CN113889284A - Infectious disease contact target tracking method based on public transport knowledge graph - Google Patents
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Abstract
The invention relates to an infectious disease contact target tracking method based on a public transport knowledge graph, which specifically comprises the following steps: s1, constructing a public transportation knowledge map based on a trip chain model by adopting a construction mode combining top-down and bottom-up; s2, obtaining travel data of a plurality of passengers, determining the travel sequence of the passengers according to the public transportation knowledge graph, and determining the type of infectious disease contact among the passengers; and S3, extracting the travel data of the infected persons in the travel data of the passengers, marking, selecting the infected persons with a preset proportion for tracking, and positioning the secondary infected individuals in the traffic system according to the contact type of the infectious diseases among the passengers. Compared with the prior art, the method has the advantages of reducing data redundancy, facilitating data expansion, improving the accuracy of judging the infectious disease contact target when a plurality of scene conversion and recursive query exist, realizing effective and rapid tracking in a large-scale contact network and the like.
Description
Technical Field
The invention relates to the field of traffic big data mining and traffic emergency management, in particular to an infectious disease contact target tracking method based on a public traffic knowledge graph.
Background
Public transportation and stations are key modes of virus transmission due to their relatively confined space and high passenger traffic. Many countries implement various control measures for public transportation including regular disinfection, forced mask wear, reduced social distance capability, changing operating schedules, and even complete suspension of service. However, the target-directed strategy of timely discovering and quarantining infected individuals is more effective than the overall management strategy. At present, however, there is still a challenge on how to efficiently mine accurate knowledge from large-scale unstructured outgoing data to alleviate disease.
In the field of infectious diseases, manual contacter tracking is commonly employed, but manual tracking is inefficient for large-scale research applications, and smart card data with fixed routes and run schedules is believed to be helpful in capturing contacters and tracking infections in public transportation systems. Previous studies have generally used relational databases, but in view of the data structure of relational databases, contacts are stored between passenger pairs, and thus are not suitable for directly representing the actual network structure, and may result in poor performance when performing multiple recursive connections and queries for contact tracking.
The knowledge graph is a technology widely applied in recent years and has important significance for efficiently constructing a high-resolution connection network. The knowledge graph is different from a traditional relational database, data are stored in the form of nodes and edges, the network scale of hundreds of billions of nodes and edges is usually supported, any object in the real world can be visually represented, and a network with rich semantics is constructed theoretically.
Disclosure of Invention
The invention aims to overcome the defect that the prior relational database has poor performance when a plurality of recursive connections and queries for contact tracking are executed, and provides an infectious disease contact target tracking method based on a public transport knowledge graph.
The purpose of the invention can be realized by the following technical scheme:
an infectious disease contact target tracking method based on a public transport knowledge graph specifically comprises the following steps:
s1, constructing a public transportation knowledge map based on a trip chain model by adopting a construction mode combining top-down and bottom-up;
s2, obtaining travel data of a plurality of passengers, determining the travel sequence of the passengers according to the public transportation knowledge graph, and determining the type of infectious disease contact among the passengers;
and S3, extracting the travel data of the infected persons in the travel data of the passengers, marking, selecting the infected persons with a preset proportion for tracking, and positioning the secondary infected individuals in the traffic system according to the contact type of the infectious diseases among the passengers.
The body is used as a mode layer in the public transport knowledge map, and corresponding data are mapped into entities and relations.
Further, the public transport knowledge graph extracts entities and relations from the travel data of passengers and is imported into a graph database as a data layer.
The process of constructing a bus knowledge map in step S1 may further include integrating multi-source data collected from smart cards, Automatic Vehicle Location (AVL) devices, shift records, and route lists for bus, Bus Rapid Transit (BRT), and subway systems.
In the step S2, the public transportation knowledge graph is simplified into a side graph G ═ V, E, and the corresponding node V is1And node VnThe connection paths of (a) are as follows:
node V1And node VnRelation E betweencAs follows:
wherein E is1、E2…En-1Is an edge in the public transportation knowledge map,
represented as a combination operator.
Further, if the passenger travels three times in the travel sequence, the corresponding expression relationship is as follows:
wherein, TpRepresenting a series of trips of passenger p during the day,
indicating the nth day of the passengerthSecond trip, ETIndicating the next trip, ET1 indicates a transfer between two travel records, ET0 means that there is no transfer between the two trip recordings.
Further, the first trip in the passenger's travel sequence
And last trip
The expression of (a) is as follows:
wherein,
indicating the presence of the substance,
indicates absence;
for trips at both ends of a continuous trip chain (i.e., continuous rides), the overall relationship is represented by context, as follows:
wherein transfer ═ 1 indicates that the passenger transfers once during the trip.
The infectious disease contact types between the passengers comprise direct contact and indirect contact, and the specific relationship is as follows:
wherein, Vp1、Vp2And Vp3Respectively representing nodes, E, corresponding to passengers p1, p2 and p3DCDenotes direct contact, EICIndicating indirect contact.
Further, the direct contact includes a common riding and a common waiting, and represents the following relationship:
wherein E isHIt is shown that there is one trip,
indicating a certain trip of passenger p, ERIndicating the behaviour of the ride vehicle, EBShows the behaviour of getting on the bus at the station, VvehicleRepresenting a vehicle scene, VstationRepresenting a station scene.
Further, two passengers riding the same public transportation means are defined as a common riding, and the determination formula is as follows:
where j, k is E {1,2}, j ≠ k, ECRA co-ride is shown as being provided,
representing the ride time of passenger j for that trip,
the getting-off time of the trip of the passenger k is represented;
two passengers get on the bus at the same station within a preset waiting time interval threshold, the common waiting is defined, and the judgment formula is as follows:
wherein E isCWIndicating a common waiting, TthresholdRepresenting a waiting interval threshold;
the trip relations in the trip sequence for the presence of direct contact and indirect contact are as follows:
wherein E isA∈{ECR，ECW}，
N-th of passenger pthAnd (5) going out.
In the step S3, the nodes where all the infected persons are located in the public transportation knowledge graph are marked as "infected", the nodes of the infected persons with a preset proportion are selected as index cases and marked as "index", the tracking is performed according to the type of infectious disease contact among the passengers, if the nodes where other passengers are located are searched, the nodes are marked as "selected", and meanwhile, the nodes are marked as "found", and the nodes are marked as "unselected".
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, by utilizing a trip chain model, multi-source data based on an intelligent card and a public transportation system are integrated, a contact network of the public transportation system with rich semantics is reconstructed based on a public transportation map, a targeted simplified contact network is extracted from a constructed knowledge map, data redundancy is reduced, and data expansion is facilitated.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic diagram of digital contact tracking according to an embodiment of the present invention, wherein fig. 2(a) to 2(e) are schematic diagrams of tracking an infected person from an index node and determining an intimate contact person.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a method for tracking infectious disease contact targets based on public transportation knowledge base specifically includes the following steps:
s1, constructing a public transportation knowledge map based on a trip chain model by adopting a construction mode combining top-down and bottom-up;
s2, obtaining travel data of a plurality of passengers, determining the travel sequence of the passengers according to the public transportation knowledge graph, and determining the type of infectious disease contact among the passengers;
and S3, extracting the travel data of the infected persons in the travel data of the passengers, marking, selecting the infected persons with a preset proportion for tracking, and positioning the secondary infected individuals in the traffic system according to the contact type of the infectious diseases among the passengers.
The body is used as a mode layer in the public transport knowledge map, and corresponding data are mapped into entities and relations.
The public transport knowledge map extracts entities and relations from travel data of passengers and introduces the entities and relations into a graph database as a data layer.
The process of constructing a bus knowledge map in step S1 further includes integrating multi-source data collected from smart cards, Automatic Vehicle Location (AVL) devices, shift records, and route lists for bus, Bus Rapid Transit (BRT), and subway systems.
In step S2, the public transportation knowledge graph is simplified to be a side graph G ═ V, E, and the corresponding node V is1And node VnThe connection paths of (a) are as follows:
node V1And node VnRelation E betweencAs follows:
wherein E is1、E2…En-1Is an edge in the public transportation knowledge map,
represented as a combination operator.
If the passenger goes three times in the trip sequence, the corresponding expression relationship is as follows:
wherein, TpRepresenting a series of trips of passenger p during the day,
indicating the nth day of the passengerthSecond trip, ETIndicating the next trip, ET1 indicates a transfer between two travel records, ET0 means that there is no transfer between the two trip recordings.
First trip in passenger's travel sequence
And last trip
The expression of (a) is as follows:
wherein,
indicating the presence of the substance,
indicates absence;
for trips at both ends of a continuous trip chain (i.e., continuous rides), the overall relationship is represented by context, as follows:
wherein transfer ═ 1 indicates that the passenger transfers once during the trip.
The types of infectious disease contact between passengers include direct contact and indirect contact, and the specific relationship is as follows:
wherein, Vp1、Vp2And Vp3Respectively representing nodes, E, corresponding to passengers p1, p2 and p3DCDenotes direct contact, EICIndicating indirect contact.
Direct contact includes a common ride and a common wait, representing the relationship as follows:
wherein E isHIt is shown that there is one trip,
indicating a certain trip of passenger p, ERIndicating the behaviour of the ride vehicle, EBShows the behaviour of getting on the bus at the station, VvehicleRepresenting a vehicle scene, VstationRepresenting a station scene.
Two passengers riding the same public transport means are defined as a common passenger, and the judgment formula is as follows:
where j, k is E {1,2}, j ≠ k, ECRA co-ride is shown as being provided,
representing the ride time of passenger j for that trip,
the getting-off time of the trip of the passenger k is represented;
two passengers get on the bus at the same station within a preset waiting time interval threshold, the common waiting is defined, and the judgment formula is as follows:
wherein E isCWIndicating a common waiting, TthresholdRepresenting a waiting interval threshold;
the trip relations in the trip sequence for the presence of direct contact and indirect contact are as follows:
wherein E isA∈{ECR，ECW}，
N-th of passenger pthAnd (5) going out.
In step S3, nodes where all infectors are located in the public transportation knowledge graph are marked as 'infection', nodes where the infectors with a preset proportion are selected as index cases and marked as 'index', the tracking is carried out according to the type of contagious disease among passengers, if nodes where other passengers are located are searched, the nodes are marked as 'selected', meanwhile, the nodes are marked as 'found', and the nodes are marked as 'unselected'.
Knowledge graph-based digital contact tracking algorithm:
in addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. An infectious disease contact target tracking method based on a public transport knowledge graph is characterized by comprising the following steps:
s1, constructing a public transportation knowledge map based on a trip chain model by adopting a construction mode combining top-down and bottom-up;
s2, obtaining travel data of a plurality of passengers, determining the travel sequence of the passengers according to the public transportation knowledge graph, and determining the type of infectious disease contact among the passengers;
and S3, extracting the travel data of the infected persons in the travel data of the passengers, marking, selecting the infected persons with a preset proportion for tracking, and positioning the secondary infected individuals in the traffic system according to the contact type of the infectious diseases among the passengers.
2. The method for tracking the infectious disease contact target based on the public transportation knowledge graph as claimed in claim 1, wherein the public transportation knowledge graph has an ontology as a model layer, and corresponding data are mapped into entities and relations.
3. The method for tracking the infectious disease contact target based on the public transportation knowledge graph as claimed in claim 2, wherein the public transportation knowledge graph extracts entities and relations from the travel data of passengers and imports the entities and relations into a graph database as a data layer.
4. The method for tracking the infectious disease contact target based on the public transportation knowledge graph as claimed in claim 1, wherein the public transportation knowledge graph is simplified to an edge graph G (V, E) in the step S2, and the corresponding node V is a node V1And node VnThe connection paths of (a) are as follows:
node V1And node VnRelation E betweencAs follows:
wherein E is1、E2…En-1Is an edge in the public transportation knowledge map,
represented as a combination operator.
5. The method for tracking the infectious disease contact target based on the public transportation knowledge graph according to claim 2, wherein if the passenger goes three times in the passenger's travel sequence, the corresponding expression relationship is as follows:
wherein, TpRepresenting a series of trips of passenger p during the day,
indicating the nth day of the passengerthSecond trip, ETIndicating the next trip, ET1 indicates a transfer between two travel records, ET0 means that there is no transfer between the two trip recordings.
6. The method for tracking the infectious disease contact target based on the public transportation knowledge graph of claim 5, wherein the passenger travels for the first time in the travel sequence
And last trip
The expression of (a) is as follows:
wherein,
indicating the presence of the substance,
indicates absence;
for trips at both ends of a continuous trip chain (i.e., continuous rides), the overall relationship is represented by context, as follows:
wherein transfer ═ 1 indicates that the passenger transfers once during the trip.
7. An infectious disease contact target tracking method based on public transportation knowledge graph according to claim 2, wherein the type of infectious disease contact between passengers comprises direct contact and indirect contact, and the specific relationship is as follows:
wherein, Vp1、Vp2And Vp3Respectively representing nodes, E, corresponding to passengers p1, p2 and p3DCDenotes direct contact, EICIndicating indirect contact.
8. The infectious disease contact target tracking method based on the public transportation knowledge graph according to claim 7, wherein the direct contact comprises a common bus taking and a common waiting, and the expression relationship is as follows:
wherein E isHIt is shown that there is one trip,
indicating a certain trip of passenger p, ERIndicating the behaviour of the ride vehicle, EBShows the behaviour of getting on the bus at the station, VvehicleRepresenting a vehicle scene, VstationRepresenting a station scene.
9. The method for tracking the infectious disease contact target based on the public transportation knowledge graph according to claim 8, wherein the determination formula of the shared bus is as follows:
where j, k is E {1,2}, j ≠ k, ECRA co-ride is shown as being provided,
representing the ride time of passenger j for that trip,
the getting-off time of the passenger on the trip is represented;
the common waiting judgment formula is as follows:
wherein E isCWIndicating a common waiting, TthresholdRepresenting a waiting interval threshold;
the trip relations in the trip sequence for the presence of direct contact and indirect contact are as follows:
wherein E isA∈{ECR,ECW},
N-th of passenger pthAnd (5) going out.
10. The method for tracking the infectious disease contact target based on the public transportation knowledge graph as claimed in claim 1, wherein in step S3, the nodes where all the infected persons are located in the public transportation knowledge graph are marked as "infected", the nodes of the infected persons with a preset proportion are selected as index cases and marked as "index", the tracking is performed according to the type of infectious disease contact among the passengers, if the nodes where other passengers are located are searched, the nodes are marked as "selected", and meanwhile, the nodes are marked as "found", and the nodes are marked as "unselected".
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147419A (en) * | 2019-04-25 | 2019-08-20 | 中国科学院深圳先进技术研究院 | Infectious disease diffusion analysis method and system based on Metro Space |
| CN110400065A (en) * | 2019-07-12 | 2019-11-01 | 北京交通大学 | Rail Transit System risk chain group construction method based on Epidemic Model |
| CN111383771A (en) * | 2020-02-26 | 2020-07-07 | 汤一平 | Epidemic disease virus field-based prevention and control system |
| CN111428045A (en) * | 2020-03-06 | 2020-07-17 | 北京明略软件系统有限公司 | Method and device for constructing knowledge graph, computer storage medium and terminal |
| CN111446000A (en) * | 2020-02-26 | 2020-07-24 | 汤一平 | System for tracing infection source based on epidemic disease virus field |
| CN111524612A (en) * | 2020-04-26 | 2020-08-11 | 腾讯科技（深圳）有限公司 | Infectious disease tracing method and device, computer equipment and storage medium |
| CN111933299A (en) * | 2020-08-14 | 2020-11-13 | 工银科技有限公司 | Infectious disease infection risk assessment method and apparatus, electronic device, and storage medium |
| CN112511982A (en) * | 2020-11-19 | 2021-03-16 | 同济大学 | Subway passenger track real-time tracing and restoring method with automatic trip semantic annotation |
| CN112786210A (en) * | 2021-01-15 | 2021-05-11 | 华南师范大学 | Epidemic propagation tracking method and system |
| CN112883195A (en) * | 2019-11-29 | 2021-06-01 | 中国科学技术大学 | Method and system for constructing traffic knowledge map of individual trip |
| CN113241188A (en) * | 2021-04-14 | 2021-08-10 | 大连理工大学 | Cross-region transmission prediction method for new infectious disease under open system |
| CN113256987A (en) * | 2021-06-30 | 2021-08-13 | 深圳市城市交通规划设计研究中心股份有限公司 | Multi-source data fusion-based resident trip chain generation method and co-multiplication query method |
-
2021
- 2021-09-16 CN CN202111086108.3A patent/CN113889284B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147419A (en) * | 2019-04-25 | 2019-08-20 | 中国科学院深圳先进技术研究院 | Infectious disease diffusion analysis method and system based on Metro Space |
| CN110400065A (en) * | 2019-07-12 | 2019-11-01 | 北京交通大学 | Rail Transit System risk chain group construction method based on Epidemic Model |
| CN112883195A (en) * | 2019-11-29 | 2021-06-01 | 中国科学技术大学 | Method and system for constructing traffic knowledge map of individual trip |
| CN111383771A (en) * | 2020-02-26 | 2020-07-07 | 汤一平 | Epidemic disease virus field-based prevention and control system |
| CN111446000A (en) * | 2020-02-26 | 2020-07-24 | 汤一平 | System for tracing infection source based on epidemic disease virus field |
| CN111428045A (en) * | 2020-03-06 | 2020-07-17 | 北京明略软件系统有限公司 | Method and device for constructing knowledge graph, computer storage medium and terminal |
| CN111524612A (en) * | 2020-04-26 | 2020-08-11 | 腾讯科技（深圳）有限公司 | Infectious disease tracing method and device, computer equipment and storage medium |
| CN111933299A (en) * | 2020-08-14 | 2020-11-13 | 工银科技有限公司 | Infectious disease infection risk assessment method and apparatus, electronic device, and storage medium |
| CN112511982A (en) * | 2020-11-19 | 2021-03-16 | 同济大学 | Subway passenger track real-time tracing and restoring method with automatic trip semantic annotation |
| CN112786210A (en) * | 2021-01-15 | 2021-05-11 | 华南师范大学 | Epidemic propagation tracking method and system |
| CN113241188A (en) * | 2021-04-14 | 2021-08-10 | 大连理工大学 | Cross-region transmission prediction method for new infectious disease under open system |
| CN113256987A (en) * | 2021-06-30 | 2021-08-13 | 深圳市城市交通规划设计研究中心股份有限公司 | Multi-source data fusion-based resident trip chain generation method and co-multiplication query method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117690600A (en) * | 2024-02-01 | 2024-03-12 | 北方健康医疗大数据科技有限公司 | Knowledge-graph-based infectious disease prediction method, system, terminal and storage medium |
| CN117690600B (en) * | 2024-02-01 | 2024-04-30 | 北方健康医疗大数据科技有限公司 | Knowledge-graph-based infectious disease prediction method, system, terminal and storage medium |
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