人群，有时还需借助媒体等手段宣传患者出现的场合，然后依靠行人的自发报告确定可能感染的人员。上述方式不仅需要耗费大量的人力，同时也存在处理效率低的问题，进而出现漫延情况不受控制的问题。
基于上述一个或多个问题，本示例实施方式提供了一种聚集风险确定方法。该方法可以应用于对各种易漫延事件的分析和管控的过程中。参考图1所示，上述聚集风险确定方法可以包括以下步骤S110至S150：
在步骤S110中，获取各监测对象的轨迹数据，并根据各轨迹数据建立轨迹数据库。
其中，监测对象的轨迹数据中包括的是监测对象在至少一个时间点所在的位置。例如，监测对象A的轨迹数据可以包括在1月1日12:00时在火车站。
在一示例性实施例中，在获取上述检测对象的轨迹数据时，可以获取不同数据来源的登记数据，该登记数据中包括各个监测对象在某时间的登记位置。将不同数据来源的登记数据按照监测对象的维度进行数据整合，可以得到每个监测对象在不同时间的位置，进而确定每个监测对象的轨迹数据。
其中，登记数据可以包括出入境、火车、国内航班、轮船、城内公交、出租车、城铁、共享单车等各个出行系统的乘坐信息，或者健康码扫码、出入登记系统、票务系统数据，或者医院就诊记录、药店等购物场景的购物登记、手机蓝牙配对记录等。通过对上述登记数据按时间、位置进行整理，然后按照监测对象维度进行整合，可以得到监测对象的轨迹数据。举例而言，监测对象在乘坐火车时，根据乘坐信息可以确定监测对象在火车出发时位于火车出发站点，在火车到站时位于火车到站站点。此时可以得到监测对象的轨迹数据，出发时间-出发站点和到站时间-到站站点。
在步骤S120中，基于轨迹数据库确定各监测对象对应的接触数据。
其中，上述接触数据可以包括接触位置和接触位置对应的接触对象。例如，监测对象B与监测对象C在同一时间去同一药店买药，针对监测对象B，接触数据可以包括药店(接触位置)-监测对象C(接触对象)。
在一示例性实施例中，上述轨迹数据还可以包括轨迹位置对应的属性数据，例如到达该轨迹位置的时间，在该轨迹位置停留的时间，该轨迹位置所覆盖的范围等。此时，参照图2所示，基于轨迹数据库确定各监测对象对应的接触数据可以通过以下过程实现：
步骤S210，在轨迹数据库中计算各监测对象的轨迹数据的交集，以确定各监测对象与其他监测对象之间存在的共同位置。
步骤S220，基于预设接触规则对共同位置对应的属性数据进行筛选，并根据筛选结果确定接触数据。
具体的，可以在轨迹数据库中计算每个监测对象之间存在的轨迹交集，以确定每个监测对象与其他监测对象之间共同出现的共同位置；然后基于预设接触规则对共同位置对应的属性数据进行筛选，并根据筛选结果确定接触数据。
需要说明的是，在确定接触数据时，可以在共同位置和对应的属性数据满足预设接触规则时，将共同位置确定为接触位置，然后将所有满足预设接触规则的属性数据所属的监测对象确定为接触对象。
举例而言，假设一个预设接触规则为存在与监测对象在同一位置扫码，且在监测对象扫码时间前后3分钟的其他监测对象，则确定该位置为接触位置，该其他监测对象则为该接触位置对应的接触对象。此时，若监测对象D在某日12：03分在共同位置1扫码，监测对象E在12：05在共同位置1扫码，扫码时间和扫码位置都符合上述预设接触规则。此时，针对监测对象D可以确定共同位置1为接触位置，监测对象E为监测对象D的接触对象；针对监测对象E可以确定共同位置1为接触位置，监测对象D为监测对象E的接触对象。
此外，还可以根据监测对象和接触对象在各个接触位置的停留时间的交集，确定监测对象和接触对象的接触时间。在确定了接触时间后，可以将接触时间与接触数据关联，以便于用户可以直观的看到监测对象与各接触对象之间的接触时间。
例如，在有扫入和扫出时间时，可以直接通过扫出时间-扫入时间为停留时间，然后根据停留时间确定交集；再如，在仅有扫入时间没有扫出时间的情况下，可以通过下一个为不同位置的扫入时间减去本次扫入时间后，通过停留时间的长短确定一个估计时间。比如，可以在该停留时间超过12小时时，将接触时间计为0.5天；又如，在仅有扫入时间没有扫出时间的情况下，下一个不同名称的扫入时间(如100米以外不同名称)，通过计算下一个不同名称的扫入时间减去扫入时间后，除以2得到本次的接触时间。
需要说明的是，在一些实施例中，也可以在确定接触数据之前先根据属性数据计算监测对象与其他监测对象之间的接触时间，并将接触时间作为属性数据的一部分，然后设置与接触时间相关的预设接触规则对属性数据进行筛选，进而根据筛选结果确定接触数据。
在一示例性实施例中，由于不同来源或者不同地点获取的轨迹数据的特性不同，对应的设置接触规则可能也存在差别。因此，在基于预设接触规则对共同位置对应的属性数据进行筛选之前，可以先根据预设归一表对轨迹数据库中的所有轨迹位置进行分类标识。提前设置不同分类对应的预设接触规则，在进行筛选时，可以根据每个轨迹位置的分类标识选择不同的预设接触规则进行筛选。例如，可以将轨迹数据分为以下一种或者多种类型：扫码出行、出入境、城内交通、购物、发热门诊、门诊、住院、火车、飞机等。
在步骤S130中，根据接触位置和接触对象，在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级。
在一示例性实施例中，参照图3所示，上述根据接触位置和接触对象在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级可以包括以下步骤S310至S320：
在步骤S310中，根据预设聚集规则对接触位置和接触对象进行筛选，基于筛选结果在接触位置中确定聚集位置，并确定聚集位置对应的接触对象。
在一示例性实施例中，在确定聚集位置时，可以根据预设聚集规则对接触位置和接触 对象进行筛选，然后根据筛选结果在发生接触的接触位置中确定满足预设聚集规则的接触位置作为聚集位置。与此同时，将满足预设聚集规则的接触对象确定为该聚集位置对应的接触对象。
需要说明的是，类似于接触规则的设置，上述预设聚集规则和事件等级规则也可以根据轨迹位置的不同进行不同的设置。在进行筛选和确定聚集事件等级时，提前设置不同分类对应的不同规则，然后可以根据每个轨迹位置的分类标识选择不同的规则进行上述筛选过程和确定聚集事件等级的过程。
举例而言，预设聚集规则可以包括：位置不同但在百米范围内或位置相同，在同一时间点或同一时间范围内(如10分钟内)停留人数超过1人，则此处为当前所有停留人的聚集位置，在在同一时间点或同一时间范围内(如10分钟内)的停留人则是当前聚集位置对应的接触对象。通过设置预设聚集规则，可以得出每个监测对象的聚集位置和每个聚集位置的接触对象。需要说明的是，一个监测对象可以有多个聚集位置，每个聚集位置的接触对象可能不同。
在步骤S320中，基于事件等级规则和聚集位置对应的接触对象，确定聚集位置对应的聚集事件等级。
在一示例性实施例中，在确定聚集位置后，可以基于事件等级规则和聚集位置对应的接触对象确定聚集位置对应的聚集事件等级。具体的，可以以聚集位置处聚集的监测对象的数量为等级区分的条件；也可以以聚集位置的监测对象密度为等级区分的条件；此外，还可以根据漫延源不同做不同的设定，本公开对此不做特殊限制。
举例而言，针对购物场景，可以设定某一聚集位置的人数超过5人，或者某一聚集位置对应的接触人超过10人时，设定此聚集位置对应的聚集事件等级为中级；再如，针对共享单车这种轨迹数据，可以在某一聚集位置的人数超过100人，或者该聚集位置对应的接触对象超过1000人，则此聚集位置对应的聚集事件等级为高级。
需要说明的是，在一些示例性实施例中，针对上述聚集事件，还可以设置聚集事件预警。例如，出现中级或高级聚集事件时，可以对聚集位置进行防控预警。
在步骤S140中，在监测对象中确定目标监测对象，并根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合。
在一示例性实施例中，目标监测对象可以包括携带漫延源的监测对象。例如，在传染疾病的场景下，目标监测对象可以是确诊患者；再如，在消息传播的场景下，目标监测对象可以是消息的第一来源。
在一示例性实施例中，在监测对象中确定了目标检测对象之后，可以根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合。
具体的，参照图4所示，可以包括以下步骤S410和S420：
在步骤S410中，将目标轨迹数据中的轨迹位置加入风险位置集合。
具体的，上述过程可以理解为，以目标监测对象的目标轨迹数据为主体，将目标监测 对象对应的目标轨迹数据中包括的轨迹位置加入风险位置集合。举例而言，在传染疾病的场景下，即将确诊患者曾经到达的轨迹位置均加入风险位置集合中。
在步骤S420中，将目标接触数据中的目标接触对象对应的接触轨迹位置加入风险位置集合。
具体的，由于目标监测对象所到的轨迹位置中，可能一些轨迹位置为目标接触位置，即目标监测对象在目标接触位置和目标接触对象进行了接触。此时目标接触人可能也成为携带者，因此将目标接触对象这一监测对象对应的接触位置也加入风险位置集合。举例而言，在传染疾病的场景下，与确诊患者F(目标监测对象)存在接触关系的监测对象G(目标接触对象)，可能与其他监测对象H(目标接触对象对应的接触对象)之间存在接触位置(监测对象G与监测对象H之间的接触位置)，则将该接触位置也作为风险位置加入风险位置集合。
在步骤S150中，根据目标接触数据和聚集事件等级，确定风险位置集合中的各风险位置对应的聚集风险等级。
在一示例性实施例中，在确定了风险位置集合后，可以根据目标接触数据和聚集事件等级，确定风险位置集合中各个风险位置对应的聚集风险等级。
具体的，可以先根据风险位置结合中各个风险位置对应的聚集事件等级确定风险位置对应的基础风险等级。例如，可以根据风险位置的对应聚集事件等级中的高级、中级、低级和无确定基础风险等级为5、4、3和2；然后基于目标接触数据对基础风险等级进行更新，获取各个风险位置对应的聚集风险等级。
在一示例性实施例中，参照图5所示，基于目标接触数据对基础风险等级进行更新可以包括以下步骤S510至步骤S530：
在步骤S510中，根据目标监测对象与目标接触对象之间的接触位置的数量对目标接触对象进行分类，得到第一接触对象和第二接触对象。
其中，第一接触对象与目标检测对象之间的接触位置的数量大于第二接触对象。在目标接触对象仅与目标监测对象存在较少数量的接触位置时，可以确定该目标接触对象与目标检测对象仅为点接触；而在目标接触对象与目标监测对象之间存在较多数量的接触位置时，很可能目标接触对象与目标监测对象为同行者。在接触情况不同时，对应的漫延可能也不同。因此可以基于接触位置的数量对目标接触对象进行分类得到第一接触对象和第二接触对象。通过上述划分第一接触对象和第二接触对象的方式，可以实现预测一级密接和二级密接的过程。
在步骤S520中，基于第一接触对象，在风险位置中确定第一风险位置，并对第一风险位置对应的基础风险等级调增第一预设等级。
具体的，在目标接触对象与目标监测对象之间存在较多数量的接触位置时，很可能已经发生感染，因此需要在风险位置中确定第一风险位置(如，目标监测对象与第一接触对象之间的接触位置)，并在第一风险位置对应的基础风险等级的基础上，对其调增第一预 设等级。需要说明的是，第一预设等级也可以根据接触位置的数量进行确定，在目标接触对象与目标监测对象之间存在的接触位置的数量不同时，选择不同的第一预设等级进行调增。
在步骤S530中，基于第二接触对象，在风险位置中确定第二风险位置，并将第二风险位置对应的基础风险等级调减第二预设等级。
具体的，在目标接触对象与目标监测对象之间存在较少数量的接触位置时，发生感染的概率较小。因此针对基于第二接触对象确定的风险位置(如风险位置中第二接触对象的轨迹位置中，除去接触位置剩余的风险位置)，即第二风险位置，可以将其风险等级进行调减第二预设等级。同样的，第二预设等级也可以根据接触位置的数量进行确定，在目标接触对象与目标监测对象之间存在的接触位置的数量不同时，选择不同的第二预设等级进行调减。
以下以传染疾病场景为例，对上述对基础风险等级进行更新的过程进行举例。
(1)根据目标监测对象I，与目标监测对象I有2个以上接触位置的目标接触对象J，与目标监测对象I只有1个接触位置的目标接触对象K，确定共20个风险位置。其中，15个为目标监测对象I的轨迹位置，剩余5个为目标接触对象K除去接触位置之外的轨迹位置。其中，15个轨迹位置中有5个为目标接触对象J的接触位置，
(2)根据20个风险位置对应的聚集事件等级，分别确定基础风险等级，假设20个的风险位置对应的聚集事件等级均为高级，则其基础风险等级都设置为5。
(3)将5个为目标接触对象J的接触位置的第一风险位置对应的基础风险等级均增加2，即这5个第一风险位置对应的聚集风险等级均为7。
(4)将5个为目标接触对象K除去接触位置之外的轨迹位置的聚集风险等级均调减3，则这5个第二风险位置对应的聚集风险等级均为2。
需要说明的是，当上述聚集风险等级调减至0后，则将其从风险位置中删除，即该风险位置不存在风险。
通过对各个风险位置的风险等级分析，可以预测以目标监测对象为漫延源头的漫延时间可能涉及的位置范围和对该位置的影响，进而便于根据不同的聚集风险等级对不同的风险位置执行不同的防控策略或措施。
需要注意的是，上述附图仅是根据本公开示例性实施例的方法所包括的处理的示意性说明，而不是限制目的。易于理解，上述附图所示的处理并不表明或限制这些处理的时间顺序。另外，也易于理解，这些处理可以是例如在多个模块中同步或异步执行的。
此外，在本公开的示例性实施方式中，还提供了一种聚集风险确定装置。参照图6所示，聚集风险确定装置600包括：数据库建立模块610，接触数据确定模块620，第一等级确定模块630，位置确定模块640和第二等级确定模块650。
其中，数据库建立模块610可以用于获取各监测对象的轨迹数据，并根据各轨迹数据建立轨迹数据库，轨迹数据包括轨迹位置；接触数据确定模块620可以用于基于轨迹数据 库确定各监测对象对应的接触数据，接触数据包括接触位置和接触位置对应的接触对象；第一等级确定模块630可以用于根据接触位置和接触对象，在轨迹数据库中确定聚集位置，并确定聚集位置对应的聚集事件等级；位置确定模块640可以用于在监测对象中确定目标监测对象，并根据目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合；第二等级确定模块650可以用于根据目标接触数据和聚集事件等级，确定风险位置集合中的各风险位置对应的聚集风险等级。
在一示例性实施例中，接触数据确定模块620用于在轨迹数据库中计算各监测对象的轨迹数据的交集，以确定各监测对象与其他监测对象之间存在的共同位置；基于预设接触规则对共同位置对应的属性数据进行筛选，并根据筛选结果确定接触数据。
在一示例性实施例中，接触数据确定模块620用于基于接触数据确定接触时间，并将接触时间与接触数据关联。
在一示例性实施例中，接触数据确定模块620用于基于预设归一表对轨迹数据库中的轨迹位置进行分类标识；基于分类标识确定共同位置对应的预设接触规则。
在一示例性实施例中，第一等级确定模块630用于根据预设聚集规则对接触位置和接触对象进行筛选，基于筛选结果在接触位置中确定聚集位置，并确定聚集位置对应的接触对象；基于事件等级规则和聚集位置对应的接触对象确定聚集位置对应的聚集事件等级。
在一示例性实施例中，位置确定模块640用于将目标轨迹数据中的轨迹位置加入风险位置集合；将目标接触数据中的目标接触对象对应的接触位置加入风险位置集合。
在一示例性实施例中，第二等级确定模块650用于根据风险位置集合中各风险位置对应的聚集事件等级，确定各风险位置对应的基础风险等级；根据目标接触数据对基础风险等级进行更新，以获取各风险位置对应的聚集风险等级。
在一示例性实施例中，第二等级确定模块650用于根据目标监测对象与目标接触对象之间的接触位置的数量对目标接触对象进行分类，得到第一接触对象和第二接触对象；其中第一接触对象与目标检测对象之间的接触位置的数量大于第二接触对象；基于第一接触对象，在风险位置中确定第一风险位置，并对第一风险位置对应的基础风险等级调增第一预设等级；基于第二接触对象，在风险位置中确定第二风险位置，并将第二风险位置对应的基础风险等级调减第二预设等级。
在一示例性实施例中，数据库建立模块610用于获取不同数据来源的登记数据，登记数据包括监测对象的登记位置；从监测对象维度对登记数据进行整合，以获取各监测对象对应的轨迹数据。
上述装置中各模块的具体细节在方法部分实施方式中已经详细说明，未披露的细节内容可以参见方法部分的实施方式内容，因而不再赘述。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块 或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
此外，图7示出了适于用来实现本公开实施例的电子设备的计算机系统的结构示意图。
需要说明的是，图7示出的电子设备的计算机系统700仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图7所示，计算机系统700包括中央处理单元(CPU)701，其可以根据存储在只读存储器(ROM)702中的程序或者从存储部分708加载到随机访问存储器(RAM)703中的程序而执行各种适当的动作和处理。在RAM 703中，还存储有系统操作所需的各种程序和数据。CPU 701、ROM 702以及RAM 703通过总线704彼此相连。输入/输出(I/O)接口705也连接至总线704。
以下部件连接至I/O接口705：包括键盘、鼠标等的输入部分706；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分707；包括硬盘等的存储部分708；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分709。通信部分709经由诸如因特网的网络执行通信处理。驱动器710也根据需要连接至I/O接口705。可拆卸介质711，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器710上，以便于从其上读出的计算机程序根据需要被安装入存储部分708。
特别地，根据本公开的实施例，下文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分709从网络上被下载和安装，和/或从可拆卸介质711被安装。在该计算机程序被中央处理单元(CPU)701执行时，执行本申请的方法和装置中限定的各种功能。在一些实施例中，计算机系统700还可以包括AI(Artificial Intelligence，人工智能)处理器，该AI处理器用于处理有关机器学习的计算操作。
需要说明的是，本公开所示的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程 序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、RF等等，或者上述的任意合适的组合。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，上述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图或流程图中的每个方框、以及框图或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现，所描述的单元也可以设置在处理器中。其中，这些单元的名称在某种情况下并不构成对该单元本身的限定。
作为另一方面，本申请还提供了一种计算机可读介质，该计算机可读介质可以是上述实施例中描述的电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被一个该电子设备执行时，使得该电子设备实现如下述实施例中的方法。例如，的电子设备可以实现如图1～图5所示的各个步骤等。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (12)

  1. 一种聚集风险确定方法，包括：
    获取各监测对象的轨迹数据，并根据各所述轨迹数据建立轨迹数据库，所述轨迹数据包括轨迹位置；
    基于所述轨迹数据库确定各所述监测对象对应的接触数据，所述接触数据包括接触位置和所述接触位置对应的接触对象；
    根据所述接触位置和所述接触对象，在所述轨迹数据库中确定聚集位置，并确定所述聚集位置对应的聚集事件等级；
    在所述监测对象中确定目标监测对象，并根据所述目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合；
    根据目标接触数据和所述聚集事件等级，确定所述风险位置集合中的各风险位置对应的聚集风险等级。
  2. 根据权利要求1所述的方法，其中，所述轨迹数据还包括所述轨迹位置对应的属性数据；
    所述基于所述轨迹数据库确定各所述监测对象对应的接触数据，包括：
    在所述轨迹数据库中计算各所述监测对象的轨迹数据的交集，以确定各所述监测对象与其他监测对象之间存在的共同位置；
    基于预设接触规则对所述共同位置对应的属性数据进行筛选，并根据筛选结果确定接触数据。
  3. 根据权利要求2所述的方法，其中，所述方法还包括：
    基于所述接触数据确定接触时间，并将所述接触时间与所述接触数据关联。
  4. 根据权利要求2所述的方法，其中，在所述基于预设接触规则对所述共同位置对应的属性数据进行筛选之前，所述方法还包括：
    基于预设归一表对所述轨迹数据库中的所述轨迹位置进行分类标识；
    基于所述分类标识确定所述共同位置对应的预设接触规则。
  5. 根据权利要求1所述的方法，其中，所述根据所述接触位置和所述接触对象，在所述轨迹数据库中确定聚集位置，并确定所述聚集位置对应的聚集事件等级，包括：
    根据预设聚集规则对所述接触位置和所述接触对象进行筛选，基于筛选结果在所述接触位置中确定聚集位置，并确定所述聚集位置对应的接触对象；
    基于事件等级规则和所述聚集位置对应的接触对象确定所述聚集位置对应的聚集事件等级。
  6. 根据权利要求1所述的方法，其中，所述根据所述目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合，包括：
    将所述目标轨迹数据中的轨迹位置加入风险位置集合；
    将所述目标接触数据中的目标接触对象对应的接触位置，加入所述风险位置集合。
  7. 根据权利要求1所述的方法，其中，所述根据目标接触数据和所述聚集事件等级，确定所述风险位置集合中的各风险位置对应的聚集风险等级，包括：
    根据所述风险位置集合中各所述风险位置对应的聚集事件等级，确定各所述风险位置对应的基础风险等级；
    根据所述目标接触数据对所述基础风险等级进行更新，以获取各所述风险位置对应的聚集风险等级。
  8. 根据权利要求7所述的方法，其中，所述根据所述目标接触数据对所述基础风险等级进行更新，包括：
    根据目标监测对象与所述目标接触对象之间的接触位置的数量对所述目标接触对象进行分类，得到第一接触对象和第二接触对象；其中第一接触对象与目标检测对象之间的接触位置的数量大于第二接触对象；
    基于所述第一接触对象，在所述风险位置中确定第一风险位置，并对所述第一风险位置对应的基础风险等级调增第一预设等级；
    基于所述第二接触对象，在所述风险位置中确定第二风险位置，并将所述第二风险位置对应的基础风险等级调减第二预设等级。
  9. 根据权利要求1所述的方法，其中，所述获取各监测对象的轨迹数据，包括：
    获取不同数据来源的登记数据，所述登记数据包括监测对象的登记位置；
    从监测对象维度对所述登记数据进行整合，以获取各所述监测对象对应的轨迹数据。
  10. 一种聚集风险确定装置，包括：
    数据库建立模块，用于获取各监测对象的轨迹数据，并根据各所述轨迹数据建立轨迹数据库，所述轨迹数据包括轨迹位置；
    接触数据确定模块，用于基于所述轨迹数据库确定各所述监测对象对应的接触数据，所述接触数据包括接触位置和所述接触位置对应的接触对象；
    第一等级确定模块，用于根据所述接触位置和所述接触对象，在所述轨迹数据库中确定聚集位置，并确定所述聚集位置对应的聚集事件等级；
    位置确定模块，用于在所述监测对象中确定目标监测对象，并根据所述目标监测对象的目标轨迹数据和目标接触数据确定风险位置集合；
    第二等级确定模块，用于根据目标接触数据和所述聚集事件等级，确定所述风险位置集合中的各风险位置对应的聚集风险等级。
  11. 一种计算机可读介质，其上存储有计算机程序，所述程序被处理器执行时实现如权利要求1至9中任一项所述的方法。
  12. 一种电子设备，包括：
    处理器；以及
    存储器，用于存储所述处理器的可执行指令；
    其中，所述处理器配置为经由执行所述可执行指令来执行权利要求1至9任一项所述的方法。
PCT/CN2021/129851 2020-12-31 2021-11-10 聚集风险确定方法及装置、计算机可读介质及电子设备 WO2022142753A1 (zh)

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