CN111477343B - Manned aircraft infects early warning system - Google Patents
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- CN111477343B CN111477343B CN202010591679.1A CN202010591679A CN111477343B CN 111477343 B CN111477343 B CN 111477343B CN 202010591679 A CN202010591679 A CN 202010591679A CN 111477343 B CN111477343 B CN 111477343B
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- 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
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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Abstract
The invention provides a manned aircraft infection early warning system which can calculate the potential infection probability of a target area according to the number of predicted infected persons and the total number of current population of the target area every day and determine the infection probability of an aircraft according to the calculated potential infection probability and the number of manned persons of the aircraft. And under the condition that the confirmed users exist in the aircraft, determining the infection probability of the non-confirmed users according to the determined infection probability of the aircraft and the distance between the positions of the aircraft seats between the non-confirmed users and the confirmed users, and under the condition that the confirmed users do not exist in the aircraft, taking the infection probability of the aircraft as the infection probability of each aircraft user, so that the infection probability of each aircraft and the corresponding infection probability of each aircraft user can be known, and effective early warning is carried out.
Description
Technical Field
The invention relates to a manned aircraft early warning system, in particular to a manned aircraft early warning system capable of analyzing the infection probability of a manned aircraft and users on the manned aircraft.
Background
The space on the manned aircraft with a certain scale is closed and narrow, and the diffusion and the transmission of microorganisms such as respiratory infectious viruses or bacteria are facilitated. The existing scheme can inquire whether a confirmed user exists on the manned aircraft or not according to the taken flight information, but does not accurately analyze the possibility that each flight and the non-confirmed users on the flights are infected on the aircraft, and carry out early warning or prompting.
Disclosure of Invention
The embodiment of the invention provides an infection early warning system for a manned aircraft, which can predict the infection probability caused by passengers on international flights and further carry out early warning.
The technical scheme adopted by the invention is as follows:
the embodiment of the invention provides a manned aircraft infection early warning system, which comprises: the system comprises a processor, a non-transient storage medium storing a computer program, a manned database and a configuration file; the manned database is stored with aircraft ID, manned number, user ID, aircraft seat position corresponding to the user ID and infection information in a correlated manner, and the configuration file is stored with area IDs of a plurality of geo-fence areas and corresponding number of people; when executed by a processor, performs the steps of:
s100, obtaining the predicted infected people number of the nth time slot in the ith geo-fenced area
；
S200, in the configuration file, searching by using the area ID of the ith geo-fenced area to obtain the number S of people in the ith geo-fenced areai；
S300, according to
Calculating a potential infection probability for the i-th geo-fenced area;
s400, acquiring a jth aircraft ID in the ith geo-fence area, and retrieving by using the jth aircraft ID in the manned database to acquire the corresponding manned number;
s500, acquiring the infection probability of the jth aircraft according to the acquired manned number corresponding to the jth aircraft ID and the calculated potential infection probability of the ith geo-fenced area;
s600, searching corresponding infection information in the manned database based on the jth aircraft ID, if the corresponding infection information is searched, executing the step S700, otherwise, executing the step S900;
s700, acquiring a confirmed user ID, an ID of an unidentified user and a corresponding aircraft seat position corresponding to the retrieved infection information from the manned database, and entering the step S800;
s800, for each retrieved non-diagnosed user, determining a distance D between the position of the aircraft seat and the diagnosed user, and calculating the infection probability of the non-diagnosed user based on the infection probability of the jth aircraft and the distance D;
s900, taking the infection probability of the jth aircraft as the infection probability of each user ID corresponding to the jth aircraft.
Optionally, the infection early warning system further includes an infection information acquisition module, which is in communication connection with one or more infection publishing platforms, and is configured to acquire the number of existing infected persons, including the number of confirmed infected persons and the number of newly added infected persons, in each geo-fenced area from the infection publishing platforms;
wherein the predicted number of infected persons for the nth time period
According to
Determining, wherein,
representing the number of existing infections in the ith geo-fenced area at the nth time period,
and
respectively a first correction coefficient and a second correction coefficient.
Optionally, a first correction factor
And determining the newly increased number of infected persons obtained from the infection publishing platform according to the nth time period and the (n-1) th time period.
Optionally, a first correction factor
Δ t, wherein Δ t is the minimum common multiple of the minimum time period of the diagnosis information issued by the infection issuing platform,
a newly increased number of infected persons for the ith geofenced area during the nth time period,
the number of newly infected individuals in the ith geo-fenced area during the (n-1) th time period,
and β are both greater than zero.
Alternatively,
，β=
。
optionally, a second correction factor
The number of aircraft users actually delivered to the geo-fenced area i within a time period to reach the geo-fenced area i is determined according to the potential infection ratios of all geo-fenced areas reaching the geo-fenced area i within the infection latency and the corresponding regional aircraft relevance degree C, which characterizes the number of aircraft users actually delivered to the geo-fenced area i within the time period to reach the geo-fenced area i.
Optionally, a second correction factor
Wherein, in the step (A),
representing the potential infection rate of the geofenced area x for the nth-kth time period,
representing the degree of aircraft association for the (n-k) th time period from geofenced area x to geofenced area i, x representing the geography of arrival at geofenced area i within the infection latencyThe fence area, h, represents the latency time within the population to elicit the infectious virus.
Optionally, the manned aircraft infection early warning system is in communication connection with a preset receiving mobile terminal, and sends the calculated aircraft infection probability and the corresponding aircraft user infection probability to the preset receiving mobile terminal.
According to the manned aircraft infection early warning system provided by the embodiment of the invention, firstly, the potential infection probability of a target area is calculated according to the number of predicted infected persons and the total number of current population of the target area every day, then, the infection probability of an aircraft is determined according to the calculated potential infection probability and the number of the persons carrying the aircraft, then, under the condition that confirmed users exist in the aircraft, the infection probability of the non-confirmed users is determined according to the determined infection probability of the aircraft and the distance between the aircraft seat positions of the non-confirmed users and the confirmed users, and under the condition that the confirmed users do not exist in the aircraft, the infection probability of the aircraft is taken as the infection probability of each aircraft user, so that the infection probability of each aircraft and the corresponding infection probability of each aircraft user can be known, and effective early warning is further carried out.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following detailed description is given with reference to specific embodiments.
The embodiment of the invention provides a manned aircraft infection early warning system, which comprises: the system comprises a processor, a non-transient storage medium storing a computer program, a manned database and a configuration file; the manned database is stored with an aircraft ID, a manned number, a user ID, an aircraft seat position corresponding to the user ID and infection information in an associated manner, wherein the aircraft ID can comprise an aircraft flight date and an aircraft number; the configuration file stores area IDs of a plurality of geo-fenced areas and corresponding number of people; when executed by a processor, performs the steps of:
s100, obtaining the predicted infected people number of the nth time slot in the ith geo-fenced area
；
S200, in the configuration file, searching by using the area ID of the ith geo-fenced area to obtain the number S of people in the ith geo-fenced areai；
S300, according to formula Qi 
Calculating a potential infection probability for the i-th geo-fenced area;
s400, acquiring a jth aircraft ID in an ith geo-fence area, and retrieving by using the jth aircraft ID in the manned database to acquire the corresponding manned number Z;
s500, acquiring the infection probability P of the jth aircraft according to the acquired manned number corresponding to the jth aircraft ID and the calculated potential infection probability of the ith geo-fenced areai；
S600, searching corresponding infection information in the manned database based on the jth aircraft ID, if the corresponding infection information is searched, executing the step S700, otherwise, executing the step S900;
s700, acquiring a confirmed user ID, an ID of an unidentified user and a corresponding aircraft seat position corresponding to the retrieved infection information from the manned database, and entering the step S800;
s800, for each retrieved non-diagnosed user, determining a distance D between the position of the aircraft seat and the diagnosed user, and calculating the infection probability of the non-diagnosed user based on the infection probability of the jth aircraft and the distance D;
s900, taking the infection probability of the jth aircraft as the infection probability of each user ID corresponding to the jth aircraft.
In an embodiment of the invention, the infection information may be automatically obtained via an aircraft information database.
In step S100, the time period may be determined according to the time when the distribution platform that distributes the infection information updates data, and in one example, the unit of the time period may be day. In step S500, in a preferred example, the infection probability of the jth aircraft may be equal to the product of the number of people corresponding to the jth aircraft ID and the calculated potential infection probability of the ith geofenced area, i.e., Pi = Qi × Z; in another preferred example, the infection probability of the j aircraft can be positively correlated with the number of people loaded corresponding to the j aircraft ID and the calculated potential infection probability of the i geofenced area, e.g., Pi = K Qi Z, where 0< K ≦ 1, and the coefficient K can be set according to actual needs.
In the embodiment of the invention, the infection early warning system further comprises an infection information acquisition module, wherein the infection information acquisition module is in communication connection with one or more infection release platforms and is used for acquiring the existing infected persons in each geo-fenced area from the infection release platforms, including the confirmed infected persons and the newly increased infected persons. The infection distribution platform may be a website that distributes infection information, such as the Hopkins university website.
In the embodiment of the present invention, in order to accurately predict the infected people in a certain geo-fenced area, in step S100, the predicted infected people in the nth time slot
Can be determined according to the following equation (1):
wherein the content of the first and second substances,
representing the number of existing infections in the ith geo-fenced area at the nth time period,
and
respectively a first correction coefficient and a second correction coefficient.
In the embodiment of the present invention, the first correction coefficient represents a correction coefficient due to a newly increased number of infected persons, that is, a newly added input coefficient. In practice, the nth time period (e.g., day) announces the number of existing diagnosed persons that is one time period different from the current time period (i.e., day) that announces the number of existing diagnosed persons of the previous time period. For real-time calculation and even judgment in advance, only the data of the current time period and the previous data can be used for calculation, and the estimation of the infection coefficient of the flight of the current time period preferably estimates the actual number of confirmed diagnoses of the current time period by using the number of existing confirmed diagnoses issued by the current time period infection issuing platform and the number of existing confirmed diagnoses issued before. Therefore, in the embodiment of the present invention, the first correction factor is preferably determined according to the number of newly increased infectious agents obtained from the infection distribution platform at the nth time slot and the (n-1) th time slot.
Preferably, in the embodiment of the present invention, the first correction coefficient
Can be determined by the following equation (2):
wherein, Δ t is the smallest common multiple of the minimum time period of the diagnosis information issued by the infection issuing platform, so that the data precision can be ensured, and in one example, Δ t can be one day;
a newly increased number of infected persons for the ith geofenced area during the nth time period,
the number of newly infected individuals in the ith geo-fenced area during the (n-1) th time period,
and β are both greater than zero, preferablyIn an exemplary embodiment of the present invention,
，β=
experiments prove that the numerical value can enable the predicted data to have better accuracy.
In the embodiment of the invention, the second correction coefficient represents the correction coefficient caused by the number of possible infected persons input to the i area by means of flights in the past from the nth-h time slot to the nth time slot, namely the input correction coefficient, and h represents the latent time of the infected virus in the crowd. In one example, the second correction coefficient
The number of aircraft users actually delivered to the geo-fenced area i within a time period to reach the geo-fenced area i can be determined based on the proportion of potential infections to all of the geo-fenced areas that reached the geo-fenced area i within the infection latency and the corresponding regional aircraft relevance C that characterizes the number of aircraft users that actually delivered to the geo-fenced area i within the time period to reach the geo-fenced area i. In one example of the present invention, the degree of association C between two geo-fenced areas can be represented by the following equation (3):
wherein the content of the first and second substances,
representing the degree of association of the aircraft from geofenced area x to geofenced area i over a period of time,
the number of people loaded, i.e., the number of users actually delivered, by the r-th aircraft r from the geo-fenced area x to the geo-fenced area i can be stored in a database according to the flight date and the aircraft numberRetrieving to obtain; n is the number of all aircraft in the geofenced area x to the geofenced area i within the time horizon, which can be retrieved from the database. In one specific example, a equals 1,
equal to 0; in another specific example, A and
positively correlated with the proportion of infected persons detected in aircraft from geofenced area x to geofenced area i.
Further, in the embodiment of the present invention, the second correction coefficient
Can be determined according to equation (4):
wherein the content of the first and second substances,
representing the potential infection rate of the geofenced area x for the nth-kth time period,
represents the aircraft relevance of the nth-k time period from geo-fenced area x to geo-fenced area i, x represents the geo-fenced area that reaches geo-fenced area i within the infection latency, h represents the latency within the crowd to elicit the infectious virus, which in one example may be 13 days.
In the embodiment of the invention, the predicted infected persons in the nth time period of each geo-fenced area comprise the existing infected persons, the infected persons caused by new increase and the infected persons caused by aviation input, so that the predicted infection prediction can be more accurate.
In the embodiment of the present invention, in step S800, the infection probability p of each undiagnosed user is calculated according to the following formula (5):
p=P+(1/D2) (5)
wherein, P represents the infection probability of the aircraft with the confirmed user, and D is the absolute distance between the aircraft seat positions between the non-confirmed user and the confirmed user, and can be calculated according to the aircraft seat map.
Further, in the embodiment of the invention, the manned aircraft infection early warning system is in communication connection with a preset receiving mobile terminal, and sends the calculated aircraft infection probability and the corresponding aircraft user infection probability to the preset receiving mobile terminal for early warning.
To sum up, the infection early warning system for manned aircraft provided by the embodiment of the invention firstly calculates the potential infection probability of the target area according to the predicted number of infected persons and the current population number of the target area every day, and then, determining the probability of infection of the aircraft based on the calculated potential probability of infection and the number of people carrying the aircraft, and then, in the case of an aircraft with a diagnosed user, determining the probability of infection for the non-diagnosed user on the basis of the determined probability of infection for the aircraft and the distance between the aircraft seat positions between the non-diagnosed user and the diagnosed user, in the case of an aircraft without the presence of diagnosed users, the probability of infection of the aircraft is taken as the probability of infection for each aircraft user, therefore, the infection probability of each aircraft and the corresponding infection probability of each aircraft user can be known, and effective and accurate early warning is carried out.
The above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. An infection early warning system for a manned aircraft, comprising: the system comprises a processor, a non-transient storage medium storing a computer program, a manned database and a configuration file; the manned database is stored with aircraft ID, manned number, user ID, aircraft seat position corresponding to the user ID and infection information in a correlated manner, and the configuration file is stored with area IDs of a plurality of geo-fence areas and corresponding number of people; when executed by a processor, performs the steps of:
s100, obtaining the predicted infected people number of the nth time slot in the ith geo-fenced area
；
S200, in the configuration file, searching by using the area ID of the ith geo-fenced area to obtain the number S of people in the ith geo-fenced areai；
S300, according to
Calculating a potential infection probability for the i-th geo-fenced area;
s400, acquiring a jth aircraft ID in the ith geo-fence area, and retrieving by using the jth aircraft ID in the manned database to acquire the corresponding manned number;
s500, acquiring the infection probability of the jth aircraft according to the acquired manned number corresponding to the jth aircraft ID and the calculated potential infection probability of the ith geo-fenced area;
s600, searching corresponding infection information in the manned database based on the jth aircraft ID, if the corresponding infection information is searched, executing the step S700, otherwise, executing the step S900;
s700, acquiring a confirmed user ID, an ID of an unidentified user and a corresponding aircraft seat position corresponding to the retrieved infection information from the manned database, and entering the step S800;
s800, for each retrieved non-diagnosed user, determining a distance D between the position of the aircraft seat and the diagnosed user, and calculating the infection probability of the non-diagnosed user based on the infection probability of the jth aircraft and the distance D;
s900, taking the infection probability of the jth aircraft as the infection probability of each user ID corresponding to the jth aircraft;
the infection early warning system also comprises an infection information acquisition module, wherein the infection information acquisition module is in communication connection with one or more infection release platforms and is used for acquiring the existing infected persons in each geo-fence area from the infection release platforms, wherein the existing infected persons comprise confirmed infected persons and newly increased infected persons;
wherein the predicted number of infected persons for the nth time period
According to
Determining, wherein,
representing the number of existing infections in the ith geo-fenced area at the nth time period,
and
respectively a first correction coefficient and a second correction coefficient;
first correction coefficient
Determining the newly increased number of infected persons obtained from the infection publishing platform according to the nth time period and the (n-1) th time period;
second correction coefficient
The number of aircraft users actually delivered to the geo-fenced area i within a time period to reach the geo-fenced area i is determined according to the potential infection ratios of all geo-fenced areas reaching the geo-fenced area i within the infection latency and the corresponding regional aircraft relevance degree C, which characterizes the number of aircraft users actually delivered to the geo-fenced area i within the time period to reach the geo-fenced area i.
2. The manned aircraft infection warning system of claim 1, wherein the first correction factor is
Δ t, wherein Δ t is the minimum common multiple of the minimum time period of the diagnosis information issued by the infection issuing platform,
a newly increased number of infected persons for the ith geofenced area during the nth time period,
the number of newly infected individuals in the ith geo-fenced area during the (n-1) th time period,
and β are both greater than zero.
3. The manned aircraft infection warning system of claim 2,
，β=
。
4. the manned aircraft infection warning system of claim 1, wherein the second correction factor is
Wherein, in the step (A),
representing the potential infection rate of the geofenced area x for the nth-kth time period,
the aircraft relevance of the nth-k time period from the geo-fenced area x to the geo-fenced area i is represented, x represents the geo-fenced area reaching the geo-fenced area i within the infection latency, and h represents the latency of causing the infection virus within the crowd.
5. The manned aircraft infection early warning system of claim 1, wherein the manned aircraft infection early warning system is in communication connection with a preset receiving mobile terminal, and sends the calculated aircraft infection probability and the corresponding aircraft user infection probability to the preset receiving mobile terminal.
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| CN1479220A (en) * | 2003-07-11 | 2004-03-03 | 李之彦 | System and method of infection source health information reporting and tracing |
| CN103390089B (en) * | 2012-05-07 | 2016-12-14 | 中国人民解放军防化学院 | A kind of epidemic Forecasting Methodology with control variable |
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